diff --git "a/3550.jsonl" "b/3550.jsonl" new file mode 100644--- /dev/null +++ "b/3550.jsonl" @@ -0,0 +1,743 @@ +{"seq_id":"618971730","text":"import os\nimport argparse\n\n\ndef get_files_size_paths(path):\n files_name_size_paths = {}\n\n for dir_path, dir_names, files_names in os.walk(path):\n for file_name in files_names:\n file_path = os.path.join(dir_path, file_name)\n file_size = os.path.getsize(file_path)\n files_name_size_paths.setdefault(\n (file_name, file_size),\n []\n ).append(file_path)\n\n return files_name_size_paths\n\n\ndef get_duplicates(files_name_size_paths):\n\n return {\n name_size: file_paths\n for name_size, file_paths in files_name_size_paths.items()\n if len(file_paths) > 1\n }\n\n\ndef pprint_duplicate(duplicate_files):\n\n for (file_name, file_size), files_paths in duplicate_files.items():\n for file_path in files_paths:\n print(' PATH: {0} SIZE: {1}'.format(file_path, file_size))\n\n\ndef get_parser_args():\n parser = argparse.ArgumentParser(\n description='Path to check for duplication'\n )\n parser.add_argument(\n 'path',\n help='input path to check'\n )\n return parser.parse_args()\n\n\nif __name__ == '__main__':\n arguments = get_parser_args()\n\n if os.path.isdir(arguments.path):\n files_name_size_paths = get_files_size_paths(arguments.path)\n duplicate_files = get_duplicates(files_name_size_paths)\n pprint_duplicate(duplicate_files)\n else:\n print(' ERROR: this is a file or directory not found!')\n","sub_path":"duplicates.py","file_name":"duplicates.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"208116189","text":"def finder(files, queries):\n result = []\n queryMap = {}\n # log each query in a hashTable\n for q in queries:\n queryMap[q] = True\n\n for f in files:\n arr = f.split(\"/\")\n # i will equal the index of the first character of f's file name\n # now we can check if that file name is in the query hashTable\n if arr[len(arr) - 1] in queryMap:\n # if so, append the current file to the result array\n result.append(f)\n\n return result\n\n\nif __name__ == \"__main__\":\n files = [\n '/bin/foo',\n '/bin/bar',\n '/usr/bin/baz'\n ]\n queries = [\n \"foo\",\n \"qux\",\n \"baz\"\n ]\n print(finder(files, queries))\n","sub_path":"hashtables/ex5/ex5.py","file_name":"ex5.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"527895188","text":"import pygame\nfrom pygame.locals import* #for event MOUSE variables\nimport os\nimport RPi.GPIO as GPIO\nimport time\nimport cv2\nimport numpy as np\n\nstart_time = time.time() #start time\ntimeOut = 1200 #timeout after 120 seconds\n\n#PiTFT\n#os.putenv('SDL_VIDEODRIVER','fbcon') #Display on PiTFT\n#os.putenv('SDL_FBDEV','/dev/fb1')\n#os.putenv('SDL_MOUSEDRV','TSLIB') #Track mouse clicks on PiTFT\n#os.putenv('SDL_MOUSEDEV','/dev/input/touchscreen')\n\npygame.init() #initialize pygame library\npygame.mouse.set_visible(True) #turn cursor on (VNC)\n#pygame.mouse.set_visible(False) #turn cursor off (piTFT)\nWHITE = 255,255,255\nBLACK = 0,0,0\nscreen = pygame.display.set_mode((320,240))\n\nXcoord=0 #initialize x,y\nYcoord=0 \nscreen.fill(BLACK) #Erase workspace\n#Image Processing\n#monitor\ncanvas =cv2.imread('aXnc7xn.png',1) #load blank canvas\nresize = cv2.imread('fruits.jpg',1)\nh,w,c = resize.shape\ncanvas = cv2.resize(canvas,(w/3,h/3))\nscreen = pygame.display.set_mode((w/3,h/3))\nresize = cv2.resize(resize,(w/3,h/3))\n\n#piTFT\n#image = cv2.imread('fruits.jpg',1)\n#image = cv2.imread('njcutuP.png',1) #load image\n#resize = cv2.resize(image, (320,240))\n#anvas = cv2.resize(canvas, (320,240))\nhsv_img = cv2.cvtColor(resize, cv2.COLOR_BGR2HSV) #Conveert from BGR to HSV\n\n#Red\nlight_red = np.array([0,102,153])\ndark_red = np.array([9,255,255])\n#orange\nlight_orange = np.array([10,102,153])\ndark_orange = np.array([20,255,255])\n#Y\nlight_Y=np.array([21, 102.0, 153.0])\ndark_Y=np.array([30.0, 255.0, 255.0])\n#G1\nlight_G1 = np.array([31,102,153])\ndark_G1 = np.array([47,255,255])\n#G2\nlight_G2 = np.array([48,102,153])\ndark_G2 = np.array([64,255,255])\n#G3\nlight_G3 = np.array([65,102,153])\ndark_G3 = np.array([80,255,255])\n#B1\nlight_B1 = np.array([81,102,153])\ndark_B1 = np.array([94,255,255])\n#B2\nlight_B2 =np.array([95,102,153])\ndark_B2 = np.array([108,255,255])\n#B3\nlight_B3 = np.array([109,102,153])\ndark_B3 =np.array([122,255,255])\n#Purple\nlight_Purple = np.array([123,102,153])\ndark_Purple = np.array([140,255,255])\n#Purp/Pink\nlight_PurpPink = np.array([141,102,153])\ndark_PurpPink = np.array([150,255,255])\n#Pink/Red\nlight_PinkRed = np.array([151,102,153])\ndark_PinkRed = np.array([179,255,255])\n#Grey\nlight_Grey = np.array([0,0.0,64])\ndark_Grey = np.array([0,0.0,255])\n#Dark\nlight_dark = np.array([0,45,20])\ndark_dark = np.array([179,101,153])\n#Color Masks\nmask_Red = cv2.inRange(hsv_img, light_red, dark_red) #red mask\nmask_Orange = cv2.inRange(hsv_img, light_orange, dark_orange) #orange mask\nmask_Yellow = cv2.inRange(hsv_img, light_Y, dark_Y) #Yellow mask\nmask_G1 = cv2.inRange(hsv_img, light_G1, dark_G1) #red mask\nmask_G2 = cv2.inRange(hsv_img, light_G2, dark_G2) #orange mask\nmask_G3 = cv2.inRange(hsv_img, light_G3, dark_G3) #Yellow mask\nmask_B1 = cv2.inRange(hsv_img, light_B1, dark_B1) #red mask\nmask_B2 = cv2.inRange(hsv_img, light_B2, dark_B2) #orange mask\nmask_B3 = cv2.inRange(hsv_img, light_B3, dark_B3) #Yellow mask\nmask_Purple = cv2.inRange(hsv_img, light_Purple, dark_Purple) #red mask\nmask_PurpPink = cv2.inRange(hsv_img, light_PurpPink, dark_PurpPink) #orange mask\nmask_PinkRed = cv2.inRange(hsv_img, light_PinkRed, dark_PinkRed) #Yellow mask\nmask_Grey = cv2.inRange(hsv_img, light_Grey, dark_Grey) #red mask\nmask_dark = cv2.inRange(hsv_img,light_dark,dark_dark) #dark mask\nmask_all = [mask_Red, mask_Orange,mask_Yellow,mask_G1,mask_G2,mask_G3,mask_B1,mask_B2,mask_B3,mask_Purple,mask_PurpPink,mask_PinkRed, mask_Grey,mask_dark]\n#Apply Masks\nresult_Red = cv2.bitwise_and(resize, resize, mask=mask_Red) #combine red mask and image\nresult_Orange = cv2.bitwise_and(resize, resize, mask=mask_Orange) #combined Orange mask and image\nresult_Yellow= cv2.bitwise_and(resize, resize, mask=mask_Yellow) #combined yellow mask and image\nresult_G1 = cv2.bitwise_and(resize, resize, mask=mask_G1) #combine G1 mask and image\nresult_G2 = cv2.bitwise_and(resize, resize, mask=mask_G2) #combined G2 mask and image\nresult_G3= cv2.bitwise_and(resize, resize, mask=mask_G3) #combined G3 mask and image\nresult_B1= cv2.bitwise_and(resize, resize, mask=mask_B1) #combine B1 mask and image\nresult_B2 = cv2.bitwise_and(resize, resize, mask=mask_B2) #combined B2 mask and image\nresult_B3= cv2.bitwise_and(resize, resize, mask=mask_B3) #combined B3 mask and image\nresult_Purple = cv2.bitwise_and(resize, resize, mask=mask_Purple) #combine Purple mask and image\nresult_PurpPink = cv2.bitwise_and(resize, resize, mask=mask_PurpPink) #combined purple/pink mask and image\nresult_PinkRed= cv2.bitwise_and(resize, resize, mask=mask_PinkRed) #combined pink/red mask and image\nresult_Grey= cv2.bitwise_and(resize, resize, mask=mask_Grey) #combined grey mask and image\nresult_dark = cv2.bitwise_and(resize,resize,mask=mask_dark)\nresult_all = [result_Red,result_Orange,result_Yellow,result_G1,result_G2,result_G3,result_B1,result_B2,result_B3,result_Purple,result_PurpPink,result_PinkRed,result_Grey,result_dark];\n#Grey Scale\nmask_Red_GS = cv2.cvtColor(result_Red, cv2.COLOR_BGR2GRAY)\nret,thresh = cv2.threshold(mask_Red_GS, 27,55 ,0)\n#Color Contours\n_,contours_Red,hier = cv2.findContours(mask_Red, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_Orange,hier = cv2.findContours(mask_Orange, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_Yellow,hier = cv2.findContours(mask_Yellow, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_G1,hier = cv2.findContours(mask_G1, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_G2,hier = cv2.findContours(mask_G2, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_G3,hier = cv2.findContours(mask_G3, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_B1,hier = cv2.findContours(mask_B1, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_B2,hier = cv2.findContours(mask_B2, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_B3,hier = cv2.findContours(mask_B3, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_Purple,hier = cv2.findContours(mask_Purple, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_PurpPink,hier = cv2.findContours(mask_PurpPink, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_PinkRed,hier = cv2.findContours(mask_PinkRed, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_Grey,hier = cv2.findContours(mask_Grey, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n_,contours_dark,hier = cv2.findContours(mask_dark, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\n\ncontours_all = [contours_Red, contours_Orange, contours_Yellow, contours_G1, contours_G2, contours_G3, contours_B1, contours_B2, contours_B3, contours_Purple, contours_PurpPink, contours_PinkRed, contours_Grey,contours_dark]\nc=0\nwhile c <13:\n cv2.drawContours(canvas, contours_all[c], -1, (0,0,0),1)\n c = c+1\n\ncv2.imwrite('canvas.png',canvas)\ncanvasPygame = pygame.image.load(\"canvas.png\")\ncanvas_rect = canvasPygame.get_rect()\nscreen.blit(canvasPygame, canvas_rect)\npygame.display.flip()\n#cv2.imshow('FINAL',canvas)\ncv2.imshow('b1',mask_B1)\ncv2.imshow('b2',mask_B2)\ncv2.imshow('b3',mask_B3)\ncv2.imshow('purple',mask_Purple)\ncv2.imshow('PurpPink',mask_PurpPink)\ncv2.imshow('pinkred',mask_PinkRed)\ncv2.imshow('dark',mask_dark)\ncv2.waitKey(0)\n#pause(20)\n#END IMAGE PROCESSING\n\n\n\n\n\n\ncanvas_screen = True\ncode_run=True \nwhile code_run:\n current_time = time.time()\n elapsed_time = current_time - start_time #calculate time elapsed\n if elapsed_time > timeOut: #quit if 30 seconds have elapsed\n print(\"time out\")\n code_run=False\n for event in pygame.event.get(): #watch for mousebutton press\n if event.type is MOUSEBUTTONUP: #touch input\n pos=pygame.mouse.get_pos()\n x,y=pos #save x,y coordinates of touch\n Xcoord=x\n Ycoord=y\n #print(str(x)+\",\"+str(y))\n if canvas_screen ==True:\n color_range=0\n while color_range <13:\n mask_check=mask_all[color_range]\n if mask_check[Ycoord,Xcoord] ==255:\n if color_range ==0:\n #orange_og = cv2.imread('oranges.png',1)\n reds = cv2.imread('red_range.png',1)\n reds_pygame = pygame.image.load(\"red_range.png\")\n reds_rect = reds_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(reds_pygame,reds_rect)\n pygame.display.flip()\n\n elif color_range ==1:\n oranges_og = cv2.imread('orange_range.png',1)\n oranges = cv2.resize(oranges_og, (320,240))\n oranges_pygame = pygame.image.load(\"orange_range.png\")\n oranges_rect = oranges_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(oranges_pygame,oranges_rect)\n pygame.display.flip()\n elif color_range ==2:\n yellows= cv2.imread('yellow_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n yellows_pygame = pygame.image.load(\"yellow_range.png\")\n yellows_rect = yellows_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(yellows_pygame,yellows_rect)\n pygame.display.flip()\n elif color_range ==3:\n g1= cv2.imread('g1_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n g1_pygame = pygame.image.load(\"g1_range.png\")\n g1_rect = g1_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(g1_pygame,g1_rect)\n pygame.display.flip()\n elif color_range ==4:\n g2= cv2.imread('g2_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n g2_pygame = pygame.image.load(\"g2_range.png\")\n g2_rect = g2_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(g2_pygame,g2_rect)\n pygame.display.flip()\n\n elif color_range ==5:\n g3 = cv2.imread('g3_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n g3_pygame = pygame.image.load(\"g3_range.png\")\n g3_rect = g3_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(g3_pygame,g3_rect)\n pygame.display.flip()\n elif color_range ==6:\n b1 = cv2.imread('b1_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n b1_pygame = pygame.image.load(\"b1_range.png\")\n b1_rect = b1_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(b1_pygame,b1_rect)\n pygame.display.flip()\n elif color_range ==7:\n b2= cv2.imread('b2_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n b2_pygame = pygame.image.load(\"b2_range.png\")\n b2_rect = b2_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(b2_pygame,b2_rect)\n pygame.display.flip()\n elif color_range ==8:\n b3 = cv2.imread('b3_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n b3_pygame = pygame.image.load(\"b3_range.png\")\n b3_rect = b3_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(b3_pygame,b3_rect)\n pygame.display.flip()\n elif color_range ==9:\n purple= cv2.imread('purple_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n purple_pygame = pygame.image.load(\"purple_range.png\")\n purple_rect = purple_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(purple_pygame,purple_rect)\n pygame.display.flip()\n elif color_range ==10:\n purplepink = cv2.imread('purplepink_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n purplepink_pygame = pygame.image.load(\"purplepink_range.png\")\n purplepink_rect = purplepink_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(purplepink_pygame,purplepink_rect)\n pygame.display.flip()\n elif color_range ==11:\n pinkred = cv2.imread('pinkred_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n pinkred_pygame = pygame.image.load(\"pinkred_range.png\")\n pinkred_rect = pinkred_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(pinkred_pygame,pinkred_rect)\n pygame.display.flip()\n else:\n grey= cv2.imread('grey_range.png',1)\n #oranges = cv2.resize(oranges_og, (320,240))\n grey_pygame = pygame.image.load(\"grey_range.png\")\n grey_rect = grey_pygame.get_rect()\n screen.fill(BLACK)\n screen.blit(grey_pygame,grey_rect)\n pygame.display.flip()\n break\n else:\n color_range=color_range+1\n canvas_screen = not canvas_screen\n else:\n #find color of pick (x,y)\n if color_range ==0:\n fillColor = reds[Ycoord,Xcoord]\n elif color_range ==1:\n fillColor = oranges[Ycoord,Xcoord]\n elif color_range ==2:\n fillColor = yellows[Ycoord,Xcoord]\n elif color_range ==3:\n fillColor = g1[Ycoord,Xcoord]\n elif color_range ==4:\n fillColor = g2[Ycoord,Xcoord]\n elif color_range ==5:\n fillColor = g3[Ycoord,Xcoord]\n elif color_range ==6:\n fillColor = b1[Ycoord,Xcoord]\n elif color_range ==7:\n fillColor = b2[Ycoord,Xcoord]\n elif color_range ==8:\n fillColor = b3[Ycoord,Xcoord]\n elif color_range ==9:\n fillColor = purple[Ycoord,Xcoord]\n elif color_range ==10:\n fillColor = purplepink[Ycoord,Xcoord]\n elif color_range ==11:\n fillColor = pinkred[Ycoord,Xcoord]\n elif color_range ==12:\n fillColor = grey[Ycoord,Xcoord]\n else:\n fillColor = resize[Ycoord,Xcoord]\n # color_range corresponds to which color range the shape choice was from\n fillB = int(fillColor[0])\n fillG = int(fillColor[1])\n fillR = int(fillColor[2])\n if color_range <13:\n cv2.fillPoly(canvas,contours_all[color_range], (fillB,fillG,fillR))\n cv2.imwrite('canvas.png',canvas)\n canvasPygame = pygame.image.load(\"canvas.png\")\n canvas_rect = canvasPygame.get_rect()\n screen.fill(BLACK)\n screen.blit(canvasPygame ,canvas_rect)\n pygame.display.flip()\n canvas_screen = not canvas_screen\n\n #cv2.imshow('Fill',canvas)\n #cv2.waitKey(0)\n","sub_path":"PiTFTImplement.py","file_name":"PiTFTImplement.py","file_ext":"py","file_size_in_byte":16496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"34395740","text":"import logging\n\nfrom django.core.management import BaseCommand\n\nfrom blocks.models import Block\n\nlogger = logging.getLogger(__name__)\n\n\nclass Command(BaseCommand):\n def add_arguments(self, parser):\n parser.add_argument(\n '-s',\n '--start-height',\n help='The block height to start the parse from',\n dest='start_height',\n default=0\n )\n parser.add_argument(\n '-n',\n '--number',\n help='use the last x blocks',\n dest='number',\n default=10000\n )\n\n def handle(self, *args, **options):\n blocks = Block.objects.filter(\n height__gte=options['start_height']\n ).exclude(\n height__isnull=True\n ).order_by(\n 'height'\n )\n\n blocks = blocks[blocks.count() - int(options['number']):]\n\n total_shares = 0\n addresses = []\n\n for block in blocks:\n if not block.solved_by:\n logger.warning('no solved by address for block {}'.format(block.height))\n block.save()\n continue\n\n if block.solved_by not in addresses:\n logger.info('New Address: {}'.format(block.solved_by.address))\n addresses.append(block.solved_by)\n total_shares += block.solved_by.balance\n\n logger.info(\n '{} blocks have been solved by {} different addresses'.format(\n blocks.count(),\n len(addresses)\n )\n )\n logger.info('using a total of {} Voting Shares'.format(total_shares))\n","sub_path":"blocks/management/commands/get_voting_shares.py","file_name":"get_voting_shares.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"90987163","text":"# BSD 3-Clause License\n#\n# Copyright (c) 2020, IPASC\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright notice, this\n# list of conditions and the following disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# 3. Neither the name of the copyright holder nor the names of its\n# contributors may be used to endorse or promote products derived from\n# this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\nimport numpy as np\nimport nrrd\n\nfrom ipasc_tool import BaseAdapter, MetaDatum\nfrom ipasc_tool import MetadataAcquisitionTags\nfrom ipasc_tool import DeviceMetaDataCreator, DetectionElementCreator, IlluminationElementCreator\n\n\nclass DKFZCAMIExperimentalSystemNrrdFileConverter(BaseAdapter):\n\n def __init__(self, nrrd_file_path):\n self.nrrd_file_path = nrrd_file_path\n [data, meta] = nrrd.read(nrrd_file_path)\n self.data = data\n self.meta = meta\n\n super().__init__()\n\n def generate_binary_data(self) -> np.ndarray:\n # the CAMI_DKFZ_FILE is captured this way: [elements, time_series, frames]\n # Needs to be reshaped in order to be in line with the IPASC definition of\n # [detectors, time_series, wavelength, frames]\n # The sample file only contains images with a single wavelength.\n # TODO adapt for multispectral images as well\n data = np.reshape(self.data, (self.meta['sizes'][0], self.meta['sizes'][1], 1, self.meta['sizes'][2]))\n return data\n\n def generate_meta_data_device(self) -> dict:\n device_creator = DeviceMetaDataCreator()\n\n device_creator.set_general_information(uuid=\"c771111c-36ba-425d-9f53-84b8ff092059\",\n fov=np.asarray([0, 0, 0, 0.0384, 0, 0.0384]))\n\n start_y_position = 0.00015\n for y_idx in range(128):\n cur_y_position = start_y_position + 0.0003 * y_idx\n detection_element_creator = DetectionElementCreator()\n detection_element_creator.set_detector_position(np.asarray([0, cur_y_position, 0]))\n detection_element_creator.set_detector_orientation(np.asarray([0, 0, 1]))\n detection_element_creator.set_detector_geometry_type(\"CUBOID\")\n detection_element_creator.set_detector_geometry(np.asarray([0.0003, 0.0003, 0.0001]))\n detection_element_creator.set_frequency_response(np.asarray([np.linspace(700, 900, 100),\n np.ones(100)]))\n detection_element_creator.set_angular_response(np.asarray([np.linspace(700, 900, 100),\n np.ones(100)]))\n\n device_creator.add_detection_element(detection_element_creator.get_dictionary())\n\n for y_idx in range(2):\n illumination_element_creator = IlluminationElementCreator()\n illumination_element_creator.set_beam_divergence_angles(0.20944)\n illumination_element_creator.set_wavelength_range(np.asarray([700, 950, 1]))\n if y_idx == 0:\n illumination_element_creator.set_illuminator_position(np.asarray([0.0083, 0.0192, -0.001]))\n illumination_element_creator.set_illuminator_orientation(np.asarray([-0.383972, 0, 1]))\n elif y_idx == 1:\n illumination_element_creator.set_illuminator_position(np.asarray([-0.0083, 0.0192, -0.001]))\n illumination_element_creator.set_illuminator_orientation(np.asarray([0.383972, 0, 1]))\n illumination_element_creator.set_illuminator_geometry(np.asarray([0, 0.025, 0]))\n illumination_element_creator.set_illuminator_geometry_type(\"CUBOID\")\n\n illumination_element_creator.set_laser_energy_profile(np.asarray([np.linspace(700, 900, 100),\n np.ones(100)]))\n illumination_element_creator.set_laser_stability_profile(np.asarray([np.linspace(700, 900, 100),\n np.ones(100)]))\n illumination_element_creator.set_pulse_width(7e-9)\n device_creator.add_illumination_element(illumination_element_creator.get_dictionary())\n\n return device_creator.finalize_device_meta_data()\n\n def set_metadata_value(self, metadata_tag: MetaDatum) -> object:\n if metadata_tag == MetadataAcquisitionTags.UUID:\n return \"TestUUID\"\n elif metadata_tag == MetadataAcquisitionTags.DATA_TYPE:\n return self.meta['type']\n elif metadata_tag == MetadataAcquisitionTags.AD_SAMPLING_RATE:\n return float(self.meta['space directions'][1][1]) / 1000000\n elif metadata_tag == MetadataAcquisitionTags.ACOUSTIC_COUPLING_AGENT:\n return \"Water\"\n elif metadata_tag == MetadataAcquisitionTags.ACQUISITION_OPTICAL_WAVELENGTHS:\n return np.asarray([700])\n elif metadata_tag == MetadataAcquisitionTags.COMPRESSION:\n return \"None\"\n elif metadata_tag == MetadataAcquisitionTags.DIMENSIONALITY:\n return \"time\"\n elif metadata_tag == MetadataAcquisitionTags.ENCODING:\n return \"raw\"\n elif metadata_tag == MetadataAcquisitionTags.SCANNING_METHOD:\n return \"Freehand\"\n elif metadata_tag == MetadataAcquisitionTags.PHOTOACOUSTIC_IMAGING_DEVICE:\n return \"c771111c-36ba-425d-9f53-84b8ff092059\"\n elif metadata_tag == MetadataAcquisitionTags.SIZES:\n return np.asarray(self.meta['sizes'])\n else:\n return None\n","sub_path":"ipasc_tool/api/adapters/DKFZ_CAMI_Experimental_System_Nrrd_File_Converter.py","file_name":"DKFZ_CAMI_Experimental_System_Nrrd_File_Converter.py","file_ext":"py","file_size_in_byte":6746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"289987548","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nimport logging\nfrom typing import List, Optional\n\nimport torch\nimport torch.nn.functional as F\nfrom reagent.evaluation.evaluation_data_page import EvaluationDataPage\nfrom reagent.evaluation.evaluator import Evaluator\nfrom reagent.optimizer import Optimizer__Union\nfrom reagent.parameters import EvaluationParameters, RLParameters\nfrom reagent.torch_utils import masked_softmax\nfrom reagent.training.reagent_lightning_module import ReAgentLightningModule\nfrom reagent.training.rl_trainer_pytorch import RLTrainerMixin\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass DQNTrainerMixin:\n # Q-value for action that is not possible. Guaranteed to be worse than any\n # legitimate action\n ACTION_NOT_POSSIBLE_VAL = -1e9\n\n def get_max_q_values(self, q_values, possible_actions_mask):\n return self.get_max_q_values_with_target(\n q_values, q_values, possible_actions_mask\n )\n\n def get_max_q_values_with_target(\n self, q_values, q_values_target, possible_actions_mask\n ):\n \"\"\"\n Used in Q-learning update.\n\n :param q_values: PyTorch tensor with shape (batch_size, state_dim). Each row\n contains the list of Q-values for each possible action in this state.\n\n :param q_values_target: PyTorch tensor with shape (batch_size, state_dim). Each row\n contains the list of Q-values from the target network\n for each possible action in this state.\n\n :param possible_actions_mask: PyTorch tensor with shape (batch_size, action_dim).\n possible_actions[i][j] = 1 iff the agent can take action j from\n state i.\n\n Returns a tensor of maximum Q-values for every state in the batch\n and also the index of the corresponding action. NOTE: looks like\n this index is only used for informational purposes only and does\n not affect any algorithms.\n\n \"\"\"\n\n # The parametric DQN can create flattened q values so we reshape here.\n q_values = q_values.reshape(possible_actions_mask.shape)\n q_values_target = q_values_target.reshape(possible_actions_mask.shape)\n # Set q-values of impossible actions to a very large negative number.\n inverse_pna = 1 - possible_actions_mask\n impossible_action_penalty = self.ACTION_NOT_POSSIBLE_VAL * inverse_pna\n q_values = q_values + impossible_action_penalty\n q_values_target = q_values_target + impossible_action_penalty\n\n if self.double_q_learning:\n # Use indices of the max q_values from the online network to select q-values\n # from the target network. This prevents overestimation of q-values.\n # The torch.gather function selects the entry from each row that corresponds\n # to the max_index in that row.\n max_q_values, max_indicies = torch.max(q_values, dim=1, keepdim=True)\n max_q_values_target = torch.gather(q_values_target, 1, max_indicies)\n else:\n max_q_values_target, max_indicies = torch.max(\n q_values_target, dim=1, keepdim=True\n )\n\n return max_q_values_target, max_indicies\n\n\nclass DQNTrainerBaseLightning(DQNTrainerMixin, RLTrainerMixin, ReAgentLightningModule):\n def __init__(\n self,\n rl_parameters: RLParameters,\n metrics_to_score=None,\n actions: Optional[List[str]] = None,\n evaluation_parameters: Optional[EvaluationParameters] = None,\n ):\n super().__init__()\n self.rl_parameters = rl_parameters\n self.time_diff_unit_length = rl_parameters.time_diff_unit_length\n self.tensorboard_logging_freq = rl_parameters.tensorboard_logging_freq\n self.calc_cpe_in_training = (\n evaluation_parameters and evaluation_parameters.calc_cpe_in_training\n )\n self._actions = actions\n\n if rl_parameters.q_network_loss == \"mse\":\n self.q_network_loss = F.mse_loss\n elif rl_parameters.q_network_loss == \"huber\":\n self.q_network_loss = F.smooth_l1_loss\n else:\n raise Exception(\n \"Q-Network loss type {} not valid loss.\".format(\n rl_parameters.q_network_loss\n )\n )\n\n if metrics_to_score:\n self.metrics_to_score = metrics_to_score + [\"reward\"]\n else:\n self.metrics_to_score = [\"reward\"]\n\n @property\n def num_actions(self) -> int:\n assert self._actions is not None, \"Not a discrete action DQN\"\n # pyre-fixme[6]: Expected `Sized` for 1st param but got `Optional[List[str]]`.\n return len(self._actions)\n\n # pyre-fixme[56]: Decorator `torch.no_grad(...)` could not be called, because\n # its type `no_grad` is not callable.\n @torch.no_grad()\n def boost_rewards(\n self, rewards: torch.Tensor, actions: torch.Tensor\n ) -> torch.Tensor:\n # Apply reward boost if specified\n reward_boosts = torch.sum(\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `reward_boosts`.\n actions.float() * self.reward_boosts,\n dim=1,\n keepdim=True,\n )\n return rewards + reward_boosts\n\n def _initialize_cpe(\n self,\n reward_network,\n q_network_cpe,\n q_network_cpe_target,\n optimizer: Optimizer__Union,\n ) -> None:\n if not self.calc_cpe_in_training:\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `reward_network`.\n self.reward_network = None\n return\n\n assert reward_network is not None, \"reward_network is required for CPE\"\n self.reward_network = reward_network\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `reward_network_optimizer`.\n self.reward_network_optimizer = optimizer\n assert (\n q_network_cpe is not None and q_network_cpe_target is not None\n ), \"q_network_cpe and q_network_cpe_target are required for CPE\"\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `q_network_cpe`.\n self.q_network_cpe = q_network_cpe\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `q_network_cpe_target`.\n self.q_network_cpe_target = q_network_cpe_target\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `q_network_cpe_optimizer`.\n self.q_network_cpe_optimizer = optimizer\n num_output_nodes = len(self.metrics_to_score) * self.num_actions\n reward_idx_offsets = torch.arange(\n 0,\n num_output_nodes,\n self.num_actions,\n dtype=torch.long,\n )\n self.register_buffer(\"reward_idx_offsets\", reward_idx_offsets)\n\n # pyre-fixme[16]: `DQNTrainerBase` has no attribute `evaluator`.\n self.evaluator = Evaluator(\n self._actions,\n self.rl_parameters.gamma,\n self.trainer,\n metrics_to_score=self.metrics_to_score,\n )\n\n def _calculate_cpes(\n self,\n training_batch,\n states,\n next_states,\n all_action_scores,\n all_next_action_scores,\n logged_action_idxs,\n discount_tensor,\n not_done_mask,\n ):\n if not self.calc_cpe_in_training:\n return\n if training_batch.extras.metrics is None:\n metrics_reward_concat_real_vals = training_batch.reward\n else:\n metrics_reward_concat_real_vals = torch.cat(\n (training_batch.reward, training_batch.extras.metrics), dim=1\n )\n\n model_propensities_next_states = masked_softmax(\n all_next_action_scores,\n training_batch.possible_next_actions_mask\n if self.maxq_learning\n else training_batch.next_action,\n self.rl_temperature,\n )\n\n ######### Train separate reward network for CPE evaluation #############\n reward_estimates = self.reward_network(states)\n reward_estimates_for_logged_actions = reward_estimates.gather(\n 1, self.reward_idx_offsets + logged_action_idxs\n )\n reward_loss = F.mse_loss(\n reward_estimates_for_logged_actions, metrics_reward_concat_real_vals\n )\n yield reward_loss\n\n ######### Train separate q-network for CPE evaluation #############\n metric_q_values = self.q_network_cpe(states).gather(\n 1, self.reward_idx_offsets + logged_action_idxs\n )\n all_metrics_target_q_values = torch.chunk(\n self.q_network_cpe_target(next_states).detach(),\n len(self.metrics_to_score),\n dim=1,\n )\n target_metric_q_values = []\n for i, per_metric_target_q_values in enumerate(all_metrics_target_q_values):\n per_metric_next_q_values = torch.sum(\n per_metric_target_q_values * model_propensities_next_states,\n 1,\n keepdim=True,\n )\n per_metric_next_q_values = per_metric_next_q_values * not_done_mask\n per_metric_target_q_values = metrics_reward_concat_real_vals[\n :, i : i + 1\n ] + (discount_tensor * per_metric_next_q_values)\n target_metric_q_values.append(per_metric_target_q_values)\n\n target_metric_q_values = torch.cat(target_metric_q_values, dim=1)\n metric_q_value_loss = self.q_network_loss(\n metric_q_values, target_metric_q_values\n )\n\n model_propensities = masked_softmax(\n all_action_scores,\n training_batch.possible_actions_mask\n if self.maxq_learning\n else training_batch.action,\n self.rl_temperature,\n )\n model_rewards = reward_estimates[\n :,\n torch.arange(\n self.reward_idx_offsets[0],\n self.reward_idx_offsets[0] + self.num_actions,\n ),\n ]\n\n self.reporter.log(\n reward_loss=reward_loss,\n model_propensities=model_propensities,\n model_rewards=model_rewards,\n )\n\n yield metric_q_value_loss\n\n def test_step(self, batch, batch_idx):\n return batch\n\n def gather_eval_data(self, test_step_outputs):\n eval_data = None\n for batch in test_step_outputs:\n edp = EvaluationDataPage.create_from_training_batch(batch, self)\n if eval_data is None:\n eval_data = edp\n else:\n eval_data = eval_data.append(edp)\n if eval_data and eval_data.mdp_id is not None:\n eval_data = eval_data.sort()\n eval_data = eval_data.compute_values(self.gamma)\n eval_data.validate()\n return eval_data\n\n def test_epoch_end(self, test_step_outputs):\n eval_data = self.gather_eval_data(test_step_outputs)\n if eval_data and eval_data.mdp_id is not None:\n cpe_details = self.evaluator.evaluate_post_training(eval_data)\n self.reporter.log(cpe_details=cpe_details)\n","sub_path":"reagent/training/dqn_trainer_base.py","file_name":"dqn_trainer_base.py","file_ext":"py","file_size_in_byte":11074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"151648279","text":"import usocket\nimport ustruct\nimport machine\nimport utime\n\nNTP_DELTA = 3155673600 - (9*60*60) # utime epoch(2000) - ntp epoch(1900) - JST\nhost = \"pool.ntp.org\"\n\n\ndef time():\n NTP_QUERY = bytearray(48)\n NTP_QUERY[0] = 0x1B\n addr = usocket.getaddrinfo(host, 123)[0][-1]\n s = usocket.usocket(usocket.AF_INET, usocket.SOCK_DGRAM)\n try:\n s.settimeout(1)\n s.sendto(NTP_QUERY, addr)\n msg = s.recv(48)\n finally:\n s.close()\n val = ustruct.unpack(\"!I\", msg[40:44])[0]\n return val - NTP_DELTA\n\n\ndef settime():\n t = time()\n\n tm = utime.localtime(t)\n machine.RTC().datetime((tm[0], tm[1], tm[2], tm[6] + 1, tm[3], tm[4], tm[5], 0))\n","sub_path":"ntptime.py","file_name":"ntptime.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"550412699","text":"n=int(input())\nfor p in range(n):\n s=str(input())\n k=int(input())\n kk=len(s)*k\n a=[39]\n b=[8]\n for i in range(len(aa)):\n if kk==a[i]:\n kk=b[i]\n break\n print(k)","sub_path":"Code/CodeRecords/2548/60829/292372.py","file_name":"292372.py","file_ext":"py","file_size_in_byte":209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"326144029","text":"#!/usr/bin/env python3\n\nimport sys\nimport json\nfrom typing import List\n\n\nclass Node:\n def __init__(self, left=None, right=None, value=None, parent=None):\n self.left = left\n self.right = right\n self.value = value\n self.parent = parent\n\n def __str__(self):\n if self.value is not None:\n return str(self.value)\n\n return \"[%s,%s]\" % (str(self.left), str(self.right))\n\n\ndef build_tree(data: str):\n if isinstance(data, int):\n return Node(value=data)\n\n n1 = build_tree(data[0])\n n2 = build_tree(data[1])\n\n tree = Node(left=n1, right=n2)\n\n n1.parent = tree\n n2.parent = tree\n\n return tree\n\n\ndef parse_tree(data: str) -> Node:\n return build_tree(json.loads(data))\n\n\ndef add_left(tree: Node, value: int) -> None:\n while tree.parent is not None:\n if tree.parent.right == tree:\n break\n tree = tree.parent\n\n tree = tree.parent\n\n if tree is None:\n return\n\n tree = tree.left\n\n while tree.value is None:\n tree = tree.right\n\n tree.value += value\n\n\ndef add_right(tree: Node, value: int) -> None:\n while tree.parent is not None:\n if tree.parent.left == tree:\n break\n tree = tree.parent\n\n tree = tree.parent\n\n if tree is None:\n return\n\n tree = tree.right\n\n while tree.value is None:\n tree = tree.left\n\n tree.value += value\n\n\ndef reduce_explode_rec(tree: Node, depth: int):\n if tree.left is None and tree.right is None:\n return tree, False\n\n if (depth >= 4 and tree.left.value is not None and\n tree.right.value is not None):\n add_left(tree, tree.left.value)\n add_right(tree, tree.right.value)\n return Node(value=0, parent=tree.parent), True\n\n left, status = reduce_explode_rec(tree.left, depth + 1)\n if status:\n tree.left = left\n left.parent = tree\n return tree, True\n\n right, status = reduce_explode_rec(tree.right, depth + 1)\n tree.right = right\n right.parent = tree\n return tree, status\n\n\ndef reduce_split_rec(tree: Node):\n if tree.left is None and tree.right is None:\n if tree.value < 10:\n return tree, False\n else:\n n1 = Node(value=(tree.value // 2))\n n2 = Node(value=tree.value - (tree.value // 2))\n n = Node(left=n1, right=n2)\n n1.parent = n\n n2.parent = n\n return n, True\n\n left, status = reduce_split_rec(tree.left)\n if status:\n tree.left = left\n left.parent = tree\n return tree, True\n\n right, status = reduce_split_rec(tree.right)\n tree.right = right\n right.parent = tree\n return tree, status\n\n\ndef reduce_operation(tree: Node) -> Node:\n while True:\n tree, flag = reduce_explode_rec(tree, 0)\n if flag:\n continue\n tree, flag = reduce_split_rec(tree)\n if not flag:\n return tree\n\n\ndef sum_operation(data: List[str]) -> Node:\n v1 = parse_tree(data[0])\n\n for i in range(1, len(data)):\n v2 = parse_tree(data[i])\n n = Node(left=v1, right=v2)\n v1.parent = n\n v2.parent = n\n v1 = reduce_operation(n)\n\n return v1\n\n\ndef magnitude(data: Node) -> int:\n if data.value is not None:\n return data.value\n return 3 * magnitude(data.left) + 2 * magnitude(data.right)\n\n\nclass TestClass():\n\n def test_1(self):\n data = parse_tree('[[[[[4,3],4],4],[7,[[8,4],9]]],[1,1]]')\n answer = '[[[[0,7],4],[[7,8],[6,0]]],[8,1]]'\n assert str(reduce_operation(data)) == answer\n\n def test_2(self):\n data = [\n '[1,1]',\n '[2,2]',\n '[3,3]',\n '[4,4]',\n ]\n answer = '[[[[1,1],[2,2]],[3,3]],[4,4]]'\n assert str(sum_operation(data)) == answer\n\n def test_3(self):\n data = [\n '[1,1]',\n '[2,2]',\n '[3,3]',\n '[4,4]',\n '[5,5]',\n ]\n answer = '[[[[3,0],[5,3]],[4,4]],[5,5]]'\n assert str(sum_operation(data)) == answer\n\n def test_4(self):\n data = [\n '[1,1]',\n '[2,2]',\n '[3,3]',\n '[4,4]',\n '[5,5]',\n '[6,6]',\n ]\n answer = '[[[[5,0],[7,4]],[5,5]],[6,6]]'\n assert str(sum_operation(data)) == answer\n\n def test_5(self):\n data = [\n '[[[0,[4,5]],[0,0]],[[[4,5],[2,6]],[9,5]]]',\n '[7,[[[3,7],[4,3]],[[6,3],[8,8]]]]',\n '[[2,[[0,8],[3,4]]],[[[6,7],1],[7,[1,6]]]]',\n '[[[[2,4],7],[6,[0,5]]],[[[6,8],[2,8]],[[2,1],[4,5]]]]',\n '[7,[5,[[3,8],[1,4]]]]',\n '[[2,[2,2]],[8,[8,1]]]',\n '[2,9]',\n '[1,[[[9,3],9],[[9,0],[0,7]]]]',\n '[[[5,[7,4]],7],1]',\n '[[[[4,2],2],6],[8,7]]',\n ]\n answer = '[[[[8,7],[7,7]],[[8,6],[7,7]]],[[[0,7],[6,6]],[8,7]]]'\n assert str(sum_operation(data)) == answer\n\n def test_6(self):\n data = [\n '[[[0,[5,8]],[[1,7],[9,6]]],[[4,[1,2]],[[1,4],2]]]',\n '[[[5,[2,8]],4],[5,[[9,9],0]]]',\n '[6,[[[6,2],[5,6]],[[7,6],[4,7]]]]',\n '[[[6,[0,7]],[0,9]],[4,[9,[9,0]]]]',\n '[[[7,[6,4]],[3,[1,3]]],[[[5,5],1],9]]',\n '[[6,[[7,3],[3,2]]],[[[3,8],[5,7]],4]]',\n '[[[[5,4],[7,7]],8],[[8,3],8]]',\n '[[9,3],[[9,9],[6,[4,9]]]]',\n '[[2,[[7,7],7]],[[5,8],[[9,3],[0,2]]]]',\n '[[[[5,2],5],[8,[3,7]]],[[5,[7,5]],[4,4]]]',\n ]\n answer = '[[[[6,6],[7,6]],[[7,7],[7,0]]],[[[7,7],[7,7]],[[7,8],[9,9]]]]'\n assert str(sum_operation(data)) == answer\n\n def test_7(self):\n data = parse_tree('[[1,2],[[3,4],5]]')\n assert magnitude(data) == 143\n\n data = parse_tree('[[[[0,7],4],[[7,8],[6,0]]],[8,1]]')\n assert magnitude(data) == 1384\n\n data = parse_tree('[[[[1,1],[2,2]],[3,3]],[4,4]]')\n assert magnitude(data) == 445\n\n data = parse_tree('[[[[3,0],[5,3]],[4,4]],[5,5]]')\n assert magnitude(data) == 791\n\n data = parse_tree('[[[[5,0],[7,4]],[5,5]],[6,6]]')\n assert magnitude(data) == 1137\n\n data = parse_tree('[[[[8,7],[7,7]],[[8,6],[7,7]]],[[[0,7],[6,6]],[8,7]]]')\n assert magnitude(data) == 3488\n\n data = parse_tree('[[[[6,6],[7,6]],[[7,7],[7,0]]],[[[7,7],[7,7]],[[7,8],[9,9]]]]')\n assert magnitude(data) == 4140\n\n\ndef main():\n data = [x.strip() for x in sys.stdin]\n result = magnitude(sum_operation(data))\n print(result)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"2021/day_18_snailfish_1.py","file_name":"day_18_snailfish_1.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"212024001","text":"from flask import render_template, url_for, request, flash,redirect\nfrom flask.ext.login import login_user, logout_user, login_required\nfrom . import main\nfrom .. forms import LoginForm,RegisterForm\nfrom .. import db\nfrom ..models import User\n\n@main.route('/login',methods=['GET','POST'])\ndef login():\n form = LoginForm()\n return render_template('login.html',form=form)\n@main.route('/register',methods=['GET','POST'])\ndef register():\n form = RegisterForm()\n if form.validate_on_submit():\n user = User(email=form.email.data,username=form.username.data,password_hash=form.password.data)\n db.session.add(user)\n db.session.commit()\n flash('You can now login.')\n return redirect(url_for('main.login'))\n return render_template('register.html',form=form)\n\n@main.route('/logout')\n@login_required\ndef logout():\n logout_user()\n flash('have been logged out')\n\n@main.route('/')\ndef index():\n return render_template('index.html')\n","sub_path":"workplace/python/app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"25735542","text":"\n#counts the number of times name \"bob\" appear on an inputed string\ns = input(\"\")\ncount = 0\nbob = \"bob\"\nfor i in range(len(s)):\n\tif len(bob) > (len(s) - i): \n\t\tbreak\n\telse:\n\t\tif bob[0] == s[i] and bob[1] == s[i+1] and bob[2] == s[i+2]:\n\t\t\tcount +=1\nprint(\"Number of times bob occurs is: \" + str(count))","sub_path":"python02.py","file_name":"python02.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"362821835","text":"DEBUG = True\n\n\nCOMPRESS_AUTO = True\nSESSION_COOKIE_SECURE = False\n\nSTATICFILES_STORAGE = 'django.contrib.staticfiles.storage.StaticFilesStorage'\nSTATIC_URL = '/static/'\nSTATIC_ROOT = '/app/static'\n\nDEFAULT_FILE_STORAGE = 'django.core.files.storage.FileSystemStorage'\nMEDIA_URL = \"/media/\"\nMEDIA_ROOT = \"/app/media\"\n\nCONN_MAX_AGE=0","sub_path":"src/webapp_2017_02_24/settings_dev.py","file_name":"settings_dev.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"199116399","text":"import sys\nimport math\n\nx = int(input())\nn = int(input())\nbricks = []\nfor i in input().split():\n bricks.append(int(i))\nbricks.sort(reverse=True)\n\nh = 1\nenergy = 0\nwhile len(bricks) >= x:\n energy += sum(bricks[:x]) * (h - 1) * 0.65\n bricks = bricks[x:]\n h = h + 1\n\nenergy += sum(bricks) * (h - 1) * 0.65\nprint(\"{:0.3f}\".format(energy))","sub_path":"practice/puzzles/easy/Brick_in_the_Wall.py","file_name":"Brick_in_the_Wall.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"565120162","text":"#!/usr/bin/python3\n\ndef ProteinToKBId(mapping):\n protein2KbId = {}\n with open(mapping, 'r') as f:\n f.readline()\n for line in f:\n items = line.strip().split(\"\\t\")\n protein2KbId[items[0]] = items[1]\n return protein2KbId\n\ndef NegPairInDataSet(corpusList, protein2KbId):\n negPair = set()\n for filename in corpusList:\n with open(filename, 'r') as f:\n for line in f:\n items = line.strip().split(\"\\t\")\n if items[-1] == '0':\n id1 = items[2]\n id2 = items[3]\n negPair.add(protein2KbId[id1] + \"\\t\" + protein2KbId[id2])\n return negPair\ndef TriplesInKB(triples):\n pairs = set()\n maxRelationId = 0\n with open(triples, 'r') as f:\n f.readline()\n for line in f:\n items = line.strip().split(\"\\t\")\n pairs.add(items[0] + \"\\t\" + items[1])\n pairs.add(items[1] + \"\\t\" + items[0])\n currentRelationId = int(items[-1])\n if currentRelationId > maxRelationId:\n maxRelationId = currentRelationId\n return pairs, maxRelationId\nfold = \"1\"\ncorpusList = [\"/home/laboratory/lab/BioCreative/2017/BC6/corpus_train\" + fold + \".txt\"]\nprotein2KBidFile = \"/home/laboratory/lab/BioCreative/codePlayer/Fast-TransX/dataNeg\" + fold + \"/entity2id.txt.new\"\ntriplesInKBFile = \"/home/laboratory/lab/BioCreative/codePlayer/Fast-TransX/dataNeg\" + fold + \"/triple2id.txt.old\"\nnewTriples2IdFile = \"/home/laboratory/lab/BioCreative/codePlayer/Fast-TransX/dataNeg\" + fold + \"/triple2id.txt\"\n\nprotein2KbId = ProteinToKBId(protein2KBidFile)\nnegPairs = NegPairInDataSet(corpusList, protein2KbId)\npairsInKB, maxRelationId = TriplesInKB(triplesInKBFile)\n\nwith open(newTriples2IdFile, 'w') as f:\n for pair in negPairs:\n if pair not in pairsInKB:\n f.write(\"{}\\t{}\\n\".format(pair, maxRelationId + 1))\n","sub_path":"KBExtractor/addNegTriple.py","file_name":"addNegTriple.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"522231829","text":"import copy\nimport collections\n\nclass BooleanNetwork:\n def __init__(self, matrix):\n # Propagation Matrix\n self.matrix = matrix\n\n # Number of genes/nodes\n self.nb_bits = len(matrix)\n\n # Number of possible states (in our example 2**6 = 64)\n self.nb_states = int(2**self.nb_bits)\n\n # For each gene, we can set a threshold. Here we assume it's 0 for all of them\n self.threshold = [0] * self.nb_bits\n\n\n def int_to_binary(self, n):\n \"\"\"\n Convert an integer to binary\n /!\\ Uses the variable: SELF.NB_BITS !\n\n :Source: https://stackoverflow.com/questions/10411085/converting-integer-to-binary-in-python\n :param n: Integer\n :return: map object -> use list comprehension to print/loop in etc\n \"\"\"\n ret = bin(n)[2:].zfill(self.nb_bits)\n return list(map(int, format(ret)))\n\n def binary_to_int(self, n):\n \"\"\"\n Converts binary number (list) to Integer\n :param n: List of bits\n :return: the list converted to int\n \"\"\"\n ret = \"\"\n for bit in n:\n ret += str(bit)\n ret_int = int(ret,2)\n return ret_int\n\n\n def get_states_sequence(self, start_state):\n \"\"\"\n From a passed starting state (INT), calculates all the following states and stops when a loop is detected.\n :param start_state: INT - number of the starting state\n :return: The sequence of states,\n \"\"\"\n\n binary_current_state = self.int_to_binary(start_state)\n\n binary_next_state = [0] * self.nb_bits\n states_sequence = [start_state]\n\n state_not_visited = True\n\n # While the state haven't already been visited...(while not orbit)\n while state_not_visited:\n\n for i in range(0, self.nb_bits):\n sum_next_state = 0\n for j in range(0, self.nb_bits):\n # print(\"Fucking i: \", i, \" and j: \", j)\n # print(binary_current_state[j] * self.matrix[j][i])\n sum_next_state += binary_current_state[j] * self.matrix[j][i]\n\n # Now we can set the next state for this gene\n if sum_next_state > self.threshold[i]:\n binary_next_state[i] = 1\n else:\n binary_next_state[i] = 0\n\n # If the next state doesn't already exist, we add it to the states list and continue\n # If not, we add it and end the while loop\n\n #print(\"CURRENT STATE: \", binary_next_state)\n if self.binary_to_int(binary_next_state) not in states_sequence:\n states_sequence.append(self.binary_to_int(binary_next_state))\n else:\n states_sequence.append(self.binary_to_int(binary_next_state))\n state_not_visited = False\n\n # The state now (starting state) is the next state\n\n binary_current_state = copy.copy(binary_next_state)\n\n return states_sequence\n\n def orbit(self, start_state):\n \"\"\"\n Return many things, orbit is a random name here\n :param start_state:\n :return: see comments below\n \"\"\"\n # Sequence from start state -> stabilization\n sequence = self.get_states_sequence(start_state)\n\n # At which state it closes its orbit\n closure = sequence[-1]\n\n # Basin = states leading to periodic orbits (cyclic attractor)\n partial_basin = sequence[0:sequence.index(closure)]\n\n # Periodic orbit (cyclic attractor) of the sequence\n periodic_orbit = sequence[sequence.index(closure):len(sequence) - 1]\n\n # Size of the orbit\n orbit_size = len(sequence) - sequence.index(closure) - 1\n\n return orbit_size, periodic_orbit, closure, partial_basin\n\n\n def count_attractors(self):\n \"\"\"\n # Returns a counter containing the sets of attractors and their occurence\n # In our case:\n # Counter({frozenset({0}): 23,\n # frozenset({1, 3, 7, 13, 55, 23, 63}): 21,\n # frozenset({39, 19, 5, 31}): 11,\n # frozenset({18, 26, 4, 36}): 9})\n #\n # Notice that unfortunately here the order of the attractor is not maintained\n # Source: https://stackoverflow.com/questions/37295981/python-creating-a-dictionary-with-key-as-a-set-and-value-as-its-count\n #\n\n :return:\n \"\"\"\n all_attractors_list = [sorted(self.orbit(i)[1]) for i in range(self.nb_states)]\n attractors_count = collections.Counter(frozenset(x) for x in all_attractors_list)\n\n return attractors_count\n\n\n def average_occupancies_in_orbit(self, orbit, print_steps = None):\n \"\"\"\n Count occurence of ABCDEF activated in each states\n :param orbit:\n :return: list of percentages like [0.25, 0.5, 0.25, 0.5, 0.5, 0.0]\n \"\"\"\n total_bits = [0 for _ in range(0, self.nb_bits)]\n percentages = [0.0 for _ in range(0, self.nb_bits)]\n\n # For every state, convert to binary and increment the occupancy\n for state in orbit:\n bin = self.int_to_binary(state)\n\n # if the optional parameter is true, print the binary states\n # allow to see more clearely the evolution of the different gene activation\n if print_steps:\n print(bin)\n #Increment here\n for i in range(0, len(bin)):\n total_bits[i] += bin[i]\n # Calculate percentages\n total_elem = len(orbit)\n for i in range(0, self.nb_bits):\n percentages[i] = total_bits[i] / total_elem\n\n return percentages\n\n\n\n def get_basin_of_attraction(self):\n \"\"\"\n\n :return:\n \"\"\"\n\n sequence_set = collections.defaultdict(list)\n\n for i in range(0, self.nb_states):\n orbit = self.orbit(i)\n unique_orbit_string = str(sorted(orbit[1]))\n # We have the \"previous steps\" and the cyclic attractor -> add the previous steps to the set corresponding to the attractor to have the full basin\n sequence_set[unique_orbit_string].extend(orbit[3])\n # To have the whole basin, we add also the cycle\n sequence_set[unique_orbit_string].extend(orbit[1])\n\n for e in sequence_set:\n sequence_set[e] = list(set(sequence_set[e]))\n\n return sequence_set","sub_path":"Assignments/Assignment6/Scripts/boolean_network.py","file_name":"boolean_network.py","file_ext":"py","file_size_in_byte":6410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"3789360","text":"import math\r\nfrom gfxhat import lcd, backlight, fonts\r\nimport time\r\nimport random\r\nfrom PIL import Image, ImageFont, ImageDraw\r\n#This function will process Radius calculation\r\n\r\ndef areaOfCircle(Radius):\r\n \r\n userRadius = int(Radius)\r\n AreaCircle = math.pi*(userRadius**2)\r\n return AreaCircle\r\n\r\n#This function will process MPG car\r\n\r\ndef carMPG(miles, gallons):\r\n \r\n userMiles = int(miles)\r\n userGallons = float(gallons)\r\n\r\n userMPG = userMiles/userGallons\r\n return userMPG\r\n\r\n#This function will process Temperature calculation\r\n\r\ndef degreeFtoC(Ferenheit):\r\n \r\n userFerenheit = int(Ferenheit)\r\n celsius = (userFerenheit - 32)*(5/9)\r\n return celsius\r\n \r\n#This unction will calculate distance between points\r\n\r\ndef distanceBPoints(x,y,x1,y1):\r\n\r\n a= x-x1;\r\n b= y-y1;\r\n\r\n distanceCalculated = (a**2 + b**2)**.5\r\n return distanceCalculated\r\n\r\n#This function will draw verticalline in gfxhat\r\n\r\ndef verticalLine(x):\r\n x = int(x);\r\n y = 0;\r\n lcd.clear();\r\n lcd.show();\r\n backlight.set_pixel(0,0,0,0)\r\n backlight.show()\r\n for y in range(0,63):\r\n lcd.set_pixel(x,y,1)\r\n y = y+1;\r\n lcd.show()\r\n \r\n \r\n#This function will draw horizontal line\r\n \r\ndef horizontalLine(x):\r\n x = int(x);\r\n y = 0;\r\n lcd.clear();\r\n lcd.show();\r\n backlight.set_pixel(0,0,0,0)\r\n backlight.show()\r\n for y in range(0,128):\r\n lcd.set_pixel(y,x,1)\r\n y = y+1;\r\n lcd.show()\r\n \r\n#This function will draw stairs in gfxhat \r\n \r\n\r\ndef stairs(x1,y1,width,height):\r\n\r\n width = int(width);\r\n height = int(height);\r\n x1 = int(x1); \r\n y1= int(y1);\r\n Y = 0;\r\n X = 0;\r\n if(y1+height < 63):\r\n \r\n while YSx or y1>Sy:\r\n if x1>Sx:\r\n print(\"This is first x1\",x1)\r\n x=x-8;\r\n vx=-vx;\r\n if y1>Sy:\r\n print(\"This is y1\",y1)\r\n y=y-8;\r\n vy=-vy;\r\n if x<0 or y<0:\r\n if x<0:\r\n print(\"this is x\",x)\r\n vx=abs(vx);\r\n x=x+vx;\r\n if y<0:\r\n print(\"this is y\",y)\r\n vy=abs(vy);\r\n y=y+vy;\r\n\r\n return [x,y,vx,vy]\r\n \r\n \r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n","sub_path":"library.py","file_name":"library.py","file_ext":"py","file_size_in_byte":4295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"343896960","text":"import sys\nDEBUG = False\nfor arg in sys.argv:\n if arg == '-v':\n DEBUG = True\n\n#Verbose for Debugging\ndef log(message):\n if DEBUG:\n print(message)\n\nimport numpy as np\n\nfileX = \"trainSubsetInput.csv\"\nfiley = \"trainSubsetTarget.csv\"\n\n\nX = np.loadtxt(open(\"trainSubsetInput.csv\", \"rb\"), delimiter=\",\")\ny = np.loadtxt(open(\"trainSubsetTarget.csv\", \"rb\"), delimiter=\",\")\ntestX = np.loadtxt(open(\"testInput.csv\", \"rb\"), delimiter=\",\")\ntesty = np.loadtxt(open(\"testTarget.csv\", \"rb\"), delimiter=\",\")\n\n\n\nindex0s = [i for i in range(len(y)) if y[i] == 0]\nindex1s = [i for i in range(len(y)) if y[i] == 1]\n\nlog(len(index0s))\nlog(len(index1s))\nlog(len(y))\n\nfrom sklearn import svm\nfrom sklearn.model_selection import GridSearchCV\n\n\n# Need a method for generating G given X and y\ndef G(X, y):\n index0s = [i for i in range(len(y)) if y[i] == 0]\n index1s = [i for i in range(len(y)) if y[i] == 1]\n X0 = X[index0s, :]\n X1 = X[index1s, :]\n \n\n G = []\n for i in range(len(y)):\n G += [shortestDistance(X[i, :], X1)] if y[i] == 0 else [shortestDistance(X[i, :], X0)]\n log(G[len(G) - 1])\n log(G[0])\n return np.median(G)\n\n\ndef shortestDistance(x, X):\n distances = [np.linalg.norm(x-xi) for xi in X]\n distances.sort()\n return distances[0]\n\ntheta = G(X, y)\ngamma = 1.0 / (2 * (theta ** 2))\n\nprint(\"Value of theta : \" + str(theta))\nprint(\"Value of gamma : \" + str(gamma))\n\nb = 2\n\n\ngammas = [gamma * (b ** i) for i in range(-3, 4)]\nCs = [b ** i for i in range(-1, 4)]\nparam_grid = {'C' : Cs, 'gamma' : gammas}\nlog(\"Paramaters under consideration\")\nlog(\"C values are : \" + str(Cs))\nlog(\"Gamma values are : \" + str(gammas))\ndef params(X, y, nfolds, param_grid):\n grid_search = GridSearchCV(svm.SVC(kernel = 'rbf'), param_grid, cv = nfolds)\n grid_search.fit(X, y)\n grid_search.best_params_\n return grid_search.best_params_\n\nlog(\"Selected Paramater are ....\")\nlog(\"---------------------------\")\nparams = params(X, y, 5, param_grid)\nlog(params)\nlog(\"---------------------------\")\n\nlog(\"Using paramaters to train model...\")\nmodel = svm.SVC(C = params['C'], gamma = params['gamma'], kernel = 'rbf')\nmodel.fit(X, y)\nlog(\"Model has been trained\")\nlog(\"Testing model on test data....\")\nlog(\"-----------------------------\")\naccuracy = model.score(testX, testy)\nlog(accuracy)\nlog(\"-----------------------------\")\nlog(\"Testing model on train data....\")\nlog(\"-----------------------------\")\naccuracy = model.score(X, y)\nlog(accuracy)\nlog(\"-----------------------------\")\n\n","sub_path":"Machine_Learning/final/temp/save.py","file_name":"save.py","file_ext":"py","file_size_in_byte":2460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"607351987","text":"from turtle import *\n\nshape(\"turtle\")\n\nn = int(input(\"So hinh thich ve: \"))\nfor i in range(3, n + 3):\n for j in range(0, i):\n forward(100)\n left(360/i)\n\nmainloop()\n","sub_path":"session02/homework/rua_hinh.py","file_name":"rua_hinh.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"243781141","text":"import os\nimport re\nimport sys\nimport shutil\nimport requests\nfrom urllib.parse import urlparse\n\npicspath = os.getenv(\"HOME\")+\"/Pictures/2ch/\"\nif not os.path.exists(picspath):\n os.makedirs(picspath)\n\ndef download(url):\n html = requests.get(url)\n board = urlparse(url).path.split('/')[1]\n thread_number = urlparse(url).path.split('/')[3].split('.')[0]\n URLS = re.findall(\"/%s/src/\\d+/\\w+.(?:jpg|gif|png|webm)\" %board, html.text)\n if not os.path.exists(\"%s%s/%s\" %(picspath, board, thread_number)):\n os.makedirs(\"%s%s/%s\" %(picspath, board, thread_number))\n for i in URLS:\n response = requests.get(\"http://2ch.hk%s\" %i, stream=True)\n with open(\"%s%s/%s/%s\" %(picspath, board, thread_number, i.split('/')[-1]), \"wb\") as outfile:\n shutil.copyfileobj(response.raw, outfile)\n\ndownload(sys.argv[1])\n","sub_path":"2chpd.py","file_name":"2chpd.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"584907857","text":"#!/usr/bin/env python\n# ***********************************************************************************************************\n#\n# Starfish Storage Corporation (\"COMPANY\") CONFIDENTIAL\n# Unpublished Copyright (c) 2011-2017 Starfish Storage Corporation, All Rights Reserved.\n#\n# NOTICE: All information contained herein is, and remains the property of COMPANY. The intellectual and\n# technical concepts contained herein are proprietary to COMPANY and may be covered by U.S. and Foreign\n# Patents, patents in process, and are protected by trade secret or copyright law. Dissemination of this\n# information or reproduction of this material is strictly forbidden unless prior written permission is\n# obtained from COMPANY. Access to the source code contained herein is hereby forbidden to anyone except\n# current COMPANY employees, managers or contractors who have executed Confidentiality and Non-disclosure\n# agreements explicitly covering such access.\n#\n# ANY REPRODUCTION, COPYING, MODIFICATION, DISTRIBUTION, PUBLIC PERFORMANCE, OR PUBLIC DISPLAY OF OR\n# THROUGH USE OF THIS SOURCE CODE WITHOUT THE EXPRESS WRITTEN CONSENT OF COMPANY IS STRICTLY PROHIBITED,\n# AND IN VIOLATION OF APPLICABLE LAWS AND INTERNATIONAL TREATIES. THE RECEIPT OR POSSESSION OF THIS SOURCE\n# CODE AND/OR RELATED INFORMATION DOES NOT CONVEY OR IMPLY ANY RIGHTS TO REPRODUCE, DISCLOSE OR DISTRIBUTE\n# ITS CONTENTS, OR TO MANUFACTURE, USE, OR SELL ANYTHING THAT IT MAY DESCRIBE, IN WHOLE OR IN PART.\n#\n# FOR U.S. GOVERNMENT CUSTOMERS REGARDING THIS DOCUMENTATION/SOFTWARE\n# These notices shall be marked on any reproduction of this data, in whole or in part.\n# NOTICE: Notwithstanding any other lease or license that may pertain to, or accompany the delivery of,\n# this computer software, the rights of the Government regarding its use, reproduction and disclosure are\n# as set forth in Section 52.227-19 of the FARS Computer Software-Restricted Rights clause.\n# RESTRICTED RIGHTS NOTICE: Use, duplication, or disclosure by the Government is subject to the\n# restrictions as set forth in subparagraph (c)(1)(ii) of the Rights in Technical Data and Computer\n# Software clause at DFARS 52.227-7013.\n#\n# ***********************************************************************************************************\n# -*- mode: python -*-\n\nfrom os.path import join\n\n\"\"\"\n full list of GPFS/DMAPI binaries available for use\n this fragment needs to be copied into spec files that will use any of the SF GPFS compiled binaries\n at this time, it can't be imported into the spec file (but that would be the ideal solution)\n\ngpfs_execs = [\n'mls', 'mrmean', 'wbee', 'dm_create_session', 'dm_destroy_session',\n'dm_getall_sessions', 'dm_getall_tokens', 'dm_query_session', 'dm_qall_sessions',\n'get_eventlist', 'set_eventlist', 'set_disp', 'get_region', 'get_fileattr', 'set_fileattr',\n'path_to_handle', 'path_to_fshandle', 'getall_disp', 'get_events',\n'dm_fsid', 'dm_handle', 'handle_to_inode', 'handle_to_fshandle', 'get_event_chunk', \n'get_allocinfo', 'probe_hole', 'punch_hole', 'makeFileStub',\n'copyIn', 'copyOut', 'copyStdin', 'copyStdout', 'set_region', 'sf_delTree',\n'sf_acl', 'sf_incrementalTreeScan', 'sf_scanSubTree', 'sf_scanInodeRange', 'sf_walkTree',\n'sf_setpool', 'sf_xattrsget', 'sf_xattrsput', 'sf_fattrsget', 'sf_fattrsput', 'sf_genericDirTree', 'sf_gScanTree',\n]\n\"\"\"\ngpfs_execs = [\n'dm_create_session', 'dm_destroy_session',\n'dm_getall_sessions', 'dm_getall_tokens', 'dm_query_session', 'dm_qall_sessions',\n'get_eventlist', 'set_eventlist', 'set_disp',\n'path_to_handle', 'getall_disp', 'get_events',\n'handle_to_inode', 'handle_to_fshandle', 'get_event_chunk',\n'get_allocinfo', 'makeFileStub',\n'copyIn', 'copyOut', 'copyStdin', 'copyStdout', 'set_region',\n'sf_acl', 'sf_scanInodeRange',\n]\n\n\ngpfs_bins = [(join('../../bin',binary),'bin') for binary in gpfs_execs]\ngpfs_libs = [('../../lib/libcommondmapi.so','lib')]\n","sub_path":"sf-gpfs/gpfsmonitor/src/sf_gpfs/event_context_spec_files.py","file_name":"event_context_spec_files.py","file_ext":"py","file_size_in_byte":3955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"372595731","text":"# -*- encoding: utf-8 -*-\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\nimport os\nimport pwd\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom ceilometer.agent.plugin_base import PollsterBase\nfrom ceilometer import sample\nfrom oslo_utils import timeutils\n\n\nOPTS = [\n cfg.StrOpt('path',\n default='/var/lib/nova/instances',\n help='test if path can access by nova'),\n cfg.BoolOpt('enable_path_access_ok_pollster_discover',\n default=False,\n help='weather enable discover this resource'),\n]\ncfg.CONF.register_opts(OPTS, group='compute')\nLOG = logging.getLogger(__name__)\n\n\nclass PathAccessOKPollster(PollsterBase):\n\n @property\n def default_discovery(self):\n return 'path_access_ok'\n\n def get_samples(self, manager, cache, resources):\n hostname = resources[0]\n volume = 0.0\n try:\n nova_entry = pwd.getpwnam('nova')\n p_stat = os.stat(cfg.CONF.compute.path)\n except OSError:\n LOG.info('%s not exist' % cfg.CONF.compute.path)\n except KeyError:\n LOG.warn('user nova not exist')\n\n if p_stat.st_mode == 16877 and p_stat.st_uid == nova_entry.pw_uid:\n volume = 1.0\n\n meta = {'hostname': hostname,\n 'path': cfg.CONF.compute.path\n }\n yield sample.Sample(\n name='path_access_ok',\n type=sample.TYPE_GAUGE,\n unit='path_access_ok',\n volume=volume,\n user_id=None,\n project_id=None,\n resource_id=hostname + '_path_access_ok',\n timestamp=timeutils.utcnow().isoformat(),\n resource_metadata=meta)\n","sub_path":"hardware/pollsters/path_access_ok.py","file_name":"path_access_ok.py","file_ext":"py","file_size_in_byte":2215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"196433124","text":"from typing import List\nimport logging\nfrom overrides import overrides\n\nfrom allennlp.common.util import JsonDict, sanitize\nfrom allennlp.predictors.predictor import Predictor\nfrom allennlp.data import DatasetReader, Instance\nfrom allennlp.models import Model\n\nlogger = logging.getLogger(__name__) # pylint: disable=invalid-name\n\n@Predictor.register(\"entailment_pair\")\nclass EntailmentPairPredictor(Predictor):\n def __init__(self, model: Model, dataset_reader: DatasetReader) -> None:\n super().__init__(model, dataset_reader)\n self._entailment_idx = self._model.vocab.get_token_index(\"entailment\", \"labels\")\n self._contradiction_idx = self._model.vocab.get_token_index(\"contradiction\", \"labels\")\n self._neutral_idx = self._model.vocab.get_token_index(\"neutral\", \"labels\")\n\n @overrides\n def _json_to_instance(self, # type: ignore\n json_dict: JsonDict) -> Instance:\n premise_text = json_dict.get(\"sentence1\", None) or json_dict.get(\"premise\", None)\n hypothesis_text = json_dict.get(\"sentence2\", None) or json_dict.get(\"hypothesis\", None)\n if premise_text and hypothesis_text:\n return self._dataset_reader.text_to_instance(premise_text, hypothesis_text)\n logger.info(\"Error parsing input\")\n return None\n\n @overrides\n def predict_json(self, inputs: JsonDict):\n instance = self._json_to_instance(inputs)\n outputs = self._model.forward_on_instance(instance)\n inputs[\"entailment_prob\"] = float(outputs[\"label_probs\"][self._entailment_idx])\n inputs[\"contradiction_prob\"] = float(outputs[\"label_probs\"][self._contradiction_idx])\n inputs[\"neutral_prob\"] = float(outputs[\"label_probs\"][self._neutral_idx])\n return sanitize(inputs)\n\n def predict_batch_json(self, inputs: List[JsonDict]) -> List[JsonDict]:\n instances = self._batch_json_to_instances(inputs)\n outputs = self.predict_batch_instance(instances)\n for input, output in zip(inputs, outputs):\n input[\"entailment_prob\"] = float(output[\"label_probs\"][self._entailment_idx])\n input[\"contradiction_prob\"] = float(output[\"label_probs\"][self._contradiction_idx])\n input[\"neutral_prob\"] = float(output[\"label_probs\"][self._neutral_idx])\n return inputs\n\n","sub_path":"lib/predictors/entailment_pair.py","file_name":"entailment_pair.py","file_ext":"py","file_size_in_byte":2314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"442697184","text":"import re\nimport urllib.request\n\n\n# result yes\n# version 3.5\ndef Schedule(a, b, c):\n \"\"\"\n a:已经下载的数据块\n b:数据库块的大小\n c:远程文件的大小\n \"\"\"\n per = 100.0 * a * b / c\n if per > 100:\n per = 100\n print('完成！')\n print('%.2f%%' % per)\n\n\ndef getHtml(url):\n page = urllib.request.urlopen(url)\n html = page.read()\n return html\n\n\ndef getImg(html):\n html = html.decode('utf-8')\n reg = r'src=\"(.*?\\.jpg)\" width'\n imgre = re.compile(reg)\n imglist = imgre.findall(html)\n # print(imglist)\n x = 0\n for imgurl in imglist:\n urllib.request.urlretrieve(imgurl, 'e:\\\\test\\\\%s.jpg' % x, Schedule) # 是不是Python3.X中把这个也改变了？\n x += 1\n\n\nhtml = getHtml('http://tieba.baidu.com/p/741081023')\n","sub_path":"python/Practice/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"521851935","text":"# THIS CODE ALLOWS THE USER TO RUN MD SIMUALTIONS ON BULK INPUTS\n\n# EXECUTION :: python initial_setup.py $PDBFILE/PDBID $CHAIN $MUTATION(like A23Y) BOTH(optional) HETATM(optional)\n\n# PYTHON MODULES\nfrom shutil import copy\nimport os\nimport subprocess\nimport requests\nimport sys\nimport gzip\nfrom Bio.PDB.MMCIFParser import MMCIFParser\n\nparser = MMCIFParser(QUIET=True)\nfrom Bio.PDB.Polypeptide import one_to_three as ott\nfrom Bio.PDB.PDBIO import PDBIO\nfrom Bio.PDB.PDBIO import Select\n\n#  mmCIF FILES\n# pathmmcif = \"/project/home17/cl9816/Project2-MD/_Dataset/cif-total\"\npathmmcif = \"/bmm/data/rcsb/data/structures/all/mmCIF\"\n\n# EXTERNAL PROGRAMS\n\n# SCWRL\n# SCWRLpath = \"/project/soft/linux64/src/scwrl4/scwrl\"\nSCWRLpath = \"/home/tkhanna1/scwrl4/Scwrl4\"\n\n\nclass user_input():\n \"\"\"\n CHECKING THE USER INPUT\n MEHTOD: F = MD from file, ID = MD from PDBID\n \"\"\"\n\n def __init__(self, inp1_pdb, inp2_chn, inp3_mutation, inp4):\n ft = inp1_pdb.split(\".\")\n if len(ft) > 1:\n self.method = \"F\"\n else:\n self.method = \"ID\"\n self.chain = inp2_chn\n self.mutations = inp3_mutation\n self.num = inp4\n\n\ndef get_structure(pdb, dir='.', fname=None, format='pdb'):\n link = 'https://files.rcsb.org/download/'\n if format == 'pdb':\n f = pdb + '.pdb'\n link += f\n elif format == 'cif':\n f = pdb + '.cif'\n link += f\n if fname:\n fp = os.path.join(dir, fname)\n else:\n fp = os.path.join(dir, f)\n if not os.path.isfile(fp):\n r = requests.get(link)\n with open(fp, 'wb') as out:\n out.write(r.content)\n\n\ndef get_pdb(inp1_pdb, inp2_chn, inp3_mutation, inp4):\n \"\"\"\n GETTING THE PDB FILE FROM PDBID\n \"\"\"\n pdb = inp1_pdb #  PDB ID\n C = inp2_chn # CHAIN\n if inp3_mutation != \"NA\":\n # pos = inp3_mutation[1:(len(inp3_mutation)-1)]\n # temp = inp3_mutation[0:1]\n # temp = inp3_mutation[(len(inp3_mutation)-1):len(inp3_mutation)]\n pos = inp3_mutation[1:-1]\n wtres = ott(inp3_mutation[0:1])\n mutres = ott(inp3_mutation[-1])\n\n # # fol = pdb[1:3]\n # pdbfile = \"{}/{}.cif.gz\".format(pathmmcif, pdb)\n # # make a copy of that file under the working dir\n # tar = gzip.open(\"{}\".format(pdbfile), \"rb\")\n # out = open(\"pdbprocess.cif\", \"wb\")\n # out.write(tar.read())\n # out.close()\n # tar.close()\n\n get_structure(pdb, '.', fname='pdbprocess.cif', format='cif')\n\n structure_id = \"{}\".format(pdb)\n filename = \"pdbprocess.cif\"\n structure = parser.get_structure(structure_id, filename)\n\n model = structure[0]\n chain = model[\"{}\".format(C)]\n c1 = chain.get_list() # LIST ALL THE RESIDUES\n\n class residue_type(Select):\n def accept_atom(self, r1):\n r2 = r1.get_id()\n p1 = r1.get_parent() # io.save(WT_filename, residue_type())\n p = p1.get_id()\n if p[0] == \" \":\n if r2[0:1] == \"H\":\n return 0\n else:\n return 1\n else:\n return 0\n\n class atom_type(Select):\n def accept_atom(self, r1):\n r2 = r1.get_id()\n p1 = r1.get_parent() # io.save(WT_filename, residue_type())\n p = p1.get_id()\n if p[0] == \" \" and \"{}\".format(p[1]) == pos:\n if r2 == \"CA\" or r2 == \"N\" or r2 == \"C\" or r2 == \"O\":\n return 1\n else:\n return 0\n elif p[0] == \" \":\n return 1\n elif p[0] != \" \":\n return 0\n\n if inp3_mutation != \"NA\":\n io = PDBIO()\n io.set_structure(chain)\n io.save(\"WT.pdb\", atom_type())\n\n f = open('WT.pdb', \"r\")\n ft = f.readlines()\n f.close()\n\n g = open('MUT.pdb', \"w\")\n\n k = 0\n list1 = []\n while k < len(ft):\n ft1 = ft[k].split()\n k1 = 0\n while k1 < len(ft1):\n if len(ft1) > 2:\n if wtres in ft1[k1]:\n if \"{}\".format(pos) in ft1[(k1 + 2)]:\n list1.append(k)\n k1 = k1 + 1\n k = k + 1\n\n k = 0\n while k < len(ft):\n t1 = ft[k]\n if k in list1:\n t1 = ft[k].replace(\"{}\".format(wtres), \"{}\".format(mutres))\n g.write(\"{}\".format(t1))\n\n k = k + 1\n\n g.close()\n\n io = PDBIO()\n io.set_structure(chain)\n io.save('WT_F.pdb', residue_type())\n\n if inp4 != \"NA\":\n # RUNNING SCWRL4.0 ON BOTH\n # subprocess.Popen(['{}/./Scwrl4'.format(SCWRLpath), '-i' 'WT.pdb', '-o','WT_F.pdb', '-h'])\n #subprocess.call([SCWRLpath, '-i', 'WT.pdb', '-o', 'WT_F.pdb', '-h'])\n # subprocess.Popen(['{}/./Scwrl4'.format(SCWRLpath), '-i' 'MUT.pdb', '-o','MUT_F.pdb', '-h'])\n # subprocess.Popen([SCWRLpath, '-i', MUT_filename, '-o', MUT_F_filename, '-h'])\n subprocess.call([SCWRLpath, '-i', 'MUT.pdb', '-o', 'MUT_F.pdb', '-h'])\n else:\n # subprocess.Popen(['{}/./Scwrl4'.format(SCWRLpath), '-i' 'MUT.pdb', '-o','MUT_F.pdb', '-h'])\n # subprocess.Popen([SCWRLpath, '-i', MUT_filename, '-o', MUT_F_filename, '-h'])\n subprocess.call([SCWRLpath, '-i', 'MUT.pdb', '-o', 'MUT_F.pdb', '-h'])\n\n else:\n io = PDBIO()\n io.set_structure(chain)\n io.save('WT_F.pdb', residue_type())\n\n\ndef main():\n \"\"\"\n MAIN FUNCTION\n \"\"\"\n inp1_pdb = sys.argv[1] # PDB/PDBID\n inp2_chn = sys.argv[2].upper() # CHAIN\n try:\n inp3_mutation = sys.argv[3].upper() # MUTATION (optional)\n except:\n inp3_mutation = \"NA\"\n try:\n inp4 = sys.argv[4] # BOTH WT AND MUT (optional)\n except:\n inp4 = \"NA\"\n # try:\n # inp5 = sys.argv[5]\t# HETATM (optional)\n # except:\n # inp5 = 0\n\n UI = user_input(inp1_pdb, inp2_chn, inp3_mutation, inp4)\n if UI.method == \"ID\":\n get_pdb(inp1_pdb, inp2_chn, inp3_mutation, inp4)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"MD_BULK_CHUNAN_MODIFIED/initial_setup-desktop.py","file_name":"initial_setup-desktop.py","file_ext":"py","file_size_in_byte":6104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"431382863","text":"import os\nimport sys\nimport json\nimport pprint\n\n\ndef list_json_files(settings):\n \"\"\"List all JSON files that maches the command line settings.\"\"\"\n for directory in settings['input_directory']:\n absolute_dir = os.path.abspath(directory)\n files = os.listdir(absolute_dir)\n json_files = []\n for item in files:\n if item.endswith(\".json\"):\n if item.startswith(settings['rw']):\n json_files.append(os.path.join(absolute_dir, item))\n\n if len(json_files) == 0:\n print(\n \"Could not find any (matching) JSON files in the specified directory \" + str(absolute_dir))\n sys.exit(1)\n\n return json_files\n\n\ndef import_json_data(filename):\n \"\"\"Returns a dictionary of imported JSON data.\"\"\"\n with open(filename) as json_data:\n try:\n d = json.load(json_data)\n except json.decoder.JSONDecodeError:\n print(f\"Failed to JSON parse {filename}\")\n sys.exit(1)\n return d\n\n\ndef import_json_dataset(fileset):\n \"\"\"Returns a list of imported raw JSON data for every file in the fileset.\n \"\"\"\n d = []\n for f in fileset:\n d.append(import_json_data(f))\n return d\n\n\ndef get_nested_value(dictionary, key):\n \"\"\"This function reads the data from the FIO JSON file based on the supplied\n key (which is often a nested path within the JSON file).\n \"\"\"\n for item in key:\n dictionary = dictionary[item]\n return dictionary\n\n\ndef get_json_mapping(mode):\n \"\"\" This function contains a hard-coded mapping of FIO nested JSON data\n to a flat dictionary.\n \"\"\"\n root = ['jobs', 0]\n jobOptions = root + ['job options']\n data = root + [mode]\n dictionary = {\n 'iodepth': (jobOptions + ['iodepth']),\n 'numjobs': (jobOptions + ['numjobs']),\n 'rw': (jobOptions + ['rw']),\n 'iops': (data + ['iops']),\n 'iops_stddev': (data + ['iops_stddev']),\n 'lat_ns': (data + ['lat_ns', 'mean']),\n 'lat_stddev': (data + ['lat_ns', 'stddev']),\n 'latency_ms': (root + ['latency_ms']),\n 'latency_us': (root + ['latency_us']),\n 'latency_ns': (root + ['latency_ns'])\n }\n\n return dictionary\n\n\ndef get_flat_json_mapping(settings, dataset):\n \"\"\"This function returns a list of simplified dictionaries based on the\n data within the supplied json data.\"\"\"\n stats = []\n for record in dataset:\n if settings['rw'] == 'randrw':\n if settings['filter'][0]:\n mode = settings['filter'][0]\n else:\n print(\n \"When processing randrw data, a -f filter (read/write) must also be specified.\")\n exit(1)\n elif settings['rw'] == 'read' or settings['rw'] == 'write':\n mode = settings['rw']\n else:\n mode = get_nested_value(\n record, ('jobs', 0, 'job options', 'rw'))[4:]\n m = get_json_mapping(mode)\n row = {'iodepth': get_nested_value(record, m['iodepth']),\n 'numjobs': get_nested_value(record, m['numjobs']),\n 'rw': get_nested_value(record, m['rw']),\n 'iops': get_nested_value(record, m['iops']),\n 'iops_stddev': get_nested_value(record, m['iops_stddev']),\n 'lat': get_nested_value(record, m['lat_ns']),\n 'lat_stddev': get_nested_value(record, m['lat_stddev']),\n 'latency_ms': get_nested_value(record, m['latency_ms']),\n 'latency_us': get_nested_value(record, m['latency_us']),\n 'latency_ns': get_nested_value(record, m['latency_ns']),\n 'type': mode}\n stats.append(row)\n return stats\n","sub_path":"fio_plot/fiolib/jsonimport.py","file_name":"jsonimport.py","file_ext":"py","file_size_in_byte":3708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"212317491","text":"import speedtest\nimport os\nimport config\nimport console\nfrom console.windows import set_title\nfrom time import localtime, strftime\nfrom time import sleep\nfrom os import system, name \n\n# clear function \ndef clear(): \n # for windows \n if name == 'nt': \n _ = system('cls') \n else: # for mac and linux(here, os.name is 'posix') \n _ = system('clear')\n\n\n#Important variables for saving\nconfig.globalDown = \"0\"\nconfig.globalUp = \"0\"\nconfig.globalPing = \"0\"\n\n\nclass tester:\n config.st = speedtest.Speedtest()\n\n def Download(self):\n download =round(config.st.download()/1000000)\n return download\n \n\n def Upload(self):\n upload =round(config.st.upload()/1000000)\n return upload\n \n\n def Ping(self):\n servernames =[]\n config.st.get_servers(servernames)\n ping = round(config.st.results.ping)\n return ping\n \n\n#Screens \ndef Main_Screen():\n clear()\n set_title('INTEST')\n opt = int(input('''\n\n ██╗███╗ ██╗████████╗███████╗███████╗████████╗\n ██║████╗ ██║╚══██╔══╝██╔════╝██╔════╝╚══██╔══╝\n ██║██╔██╗ ██║ ██║ █████╗ ███████╗ ██║ \n ██║██║╚██╗██║ ██║ ██╔══╝ ╚════██║ ██║ \n ██║██║ ╚████║ ██║ ███████╗███████║ ██║ \n ╚═╝╚═╝ ╚═══╝ ╚═╝ ╚══════╝╚══════╝ ╚═╝ \n The Internet tester\n By Mihal \n \n [i] Select:\n 1) Download Speed\n 2) Upload Speed\n 3) Ping\n\n\n [?] Choice: '''))\n if opt == 1:\n config.globalDown = tester().Download()\n print(' [i] Download: ',config.globalDown, 'Mbps')\n end_controler()\n elif opt == 2:\n config.globalUp = tester().Upload()\n print(' [i] Upload: ',config.globalUp, 'Mbps')\n end_controler()\n elif opt == 3:\n config.globalPing = tester().Ping()\n print(' [i] Ping',config.globalPing,'ms')\n end_controler()\n else:\n print(\"\\n [!] Invalid choice!\")\n print(\" [i] Restarting...\")\n sleep(1)\n clear()\n Main_Screen()\n\ndef end_controler():\n end_control_int = input(\" [?] Do you want to test something else? [Y/N]: \")\n if(end_control_int == 'Y' or end_control_int == 'y'):\n Main_Screen()\n elif(end_control_int == 'N' or end_control_int == 'n'):\n SaveResults = input(\" [?] Do you want to Save results? [Y/N]: \")\n if (SaveResults == 'Y' or SaveResults == 'y'):\n Save_screen()\n elif(SaveResults == 'N' or SaveResults == 'n'):\n End_Screen()\n else:\n Save_screen()\n else:\n Main_Screen()\n\ndef Save_screen():\n print(\" [i] Saving Results...\")\n config.FN = str(strftime(\"Results %Y-%m-%d-%H-%M-%S.txt\", localtime()))\n config.SF = open(config.FN, \"w\")\n config.SF.write(\"Results from \")\n config.SF.write(str(strftime(\"%Y-%m-%d %H:%M:%S\", localtime())))\n config.SF.write('\\n\\n')\n if (config.globalDown != \"0\"): #Download if\n config.SF.write(\"Download Speed: \")\n config.SF.write(str(config.globalDown))\n config.SF.write(\" Mbps\")\n config.SF.write('\\n')\n config.globalDown_Saved = bool(True)\n else:\n config.globalDown_Saved = bool(True)\n if (config.globalUp != \"0\"): #Upload if\n config.SF.write(\"Upload Speed: \")\n config.SF.write(str(config.globalUp))\n config.SF.write(\" Mbps\")\n config.SF.write('\\n')\n config.globalUp_Saved = bool(True)\n else:\n config.globalUp_Saved = bool(True)\n if(config.globalPing != \"0\"): #Ping if\n config.SF.write(\"Ping: \")\n config.SF.write(str(config.globalPing))\n config.SF.write(\" ms\")\n config.globalPing_Saved = bool(True)\n else:\n config.globalPing_Saved = bool(True)\n if(config.globalDown_Saved == True and config.globalUp_Saved == True and config.globalPing_Saved == True):\n config.SF.close()\n else:\n print(\" [!] Saving Failed!\")\n sleep(2)\n exit()\n clear()\n print(\" [i] Saved to \",os.path.dirname(__file__),\"\\\\\",config.FN)\n print(\" [i] Successfully Saved!\")\n sleep(5)\n End_Screen()\n\ndef End_Screen():\n clear()\n print('Exiting.')\n sleep(1)\n clear()\n print('Exiting..')\n sleep(1)\n clear()\n print('Exiting...')\n clear()\n exit()\n\n#Screens End\n\n#Program start Here(After all variables and calls)\nhas_started = None\nif(has_started == None):\n Main_Screen()\n has_started = True\nelse:\n pass","sub_path":"Versions/!Prototype/INTEST.py","file_name":"INTEST.py","file_ext":"py","file_size_in_byte":5149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"100646712","text":"########################################\n# CS63: Artificial Intelligence, Lab 4\n# Spring 2018, Swarthmore College\n########################################\n# NOTE: you should not need to modify this file.\n########################################\n\nimport numpy as np\nfrom scipy.ndimage import label\n\nfrom BoardGames import _base_game\n\n_adj = np.ones([3,3], int)\n_adj[0,0] = 0\n_adj[2,2] = 0\n\nRED = u\"\\033[1;31m\"\nBLUE = u\"\\033[1;34m\"\nRESET = u\"\\033[0;0m\"\nSQUARE = u\"\\u2588\"\n\nRED_BORDER = RED + \"-\" + RESET\nBLUE_BORDER = BLUE + \"\\\\\" + RESET\n\n\n\nclass HexGame(_base_game):\n def __init__(self, size=8):\n self.size = size\n self.turn = 1\n self.board = np.zeros([size, size], int)\n\n self._moves = None\n self._terminal = None\n self._winner = None\n self._repr = None\n self._hash = None\n\n def __repr__(self):\n if self._repr is None:\n self._repr = u\"\\n\" + (\" \" + RED_BORDER)*self.size +\"\\n\"\n for i in range(self.size):\n self._repr += \" \" * i + BLUE_BORDER + \" \"\n for j in range(self.size):\n self._repr += self._print_char(self.board[i,j]) + \" \"\n self._repr += BLUE_BORDER + \"\\n\"\n self._repr += \" \"*(self.size) + \" \" + (\" \" + RED_BORDER) * self.size\n return self._repr\n\n def makeMove(self, move):\n \"\"\"Returns a new ConnectionGame in which move has been played.\n A move is a column into which a piece is dropped.\"\"\"\n hg = HexGame(self.size)\n hg.board = np.array(self.board)\n hg.board[move[0], move[1]] = self.turn\n hg.turn = -self.turn\n return hg\n\n @property\n def availableMoves(self):\n if self._moves is None:\n self._moves = list(zip(*np.nonzero(np.logical_not(self.board))))\n return self._moves\n\n @property\n def isTerminal(self):\n if self._terminal is not None:\n return self._terminal\n if self.turn == 1:\n clumps = label(self.board < 0, _adj)[0]\n else:\n clumps = label(self.board.T > 0, _adj)[0]\n spanning_clumps = np.intersect1d(clumps[0], clumps[-1])\n self._terminal = np.count_nonzero(spanning_clumps)\n return self._terminal\n\n @property\n def winner(self):\n if self.isTerminal:\n return -self.turn\n return 0\n","sub_path":"Hex.py","file_name":"Hex.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"388368474","text":"#!/usr/bin/env python3\n\nfrom cryptofeed import FeedHandler\nfrom cryptofeed.defines import TRADES\n\nfrom cryptoblotter.exchanges import CoinbaseBlotter\nfrom cryptoblotter.trades import SequentialIntegerTradeCallback, ThreshCallback\nfrom cryptoblotter.trades.constants import VOLUME\n\n\nasync def trades(trade):\n print(trade)\n\n\nif __name__ == \"__main__\":\n fh = FeedHandler()\n fh.add_feed(\n CoinbaseBlotter(\n symbols=[\"BTC-USD\"],\n channels=[TRADES],\n callbacks={\n TRADES: SequentialIntegerTradeCallback(\n ThreshCallback(\n trades,\n thresh_attr=VOLUME,\n thresh_value=1000,\n window_seconds=60,\n )\n )\n },\n )\n )\n fh.run()\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"598332895","text":"\n# coding: utf-8\n\n# In[7]:\n\nfrom random import randint\nimport bernoulli_reward\n\n\ndef CFL(c,users,time_slots, sample_count, observed_mean, reward_distribution): #defining CFL . Passing parameters are: c-no of channels , users-no of users , time_slots\n b=0.1\n p = [[1/c for i in range(0,c)] for j in range(0,users)] #matrix for probability of each user selecting a particular channel .\n selections = [-1 for i in range(0,users)] # list that will store channels selected by each user in a particular time slot.\n #-1 in selection denotes that no channel is selected.\n allocations = [-1 for i in range(0,users)] # it will store the final selections which has no collisions.\n while(time_slots < c):\n print(\"time slot : \",time_slots)\n for u in range(0,users): # 'u' is the iterator for users\n channelNumber=0\n while channelNumber < c:\n randNumber1 = randint(0,100) # generating a random number to check chances of a channel to get selected\n \n if randNumber1 < p[u][channelNumber]*100 :\n #channel is selected\n selections[u]=channelNumber\n break\n \n channelNumber=channelNumber+1\n \n print(\"selection of each user : \",selections)\n \n \n \n # To check collisions \n count = [0 for i in range(0,c)] # it stores number of users wanting same channel\n colliding_channels=[] # it stores list of channels colliding\n for u in range(0,users):\n if selections[u] !=-1:\n count[selections[u]]+=1\n \n for i in range(0,c):\n if count[i] >1: # checks if a count of users wanting a channel is more than 1 than there is collision\n colliding_channels.append(i)\n \n \n \n print(\"collinding channels : \",colliding_channels)\n \n for u in range(0,users):\n channelNumber = selections[u]\n if selections[u] in colliding_channels: # if selection of user belongs to 'colliding_channels' list then failure else success\n #failure\n \n p[u][channelNumber] = (1-b)*p[u][channelNumber]\n for j in range(0,c):\n if j!=channelNumber:\n p[u][j] = ( (1-b)*p[u][j] ) + ( b/(c-1) ) \n \n \n ### increment no of times user 'u' played on channel 'channelNumber', reward 0 \n '''\n sample_count[u][channelNumber] = sample_count[u][channelNumber] + 1\n print('updating sample count of (u, channelNumber)', u, channelNumber)\n print(sample_count[u])\n observed_mean[u][channelNumber] = (observed_mean[u][channelNumber] * (sample_count[u][channelNumber] - 1))/sample_count[u][channelNumber]\n print('updating observed_mean of (u, channelNumber)', u, channelNumber)\n print(observed_mean[u])\n '''\n elif selections[u] != -1: # checks if any channel is selected by user 'u' or not , if a channel is selected then success\n #success\n \n p[u] = [0 for j in range(0,c)] \n p[u][channelNumber]=1\n\n time_slots+=1\n \n\n\n ### generate reward for user 'u' on channel 'channelNumber' acc to prob distribution\n '''\n instantanious_reward = bernoulli_reward.find_reward(reward_distribution[u][channelNumber])\n print('instantanious_reward:', instantanious_reward)\n ### increment no of times user 'u' played on channel 'channelNumber'\n sample_count[u][channelNumber] = sample_count[u][channelNumber] + 1\n print('updating sample count of (u, channelNumber)', u, channelNumber)\n print(sample_count[u])\n ### add reward to mean reward\n observed_mean[u][channelNumber] = (observed_mean[u][channelNumber]*(sample_count[u][channelNumber]-1) + instantanious_reward)/sample_count[u][channelNumber]\n print('updating observed_mean of (u, channelNumber)', u, channelNumber)\n print(observed_mean[u])\n '''\n \n #time_slots+=1\n\n \n '''\n for p1 in p:\n print (p1)\n '''\n print()\n print()\n #if -1 not in selections and len(colliding_channels) == 0 :\n \n\n return selections,time_slots\n \n \n\n ##MAIN \n\n \ntime_slots=0\nc=5\nusers=4\nreward_distribution = [[0.3,0.1,0.5,0.4,0.2],\n [0.4,0.2,0.7,0.4,0.1],\n [0.8,0.4,0.2,0.4,0.1],\n [0.5,0.6,0.2,0.1,0.4]]\n #[0.1,0.7,0.5,0.3,0.6] \nobserved_mean = [[0.1*randint(0, 8) for i in range(c)] for i in range(users)]\nsample_count = [[1 for i in range(c)] for j in range(users)]\n\n'''\nprint('Initial observed_mean:')\nfor mean in observed_mean:\n print(mean)\n\nprint('Initial Sample count')\nfor sample in sample_count:\n print(sample)\n'''\n\n\nallocations, time_slots = CFL(c ,users,0, sample_count, observed_mean, reward_distribution)\n\n\n'''\n\nprint('Observed mean after CFL')\nfor mean in observed_mean:\n print(mean)\n\nprint()\nprint('Sample count after CFL')\nfor sample in sample_count:\n print(sample)\n'''\n\nprint(\"final allocations : \",allocations)\nprint(\"time slots : \",time_slots)\n\n\n\n# In[ ]:\n\n\n\n\n\n\n# In[ ]:\n","sub_path":"CSM-MAB/cfl_new_test.py","file_name":"cfl_new_test.py","file_ext":"py","file_size_in_byte":5571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"47691422","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Author: yang37\n# @Date: 2017-06-12 17:03:43\n# @Last Modified by: chaomy\n# @Last Modified time: 2018-04-03 16:46:58\n\n\nimport re\nimport glob\nimport numpy as np\nimport os\nimport shutil\nimport ase\nimport ase.io\n\n\nclass get_data_va(object):\n\n def vasp_energy_stress_vol(self, fnm=\"OUTCAR\"):\n stress = np.arange(6, dtype=\"float\")\n stress.shape = ([6, 1])\n os.system(\"tail -n 1000 {} > outcar_save\".format(fnm))\n raw = self.mreadlines(\"outcar_save\")\n real_N = r\"(-?\\d*\\.\\d{5})\"\n real_Num = r\"(-?\\d*\\.\\d*)\"\n In = r\"\\s*\"\n for line in raw:\n matchV = \\\n re.search(r\"\\s*volume of cell : \\s*\" + real_Num,\n line)\n matchE = \\\n re.search(r\"\\s*energy\\s*without\\s*entropy=\\s*\" +\n real_Num + In + r\"energy\\(sigma->0\\)\\s*=\\s*\" +\n real_Num, line)\n matchS = re.search(r\"^\\s*in kB\\s*\" + real_N +\n In + real_N + In + real_N + In + real_N +\n In + real_N + In + real_N, line)\n if matchS:\n for i in range(6):\n stress[i] = float(matchS.group(i + 1))\n if matchE:\n energy = float(matchE.group(2))\n if matchV:\n volume = float(matchV.group(1))\n # atoms = ase.io.read(\"CONTCAR\", format='vasp')\n # print atoms.get_cell()\n return (energy, stress, volume)\n\n def vasp_energy_stress_vol_quick(self, voltag=True, atomstag=True):\n os.system(\"tail -n 10000 OUTCAR > outcar_save\")\n stress = np.arange(6, dtype=\"float\")\n stress.shape = ([6, 1])\n real_Num = r\"(-?\\d*\\.\\d*)\"\n In = r\"\\s*\"\n atoms = ase.io.read(\"CONTCAR\", format='vasp')\n raw = self.mread(\"outcar_save\")\n\n # see whether the job finishes\n info = r\"General timing and accounting informations\"\n find_info = re.compile(info)\n result = find_info.findall(raw)\n if result is not None:\n # vol_line = r\"volume of cell\\s*:\\s*(-?\\d*\\.\\d*)\"\n # find_vol = re.compile(vol_line)\n energy_line = r\"\\s*energy\\s*without\\s*entropy=\\s*\" + \\\n real_Num + In + r\"energy\\(sigma->0\\)\\s*=\\s*\" + \\\n real_Num\n find_energy = re.compile(energy_line)\n energy = float(find_energy.findall(raw)[-1][-1])\n\n cell = atoms.get_cell()\n vol = np.linalg.norm(cell)\n return (energy, vol, atoms)\n else:\n return (0, 0, atoms)\n\n def collect_outcar(self):\n dir_list = glob.glob(\"./*\")\n for i in range(len(dir_list)):\n if os.path.isdir(dir_list[i]):\n origin_file = '%s/Continue/OUTCAR' % (dir_list[i])\n moved_file = './O-%s-T-OUTCAR' % (dir_list[i][2:])\n shutil.copyfile(origin_file,\n moved_file)\n\n raw = self.mreadlines(moved_file)\n print(raw[-10:])\n\n def read_vasp_poscar(self, filename=\"POSCAR\"):\n raw = self.mreadlines()\n self.lattice_constant = float(raw[1])\n print(self.lattice_constant)\n supercell_base = np.zeros([3, 3], \"float\")\n for i in range(3):\n for j in range(3):\n supercell_base[i, j] = float(raw[2 + i].split()[j])\n atom_number = int(raw[5])\n print(atom_number)\n comment = str(raw[6])\n print(comment)\n atom_position = np.zeros([3, atom_number], \"float\")\n for j in range(atom_number):\n for i in range(3):\n atom_position[i, j] = float(raw[7 + j].split()[i])\n return atom_number, np.mat(supercell_base), comment, atom_position\n\n def cal_xy_area_read_poscar(self):\n atoms = ase.io.read(\"POSCAR\", format=\"vasp\")\n supercell_base = atoms.get_cell()\n xy_area = self.cal_poscar_xy_area(supercell_base)\n return xy_area\n\n def cal_poscar_xy_area(self, supercell_base):\n vect_x = np.array(supercell_base[0, :])\n vect_y = np.array(supercell_base[1, :])\n xy_area = np.linalg.norm(np.cross(vect_x, vect_y))\n return xy_area\n\n def read_OSZICAR(self, tag=\"dft\"):\n step = []\n temp = []\n energy = []\n raw = self.mreadlines(\"OSZICAR\")\n for i in range(len(raw)):\n line_segs = raw[i].split()\n if line_segs[3] == \"E=\":\n print(line_segs)\n step.append(int(line_segs[0]))\n temp.append(float(line_segs[2]))\n energy.append(float(line_segs[4]))\n step = np.array(step)\n temp = np.array(temp)\n energy = np.array(energy)\n return (step, temp, energy)\n\n # old version, depreciate\n def get_VA_tensile_data(self, infile):\n Sxx, Strain = [], []\n with open('./VA-DATA', 'r') as fid:\n for line in fid:\n match2 = \\\n re.search(r\"delta (\\d*.\\d*)\\s*Sxx\\s*(-?\\d*.\\d*)$\", line)\n if match2:\n Strain.append(float(match2.group(1)))\n Sxx.append(0.1 * np.abs(float(match2.group(2))))\n fid.close()\n VA_Stress, VA_Strain = \\\n np.array(Sxx), np.array(Strain)\n\n VA_Strain = VA_Strain[0:20]\n VA_Stress = VA_Stress[0:20]\n with open(\"monitor.txt\", 'a') as fid:\n print(\"Vasp Strain is , \", VA_Strain, file=fid)\n print(\"Vasp Stress is , \", VA_Stress, file=fid)\n print(\"VASP length\", len(VA_Strain), file=fid)\n fid.close()\n return (VA_Strain, VA_Stress)\n","sub_path":"get_data_va.py","file_name":"get_data_va.py","file_ext":"py","file_size_in_byte":5726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"327733716","text":"from os import PathLike\nfrom pathlib import Path\nfrom typing import Any, Iterable, Tuple\nfrom unittest.mock import patch\n\nimport numpy as np\nimport pytest\nimport xarray as xr\nfrom basic_modeling_interface import Bmi\nfrom numpy.testing import assert_array_equal\n\nfrom ewatercycle.models.abstract import AbstractModel\n\n\nclass MockedModel(AbstractModel):\n def setup(self, *args, **kwargs) -> Tuple[PathLike, PathLike]:\n if 'bmi' in kwargs:\n # sub-class of AbstractModel should construct bmi\n # using grpc4bmi Docker or Singularity client\n self.bmi = kwargs['bmi']\n return Path('foobar.cfg'), Path('.')\n\n def get_value_as_xarray(self, name: str) -> xr.DataArray:\n return xr.DataArray(\n data=[[1.0, 2.0]],\n dims=[\"time\", \"x\"],\n name='Temperature',\n attrs=dict(units=\"degC\"),\n )\n\n @property\n def parameters(self) -> Iterable[Tuple[str, Any]]:\n return [('area', 42)]\n\n\n@pytest.fixture\n@patch('basic_modeling_interface.Bmi')\ndef bmi(MockedBmi):\n return MockedBmi()\n\n\n@pytest.fixture\ndef model(bmi: Bmi):\n m = MockedModel()\n m.setup(bmi=bmi)\n return m\n\ndef test_construct():\n with pytest.raises(TypeError) as excinfo:\n AbstractModel()\n msg = str(excinfo.value)\n assert \"Can't instantiate abstract class\" in msg\n assert 'setup' in msg\n assert 'parameters' in msg\n\n\ndef test_setup(model):\n result = model.setup()\n\n expected = Path('foobar.cfg'), Path('.')\n assert result == expected\n\n\ndef test_initialize(model: MockedModel, bmi):\n config_file = 'foobar.cfg'\n model.initialize(config_file)\n\n bmi.initialize.assert_called_once_with(config_file)\n\n\ndef test_finalize(model: MockedModel, bmi):\n model.finalize()\n\n bmi.finalize.assert_called_once_with()\n\n\ndef test_update(model: MockedModel, bmi):\n model.update()\n\n bmi.update.assert_called_once_with()\n\n\ndef test_get_value(bmi, model: MockedModel):\n expected = np.array([1.0, 2.0])\n bmi.get_value.return_value = expected\n\n value = model.get_value('discharge')\n\n assert_array_equal(value, expected)\n\n\ndef test_set_value(model: MockedModel, bmi):\n value = np.array([1.0, 2.0])\n model.set_value('precipitation', value)\n\n bmi.set_value.assert_called_once_with('precipitation', value)\n\n\ndef test_start_time(bmi, model: MockedModel):\n bmi.get_start_time.return_value = 42.0\n\n time = model.start_time\n\n assert time == pytest.approx(42.0)\n\n\ndef test_end_time(bmi, model: MockedModel):\n bmi.get_end_time.return_value = 42.0\n\n time = model.end_time\n\n assert time == pytest.approx(42.0)\n\n\ndef test_time(bmi, model: MockedModel):\n bmi.get_current_time.return_value = 42.0\n\n time = model.time\n\n assert time == pytest.approx(42.0)\n\n\ndef test_time_units(bmi, model: MockedModel):\n bmi.get_time_units.return_value = 'd'\n\n units = model.time_units\n\n assert units == 'd'\n\n\ndef test_time_step(bmi, model: MockedModel):\n bmi.get_time_step.return_value = 1.0\n\n step = model.time_step\n\n assert step == pytest.approx(1.0)\n\n\ndef test_output_var_names(bmi, model: MockedModel):\n bmi.get_output_var_names.return_value = ('discharge', )\n\n names = model.output_var_names\n\n assert names == ('discharge', )\n\n\ndef test_get_value_as_xarray(model: MockedModel):\n expected = xr.DataArray(\n data=[[1.0, 2.0]],\n dims=[\"time\", \"x\"],\n name='Temperature',\n attrs=dict(units=\"degC\"),\n )\n\n dataarray = model.get_value_as_xarray(\"Temperature\")\n\n xr.testing.assert_equal(dataarray, expected)\n","sub_path":"tests/models/test_abstract.py","file_name":"test_abstract.py","file_ext":"py","file_size_in_byte":3607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"614851263","text":"from models.base_model import BaseModel\r\nfrom models.user import User\r\nimport peewee as pw\r\n\r\nclass Client(BaseModel):\r\n name = pw.CharField(unique=True)\r\n industry = pw.CharField()\r\n country = pw.CharField()\r\n\r\n def validate(self):\r\n # new client validation (check for duplicates)\r\n duplicate_name = Client.get_or_none(Client.name == self.name)\r\n\r\n if duplicate_name and not duplicate_name.id == self.id:\r\n self.errors.append('Client already exists in system.')","sub_path":"models/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"634640601","text":"a = [int(s) for s in input().split()]\nn = int(input())\n\nfor i in range(len(a)):\n if n > a[i]:\n a.insert(i, n)\n print(i+1)\n break\nelse:\n print(len(a) + 1)\n","sub_path":"contests/contest_6/K.py","file_name":"K.py","file_ext":"py","file_size_in_byte":181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"591787995","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport os, sys\nimport matplotlib.pylab as pl\nimport matplotlib.colors as cl\nimport matplotlib\n\ndef load_arrays():\n files = os.listdir(\".\")\n files.sort()\n files.reverse()\n print(files)\n\n arrs_static = []\n arrs_middle = []\n arrs_info = []\n arrs_mid_point = []\n for i in files:\n if '_' in i or \".py\" in i or \"png\" in i:\n continue\n\n x = np.load(i, allow_pickle=True)\n print(x.shape)\n\n arrs_static.append(x)\n\n arrs_middle.append(np.load(i[:-4]+\"_middle.npy\", allow_pickle=True))\n arrs_info.append(np.load(i[:-4]+\"_info.npy\", allow_pickle=True))\n arrs_mid_point.append(np.load(i[:-4]+\"_mid_point.npy\", allow_pickle=True))\n\n\n return arrs_static, arrs_middle, np.array(arrs_info), arrs_mid_point\n\n\ndef plot_force_over_time(length_scale=None):\n\n\n arrs, _, info, _ = load_arrays()\n \n p = 8\n\n arrs = arrs[p:]\n info = info[p:]\n lengths = info[:, -1]\n time_ratio = info[:, 1] / 2\n arrs = arrs[:]\n\n forces = []\n time_ratios = []\n times = []\n for i, m in enumerate(arrs):\n l = m[40:100, 0, :, 1]\n\n print(l.shape)\n # f = lengths[i] ** -2\n x = np.sum(l, axis=1) * -1\n print(\"x.shape\", x.shape)\n forces.extend(x)\n time_ratios.extend(np.ones(x.shape)*time_ratio[i])\n times.extend(np.arange(len(x)))\n\n forces = np.array(forces)\n time_ratios = np.array(time_ratios)\n times = np.array(times)*0.001+0.04\n #print(forces.shape, times.shape, time_ratios.shape)\n plt.scatter(times, forces, c=time_ratios, cmap=None, norm=cl.LogNorm(), s=0.5)\n\n\n print(forces.shape)\n\n\n #plt.xlabel(\"Mesh Density (number of nodes)^(1/3)\")\n #plt.ylabel(\"Total force on Static end\")\n #plt.title(\"Force at fixed end time= \" + str(t * 0.001) + \" sec\")\n cb = plt.colorbar()\n cb.set_label(\"Compute Time:Simulation Time Ratio\")\n plt.ylabel(\"Total Force on Fixed side\")\n plt.xlabel(\"Time (sec)\")\n plt.title(\"Phase Graph Zoom\")\n #plt.savefig(\"PhaseGraph_small_2.png\")\n plt.show()\n\n\n#plot_force_over_time()\n#exit()\n\ndef plot_force_over_scale_at_wall(t):\n arrs, _, info,_ = load_arrays()\n lengths = info[:, 0]\n print(lengths)\n colors = pl.cm.jet(np.linspace(0, 1, len(arrs)))\n\n forces = []\n for i,m in enumerate(arrs):\n print(m.shape)\n l = m[:, 0, :, 1]\n x = np.mean(l, axis=1)\n print(x.shape)\n \n plt.plot(x, color=colors[i])\n forces.append(x)\n\n forces = np.array(forces)\n plt.show()\n #plt.scatter(lengths, forces)\n #plt.xlim(max(lengths), min(lengths))\n #print(np.where(info == length_scale)[0])\n #forces = arrs[np.where(info==length_scale)[0][0]]\n\ndef plot_force_over_scale_at_middle(t):\n _, _, info, arr = load_arrays()\n for i in arr:\n print(\"hi\", i.shape)\n \n\n colors = pl.cm.jet(np.linspace(0, 1, len(arr)))\n pin = 20\n \n for pin in range(2002):\n i = 0\n for inf, m in zip(info, arr):\n l = m[pin, 0, 1]\n #l = np.sum(l, axis=1)\n plt.scatter(inf[0], l, color=colors[i])\n i += 1\n \n plt.savefig(str(pin)+\".png\")\n plt.close()\n plt.clf()\n print(pin)\n\n plt.show()\n\n\ndef plot_force_over_lenth_at_wall(t):\n arrs, _, info, _ = load_arrays()\n p = 8\n arrs = arrs[p:]\n info = info[p:]\n lengths = info[:, -1]\n time_ratio = info[:, 1] / 2\n arrs = arrs[:]\n print(lengths)\n\n\n #exit()\n for t in [400,]:\n forces = []\n for i, m in enumerate(arrs):\n \n l = m[t, 0, :, 1]\n \n print(l.shape)\n #f = lengths[i] ** -2\n x = np.sum(l)*-1\n print(x.shape)\n forces.append(x)\n \n forces = np.array(forces)\n print(forces.shape)\n x = np.power(lengths, 1.0 / 3.0)\n y = forces\n plt.scatter(x,y, c=time_ratio,\n norm=cl.LogNorm(), cmap=None)\n\n ax = plt.gca()\n for i in range(len(time_ratio)):\n print(i)\n if not i in [13, 15, 17, 19, 23]:\n continue\n s = \"{:.1e}\".format(time_ratio[i])\n s = s[:]\n ax.annotate(s, (x[i], y[i]))\n #plt.yscale(\"log\")\n #plt.xscale(\"log\")\n plt.xlabel(\"Mesh Density (number of nodes)^(1/3)\")\n plt.ylabel(\"Total force on Static end\")\n plt.title(\"Force at fixed end time= \"+ str(t*0.001) + \" sec\")\n cb = plt.colorbar()\n cb.set_label(\"Compute Time:Simulation Time Ratio\")\n plt.show()\n #plt.savefig(\"ForceFixedEnd\" + str(t).zfill(3)+\".png\")\n plt.clf()\n \n \n \n \n \nplot_force_over_lenth_at_wall(200)\nexit()\n\ndef scale_vs_density():\n _, _, info, _ = load_arrays()\n plt.loglog(info[:, 1], info[:,0])\n plt.show()\n exit()\n info = info[8:]\n num_nodes = info[:, -1]\n length_scale = info[:,0]\n time_ratio = info[:, 1]/2\n\n fig, ax = plt.subplots()\n\n num_nodes = np.power(num_nodes, 1.0/3.0)\n im = ax.scatter(length_scale, num_nodes,\n c=time_ratio,cmap=None , norm=cl.LogNorm())\n cb = fig.colorbar(im)\n cb.set_label(\"Compute Time : Simulation Time Ratio\")\n\n ax.set_ylabel(\"Mesh Density (number of nodes)^(1/3)\")\n #alt ax.set_ylabel(\"number of nodes\")\n\n ax.set_xlabel(\"Length Scale in Gmsh (clscale)\")\n\n #ax.set_yscale(\"log\")\n #ax.set_xscale(\"log\")\n\n fig.suptitle(\"Mesh length scale vs density with compute time\")\n #ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n ax = plt.gca()\n for i in range(len(time_ratio)):\n print(i)\n if not i in [14,16, 18, 20, 24,26]:\n continue\n s = \"{:.1e}\".format(time_ratio[i])\n s = s[:]\n ax.annotate(s, (length_scale[i], num_nodes[i]))\n plt.savefig(\"Scale_density_compute_time_annotated.png\")\n #plt.show()\n\nscale_vs_density()\nexit()\n\n","sub_path":"density_study/rectangle/data/data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":5998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"122916","text":"\"\"\"\n\n854.\nHard\n\n\"\"\"\n\nimport collections\n\n\nclass Solution:\n def kSimilarity(self, A: str, B: str) -> int:\n A, B = self.trim(A, B)\n if A == B:\n return 0\n memo = {}\n self.target_swap(A, B, memo)\n return memo[(A, B)]\n\n @staticmethod\n def trim(A, B):\n newA, newB = [], []\n\n for a, b in zip(A, B):\n if a != b:\n newA.append(a)\n newB.append(b)\n\n return ''.join(newA), ''.join(newB)\n\n def target_swap(self, s, target, memo):\n \"\"\"\n The minimum number of swaps to transform s to target\n :param s:\n :param target:\n :param memo:\n :return:\n \"\"\"\n\n if s == target:\n return 0\n\n if (s, target) in memo:\n return memo[(s, target)]\n\n ans = float('inf')\n s, target = self.trim(s, target)\n\n for i, c in enumerate(s):\n if c == target[0]:\n new_s = self.swap(s, 0, i)[1:]\n new_target = target[1:]\n res = 1 + self.target_swap(new_s, new_target, memo)\n ans = min(ans, res)\n memo[(s, target)] = ans\n return ans\n\n @staticmethod\n def swap(s, i, j):\n l = [c for c in s]\n l[i], l[j] = l[j], l[i]\n return ''.join(l)\n\n def kSimilarity_solution(self, A, B):\n if A == B: return 0\n dq, seen, step, n = collections.deque([A]), {A}, 0, len(A)\n while dq:\n sz = len(dq)\n for _ in range(sz):\n cur, i = dq.popleft(), 0\n while i < n and cur[i] == B[i]:\n i += 1\n for j in range(i + 1, n):\n if B[j] != cur[i] or cur[j] == B[j]: continue\n nxt = cur[:i] + cur[j] + cur[i + 1: j] + cur[i] + cur[j + 1:]\n if nxt not in seen:\n seen.add(nxt)\n if nxt == B: return step + 1\n dq.append(nxt)\n step += 1\n\n\nif __name__ == \"__main__\":\n\n sol = Solution()\n method = sol.kSimilarity\n\n cases = [\n\n (method, (\"abc\",\"bca\"), 2),\n (method, (\"abac\",\"baca\"), 2),\n (method, (\"aabc\",\"abca\"), 2),\n (method, (\"cbca\",\"abcc\"), 1),\n (method, (\"abbcd\",\"bcadb\"), 3),\n (method, (\"abccaacceecdeea\",\"bcaacceeccdeaae\"), 9),\n (method, (\"fffeaacbdbdafcfbbafb\",\"abcbdfafffefabdbbafc\"), 10),\n (method, (\"abccab\",\"abccab\"), 0),\n (method, (\"abcdeabcdeabcdeabcde\",\"aaaabbbbccccddddeeee\"), 8),\n ]\n\n for i, (func, case, expected) in enumerate(cases):\n ans = func(*case)\n if ans == expected:\n print(\"Case {:d} Passed\".format(i + 1))\n else:\n print(\"Case {:d} Failed; Expected {:s} != {:s}\".format(i + 1, str(expected), str(ans)))","sub_path":"algo/search/k_similar_strings.py","file_name":"k_similar_strings.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"185599329","text":"from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk\nfrom matplotlib.figure import Figure\nfrom tkinter import *\n\nfrom plotting import *\nfrom methods import *\n\ndef input():\n x0 = float(x0_entry.get())\n y0 = float(y0_entry.get())\n X = float(X_entry.get())\n n = int(n_entry.get())\n n0 = int(N0_entry.get())\n n1 = int(N1_entry.get())\n\n exact = exact_solution(x0, y0, X, n)\n euler = euler_method(x0, y0, X, n)\n improved = improved_euler_method(x0, y0, X, n)\n runge_kutta = runge_kutta_method(x0, y0, X, n)\n\n error1 = compute_error(exact[1], euler[1])\n error2 = compute_error(exact[1], improved[1])\n error3 = compute_error(exact[1], runge_kutta[1])\n\n g_error = global_error(x0, y0, X, n0, n1)\n\n plotMethods(exact, euler, improved, runge_kutta, f1, a, dataPlot1, x0, X)\n plotErrors(error1, error2, error3, f1, a2, exact, dataPlot1, x0, X)\n plot_total_errors(g_error[0], g_error[1], g_error[2], f1, a3, dataPlot1, n0, n1)\n\nmaster = Tk()\n\nf_top = Frame()\nf_mid = Frame()\nf_bot = Frame()\nmaster.title(\"Computational methods\")\n\nx0_entry = Entry(f_top, width=15)\ny0_entry = Entry(f_top, width=15)\nX_entry = Entry(f_top, width=15)\nn_entry = Entry(f_top, width=15)\nN0_entry = Entry(f_top, width=15)\nN1_entry = Entry(f_top, width=15)\n\nx0_label = Label(f_mid, width=15, text = \"enter x0\")\ny0_label = Label(f_mid, width=15, text = \"enter y0\")\nX_label = Label(f_mid, width=15, text = \"enter X\")\nn_label = Label(f_mid, width=15, text = \"enter n\")\nN0_label = Label(f_mid, width=15, text = \"enter N0\")\nN1_label = Label(f_mid, width=15, text = \"enter N1\")\n\nbutton = Button(f_bot, width=90, text=\"Submit\", command=input)\n\nf_top.pack()\nf_mid.pack()\nf_bot.pack()\n\nx0_entry.pack(side=LEFT)\ny0_entry.pack(side=LEFT)\nX_entry.pack(side=LEFT)\nn_entry.pack(side=LEFT)\nN0_entry.pack(side=LEFT)\nN1_entry.pack(side=LEFT)\n\nx0_label.pack(side=LEFT)\ny0_label.pack(side=LEFT)\nX_label.pack(side=LEFT)\nn_label.pack(side=LEFT)\nN0_label.pack(side=LEFT)\nN1_label.pack(side=LEFT)\nbutton.pack()\n\nf1 = Figure(figsize=(2,2), dpi=300)\na = f1.add_subplot(131)\na2 = f1.add_subplot(132)\na3 = f1.add_subplot(133)\n\ndataPlot1 = FigureCanvasTkAgg(f1, master=master)\nmaster.geometry('1200x800')\nmaster.mainloop()\n","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"274377155","text":"from datavault import mail_sender\nfrom flask_mail import Message\n\n\nclass EmailMessage():\n\tdef __init__(self, subject, body, recipients):\n\t\tself.subject = subject\n\t\tself.body = body\n\t\tself.recipients = recipients\n\n\tdef send(self):\n\t\tmsg = Message(self.subject, recipients = self.recipients)\n\t\tmsg.body = self.body\n\t\tmail_sender.send(msg)\n\n\n","sub_path":"datavault/tools/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"495765946","text":"from Graph import Graph\n\n\ndef findPath(graph):\n # Count the number of nodes with an odd number of edges\n vertexList = graph.getVertexList()\n # Nodes with an odd number of\n oddVertices = []\n\n # Count number of vertices with odd number of edges\n for vertex in vertexList:\n if len(graph.getNeighbors(vertex)) % 2 != 0:\n oddVertices.append(vertex)\n\n if len(oddVertices) == 0:\n # Start anywhere\n start = vertexList[0]\n elif len(oddVertices) == 2:\n # Start at odd vertex\n start = oddVertices[0]\n else:\n raise NameError(\"Path not possible\")\n\n # Perform algorithm\n\n current = start # Current node\n stack = [] # Stack of nodes\n path = [] # List of nodes\n\n # End if no neighbor and stack empty\n while True: # Sorry...\n adjacentList = graph.getNeighbors(current)\n # If no neighbor, add current to path and select new current from stack\n if len(adjacentList) == 0:\n path.append(current)\n if len(stack) == 0:\n # Finished\n break\n else:\n current = stack.pop()\n\n # If neighbor, select any neighbor. Remove edge between self and\n # neighbor, add current to stack and then set neighbor as current\n else:\n next = adjacentList[0]\n graph.removeEdge(current, next)\n stack.append(current)\n current = next\n return path\n","sub_path":"LAB2/findPath.py","file_name":"findPath.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"124264337","text":"#Rotation of algorithms can be implemented using\n#different algorithms like:\n#-one by one algorithm\n#-juggling algorithm\n#-reversal algorithm\n#-back swap algorthm\n\n\ndef oneByOne(arr, d, n): \n for i in range(d): \n temp = arr[0] \n for i in range(n-1): \n arr[i] = arr[i + 1] \n arr[n-1] = temp \n\n\n \n \ndef printArray(arr, size): \n for i in range(size): \n print (\"% d\"% arr[i], end =\" \") \n \narr = [1, 2, 3, 4, 5, 6, 7] \nprint(*arr)\noneByOne(arr, 2, 7) \nprint(*arr)\narr = [1, 2, 3, 4, 5, 6, 7] \nprint(*arr)\n","sub_path":"dataStructures/arrays/array_rotate.py","file_name":"array_rotate.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"356160947","text":"\n# This file contains the definitions of the status codes and their reasons to\n# to be used in the smoke test cases for Snmp Operations and Discover.\n\n# Status codes and their reasons to be used in the test cases\n\n# When response is \"OK\"\nOK = \"OK\" \nStatus_Code_OK = 200\n\n# When response is \"Method Not Allowed\"\nMethod_Not_Allowed = \"Method Not Allowed\" \nStatus_Code_Method_Not_Allowed = 405\n\n# When response is \"Internal Server Error\" \nInternal_Server_Error = \"Internal Server Error\" \nStatus_Code_Internal_Server_Error = 500\n\n# When response is \"Not Found\"\nNot_Found = \"Not Found\" \nStatus_Code_Not_Found = 404\n\n# When response is \"Bad Request\"\nBad_Request = \"Bad Request\"\nStatus_Code_Bad_Request = 400\n\n# When response is \"No Content\"\nNo_Content = \"No Content\"\nStatus_Code_No_Content = 204\n","sub_path":"integration/tests/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"525753545","text":"# -*- coding: utf-8 -*-\r\nfrom tkinter import Tk,Label,Button,Frame\r\n \r\nproceso=0\r\n \r\ndef iniciar(h=0, m=0, s=0, ms=0):\r\n global proceso\r\n \r\n #Verificamos si los segundos y los minutos son mayores a 60\r\n #Verificamos si las horas son mayores a 24\r\n if ms>=1000:\r\n ms=0\r\n s=s+1\r\n if s >= 60:\r\n s=0\r\n m=m+1\r\n if m >= 60:\r\n m=0\r\n h=h+1\r\n if h >= 24:\r\n h=0\r\n \r\n #etiqueta que muestra el cronometro en pantalla\r\n time['text'] = str(h)+\":\"+str(m)+\":\"+str(s)+\":\"+str(ms)\r\n \r\n # iniciamos la cuenta progresiva de los segundos\r\n proceso=time.after(1, iniciar, (h), (m), (s),(ms+1))\r\n \r\ndef parar():\r\n global proceso\r\n time.after_cancel(proceso)\r\n \r\nroot = Tk()\r\nroot.title('Cronômetro')\r\n \r\ntime = Label(root, fg='red', width=20, font=(\"\",\"18\"))\r\ntime.pack()\r\n \r\n# si queremos que se autoejecuta al iniciar el programa hay que desomentar\r\n# esta linea y comentar los botones\r\n#iniciar()\r\n \r\n# Generamos un frame para poner los botones de iniciar y parar\r\nframe=Frame(root)\r\nbtnIniciar=Button(frame, fg='blue', text='Iniciar', command=iniciar)\r\nbtnIniciar.grid(row=1, column=1)\r\nbtnParar=Button(frame, fg='blue', text='Parar', command=parar)\r\nbtnParar.grid(row=1, column=2)\r\nframe.pack()\r\n \r\nroot.mainloop()","sub_path":"Cronômetro 1.py","file_name":"Cronômetro 1.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"357278270","text":"#!/usr/bin/python\n\nimport sys, requests\n\n\ndef check():\n data={}\n\n consumers = requests.get('http://localhost:9000/consumers').json()\n\n for consumer in consumers:\n\n consumerInfos=requests.get('http://localhost:9000/consumers/'+consumer['groupId']).json()\n\n for consumerInfo in consumerInfos:\n data['{consumer_group}-{topic}-{partition}-lag'.format(consumer_group=consumer['groupId'],topic=consumerInfo['topic'],partition=consumerInfo['partition'])]=consumerInfo['lag']\n data['{consumer_group}-{topic}-{partition}-log_end_offset'.format(consumer_group=consumer['groupId'],topic=consumerInfo['topic'],partition=consumerInfo['partition'])]=consumerInfo['log_end_offset']\n data['{consumer_group}-{topic}-{partition}-offset'.format(consumer_group=consumer['groupId'],topic=consumerInfo['topic'],partition=consumerInfo['partition'])]=consumerInfo['offset']\n\n print(data)\n \n return data\n\n\nif __name__ == \"__main__\":\n check()\n","sub_path":"docker-compose/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"464631895","text":"#%%\nimport time\nimport pandas as pd\nimport os\nimport torch\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.nn as nn\n\nTRAIN_TXT = os.path.expanduser('~/data/datasets/EPIC_KITCHENS_2018/train.txt')\nVAL_TXT = os.path.expanduser('~/data/datasets/EPIC_KITCHENS_2018/val.txt')\nLABELS_DIR = os.path.expanduser('~/data/datasets/EPIC_KITCHENS_2018/custom_labels_30')\nFRAMES_DIR = os.path.expanduser('~/data/datasets/EPIC_KITCHENS_2018/frames_30')\nFEATS_DIR = os.path.expanduser('./feats')\nFEATS_EXT = '_base.pt'\n\n\n#%%\ndef get_feats_and_labels(video_name):\n f_csv_labels = os.path.join(LABELS_DIR, video_name + '.csv')\n\n df_labels = pd.read_csv(f_csv_labels)\n df_labels = df_labels.set_index('path', drop=False)\n\n all_frame_names = df_labels.path.apply(lambda x: os.path.splitext(os.path.basename(x))[0])\n feat_files = all_frame_names.apply(lambda x: os.path.join(FEATS_DIR, x + FEATS_EXT))\n feat_files_exist = feat_files.apply(os.path.exists)\n df_feats = pd.DataFrame({'frame_name': all_frame_names, 'frame_path': df_labels.path, 'feat_file': feat_files,\n 'feats_exist': feat_files_exist})\n df_feats = df_feats.set_index('frame_path')\n print(df_feats.feats_exist.sum(), '/', len(df_feats.feats_exist), 'feature files exist.')\n if df_feats.feats_exist.sum() == 0:\n print('features exist in ', FEATS_DIR, '?')\n\n df_label_feats = pd.concat([df_feats, df_labels], axis=1)\n assert len(df_label_feats) == len(df_feats) == len(df_labels)\n\n return df_label_feats\n\n#%%\nwith open(TRAIN_TXT, 'r') as f:\n train_videopaths = [s.strip() for s in f.readlines()]\n\nwith open(VAL_TXT, 'r') as f:\n val_videopaths = [s.strip() for s in f.readlines()]\n\n#%%\n\ntrain_videonames = [os.path.splitext(os.path.basename(pth))[0] for pth in train_videopaths]\nval_videonames = [os.path.splitext(os.path.basename(pth))[0] for pth in val_videopaths]\n\n#%%\nval_df_label_feats_list = []\nfor videoname in val_videonames:\n print(videoname)\n val_df_label_feats_list.append(get_feats_and_labels(videoname))\n\n\n#%%\ntrain_df_label_feats_list = []\nfor videoname in train_videonames:\n print(videoname)\n train_df_label_feats_list.append(get_feats_and_labels(videoname))\n\n#%%\nclass FeatsDataset(Dataset):\n def __init__(self, df_feats_labels, feats_in_batch_form=True, transform=None):\n \"\"\"\n Args:\n csv_file (string): Path to the csv file with annotations.\n root_dir (string): Directory with all the images.\n transform (callable, optional): Optional transform to be applied\n on a sample.\n \"\"\"\n self.df_labels_feats_files = df_feats_labels\n self.transform = transform\n self.feats_in_batch_form = feats_in_batch_form\n\n def __len__(self):\n return len(self.df_labels_feats_files)\n\n def __getitem__(self, idx):\n df_slice = self.df_labels_feats_files.iloc[idx]\n feats_file = df_slice['feat_file']\n feats = torch.load(feats_file)\n if self.feats_in_batch_form:\n assert feats.shape[0] == 1\n feats = feats[0, ...]\n label = df_slice['labels']\n\n sample = (feats, label)\n\n if self.transform:\n sample = self.transform(sample)\n\n return sample\n#%%\ndf_label_feats_train = pd.concat(train_df_label_feats_list, axis=0, ignore_index=True)\ndf_label_feats_val = pd.concat(val_df_label_feats_list, axis=0, ignore_index=True)\ntrain_dataset = FeatsDataset(df_label_feats_train)\nval_dataset = FeatsDataset(df_label_feats_val)\n\n#%%\n# Make dataset/dataloader\ntrain_batch_size = 16\nval_batch_size = 1\ntrain_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)\nval_dataloader = DataLoader(val_dataset, batch_size=val_batch_size, shuffle=False)\n\n#%%\n# Train model\n\nclass CustomSimpleNet(nn.Module):\n def __init__(self, in_channels=1024, global_pooling_type='avg'):\n super(CustomSimpleNet, self).__init__()\n conv1x1_out_channels = in_channels\n self.conv1x1 = nn.Conv2d(in_channels=in_channels, out_channels=conv1x1_out_channels, kernel_size=1)\n self.global_pool_avg = nn.AdaptiveAvgPool2d(output_size=(1,1))\n self.global_pool_max = nn.AdaptiveMaxPool2d(output_size=(1,1))\n self.fc = nn.Linear(in_features=conv1x1_out_channels, out_features=2)\n self.global_pooling_type = global_pooling_type\n\n @property\n def global_pool(self):\n if self.global_pooling_type == 'avg':\n global_pool = self.global_pool_avg\n elif self.global_pooling_type == 'max':\n global_pool = self.global_pool_max\n else:\n raise ValueError\n return global_pool\n\n def forward(self, x):\n # x = F.relu(self.conv1x1(x))\n x = self.conv1x1(x)\n x = self.global_pool(x)\n x = self.fc(x.view(x.shape[0], -1))\n return x\n\n def initialize_weights(self):\n self.apply(weight_reset)\n\n\ndef weight_reset(m):\n if (\n isinstance(m, nn.Conv1d)\n or isinstance(m, nn.Conv2d)\n or isinstance(m, nn.Linear)\n or isinstance(m, nn.Conv3d)\n or isinstance(m, nn.ConvTranspose1d)\n or isinstance(m, nn.ConvTranspose2d)\n or isinstance(m, nn.ConvTranspose3d)\n or isinstance(m, nn.BatchNorm1d)\n or isinstance(m, nn.BatchNorm2d)\n or isinstance(m, nn.BatchNorm3d)\n or isinstance(m, nn.GroupNorm)\n ):\n m.reset_parameters()\nimport torch.optim as optim\n\n\nclass Trainer(object):\n def __init__(self, model: nn.Module, dataloader_train: DataLoader, dataloader_val: DataLoader=None, cuda=True):\n self.cuda = cuda\n self.model = model\n if cuda:\n self.model.cuda()\n self.dataloader_train = dataloader_train\n self.dataloader_val = dataloader_val\n self.optimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.3)\n self.loss_fcn = nn.CrossEntropyLoss()\n\n def train(self, n_epoch):\n for epoch in range(n_epoch): # loop over the dataset multiple times\n running_loss = 0.0\n for i, data in enumerate(self.dataloader_train):\n # get the inputs; data is a list of [inputs, labels]\n inputs, labels = data\n loss = self.train_step(data)\n # print statistics\n running_loss += loss.item()\n if i % 2000 == 1999: # print every 2000 mini-batches\n print('[%d, %5d] loss: %.3f' %\n (epoch + 1, i + 1, running_loss / 2000))\n running_loss = 0.0\n\n def train_step(self, data):\n x, lbl = data\n if self.cuda:\n device = torch.device(\"cuda\")\n x, lbl = x.to(device), lbl.to(device)\n # zero the parameter gradients\n self.optimizer.zero_grad()\n # forward + backward + optimize\n outputs = self.model(x)\n loss = self.loss_fcn(outputs, lbl)\n loss.backward()\n self.optimizer.step()\n return loss\n\n def val_step(self, data):\n x, lbl = data\n if self.cuda:\n device = torch.device(\"cuda\")\n x, lbl = x.to(device), lbl.to(device)\n # zero the parameter gradients\n self.optimizer.zero_grad()\n # forward + backward + optimize\n outputs = self.model(x)\n loss = self.loss_fcn(outputs, lbl)\n assert len(outputs.shape) == 2 and outputs.shape[1] == 2\n pred_lbl = torch.argmax(outputs, dim=1)\n return loss, pred_lbl\n\n def evaluate(self):\n was_training = False\n if self.model.training:\n was_training = True\n self.model.eval()\n confusion_mat = torch.zeros((2,2), dtype=int)\n with torch.no_grad():\n for epoch in range(1): # loop over the dataset multiple times\n running_loss = 0.0\n for i, data in enumerate(self.dataloader_train):\n # get the inputs; data is a list of [inputs, labels]\n inputs, labels = data\n loss, pred_lbls = self.val_step(data)\n for lbl, pred_lbl in zip(data[1], pred_lbls):\n confusion_mat[lbl, pred_lbl] += 1\n # print statistics\n running_loss += loss.item()\n if was_training:\n self.model.train()\n return {'running_loss': running_loss,\n 'avg_loss': running_loss / len(self.dataloader_train),\n 'confusion_mat': confusion_mat}\n\n#%%\ndevice = torch.device(\"cuda\")\n\ntrainer = Trainer(CustomSimpleNet(1024, 'avg'), train_dataloader)\n\n#%%\n# Confirm we can bring loss to 0 on a single image\nexample_sample = [x.to(device) for x in next(iter(val_dataloader))]\n\n#%%\n# Confirm we can bring loss to 0 on a single image\nlosses = []\nfor i in range(200):\n losses.append(trainer.train_step(example_sample).item())\n\n#%%\nfrom bokeh.plotting import figure, output_file, show, output_notebook\n\noutput_file(\"line.html\")\np = figure(plot_width=400, plot_height=400)\n# add a line renderer\np.line(range(len(losses)), losses, line_width=2)\np.xaxis.axis_label = 'iterations'\np.yaxis.axis_label = 'loss'\nshow(p)\n\n#%%\ntrainer.model.apply(weight_reset)\nval_dicts = []\nstart = time.time()\nn_epochs = 20\nfor ep in range(n_epochs):\n val_dicts.append(trainer.evaluate())\n trainer.train(1)\n print(val_dicts[ep])\nend = time.time()\nprint(f\"Time elapsed over {n_epochs} epochs of length {len(trainer.dataloader_train)}:\", end - start)\n\n#%%\nfrom bokeh.plotting import figure, output_file, show, output_notebook\nval_losses = [d['avg_loss'] for d in val_dicts]\noutput_file(\"line2.html\")\np = figure(plot_width=400, plot_height=400)\n# add a line renderer\np.line(range(len(val_losses)), val_losses, line_width=2)\np.xaxis.axis_label = 'epochs'\np.yaxis.axis_label = 'average training loss'\nshow(p)\n\nn_neg = sum(val_dicts[0]['confusion_mat'][0, :]).item()\nn_pos = sum(val_dicts[0]['confusion_mat'][1, :]).item()\ntrue_neg = [d['confusion_mat'][0, 0].item() for d in val_dicts]\ntrue_pos = [d['confusion_mat'][1, 1].item() for d in val_dicts]\noutput_file(\"line3.html\")\np = figure(plot_width=400, plot_height=400)\n# add a line renderer\np.line(range(len(true_neg)), [x / n_neg for x in true_neg], line_width=2, color='blue', legend_label=f\"TN (max={n_neg}\")\np.line(range(len(true_pos)), [x / n_pos for x in true_pos], line_width=2, color='green', legend_label=f\"TP (max={n_pos})\")\np.xaxis.axis_label = 'epochs'\np.legend.location = \"top_left\"\nshow(p)\n\n#%%\nx = 2\n","sub_path":"interactive-train-classifier-on-feats.py","file_name":"interactive-train-classifier-on-feats.py","file_ext":"py","file_size_in_byte":10538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"288203942","text":"# 백준 2839 김은비\r\n\r\nBag = 0 # 필요한 봉지의 개수\r\nKg = int(input()) # 설탕의 무게\r\n\r\nwhile Kg >= 0: # 설탕의 무게가 0 이상이면\r\n if (Kg % 5) == 0: # 5의 배수이면\r\n Bag += Kg // 5 # 5로 나눈 몫을 구하고\r\n print(Bag) # 필요한 봉지의 개수 출력\r\n break\r\n # 5의 배수가 될 때까지\r\n Kg -= 3 # 설탕-3\r\n Bag += 1 # 봉지+1\r\nelse: \r\n print(\"-1\")\r\n","sub_path":"2839_SugarDelivery.py","file_name":"2839_SugarDelivery.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"568906289","text":"from django.contrib import admin\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import reverse\nfrom django.templatetags.static import static\nfrom django.utils.html import format_html\nfrom django.utils.http import url_has_allowed_host_and_scheme\n\nfrom star_burger.settings import ALLOWED_HOSTS\nfrom .models import Order, OrderElement\nfrom .models import Product\nfrom .models import ProductCategory\nfrom .models import Restaurant\nfrom .models import RestaurantMenuItem\n\n\nclass RestaurantMenuItemInline(admin.TabularInline):\n model = RestaurantMenuItem\n extra = 0\n\n\n@admin.register(Restaurant)\nclass RestaurantAdmin(admin.ModelAdmin):\n search_fields = [\n 'name',\n 'address',\n 'contact_phone',\n ]\n list_display = [\n 'name',\n 'address',\n 'contact_phone',\n ]\n inlines = [\n RestaurantMenuItemInline\n ]\n\n@admin.register(Product)\nclass ProductAdmin(admin.ModelAdmin):\n list_display = [\n 'get_image_list_preview',\n 'name',\n 'category',\n 'price',\n ]\n list_display_links = [\n 'name',\n ]\n list_filter = [\n 'category',\n ]\n search_fields = [\n # FIXME SQLite can not convert letter case for cyrillic words properly, so search will be buggy.\n # Migration to PostgreSQL is necessary\n 'name',\n 'category__name',\n ]\n\n inlines = [\n RestaurantMenuItemInline\n ]\n fieldsets = (\n ('Общее', {\n 'fields': [\n 'name',\n 'category',\n 'image',\n 'get_image_preview',\n 'price',\n ]\n }),\n ('Подробно', {\n 'fields': [\n 'special_status',\n 'description',\n ],\n 'classes': [\n 'wide'\n ],\n }),\n )\n\n readonly_fields = [\n 'get_image_preview',\n ]\n\n class Media:\n css = {\n \"all\": (\n static(\"admin/foodcartapp.css\")\n )\n }\n\n def get_image_preview(self, obj):\n if not obj.image:\n return 'выберите картинку'\n return format_html('',\n url=obj.image.url)\n\n get_image_preview.short_description = 'превью'\n\n def get_image_list_preview(self, obj):\n if not obj.image or not obj.id:\n return 'нет картинки'\n edit_url = reverse('admin:foodcartapp_product_change', args=(obj.id,))\n return format_html(\n '',\n edit_url=edit_url, src=obj.image.url)\n\n get_image_list_preview.short_description = 'превью'\n\n\n@admin.register(ProductCategory)\nclass ProductAdmin(admin.ModelAdmin):\n pass\n\n\nclass OrderElementsInline(admin.TabularInline):\n model = OrderElement\n extra = 0\n\n\n@admin.register(Order)\nclass OrderAdmin(admin.ModelAdmin):\n inlines = [\n OrderElementsInline\n ]\n fields = ['status', 'payment_method', 'address', 'firstname', 'lastname',\n 'phonenumber', 'comment', 'created_at', 'called_at',\n 'delivered_at']\n\n def response_post_save_change(self, request, obj):\n res = super().response_post_save_change(request, obj)\n if request.GET.get('next') and url_has_allowed_host_and_scheme(request.GET['next'], ALLOWED_HOSTS):\n return HttpResponseRedirect(request.GET['next'])\n else:\n return res\n\n def save_related(self, request, form, formsets, change):\n for formset in formsets:\n if formset.model == OrderElement:\n order_items = formset.save(commit=False)\n for order_item in order_items:\n print(order_item)\n if order_item.price is None:\n order_item.price = order_item.product.price\n formset.save()\n","sub_path":"foodcartapp/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"530608468","text":"import multiprocessing as mp\n\nn = int(input())\nnums = [i for i in range(1, n+1)]\n\ndef sol(nums):\n front, back = [], []\n for i in range(0, -len(nums), -1):\n if i == 0:\n back = [nums[i]] + back\n elif i == -1:\n front += [nums[i]]\n else:\n if i % 2 == 0:\n front += [nums[i]]\n else:\n back = [nums[i]] + back\n res = front + back\n return res\nprint(*sol(nums))\n\n","sub_path":"python_algo/baekjoon/2220.py","file_name":"2220.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"398660346","text":"def sigmoid(x): \n return 1.0 / (1.0 + np.exp(-x)) # sigmoid \"squashing\" function to interval [0,1]\n\ndef prepro(I):\n \"\"\" prepro 210x160x3 uint8 frame into 6400 (80x80) 1D float vector \"\"\"\n I = I[35:195] # crop\n I = I[::2,::2,0] # downsample by factor of 2\n I[I == 144] = 0 # erase background (background type 1)\n I[I == 109] = 0 # erase background (background type 2)\n I[I != 0] = 1 # everything else (paddles, ball) just set to 1\n return I.astype(np.float).ravel()\n\ndef discount_rewards(r):\n \"\"\" take 1D float array of rewards and compute discounted reward \"\"\"\n discounted_r = np.zeros_like(r)\n running_add = 0\n for t in reversed(xrange(0, r.size)):\n if r[t] != 0: running_add = 0 # reset the sum, since this was a game boundary (pong specific!)\n running_add = running_add * gamma + r[t]\n discounted_r[t] = running_add\n return discounted_r\n\ndef policy_forward(x):\n h = np.dot(model['W1'], x)\n h[h<0] = 0 # ReLU nonlinearity\n logp = np.dot(model['W2'], h)\n p = sigmoid(logp)\n return p, h # return probability of taking action 2, and hidden state\n\ndef policy_backward(eph, epdlogp):\n \"\"\" backward pass. (eph is array of intermediate hidden states) \"\"\"\n dW2 = np.dot(eph.T, epdlogp).ravel()\n dh = np.outer(epdlogp, model['W2'])\n dh[eph <= 0] = 0 # backpro prelu\n dW1 = np.dot(dh.T, epx)\n return {'W1':dW1, 'W2':dW2}","sub_path":"simulation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"227547229","text":"import matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\n\nhand = mpimg.imread(\"0.JPG\")\n# print(\"hand shape: \", hand.shape)\n# plt.imshow(hand)\n# plt.show()\n\n###################################\nimport cv2\n\nhand_hsv = cv2.cvtColor(hand, cv2.COLOR_RGB2HSV)\n\nh = hand_hsv[:, :, 0]\ns = hand_hsv[:, :, 1]\nv = hand_hsv[:, :, 2]\n\n# f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5))\n# ax1.set_title(\"H\")\n# ax1.imshow(h, cmap=\"gray\")\n# ax2.set_title(\"S\")\n# ax2.imshow(s, cmap=\"gray\")\n# ax3.set_title(\"V\")\n# ax3.imshow(v, cmap=\"gray\")\n# plt.show()\n####################################\n# r = hand[:, :, 0]\n# g = hand[:, :, 1]\n# b = hand[:, :, 2]\n\n# f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5))\n# ax1.set_title(\"R\")\n# ax1.imshow(r, cmap=\"gray\")\n# ax2.set_title(\"G\")\n# ax2.imshow(g, cmap=\"gray\")\n# ax3.set_title(\"B\")\n# ax3.imshow(b, cmap=\"gray\")\n# plt.show()\n####################################\nclahe = cv2.createCLAHE(clipLimit=4.0, tileGridSize=(10,10))\nh_clahe = clahe.apply(h)\n# plt.imshow(h_clahe, cmap=\"gray\")\n# plt.show()\n###################################\ntop_crop_y = 0\nbottom_crop_y = 3000\n# top_crop_y = 1100\n# bottom_crop_y = 3150\n\nplt.imshow(hand)\nplt.axhline(y=top_crop_y)\nplt.axhline(y=bottom_crop_y)\nplt.show()\n###################################\nimport numpy as np\nimport pandas as pd\n\nhand_flattened = []\n\nfor index in range(3):\n hand_top = np.array(hand_hsv[:top_crop_y, :, index]).flatten()\n hand_bottom = np.array(hand_hsv[bottom_crop_y:, :, index]).flatten()\n top_and_bottom = np.append(hand_top, hand_bottom)\n top_and_bottom_series = pd.Series(top_and_bottom)\n print(top_and_bottom_series.describe())\n hand_flattened.append(top_and_bottom_series)","sub_path":"legacy/chromakey/removebg.py","file_name":"removebg.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"249733205","text":"\n\ndef StringBreak(s,d):\n\n dp = [ 0 for j in range(len(s) + 1)]\n dp[0] = True\n for i in range(1,len(s)+1):\n for j in range(0,i):\n if dp[j] and s[j:i] in d:\n dp[i] = True\n print(\"True\")\n break\n\n if True in dp:\n return True\n else:\n return False\n\n\n\nif __name__==\"__main__\":\n s = \"helloworld\"\n d = {'h','e','he','ll','world'}\n print(type(d))\n print(StringBreak(s,d))","sub_path":"DyammicProgramming/StringBreak.py","file_name":"StringBreak.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"438166239","text":"import unittest\nimport os\nimport shutil\nfrom mincemeatpy.hdfs_client import HDFSClient\n\nclass TestHDFSClient(unittest.TestCase):\n directory = \"hdfs\"\n content = \"hello world\"\n local_file = \"hdfs/temp1.txt\"\n remote_file = \"/temp1.txt\"\n\n # def setUp(self):\n # self.hdfs_client = HDFSClient()\n\n @classmethod\n def create_files(cls):\n if not os.path.exists(cls.directory):\n os.makedirs(cls.directory)\n file = open(cls.local_file, 'w')\n file.write(cls.content)\n file.close()\n\n @classmethod\n def delete_files(cls):\n shutil.rmtree(cls.directory)\n\n @classmethod\n def setUpClass(cls):\n cls.hdfs_client = HDFSClient()\n cls.create_files()\n\n @classmethod\n def tearDownClass(cls):\n cls.delete_files()\n\n # def test_test(self):\n # self.assertFalse(self.hdfs_client.test(self.remote_file))\n\n # def test_put(self):\n # self.assertTrue(self.hdfs_client.put(self.local_file, self.remote_file))\n\n # def test_get(self):\n # path = \"temp/temp2.txt\"\n # self.assertTrue(self.hdfs_client.get(cls.remote_file, path))\n\n # def test_rmr(self):\n # path = \"/temp1.txt\"\n # self.assertFalse(self.hdfs_client.test(path))\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"map-reduce with caching on HDFS/test/test_hdfs_client.py","file_name":"test_hdfs_client.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"195901183","text":"from Queue import Queue\nfrom PyQt4.QtGui import QFileDialog\n\n__author__ = 'clark'\n# coding: utf-8\n\nimport os\nimport sys\nimport subprocess\nimport ctypes\nfrom controller.download_input import tabDownload_InputBox\nfrom PyQt4 import QtGui, QtCore\nfrom PyQt4.QtCore import pyqtSignal\nfrom storagon_app.ClientAPI_SDK import StoragonSDK\nfrom storagon_thread import UploadThread\nfrom ui.tabUpload import Ui_tabUploadForm\n\n\ndef resource_path(relative_path):\n \"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)\n\nfile_config = 'config.yaml'\nserverURL = 'http://junshare.com'\n# serverURL = 'http://localhost:8000'\n\n\nclass tabUpload(QtGui.QWidget):\n trigger = pyqtSignal(long, str)\n input_link_signal = pyqtSignal(str)\n\n def __init__(self, mainWindow, upload_complete_signal):\n super(tabUpload, self).__init__(mainWindow)\n self.mainWindow = mainWindow\n self.storagonSDK = StoragonSDK(serverURL)\n self.upload_complete_signal = upload_complete_signal\n self.Ui_tabDownloadForm = Ui_tabUploadForm()\n self.Ui_tabDownloadForm.setupUi(self)\n self.table = self.Ui_tabDownloadForm.tableWidget\n self.table.setContextMenuPolicy(QtCore.Qt.CustomContextMenu)\n self.table.customContextMenuRequested.connect(self.openDownloadMenu)\n self.setTableWidth()\n self.show()\n self.mainWindow = mainWindow\n self.trigger.connect(self.updateStatus)\n self.input_link_signal.connect(self.chooseFileToDownload)\n self.queue = Queue(maxsize=10)\n # self.Ui_tabDownloadForm.btn_addfile.setIcon(QtGui.QIcon(resource_path('add.png')))\n self.clip = self.mainWindow.clip\n # self.Ui_tabDownloadForm.btn_addfile.setIconSize(QtCore.QSize(24, 24))\n self.Ui_tabDownloadForm.choose_upload_file.setIcon(QtGui.QIcon(resource_path('upload2.png')))\n self.max_thread_upload = 4\n self.running_thread = 0\n self.fileSavePath = os.path.expanduser(\"~\")\n self.upload_file = None\n self.Ui_tabDownloadForm.choose_upload_file.clicked.connect(self.choose_path)\n self.file_to_download = []\n self.threaddownload = {}\n self.temp_filename = ''\n\n def choose_path(self):\n f = str(QFileDialog.getOpenFileName(self, \"Select File to Upload\"))\n self.upload_file = f\n self.upload()\n\n def upload(self):\n # login = self.storagonSDK.login(self.mainWindow.username, self.mainWindow.password)\n # if login:\n if self.mainWindow.logined:\n filename = str(self.upload_file).split('/').pop().split('#')[0]\n self.chooseFileToDownload(filename)\n if filename in self.threaddownload.keys():\n th = self.threaddownload[filename]\n # thread bi pause, gio cho download tiep\n if th.downloadFolder == self.fileSavePath and th.pausenow:\n th.pausenow = False\n else:\n self.th = UploadThread(self, filename, self.upload_file, self.mainWindow.username, self.mainWindow.password, folder_id=None)\n self.th.start()\n self.threaddownload[filename] = self.th\n self.mainWindow.trayIcon.showMessage('Uploading ', filename.decode('utf-8'))\n self.th.finished.connect(lambda: self.upload_complete_signal.emit(str(filename)))\n else:\n self.mainWindow.alertAutoClose('Error', \"You have not login\")\n # sys.exit(1)\n\n def updateStatus(self, threadID, status=None, file_size=None, speed=None, links=None, error=None):\n # print \"Update status here\"\n threadID = unicode(threadID)\n num_row = self.table.rowCount()\n for i in range(num_row):\n try:\n filename = unicode(self.table.item(i, 0).text())\n except:\n continue\n if filename == threadID:\n if file_size:\n size_item = self.table.item(i, 1)\n size_item.setText(str(file_size) + ' byte')\n if speed:\n status_item = self.table.item(i, 2)\n message = status + \" \" + speed\n status_item.setText(str(message))\n elif error:\n error_item = self.table.item(i, 2)\n error_item.setText(\"Error: \" + str(error))\n self.running_thread -= 1\n elif status:\n status_item = self.table.item(i, 2)\n status_item.setText(status)\n # self.table.update(self.table.indexFromItem(status_item))\n if status == 'Completed':\n print(\"Upload Complete !\")\n self.running_thread -= 1\n status_item = self.table.item(i, 2)\n status_item.setText(status)\n if links:\n status_item = self.table.item(i, 3)\n status_item.setText(links)\n return True\n\n def setTableWidth(self):\n width = QtGui.QDesktopWidget().width() / 8\n for i in range(self.table.columnCount()):\n self.table.setColumnWidth(i, width)\n\n def openDownloadMenu(self, position):\n menu = QtGui.QMenu(self)\n Action_pause = menu.addAction('&Pause')\n Action_pause.triggered.connect(self.run_pause_selected)\n Action_resume = menu.addAction('&Resume')\n Action_resume.triggered.connect(self.run_resume_selected)\n Action_stop = menu.addAction('&Stop And Remove')\n Action_stop.triggered.connect(self.run_remove_selected)\n Action_coppy = menu.addAction('&Coppy Download Link')\n Action_coppy.triggered.connect(self.coppy_download_link)\n menu.exec_(self.table.viewport().mapToGlobal(position))\n\n def openFolderPath(self, path):\n if sys.platform == 'darwin':\n subprocess.call(['open', '--', path])\n elif sys.platform == 'linux2':\n os.system('xdg-open {0}'.format(path))\n elif sys.platform == 'win32':\n print (\"Path : %s\" % path)\n ctypes.windll.shell32.ShellExecuteW(None, u'open', u'explorer.exe',\n u'/n,/select, ' + path, None, 1)\n\n def coppy_download_link(self):\n rows = self.table.selectionModel().selectedRows()\n for row in rows:\n r = row.row()\n status = str(self.table.item(r, 2).text())\n if status.find('Completed') != -1:\n link_download = unicode(self.table.item(r, 3).text())\n self.clip.setText(link_download)\n QtGui.QMessageBox.information(self, 'Infomation', \"Link Coppied\", QtGui.QMessageBox.Ok)\n\n def run_pause_selected(self):\n row_selected = list(set([item.row() for item in self.table.selectedItems()]))\n\n if not row_selected:\n QtGui.QMessageBox.warning(self, 'Warning', \"Please Choose File(s) To Pause\", QtGui.QMessageBox.Ok)\n else:\n for row in row_selected:\n filename = unicode(self.table.item(row, 0).text())\n for threadID in self.threaddownload.keys():\n if threadID == filename:\n th = self.threaddownload[threadID]\n if not th.pausenow:\n th.pausenow = True\n self.running_thread -= 1\n status_item = self.table.item(row, 2)\n message = \"Paused\"\n status_item.setText(str(message))\n\n def run_resume_selected(self):\n row_selected = list(set([item.row() for item in self.table.selectedItems()]))\n if not row_selected:\n row_selected = range(self.table.rowCount())\n error = 0\n list_remove_thread = []\n for row in row_selected:\n filename = unicode(self.table.item(row, 0).text())\n for threadID in self.threaddownload.keys():\n if threadID == filename:\n th = self.threaddownload[threadID]\n if th.pausenow:\n if self.running_thread < self.maxthreaddownload:\n th.pausenow = False\n self.running_thread += 1\n else:\n error = 1\n else:\n status = self.table.item(row, 2).text()\n # Thread bi loi va gio nguoi dung resume\n if status.find('Error') != -1:\n if self.running_thread < self.maxthreaddownload:\n self.fileToDownload.append(filename)\n list_remove_thread.append(filename)\n self.running_thread -= 1\n else:\n error = 1\n if list_remove_thread:\n for th in list_remove_thread:\n del self.threaddownload[th]\n self.download()\n if error == 1:\n QtGui.QMessageBox.warning(self, 'Warning', \"Number Of Running Thread Is Maximum. Please\" +\n \"Wait Until Other Files Completed\", QtGui.QMessageBox.Ok)\n\n def run_remove_selected(self):\n row_selected = list(set([item.row() for item in self.table.selectedItems()]))\n while len(row_selected):\n row = row_selected.pop(0)\n if len(row_selected):\n row_selected = [row - 1 for row in row_selected]\n file_path = unicode(self.table.item(row, 0).text())\n status = unicode(self.table.item(row, 2).text())\n self.table.removeRow(row)\n\n def showinputbox(self):\n if self.inputbox is None:\n self.inputbox = tabDownload_InputBox(tab_download=self)\n\n def chooseFileToDownload(self, filename):\n curRow = self.table.rowCount()\n self.table.insertRow(curRow)\n self.table.setItem(curRow, 0, QtGui.QTableWidgetItem(filename.decode('utf-8')))\n self.table.setItem(curRow, 1, QtGui.QTableWidgetItem(u'Updating...'))\n self.table.setItem(curRow, 2, QtGui.QTableWidgetItem(u'Ready'))\n self.table.setItem(curRow, 3, QtGui.QTableWidgetItem())","sub_path":"controller/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":10621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"23529024","text":"from django.contrib.auth.models import AbstractUser\nfrom django.core.validators import RegexValidator\nfrom django.db import models\n\n\nclass User(AbstractUser):\n\n nickname = models.CharField(max_length=20, unique=True)\n phone_number = models.CharField(\n max_length=13,\n blank=True,\n validators=[RegexValidator(r\"010-?[1-9]\\d{3}-?\\d{4}$\")],\n )\n avatar = models.ImageField(\n blank=True,\n upload_to=\"accounts/avatar/%Y/%m/%d\",\n help_text=\"48px * 48px 크기의 png/jpg 파일을 업로드해주세요\",\n )\n\n @property\n def name(self):\n return f\"{self.first_name} {self.last_name}\".strip()\n\n @property\n def avatar_url(self):\n if self.avatar:\n return self.avatar.url\n else:\n return resolve_url(\"/static/avatar.png\")\n\n","sub_path":"backend/accounts/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"520371881","text":"import functools\n\nclass Solution:\n def singleNumber(self, nums: List[int]) -> List[int]:\n xor = functools.reduce(operator.xor, nums)\n position = xor & -xor\n result = [0] * 2\n for num in nums:\n if position & num: result[0] ^= num\n else: result[1] ^= num\n return result\n","sub_path":"260singleNumberIII.py","file_name":"260singleNumberIII.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"589214668","text":"# Triangulate 3D joints\n#\n# Dylan Campbell \n\nimport os\nimport cv2 as cv\nimport argparse\nimport json\nimport pickle\nimport subprocess\nimport numpy as np\nfrom glob import glob\nimport itertools\nfrom multiprocessing import Pool\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom joint_ids import *\nfrom dataset_ids import *\n\nfrom pdb import set_trace as st\n\nnp.seterr(all='raise')\n\nscenes = get_scenes()\ncams = get_cams()\n\nconnectivity_ikea = get_ikea_connectivity() # == COCO format\nconnectivity_body25 = get_body25_connectivity()\n\ndef triangulate_joints(args):\n camera_parameters = get_camera_parameters(args)\n\n scan_folders = get_scan_dirs(args.dataset_dir)\n\n # Select save format:\n if args.save_format == 'ikea':\n joint_names = get_ikea_joint_names()\n connectivity = connectivity_ikea\n elif args.save_format == 'body25':\n joint_names = get_body25_joint_names()[:25]\n connectivity = connectivity_body25\n\n if 'keypoint_rcnn' in args.input_predictions:\n input_joint_names = get_ikea_joint_names()\n elif 'openpose' in args.input_predictions:\n input_joint_names = get_body25_joint_names()\n input_joint_names_dict = {name: i for i, name in enumerate(input_joint_names)}\n\n num_joints = len(joint_names)\n\n with open(os.path.join(args.dataset_dir, 'test_cross_env.txt'), 'r') as f:\n test_paths = f.read().splitlines()\n with open(os.path.join(args.dataset_dir, 'train_cross_env.txt'), 'r') as f:\n train_paths = f.read().splitlines()\n\n reproj_gt_meter = AverageMeter('Reprojection error')\n reproj_gt_meter_train = AverageMeter('Reprojection error')\n reproj_gt_meter_test = AverageMeter('Reprojection error')\n pck_gt_meter = AverageMeter('PCK')\n pck_gt_meter_train = AverageMeter('PCK')\n pck_gt_meter_test = AverageMeter('PCK')\n\n for i, scan_folder in enumerate(scan_folders):\n print(f\"\\nProcessing {i} of {len(scan_folders)}: {' '.join(scan_folder.split('/')[-2:])}\")\n\n # Determine scene ID:\n label = scan_folder.split('/')[-1]\n scene = label.split('_')[3]\n assert scene in scenes\n\n prediction_path = os.path.join('predictions', 'pose2d', args.input_predictions)\n \n use_all_frames = False\n if use_all_frames:\n # Use all frames:\n # Check all cams, since some have more frames than others...\n json_mask = os.path.join(scan_folder, 'dev1', prediction_path, 'scan_video_????????????_keypoints.json')\n json_files1 = glob(json_mask)\n json_mask = os.path.join(scan_folder, 'dev2', prediction_path, 'scan_video_????????????_keypoints.json')\n json_files2 = glob(json_mask)\n json_mask = os.path.join(scan_folder, 'dev3', prediction_path, 'scan_video_????????????_keypoints.json')\n json_files3 = glob(json_mask)\n json_index = np.argmin([len(json_files1), len(json_files2), len(json_files3)])\n json_files = [json_files1, json_files2, json_files3][json_index]\n keypoint_filenames = sorted([os.path.basename(json_file) for json_file in json_files])\n else:\n # Use frames with GT 2D annotations:\n json_mask = os.path.join(scan_folder, 'dev3', 'pose2d', '??????.json') # GT 2D annotations\n json_files = sorted(glob(json_mask)) # eg //dev3/pose2d/000000.json\n frame_strs = [os.path.splitext(os.path.basename(json_file))[0] for json_file in json_files] # eg 000000\n keypoint_filenames = sorted([f'scan_video_000000{frame_str}_keypoints.json' for frame_str in frame_strs]) # eg scan_video_000000000000_keypoints.json\n\n for file_index, keypoint_filename in enumerate(keypoint_filenames):\n joints2d = np.zeros((num_joints, 3, 3))\n for cam in cams:\n json_file = os.path.join(scan_folder, cam, prediction_path, keypoint_filename)\n if not os.path.exists(json_file):\n continue # Predictions don't exist (missing video frame)\n with open(json_file) as f:\n pose2d = json.load(f)\n if len(pose2d[\"people\"]) == 0:\n continue\n keypoints = None\n # max_length = 0.0\n max_score = -np.Inf\n # Choose highest scoring person in frame:\n for person_id, person in enumerate(pose2d[\"people\"]):\n kps = np.array(person[\"pose_keypoints_2d\"]).reshape(-1, 3) # [x1, y1, c1], [x2, ... in COCO or body25 format\n average_score = np.mean(kps[:, 2])\n if average_score > max_score:\n max_score = average_score\n keypoints = kps\n # Convert to ikea joints:\n for j, joint_name in enumerate(joint_names):\n joint_id = input_joint_names_dict[joint_name]\n joints2d[j, cams.index(cam), :] = keypoints[joint_id, :]\n # Undistort points:\n do_undistort = False\n if do_undistort:\n for cam_id, cam in enumerate(cams):\n joints2d_cam = joints2d[:, cam_id, :2] # 17x2\n K = camera_parameters[scene][cam][\"K\"]\n dist_coefs = camera_parameters[scene][cam][\"dist_coefs\"]\n joints2d_cam_undistorted = cv.undistortPoints(joints2d_cam.T, K, dist_coefs, None, None, K).squeeze() # input 2xN/Nx2, output 1xN/Nx1 2-channel\n joints2d[:, cam_id, :2] = joints2d_cam_undistorted\n\n # Loop over joints:\n joints3d = np.zeros((num_joints, 4)) # 17x4\n for j in range(num_joints):\n joint2d = joints2d[j, :, :] # 3x3\n if np.count_nonzero(joint2d[:, 2] >= args.score_threshold) < 2: # Skip if insufficient good detections for triangulation\n continue\n\n Ps = []\n xs = []\n C = 1.0\n for cam_id, cam in enumerate(cams):\n if joint2d[cam_id, 2] >= args.score_threshold:\n Ps.append(camera_parameters[scene][cam][\"P\"].astype(float))\n xs.append(np.array([joint2d[cam_id, 0], joint2d[cam_id, 1], 1.0]).astype(float)) # homogeneous\n C *= joint2d[cam_id, 2]\n\n if len(Ps) == 2:\n # Triangulate points from 2 views:\n X = cv.triangulatePoints(Ps[0], Ps[1], xs[0][:2], xs[1][:2]) # dev1+dev3 (preferred pair)\n X /= X[3]\n X = X.squeeze()\n X[3] = C\n else:\n # Triangulate from all 2-view pairs and average (suboptimal):\n X1 = cv.triangulatePoints(Ps[0], Ps[1], xs[0][:2], xs[1][:2]) # dev1+dev2\n X2 = cv.triangulatePoints(Ps[0], Ps[2], xs[0][:2], xs[2][:2]) # dev1+dev3\n X3 = cv.triangulatePoints(Ps[1], Ps[2], xs[1][:2], xs[2][:2]) # dev2+dev3\n X1 /= X1[3]\n X2 /= X2[3]\n X3 /= X3[3]\n X1 = X1.squeeze()\n X2 = X2.squeeze()\n X3 = X3.squeeze()\n\n X = np.mean((X1, X2, X3), axis=0)\n X[3] = C\n\n joints3d[j, :] = X\n\n # Filter any points that are far from the median of the others (dimension-wise):\n non_zero_indices = joints3d[:, 3] > 0.0\n if non_zero_indices.any(): # At least one joint\n joints3d_median = np.median(joints3d[non_zero_indices, :], axis=0) # excluding zeros\n error = np.abs(joints3d[:, :3] - joints3d_median[:3])\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0 and any(error[j, :] > args.distance_to_median_threshold): # 200 cm\n joints3d[j, :] = np.zeros(4)\n\n # Filter any points that are far from any other point:\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0:\n distances = []\n for j2 in range(num_joints):\n if joints3d[j2, 3] > 0.0 and j != j2:\n distances.append(np.linalg.norm(joints3d[j, :3] - joints3d[j2, :3]))\n if distances and np.array(distances).min() > args.distance_to_closest_threshold: # 100 cm\n joints3d[j, :] = np.zeros(4)\n\n # Compute reprojection errors in each view:\n # Discard joints with large reprojection error in any view\n for cam_id, cam in enumerate(cams):\n P = camera_parameters[scene][cam][\"P\"]\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0: # Skip joints that were not triangulated\n if joints2d[j, cam_id, 2] > args.score_threshold: # Skip 2D joints that were not well detected\n x2d = joints2d[j, cam_id, :2]\n x3dproj = P @ np.array([joints3d[j, 0], joints3d[j, 1], joints3d[j, 2], 1.0]) # Project to 2D\n x3dproj /= x3dproj[2]\n x3dproj = x3dproj[:2]\n reprojection_error = np.linalg.norm(x2d - x3dproj)\n # print(f\"{cam} {joint_names[j]} \\t\\t {reprojection_error:3.1f}\")\n if reprojection_error > args.reprojection_threshold:\n joints3d[j, :] = np.zeros(4)\n\n # Compare against GT 2D annotations:\n # Discard joints with large reprojection error in this view\n if not use_all_frames:\n with open(json_files[file_index]) as f:\n pose2d_gt = json.load(f)\n cam = 'dev3'\n P = camera_parameters[scene][cam][\"P\"]\n # GET GT PARAMS\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0: # Skip joints that were not triangulated\n joint_name = get_ikea_joint_names()[j]\n position_gt = np.array(pose2d_gt[joint_name][\"position\"])\n confidence_gt = pose2d_gt[joint_name][\"confidence\"]\n x2d = position_gt\n x3dproj = P @ np.array([joints3d[j, 0], joints3d[j, 1], joints3d[j, 2], 1.0]) # Project to 2D\n x3dproj /= x3dproj[2]\n x3dproj = x3dproj[:2]\n reprojection_error = np.linalg.norm(x2d - x3dproj)\n # print(f\"{cam} {joint_names[j]} \\t\\t {reprojection_error:3.1f}\")\n if reprojection_error > args.reprojection_threshold:\n joints3d[j, :] = np.zeros(4)\n\n # Quantify pseudoGT error:\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0: # Skip joints that were not triangulated\n joint_name = get_ikea_joint_names()[j]\n position_gt = np.array(pose2d_gt[joint_name][\"position\"])\n confidence_gt = pose2d_gt[joint_name][\"confidence\"]\n x2d = position_gt\n x3dproj = P @ np.array([joints3d[j, 0], joints3d[j, 1], joints3d[j, 2], 1.0]) # Project to 2D\n x3dproj /= x3dproj[2]\n x3dproj = x3dproj[:2]\n reprojection_error = np.linalg.norm(x2d - x3dproj)\n\n if confidence_gt == 3:\n reproj_gt_meter.update(reprojection_error, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in train_paths:\n reproj_gt_meter_train.update(reprojection_error, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in test_paths:\n reproj_gt_meter_test.update(reprojection_error, 1)\n if reprojection_error < 10.0: # PCK @ 10 pixels\n pck_gt_meter.update(1.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in train_paths:\n pck_gt_meter_train.update(1.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in test_paths:\n pck_gt_meter_test.update(1.0, 1)\n else:\n pck_gt_meter.update(0.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in train_paths:\n pck_gt_meter_train.update(0.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in test_paths:\n pck_gt_meter_test.update(0.0, 1)\n else:\n if confidence_gt == 3:\n pck_gt_meter.update(0.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in train_paths:\n pck_gt_meter_train.update(0.0, 1)\n if '/'.join(scan_folder.split('/')[-2:]) in test_paths:\n pck_gt_meter_test.update(0.0, 1)\n\n # Plot results:\n # do_plot = True\n do_plot = False\n if do_plot:\n if file_index % 1 == 0:\n print(file_index)\n print(scan_folder)\n for cam_id, cam in enumerate(cams):\n if not use_all_frames:\n image_path = os.path.join(scan_folder, cam, 'images', frame_strs[file_index] + '.png')\n img = cv.imread(image_path)\n plt.imshow(img)\n P = camera_parameters[scene][cam][\"P\"]\n for j in range(num_joints):\n if joints2d[j, cam_id, 2] > args.score_threshold:\n plt.scatter(joints2d[j, cam_id, 0], joints2d[j, cam_id, 1], c='w')\n if joints3d[j, 3] > 0.0:\n x = P @ np.array([joints3d[j, 0], joints3d[j, 1], joints3d[j, 2], 1.0]) # Project to 2D\n x /= x[2]\n plt.scatter(x[0], x[1], c='r', s=16)\n for limb in connectivity:\n if joints2d[limb[0], cam_id, 2] > args.score_threshold and joints2d[limb[1], cam_id, 2] > args.score_threshold:\n plt.plot([joints2d[limb[0], cam_id, 0], joints2d[limb[1], cam_id, 0]], [joints2d[limb[0], cam_id, 1], joints2d[limb[1], cam_id, 1]], c='w')\n if joints3d[limb[0], 3] > 0.0 and joints3d[limb[1], 3] > 0.0:\n x1 = P @ np.array([joints3d[limb[0], 0], joints3d[limb[0], 1], joints3d[limb[0], 2], 1.0]) # Project to 2D\n x1 /= x1[2]\n x2 = P @ np.array([joints3d[limb[1], 0], joints3d[limb[1], 1], joints3d[limb[1], 2], 1.0]) # Project to 2D\n x2 /= x2[2]\n plt.plot([x1[0], x2[0]], [x1[1], x2[1]], c='r')\n plt.axis('equal')\n # plt.gca().invert_yaxis()\n plt.show()\n\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n ax.set_aspect('equal')\n ax.view_init(elev=-89., azim=-89.)\n for j in range(num_joints):\n if joints3d[j, 3] > 0.0:\n ax.scatter(joints3d[j, 0], joints3d[j, 1], joints3d[j, 2], c='k') # dev1\n for limb in connectivity:\n if joints3d[limb[0], 3] > 0.0 and joints3d[limb[1], 3] > 0.0:\n ax.plot([joints3d[limb[0], 0], joints3d[limb[1], 0]], [joints3d[limb[0], 1], joints3d[limb[1], 1]], [joints3d[limb[0], 2], joints3d[limb[1], 2]])\n ax.set_xlabel('X')\n ax.set_ylabel('Y')\n ax.set_zlabel('Z')\n set_axes_equal(ax)\n plt.show()\n\n # Save 3D joints [X,Y,Z,C]\n frame_index = int(keypoint_filename.split('_')[2])\n for cam in cams:\n # Transform into camera coordinate system\n R = camera_parameters[scene][cam][\"R\"]\n T = camera_parameters[scene][cam][\"T\"]\n joints3d_cam = joints3d.copy()\n non_zero_indices = joints3d[:, 3] > 0.0\n joints3d_cam[non_zero_indices, :3] = (R @ joints3d_cam[non_zero_indices, :3].T + T).T\n\n # Rearrange into dictionary for writing:\n output_dict = {}\n # IKEA ANNOTATION STYLE:\n # for joint_id, joint_name in enumerate(joint_names):\n # output_dict[joint_name] = {\"position\": list(joints3d_cam[joint_id, :3]), \"score\": joints3d_cam[joint_id, 3]}\n # OPENPOSE ANNOTATION STYLE:\n output_dict[\"format\"] = args.save_format\n output_dict[\"pose_keypoints_3d\"] = list(joints3d_cam.reshape(-1)) # [X1,Y1,Z1,C1,...XN,YN,ZN,CN]\n\n\n # output_path = os.path.join(scan_folder, cam, 'pose3d', f'format_{args.save_format}')\n output_path = os.path.join(scan_folder, cam, 'pose3d') # Directly store in pose3d\n os.makedirs(output_path, exist_ok=True)\n json_file = os.path.join(output_path, f\"{frame_index:06}.json\")\n with open(json_file, 'w') as f:\n json.dump(output_dict, f)\n\n errors_2d_gt = {\"reproj\": reproj_gt_meter.avg,\n \"reproj_train\": reproj_gt_meter_train.avg,\n \"reproj_test\": reproj_gt_meter_test.avg,\n \"pck\": pck_gt_meter.avg,\n \"pck_train\": pck_gt_meter_train.avg,\n \"pck_test\": pck_gt_meter_test.avg}\n with open(os.path.join('./', 'pseudoGT_errors_2d.pkl'), 'wb') as f:\n pickle.dump(errors_2d_gt, f)\n print('Averages:')\n print(f\"reproj_gt: {reproj_gt_meter.avg}, reproj_gt_train: {reproj_gt_meter_train.avg}, reproj_gt_test: {reproj_gt_meter_test.avg}, pck_gt: {pck_gt_meter.avg}, pck_gt_train: {pck_gt_meter_train.avg}, pck_gt_test: {pck_gt_meter_test.avg}\")\n print('Counts:')\n print(f\"reproj_gt: {reproj_gt_meter.count}, reproj_gt_train: {reproj_gt_meter_train.count}, reproj_gt_test: {reproj_gt_meter_test.count}, pck_gt: {pck_gt_meter.count}, pck_gt_train: {pck_gt_meter_train.count}, pck_gt_test: {pck_gt_meter_test.count}\")\n\n\ndef get_camera_parameters(args):\n calib_path = os.path.join(args.dataset_dir, 'Calibration')\n camera_parameters_file = os.path.join(calib_path, 'camera_parameters.pkl')\n if os.path.exists(camera_parameters_file):\n with open(camera_parameters_file, 'rb') as f:\n camera_parameters = pickle.load(f)\n else:\n camera_parameters = {}\n for scene in scenes:\n scene_path = os.path.join(calib_path, scene)\n camera_parameters[scene] = {}\n for cam in cams:\n cam_path = os.path.join(scene_path, cam)\n with open(os.path.join(cam_path, 'camera_parameters.pkl'), 'rb') as f:\n camera_parameters[scene][cam] = pickle.load(f)\n P = camera_parameters[scene][cam][\"K\"] @ cv.hconcat((camera_parameters[scene][cam][\"R\"], camera_parameters[scene][cam][\"T\"]))\n camera_parameters[scene][cam][\"P\"] = P\n with open(camera_parameters_file, 'wb') as f:\n pickle.dump(camera_parameters, f)\n return camera_parameters\n\ndef set_axes_equal(ax):\n '''Make axes of 3D plot have equal scale so that spheres appear as spheres,\n cubes as cubes, etc.. This is one possible solution to Matplotlib's\n ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n\n Input\n ax: a matplotlib axis, e.g., as output from plt.gca().\n '''\n\n x_limits = ax.get_xlim3d()\n y_limits = ax.get_ylim3d()\n z_limits = ax.get_zlim3d()\n\n x_range = abs(x_limits[1] - x_limits[0])\n x_middle = np.mean(x_limits)\n y_range = abs(y_limits[1] - y_limits[0])\n y_middle = np.mean(y_limits)\n z_range = abs(z_limits[1] - z_limits[0])\n z_middle = np.mean(z_limits)\n\n # The plot bounding box is a sphere in the sense of the infinity\n # norm, hence I call half the max range the plot radius.\n plot_radius = 0.5*max([x_range, y_range, z_range])\n\n ax.set_xlim3d([x_middle - plot_radius, x_middle + plot_radius])\n ax.set_ylim3d([y_middle - plot_radius, y_middle + plot_radius])\n ax.set_zlim3d([z_middle - plot_radius, z_middle + plot_radius])\n\ndef triangulate_nviews(P, ip):\n \"\"\"\n Triangulate a point visible in n camera views.\n P is a list of camera projection matrices.\n ip is a list of homogenised image points. eg [ [x, y, 1], [x, y, 1] ], OR,\n ip is a 2d array - shape nx3 - [ [x, y, 1], [x, y, 1] ]\n len of ip must be the same as len of P\n \"\"\"\n if not len(ip) == len(P):\n raise ValueError('Number of points and number of cameras not equal.')\n n = len(P)\n M = np.zeros([3*n, 4+n])\n for i, (x, p) in enumerate(zip(ip, P)):\n M[3*i:3*i+3, :4] = p\n M[3*i:3*i+3, 4+i] = -x\n V = np.linalg.svd(M)[-1]\n X = V[-1, :4]\n return X / X[3]\n\ndef get_scan_dirs(input_path):\n \"\"\"Get all scan directories (scan = an assembly)\n \"\"\"\n scan_path_list = []\n category_path_list = get_subdirs(input_path)\n for category in category_path_list:\n if os.path.basename(category) != 'Calibration':\n category_scans = get_subdirs(category)\n for category_scan in category_scans:\n scan_path_list.append(category_scan)\n return scan_path_list\n\ndef get_subdirs(input_path):\n subdirs = [os.path.join(input_path, dir_i) for dir_i in os.listdir(input_path)\n if os.path.isdir(os.path.join(input_path, dir_i))]\n subdirs.sort()\n return subdirs\n\nclass AverageMeter(object):\n \"\"\"Computes and stores the average and current value\"\"\"\n def __init__(self, name, fmt=':f'):\n self.name = name\n self.fmt = fmt\n self.reset()\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n def __str__(self):\n fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'\n return fmtstr.format(**self.__dict__)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset_dir', type=str, default='/home/djcam/Documents/HDD/datasets/ikea/ikea_asm/',\n help='directory of the IKEA assembly dataset')\n parser.add_argument('--save_format', type=str, default='ikea',\n help='output body format [ikea, body25]')\n parser.add_argument('--input_predictions', type=str, default='keypoint_rcnn',\n help='input 2D predictions [keypoint_rcnn, openpose]')\n parser.add_argument('--score_threshold', type=float, default=0.5,\n help='score threshold for 2D joint detections to be used in triangulation')\n parser.add_argument('--reprojection_threshold', type=float, default=30.0,\n help='reprojection error threshold (joints with greater reproj error will be removed)')\n parser.add_argument('--distance_to_median_threshold', type=float, default=200.0,\n help='triangulated points are discarded if they are further from the median position than this distance (cm)')\n parser.add_argument('--distance_to_closest_threshold', type=float, default=100.0,\n help='triangulated points are discarded if they are further from the closest point than this distance (cm)')\n args = parser.parse_args()\n\n triangulate_joints(args)","sub_path":"human_pose/triangulate.py","file_name":"triangulate.py","file_ext":"py","file_size_in_byte":24329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"12656201","text":"import ytp\nytp.load_api(keyboard, mouse, store, system, window, clipboard, highlevel, dialog, engine)\n\nkey = '0'\n\ndef main():\n ytp.multiplier(key, '11')\n\nif ytp.is_numlock_on():\n keyboard.send_key(key)\nelse:\n main()\n","sub_path":"yt/multiplier_10.py","file_name":"multiplier_10.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"393928751","text":"import json\nimport hashlib\nf = open('test.level','r',encoding='utf-8')\nstrs = f.read()\nf.close()\njs = json.loads(strs)\nlevel = js[\"level_data\"]\nprint(level)\nstrs = \"GG思密达\"\nb = bytes(level)\nprint(b)\nprint(list(b))\nm2 = hashlib.md5() \nm2.update(b)\nprint(m2.hexdigest().upper())\nprint(js[\"level_id\"])","sub_path":"test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"225746997","text":"\"\"\"\nSupport for Mikrotik routers as device tracker.\n\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/device_tracker.mikrotik/\n\"\"\"\nimport logging\nimport threading\nfrom datetime import timedelta\n\nimport voluptuous as vol\n\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.components.device_tracker import (\n DOMAIN, PLATFORM_SCHEMA, DeviceScanner)\nfrom homeassistant.const import (CONF_HOST,\n CONF_PASSWORD,\n CONF_USERNAME,\n CONF_PORT)\nfrom homeassistant.util import Throttle\n\nREQUIREMENTS = ['librouteros==1.0.2']\n\n# Return cached results if last scan was less then this time ago.\nMIN_TIME_BETWEEN_SCANS = timedelta(seconds=10)\n\nMTK_DEFAULT_API_PORT = '8728'\n\n_LOGGER = logging.getLogger(__name__)\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({\n vol.Required(CONF_HOST): cv.string,\n vol.Required(CONF_USERNAME): cv.string,\n vol.Required(CONF_PASSWORD): cv.string,\n vol.Optional(CONF_PORT, default=MTK_DEFAULT_API_PORT): cv.port\n})\n\n\ndef get_scanner(hass, config):\n \"\"\"Validate the configuration and return MTikScanner.\"\"\"\n scanner = MikrotikScanner(config[DOMAIN])\n return scanner if scanner.success_init else None\n\n\nclass MikrotikScanner(DeviceScanner):\n \"\"\"This class queries a Mikrotik router.\"\"\"\n\n def __init__(self, config):\n \"\"\"Initialize the scanner.\"\"\"\n self.last_results = {}\n\n self.host = config[CONF_HOST]\n self.port = config[CONF_PORT]\n self.username = config[CONF_USERNAME]\n self.password = config[CONF_PASSWORD]\n\n self.lock = threading.Lock()\n\n self.connected = False\n self.success_init = False\n self.client = None\n\n self.success_init = self.connect_to_device()\n\n if self.success_init:\n _LOGGER.info(\"Start polling Mikrotik router...\")\n self._update_info()\n else:\n _LOGGER.error(\"Connection to Mikrotik failed\")\n\n def connect_to_device(self):\n \"\"\"Connect to Mikrotik method.\"\"\"\n # pylint: disable=import-error\n import librouteros\n try:\n self.client = librouteros.connect(\n self.host,\n self.username,\n self.password,\n port=int(self.port)\n )\n\n routerboard_info = self.client(cmd='/system/routerboard/getall')\n\n if routerboard_info:\n _LOGGER.info(\"Connected to Mikrotik %s with IP %s\",\n routerboard_info[0].get('model', 'Router'),\n self.host)\n self.connected = True\n\n except (librouteros.exceptions.TrapError,\n librouteros.exceptions.ConnectionError) as api_error:\n _LOGGER.error(\"Connection error: %s\", api_error)\n\n return self.connected\n\n def scan_devices(self):\n \"\"\"Scan for new devices and return a list with found device MACs.\"\"\"\n self._update_info()\n return [device for device in self.last_results]\n\n def get_device_name(self, mac):\n \"\"\"Return the name of the given device or None if we don't know.\"\"\"\n with self.lock:\n return self.last_results.get(mac)\n\n @Throttle(MIN_TIME_BETWEEN_SCANS)\n def _update_info(self):\n \"\"\"Retrieve latest information from the Mikrotik box.\"\"\"\n with self.lock:\n _LOGGER.info(\"Loading wireless device from Mikrotik...\")\n\n wireless_clients = self.client(\n cmd='/interface/wireless/registration-table/getall'\n )\n device_names = self.client(cmd='/ip/dhcp-server/lease/getall')\n\n if device_names is None or wireless_clients is None:\n return False\n\n mac_names = {device.get('mac-address'): device.get('host-name')\n for device in device_names\n if device.get('mac-address')}\n\n self.last_results = {\n device.get('mac-address'):\n mac_names.get(device.get('mac-address'))\n for device in wireless_clients\n }\n\n return True\n","sub_path":"homeassistant/components/device_tracker/mikrotik.py","file_name":"mikrotik.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"594880236","text":"from functools import wraps\r\nfrom flask import current_app\r\nfrom flask_login import current_user\r\n\r\nfrom urllib.parse import urlparse, urljoin\r\nfrom flask import request, url_for, redirect\r\n\r\n\r\ndef require_username(username, fallback_view):\r\n def wrapper(func):\r\n @wraps(func)\r\n def decorated_view(*args, **kwargs):\r\n if not current_user.is_authenticated:\r\n return current_app.login_manager.unauthorized()\r\n if current_user.username != username:\r\n return redirect(url_for(fallback_view))\r\n return func(*args, **kwargs)\r\n return decorated_view\r\n return wrapper\r\n\r\n\r\ndef is_safe_url(target):\r\n ref_url = urlparse(request.host_url)\r\n test_url = urlparse(urljoin(request.host_url, target))\r\n return test_url.scheme in ('http', 'https') and ref_url.netloc == test_url.netloc\r\n","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"277063026","text":"#!/usr/bin/env python\n\n\nif __name__ == '__main__':\n dictionary = {'name': 'Chris', 'city': 'Seattle', 'cake': 'Chocolate'}\n print(dictionary)\n del dictionary['cake']\n print(dictionary)\n dictionary.setdefault('fruit', 'Mango')\n for k, v in dictionary.items():\n print(k)\n print(v)\n print('cake' in dictionary)\n print('Mango' in dictionary.values())\n\n dictionary2 = dictionary.copy()\n for k, v in dictionary.items():\n dictionary2[k] = 't' * v.count('t')\n print(dictionary2)\n\n s2 = set()\n s3 = set()\n s4 = set()\n for x in range(0, 21):\n if x % 2 == 0:\n s2.add(x)\n if x % 3 == 0:\n s3.add(x)\n if x % 4 == 0:\n s4.add(x)\n print(s2)\n print(s3)\n print(s4)\n print(s3.issubset(s2))\n print(s4.issubset(s2))\n\n s5 = set(['p', 'y', 't', 'h', 'o', 'n'])\n s4.add('i')\n fs = frozenset(('m', 'a', 'r', 'a', 't', 'h', 'o', 'n'))\n print(fs.union(s5))\n print(fs.intersection(s5))\n","sub_path":"students/josh_hicks/session04/dict_lab.py","file_name":"dict_lab.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"225534340","text":"import json\nimport jsonlines\nfrom tensorflow.keras.preprocessing.text import Tokenizer #tokenizer:分词器，产生词典并创建词向量\n#要依据单词的编码将句子序列化，得到句子后要对句子进行处理，使得句子编码的长度相同。\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences #保证编码后 句子长度的一致性\n\n\nsource_file = 'F:\\\\DL_datasets\\\\archive(1)\\\\Sarcasm_Headlines_Dataset.json'\narticle_link_list =[]\nsentences = []\nlabels = []\nwith open(source_file,'r+') as f:\n for item in jsonlines.Reader(f):\n article_link_list.append(item['article_link'])\n labels.append(item['is_sarcastic'])\n sentences.append(item['headline'])\n\ntokenizer = Tokenizer(oov_token='= span[1]):\n answer_span.append(idx)\n if len(answer_span) == 0:\n # there is no answer in context_tokens (mb because of para_limit)\n continue\n y1, y2 = answer_span[0], answer_span[-1]\n y1s.append(y1)\n y2s.append(y2)\n if len(answer_texts) == 0 and len(qa[\"answers\"]) != 0:\n # all answers are in the end of long context\n # skipping such QAs\n continue\n example = {\"context_tokens\": context_tokens, \"context_chars\": context_chars,\n \"context_bpe\": context_bpe, \"context_pos\": context_pos,\n \"ques_tokens\": ques_tokens, \"ques_chars\": ques_chars,\n \"ques_bpe\": ques_bpe, \"ques_pos\": ques_pos,\n \"y1s\": y1s, \"y2s\": y2s, \"id\": total}\n examples.append(example)\n eval_examples[str(total)] = {\n \"context\": context, \"spans\": spans,\n \"answers\": answer_texts, \"uuid\": qa[\"id\"],\n \"context_raw\": context_raw,\n \"raw2prepro\": r2p,\n \"prepro2raw\": p2r,\n }\n random.shuffle(examples)\n print(\"{} questions in total\".format(len(examples)))\n return examples, eval_examples\n\n\ndef get_embedding(counter, data_type, limit=-1, emb_file=None, size=None, vec_size=None):\n print(\"Generating {} embedding...\".format(data_type))\n embedding_dict = {}\n filtered_elements = [k for k, v in counter.items() if v > limit]\n if emb_file is not None:\n assert size is not None\n assert vec_size is not None\n with open(emb_file, \"r\", encoding=\"utf-8\") as fh:\n print('Using pretrained embdgs: {}'.format(emb_file))\n for line in tqdm(fh, total=size):\n array = line.split()\n word = \"\".join(array[0:-vec_size])\n vector = list(map(float, array[-vec_size:]))\n if word in counter and counter[word] > limit:\n embedding_dict[word] = vector\n print(\"{} / {} tokens have corresponding {} embedding vector\".format(\n len(embedding_dict), len(filtered_elements), data_type))\n else:\n assert vec_size is not None\n for token in filtered_elements:\n embedding_dict[token] = [np.random.normal(\n scale=0.1) for _ in range(vec_size)]\n print(\"{} tokens have corresponding embedding vector\".format(\n len(filtered_elements)))\n\n NULL = \"--NULL--\"\n OOV = \"--OOV--\"\n token2idx_dict = {token: idx for idx,\n token in enumerate(embedding_dict.keys(), 2)}\n token2idx_dict[NULL] = 0\n token2idx_dict[OOV] = 1\n embedding_dict[NULL] = [0. for _ in range(vec_size)]\n embedding_dict[OOV] = [0. for _ in range(vec_size)]\n idx2emb_dict = {idx: embedding_dict[token]\n for token, idx in token2idx_dict.items()}\n emb_mat = [idx2emb_dict[idx] for idx in range(len(idx2emb_dict))]\n return emb_mat, token2idx_dict\n\n\ndef build_features(config, examples, data_type, out_file, word2idx_dict, char2idx_dict,\n bpe2idx_dict=None, pos2idx_dict=None, is_test=False):\n\n para_limit = config.test_para_limit if is_test else config.para_limit\n ques_limit = config.test_ques_limit if is_test else config.ques_limit\n char_limit = config.char_limit\n bpe_limit = config.bpe_limit\n\n def filter_func(example):\n return len(example[\"context_tokens\"]) > para_limit or len(example[\"ques_tokens\"]) > ques_limit\n\n print(\"Processing {} examples...\".format(data_type))\n writer = tf.python_io.TFRecordWriter(out_file)\n total = 0\n total_ = 0\n meta = {}\n for example in tqdm(examples):\n total_ += 1\n\n if filter_func(example):\n continue\n\n total += 1\n context_idxs = np.zeros([para_limit], dtype=np.int32)\n context_char_idxs = np.zeros([para_limit, char_limit], dtype=np.int32)\n context_bpe_idxs = np.zeros([para_limit, bpe_limit], dtype=np.int32)\n context_pos_idxs = np.zeros([para_limit], dtype=np.int32)\n ques_idxs = np.zeros([ques_limit], dtype=np.int32)\n ques_char_idxs = np.zeros([ques_limit, char_limit], dtype=np.int32)\n ques_bpe_idxs = np.zeros([ques_limit, bpe_limit], dtype=np.int32)\n ques_pos_idxs = np.zeros([ques_limit], dtype=np.int32)\n y1 = np.zeros([para_limit], dtype=np.float32)\n y2 = np.zeros([para_limit], dtype=np.float32)\n\n def _get_word(word):\n for each in (word, word.lower(), word.capitalize(), word.upper()):\n if each in word2idx_dict:\n return word2idx_dict[each]\n return 1\n\n def _get_emb(k, k2idx_dict):\n if k in k2idx_dict:\n return k2idx_dict[k]\n return 1\n\n for i, token in enumerate(example[\"context_tokens\"]):\n context_idxs[i] = _get_word(token)\n\n for i, token in enumerate(example[\"ques_tokens\"]):\n ques_idxs[i] = _get_word(token)\n\n if config.use_char:\n for i, token in enumerate(example[\"context_chars\"]):\n for j, char in enumerate(token):\n if j == char_limit:\n break\n context_char_idxs[i, j] = _get_emb(char, char2idx_dict)\n\n for i, token in enumerate(example[\"ques_chars\"]):\n for j, char in enumerate(token):\n if j == char_limit:\n break\n ques_char_idxs[i, j] = _get_emb(char, char2idx_dict)\n\n if config.use_bpe:\n for i, token in enumerate(example[\"context_bpe\"]):\n for j, bpe in enumerate(token):\n if j == bpe_limit:\n break\n context_bpe_idxs[i, j] = _get_emb(bpe, bpe2idx_dict)\n\n for i, token in enumerate(example[\"ques_bpe\"]):\n for j, bpe in enumerate(token):\n if j == bpe_limit:\n break\n ques_bpe_idxs[i, j] = _get_emb(bpe, bpe2idx_dict)\n\n if config.use_pos:\n for i, token in enumerate(example[\"context_pos\"]):\n context_pos_idxs[i] = _get_emb(token, pos2idx_dict)\n\n for i, token in enumerate(example[\"ques_pos\"]):\n ques_pos_idxs[i] = _get_emb(token, pos2idx_dict)\n\n # if we have no answers in file (it means we are in predict mode)\n # then add dummy answers\n start, end = 0, 0\n if len(example[\"y1s\"]) > 0:\n start, end = example[\"y1s\"][-1], example[\"y2s\"][-1]\n\n y1[start], y2[end] = 1.0, 1.0\n\n record = tf.train.Example(features=tf.train.Features(feature={\n \"context_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_idxs.tostring()])),\n \"ques_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_idxs.tostring()])),\n \"context_char_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_char_idxs.tostring()])),\n \"ques_char_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_char_idxs.tostring()])),\n \"context_bpe_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_bpe_idxs.tostring()])),\n \"ques_bpe_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_bpe_idxs.tostring()])),\n \"context_pos_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[context_pos_idxs.tostring()])),\n \"ques_pos_idxs\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[ques_pos_idxs.tostring()])),\n \"y1\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[y1.tostring()])),\n \"y2\": tf.train.Feature(bytes_list=tf.train.BytesList(value=[y2.tostring()])),\n \"id\": tf.train.Feature(int64_list=tf.train.Int64List(value=[example[\"id\"]]))\n }))\n writer.write(record.SerializeToString())\n print(\"Build {} / {} instances of features in total\".format(total, total_))\n meta[\"total\"] = total\n writer.close()\n return meta\n\n\ndef build_features_notfdata(config, examples, data_type, word2idx_dict, char2idx_dict,\n bpe2idx_dict=None, pos2idx_dict=None, is_test=False):\n\n para_limit = config.test_para_limit if is_test else config.para_limit\n ques_limit = config.test_ques_limit if is_test else config.ques_limit\n char_limit = config.char_limit\n bpe_limit = config.bpe_limit\n\n def filter_func(example):\n return len(example[\"context_tokens\"]) > para_limit or len(example[\"ques_tokens\"]) > ques_limit\n\n print(\"Processing {} examples...\".format(data_type))\n total = 0\n total_ = 0\n meta = {}\n records = []\n for example in tqdm(examples):\n total_ += 1\n\n if filter_func(example):\n continue\n\n total += 1\n context_idxs = np.zeros([para_limit], dtype=np.int32)\n context_char_idxs = np.zeros([para_limit, char_limit], dtype=np.int32)\n context_bpe_idxs = np.zeros([para_limit, bpe_limit], dtype=np.int32)\n context_pos_idxs = np.zeros([para_limit], dtype=np.int32)\n ques_idxs = np.zeros([ques_limit], dtype=np.int32)\n ques_char_idxs = np.zeros([ques_limit, char_limit], dtype=np.int32)\n ques_bpe_idxs = np.zeros([ques_limit, bpe_limit], dtype=np.int32)\n ques_pos_idxs = np.zeros([ques_limit], dtype=np.int32)\n y1 = np.zeros([para_limit], dtype=np.float32)\n y2 = np.zeros([para_limit], dtype=np.float32)\n\n def _get_word(word):\n for each in (word, word.lower(), word.capitalize(), word.upper()):\n if each in word2idx_dict:\n return word2idx_dict[each]\n return 1\n\n def _get_emb(k, k2idx_dict):\n if k in k2idx_dict:\n return k2idx_dict[k]\n return 1\n\n for i, token in enumerate(example[\"context_tokens\"]):\n context_idxs[i] = _get_word(token)\n\n for i, token in enumerate(example[\"ques_tokens\"]):\n ques_idxs[i] = _get_word(token)\n\n if config.use_char:\n for i, token in enumerate(example[\"context_chars\"]):\n for j, char in enumerate(token):\n if j == char_limit:\n break\n context_char_idxs[i, j] = _get_emb(char, char2idx_dict)\n\n for i, token in enumerate(example[\"ques_chars\"]):\n for j, char in enumerate(token):\n if j == char_limit:\n break\n ques_char_idxs[i, j] = _get_emb(char, char2idx_dict)\n\n if config.use_bpe:\n for i, token in enumerate(example[\"context_bpe\"]):\n for j, bpe in enumerate(token):\n if j == bpe_limit:\n break\n context_bpe_idxs[i, j] = _get_emb(bpe, bpe2idx_dict)\n\n for i, token in enumerate(example[\"ques_bpe\"]):\n for j, bpe in enumerate(token):\n if j == bpe_limit:\n break\n ques_bpe_idxs[i, j] = _get_emb(bpe, bpe2idx_dict)\n\n if config.use_pos:\n for i, token in enumerate(example[\"context_pos\"]):\n context_pos_idxs[i] = _get_emb(token, pos2idx_dict)\n\n for i, token in enumerate(example[\"ques_pos\"]):\n ques_pos_idxs[i] = _get_emb(token, pos2idx_dict)\n\n # if we have no answers in file (it means we are in predict mode)\n # then add dummy answers\n start, end = 0, 0\n if len(example[\"y1s\"]) > 0:\n start, end = example[\"y1s\"][-1], example[\"y2s\"][-1]\n\n y1[start], y2[end] = 1.0, 1.0\n\n record = {\n \"context_idxs\": context_idxs,\n \"ques_idxs\": ques_idxs,\n \"context_char_idxs\": context_char_idxs,\n \"ques_char_idxs\": ques_char_idxs,\n \"context_bpe_idxs\": context_bpe_idxs,\n \"ques_bpe_idxs\": ques_bpe_idxs,\n \"context_pos_idxs\": context_pos_idxs,\n \"ques_pos_idxs\": ques_pos_idxs,\n \"y1\": y1,\n \"y2\": y2,\n \"id\": example[\"id\"],\n }\n records.append(record)\n print(\"Build {} / {} instances of features in total\".format(total, total_))\n meta[\"total\"] = total\n return records, meta\n\n\ndef save(filename, obj, message=None):\n if message is not None:\n print(\"Saving {}...\".format(message))\n with open(filename, \"w\") as fh:\n json.dump(obj, fh)\n\n\ndef train_bpe(config):\n print('Start BPE training...')\n from subword_nmt.learn_bpe import main as learn_bpe\n train = json.load(open(config.train_file, 'r'))\n train_texts = []\n for p in train['data'][0]['paragraphs']:\n train_texts.append(preprocess_string(' '.join(word_tokenize(p['context'])),\n remove_unicode=config.remove_unicode))\n for qas in p['qas']:\n train_texts.append(preprocess_string(' '.join(word_tokenize(qas['question'])),\n remove_unicode=config.remove_unicode))\n\n learn_bpe(train_texts, outfile=open(config.bpe_codes_file, 'w'), num_symbols=config.bpe_merges_count)\n print('BPE trained. BPE codes saved to {}'.format(config.bpe_codes_file))\n\n\ndef prepro(config):\n word_counter, char_counter, bpe_counter, pos_counter = Counter(), None, None, None\n bpe_model = None\n pos_model = None\n if config.use_bpe:\n if not config.use_bpe_pretrained_codes:\n # train bpe on train set\n train_bpe(config)\n bpe_model = BPE(open(config.bpe_codes_file, 'r'))\n else:\n print('Loading BPE codes: {}'.format(config.bpe_pretrained_codes_file))\n bpe_model = BPE(open(config.bpe_pretrained_codes_file, 'r'))\n bpe_counter = Counter()\n\n if config.use_char:\n char_counter = Counter()\n\n if config.use_pos:\n pos_model = Mystem()\n pos_counter = Counter()\n\n train_examples, train_eval = process_file(\n config, config.train_file, \"train\", word_counter, char_counter, bpe_counter, pos_counter,\n config.remove_unicode, bpe_model, pos_model, is_test=False)\n dev_examples, dev_eval = process_file(\n config, config.dev_file, \"dev\", word_counter, char_counter, bpe_counter, pos_counter,\n config.remove_unicode, bpe_model, pos_model, is_test=False)\n test_examples, test_eval = process_file(\n config, config.test_file, \"test\",\n remove_unicode=config.remove_unicode, bpe_model=bpe_model, pos_model=pos_model, is_test=True)\n\n word_emb_file = config.fasttext_file if config.fasttext else config.glove_word_file\n char_emb_file = config.glove_char_file if config.pretrained_char else None\n char_emb_size = config.glove_char_size if config.pretrained_char else None\n char_emb_dim = config.glove_dim if config.pretrained_char else config.char_dim\n bpe_emb_file = config.glove_bpe_file if config.pretrained_bpe_emb else None\n bpe_emb_size = config.glove_bpe_size if config.pretrained_bpe_emb else None\n bpe_emb_dim = config.bpe_glove_dim if config.pretrained_bpe_emb else config.bpe_dim\n\n word_emb_mat, word2idx_dict = get_embedding(\n word_counter, \"word\", emb_file=word_emb_file, size=config.glove_word_size, vec_size=config.glove_dim)\n char_emb_mat, char2idx_dict = None, None\n if config.use_char:\n char_emb_mat, char2idx_dict = get_embedding(\n char_counter, \"char\", emb_file=char_emb_file, size=char_emb_size, vec_size=char_emb_dim)\n bpe_emb_mat, bpe2idx_dict = None, None\n if config.use_bpe:\n bpe_emb_mat, bpe2idx_dict = get_embedding(\n bpe_counter, \"bpe\", emb_file=bpe_emb_file, size=bpe_emb_size, vec_size=bpe_emb_dim)\n\n pos_emb_mat, pos2idx_dict = None, None\n if config.use_pos:\n pos_emb_mat, pos2idx_dict = get_embedding(\n pos_counter, \"pos\", emb_file=None, size=None, vec_size=config.pos_dim)\n\n pickle.dump(word2idx_dict, open(config.word2idx_dict_file, 'wb'))\n pickle.dump(char2idx_dict, open(config.char2idx_dict_file, 'wb'))\n pickle.dump(bpe2idx_dict, open(config.bpe2idx_dict_file, 'wb'))\n pickle.dump(pos2idx_dict, open(config.pos2idx_dict_file, 'wb'))\n\n build_features(config, train_examples, \"train\",\n config.train_record_file, word2idx_dict, char2idx_dict, bpe2idx_dict, pos2idx_dict)\n dev_meta = build_features(config, dev_examples, \"dev\",\n config.dev_record_file, word2idx_dict, char2idx_dict, bpe2idx_dict, pos2idx_dict)\n test_meta = build_features(config, test_examples, \"test\",\n config.test_record_file, word2idx_dict, char2idx_dict, bpe2idx_dict, pos2idx_dict, is_test=True)\n\n save(config.word_emb_file, word_emb_mat, message=\"word embedding\")\n save(config.char_emb_file, char_emb_mat, message=\"char embedding\")\n save(config.bpe_emb_file, bpe_emb_mat, message=\"bpe embedding\")\n save(config.pos_emb_file, pos_emb_mat, message=\"pos embedding\")\n save(config.train_eval_file, train_eval, message=\"train eval\")\n save(config.dev_eval_file, dev_eval, message=\"dev eval\")\n save(config.test_eval_file, test_eval, message=\"test eval\")\n save(config.dev_meta, dev_meta, message=\"dev meta\")\n save(config.test_meta, test_meta, message=\"test meta\")\n\n\ndef prepro_predict(config):\n\n if config.use_bpe and config.use_bpe_pretrained_codes:\n bpe_model = BPE(open(config.bpe_pretrained_codes_file, 'r'))\n elif config.use_bpe and not config.use_bpe_pretrained_codes:\n bpe_model = BPE(open(config.bpe_codes_file, 'r'))\n else:\n bpe_model = None\n\n predict_examples, predict_eval = process_file(\n config, config.predict_file, \"predict\",\n remove_unicode=config.remove_unicode, bpe_model=bpe_model, is_test=True)\n\n word2idx_dict = pickle.load(open(config.word2idx_dict_file, 'rb'))\n char2idx_dict = pickle.load(open(config.char2idx_dict_file, 'rb'))\n bpe2idx_dict = pickle.load(open(config.bpe2idx_dict_file, 'rb'))\n pos2idx_dict = pickle.load(open(config.pos2idx_dict_file, 'rb'))\n\n predict_meta = build_features(config, predict_examples, \"predict\",\n config.predict_record_file, word2idx_dict, char2idx_dict,\n bpe2idx_dict, pos2idx_dict, is_test=True)\n\n save(config.predict_eval_file, predict_eval, message=\"predict eval\")\n save(config.predict_meta, predict_meta, message=\"predict meta\")\n\n\ndef prepro_test_sber(config):\n if config.use_bpe and config.use_bpe_pretrained_codes:\n bpe_model = BPE(open(config.bpe_pretrained_codes_file, 'r'))\n elif config.use_bpe and not config.use_bpe_pretrained_codes:\n bpe_model = BPE(open(config.bpe_codes_file, 'r'))\n else:\n bpe_model = None\n\n word2idx_dict = pickle.load(open(config.word2idx_dict_file, 'rb'))\n char2idx_dict = pickle.load(open(config.char2idx_dict_file, 'rb'))\n bpe2idx_dict = pickle.load(open(config.bpe2idx_dict_file, 'rb'))\n pos2idx_dict = pickle.load(open(config.pos2idx_dict_file, 'rb'))\n\n for datafile, datatype in zip([config.sber_public_file, config.sber_private_file], ['public', 'private']):\n datafile_squad = os.path.join(config.target_dir, \"{}.json_squad\".format(datatype))\n sber2squad(datafile, outfile=datafile_squad)\n data_examples, data_eval = process_file(\n config, datafile_squad, datatype,\n remove_unicode=config.remove_unicode, bpe_model=bpe_model, is_test=True)\n\n data_meta = build_features(config, data_examples, datatype,\n os.path.join(config.target_dir, \"{}.tfrecords\".format(datatype)),\n word2idx_dict, char2idx_dict, bpe2idx_dict, pos2idx_dict, is_test=True)\n\n save(os.path.join(config.target_dir, \"{}_eval.json\".format(datatype)), data_eval, message=\"{} eval\".format(datatype))\n save(os.path.join(config.target_dir, \"{}_meta.json\".format(datatype)), data_meta, message=\"{} meta\".format(datatype))\n\n","sub_path":"prepro.py","file_name":"prepro.py","file_ext":"py","file_size_in_byte":26455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"611093132","text":"from itertools import chain\nimport bz2\nfrom datetime import datetime\nimport lzma\nimport os\nimport re\nimport stat\nimport zlib\n\ntry:\n # use uchardet bindings if available\n import cchardet as chardet\nexcept ImportError:\n import chardet\nfrom snakeoil import klass\nfrom snakeoil.osutils import sizeof_fmt\n\nfrom . import magic, const\nfrom .exceptions import BiteError\nfrom .utc import utc, parse_date\n\n\ndef decompress(fcn):\n \"\"\"Decorator that decompresses returned data.\n\n libmagic is used to identify the MIME type of the data and the function\n will keep decompressing until no supported compression format is identified.\n \"\"\"\n def wrapper(cls, raw=False, *args, **kw):\n data = fcn(cls)\n\n if raw:\n # return raw data without decompressing\n return data\n\n mime_type, mime_subtype = magic.from_buffer(data, mime=True).split('/')\n while mime_subtype in ('x-bzip2', 'x-bzip', 'bzip', 'x-gzip', 'gzip', 'x-xz'):\n if mime_subtype in ('x-bzip2', 'x-bzip', 'bzip'):\n data = bz2.decompress(data)\n elif mime_subtype in ('x-gzip', 'gzip'):\n data = zlib.decompress(data, 16 + zlib.MAX_WBITS)\n elif mime_subtype in ('x-xz'):\n data = lzma.decompress(data)\n mime_type, mime_subtype = magic.from_buffer(data, mime=True).split('/')\n return data\n return wrapper\n\n\nclass DateTime(object):\n \"\"\"Object that converts/stores a given datetime object.\"\"\"\n\n def __init__(self, datetime):\n # TODO: handle different time zones?\n self.token = datetime if isinstance(datetime, str) else None\n if self.token is not None:\n datetime = parse_date(datetime)\n self._datetime = datetime.replace(tzinfo=utc)\n\n def __str__(self):\n if self.token is not None:\n return f'{self.token!r} -- {self.local}'\n return self.local\n\n def __repr__(self):\n return repr(self._datetime)\n\n def isoformat(self, **kw):\n \"\"\"Return a string representing the date and time in ISO 8601 format.\"\"\"\n return self._datetime.isoformat(**kw)\n\n @property\n def utcformat(self):\n \"\"\"Return a string representing the date and time in ISO 8601 format, assuming UTC.\"\"\"\n return self._datetime.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n @property\n def local(self):\n \"\"\"Return datetime string converted to the system timezone.\"\"\"\n return self._datetime.astimezone().strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n\n def replace(self, **kw):\n \"\"\"Return a modified datetime with kwargs specifying new attributes.\"\"\"\n return self._datetime.replace(**kw)\n\n def strftime(self, fmt):\n \"\"\"Return a modified datetime with kwargs specifying new attributes.\"\"\"\n return self._datetime.strftime(fmt)\n\n def __eq__(self, x):\n return self._datetime == x\n\n def __gt__(self, x):\n return self._datetime > x\n\n def __ge__(self, x):\n return self._datetime >= x\n\n def __lt__(self, x):\n return self._datetime < x\n\n def __le__(self, x):\n return self._datetime <= x\n\n\nclass TimeInterval(object):\n \"\"\"Object that converts/stores a given time interval.\"\"\"\n\n def __init__(self, interval):\n # TODO: handle different time zones?\n self.token = interval if isinstance(interval, str) else None\n if self.token is not None:\n if not self.token:\n raise ValueError(f'invalid time interval: {interval!r}')\n start, _sep, end = interval.partition('/')\n start = parse_date(start) if start else None\n end = parse_date(end) if end else None\n interval = (start, end)\n # assume singular datetime obj input means that time or later\n elif isinstance(interval, datetime):\n interval = (interval, None)\n\n try:\n start, end = interval\n except ValueError:\n raise ValueError(f'invalid time interval: {interval!r}')\n\n self.start = DateTime(start) if start else start\n self.end = DateTime(end) if end else end\n\n if self.start and self.end and self.start > self.end:\n raise ValueError(\n 'invalid time interval: start time after end time '\n f'({self.start} -> {self.end})')\n\n def __repr__(self):\n return repr((self.start, self.end))\n\n def __str__(self):\n l = []\n if self.token:\n l.extend((repr(self.token), '--'))\n\n if self.start and self.end:\n l.append(f'between {self.start} and {self.end}')\n elif self.start:\n l.append(f'after {self.start}')\n else:\n l.append(f'before {self.end}')\n\n return ' '.join(l)\n\n def __contains__(self, obj):\n if not isinstance(obj, datetime):\n return False\n if self.start is not None and obj < self.start:\n return False\n if self.end is not None and obj > self.end:\n return False\n return True\n\n def __iter__(self):\n return iter((self.start, self.end))\n\n\nclass IntRange(object):\n \"\"\"Object that converts/stores a given integer range.\"\"\"\n\n def __init__(self, interval):\n self.token = interval if isinstance(interval, str) else None\n if self.token is not None:\n if not self.token:\n raise ValueError(f'invalid range: {interval!r}')\n start, _sep, end = interval.partition('..')\n start = int(start) if start else None\n end = int(end) if end else None\n interval = (start, end)\n\n try:\n self.start, self.end = interval\n except ValueError:\n raise ValueError(f'invalid range: {interval!r}')\n\n if self.start and self.end and self.start > self.end:\n raise ValueError(\n 'invalid range: start occurs after end '\n f'({self.start} -> {self.end})')\n\n def __repr__(self):\n return repr((self.start, self.end))\n\n def __str__(self):\n l = []\n if self.token:\n l.extend((repr(self.token), '--'))\n\n if self.start and self.end:\n l.append(f'between {self.start} and {self.end}')\n elif self.start:\n l.append(f'>= {self.start}')\n else:\n l.append(f'<= {self.end}')\n\n return ' '.join(l)\n\n def __iter__(self):\n return iter((self.start, self.end))\n\n\nclass Item(object):\n \"\"\"Generic bug/issue/ticket object used by a service.\"\"\"\n\n attributes = {}\n attribute_aliases = {}\n type = None\n\n _print_fields = (\n ('title', 'Title'),\n ('id', 'ID'),\n ('created', 'Reported'),\n ('modified', 'Updated'),\n ('comments', 'Comments'),\n ('attachments', 'Attachments'),\n ('changes', 'Changes'),\n )\n\n def __init__(self, id=None, title=None, creator=None, owner=None, created=None,\n modified=None, status=None, url=None, blocks=None,\n depends=None, cc=None, comments=None, attachments=None, changes=None, **kw):\n self.id = id # int\n self.title = title # string\n self.creator = creator # string\n self.owner = owner # string\n self.created = created # date object\n self.modified = modified # date object\n self.status = status # string\n self.url = url # string\n self.cc = cc # set\n self.blocks = blocks # set\n self.depends = depends # set\n self.comments = comments # list of Comment objects\n self.attachments = attachments # dict of lists of Attachment objects\n self.changes = changes # list of Change objects\n\n @klass.jit_attr\n def events(self):\n \"\"\"Sorted list of all item events.\n\n Currently this relates to all comments and changes made to an item.\n \"\"\"\n comments = self.comments if self.comments is not None else ()\n changes = self.changes if self.changes is not None else ()\n return sorted(chain(comments, changes), key=lambda event: event.created)\n\n def _custom_str_fields(self):\n \"\"\"Custom field output for string rendering.\"\"\"\n return ()\n\n def __str__(self):\n lines = []\n\n for field, title in self._print_fields:\n value = getattr(self, field, None)\n if value is None:\n continue\n\n if field in ('changes', 'comments', 'attachments'):\n value = len(value)\n\n # Initial comment is the description\n if field == 'comments': value -= 1\n\n if isinstance(value, (list, tuple)):\n value = ', '.join(map(str, value))\n\n lines.append(f'{title:<12}: {value}')\n\n lines.extend(sorted(self._custom_str_fields()))\n return '\\n'.join(lines)\n\n def __getattr__(self, name):\n if name in self.attributes:\n return None\n elif name in self.attribute_aliases:\n return getattr(self, self.attribute_aliases[name])\n else:\n raise AttributeError(f'missing field: {name}')\n\n # allow items to be used as mapping args to functions\n def __getitem__(self, key):\n return self.__dict__[key]\n\n def keys(self):\n return self.__dict__.keys()\n\n\nclass Change(object):\n \"\"\"Generic change event on a service.\"\"\"\n\n change_aliases = {}\n\n def __init__(self, creator, created, changes, id=None, count=None):\n self.id = id # int\n self.creator = creator # string\n self.created = created # date object\n self.changes = changes # dict\n self.count = count # id\n\n def __str__(self):\n lines = [f'Change #{self.count} by {self.creator}, {self.created}']\n lines.append('-' * const.COLUMNS)\n for k, v in self.changes.items():\n try:\n removed, added = v\n if removed and added:\n lines.append(f'{k.capitalize()}: {removed} -> {added}')\n elif removed:\n lines.append(f'{k.capitalize()}: -{removed}')\n else:\n lines.append(f'{k.capitalize()}: +{added}')\n except ValueError:\n lines.append(f'{k.capitalize()}: {v}')\n return '\\n'.join(lines)\n\n def match(self, fields):\n for field in fields:\n if ':' in field:\n key, value = field.split(':')\n else:\n key = field\n value = None\n\n key = self.change_aliases.get(key, key)\n\n if not value:\n return key in self.changes\n else:\n try:\n removed, added = self.changes[key]\n if value.startswith('-'):\n return removed == value[1:]\n elif value.startswith('+'):\n return added == value[1:]\n else:\n return value in self.changes[key]\n except KeyError:\n return False\n except ValueError:\n return value == self.changes[key]\n\n\nclass Comment(Change):\n \"\"\"Generic comment on a service.\"\"\"\n\n def __init__(self, creator, created, modified=None,\n id=None, count=None, changes=None, text=None):\n self.modified = modified\n self.text = text\n super().__init__(id=id, creator=creator, created=created, changes=changes, count=count)\n\n def __str__(self):\n lines = []\n if self.count == 0:\n lines.append(f'Description by {self.creator}, {self.created}')\n else:\n lines.append(f'Comment #{self.count} by {self.creator}, {self.created}')\n lines.append('-' * const.COLUMNS)\n if self.text:\n lines.append(self.text)\n return '\\n'.join(lines)\n\n @property\n def reply(self):\n lines = [f'In reply to {self.creator} from comment #{self.count}:']\n lines.extend(f'> {line}' for line in self.text.splitlines())\n return '\\n'.join(lines)\n\n\nclass Attachment(object):\n \"\"\"Generic attachment to an item on a service.\"\"\"\n\n def __init__(self, id=None, filename=None, url=None, size=None,\n mimetype=None, data=None, creator=None, created=None, modified=None):\n self.id = id\n # make sure the file name is valid\n # TODO: fix this to remove all os.path.sep chars\n self.filename = filename\n if self.filename is not None:\n self.filename = os.path.basename(re.sub(r'\\.\\.', '', self.filename))\n self.url = url\n self.size = size\n self.mimetype = mimetype\n self.data = data\n self.creator = creator\n self.created = created\n self.modified = modified\n\n # don't trust the content type -- users often set the wrong mimetypes\n if self.data is not None:\n mimetype = magic.from_buffer(self.read(), mime=True)\n if mimetype == 'application/octet-stream':\n # assume these are plaintext\n self.mimetype = 'text/plain'\n else:\n self.mimetype = mimetype\n\n def __str__(self):\n l = ['Attachment:']\n if self.id is not None:\n l.append(f'[ID: {self.id}]')\n l.append(f'[{self.filename}]')\n if self.size is not None:\n l.append(f'({sizeof_fmt(self.size)})')\n return ' '.join(l)\n\n @decompress\n def read(self):\n if isinstance(self.data, str):\n return self.data.encode()\n return self.data\n\n def write(self, path):\n try:\n with open(path, 'wb+') as f:\n os.chmod(path, stat.S_IREAD | stat.S_IWRITE)\n f.write(self.read(raw=True))\n except Exception as e:\n # toss file stub if it got created\n try:\n os.remove(path)\n except FileNotFoundError:\n pass\n\n if isinstance(e, IOError):\n raise BiteError(f'failed writing file: {path!r}: {e.strerror}')\n raise\n\n\nclass TarAttachment(object):\n def __init__(self, tarfile, cfile):\n self.tarfile = tarfile\n self.cfile = cfile\n\n @decompress\n def read(self):\n return self.tarfile.extractfile(self.cfile).read()\n\n def data(self):\n data = self.read()\n mime = magic.from_buffer(data, mime=True)\n if mime.startswith('text'):\n for encoding in ('utf-8', 'latin-1'):\n try:\n return data.decode(encoding)\n except UnicodeDecodeError:\n pass\n # fallback to detecting the encoding\n encoding = chardet.detect(data)['encoding']\n return data.decode(encoding)\n else:\n return 'Non-text data: ' + mime + '\\n'\n","sub_path":"src/bite/objects.py","file_name":"objects.py","file_ext":"py","file_size_in_byte":14843,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"137193013","text":"# -*- coding: utf-8 -*-\n'''\nCreated on Oct 9, 2013\n@author: lin\n'''\n\nimport pymysql\npymysql.install_as_MySQLdb()\nimport db.db_conf\n\ndef init_mfexchange_user(conn):\n \n sql1 = \"UPDATE users set phone_number=user_id;\"\n cursor = conn.cursor()\n conn.select_db(\"mfexchange\")\n cursor.execute(sql1)\n conn.commit()\n \n sql2 = \"UPDATE mis_users set phone_number=user_id;\"\n cursor = conn.cursor()\n conn.select_db(\"mfexchange\")\n cursor.execute(sql2)\n conn.commit()\n \n sql3 = \"UPDATE users set realName=substr(realName,1,2) WHERE LENGTH(realName) > 6;\"\n print (sql3)\n cursor = conn.cursor()\n conn.select_db(\"mfexchange\")\n cursor.execute(sql3)\n conn.commit()\n \n# sql4 = \"UPDATE users set realName=concat (realName,'微') WHERE LENGTH(realName) > 3;\"\n# print sql4\n# cursor = conn.cursor()\n# conn.select_db(\"mfexchange\")\n# cursor.execute(sql4)\n# conn.commit()\n# \n# sql5 = \"UPDATE users set realName=concat (realName,'微') WHERE LENGTH(realName) > 3;\"\n# print sql5\n# cursor = conn.cursor()\n# conn.select_db(\"mfexchange\")\n# cursor.execute(sql5)\n# conn.commit()\n \n sql6 = \"UPDATE users set phone_number=15811297594 , password='1b94d3fef4fc0a7b5707b761e7fbd74b8e0ebfaf14caa013c9c763ff02f85552' WHERE user_id='2335';\"\n cursor = conn.cursor()\n conn.select_db(\"mfexchange\")\n cursor.execute(sql6)\n conn.commit()\n \n sql7 = \"UPDATE users set phone_number=13611293208 ,password='fcbdf647ea2ae6bff9c859e0403cd219648349d27e11d9fc0304c0d681538eb4' WHERE user_id='3618';\"\n cursor = conn.cursor()\n conn.select_db(\"mfexchange\")\n cursor.execute(sql7)\n conn.close()\n conn.commit()\n \n # update_user_passwd \ndef update_user_passwd(db_name, user_id):\n sql = \"UPDATE users set username='weijinsuo',`password`='43cc73be1e0389f978b0142f61798dc93a54b2eb44df61b59892aec37814135a' WHERE user_id=\\'\" + user_id + \"\\' or phone_number=\\'\" + user_id + \"\\';\"\n# sql2 = \"insert ignore into security_resource_mapping(username, resource_id) select \\'\"+name+\"\\' ,_group from (select distinct(_group) as _group from resource where _group != '') as a\"\n# sql3 = \"insert ignore into security_mapping(username, data_id) select \\'\"+name+\"\\', id from resource where _group in (select distinct(resource_id) from security_resource_mapping where username =\\'\"+name+\"\\')\".format(name,name)\n print (sql)\n cursor = db_name.cursor()\n db_name.select_db(\"mfexchange\")\n ru = cursor.execute(sql)\n db_name.commit()\n if (ru > 0) == True:\n return \"修改成功\"\n return \"修改失败\" \n\ndef update_order_info(db_conn, loanId, currentMomey, order_status):\n sql = \"update loan set currentMomey='\" + currentMomey + \"' , `status`='\" + order_status + \"' WHERE loan_id='\" + loanId + \"'\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"mfexchange\")\n cursor.execute(sql)\n db_conn.commit()\n cursor.close()\n db_conn.close()\n return \"修改成功\"\n\ndef select_useridByphone_number(db_conn, userid):\n id = 'null'\n sql = \"SELECT id from TB_USER WHERE LOGINNAME='\"+ userid +\"'\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"Biz\")\n cursor.execute(sql)\n db_conn.commit()\n res = cursor.fetchone()\n id = res[0]\n print ('userid db values is:'+id)\n return id\n\n\ndef update_user_account(db_conn, phone_number, account):\n \n sql = \"SELECT user_id from users WHERE user_id='\" + phone_number + \"' or phone_number='\" + phone_number + \"'\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"mfexchange\")\n cursor.execute(sql)\n db_conn.commit()\n res = cursor.fetchone()\n userId = res[0]\n# print userId\n# print account\n# print type(userId)\n# print type(account)\n sqlacountBalance = \"SELECT acountBalance from account WHERE userid='\" + str(userId) + \"'\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"mfexchange\")\n cursor.execute(sqlacountBalance)\n db_conn.commit()\n res1 = cursor.fetchone()\n acountBalance = res1[0]\n print (acountBalance0)\n \n \n sqlavailableFunds = \"SELECT acountBalance from account WHERE userid='\" + str(userId) + \"'\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"mfexchange\")\n cursor.execute(sqlavailableFunds)\n db_conn.commit()\n res2 = cursor.fetchone()\n availableFunds = res2[0]\n\n sql1 = \"UPDATE account a set a.acountBalance=a.acountBalance+'\" + account + \"',a.availableFunds=a.availableFunds+'\" + account + \"' WHERE userid = '\" + str(userId) + \"'\"\n cursor = db_conn.cursor()\n db_conn.query(\"set names 'utf8'\")\n db_conn.select_db(\"mfexchange\")\n cursor.execute(sql1)\n db_conn.commit()\n \n sql2 = \"INSERT INTO `account_log` (`userid`,`to_userid`,`orderid`,`status`,`type`,`total`,`addtime`,`adduser`,`remark`,`currency`,`source_account`,`dest_account`,`availableFound`,`accountBalance`) VALUES ('\" + str(userId) + \"', 0, ROUND(ROUND(RAND(),16)*10000000000000000), 'yes', 'chongzhi', '\" + account + \"', NOW(), 2335, '充值', 'RMB', NULL, NULL, '\" + str(availableFunds) + \"'+'\" + account + \"', '\" + str(acountBalance) + \"'+'\" + account + \"')\"\n cursor = db_conn.cursor()\n db_conn.select_db(\"mfexchange\")\n ru_i = cursor.execute(sql2)\n db_conn.commit()\n cursor.close()\n db_conn.close()\n if (ru_i > 0) == True:\n return \"机号码或UserId不存在\" \n return \"增加成功\"\n\ndef find_phone_numberCode(db_conn, phone_number):\n try:\n smsContext = 'null'\n sql = \"SELECT actual_msg as smsContext from sms_record WHERE mobileNum='\" + phone_number + \"' ORDER BY id desc LIMIT 1\"\n print (sql)\n cursor = db_conn.cursor()\n db_conn.select_db(\"smsgateway\")\n cursor.execute(sql)\n db_conn.commit()\n res = cursor.fetchone()\n smsContext = res[0]\n print (smsContext)\n# print smsContext[4:]\n return smsContext\n cursor.close\n db_conn.close\n# mail = \"\"\n# for message in messagelist:\n# mail = mail + message\n# web.config.smtp_server = 'mail.corp.qunar.com'\n# web.sendmail('ots_beta@qunar.com', 'ots_beta@qunar.com', ota_message, mail)\n except Exception as e:\n print (e)\n return '数据库没有短信！' \n finally:\n cursor.close\n db_conn.close\n\n\nif __name__ == \"__main__\":\n\n db_conn = db_conf.get_db('beta_1')\n# find_phone_numberCode(db_conn,\"15811297594\")\n \n \n","sub_path":"db/mfexchange_sql.py","file_name":"mfexchange_sql.py","file_ext":"py","file_size_in_byte":6454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"525607255","text":"import cgi\nimport os.path\nimport re\nimport urllib\nimport urlparse\n\nimport tornado.escape\n\ndef load_basic_parameters(handler, prefix=\"\", url=\"\"):\n uri = url if url else handler.request.uri\n # Hmmm, some servers send UTF-8 percent encoded and some don't...\n uri = tornado.escape.url_unescape(uri)\n # XXX WTF?? it's double-escaped??? something's weird here. FIX\n uri = tornado.escape.url_unescape(uri)\n\n if handler.prefix and uri.find(handler.prefix) == 0:\n uri = uri[len(handler.prefix):]\n\n if prefix and uri.find(prefix) == 0:\n uri = uri[len(prefix):]\n\n implied_profile = None\n hostname_user = None\n if handler.models:\n hostname_user = handler.get_user_by_hostname()\n\n if handler.constants['single_user_site'] and handler.models:\n implied_profile = handler.get_author_username()\n elif hostname_user:\n implied_profile = hostname_user.username\n elif handler.current_user is not None:\n implied_profile = handler.current_user.get('username', None)\n\n parsed_url = urlparse.urlparse(uri)\n uri_array = parsed_url.path.split(\"/\")\n uri_array.pop(0)\n uri_array = [sanitize_form_value(x) for x in uri_array]\n\n uri_dict = {\n 'uri': uri,\n 'profile': None,\n 'section': None,\n 'album': None,\n 'name': None,\n 'modifier': None,\n 'private': None,\n }\n\n if not hostname_user and (len(uri_array) == 0 or uri_array[0] == \"\"):\n uri_dict['profile'] = (implied_profile or\n (handler.models.users.get(1).username if handler.models else ''))\n uri_dict['section'] = 'main'\n uri_dict['name'] = 'main'\n return uri_dict\n\n if uri_array[0] == 'private':\n uri_dict['private'] = '/'.join(uri_array[1:])\n return uri_dict\n\n if uri_array[0] in handler.constants['reserved_names']:\n uri_array.insert(0, implied_profile)\n elif ((handler.constants['single_user_site'] or hostname_user) and\n uri_array[0] != implied_profile):\n uri_array.insert(0, implied_profile)\n\n uri_dict['profile'] = uri_array[0]\n\n if (len(uri_array) > 2 and uri_array[2] != \"\" and\n uri_array[-2] == \"page\" and uri_array[-1] != \"\"):\n uri_dict[\"modifier\"] = uri_array[-1]\n uri_array.pop()\n uri_array.pop()\n\n if len(uri_array) == 1:\n uri_array.append('home')\n elif uri_array[1] == \"\":\n uri_array[1] = 'home'\n\n if len(uri_array) > 2 and uri_array[2] != \"\":\n uri_dict[\"section\"] = uri_array[1]\n uri_dict[\"name\"] = uri_array[-1]\n else:\n uri_dict[\"section\"] = 'main'\n uri_dict[\"name\"] = uri_array[1]\n\n uri_dict[\"name\"] = reverse_href(urllib.unquote_plus(uri_dict[\"name\"]))\n\n return uri_dict\n\ndef sanitize_form_value(value):\n main_regex = re.compile(\"<|>|&#|script\\:\", re.IGNORECASE)\n value = main_regex.sub(\"_\", value)\n # prevent something that is already & from becoming &amp;\n value = re.sub(\"&\", \"&\", value)\n value = re.sub(\"&\", \"&\", value)\n\n if value.find('?') != -1:\n value = value[:value.find('?')] # get rid of arguments\n\n return value;\n\ndef href(url):\n return url.replace(\" \", \"+\")\n\ndef reverse_href(url):\n return url.replace(\"+\", \" \")\n\ndef add_base_uris(handler, view):\n return re.compile(r'([\"\\'])(static/resource)', re.M | re.U).sub(\n r'\\1' + handler.base_uri + r'\\2', view)\n\n# xxx, this is now done in wysiwyg\ndef linkify_tags(handler, username, text):\n return re.compile(r'#(\\w+)(?![^<&]*([>;]))', re.M | re.U).sub(\n r' #\\1', text)\n\ndef clean_name(name):\n return re.compile(r'[\\W]+', re.M | re.U).sub('', name.replace(\n \" \", \"_\").replace(\"-\", \"_\")).replace(\"_\", \"-\")[:255]\n\ndef check_legit_filename(full_path):\n leafname = os.path.basename(full_path)\n # _current_theme is used internally for themes\n if leafname in ('crossdomain.xml', 'clientaccesspolicy.xml', '.htaccess',\n '.htpasswd', '_current_theme'):\n raise tornado.web.HTTPError(400, \"i call shenanigans\")\n\ndef clean_filename(name):\n if name == '..' or name == '.':\n return ''\n\n check_legit_filename(name)\n\n if name.startswith('/'): # get rid of leading /\n name = name[1:]\n return re.compile(r'[\\\\\\/]\\.\\.|\\.\\.[\\\\\\/]', re.M | re.U).sub('', name)\n\ndef content_url(handler, item, host=False, **arguments):\n url = \"\"\n\n if host:\n url += handler.request.protocol + \"://\" + handler.request.host\n\n if not handler.constants['http_hide_prefix']:\n url += handler.prefix\n\n if (not handler.constants['single_user_site'] and not handler.hostname_user\n and item.name != 'main'):\n url += '/' + item.username\n\n if item.section != 'main':\n url += '/' + item.section\n\n if item.album and item.album != 'main':\n url += '/' + item.album\n\n if item.name != 'home' and item.name != 'main':\n url += '/' + item.name\n elif item.name == 'home' and handler.hostname_user:\n url += '/'\n\n if arguments:\n for arg in arguments:\n arguments[arg] = arguments[arg].encode('utf-8')\n url += '?' + urllib.urlencode(arguments)\n\n return href(url)\n\ndef nav_url(handler, host=False, username=\"\", section=\"\", album=\"\", name=\"\",\n page=None, **arguments):\n url = \"\"\n\n if host:\n url += handler.request.protocol + \"://\" + handler.request.host\n\n if not handler.constants['http_hide_prefix']:\n url += handler.prefix\n\n if (not handler.constants['single_user_site'] and not handler.hostname_user\n and username):\n url += '/' + username\n\n if section:\n url += '/' + section\n if album:\n url += '/' + album\n if name:\n url += '/' + name\n\n if page:\n url += '/page/' + str(page)\n\n args = \"\"\n if arguments:\n for arg in arguments:\n arguments[arg] = arguments[arg].encode('utf-8')\n args = '?' + urllib.urlencode(arguments)\n\n return (href(url) + args) or \"/\"\n\ndef resource_directory(handler, section=\"\", album=\"\"):\n path = handler.application.settings[\"resource_path\"]\n\n path = os.path.join(path, handler.get_author_username())\n\n if section:\n path += '/' + section\n\n if album:\n path += '/' + album\n\n return path\n\ndef resource_url(handler, section=\"\", album=\"\", resource=\"\", filename=\"\",\n host=False):\n if filename:\n return handler.application.settings[\"resource_url\"] \\\n + filename.replace(handler.application.settings[\"resource_path\"], '')\n\n path = \"\"\n if host:\n path += handler.request.protocol + \"://\" + handler.request.host\n if not handler.constants['http_hide_prefix']:\n path += handler.prefix\n path += '/'\n\n path += handler.application.settings[\"resource_url\"] + '/'\n\n path += handler.get_author_username()\n\n if section:\n path += '/' + section + '/'\n\n if album:\n path += album + '/'\n\n if resource:\n path += resource + '/'\n\n return path\n","sub_path":"logic/url_factory.py","file_name":"url_factory.py","file_ext":"py","file_size_in_byte":6644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"131894180","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 18 18:12:44 2017\n\n@author: ryad\n\"\"\"\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#importing the data set \ndataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\\t',quoting = 3)\n\n#Cleaning the Texts \nimport re \nfrom nltk.corpus import stopwords \nfrom nltk.stem.porter import PorterStemmer\n\n\n\ntext_cleaned = []\n\n\nfor i in range(0,1000):\n \n review = re.sub('[^a-zA-Z]' ,' ',dataset['Review'][i])\n review = review.lower()\n review = review.split()\n ps = PorterStemmer()\n review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n review = ' '.join(review)\n text_cleaned.append(review)\n \n#Creating bag of words model\nfrom sklearn.feature_extraction.text import CountVectorizer\ncountvectorizer = CountVectorizer()\nX = countvectorizer.fit_transform(text_cleaned).toarray()\ny = dataset.iloc[:,1].values\n\n\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n\n\n\nfrom sklearn.naive_bayes import GaussianNB\nclassifier = GaussianNB()\nclassifier.fit(X_train,y_train)\n# Predecting the Test set results \ny_pred = classifier.predict(X_test)\nprint(y_pred)\n\n#Making the consfusion matrix\nfrom sklearn.metrics import confusion_matrix\ncm_pred = confusion_matrix(y_test,y_pred)\ncm_train = confusion_matrix(y_train,classifier.predict(X_train))\n\naccuracy = (cm_pred[0,0]+cm_pred[1,1])/(cm_pred[0,0]+cm_pred[1,1]+cm_pred[0,1]+cm_pred[1,0])\nprecision = (cm_pred[1,1])/(cm_pred[1,1]+cm_pred[0,1])\nrecall = (cm_pred[1,1])/(cm_pred[1,1]+cm_pred[1,0])\nF1_score =2*precision*recall/(precision+recall)\n\n","sub_path":"NAt_proc_lang.py","file_name":"NAt_proc_lang.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"573662639","text":"from flask import Blueprint, jsonify, request, url_for, current_app, redirect\nfrom flask_restful import Resource, Api, abort\nfrom boonai.model import TrainedModel\nfrom boonai.project.api.machine_learning.algorithm_selection import functions_dict\nfrom boonai.project import db\nimport requests\nimport pandas as pd\nfrom io import StringIO\nimport pickle\nfrom boonai.project.site.helper import url_join\n\n\ndef row_to_dict(row):\n return dict(\n (col, getattr(row, col))\n for col\n in row.__table__.columns.keys()\n )\n\n\nclass All(Resource):\n def get(self):\n # Get list of all models\n\n user_id = request.args.get('userid')\n project_id = request.args.get('projectid')\n\n query = TrainedModel.query\n if user_id:\n query = query.filter_by(user_id=user_id)\n if project_id:\n query = query.filter_by(project_id=project_id)\n trained_models = query.all()\n trained_models_dict = [\n row_to_dict(row)\n for row in trained_models\n ]\n\n for model_dict in trained_models_dict:\n model_dict['links'] = [\n {\n \"rel\": \"self\",\n \"href\": url_join(\n base_url=request.base_url,\n url=model_dict['id']\n )\n },\n {\n \"rel\": \"file\",\n \"href\": url_join(\n base_url=current_app.config['STORAGE_API'],\n url=model_dict['file_id']\n )\n }\n ]\n\n return {\n 'content': trained_models_dict,\n 'links': [\n {\n \"rel\": \"self\",\n \"href\": request.base_url\n }\n ]\n }\n\n def post(self):\n # Train the model with a submited dataset and store it\n\n storage_api = current_app.config['STORAGE_API']\n datasets_api = current_app.config['DATASETS_API']\n\n posted_json = request.get_json()\n name = posted_json['name']\n description = posted_json['description']\n dataset_id = posted_json['dataset_id']\n algorithm_id = int(posted_json['algorithm_id'])\n user_id = posted_json['user_id']\n project_id = posted_json['project_id']\n\n dataset_uri = datasets_api + '/' + dataset_id\n r_dataset = requests.get(dataset_uri)\n dataset_links = r_dataset.json()['links']\n storage_uri = [\n l['href']\n for l in dataset_links\n if l['rel'] == 'file'\n ][0]\n\n storage_id = storage_uri.split('/')[-1] # TODO: fix db entries and remove this\n\n r_storage = requests.get(storage_api + '/' + storage_id)\n dataset = r_storage.content.decode('utf-8')\n csv = StringIO(dataset)\n df = pd.read_csv(csv)\n\n func = functions_dict[algorithm_id] # TODO disable multithreading, cannot be run from thread\n\n fitted_model = func(df.ix[:, 0], df.ix[:, 1])\n # TODO: get some stats from training, like test scores\n\n model_pickle = pickle.dumps(fitted_model)\n r = requests.post(storage_api, data=model_pickle)\n file_id = int(r.content)\n trained_model = TrainedModel(\n name=name,\n description=description,\n algorithm_id=int(algorithm_id),\n dataset_id=int(dataset_id), # TODO enter correct data\n user_id=user_id,\n project_id=project_id,\n file_id=int(file_id)\n )\n\n # TODO: Uncomment when done\n db.session.add(trained_model)\n db.session.commit()\n\n return redirect(\n url_for(\n 'machine_learning_models.single',\n model_id=trained_model.id\n )\n )\n\n\nclass Single(Resource):\n def get(self, model_id):\n # get sepcific model\n\n tm = TrainedModel.query.filter_by(id=model_id).first()\n content_dict = row_to_dict(tm)\n return {\n 'content': content_dict,\n 'links': [\n {\n \"rel\": \"self\",\n \"href\": url_join(\n base_url=request.base_url,\n url=model_id\n )\n },\n {\n \"rel\": \"file\",\n \"href\": url_join(\n current_app.config['STORAGE_API'],\n content_dict['file_id']\n )\n }\n ]\n\n }\n\n def post(self, model_id):\n posted_file = request.get_data('content')\n csv = StringIO(posted_file.decode('utf-8'))\n df = pd.read_csv(csv, encoding='utf-8')\n tm = TrainedModel.query.filter_by(id=model_id).first()\n storage_api = current_app.config['STORAGE_API']\n r = requests.get('{}/{}'.format(storage_api, tm.file_id))\n pickled_model = r.content\n trained_model = pickle.loads(pickled_model)\n y = trained_model.predict(df.ix[:, 0])\n\n result = {'X': list(df.ix[:, 0].values), 'y': y.tolist()}\n\n self_href = url_join(\n base_url=request.base_url,\n url=url_for(\n 'machine_learning_models.single',\n model_id=model_id\n )\n )\n return {\n 'content': result,\n 'links': [\n {\n \"rel\": \"self\",\n \"href\": self_href\n }\n ]\n }\n\n\nmodels_blueprint = Blueprint('machine_learning_models', __name__)\ndata_api = Api(models_blueprint, '/v1')\ndata_api.add_resource(All, '/machine-learning/models')\ndata_api.add_resource(Single, '/machine-learning/models/')\n","sub_path":"boonai/project/api/machine_learning/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"385876858","text":"import pygame\nfrom pygame.sprite import Sprite\n\n\nclass Enemy(Sprite):\n \"\"\" Enemy class, the alien of the game \"\"\"\n\n def __init__(self, hub):\n \"\"\" Initialize Default values\"\"\"\n super().__init__()\n self.game_hub = hub\n self.game_mode = self.game_hub.game_mode\n self.value = 50\n # Load the enemy image\n self.image = pygame.image.load('imgs/Enemies/enemyRed1.png')\n self.image = pygame.transform.scale(self.image, (50, 50))\n self.rect = self.image.get_rect()\n\n self.movingRight = True\n self.velocity = self.game_mode.enemy_speed_factor\n self.movingDown = False\n\n def update(self):\n \"\"\" Update the logic of enemy\"\"\"\n if self.movingRight:\n self.rect.x += self.velocity\n else:\n self.rect.x -= self.velocity\n\n self.check_boundaries()\n\n def draw(self):\n \"\"\" Draw the enemy onto the screen \"\"\"\n self.game_hub.main_screen.blit(self.image, self.rect)\n\n def check_boundaries(self):\n \"\"\" Check if the enemy hit the border of the screen \"\"\"\n if self.rect.left < 0 or self.rect.right > self.game_hub.WINDOW_WIDTH:\n self.movingDown = True\n self.movingRight = not self.movingRight\n self.rect.y += 50\n","sub_path":"enemies/enemy.py","file_name":"enemy.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"368482668","text":"import os\nimport sys\nimport osr\n\nfrom osgeo import gdal, ogr\nfrom shapely.geometry.polygon import Polygon\n\n# utility functions\nsys.path.append( os.path.join( os.path.dirname( sys.path[0]), '../utility' ) )\nfrom fs import getFileList, getFile\n\n\nclass Clipper:\n\n def __init__( self ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n # increase system memory usage\n os.environ['GDAL_CACHEMAX'] = '2048'\n gdal.UseExceptions()\n\n self._epsg = 28412\n\n return\n\n\n def process( self, scene_path, aois, out_path=None, distance=100, ext='*_reflectance.tif' ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n aoi_paths = []\n\n # get exported geotiff list\n files = getFileList( scene_path, ext )\n for f in files: \n\n # open image\n ds = gdal.Open( f )\n if ds is not None:\n\n # get aoi to image transform\n extent = self.getExtent ( ds )\n coord_tx = self.getCoordinateTransform( ds )\n\n # for each aoi\n for aoi in aois:\n\n # get buffered bounding box coordinates\n bbox = self.getBoundingBox( aoi[ 'bbox'], coord_tx[ 'aoi_image' ], distance=distance )\n if self.overlapsScene( extent, bbox ) is True:\n\n # create aoi sub-path\n aoi_path = os.path.join( scene_path, aoi[ 'name'] + '/' )\n if not os.path.exists( aoi_path ):\n os.makedirs(aoi_path, 0o755 )\n\n # generate aoi sub-image aligned with bbox\n aoi_pathname = os.path.join( aoi_path, os.path.basename( f ) )\n print ( 'Creating AoI image: {}'.format( aoi_pathname ) )\n\n # reproject bbox to local utm - setup warp options\n bbox = self.getBoundingBox( aoi[ 'bbox'], coord_tx[ 'aoi_local' ], distance=distance )\n options = '-t_srs EPSG:{} -tr 15 -15 -te {} {} {} {}'. format ( self._epsg, bbox[ 'ulx'], bbox[ 'lry' ], bbox[ 'lrx' ], bbox[ 'uly'] )\n\n gdal.Warp( aoi_pathname, ds, options=options )\n\n # record aoi image location\n aoi_paths.append ( aoi_path )\n\n return list( set( aoi_paths ) )\n\n\n def getExtent( self, ds ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n # create transform\n geo = ds.GetGeoTransform()\n return { \n 'ulx' : geo[ 0 ],\n 'uly' : geo[ 3 ],\n 'lrx' : geo[ 0 ] + ( ds.RasterXSize * geo[ 1 ] ),\n 'lry' : geo[ 3 ] + ( ds.RasterYSize * geo[ 5 ] )\n } \n\n\n def getCoordinateTransform( self, ds ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n # retrieve srs from image\n image = osr.SpatialReference( wkt=ds.GetProjection() )\n\n # aoi in lat / lon\n aoi = osr.SpatialReference()\n aoi.ImportFromEPSG( 4326 )\n\n # output in local utm\n local = osr.SpatialReference()\n local.ImportFromEPSG( self._epsg )\n\n # create transform\n return { 'aoi_image' : osr.CoordinateTransformation( aoi, image ), \n 'aoi_local' : osr.CoordinateTransformation( aoi, local ) }\n\n\n def getBoundingBox( self, aoi, coord_tx, distance=100 ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n # sort lat / lon min and max\n lat = [ min ( aoi[ 0 ], aoi[ 2 ] ), max( aoi[ 0 ], aoi[ 2 ] ) ]\n lon = [ min ( aoi[ 1 ], aoi[ 3 ] ), max( aoi[ 1 ], aoi[ 3 ] ) ]\n\n # transform lat / lon to image crs\n ulx, uly, ulz = coord_tx.TransformPoint( lon[ 0 ], lat[ 1 ] )\n lrx, lry, lrz = coord_tx.TransformPoint( lon[ 1 ], lat[ 0 ] )\n\n # lock to 30m grid\n ulx = ulx + ( 30.0 - ulx % 30 )\n uly = uly + ( 30.0 - uly % 30 )\n\n lrx = lrx + ( 30.0 - lrx % 30 )\n lry = lry + ( 30.0 - lry % 30 )\n\n # return bbox window\n return {\n 'ulx' : ulx - distance, \n 'uly' : uly + distance, \n 'lrx' : lrx + distance, \n 'lry' : lry - distance \n }\n\n\n def overlapsScene( self, extent, bbox ):\n\n \"\"\"\n Placeholder\n \"\"\"\n\n # create scene extent polygon\n p1 = Polygon( [ ( extent[ 'ulx' ], extent[ 'uly' ] ),\n ( extent[ 'lrx' ], extent[ 'uly' ] ),\n ( extent[ 'lrx' ], extent[ 'lry' ] ),\n ( extent[ 'ulx' ], extent[ 'lry' ] ),\n ( extent[ 'ulx' ], extent[ 'uly' ] ) ] )\n \n # create aoi polygon\n p2 = Polygon( [ ( bbox[ 'ulx' ], bbox[ 'uly' ] ),\n ( bbox[ 'lrx' ], bbox[ 'uly' ] ),\n ( bbox[ 'lrx' ], bbox[ 'lry' ] ),\n ( bbox[ 'ulx' ], bbox[ 'lry' ] ),\n ( bbox[ 'ulx' ], bbox[ 'uly' ] ) ] )\n \n return p1.intersects( p2 )\n\n","sub_path":"src/aster/clipper/clipper.py","file_name":"clipper.py","file_ext":"py","file_size_in_byte":5050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"382493710","text":"from sklearn.feature_extraction.text import TfidfVectorizer\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nimport numpy as np\nimport jieba\nimport re\n\n\ndef get_title_context():\n \"\"\"\n 将文档进行分词，返回结果，用空格分隔；\n :return:\n \"\"\"\n with open('SogouCS.WWW08.txt') as f:\n file = f.read()\n file_string = \"\"\n for i in file:\n file_string += i\n document = re.compile('' + '(.*?)' + '', re.S)\n title = re.compile('' + '(.*?)' + '', re.S)\n context = re.compile('' + '(.*?)' + '', re.S)\n documents = document.findall(file_string)\n titles, contexts = [], []\n for lines in documents:\n titles_tmp = title.findall(lines)\n contexts_tmp = context.findall(lines)\n contexts_tmp = str(contexts_tmp).replace('\\\\n', '')\n titles.append(titles_tmp)\n contexts.append(contexts_tmp)\n c = []\n for item in contexts:\n tmp = jieba.cut(item,cut_all=False)\n c.append(' '.join(tmp))\n return titles, c, documents\n\n\ndef transform(dataset, n_features):\n \"\"\"\n :param n_features: 从文档中提取出tfidf值排名前n_features的单词\n 将每篇文档都表示成n_features的向量，如不含该词则对应的值为0；\n \"\"\"\n vectorizer = TfidfVectorizer(max_df=100, max_features=n_features, min_df=10, use_idf=True,\n dtype=float)\n X = vectorizer.fit_transform(dataset)\n # print(X.shape)\n # vectorizer.get_feature_names()返回满足结果的前n_features个词;\n # print(vectorizer.get_feature_names())\n\n return X, vectorizer\n\n\ndef train(X, vectorizer, true_k=10, minibatch=False, showLable=False):\n # 使用采样数据还是原始数据训练k-means，\n km = KMeans(n_clusters=true_k, init='k-means++', max_iter=300, n_init=1,\n verbose=False)\n km.fit(X)\n if showLable:\n print(\"Top terms per cluster:\")\n order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n terms = vectorizer.get_feature_names()\n print(vectorizer.get_stop_words())\n for i in range(true_k):\n print(\"Cluster %d:\" % i, end='')\n for ind in order_centroids[i, :10]:\n print(' %s' % terms[ind], end='')\n print()\n result = list(km.predict(X))\n # print(result)\n print('Cluster distribution:')\n print(dict([(i, result.count(i)) for i in result]))\n return -km.score(X)\n\n\ndef test():\n '''测试选择最优参数'''\n title, dataset, document = get_title_context()\n # dataset = loadDataset()\n print(\"%d documents\" % len(dataset))\n X, vectorizer = transform(dataset, n_features=500)\n true_ks = []\n scores = []\n # for i in range(3, 80, 1):\n # score = train(X, vectorizer, true_k=i) / len(dataset)\n # # print(i, score)\n # true_ks.append(i)\n # scores.append(score)\n # plt.figure(figsize=(8, 4))\n # plt.plot(true_ks, scores, label=\"error\", color=\"red\", linewidth=1)\n # plt.xlabel(\"n_features\")\n # plt.ylabel(\"error\")\n # plt.legend()\n # plt.show()\n\n\ndef out():\n '''在最优参数下输出聚类结果'''\n title, dataset, document = get_title_context()\n X,vectorizer = transform(dataset,n_features=500)\n score = train(X,vectorizer,true_k=12,showLable=True)/len(dataset)\n print (score)\n\n# test()\nout()\n# title, context, document = get_title_context()\n# X, vectorizer = transform(context)\n","sub_path":"kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":3588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"33559020","text":"from config.setup import *\nfrom modules.devices.model import *\n\n@socketio.on('devices')\ndef handleSocketMessage(response, methods=['GET', 'POST']):\n try:\n data = response\n\n slug = data['device']\n index = data['index']\n value = data['value']\n\n if slug:\n device = SingleDevice(slug)\n device.addData(index, value)\n except:\n return False","sub_path":"modules/devices/socketio.py","file_name":"socketio.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"139070959","text":"# Databricks notebook source\n# MAGIC %fs ls /mnt/databrickssparkdeveloper/inputs/\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 1: Carregar os tres arquivos (presentes no diretorio dbfs:/mnt/databrickssparkdeveloper) em DataFrames ###\n\n# COMMAND ----------\n\ninput_path = \"/mnt/databrickssparkdeveloper/inputs/\"\n\n# COMMAND ----------\n\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType, TimestampType, ArrayType\n\nanswers_fields = [[\"Id\", IntegerType()],\n [\"OwnerUserId\", DoubleType()],\n [\"CreationDate\", TimestampType()],\n [\"ParentId\", IntegerType()],\n [\"Score\", IntegerType()],\n [\"Body\", StringType()]]\n\nanswers_schema = StructType([StructField(x[0], x[1]) for x in answers_fields])\n\n\nquestions_fields = [[\"Id\", IntegerType()],\n [\"OwnerUserId\", DoubleType()],\n [\"CreationDate\", TimestampType()],\n [\"Score\", IntegerType()],\n [\"Title\", StringType()],\n [\"Body\", StringType()]]\n\nquestions_schema = StructType([StructField(x[0], x[1]) for x in questions_fields])\n\n\ntags_fields = [[\"Id\", IntegerType()],\n [\"Tags\", ArrayType(StringType())]]\n\ntags_schema = StructType([StructField(x[0], x[1]) for x in tags_fields])\n\n# COMMAND ----------\n\nquestionsDF = spark.read.csv(input_path + \"Questions.csv\", schema=questions_schema, header=True)\n\nanswersDF = spark.read.csv(input_path + \"Answers.csv\", schema=answers_schema, header=True)\n\ntagsDF = spark.read.json(input_path + \"Tags.json\", schema=tags_schema)\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 2: Criar uma tabela 'count_tags' com a quantidade de vezes que cada tag apareceu junto com a tag principal Python para todas as perguntas. ###\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import explode_outer, broadcast\n\nexploded_tagsDF = tagsDF.select(\"Id\", explode_outer(\"Tags\").alias(\"Tag\"))\n\ncount_tagsDF = exploded_tagsDF.groupBy('Tag').count().orderBy('count', ascending=False).filter(\"Tag <> 'python'\")\n\ncount_tagsDF.explain(True)\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 3: Criar uma tabela 'users_rank' contendo um ranking de scores (de maneira decrescente) mantendo todas as informacoes presentes sobre a resposta ###\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import desc, dense_rank, col, broadcast\nfrom pyspark.sql.window import Window\n\n\nanswersDF_filtered = answersDF.filter(col(\"OwnerUserId\").isNotNull()) \\\n .filter(col(\"CreationDate\").between(\"2012-01-01\", \"2016-01-01\"))\n\nspark.conf.set(\"spark.sql.shuffle.partitions\", \"100\")\n\n\nanswers_rank = answersDF_filtered.withColumn(\"rank\", dense_rank().over(Window.partitionBy().orderBy(desc(\"Score\"))))\n\njoin_key = [answers_rank.ParentId == tagsDF.Id]\n\nanswers_rank_joined = answers_rank.join(broadcast(tagsDF), join_key).drop(tagsDF.Id)\n\nanswers_rank_joined.repartition(24).write.mode(\"overwrite\").saveAsTable(\"answers_rank\")\n\n# COMMAND ----------\n\n# MAGIC %sql select * from answers_rank order by rank asc\n\n# COMMAND ----------\n\nfrom pyspark.sql.types import LongType, StructField\nfrom pyspark.sql.functions import desc\n\ndef dfZipWithIndex(df, orderByCol, colName, offset=1):\n \"\"\"\n https://stackoverflow.com/questions/30304810/dataframe-ified-zipwithindex\n Enumerates dataframe rows is native order, like rdd.ZipWithIndex(), but on a dataframe \n and preserves a schema\n :param df: source dataframe\n :param offset: adjustment to zipWithIndex()'s index\n :param colName: name of the index column\n \"\"\"\n\n new_schema = StructType(df.schema.fields + [StructField(colName,LongType(),True)])\n\n zipped_rdd = df.orderBy(desc(orderByCol)).rdd.zipWithIndex()\n\n new_rdd = zipped_rdd.map(lambda x: (list(x[0]) + [x[1]+offset]))\n\n return spark.createDataFrame(new_rdd, new_schema)\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import desc, dense_rank, col\nfrom pyspark.sql.functions import broadcast\n\nanswersDF_filtered = answersDF.filter(\"OwnerUserId is not null\").filter(col(\"CreationDate\").between(\"2012-01-01\", \"2016-12-31\"))\n\nanswers_rank = dfZipWithIndex(answersDF_filtered, orderByCol=\"Score\", colName='rank')\n\nanswers_rank_joined = answers_rank.join(broadcast(tagsDF), answers_rank.ParentId == tagsDF.Id).drop(tagsDF.Id)\n\nanswers_rank_joined.write.mode(\"overwrite\").saveAsTable(\"answers_rank\")\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 4: Contar quantas vezes a palavra 'spark' apareceu em todos os titulos de todas as perguntas. Retornar o valor final como um numero (inteiro) ###\n\n# COMMAND ----------\n\nquestionsDF.cache().count()\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import col, lower\n\nspark_questions = questionsDF.select(\"Title\")\\\n.filter(lower(col(\"Title\")).contains(\"spark\"))\n\nrdd_spark_words = spark_questions.rdd.flatMap(lambda x: x[0].split(\" \"))\\\n.filter(lambda x: 'spark' in x.lower())\n\nrdd_spark_words.count()\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import col, split, explode_outer, lower\ndf_count_2 = questionsDF.select(explode_outer(split(lower(col(\"Title\")), \" \")).alias(\"words\"))\\\n .filter(\"words like '%spark%'\")\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 5 Utilizando os dado de respostas (Answers), criar um JSON contendo cada OwnerUserId juntamente com o seu score medio e as tags utilizadas em todas as perguntas de suas respostas.###\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import col, avg, max, collect_list, desc\n\njoinKey = \"Id\"\n\njson_parsed = (answersDF.filter(\"OwnerUserId is not null\").select(\"OwnerUserId\", \"CreationDate\", \"Score\", col(\"ParentId\").alias(\"Id\"))\n \n .join(tagsDF, joinKey)\n .drop(\"Id\")\n \n .groupBy(\"OwnerUserId\").agg(\n avg(\"Score\").alias(\"AvgScore\"),\n collect_list(\"Tags\").alias(\"Tags\"))\n )\n\ndisplay(json_parsed)\n\n# COMMAND ----------\n\n# MAGIC %md ### Parte 6: \n# MAGIC * Criar uma visao chamada YearPythonCount contendo quantas perguntas foram feitas em cada ano contendo a tag python.\n# MAGIC * Criar uma visao chamada DaysOfWeekCount contendo a quantidade de perguntas feitas contendo a tag python para cada dia da semana. Criar uma coluna com o ranking dos dias, e outra com os nomes dos dias da semana em escrito (Segunda-Feira, Terca-Feira, etc...).\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import year, dayofweek, when, lit, dense_rank\nfrom pyspark.sql.window import Window\n\n# VISAO 1\nYearPythonCountDF = questionsDF.select(year(\"CreationDate\").alias(\"year\"), \"CreationDate\")\\\n.groupBy(\"year\").count()\n\nYearPythonCountDF.write.mode(\"overwrite\").saveAsTable(\"YearPythonCountDF\")\n\n\n# COMMAND ----------\n\n# VISAO 2\n\nw = Window.orderBy(desc(\"count\"))\n\nDaysOfWeekCountDF = questionsDF.select(dayofweek(\"CreationDate\").alias(\"NumDayOfWeek\"))\\\n .withColumn(\"DayOfWeek\", when(col('NumDayOfWeek') == 1, lit(\"Monday\"))\\\n .when(col('NumDayOfWeek') == 2, lit(\"Tuesday\"))\\\n .when(col('NumDayOfWeek') == 3, lit(\"Wednesday\"))\\\n .when(col('NumDayOfWeek') == 4, lit(\"Thursday\"))\\\n .when(col('NumDayOfWeek') == 5, lit(\"Friday\"))\\\n .when(col('NumDayOfWeek') == 6, lit(\"Saturday\"))\\\n .when(col('NumDayOfWeek') == 7, lit(\"Sunday\"))\\\n .otherwise(lit(None)))\\\n .groupBy(\"DayOfWeek\").count()\\\n .withColumn(\"Rank\", dense_rank().over(w))\n\ndisplay(DaysOfWeekCountDF)\n\n# DaysOfWeekCountDF.write.mode(\"overwrite\").saveAsTable(\"DaysOfWeekCountDF\")\n","sub_path":"Desafios/Resolucoes/Desafio 2 - DataFrames Resolucao.py","file_name":"Desafio 2 - DataFrames Resolucao.py","file_ext":"py","file_size_in_byte":7721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"623932173","text":"import constants as k\nimport ArrayManager as am\nfrom decimal import Decimal, getcontext\nimport os\nimport time\n\n# append output to a file.\ndef createoutputfile(texttowrite, filename):\n try:\n with open(filename, 'a') as f:\n f.write(texttowrite + \"\\n\")\n except IOError:\n print(\"WARNING: There was a problem accessing \" + k.OUTPUT_FILE)\n\n# def minstohours(minutevalue):\n# if minutevalue <= 60:\n# returnvalue = str(minutevalue) + \" minutes\"\n# else:\n# returnvalue = str(round((minutevalue/60),1)) + \" hours\"\n# return returnvalue\n\n# ###############################################\n# Main Parts STarts Here\n# purge old file for writing...\n# ###############################################\n\nwhile True:\n try:\n importarray = []\n # Load up file into array\n # print(k.FILE_BINNED_MINS)\n importarray = am.CreateRawArray(k.FILE_BINNED_MINS)\n\n # remove the first line which may contain text header\n importarray.pop(0)\n\n # what is the size of the sliding window we want to use to show that a blip happened in a certain interval?\n windowsizeminutes = 60\n windowinterval = k.MAG_READ_FREQ * windowsizeminutes\n\n try:\n os.remove(k.OUTPUT_FILE)\n except OSError:\n print(\"WARNING: could not delete \" + k.OUTPUT_FILE)\n\n # Possible create a sub-list to only get values for the lasty 2 hours??\n\n # Reverse array, make the most recent time index[0]\n importarray.reverse()\n\n hr = 0\n outputlist = []\n datestring = importarray[0].dateTime\n\n # we are going to parse thru the data, dealing with chunks of 60 mins at a time\n for i in range(0, len(importarray), windowinterval):\n maxv = Decimal(0)\n minv = Decimal(0)\n\n if i + windowinterval < len(importarray):\n for j in range(i, i + windowinterval):\n # determin max and min values for this window interval\n if Decimal(importarray[j].raw_x) >= maxv:\n maxv = Decimal(importarray[j].raw_x)\n elif Decimal(importarray[j].raw_x) <= minv:\n minv = Decimal(importarray[j].raw_x)\n\n # calculate the variation\n hr = hr + 1\n fieldstrength = Decimal(0)\n fieldstrength = maxv - minv\n\n if fieldstrength <= Decimal(k.MAG_THRESHOLD_NORMAL):\n spancolour = k.COLOUR_N\n elif fieldstrength > Decimal(k.MAG_THRESHOLD_NORMAL) and fieldstrength <= Decimal(k.MAG_THRESHOLD_MEDIUM):\n spancolour = k.COLOUR_N_M\n elif fieldstrength > Decimal(k.MAG_THRESHOLD_MEDIUM) and fieldstrength <= Decimal(k.MAG_THRESHOLD_HIGH):\n spancolour = k.COLOUR_M_H\n # this is an alert condition\n elif fieldstrength > Decimal(k.MAG_THRESHOLD_HIGH):\n spancolour = k.COLOUR_H\n # this is an alert condition\n\n spantag = \"\"\n outputtext = spantag + str(hr) + \" hr
ago\" + \"\"\n print(outputtext)\n outputlist.append(outputtext)\n\n\n outputlist.reverse()\n createoutputfile(\"\",k.OUTPUT_FILE)\n for item in outputlist:\n createoutputfile(item, k.OUTPUT_FILE)\n createoutputfile(\"
\",k.OUTPUT_FILE)\n\n createoutputfile(\"
\",k.OUTPUT_FILE)\n createoutputfile(\"
Normal and \", k.OUTPUT_FILE)\n createoutputfile(\"Minor activity are typical.
\", k.OUTPUT_FILE)\n createoutputfile(\"Continuous intervals of Moderate\", k.OUTPUT_FILE)\n createoutputfile(\"and HIGH activity may mean active aurora. Updates every 10 minutes.

\", k.OUTPUT_FILE)\n createoutputfile(\"Last updated at \" + datestring + \"\", k.OUTPUT_FILE)\n createoutputfile(\"
\",k.OUTPUT_FILE)\n\n except:\n print(\"dreadfully lazy try/except here!\")\n\n time.sleep(600)","sub_path":"!Archive/testSpike/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"143780734","text":"import sys, pygame\nimport os\npygame.init()\n\nsize = width, height = 700, 500\nspeed = 5\ncolour = 255, 255, 255\nblack = 0,0,0\nRED = 0,255,255\nBLUE = 255,0,255\nBULLET_COUNT = 4\n\nHEALTH_FONT = pygame.font.SysFont('comicsans', 40)\nWINNER_FONT = pygame.font.SysFont('comicsans', 100)\n\nscreen = pygame.display.set_mode(size)\npygame.display.set_caption(\"Space War\")\n\nbackground = pygame.image.load(os.path.join('asset','space.png'))\nbackground = pygame.transform.scale(background,(width,height))\nplayer_ship_1 = pygame.image.load(os.path.join('asset',\"player_1.png\"))\nplayer_ship_1 = pygame.transform.scale(player_ship_1,(100,100))\n\nplayer_ship_2 = pygame.image.load(os.path.join('asset',\"player_1.png\"))\nplayer_ship_2 = pygame.transform.scale(player_ship_1,(100,100))\n\nballrect_1 = player_ship_1.get_rect()\nballrect_2 = player_ship_2.get_rect()\n\nballrect_1.x = width-100\nballrect_1.y = height-100\n\nclock = pygame.time.Clock()\n\nplay_1_bullets = []\nplay_2_bullets = []\n\nbullet_sound = pygame.mixer.Sound(os.path.join(\"asset\",\"laser-cannon-shot.wav\"))\ngame_over_sound = pygame.mixer.Sound(os.path.join(\"asset\",\"spaceship-system-break-down.wav\"))\nhit = pygame.mixer.Sound(os.path.join(\"asset\",\"explosion.wav\"))\n\ndef main():\n\n player_1_health = 10\n player_2_health = 10\n\n while True:\n\n clock.tick(60) \n for event in pygame.event.get():\n if event.type == pygame.QUIT: sys.exit()\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE and len(play_1_bullets) height-100):\n ballrect_1.y += speed\n\n if keys[pygame.K_RIGHT]:\n if not (ballrect_1.x > width-100):\n ballrect_1.x += speed \n\n if keys[pygame.K_LEFT]:\n if not (ballrect_1.x < border.x):\n ballrect_1.x -= speed \n\n\n # player 2 controls \n if keys[pygame.K_w]:\n if not (ballrect_2.y < 0):\n ballrect_2.y -= speed\n\n if keys[pygame.K_s]:\n if not (ballrect_2.y > height-100):\n ballrect_2.y += speed\n\n if keys[pygame.K_d]:\n if not (ballrect_2.x > border.x-100):\n ballrect_2.x += speed \n\n if keys[pygame.K_a]:\n if not (ballrect_2.x < 0):\n ballrect_2.x -= speed \n\n # bullets movement \n for bullet in play_1_bullets:\n bullet.x += 3 \n if ballrect_1.colliderect(bullet):\n pygame.mixer.Sound.play(hit)\n pygame.mixer.music.stop()\n player_1_health-=1\n play_1_bullets.remove(bullet)\n if bullet.x >= width:\n play_1_bullets.remove(bullet)\n\n for bullet in play_2_bullets:\n bullet.x -= 3 \n if ballrect_2.colliderect(bullet):\n pygame.mixer.Sound.play(hit)\n pygame.mixer.music.stop()\n player_2_health-=1\n play_2_bullets.remove(bullet)\n if bullet.x <= 0:\n play_2_bullets.remove(bullet) \n\n\n border = pygame.Rect(width//2,0,5,height) \n screen.fill(colour)\n screen.blit(background,(0,0))\n player_1_health_text = HEALTH_FONT.render(\n \"Player 2: \" + str(player_1_health), 1, colour)\n player_2_health_text = HEALTH_FONT.render(\n \"Player 1: \" + str(player_2_health), 1, colour)\n screen.blit(player_1_health_text, (width - player_1_health_text.get_width() - 10, 10))\n screen.blit(player_2_health_text, (10, 10))\n\n for bullet in play_1_bullets:\n pygame.draw.rect(screen,RED,bullet)\n for bullet in play_2_bullets:\n pygame.draw.rect(screen,BLUE,bullet) \n\n pygame.draw.rect(screen,black,border)\n screen.blit(player_ship_1, ballrect_1)\n screen.blit(player_ship_2, ballrect_2)\n\n if player_1_health<=0 or player_2_health<=0:\n\n pygame.mixer.Sound.play(game_over_sound)\n pygame.mixer.music.stop()\n\n game_over = WINNER_FONT.render(\"GAME OVER!!!\",1,(0,255,0))\n screen.blit(game_over,(width//2 - game_over.get_width()//2,height//2 - game_over.get_height()))\n\n if player_1_health<=0:\n player_won = WINNER_FONT.render(\"Player 1 WON!!\",1,(0,255,0))\n screen.blit(player_won,(width//2 - player_won.get_width()//2,height//2))\n\n if player_2_health<=0:\n player_won = WINNER_FONT.render(\"Player 2 WON!!\",1,(0,255,0))\n screen.blit(player_won,(width//2 - player_won.get_width()//2,height//2)) \n\n pygame.display.update()\n play_1_bullets.clear()\n play_2_bullets.clear()\n pygame.time.delay(5000)\n break \n\n pygame.display.flip()\n\n main() \n\nif __name__ == '__main__':\n main() ","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"51261026","text":"\"\"\"\n0. Load config\n1. Load dataset\n2. Load Trainer\n3. train\n4. eval\n\"\"\"\nfrom config import Config\nfrom trainer import Trainer\nfrom utils import init_logger, load_tokenizer\nfrom data_loader import load_and_cache_examples\n\n\ndef main():\n config = Config('config.ini')\n init_logger()\n tokenizer = load_tokenizer(config)\n train_dataset = load_and_cache_examples(config, tokenizer, evaluate=False)\n test_dataset = load_and_cache_examples(config, tokenizer, evaluate=True)\n trainer = Trainer(config, train_dataset, test_dataset)\n\n if config.do_train:\n trainer.train()\n\n if config.do_eval:\n trainer.evaluate()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"339346931","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /net/orion/data/home/tack/projects/kaa/metadata/build/lib.linux-x86_64-2.6/kaa/metadata/video/flv.py\n# Compiled at: 2009-05-22 11:00:08\n__all__ = [\n 'Parser']\nimport sys, struct, logging, core\nlog = logging.getLogger('metadata')\nFLV_TAG_TYPE_AUDIO = 8\nFLV_TAG_TYPE_VIDEO = 9\nFLV_TAG_TYPE_META = 18\nFLV_AUDIO_CHANNEL_MASK = 1\nFLV_AUDIO_SAMPLERATE_MASK = 12\nFLV_AUDIO_CODECID_MASK = 240\nFLV_AUDIO_SAMPLERATE_OFFSET = 2\nFLV_AUDIO_CODECID_OFFSET = 4\nFLV_AUDIO_CODECID = (1, 2, 85, 1)\nFLV_VIDEO_CODECID_MASK = 15\nFLV_VIDEO_CODECID = ('FLV1', 'MSS1', 'VP60')\nFLV_DATA_TYPE_NUMBER = 0\nFLV_DATA_TYPE_BOOL = 1\nFLV_DATA_TYPE_STRING = 2\nFLV_DATA_TYPE_OBJECT = 3\nFLC_DATA_TYPE_CLIP = 4\nFLV_DATA_TYPE_REFERENCE = 7\nFLV_DATA_TYPE_ECMARRAY = 8\nFLV_DATA_TYPE_ENDOBJECT = 9\nFLV_DATA_TYPE_ARRAY = 10\nFLV_DATA_TYPE_DATE = 11\nFLV_DATA_TYPE_LONGSTRING = 12\nFLVINFO = {'creator': 'copyright'}\n\nclass FlashVideo(core.AVContainer):\n \"\"\"\n Experimental parser for Flash videos. It requires certain flags to\n be set to report video resolutions and in most cases it does not\n provide that information.\n \"\"\"\n table_mapping = {'FLVINFO': FLVINFO}\n\n def __init__(self, file):\n core.AVContainer.__init__(self)\n self.mime = 'video/flv'\n self.type = 'Flash Video'\n data = file.read(13)\n if len(data) < 13 or struct.unpack('>3sBBII', data)[0] != 'FLV':\n raise core.ParseError()\n for i in range(10):\n if self.audio and self.video:\n break\n data = file.read(11)\n if len(data) < 11:\n break\n chunk = struct.unpack('>BH4BI', data)\n size = (chunk[1] << 8) + chunk[2]\n if chunk[0] == FLV_TAG_TYPE_AUDIO:\n flags = ord(file.read(1))\n if not self.audio:\n a = core.AudioStream()\n a.channels = (flags & FLV_AUDIO_CHANNEL_MASK) + 1\n srate = flags & FLV_AUDIO_SAMPLERATE_MASK\n a.samplerate = 44100 << (srate >> FLV_AUDIO_SAMPLERATE_OFFSET) >> 3\n codec = (flags & FLV_AUDIO_CODECID_MASK) >> FLV_AUDIO_CODECID_OFFSET\n if codec < len(FLV_AUDIO_CODECID):\n a.codec = FLV_AUDIO_CODECID[codec]\n self.audio.append(a)\n file.seek(size - 1, 1)\n elif chunk[0] == FLV_TAG_TYPE_VIDEO:\n flags = ord(file.read(1))\n if not self.video:\n v = core.VideoStream()\n codec = (flags & FLV_VIDEO_CODECID_MASK) - 2\n if codec < len(FLV_VIDEO_CODECID):\n v.codec = FLV_VIDEO_CODECID[codec]\n self.video.append(v)\n file.seek(size - 1, 1)\n elif chunk[0] == FLV_TAG_TYPE_META:\n log.info('metadata %s', str(chunk))\n metadata = file.read(size)\n try:\n while metadata:\n length, value = self._parse_value(metadata)\n if isinstance(value, dict):\n log.info('metadata: %s', value)\n if value.get('creator'):\n self.copyright = value.get('creator')\n if value.get('width'):\n self.width = value.get('width')\n if value.get('height'):\n self.height = value.get('height')\n if value.get('duration'):\n self.length = value.get('duration')\n self._appendtable('FLVINFO', value)\n if not length:\n break\n metadata = metadata[length:]\n\n except (IndexError, struct.error, TypeError):\n pass\n\n else:\n log.info('unkown %s', str(chunk))\n file.seek(size, 1)\n file.seek(4, 1)\n\n def _parse_value(self, data):\n \"\"\"\n Parse the next metadata value.\n \"\"\"\n if ord(data[0]) == FLV_DATA_TYPE_NUMBER:\n value = struct.unpack('>d', data[1:9])[0]\n return (\n 9, value)\n else:\n if ord(data[0]) == FLV_DATA_TYPE_BOOL:\n return (2, bool(data[1]))\n if ord(data[0]) == FLV_DATA_TYPE_STRING:\n length = (ord(data[1]) << 8) + ord(data[2])\n return (\n length + 3, data[3:length + 3])\n if ord(data[0]) == FLV_DATA_TYPE_ECMARRAY:\n init_length = len(data)\n num = struct.unpack('>I', data[1:5])[0]\n data = data[5:]\n result = {}\n for i in range(num):\n length = (ord(data[0]) << 8) + ord(data[1])\n key = data[2:length + 2]\n data = data[length + 2:]\n length, value = self._parse_value(data)\n if not length:\n return (0, result)\n result[key] = value\n data = data[length:]\n\n return (init_length - len(data), result)\n log.info('unknown code: %x. Stop metadata parser', ord(data[0]))\n return (0, None)\n\n\nParser = FlashVideo","sub_path":"pycfiles/kaa_metadata-0.7.8dev_r4569_20111003-py2.6-linux-x86_64/flv.py","file_name":"flv.py","file_ext":"py","file_size_in_byte":5554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"391630419","text":"\nfrom rest_framework import permissions\nfrom .models import ProjectAccessModel, SystemAccessModel\n\n\nclass ProjectRelatedPermission(permissions.BasePermission):\n message = \"Access Denied\"\n\n def has_permission(self, request, view):\n \"\"\" global permission applies to list and detail view \"\"\"\n if request.user.is_superuser:\n return True\n try:\n project_access = ProjectAccessModel.objects.get(\n project_id=view.kwargs.get('project_id'), user=request.user)\n except ProjectAccessModel.DoesNotExist:\n project_access = None\n\n if project_access is not None:\n if request.method in permissions.SAFE_METHODS:\n return True\n if project_access.moderator:\n return True\n\n return False\n\n\nclass SystemRelatedPermission(permissions.BasePermission):\n message = \"Access Denied\"\n\n def has_permission(self, request, view):\n \"\"\" global permission applies to list and detail view \"\"\"\n if request.user.is_superuser:\n return True\n try:\n system_access = SystemAccessModel.objects.get(\n system_id=view.kwargs.get('system_id'), user=request.user)\n project = system_access.system.project\n project_access = ProjectAccessModel.objects.get(\n project=project, user=request.user)\n if project_access.moderator:\n return True\n except SystemAccessModel.DoesNotExist:\n system_access = None\n if system_access is not None:\n if request.method in permissions.SAFE_METHODS:\n return True\n if system_access.moderator:\n return True\n return False\n","sub_path":"apps/system/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"377057902","text":"#!/usr/bin/env python\n\n#mat = '/home/tpillone/work/projets/2018_10_MYCOST/2018_07_4_strains/typing/freebayes_joint_genotyping/cgMLST/bwa/distances_in_snp.tsv'\n# /home/tpillone/work/projets/2018_10_MYCOST/2018_07_4_strains/typing/freebayes_joint_genotyping/cgMLST/bwa\n\nimport numpy\nimport networkx\nimport pandas\nimport json\nimport itertools\nimport matplotlib.pyplot as plt\n\n\n\n\n\ndef find_clusters(G, node_list):\n comb = itertools.combinations(node_list, 2)\n groups = []\n for i in comb:\n if i[0] == i[1]:\n continue\n try:\n print (G[i[0]][i[1]])\n except:\n if len(groups)==0:\n no_match=True\n else:\n no_match = False\n for n, group in enumerate(groups):\n if i[0] in group and i[1] in group:\n continue\n elif i[0] in group and i[1] not in group:\n groups[n].append(i[1])\n elif i[1] in group and i[0] not in group:\n groups[n].append(i[1])\n else:\n no_match=True\n if no_match:\n groups.append([i[0], i[1]])\n return groups\n\ndef merge_group_nodes(G, groups, node_list):\n for group in groups:\n median_dico = {}\n for node in node_list:\n if node in group:\n continue\n data = []\n for member in group:\n data.append(G[member][node]['weight'])\n m = numpy.median(data)\n median_dico[node] = m\n mapping = {group[0]: '\\n'.join(group)}\n G = networkx.relabel_nodes(G, mapping)\n for i in group[1:len(group)]:\n G.remove_node(i)\n for i in node_list:\n if i in group:\n continue\n G['\\n'.join(group)][i]['weight'] = median_dico[i]\n return G\n\n\n# this function is used to convert networkx to Cytoscape.js JSON format\n# returns string of JSON\ndef convert2cytoscapeJSON(G):\n # load all nodes into nodes array\n final = {}\n final[\"nodes\"] = []\n final[\"edges\"] = []\n for node in G.nodes():\n nx = {}\n nx[\"data\"] = {}\n nx[\"data\"][\"id\"] = node\n nx[\"data\"][\"label\"] = node\n final[\"nodes\"].append(nx.copy())\n #load all edges to edges array\n for edge in G.edges(data=True):\n print(edge)\n nx = {}\n nx[\"data\"]={}\n nx[\"data\"][\"id\"]=edge[0]+edge[1]\n nx[\"data\"][\"strength\"] = edge[2][\"weight\"]\n nx[\"data\"][\"source\"]=edge[0]\n nx[\"data\"][\"target\"]=edge[1]\n final[\"edges\"].append(nx)\n return json.dumps(final)\n\n\ndef get_MN_tree(dist_matrix):\n\n m = pandas.read_csv(dist_matrix, delimiter='\\t', header=0, index_col=0)\n\n nodes = list(m.index)\n\n G = networkx.from_pandas_adjacency(m) # networkx.from_numpy_matrix(A)\n\n groups = find_clusters(G, nodes)\n\n G = merge_group_nodes(G, groups, nodes)\n\n T=networkx.minimum_spanning_tree(G)\n\n return T\n","sub_path":"chlamdb/network_d3/minimum_spanning_tree.py","file_name":"minimum_spanning_tree.py","file_ext":"py","file_size_in_byte":2977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"285910424","text":"import numpy as np\n\n\ndef load_testing():\n return load(0, 10000)\n\n\ndef load_training():\n return load(10000, 60000)\n\n\ndef load(start, number_samples):\n mnist8m_file = open(\"../dataset/mnist8m/mnist8m.scale\", 'rb')\n\n images = np.zeros((number_samples, 28*28))\n labels = np.zeros(number_samples)\n count = 0\n for line in mnist8m_file:\n if count < start:\n count += 1\n continue\n\n ind = count - start\n\n if ind >= number_samples:\n break\n\n labels[ind] = int(line[0:1])\n for pixel in line[2:].split():\n pixel = pixel.split(':')\n images[ind, int(pixel[0])-1] = pixel[1]\n\n count += 1\n\n mnist8m_file.close()\n\n # normalize\n images = images*2.0 - 1\n\n return images, labels","sub_path":"source/loaders/load_8m_scale.py","file_name":"load_8m_scale.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"488608980","text":"from django import forms\nfrom .models import Question,Choice,Category\nfrom django.forms.formsets import BaseFormSet\nfrom django.forms import TextInput\n\nclass QuestionForm(forms.ModelForm):\n\n\tdef __init__(self, *args, **kwargs): \n\t\tsuper(QuestionForm, self).__init__(*args, **kwargs)\n\t\tself.fields['question_text'].label = ''\n\n\tclass Meta:\n\t\tmodel = Question\n\t\tfields = ('question_text',)\n\t\twidgets = {'question_text': TextInput(attrs={'placeholder': 'Enter Your Question Here',}),}\n\nclass ChoiceForm(forms.ModelForm):\n\t\n\tdef __init__(self, *args, **kwargs): \n\t\tsuper(ChoiceForm, self).__init__(*args, **kwargs)\n\t\tself.fields['choice_text'].label = ''\n\t\n\tclass Meta:\n\t\tmodel = Choice\n\t\tfields = ('choice_text',)\n\t\twidgets = {'choice_text': TextInput(attrs={'placeholder': 'Enter A Choice Here',}),}\n\nclass CategoryForm(forms.ModelForm):\n\n\tdef __init__(self, *args, **kwargs): \n\t\tsuper(CategoryForm, self).__init__(*args, **kwargs)\n\t\tself.fields['category_text'].label = ''\n\t\n\tclass Meta:\n\t\tmodel = Category\n\t\tfields = ('category_text',)\n\t\twidgets = {'category_text': TextInput(attrs={'placeholder': 'Enter A Category',}),}","sub_path":"pollSite/polls/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"482090241","text":"import pygame\n\n\npygame.init()\n\nscreen = pygame.display.set_mode((400, 600))\nsquare = pygame.Surface((50, 70))\nbg = pygame.image.load(\"images/002.png\").convert_alpha()\nbg_rect = bg.get_rect()\nx, y, w, h = bg.get_rect()\nsize = bg.get_size()\nprint(w, h)\nprint(size)\nprint(bg_rect)\nsquare.fill((0, 180, 250))\nsw, sh = screen.get_size()\nwhile True:\n screen.fill((128, 255, 128))\n screen.blit(pygame.transform.scale(bg, (sw, sh)),(0, 0))\n screen.blit(square, (300, 100))\n if pygame.event.get(pygame.QUIT):\n break\n pygame.display.update()\n\npygame.quit()","sub_path":"00031move.py","file_name":"00031move.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"344211897","text":"__author__ = \"Nikola\"\n\nimport os\nimport time\n\nfrom Graph.graph import Graph\nfrom Graph.page import Page\nfrom TrieParser.parser import Parser\nfrom TrieParser.trie import Trie\nfrom TrieParser.trie2 import Trie2\n\n\nclass HtmlLoader(object):\n \"\"\"\n Class which is in charge of everything data-wise. Within its fields, it holds the Trie structure,\n Graph and a dictionary with every page name linked to a certain page number (later used as ID).\n \"\"\"\n\n def __init__(self):\n self.trie = Trie() # Only change Trie <-> Trie2 here to test the other class\n self.graph = Graph()\n self.pages = []\n self.dict = {} # Dictionary is used to keep record of pages, in the format :\n self.files = []\n\n def loadTrieViaHTML(self, path):\n \"\"\"\n Collects all the '.html' files from the given path and its subfolders into a list. Then proceeds to\n call Parser.parse() for each file in the list. Words from every file are then inserted into the Trie\n structure. After filling the Trie, it creates the Graph structure.\n \"\"\"\n parser = Parser()\n\n start = time.time()\n \"\"\"\n By using 'self.getAllFiles(path), we collect the absolute paths for every '.html' file in the given\n directory. Paths are kept within the list 'self.files'. \n Using a for loop and a parser, we iterate through the list, and parse every file, add its words\n to the Trie structure, and subsequently build a Graph.\n \"\"\"\n\n page_counter = -1\n self.getHtmlFiles(path)\n\n for file in self.files:\n page_counter += 1\n self.dict[page_counter] = file\n\n parser.parse(file) # Parse the page at the given path\n\n page = Page(file, parser.links, len(parser.words)) # Create a new Page object to be used for Graphing\n self.pages.append(page)\n\n for word in parser.words: # Insert every word from the page into Trie\n self.trie.insertWord(word, page_counter)\n\n \" Graph creation below: \"\n \" Creating a Vertex for every page \"\n for page in self.pages:\n self.graph.insert_vertex(Graph.Vertex(page.path))\n\n \" Adding edges for every link between pages \"\n for page in self.pages:\n for link in page.links:\n self.graph.insert_edge(Graph.Vertex(page.path), Graph.Vertex(link))\n\n end = time.time()\n print(\"Parsed files, loaded Trie and formed a Graph in \" + str((end - start).__round__(2)) + \" seconds.\")\n\n\n \" Returns a page name corresponding to the page number which is passed as a parameter. \"\n def getPageName(self, pageNum):\n return self.dict.get(pageNum)\n\n \" Return a corresponding page number for a given page name. \"\n def getPageNum(self, pageName):\n for key in self.dict.keys():\n if self.dict[key] == pageName:\n return key\n return -1\n\n \" Iterates through all the files and subfolders in the given path folder, and adds .html file names to self.files \"\n def getHtmlFiles(self, path):\n for file in os.scandir(path):\n filepath = file.path\n if file.name.endswith('html'):\n self.files.append(filepath)\n elif file.is_dir():\n self.getHtmlFiles(filepath)\n","sub_path":"TrieParser/HtmlLoader.py","file_name":"HtmlLoader.py","file_ext":"py","file_size_in_byte":3447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"455945090","text":"# from Tkinter import * # 2.7\r\nfrom tkinter import * \r\nimport turtle\r\n# import tkMessageBox as mbx # 2.7\r\nfrom tkinter import messagebox as mbx\r\nimport sys\r\n\r\n\r\n\r\n\r\nclass drawing_app:\r\n def __init__(self, master):\r\n\r\n # Initiate environment\r\n self.speedSet = Entry(master)\r\n self.speedSet.grid(row=3, column=5)\r\n self.speedset = Label(master,text=\"set speed\")\r\n self.speedset.grid(row=3, column=4)\r\n\r\n self.colorset = Button(master, text=\"setcolor\", command=self.setcolor)\r\n self.colorset.grid(row=4, column=4)\r\n\r\n self.colorentry = Entry(master)\r\n self.colorentry.grid(row=4, column=5)\r\n\r\n self.title = Label(master,text=\"my drawing app with turtle\")\r\n self.title.grid(row=0, column=3)\r\n\r\n self.left = Button(master,text=\"left\", command=self.goleft)\r\n self.left.grid(row=1, column=1)\r\n\r\n self.right = Button(master,text=\"right\", command=self.goright)\r\n self.right.grid(row=1, column=2)\r\n\r\n self.forward = Button(master,text=\"forward\", command=self.goforward)\r\n self.forward.grid(row=1, column=3)\r\n\r\n self.backward = Button(master,text=\"backward\", command=self.gobackward)\r\n self.backward.grid(row=1, column=4)\r\n\r\n self.save = Button(master,text=\"save\", command=self.saveimage)\r\n self.save.grid(row=1, column=5)\r\n\r\n\r\n def goright(self):\r\n try:\r\n speed = int(self.speedSet.get())\r\n turtle.right(speed)\r\n except:\r\n mbx.showerror(\"error\", \" the input was not a numerical character\")\r\n\r\n def goleft(self):\r\n try:\r\n speed = int(self.speedSet.get())\r\n turtle.left(speed)\r\n except:\r\n mbx.showerror(\"error\",\" the input was not a numerical character\")\r\n\r\n\r\n def goforward(self):\r\n try:\r\n speed = int(self.speedSet.get())\r\n turtle.forward(speed)\r\n except:\r\n mbx.showerror(\"error\", \" the input was not a numerical character\")\r\n\r\n\r\n\r\n def gobackward(self):\r\n try:\r\n speed = int(self.speedSet.get())\r\n turtle.backward(speed)\r\n except:\r\n mbx.showerror(\"error\", \" the input was not a numerical character\")\r\n\r\n def setcolor(self):\r\n color = self.colorentry.get()\r\n turtle.color(color)\r\n\r\n def saveimage(self):\r\n ts = turtle.getscreen()\r\n ts.getcanvas().postscript(file=\"c:\\\\users\\owner\\\\pictures\\img.jpg\")\r\n mbx.showinfo(\"done\",\"image is saved\")\r\n\r\n\r\nroot = Tk()\r\n# Make object app using drawing_appp class\r\nobj = drawing_app(root)\r\nroot.mainloop()\r\n\r\n \r\n \r\n\r\n\r\n\r\n","sub_path":"drawme.py","file_name":"drawme.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"615439989","text":"from django.shortcuts import render, render_to_response, redirect\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.views import View\nfrom django.http import JsonResponse\nfrom django.core import serializers\nfrom ssc.forms import *\nfrom ssc.models import *\nfrom ssc.utilities import *\nfrom SSC_KSTU.settings import DEBUG, BASE_URL\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.files.storage import FileSystemStorage\nimport json\nfrom django.shortcuts import get_object_or_404\n\n# Create your views here.\n\n# Текущий ректор\ntry:\n rector_name = Rector.objects.filter(status=True)[0].name\nexcept:\n rector_name = 'Ибатову Марату Кенесовичу'\n\n\n# главная страница\ndef index(request):\n return render(request, 'ssc/index.html')\n\n\nclass TemplateView(View):\n \"\"\"\n Шаблон класс-представление\n \"\"\"\n form_class = None\n template_name = None\n context = None\n app_type = None\n app_ref = None\n\n def get(self, request):\n form = self.form_class()\n self.context['form'] = form\n return render(request, self.template_name, self.context)\n\n def post(self, request):\n form = self.form_class(request.POST, request.FILES)\n self.context['form'] = form\n\n files = request.FILES\n fs = FileSystemStorage()\n\n if form.is_valid():\n for _, file in files.items():\n fs.save(file.name, file)\n data = form.save()\n\n # создаем уведомление\n base_url = get_current_site(request)\n url_for_app = f'{base_url}/admin/ssc/{self.app_ref}/{data.id}/change/'\n\n n = Notification(application_type=self.app_type, url_for_application=url_for_app)\n n.save()\n\n return render(request, 'ssc/complete.html')\n\n if DEBUG:\n print(form.errors)\n\n return render(request, self.template_name, self.context)\n\n\ndef bachelor(request):\n context = {\n 'status': statuses.get('bachelor')\n }\n return render(request, 'ssc/bachelor.html', context)\n\n\ndef postgraduate(request):\n context = {\n 'status': statuses.get('postgraduate')\n }\n return render(request, 'ssc/postgraduate.html', context)\n\n\nclass AbroadView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Прием документов для участия в конкурсе на обучение за рубежом, в том числе академической мобильности\"\n Государственная услуга\n \"\"\"\n form_class = AbroadForm\n template_name = 'ssc/abroad.html'\n context = {'status': statuses.get('abroad')}\n app_type = 'Академическая мобильность'\n app_ref = 'abroad'\n\n @login_required\n def render(self, obj_id):\n app = Abroad.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=abroad&id={obj_id}')\n }\n return render_pdf('applications/abroad.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\ndef certificate(request):\n context = {\n 'status': statuses.get('certificate')\n }\n return render(request, 'ssc/certificate.html', context)\n\n\nclass HostelView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Предоставление общежития обучающимся в высших учебных заведениях\"\n Государственная услуга\n \"\"\"\n form_class = HostelForm\n template_name = 'ssc/hostel.html'\n context = {'status': statuses.get('hostel')}\n app_type = 'Общежитие'\n app_ref = 'hostel'\n\n @login_required\n def render(self, obj_id):\n app = Hostel.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=hostel&id={obj_id}')\n }\n return render_pdf('applications/hostel.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\nclass DuplicateView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Выдача справки лицам, не завершившим высшее и послевузовское образование\"\n Государственная услуга\n \"\"\"\n # form_class = DuplicateForm\n template_name = 'ssc/duplicate.html'\n context = {'status': statuses.get('duplicate')}\n # mail_template = 'mails/duplicate.html'\n app_type = 'Дубликаты документов'\n app_ref = 'duplicate'\n\n def get(self, request):\n # form = self.form_class()\n # self.context['form'] = form\n return render(request, self.template_name, self.context)\n\n # @login_required\n # def render(self, obj_id):\n # app = Duplicate.objects.get(id=obj_id)\n # if app.status not in ('Не проверено', 'Отозвано на исправление'):\n # context = {\n # 'rector_name': rector_name,\n # 'app': app,\n # 'qr_code': generate_qr_code('http://www.kstu.kz/')\n # }\n # return render_pdf('applications/duplicate.html', context)\n # else:\n # return HttpResponse('

Заявление не потверждено

')\n\n\nclass AcademicLeaveView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Предоставление академических отпусков обучающимся в организациях образования\"\n Государственная услуга\n \"\"\"\n form_class = AcademicLeaveForm\n template_name = 'ssc/academic-leave.html'\n context = {'status': statuses.get('academic-leave')}\n app_type = 'Академический отпуск'\n app_ref = 'academicleave'\n\n @login_required\n def render(self, obj_id):\n app = AcademicLeave.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=academic_leave&id={obj_id}')\n }\n return render_pdf('applications/academic-leave.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\nclass ReferenceView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Выдача справки лицам, не завершившим высшее и послевузовское образование\"\n Государственная услуга\n \"\"\"\n form_class = ReferenceForm\n template_name = 'ssc/reference.html'\n context = {'status': statuses.get('reference')}\n app_type = \"Академическая справка\"\n app_ref = \"reference\"\n\n @login_required\n def render(self, obj_id):\n app = Reference.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=reference&id={obj_id}')\n }\n return render_pdf('applications/reference.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\ndef transfer_and_recovery(request):\n context = {\n 'status': statuses.get('transfer-and-recovery')\n }\n return render(request, 'ssc/transfer-and-recovery.html', context)\n\n\nclass TransferView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Перевод в другой ВУЗ\"\n Внутривузовская услуга\n \"\"\"\n form_class = TransferForm\n template_name = 'ssc/transfer.html'\n context = {}\n app_type = \"Перевод в другой ВУЗ\"\n app_ref = 'transfer'\n\n @login_required\n def render(self, obj_id):\n app = Transfer.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=transfer&id={obj_id}')\n }\n return render_pdf('applications/transfer.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\nclass TransferKSTUView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Перевод в КарГТУ\"\n Внутривузовская услуга\n \"\"\"\n form_class = TransferKSTUForm\n template_name = 'ssc/transfer-kstu.html'\n context = {}\n app_type = 'Перевод в КарГТУ'\n app_ref = 'transferkstu'\n\n @login_required\n def render(self, obj_id):\n app = TransferKSTU.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=transfer_kstu&id={obj_id}')\n }\n return render_pdf('applications/transfer-kstu.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\nclass RecoveryView(TemplateView):\n \"\"\"\n Представления для подачи заявления по услуге\n \"Восстановление в число обучающихся\"\n Внутривузовская услуга\n \"\"\"\n form_class = RecoveryForm\n template_name = 'ssc/recovery.html'\n context = {}\n app_type = 'Восстановление в число обучающихся'\n app_ref = 'recovery'\n\n @login_required\n def render(self, obj_id):\n app = Recovery.objects.get(id=obj_id)\n if app.status not in ('Не проверено', 'Отозвано на исправление'):\n context = {\n 'rector_name': rector_name,\n 'app': app,\n 'qr_code': generate_qr_code(f'{BASE_URL}/check_order?order_type=recovery&id={obj_id}')\n }\n return render_pdf('applications/recovery.html', context)\n else:\n return HttpResponse('

Заявление не потверждено!

')\n\n\n@login_required\ndef get_notifications(request):\n \"\"\"\n Получить все уведомления\n \"\"\"\n notifications = Notification.objects.filter(is_showed=False).order_by('-date')\n notifications = serializers.serialize(\"json\", notifications)\n data = json.loads(notifications, encoding='utf8')\n return JsonResponse(data, safe=False)\n\n\n# отметить уведомление как прочитанное\ndef mark_as_read(request, obj_id):\n n = Notification.objects.get(id=obj_id)\n n.is_showed = True\n n.save()\n return HttpResponse(\"OK\", status=200)\n\n\n@login_required(login_url='/admin/login')\ndef stats(request):\n \"\"\"\n Выгрузка по статистике\n \"\"\"\n template = 'custom_admin/stats.html'\n return render(request, template)\n\n\ndef page_not_found(request, exception):\n return render(request, template_name='error_handlers/404.html', status=404)\n\n\ndef internal_server_error(request):\n return render(request, template_name='error_handlers/500.html', status=500)\n\n\ndef check_order(request):\n \"\"\"\n Проверка\n заявления\n \"\"\"\n template = 'ssc/verification.html'\n model_dictionary = {'academic_leave': AcademicLeave,\n 'hostel': Hostel,\n 'reference': Reference,\n 'abroad': Abroad,\n 'transfer_kstu': TransferKSTU,\n 'transfer': Transfer,\n 'recovery': Recovery}\n\n if request.method == 'GET':\n order_type = request.GET.get('order_type')\n order_id = request.GET.get('id')\n\n if order_type is None or order_id is None:\n return page_not_found(request, 'Не существует')\n\n model = model_dictionary[order_type]\n obj = get_object_or_404(model, id=order_id)\n\n order_type = model._meta.verbose_name.capitalize()\n\n context = {'last_name': obj.last_name,\n 'first_name': obj.first_name,\n 'patronymic': obj.patronymic,\n 'individual_identification_number': obj.individual_identification_number,\n 'date': obj.date_of_application,\n 'type': order_type}\n\n return render(request, template, context)\n\n return page_not_found(request, 'Не существует')\n\n\n\n","sub_path":"ssc/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"113812386","text":"import logging \n\nclass BaseParser: \n @classmethod\n def instance(cls, *args, **kwargs):\n \"\"\"Singleton getter\n \"\"\"\n if cls._instance is None:\n cls._instance = cls(*args, **kwargs)\n loaded = cls._instance.reload()\n logging.getLogger('back-robot').info('Loaded %r', loaded)\n\n return cls._instance\n\n @classmethod\n def add_config_path(cls, path):\n cls._config_paths.append(path)\n cls.reload()\n\n @classmethod\n def reload(cls):\n cls.instance().read(cls._config_paths)\n","sub_path":"vibm/nomads/nomads/backend/utils/config_parser.py","file_name":"config_parser.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"244910886","text":"\"\"\"\nCommon functions for Nginx found between collectd and telegraf configurators\n\"\"\"\nimport re\n\nimport common.install_utils as utils\nimport common.config as config\nimport plugin_dir.utils.plugin_utils as p_utils\n\n\ndef title():\n utils.cprint(\n ' **** ** ** \\n'\n '/**/** /** ***** // \\n'\n '/**//** /** **///** ** ******* ** **\\n'\n '/** //** /**/** /**/**//**///**//** ** \\n'\n '/** //**/**//******/** /** /** //*** \\n'\n '/** //**** /////**/** /** /** **/** \\n'\n '/** //*** ***** /** *** /** ** //**\\n'\n '// /// ///// // /// // // // \\n')\n\n\ndef overview():\n utils.cprint()\n utils.cprint(\n 'To collect metrics from Nginx server, the following\\n'\n 'steps need to be taken:\\n'\n '1. http_stub_status_module for nginx needs to be enabled.\\n'\n '2. Enable the nginx-status page for each virtual host.\\n')\n\n _ = utils.cinput('Press Enter to continue')\n\n\ndef check_dependency():\n utils.print_step('Checking dependency')\n utils.print_step(' Checking http_stub_status_module')\n\n cmd_res = utils.get_command_output(\n 'nginx -V 2>&1 | grep -o '\n 'with-http_stub_status_module')\n if cmd_res is None:\n utils.print_warn(\n 'http_stub_status_module is not enabled.\\n'\n 'This module is required to enable the '\n 'nginx-status page.')\n self.raise_error('http_stub_status_module')\n utils.print_success()\n\n\ndef check_server_url(url, url_list=[]):\n \"\"\"\n check if the url provided is a valid server-status page\n\n Input:\n url string: the url provided by the user\n url_list []string: list of url user has monitored already\n Output:\n ret_val bool:\n True if user provides a valid url\n False otherwise\n \"\"\"\n ret_val = False\n\n if not p_utils.check_url(url, url_list):\n return False\n\n res = utils.get_command_output('curl -s {url}'.format(url=url))\n status = check_nginx_status(res)\n if not status:\n utils.print_warn(\n 'The url you have provided '\n 'does not seem to be the correct server_status '\n 'page. Incorrect server-status will not be '\n 'recorded.')\n record = utils.ask(\n 'Would you like to record this url anyway?', 'no')\n if record:\n ret_val = True\n else:\n ret_val = True\n\n return ret_val\n\n\ndef check_nginx_status(payload):\n \"\"\"\n Loose regex check on the content of the page\n \"\"\"\n nginx_re = re.search(\n r'active connections:\\s*\\d+\\s*'\n 'server accepts handled requests', payload, re.I)\n nginx_re2 = re.search(\n r'reading: \\d+ writing: \\d+ waiting: \\d+', payload, re.I)\n\n if nginx_re is None or nginx_re2 is None:\n return False\n\n return True\n\n\ndef plugin_usage():\n utils.cprint(\n 'To monitor a nginx server, '\n 'you must enable a server-status page.\\n'\n 'To enable a server-status page, the following code in quote\\n'\n '\"location /server-status{\\n'\n ' stub_status on;\\n'\n ' access_log off;\\n'\n ' allow 127.0.0.1;\\n'\n ' # deny all;\\n'\n '}\"\\n'\n 'must be included within a server block '\n 'for the .conf file of your server.\\n\\n'\n 'To check whether the server-status page is working, '\n 'please visit\\n'\n '\\tyour-server-name/server-status\\n')\n\nif __name__ == '__main__':\n utils.print_step('Testing module {}'.format(__loader__.fullname))\n utils.cprint(get_sample_config('nginx'))\n","sub_path":"app-config/WF-PCInstaller/plugin_dir/utils/nginx_utils.py","file_name":"nginx_utils.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"184919565","text":"import vmware.base.appmgmt as appmgmt\nimport vmware.common.base_facade as base_facade\nimport vmware.common.constants as constants\nimport vmware.common.global_config as global_config\nimport vmware.nsx.manager.appliancemanagement.ntp.api.ntp_api_client \\\n as ntp_api_client\nimport vmware.nsx.manager.appliancemanagement.ntp.cli.ntp_cli_client \\\n as ntp_cli_client\n\npylogger = global_config.pylogger\n\n\nclass NtpFacade(appmgmt.ApplianceManagement, base_facade.BaseFacade):\n\n \"\"\"\n Ntpfacade class to perform CRUDAQ\n \"\"\"\n\n DEFAULT_EXECUTION_TYPE = constants.ExecutionType.API\n DEFAULT_IMPLEMENTATION_VERSION = \"NSX70\"\n\n def __init__(self, parent=None):\n super(NtpFacade, self).__init__(parent)\n self.nsx_manager_obj = parent\n\n # instantiate client objects\n api_client = ntp_api_client.NtpAPIClient(\n parent=parent.get_client(constants.ExecutionType.API))\n cli_client = ntp_cli_client.NtpCLIClient(\n parent=parent.get_client(constants.ExecutionType.CLI))\n\n # Maintain the list of client objects.\n self._clients = {constants.ExecutionType.API: api_client,\n constants.ExecutionType.CLI: cli_client}\n","sub_path":"SystemTesting/pylib/vmware/nsx/manager/appliancemanagement/ntp/ntp_facade.py","file_name":"ntp_facade.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"112550394","text":"#\n# CIFAR VGG models.\n#\n# Hyperspherical Prototypical Networks.\n#\n\nimport math\nimport torch\nimport torch.nn as nn\nimport torch.nn.init as init\nimport torch.nn.functional as F\n\n#\n#\n#\nclass CIFARvgg(nn.Module):\n \n #\n #\n #\n def __init__(self, features, output_dim):\n super(CIFARvgg, self).__init__()\n self.features = features\n\n self.classifier = nn.Sequential(\n nn.Dropout(),\n nn.Linear(512, 512),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(512, 512),\n nn.ReLU(True),\n nn.Linear(512, output_dim),\n )\n \n # Initialize weights\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n m.weight.data.normal_(0, math.sqrt(2. / n))\n m.bias.data.zero_()\n\n #\n #\n #\n def forward(self, x):\n x = self.features(x)\n x = x.view(x.size(0), -1)\n x = self.classifier(x)\n return x\n\n#\n#\n#\ndef make_layers(cfg, batch_norm=False):\n layers = []\n in_channels = 3\n for v in cfg:\n if v == 'M':\n layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n else:\n conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)\n if batch_norm:\n layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]\n else:\n layers += [conv2d, nn.ReLU(inplace=True)]\n in_channels = v\n return nn.Sequential(*layers)\n\n#\n#\n#\ncfg = {\n 'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n 'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n 'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', \n 512, 512, 512, 512, 'M'],\n}\n\n\ndef CIFARvgg11(output_dim):\n \"\"\"VGG 11-layer model (configuration \"A\")\"\"\"\n return CIFARvgg(make_layers(cfg['A']), output_dim)\n\n\ndef CIFARvgg11_bn(output_dim):\n \"\"\"VGG 11-layer model (configuration \"A\") with batch normalization\"\"\"\n return CIFARvgg(make_layers(cfg['A'], batch_norm=True), output_dim)\n\n\ndef CIFARvgg13(output_dim):\n \"\"\"VGG 13-layer model (configuration \"B\")\"\"\"\n return CIFARvgg(make_layers(cfg['B']), output_dim)\n\n\ndef CIFARvgg13_bn(output_dim):\n \"\"\"VGG 13-layer model (configuration \"B\") with batch normalization\"\"\"\n return CIFARvgg(make_layers(cfg['B'], batch_norm=True), output_dim)\n\n\ndef CIFARvgg16(output_dim):\n \"\"\"VGG 16-layer model (configuration \"D\")\"\"\"\n return CIFARvgg(make_layers(cfg['D']), output_dim)\n\n\ndef CIFARvgg16_bn(output_dim):\n \"\"\"VGG 16-layer model (configuration \"D\") with batch normalization\"\"\"\n return CIFARvgg(make_layers(cfg['D'], batch_norm=True), output_dim)\n\n\ndef CIFARvgg19(output_dim):\n \"\"\"VGG 19-layer model (configuration \"E\")\"\"\"\n return CIFARvgg(make_layers(cfg['E']), output_dim)\n\n\ndef CIFARvgg19_bn(output_dim):\n \"\"\"VGG 19-layer model (configuration 'E') with batch normalization\"\"\"\n return CIFARvgg(make_layers(cfg['E'], batch_norm=True), output_dim)\n","sub_path":"models/cifar/vgg.py","file_name":"vgg.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"644699608","text":"# 2106. Maximum Fruits Harvested After at Most K Steps\n# 2021/12/13\n# vwc 271\n\n# Runtime: 3064 ms, faster than 25.00% of Python3 online submissions for Maximum Fruits Harvested After at Most K Steps.\n# Memory Usage: 61.4 MB, less than 25.00% of Python3 online submissions for Maximum Fruits Harvested After at Most K Steps.\n\n# optimal strategy: only turn around at most 1 time\n# move left x steps, then move right k - x steps\n# or mover right x steps, then move left k - steps\n\n# remarks: start pos could be outside the range, makes the code not that elegant\n\nclass Solution:\n def maxTotalFruits(self, fruits: List[List[int]], startPos: int, k: int) -> int:\n l, r = fruits[0][0], fruits[-1][0]\n counts = [0] * (r + 1)\n for p, a in fruits:\n counts[p] = a\n sums = [0]\n for x in counts:\n sums.append(sums[-1] + x)\n ans = 0\n for x in range(k + 1):\n # startPos - x, start - x + (k - x)\n lower, upper = startPos - x, min(r, startPos - x + (k - x))\n if lower > r: continue\n if lower < l: break\n\n if upper <= startPos:\n ans = max(ans, (sums[startPos + 1] if startPos <= r else sums[-1]) - sums[lower])\n else:\n ans = max(ans, sums[upper + 1] - sums[lower])\n\n for x in range(k + 1):\n lower, upper = max(0, startPos + x - (k - x)), min(r, startPos + x)\n if lower > r: break\n\n if lower >= startPos:\n ans = max(ans, sums[upper + 1] - sums[startPos])\n else:\n ans = max(ans, sums[upper + 1] - sums[lower])\n\n return ans\n\n\n# 2021/12/13\n# Runtime: 2832 ms, faster than 50.00% of Python3 online submissions for Maximum Fruits Harvested After at Most K Steps.\n# Memory Usage: 61.6 MB, less than 25.00% of Python3 online submissions for Maximum Fruits Harvested After at Most K Steps.\n\n# sliding window\n# left, right index is confused, not easy to come up during an interview\n# customized size function\n# maintaining a window of size k\n\nclass Solution:\n def maxTotalFruits(self, fruits: List[List[int]], startPos: int, k: int) -> int:\n lower, upper = fruits[0][0], fruits[-1][0]\n counts = [0] * (upper + 1)\n for p, a in fruits:\n counts[p] = a\n sums = [0]\n l_most, r_most = startPos - k, startPos + k\n for x in counts:\n sums.append(sums[-1] + x)\n if l_most > upper or r_most < lower:\n return 0\n l, ans = max(0, l_most), 0\n for r in range(l, min(r_most + 1, upper + 1)):\n while l <= r and self.size(l, r, startPos) > k:\n l += 1\n ans = max(ans, sums[r + 1] - sums[l])\n return ans\n\n def size(self, l, r, start):\n if r <= start:\n d = start - l\n elif l >= start:\n d = r - start\n else:\n d = r - l + min(start - l, r - start)\n return d","sub_path":"2106. Maximum Fruits Harvested After at Most K Steps.py","file_name":"2106. Maximum Fruits Harvested After at Most K Steps.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"651909288","text":"from app.configurations.database import db\nfrom sqlalchemy.orm import relationship, backref\nfrom sqlalchemy import Column, Integer, String, Boolean, ForeignKey, Date, Float\n\n\nclass Transaction(db.Model):\n __tablename__ = \"transaction\"\n\n id = Column(Integer, primary_key=True)\n\n date = Column(Date, nullable=False)\n type = Column(String, nullable=False)\n coin = Column(String, nullable=False)\n fiat = Column(String, nullable=False)\n price_per_coin = Column(Float, nullable=False)\n avg_price_brl = Column(Float, nullable=False)\n avg_price_usd = Column(Float, nullable=False)\n net_quantity = Column(Float, nullable=False)\n quantity = Column(Float, nullable=False)\n foreign_exch = Column(Boolean, nullable=False)\n\n user_id = Column(Integer, ForeignKey(\"user.id\"), nullable=False)\n\n accounting = relationship(\n \"Accounting\", uselist=False, backref=backref(\"transaction\")\n )\n\n def serialized(self):\n return {\n \"id\": self.id,\n \"date\": self.date,\n \"type\": self.type,\n \"coin\": self.coin,\n \"fiat\": self.fiat,\n \"price_per_coin\": self.price_per_coin,\n \"avg_price_brl\": self.avg_price_brl,\n \"avg_price_usd\": self.avg_price_usd,\n \"net_quantity\": self.net_quantity,\n \"quantity\": self.quantity,\n \"foreign_exch\": self.foreign_exch,\n }\n","sub_path":"app/models/transactions_model.py","file_name":"transactions_model.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"603917050","text":"\n\n#calss header\nclass _EXIGENCY():\n\tdef __init__(self,): \n\t\tself.name = \"EXIGENCY\"\n\t\tself.definitions = [u'the difficulties of a situation, especially one that causes urgent demands: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_exigency.py","file_name":"_exigency.py","file_ext":"py","file_size_in_byte":360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"42259246","text":"\nfrom controlador import modulo_jaccard as ja\nfrom controlador import modulo_coseno as cs\nfrom controlador import modulo_tweets as tw\nfrom controlador import modulo_lec_escri as lc\nfrom controlador import modulo_maquinavec as mv\nfrom controlador import nlp as nl\nfrom controlador import modulo_textblob as txtbl\nfrom controlador import modulo_topicmo as tpm\nfrom controlador import modulo_voting as vt\nfrom threading import Thread\n\n##############CALCULADOR########################\ndef categorizar(positivo, negativo):\n temp1 = []\n for x,temp in enumerate(positivo):\n if temp > negativo[x]:\n temp1.append(1)\n elif temp == negativo[x]:\n temp1.append(0)\n elif temp < negativo[x]:\n temp1.append(-1)\n return temp1\n##########################################################\n\n\n###########LITERAL 1###################\ndef literal1(n, fechaInicio, fechaFin):\n \n print(\"Procesos\")\n #Consultando Tweets\n tweet, fecha = tw.obtenerTweets(n, fechaInicio, fechaFin)\n #variable q contiene los tweets generales\n temp = tweet[:]\n #variable q contiene los tweets generales\n tempo = temp[:]\n #Proceso NLP\n tweet = nl.minusculas(tweet)\n tweet = nl.eliminarce(tweet)\n tweet = nl.tokenizar(tweet)\n tweet = nl.qstopwords(tweet, 1)\n \n #Proceso Nube\n vec_nube_temp = []\n for review in tweet:\n vec_nube_temp += review\n\n #Obteniendo Diccionarios\n dicposi = lc.leerTxt('modelo/dic_posi.txt')\n dicneg = lc.leerTxt('modelo/dic_neg.txt')\n\n #Cantidad Tweets\n print(\"Cantidad de tweets: \" +str(len(tweet)))\n\n print(\"\\n-- Topic Modeling --\")\n #tpm.topicmodeling(tweet)\n #hilo = Thread(target=tpm.topicmodeling, args=(tweet,))\n #hilo.start()\n #hilo.join()\n\n print(\"\\n-- Jaccard --\")\n #Jaccard de Negativos\n negativo = ja.vectores(tweet, dicneg)\n #Jaccard de Positivos\n positivo = ja.vectores(tweet, dicposi)\n #Obteniendo Resultados\n rsJaccard = categorizar(positivo, negativo)\n \n print(\"\\n-- Coseno --\")\n #Coseno de Negativos\n vectorneg = cs.coseno(tweet,dicneg)\n #Coseno de Positivos\n vectorpos = cs.coseno(tweet,dicposi)\n #Obteniendo Resultados, columnas = vectorneg[0,1:]\n # filas = vectorneg[1:,0]\n rsCoseno = categorizar(vectorpos[1:, 0], vectorneg[1:, 0])\n\n print(\"\\n-- TextBlob --\")\n rsTextBlob = txtbl.textblob(tempo)\n\n print(\"\\n-- SVM --\")\n rsSVM = mv.maqvec(tweet)\n\n print(\"\\n-- Voting --\")\n rsVoting = vt.voting(rsJaccard,rsCoseno,rsTextBlob,rsSVM)\n \n print(\"-- Envio al servidor --\")\n rs = []\n rs.append(fecha) #fecha\n rs.append(temp) #tweet general\n rs.append(rsJaccard)#Jaccard\n rs.append(rsCoseno)#Coseno\n rs.append(rsTextBlob)#TextBlob\n rs.append(rsSVM)#SVM\n rs.append(rsVoting)#Voting\n rs.append(\" \".join(review2 for review2 in vec_nube_temp))#Datos para la nube\n \n return rs","sub_path":"controlador/procesos.py","file_name":"procesos.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"435395664","text":"import pygame\n\npygame.init()\nscreen = pygame.display.set_mode((800,600))\n\nx,y = 30,30\n\ndef events():\n global x,y\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return event.type\n if event.type == pygame.KEYDOWN or event.type == PY:\n if event.key == pygame.K_UP:\n y-=10\n if event.key == pygame.K_DOWN:\n y+=10\n if event.key == pygame.K_RIGHT:\n x+=10\n if event.key == pygame.K_LEFT:\n x-=10\n print(x,y)\ndef loop():\n pass\n\ndef render():\n global x,y\n screen.fill((0, 0, 0))\n pygame.draw.rect(screen, (0, 128, 255), pygame.Rect(x,y,30,30))\n pygame.display.flip()\n\n\nwhile True:\n if events() == pygame.QUIT:\n print('ok')\n break\n loop()\n render()","sub_path":"flappy.py","file_name":"flappy.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"446807986","text":"import logging\nimport sys\nfrom marshmallow import Schema, fields, post_load, ValidationError, EXCLUDE\n\nfrom .. import utils\nfrom . import HivenObject\nfrom .. import exception as errs\n\nlogger = logging.getLogger(__name__)\n\n__all__ = ('Context', 'ContextSchema')\n\n\nclass ContextSchema(Schema):\n # Validations to check for the datatype and that it's passed correctly =>\n # will throw exception 'ValidationError' in case of an faulty data parsing\n\n room = fields.Raw(required=True)\n author = fields.Raw(required=True)\n created_at = fields.Str(required=True)\n house = fields.Raw(default=None)\n\n @post_load\n def make(self, data, **kwargs):\n \"\"\"\n Returns an instance of the class using the @classmethod inside the Class to initialise the object\n\n :param data: Dictionary that will be passed to the initialisation\n :param kwargs: Additional Data that can be passed\n :return: A new Context Object\n \"\"\"\n return Context(**data, **kwargs)\n\n\n# Creating a Global Schema for reuse-purposes\nGLOBAL_SCHEMA = ContextSchema()\n\n\nclass Context(HivenObject):\n \"\"\"\n Represents a Command Context for a triggered command in the CommandListener\n \"\"\"\n def __init__(self, **kwargs):\n \"\"\"\n Object Instance Construction\n\n :param kwargs: Additional Parameter of the class that will be initialised with it\n \"\"\"\n self._room = kwargs.get('room')\n self._author = kwargs.get('author')\n self._house = kwargs.get('house')\n self._created_at = kwargs.get('created_at')\n\n @classmethod\n async def from_dict(cls, data: dict, http, **kwargs):\n \"\"\"\n Creates an instance of the Context Class with the passed data\n\n :param data: Dict for the data that should be passed\n :param http: HTTP Client for API-interaction and requests\n :param kwargs: Additional parameter or instances required for the initialisation\n :return: The newly constructed Context Instance\n \"\"\"\n try:\n instance = GLOBAL_SCHEMA.load(data, unknown=EXCLUDE)\n\n except ValidationError as e:\n utils.log_validation_traceback(cls, e)\n raise errs.InvalidPassedDataError(f\"Failed to perform validation in '{cls.__name__}'\", data=data) from e\n\n except Exception as e:\n utils.log_traceback(msg=f\"Traceback in '{cls.__name__}' Validation:\",\n suffix=f\"Failed to initialise {cls.__name__} due to exception:\\n\"\n f\"{sys.exc_info()[0].__name__}: {e}!\")\n raise errs.InitializationError(f\"Failed to initialise {cls.__name__} due to exception:\\n\"\n f\"{sys.exc_info()[0].__name__}: {e}!\") from e\n else:\n # Adding the http attribute for API interaction\n instance._http = http\n\n return instance\n\n @property\n def house(self):\n return self._house\n\n @property\n def author(self):\n return self._author\n\n @property\n def room(self):\n return self._room\n\n @property\n def created_at(self):\n return self._created_at\n","sub_path":"openhivenpy/types/context.py","file_name":"context.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"51606024","text":"import gc\nfrom scipy import sparse\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom utils import raw_data_path, feature_data_path, result_path, cache_pkl_path, dump_pickle, load_pickle\ndef gen_features():\n\n data = load_pickle(raw_data_path+\"preprocess.pkl\")\n print (\"read finish\")\n\n one_hot_feature=[ 'LBS','age','carrier','consumptionAbility','education','gender','house','os','marriageStatus','advertiserId','campaignId', 'creativeId',\n 'adCategoryId', 'productId', 'productType']\n vector_feature=['interest1','interest2','interest5','kw1','kw2','topic1','topic2']\n\n data = data[one_hot_feature + vector_feature + ['label','creativeSize']]\n\n print('start!')\n for feature in one_hot_feature:\n try:\n data[feature] = LabelEncoder().fit_transform(data[feature].apply(int))\n except:\n data[feature] = LabelEncoder().fit_transform(data[feature])\n\n train = data[data.label != -1]\n test = data[data.label == -1]\n test = test.drop('label', axis=1)\n train_y = train.pop('label')\n\n\n train_x = train[['creativeSize']].values\n test_x = test[['creativeSize']].values\n\n oc_encoder = OneHotEncoder()\n for feature in one_hot_feature:\n print (feature)\n gc.collect()\n oc_encoder.fit(data[feature].values.reshape(-1, 1))\n train_a=oc_encoder.transform(train[feature].values.reshape(-1, 1))\n test_a = oc_encoder.transform(test[feature].values.reshape(-1, 1))\n\n train_x = sparse.hstack((train_x, train_a))\n test_x = sparse.hstack((test_x, test_a))\n print('one-hot prepared !')\n\n ct_encoder = CountVectorizer(min_df=0.0009)\n for feature in vector_feature:\n gc.collect()\n print(feature)\n ct_encoder.fit(data[feature])\n train_a = ct_encoder.transform(train[feature])\n test_a = ct_encoder.transform(test[feature])\n\n train_x = sparse.hstack((train_x, train_a))\n test_x = sparse.hstack((test_x, test_a))\n print('cv prepared !')\n\n del data,train,test\n gc.collect()\n\n sparse.save_npz(raw_data_path+'onehot_train.npz', train_x)\n sparse.save_npz(raw_data_path+'onehot_test.npz', test_x)\n\nif __name__== '__main__':\n gen_features()","sub_path":"code/_2_4_gen_onehotFeatures.py","file_name":"_2_4_gen_onehotFeatures.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"415482260","text":"def dedup(items):\r\n\tseen = set()\r\n\tfor item in items:\r\n\t\tif item not in seen:\r\n\t\t\tyield item\r\n\t\t\tseen.add(item)\r\n\tprint(seen)\r\n\r\nlist1 = [1,2,3,5,3,1,5,1,35,6,1,3]\r\ns = list(dedup(list1))\r\nprint(s)","sub_path":"01.my_learn_python/99.test/03.test.py","file_name":"03.test.py","file_ext":"py","file_size_in_byte":197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"283193360","text":"\nimport pickle\nimport numpy as np\n\ndef cos_sim(v1, v2):\n if (np.linalg.norm(v1)*np.linalg.norm(v2) == 0):\n return 0\n return np.dot(v1, v2) / (np.linalg.norm(v1)*np.linalg.norm(v2))\n\nif __name__ == '__main__':\n eng_cos = dict()\n with open('word_context_pca.dump', 'rb') as i_f:\n word_name, pca_matrix = pickle.load(i_f)\n\n vec_Eng = pca_matrix[word_name['England']]\n for word in word_name:\n if word == 'England':\n continue\n vec = pca[word_name[word]]\n sim = cos_sim(vec_Eng, vec)\n eng_cos[word] = sim\n\n count = 0\n for k, v in sorted(eng_cos.items(), key = lambda x:x[1], reverse = True):\n count += 1\n print(count, k, v)\n if count == 10:\n break\n","sub_path":"naruhisa/chapter09/knock88.py","file_name":"knock88.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"140977169","text":"import os\nfrom flask import Flask, flash, Response, request, send_file, redirect, url_for, render_template, send_from_directory\nfrom werkzeug.utils import secure_filename\nfrom PIL import Image\nimport time\nimport json\n\nfrom pubmex.pubmexinference import PubMexInference\n\nUPLOAD_FOLDER = '/home/appuser/detectron2_repo/app/uploads/'\nALLOWED_EXTENSIONS = {'pdf'}\n\napp = Flask(__name__)\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\napp.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0\napp.secret_key = os.urandom(24)\n\n\npubmex = PubMexInference(\n model_dump='/home/appuser/detectron2_repo/app/models/final/model_final.pth', \n config_file='/home/appuser/detectron2_repo/app/configs/final/train_config.yaml',\n use_cuda=False,\n )\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n@app.route('/uploadpdf', methods=[\"GET\", \"POST\"])\ndef upload_file():\n output = {}\n\n if request.method == 'POST':\n if 'file' not in request.files:\n output[\"flash\"] = \"Please upload a PDF document above!\"\n return Response(json.dumps(output), mimetype='text/json')\n \n file = request.files['file']\n print(file.filename)\n if file.filename == '':\n output[\"flash\"] = \"Please upload a PDF document above!\"\n return Response(json.dumps(output), mimetype='text/json')\n if not allowed_file(file.filename):\n output[\"flash\"] = 'Wrong file format. Please upload a PDF document!'\n return Response(json.dumps(output), mimetype='text/json')\n \n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n\n file_saved = False\n waited = 0\n while not file_saved:\n if not os.path.isfile(os.path.join(app.config['UPLOAD_FOLDER'], filename)):\n time.sleep(1)\n waited += 1\n print(\"waiting\")\n else:\n file_saved = True\n if waited > 20:\n return redirect(request.host_url)\n for i in range(3):\n while True:\n try:\n v, metadata = pubmex.predict(app.config['UPLOAD_FOLDER'] + filename)\n img = Image.fromarray(v.get_image()[:, :, ::-1])\n img_path = app.config['UPLOAD_FOLDER'] + filename[:-4] + \".jpg\"\n img.save(img_path)\n output[\"output\"] = metadata\n output[\"image_path\"] = app.config['UPLOAD_FOLDER'] + filename[:-4] + \".jpg\"\n return Response(json.dumps(output), mimetype='text/json')\n except:\n output[\"flash\"] = \"Something went wrong uploading the file - please try again.\"\n if i == 2:\n return Response(json.dumps(output), mimetype='text/json')\n continue\n break\n return render_template(\"index.html\")\n\n@app.route('/deletefile/')\ndef delete_file(filename):\n print(\"remove file\")\n flash(\"Deleted file {}\".format(filename))\n try:\n os.remove(app.config['UPLOAD_FOLDER'] + filename)\n os.remove(app.config['UPLOAD_FOLDER'] + filename[:-4] + \".jpg\")\n\n return redirect(url_for('upload_file', filename=filename)) \n except:\n pass\n\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0')\n\n\n","sub_path":"pubmexwebdemo/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"125376262","text":"import tempfile\nfrom django.conf import settings, global_settings\n\n# Silence the warning about an insecure SECRET_KEY\nglobal_settings.SECRET_KEY = 'SUPER_SAFE_TESTING_KEY'\n\nsettings.configure(default_settings=global_settings)\nfrom graphite.settings import * # noqa\n\nfrom django import VERSION\n\nif VERSION < (1, 6):\n TEST_RUNNER = 'discover_runner.DiscoverRunner'\n\nCACHES = {\n 'default': {\n 'BACKEND': 'django.core.cache.backends.dummy.DummyCache',\n },\n}\n\nLOG_DIR = tempfile.mkdtemp(prefix='graphite-log-test')\nURL_PREFIX = '/graphite'\n","sub_path":"webapp/tests/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"583976659","text":"from PyQt5 import QtCore, QtGui, QtWidgets\r\nimport SQLStatements\r\n\r\n\r\n\r\n\r\nclass Ui_Profile(object):\r\n\r\n\r\n def setupUi(self, progress):\r\n #set window title\r\n progress.setWindowTitle(\"Profile\")\r\n progress.resize(1188, 730)\r\n progress.setStyleSheet(\"background-color: qlineargradient(spread:pad, x1:0.267, y1:0.642, x2:0.778, y2:0.323864, stop:0 rgba(0, 150, 136, 255), stop:1 rgba(63, 81, 181, 255));\\n\"\"\")\r\n self.progress_2 = QtWidgets.QWidget(progress)\r\n QtCore.QMetaObject.connectSlotsByName(progress)\r\n progress.setCentralWidget(self.progress_2)\r\n\r\n self.frameWhiteHeader = QtWidgets.QFrame(self.progress_2)\r\n self.frameWhiteHeader.setGeometry(QtCore.QRect(0, 0, 1191, 51))\r\n self.frameWhiteHeader.setStyleSheet(\"background-color: rgb(255, 255, 255);\")\r\n self.frameWhiteHeader.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frameWhiteHeader.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.frameGreyBackground = QtWidgets.QFrame(self.progress_2)\r\n self.frameGreyBackground.setGeometry(QtCore.QRect(0, 50, 181, 761))\r\n self.frameGreyBackground.setStyleSheet(\"background-color: rgb(96, 125, 139);\")\r\n self.frameGreyBackground.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frameGreyBackground.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.frame_2 = QtWidgets.QFrame(self.progress_2)\r\n self.frame_2.setGeometry(QtCore.QRect(180, 50, 1011, 51))\r\n self.frame_2.setStyleSheet(\"background-color: rgb(224, 224, 224);\")\r\n self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.frameGreyHeader = QtWidgets.QFrame(self.frame_2)\r\n self.frameGreyHeader.setGeometry(QtCore.QRect(140, 50, 1011, 51))\r\n self.frameGreyHeader.setStyleSheet(\"background-color: rgb(240, 240, 240);\")\r\n self.frameGreyHeader.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frameGreyHeader.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frameGreyHeader.setObjectName(\"frameGreyHeader\")\r\n\r\n self.frameUserPhoto = QtWidgets.QFrame(self.frameGreyBackground)\r\n self.frameUserPhoto.setGeometry(QtCore.QRect(30, 40, 120, 120))\r\n self.frameUserPhoto.setStyleSheet(\"image: url(:/images/default-user.png);\\n\"\"border-radius: 90px;\")\r\n self.frameUserPhoto.setFrameShape(QtWidgets.QFrame.NoFrame)\r\n self.frameUserPhoto.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frameUserPhoto.setLineWidth(27)\r\n\r\n self.labelName = QtWidgets.QLabel(self.frameGreyBackground)\r\n self.labelName.setGeometry(QtCore.QRect(50, 170, 100, 21))\r\n self.labelName.setStyleSheet(\"font: 11pt \\\"MS Shell Dlg 2\\\";\\n\"\"color: rgb(255, 255, 255);\")\r\n \r\n #set buttons\r\n self.buttonProfile = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.buttonProfile.setGeometry(QtCore.QRect(0, 0, 85, 51))\r\n self.buttonProfile.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.buttonProfile.setFlat(True)\r\n self.buttonProfile.setText(\" Profile | \")\r\n\r\n self.buttonDiary = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.buttonDiary.setGeometry(QtCore.QRect(88, 0, 90, 51))\r\n self.buttonDiary.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.buttonDiary.setFlat(True)\r\n self.buttonDiary.setText(\"Diary | \")\r\n\r\n self.buttonPlans = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.buttonPlans.setGeometry(QtCore.QRect(145, 0, 85, 51))\r\n self.buttonPlans.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.buttonPlans.setFlat(True)\r\n self.buttonPlans.setText(\" Plans | \")\r\n\r\n self.buttonProgress = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.buttonProgress.setGeometry(QtCore.QRect(230, 0, 100, 51))\r\n self.buttonProgress.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.buttonProgress.setFlat(True)\r\n self.buttonProgress.setText(\" Progress | \")\r\n\r\n self.buttonNutrition = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.buttonNutrition.setGeometry(QtCore.QRect(330, 0, 100, 51))\r\n self.buttonNutrition.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.buttonNutrition.setFlat(True)\r\n self.buttonNutrition.setText(\" Nutrition | \")\r\n\r\n self.signOut = QtWidgets.QPushButton(self.frameWhiteHeader)\r\n self.signOut.setGeometry(QtCore.QRect(1050, 0, 110, 51))\r\n self.signOut.setStyleSheet(\"font: 75 10pt \\\"Lato\\\";\")\r\n self.signOut.setFlat(True)\r\n self.signOut.setText(\" Sign Out\")\r\n\r\n self.buttonLeftArrow = QtWidgets.QPushButton(self.frame_2)\r\n self.buttonLeftArrow.setGeometry(QtCore.QRect(0, 0, 61, 51))\r\n self.buttonLeftArrow.setStyleSheet(\"border: 1px solid #C8C8C8;\")\r\n self.buttonLeftArrow.setFlat(True)\r\n self.buttonLeftArrow.setText(\"<\")\r\n\r\n self.buttonRightArrow = QtWidgets.QPushButton(self.frame_2)\r\n self.buttonRightArrow.setGeometry(QtCore.QRect(950, 0, 61, 51))\r\n self.buttonRightArrow.setStyleSheet(\"border: 1px solid #C8C8C8;\")\r\n self.buttonRightArrow.setFlat(True)\r\n self.buttonRightArrow.setText(\">\")\r\n\r\n self.frameGreyHeader = QtWidgets.QFrame(self.frame_2)\r\n self.frameGreyHeader.setGeometry(QtCore.QRect(140, 50, 1011, 51))\r\n self.frameGreyHeader.setStyleSheet(\"background-color: rgb(240, 240, 240);\")\r\n self.frameGreyHeader.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frameGreyHeader.setFrameShadow(QtWidgets.QFrame.Raised)\r\n\r\n self.dateEdit = QtWidgets.QDateEdit(self.frame_2)\r\n self.dateEdit.setGeometry(QtCore.QRect(410, 10, 181, 31))\r\n self.dateEdit.setDateTime(QtCore.QDateTime.currentDateTime())\r\n\r\n self.frameWhiteHeader.raise_()\r\n self.frameGreyBackground.raise_()\r\n self.frame_2.raise_()\r\n \r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n progress = QtWidgets.QMainWindow()\r\n ui = Ui_Profile()\r\n ui.setupUi(progress)\r\n progress.show()\r\n sys.exit(app.exec_())\r\n","sub_path":"Profile.py","file_name":"Profile.py","file_ext":"py","file_size_in_byte":6592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"283816311","text":"\nimport argparse\nimport os\nimport warnings\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.exceptions import DataConversionWarning\nfrom sklearn.compose import make_column_transformer\n\nimport boto3\nos.system('pip install sagemaker')\nimport sagemaker\nfrom sagemaker import get_execution_role\n\nwarnings.filterwarnings(action='ignore', category=DataConversionWarning)\n\nif __name__=='__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--train-test-split-ratio', type=float, default=0.3)\n parser.add_argument('--random-split', type=int, default=0)\n args, _ = parser.parse_known_args()\n \n print('Received arguments {}'.format(args))\n\n input_data_path = os.path.join('/opt/ml/processing/input', 'rawdata.csv')\n \n print('Reading input data from {}'.format(input_data_path))\n df = pd.read_csv(input_data_path)\n df.sample(frac=1)\n \n COLS = df.columns\n #newcolorder = ['PAY_AMT1','BILL_AMT1'] + list(COLS[1:])[:11] + list(COLS[1:])[12:17] + list(COLS[1:])[18:]\n newcolorder = list(COLS[1:])\n rest_col = newcolorder[:11]\n bill_col = newcolorder[11:17]\n pay_col = newcolorder[17:]\n \n \n split_ratio = args.train_test_split_ratio\n random_state=args.random_split\n \n #print('DF', df)\n \n X_train, X_test, y_train, y_test = train_test_split(df.drop('Label', axis=1), df['Label'], \n test_size=split_ratio,\n random_state=random_state)\n \n #print('X_TRAIN',X_train)\n #NOTE:, random_state=random_state\n #split into X_train_pay and X_train_bill\n X_train_rest = X_train.iloc[:,:11]\n X_train_bill = X_train.iloc[:,11:17]\n X_train_pay = X_train.iloc[:,17:] \n \n #print('TRAIN_BILL',X_train_bill.head())\n #print('TRAIN_PAY',X_train_pay.head())\n #split into X_test_pay and X_test_bill\n X_test_rest = X_test.iloc[:,:11]\n X_test_bill = X_test.iloc[:,11:17]\n X_test_pay = X_test.iloc[:,17:] \n \n #define scaler \n bill_scaler = MinMaxScaler()\n pay_scaler = StandardScaler()\n\n # execute fit_transform on train\n X_train_bill_scaled = bill_scaler.fit_transform(X_train_bill)\n X_train_pay_scaled = pay_scaler.fit_transform(X_train_pay)\n print('Train_rest',X_train_rest)\n print('TRAIN_BILL_scaled',X_train_bill_scaled)\n print('TRAIN_PAY_scaled',X_train_pay_scaled)\n \n #execute transform on test\n X_test_bill_scaled = bill_scaler.transform(X_test_bill)\n X_test_pay_scaled = pay_scaler.transform(X_test_pay)\n #print('TEST_BILL_scaled',X_test_bill_scaled)\n #print('TEST_PAY_scaled',X_test_pay_scaled)\n #print(type(X_train_rest))\n #print(type(X_train_bill_scaled))\n \"\"\"\n #not working\n preprocess = make_column_transformer(\n (['PAY_AMT1'], StandardScaler()),\n (['BILL_AMT1'], MinMaxScaler()),\n remainder='passthrough')\n \n print('Running preprocessing and feature engineering transformations')\n print(newcolorder)\n\n train_features = pd.DataFrame(preprocess.fit_transform(X_train), columns = newcolorder)\n test_features = pd.DataFrame(preprocess.transform(X_test), columns = newcolorder)\n \"\"\"\n #print('X_train_rest',X_train_rest)\n train_index = X_train_rest.index\n train_features = X_train_rest\n train_features = train_features.join(pd.DataFrame(X_train_bill_scaled,columns=bill_col, index=train_index))\n train_features = train_features.join(pd.DataFrame(X_train_pay_scaled, columns=pay_col, index=train_index))\n test_features = X_test_rest \n test_index = X_test_rest.index\n test_features = test_features.join(pd.DataFrame(X_test_bill_scaled,columns=bill_col, index=test_index))\n test_features = test_features.join(pd.DataFrame(X_test_pay_scaled, columns=pay_col, index=test_index))\n \n \n print('train_features',train_features)\n #print('test_features',test_features)\n \n # concat to ensure Label column is the first column in dataframe\n print('y_train',y_train)\n train_full = pd.DataFrame(y_train.values, columns=['Label'],index=train_index).join(train_features)\n test_full = pd.DataFrame(y_test.values, columns=['Label'],index=test_index).join(test_features)\n \n print('TRAIN_FULL',train_full)\n print('TEST_FULL',test_full)\n print('Train data shape after preprocessing: {}'.format(train_features.shape))\n print('Test data shape after preprocessing: {}'.format(test_features.shape))\n \n train_features_headers_output_path = os.path.join('/opt/ml/processing/train_headers', 'train_data_with_headers.csv')\n \n train_features_output_path = os.path.join('/opt/ml/processing/train', 'train_data.csv')\n \n test_features_output_path = os.path.join('/opt/ml/processing/test', 'test_data.csv')\n \n print('Saving training features to {}'.format(train_features_output_path))\n train_full.to_csv(train_features_output_path, header=False, index=False)\n print(\"Complete\")\n \n print(\"Save training data with headers to {}\".format(train_features_headers_output_path))\n train_full.to_csv(train_features_headers_output_path, index=False)\n \n print('Saving test features to {}'.format(test_features_output_path))\n test_full.to_csv(test_features_output_path, header=False, index=False)\n print(\"Complete\")\n\n \n \n \n \n #uploading testdata to s3 for monitoring demonstration \n \n\n\n\n os.environ['AWS_DEFAULT_REGION'] = 'ap-southeast-1'\n role = get_execution_role()\n sess = sagemaker.Session()\n region = boto3.session.Session().region_name\n print(\"Region = {}\".format(region))\n sm = boto3.Session().client('sagemaker')\n \n rawbucket = 'sagemaker-ap-southeast-1-692165707308'\n prefix = 'sagemaker-modelmonitor' # use this prefix to store all files pertaining to this workshop.\n dataprefix = prefix + '/data'\n testdataprefix = prefix + '/test_data'\n \n \n \n sess.upload_data(test_features_output_path,bucket=rawbucket,key_prefix=dataprefix)","sub_path":"mlops-pipeline-demo/sagemaker-pipeline-demo-1-p-kjjql6k63ho9-modelbuild/pipelines/credit/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":6119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"286157680","text":"import numpy as np\nimport pywt\nimport cv2\nfrom PIL import Image\nfrom matplotlib import pyplot as plt\n\ndef w2d(img, mode='symmetric' , level=1):\n imArray = cv2.imread(img)\n #Datatype conversions\n #convert to grayscale\n imArray = cv2.cvtColor( imArray,cv2.COLOR_RGB2GRAY )\n #convert to float\n imArray = np.float32(imArray)\n imArray /= 255\n # compute coefficients\n coeffs=pywt.wavedec2(imArray, mode, level=level)\n\n #Process Coefficients\n coeffs_H=list(coeffs)\n coeffs_H[0] *= 0\n\n # reconstruction\n imArray_H=pywt.waverec2(coeffs_H, mode)\n imArray_H *= 255\n i = Image.fromarray(imArray_H)\n plt.imshow(i, cmap='Greys_r')\n plt.show()\n\nw2d('./imagenes/mdb001.png','db1',8)","sub_path":"PROCESAMIENTO_DE_IMAGENES/PROYECTO/pruebas2.py","file_name":"pruebas2.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"152037490","text":"#!/usr/bin/env python\r\n\"\"\"A representation of the Tissue used in an IP Experiment. \r\n\"\"\"\r\n# load the modules \r\nfrom __future__ import annotations\r\nimport pandas as pd\r\nimport numpy as np \r\nfrom typing import Union, List, Dict \r\nfrom IPTK.Classes.Database import CellularLocationDB, GeneExpressionDB\r\n# define the tissue class \r\nclass ExpressionProfile: \r\n\t\"\"\"a representation of tissue reference expression value. \r\n\t\"\"\"\r\n\tdef __init__(self, name: str,\r\n\t \texpression_table: pd.DataFrame, \r\n\t\taux_proteins: pd.DataFrame = None)->ExpressionProfile: \r\n\t\t\"\"\"Create an expression profile instance from an expression table. \r\n\r\n\t\t:param name: the name of the tissue \r\n\t\t:type name: str\r\n\t\t:param expression_table: A dataframe with the following three columns gene id, gene name, and the expression value. \r\n\t\t:type expression_table: pd.DataFrame\r\n\t\t:param aux_proteins: A table that contain the expression table of auxillary proteins that does not belong to the tissue per sa,\r\n\t\tfor example, pathogen-derived genes, defaults to None\r\n\t\t:type: pd.DataFrame, optional\r\n\t\t:raises ValueError: if the tissue name is None\r\n\t\t:raises ValueError: if the expression table is not a pandas dataframe instance \r\n\t\t:raises ValueError: if the expression table does not have the correct shape \r\n\t\t:raises ValueError: if the aux_proteins does not have the correct shape\r\n\t\t:return: an expression profile instance \r\n\t\t:rtype: ExpressionProfile\r\n\t\t\"\"\"\r\n\t\tif name is not None:\r\n\t\t\tself._name=name\r\n\t\telse:\r\n\t\t\traise ValueError(f'The tissue name can not be None')\r\n\t\t\r\n\t\tif not isinstance(expression_table, pd.DataFrame):\r\n\t\t\traise ValueError(f'Expression table must be a pd.DataFrame instance, however, your input is of type: {type(expression_table)}')\r\n\t\t\r\n\t\tif expression_table.shape[1] != 3: \r\n\t\t\traise ValueError(f\"The provided expression table must have three columns, however, your table have: {expression_table.shape[1]}\")\r\n\t\t\r\n\t\tself._exp_map=expression_table\r\n\t\t# set the columns name \r\n\t\tself._exp_map.columns=['gene', 'gene_name','exp_value']\r\n\t\t# parse the auxillary table \r\n\t\tif aux_proteins is not None:\r\n\t\t\tif aux_proteins.shape[1]!=3:\r\n\t\t\t\traise ValueError(f'The provided auxillary proteins table must have three columns, however, your table have: {aux_proteins.shape[1]}')\r\n\t\t\taux_proteins.columns=['gene', 'gene_name','exp_value']\r\n\t\t\t# concatenate the results \r\n\t\t\tself._exp_map=pd.concat([self._exp_map,aux_proteins],axis=0)\r\n\t\treturn\r\n\r\n\tdef get_gene_id_expression(self,gene_id: str)-> float: \r\n\t\t\"\"\"\r\n\t\t:param gene_id: the gene id to retrive its expression value from the database\r\n\t\t:type gene_id: str\r\n\t\t:raises KeyError: if the provided id is not defined in the instance table \r\n\t\t:return: the expression value of the provided gene id.\r\n\t\t:rtype: float\r\n\t\t\"\"\"\r\n\t\tif gene_id not in self._exp_map.iloc[:,0].tolist(): \r\n\t\t\traise KeyError(f\"The provided gene id: {gene_id} is not defined in the database\")\r\n\t\treturn self._exp_map.loc[self._exp_map.iloc[:,0]==gene_id,:].iloc[0,2]\r\n\t\r\n\tdef get_gene_name_expression(self,gene_name: str)-> float: \r\n\t\t\"\"\"\r\n\t\t:param gene_name: the gene name to retrive its expression value from the database\r\n\t\t:type gene_name: str\r\n\t\t:raises KeyError: if the provided id is not defined in the instance table \r\n\t\t:return: the expression value of the provided gene name. \r\n\t\t:rtype: float\r\n\t\t\"\"\"\r\n\t\tif gene_name not in self._exp_map.iloc[:,1].tolist(): \r\n\t\t\traise KeyError(f\"The provided gene name: {gene_name} is not defined in the database.\")\r\n\t\treturn self._exp_map.loc[self._exp_map.iloc[:,1]==gene_name,:].iloc[0,2]\r\n\r\n\tdef get_name(self)->str:\r\n\t\t\"\"\"\r\n\t\t:return: the name of the tissue where the expression profile was obtained\r\n\t\t:rtype: str\r\n\t\t\"\"\"\r\n\t\treturn self._name\r\n\r\n\tdef get_table(self)->pd.DataFrame:\r\n\t\t\"\"\"\r\n\t\t:return: return a table that contain the expression of all the transcripts in the current profile \\\r\n\t\tincluding core and auxiliary proteins\r\n\t\t:rtype: pd.DataFrame\r\n\t\t\"\"\"\r\n\t\treturn self._exp_map\r\n\t\r\n\tdef __len__(self)->int: \r\n\t\t\"\"\"\r\n\t\t:return: return the number of unique transcripts in the profile \r\n\t\t:rtype: int\r\n\t\t\"\"\"\r\n\t\treturn len(set(self._exp_map.shape[0]))\r\n\r\n\tdef __str__(self)->str:\r\n\t\t\"\"\" \r\n\t\t:return: a string representation for the class instance \r\n\t\t:rtype: str\r\n\t\t\"\"\"\r\n\t\treturn f'{self._name} with an expression profile covering {self._exp_map.shape[0]} genes.'\r\n\t\r\n\tdef __repr__(self)->str:\r\n\t\treturn str(self)\r\n\r\nclass Tissue:\r\n\tdef __init__(self, name: str, main_exp_value: GeneExpressionDB, \r\n\tmain_location: CellularLocationDB, aux_exp_value: GeneExpressionDB = None,\r\n\taux_location: CellularLocationDB = None) -> Tissue:\r\n\t\t\"\"\"The initializer of the Tissue class \r\n\r\n\t\t:param name: The name of the tissue \r\n\t\t:type name: str\r\n\t\t:param main_exp_value: A GeneExpressionDB instace containing the gene expression accross different tissues \r\n\t\t:type main_exp_value: GeneExpressionDB\r\n\t\t:param main_location: A CellularLocationDB instance that contain the sub cellular locations for the proteins expressed in the tissue\r\n\t\t:type main_location: A CellularLocationDB \r\n\t\t:param aux_exp_value: A GeneExpressionDB instance that contain the expression table of auxillary proteins that does not belong to the tissue per sa,\r\n\t \tfor example, pathogen-derived genes or extra-cellular matrix, defaults to None.\r\n\t\t:type aux_exp_value: GeneExpressionDB, optional\r\n\t\t:param aux_location: CellularLocationDB instance that contain the sub cellular locations for proteins that does not belong to the tissue of interest per sa\r\n\t \tfor example, pathogen-derived proteins or media-added proteins, defaults to None\r\n\t\t:type aux_location: CellularLocationDB, optional\r\n\t\t:raises KeyError: if the provided tissue name is not defined in the gene expression database \r\n\t\t:return: [description]\r\n\t\t:rtype: Tissue\r\n\t\t\"\"\"\r\n\t\tif name not in main_exp_value.get_tissues():\r\n\t\t\traise KeyError(f'The provided tissue name: {name} is not in the provided main expression database')\r\n\t\t# add the expression profile \r\n\t\tif aux_exp_value is not None: \r\n\t\t\tself._exp_prof: ExpressionProfile = ExpressionProfile(name=name,\r\n\t\t\texpression_table=main_exp_value.get_expression_in_tissue(name),\r\n\t\t\taux_proteins=aux_exp_value.get_table())\r\n\t\telse: \r\n\t\t\tself._exp_prof: ExpressionProfile = ExpressionProfile(\r\n\t\t\t\tname=name, expression_table=main_exp_value.get_expression_in_tissue(name))\t\r\n\t\t# add the cellular location profile: \r\n\t\tself._cell_loc = main_location\r\n\t\tif aux_location is not None:\r\n\t\t\tself._cell_loc.add_to_database(aux_location)\r\n\t\r\n\tdef get_expression_profile(self)->ExpressionProfile: \r\n\t\t\"\"\"\r\n\t\t:return: the expresion profile of the current tissue \r\n\t\t:rtype: ExpressionProfile\r\n\t\t\"\"\"\r\n\t\treturn self._exp_prof\r\n\t\r\n\tdef get_name(self)->str:\r\n\t\t\"\"\"\r\n\t\t:return: the name of the tissue \r\n\t\t:rtype: str\r\n\t\t\"\"\"\r\n\t\treturn self._exp_prof.get_name()\r\n\r\n\tdef get_subCellular_locations(self) ->CellularLocationDB:\r\n\t\t\"\"\" \r\n\t\t:return: the sub-cellular localization of all the proteins stored in current instance resources. \r\n\t\t:rtype: CellularLocationDB\r\n\t\t\"\"\"\r\n\t\treturn self._cell_loc\r\n\t\r\n\tdef __str__(self)->str:\r\n\t\t\"\"\"\r\n\t\t:return: a string representation for the current instance\r\n\t\t:rtype: str\r\n\t\t\"\"\"\r\n\t\treturn f'{self._exp_prof.get_name()} with an associated expression profile covering: {len(self._exp_prof)} genes and a sub-cellular location covering: {len(self._cell_loc)} genes.'\r\n\t\t","sub_path":"lib_exp_acc/IPTK/Classes/Tissue.py","file_name":"Tissue.py","file_ext":"py","file_size_in_byte":7332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"193207607","text":"import torch\nimport torch.nn as nn\nfrom torch.nn.parallel import data_parallel\nfrom torch.autograd import Variable\nfrom torch.nn.functional import log_softmax\nfrom seq2seq.tools.utils import batch_padded_sequences\n\n\nclass Seq2Seq(nn.Module):\n\n def __init__(self, encoder=None, decoder=None, bridge=None, batch_first=False):\n super(Seq2Seq, self).__init__()\n self.encoder = encoder\n self.decoder = decoder\n if bridge is not None:\n self.bridge = bridge\n self.batch_first = batch_first\n\n def encode(self, inputs, hidden=None, devices=None):\n if isinstance(devices, tuple):\n return data_parallel(self.encoder, (inputs, hidden),\n device_ids=devices,\n dim=0 if self.batch_first else 1)\n else:\n return self.encoder(inputs, hidden)\n\n def decode(self, inputs, context, get_attention=None, devices=None):\n if isinstance(devices, tuple):\n inputs = (inputs, context, get_attention) if get_attention else (\n inputs, context)\n return data_parallel(self.decoder, inputs,\n device_ids=devices,\n dim=0 if self.batch_first else 1)\n else:\n if get_attention:\n return self.decoder(inputs, context, get_attention=get_attention)\n else:\n return self.decoder(inputs, context)\n\n def forward(self, input_encoder, input_decoder, encoder_hidden=None, devices=None):\n if not isinstance(devices, dict):\n devices = {'encoder': devices, 'decoder': devices}\n context = self.encode(input_encoder, encoder_hidden,\n devices=devices.get('encoder', None))\n if hasattr(self, 'bridge'):\n context = self.bridge(context)\n output, hidden = self.decode(\n input_decoder, context, devices=devices.get('decoder', None))\n return output\n\n def clear_state(self):\n pass\n\n def generate(self, input_list, state_list, k=1, feed_all_timesteps=False, get_attention=False):\n # assert isinstance(input_list, list) or isinstance(input_list, tuple)\n # assert isinstance(input_list[0], list) or isinstance(\n # input_list[0], tuple)\n\n time_dim = 1 if self.batch_first else 0\n view_shape = (-1, 1) if self.batch_first else (1, -1)\n\n # For recurrent models, the last input frame is all we care about,\n # use feed_all_timesteps whenever the whole input needs to be fed\n if feed_all_timesteps:\n inputs = [torch.LongTensor(inp) for inp in input_list]\n inputs = batch_padded_sequences(\n inputs, batch_first=self.batch_first)\n else:\n inputs = torch.LongTensor(\n [inputs[-1] for inputs in input_list]).view(*view_shape)\n\n inputs_var = Variable(inputs, volatile=True)\n if next(self.decoder.parameters()).is_cuda:\n inputs_var = inputs_var.cuda()\n states = self.merge_states(state_list)\n\n if get_attention:\n logits, new_states, attention = self.decode(\n inputs_var, states, get_attention=True)\n attention = attention.select(time_dim, -1).data\n else:\n attention = None\n logits, new_states = self.decode(inputs_var, states)\n # use only last prediction\n logits = logits.select(time_dim, -1).contiguous()\n logprobs = log_softmax(logits.view(-1, logits.size(-1)))\n logprobs, words = logprobs.data.topk(k, 1)\n new_states = [self.select_state(new_states, i)\n for i in range(len(input_list))]\n return words, logprobs, new_states, attention\n\n def merge_states(self, state_list):\n if isinstance(state_list[0], tuple):\n return tuple([self.merge_states(s) for s in zip(*state_list)])\n else:\n if state_list[0] is None:\n return None\n if state_list[0].dim() == 3 and not self.batch_first:\n batch_dim = 1\n else:\n batch_dim = 0\n return torch.cat(state_list, batch_dim)\n\n def select_state(self, state, i):\n if isinstance(state, tuple):\n return tuple(self.select_state(s, i) for s in state)\n else:\n if state is None:\n return None\n if state.dim() == 3 and not self.batch_first:\n batch_dim = 1\n else:\n batch_dim = 0\n return state.narrow(batch_dim, i, 1)\n","sub_path":"seq2seq/models/seq2seq.py","file_name":"seq2seq.py","file_ext":"py","file_size_in_byte":4625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"227843742","text":"import re\nimport asyncio\nimport logging\nimport time\nimport ipaddress\nimport os\nimport pathlib\nimport inspect\nimport uuid\nimport colorama\nimport functools\nfrom logging.handlers import WatchedFileHandler\nfrom typing import Any, Dict, List, Tuple, Union, Optional, Callable, SupportsInt, Awaitable, Mapping, Iterable # noqa\nfrom multidict import CIMultiDict, CIMultiDictProxy\nfrom aiohttp import web, web_server, web_protocol, web_urldispatcher, hdrs, WSMsgType\nfrom aiohttp.web_fileresponse import FileResponse\nfrom aiohttp.http import HttpVersion\nfrom aiohttp.helpers import BasicAuth\nfrom aiohttp.streams import EofStream\nfrom tomodachi.invoker import Invoker\nfrom tomodachi.helpers.dict import merge_dicts\nfrom tomodachi.helpers.middleware import execute_middlewares\n\n\nclass HttpException(Exception):\n def __init__(self, *args: Any, **kwargs: Any) -> None:\n self._log_level = kwargs.get('log_level') if kwargs and kwargs.get('log_level') else 'INFO'\n\n\nclass RequestHandler(web_protocol.RequestHandler): # type: ignore\n def __init__(self, *args: Any, **kwargs: Any) -> None:\n self._server_header = kwargs.pop('server_header', None) if kwargs else None\n self._access_log = kwargs.pop('access_log', None) if kwargs else None\n super().__init__(*args, **kwargs) # type: ignore\n\n @staticmethod\n def get_request_ip(request: Any, context: Optional[Dict] = None) -> Optional[str]:\n if request._cache.get('request_ip'):\n return str(request._cache.get('request_ip', ''))\n\n if request.transport:\n if not context:\n context = {}\n real_ip_header = context.get('options', {}).get('http', {}).get('real_ip_header', 'X-Forwarded-For')\n real_ip_from = context.get('options', {}).get('http', {}).get('real_ip_from', [])\n if isinstance(real_ip_from, str):\n real_ip_from = [real_ip_from]\n\n peername = request.transport.get_extra_info('peername')\n request_ip = None\n if peername:\n request_ip, _ = peername\n if real_ip_header and real_ip_from and request.headers.get(real_ip_header) and request_ip and len(real_ip_from):\n if any([ipaddress.ip_address(request_ip) in ipaddress.ip_network(cidr) for cidr in real_ip_from]):\n request_ip = request.headers.get(real_ip_header).split(',')[0].strip().split(' ')[0].strip()\n\n request._cache['request_ip'] = request_ip\n return request_ip\n\n return None\n\n @staticmethod\n def colorize_status(text: Optional[Union[str, int]], status: Optional[Union[str, int, bool]] = False) -> str:\n if status is False:\n status = text\n status_code = str(status) if status else None\n if status_code and not logging.getLogger('transport.http').handlers:\n output_text = str(text) if text else ''\n color = None\n\n if status_code == '101':\n color = colorama.Fore.CYAN\n elif status_code[0] == '2':\n color = colorama.Fore.GREEN\n elif status_code[0] == '3' or status_code == '499':\n color = colorama.Fore.YELLOW\n elif status_code[0] == '4':\n color = colorama.Fore.RED\n elif status_code[0] == '5':\n color = colorama.Fore.WHITE + colorama.Back.RED\n\n if color:\n return '{}{}{}'.format(color, output_text, colorama.Style.RESET_ALL)\n return output_text\n\n return str(text) if text else ''\n\n def handle_error(self, request: Any, status: int = 500, exc: Any = None, message: Optional[str] = None) -> web.Response:\n \"\"\"Handle errors.\n\n Returns HTTP response with specific status code. Logs additional\n information. It always closes current connection.\"\"\"\n if self.transport is None:\n # client has been disconnected during writing.\n if self._access_log:\n request_ip = RequestHandler.get_request_ip(request, None)\n version_string = None\n if isinstance(request.version, HttpVersion):\n version_string = 'HTTP/{}.{}'.format(request.version.major, request.version.minor)\n logging.getLogger('transport.http').info('[{}] [{}] {} {} \"{} {}{}{}\" - {} \"{}\" -'.format(\n RequestHandler.colorize_status('http', 499),\n RequestHandler.colorize_status(499),\n request_ip or '',\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n request.method,\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n ' {}'.format(version_string) if version_string else '',\n request.content_length if request.content_length is not None else '-',\n request.headers.get('User-Agent', '').replace('\"', '')\n ))\n\n headers = {}\n headers[hdrs.CONTENT_TYPE] = 'text/plain; charset=utf-8'\n\n msg = '' if status == 500 or not message else message\n\n headers[hdrs.CONTENT_LENGTH] = str(len(msg))\n headers[hdrs.SERVER] = self._server_header or ''\n resp = web.Response(status=status, # type: ignore\n text=msg,\n headers=headers) # type: web.Response\n resp.force_close() # type: ignore\n\n # some data already got sent, connection is broken\n if request.writer.output_size > 0 or self.transport is None:\n self.force_close() # type: ignore\n elif self.transport is not None:\n request_ip = RequestHandler.get_request_ip(request, None)\n if not request_ip:\n peername = request.transport.get_extra_info('peername')\n if peername:\n request_ip, _ = peername\n if self._access_log:\n logging.getLogger('transport.http').info('[{}] [{}] {} {} \"INVALID\" {} - \"\" -'.format(\n RequestHandler.colorize_status('http', status),\n RequestHandler.colorize_status(status),\n request_ip or '',\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n len(msg)\n ))\n\n return resp\n\n\nclass Server(web_server.Server): # type: ignore\n def __init__(self, *args: Any, **kwargs: Any) -> None:\n self._server_header = kwargs.pop('server_header', None) if kwargs else None\n self._access_log = kwargs.pop('access_log', None) if kwargs else None\n super().__init__(*args, **kwargs) # type: ignore\n\n def __call__(self) -> RequestHandler:\n return RequestHandler(\n self, loop=self._loop, server_header=self._server_header, access_log=self._access_log,\n **self._kwargs)\n\n\nclass DynamicResource(web_urldispatcher.DynamicResource): # type: ignore\n def __init__(self, pattern: Any, *, name: Optional[str] = None) -> None:\n self._routes = [] # type: List\n self._name = name\n self._pattern = pattern\n self._formatter = ''\n\n\nclass Response(object):\n def __init__(self, *, body: Optional[Union[bytes, str]] = None, status: int = 200, reason: Optional[str] = None, headers: Optional[Union[Dict, CIMultiDict, CIMultiDictProxy]] = None, content_type: Optional[str] = None, charset: Optional[str] = None) -> None:\n if headers is None:\n headers = CIMultiDict()\n elif not isinstance(headers, (CIMultiDict, CIMultiDictProxy)):\n headers = CIMultiDict(headers)\n\n self._body = body\n self._status = status\n self._reason = reason\n self._headers = headers\n self.content_type = content_type if hdrs.CONTENT_TYPE not in headers else None\n self.charset = charset if hdrs.CONTENT_TYPE not in headers else None\n\n self.missing_content_type = hdrs.CONTENT_TYPE not in headers and not content_type and not charset\n\n def get_aiohttp_response(self, context: Dict, default_charset: Optional[str] = None, default_content_type: Optional[str] = None) -> web.Response:\n if self.missing_content_type:\n self.charset = default_charset\n self.content_type = default_content_type\n\n charset = self.charset\n if hdrs.CONTENT_TYPE in self._headers and ';' in self._headers[hdrs.CONTENT_TYPE]:\n try:\n charset = str([v for v in self._headers[hdrs.CONTENT_TYPE].split(';') if 'charset=' in v][0]).replace('charset=', '').strip()\n except IndexError:\n pass\n elif hdrs.CONTENT_TYPE in self._headers and ';' not in self._headers[hdrs.CONTENT_TYPE]:\n charset = None\n\n if self._body and not isinstance(self._body, bytes) and charset:\n body = self._body\n try:\n body_value = body.encode(charset.lower())\n except (ValueError, LookupError, UnicodeEncodeError) as e:\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(e)))\n raise web.HTTPInternalServerError() from e # type: ignore\n elif self._body:\n body_value = self._body.encode() if not isinstance(self._body, bytes) else self._body\n else:\n body_value = b''\n\n response = web.Response(body=body_value, # type: ignore\n status=self._status,\n reason=self._reason,\n headers=self._headers,\n content_type=self.content_type,\n charset=self.charset) # type: web.Response\n return response\n\n\nclass HttpTransport(Invoker):\n async def request_handler(cls: Any, obj: Any, context: Dict, func: Any, method: str, url: str, ignore_logging: Union[bool, List[int], Tuple[int]] = False, pre_handler_func: Optional[Callable] = None) -> Any:\n pattern = r'^{}$'.format(re.sub(r'\\$$', '', re.sub(r'^\\^?(.*)$', r'\\1', url)))\n compiled_pattern = re.compile(pattern)\n\n default_content_type = context.get('options', {}).get('http', {}).get('content_type', 'text/plain')\n default_charset = context.get('options', {}).get('http', {}).get('charset', 'utf-8')\n\n if default_content_type is not None and \";\" in default_content_type:\n # for backwards compability\n try:\n default_charset = str([v for v in default_content_type.split(';') if 'charset=' in v][0]).replace('charset=', '').strip()\n default_content_type = str([v for v in default_content_type.split(';')][0]).strip()\n except IndexError:\n pass\n\n async def handler(request: web.Request) -> Union[web.Response, web.FileResponse]:\n result = compiled_pattern.match(request.path)\n values = inspect.getfullargspec(func)\n kwargs = {k: values.defaults[i] for i, k in enumerate(values.args[len(values.args) - len(values.defaults):])} if values.defaults else {}\n if result:\n for k, v in result.groupdict().items():\n kwargs[k] = v\n\n @functools.wraps(func)\n async def routine_func(*a: Any, **kw: Any) -> Union[str, bytes, Dict, List, Tuple, web.Response, Response]:\n routine = func(*(obj, request, *a), **merge_dicts(kwargs, kw))\n return_value = (await routine) if isinstance(routine, Awaitable) else routine # type: Union[str, bytes, Dict, List, Tuple, web.Response, Response]\n return return_value\n\n if pre_handler_func:\n await pre_handler_func(obj, request)\n\n return_value = await execute_middlewares(func, routine_func, context.get('http_middleware', []), *(obj, request))\n response = await resolve_response(return_value, request=request, context=context, default_content_type=default_content_type, default_charset=default_charset)\n return response\n\n context['_http_routes'] = context.get('_http_routes', [])\n route_context = {'ignore_logging': ignore_logging}\n if isinstance(method, list) or isinstance(method, tuple):\n for m in method:\n context['_http_routes'].append((m.upper(), pattern, handler, route_context))\n else:\n context['_http_routes'].append((method.upper(), pattern, handler, route_context))\n\n start_func = cls.start_server(obj, context)\n return (await start_func) if start_func else None\n\n async def static_request_handler(cls: Any, obj: Any, context: Dict, func: Any, path: str, base_url: str, ignore_logging: Union[bool, List[int], Tuple[int]] = False) -> Any:\n if '?P' not in base_url:\n pattern = r'^{}(?P.+?)$'.format(re.sub(r'\\$$', '', re.sub(r'^\\^?(.*)$', r'\\1', base_url)))\n else:\n pattern = r'^{}$'.format(re.sub(r'\\$$', '', re.sub(r'^\\^?(.*)$', r'\\1', base_url)))\n compiled_pattern = re.compile(pattern)\n\n if path.startswith('/'):\n path = os.path.dirname(path)\n else:\n path = '{}/{}'.format(os.path.dirname(context.get('context', {}).get('_service_file_path')), path)\n\n if not path.endswith('/'):\n path = '{}/'.format(path)\n\n async def handler(request: web.Request) -> web.Response:\n result = compiled_pattern.match(request.path)\n filename = result.groupdict()['filename'] if result else ''\n filepath = '{}{}'.format(path, filename)\n\n try:\n if os.path.commonprefix((os.path.realpath(filepath), os.path.realpath(path))) != os.path.realpath(path) or os.path.isdir(filepath) or not os.path.exists(filepath):\n raise web.HTTPNotFound() # type: ignore\n\n pathlib.Path(filepath).open('r')\n\n response = FileResponse(path=filepath, # type: ignore\n chunk_size=256 * 1024) # type: web.Response\n return response\n except PermissionError as e:\n raise web.HTTPForbidden() # type: ignore\n\n route_context = {'ignore_logging': ignore_logging}\n context['_http_routes'] = context.get('_http_routes', [])\n context['_http_routes'].append(('GET', pattern, handler, route_context))\n\n start_func = cls.start_server(obj, context)\n return (await start_func) if start_func else None\n\n async def error_handler(cls: Any, obj: Any, context: Dict, func: Any, status_code: int) -> Any:\n default_content_type = context.get('options', {}).get('http', {}).get('content_type', 'text/plain')\n default_charset = context.get('options', {}).get('http', {}).get('charset', 'utf-8')\n\n if default_content_type is not None and \";\" in default_content_type:\n # for backwards compability\n try:\n default_charset = str([v for v in default_content_type.split(';') if 'charset=' in v][0]).replace('charset=', '').strip()\n default_content_type = str([v for v in default_content_type.split(';')][0]).strip()\n except IndexError:\n pass\n\n async def handler(request: web.Request) -> web.Response:\n request._cache['error_status_code'] = status_code\n\n values = inspect.getfullargspec(func)\n kwargs = {k: values.defaults[i] for i, k in enumerate(values.args[len(values.args) - len(values.defaults):])} if values.defaults else {}\n\n @functools.wraps(func)\n async def routine_func(*a: Any, **kw: Any) -> Union[str, bytes, Dict, List, Tuple, web.Response, Response]:\n routine = func(*(obj, request, *a), **merge_dicts(kwargs, kw))\n return_value = (await routine) if isinstance(routine, Awaitable) else routine # type: Union[str, bytes, Dict, List, Tuple, web.Response, Response]\n return return_value\n\n return_value = await execute_middlewares(func, routine_func, context.get('http_middleware', []), *(obj, request))\n response = await resolve_response(return_value, request=request, context=context, status_code=status_code, default_content_type=default_content_type, default_charset=default_charset)\n return response\n\n context['_http_error_handler'] = context.get('_http_error_handler', {})\n context['_http_error_handler'][int(status_code)] = handler\n\n start_func = cls.start_server(obj, context)\n return (await start_func) if start_func else None\n\n async def websocket_handler(cls: Any, obj: Any, context: Dict, func: Any, url: str) -> Any:\n pattern = r'^{}$'.format(re.sub(r'\\$$', '', re.sub(r'^\\^?(.*)$', r'\\1', url)))\n compiled_pattern = re.compile(pattern)\n\n access_log = context.get('options', {}).get('http', {}).get('access_log', True)\n\n async def _pre_handler_func(obj: Any, request: web.Request) -> None:\n request._cache['is_websocket'] = True\n request._cache['websocket_uuid'] = str(uuid.uuid4())\n\n @functools.wraps(func)\n async def _func(obj: Any, request: web.Request, *a: Any, **kw: Any) -> None:\n websocket = web.WebSocketResponse() # type: ignore\n\n request_ip = RequestHandler.get_request_ip(request, context)\n try:\n await websocket.prepare(request)\n except Exception:\n try:\n await websocket.close()\n except Exception:\n pass\n\n if access_log:\n logging.getLogger('transport.http').info('[{}] {} {} \"CANCELLED {}{}\" {} \"{}\" {}'.format(\n RequestHandler.colorize_status('websocket', 101),\n request_ip,\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n request._cache.get('websocket_uuid', ''),\n request.headers.get('User-Agent', '').replace('\"', ''),\n '-'\n ))\n\n return\n\n context['_http_open_websockets'] = context.get('_http_open_websockets', [])\n context['_http_open_websockets'].append(websocket)\n\n if access_log:\n logging.getLogger('transport.http').info('[{}] {} {} \"OPEN {}{}\" {} \"{}\" {}'.format(\n RequestHandler.colorize_status('websocket', 101),\n request_ip,\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n request._cache.get('websocket_uuid', ''),\n request.headers.get('User-Agent', '').replace('\"', ''),\n '-'\n ))\n\n result = compiled_pattern.match(request.path)\n values = inspect.getfullargspec(func)\n kwargs = {k: values.defaults[i] for i, k in enumerate(values.args[len(values.args) - len(values.defaults):])} if values.defaults else {}\n if result:\n for k, v in result.groupdict().items():\n kwargs[k] = v\n\n if len(values.args) - (len(values.defaults) if values.defaults else 0) >= 3:\n # If the function takes a third required argument the value will be filled with the request object\n a = a + (request,)\n if 'request' in values.args and (len(values.args) - (len(values.defaults) if values.defaults else 0) < 3 or values.args[2] != 'request'):\n kwargs['request'] = request\n\n try:\n routine = func(*(obj, websocket, *a), **merge_dicts(kwargs, kw))\n callback_functions = (await routine) if isinstance(routine, Awaitable) else routine # type: Optional[Union[Tuple, Callable]]\n except Exception as e:\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(e)))\n try:\n await websocket.close()\n except Exception:\n pass\n\n try:\n context['_http_open_websockets'].remove(websocket)\n except Exception:\n pass\n\n if access_log:\n logging.getLogger('transport.http').info('[{}] {} {} \"{} {}{}\" {} \"{}\" {}'.format(\n RequestHandler.colorize_status('websocket', 500),\n request_ip,\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n RequestHandler.colorize_status('ERROR', 500),\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n request._cache.get('websocket_uuid', ''),\n request.headers.get('User-Agent', '').replace('\"', ''),\n '-'\n ))\n\n return\n\n _receive_func = None\n _close_func = None\n\n if callback_functions and isinstance(callback_functions, tuple):\n try:\n _receive_func, _close_func = callback_functions\n except ValueError:\n _receive_func, = callback_functions\n elif callback_functions:\n _receive_func = callback_functions\n\n try:\n async for message in websocket:\n if message.type == WSMsgType.TEXT:\n if _receive_func:\n try:\n await _receive_func(message.data)\n except Exception as e:\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(e)))\n elif message.type == WSMsgType.ERROR:\n if not context.get('log_level') or context.get('log_level') in ['DEBUG']:\n ws_exception = websocket.exception()\n if isinstance(ws_exception, (EofStream, RuntimeError)):\n pass\n elif isinstance(ws_exception, Exception):\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(ws_exception)))\n else:\n logging.getLogger('transport.http').warning('Websocket exception: \"{}\"'.format(ws_exception))\n elif message.type == WSMsgType.CLOSED:\n break # noqa\n except Exception as e:\n pass\n finally:\n if _close_func:\n try:\n await _close_func()\n except Exception as e:\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(e)))\n try:\n await websocket.close()\n except Exception:\n pass\n\n try:\n context['_http_open_websockets'].remove(websocket)\n except Exception:\n pass\n\n return await cls.request_handler(cls, obj, context, _func, 'GET', url, pre_handler_func=_pre_handler_func)\n\n async def start_server(obj: Any, context: Dict) -> Optional[Callable]:\n if context.get('_http_server_started'):\n return None\n context['_http_server_started'] = True\n\n server_header = context.get('options', {}).get('http', {}).get('server_header', 'tomodachi')\n access_log = context.get('options', {}).get('http', {}).get('access_log', True)\n\n logger_handler = None\n if isinstance(access_log, str):\n try:\n wfh = WatchedFileHandler(filename=access_log)\n except FileNotFoundError as e:\n logging.getLogger('transport.http').warning('Unable to use file for access log - invalid path (\"{}\")'.format(access_log))\n raise HttpException(str(e)) from e\n except PermissionError as e:\n logging.getLogger('transport.http').warning('Unable to use file for access log - invalid permissions (\"{}\")'.format(access_log))\n raise HttpException(str(e)) from e\n wfh.setLevel(logging.DEBUG)\n logging.getLogger('transport.http').setLevel(logging.DEBUG)\n logging.getLogger('transport.http').info('Logging to \"{}\"'.format(access_log))\n logger_handler = wfh\n logging.getLogger('transport.http').addHandler(logger_handler)\n\n async def _start_server() -> None:\n loop = asyncio.get_event_loop()\n\n logging.getLogger('aiohttp.access').setLevel(logging.WARNING)\n\n @web.middleware\n async def middleware(request: web.Request, handler: Callable) -> web.Response:\n async def func() -> web.Response:\n request_ip = RequestHandler.get_request_ip(request, context)\n if request.headers.get('Authorization'):\n try:\n request._cache['auth'] = BasicAuth.decode(request.headers.get('Authorization'))\n except ValueError:\n pass\n\n if access_log:\n timer = time.time()\n response = web.Response(status=503, # type: ignore\n headers={}) # type: web.Response\n try:\n response = await handler(request)\n response.headers[hdrs.SERVER] = server_header or ''\n except web.HTTPException as e:\n error_handler = context.get('_http_error_handler', {}).get(e.status, None)\n if error_handler:\n response = await error_handler(request)\n response.headers[hdrs.SERVER] = server_header or ''\n else:\n response = e\n response.headers[hdrs.SERVER] = server_header or ''\n response.body = str(e).encode('utf-8')\n except Exception as e:\n error_handler = context.get('_http_error_handler', {}).get(500, None)\n logging.getLogger('exception').exception('Uncaught exception: {}'.format(str(e)))\n if error_handler:\n response = await error_handler(request)\n response.headers[hdrs.SERVER] = server_header or ''\n else:\n response = web.HTTPInternalServerError() # type: ignore\n response.headers[hdrs.SERVER] = server_header or ''\n response.body = b''\n finally:\n if not request.transport:\n response = web.Response(status=499, # type: ignore\n headers={}) # type: web.Response\n response._eof_sent = True\n\n if access_log:\n request_time = time.time() - timer\n version_string = None\n if isinstance(request.version, HttpVersion):\n version_string = 'HTTP/{}.{}'.format(request.version.major, request.version.minor)\n\n if not request._cache.get('is_websocket'):\n status_code = response.status if response is not None else 500\n ignore_logging = getattr(handler, 'ignore_logging', False)\n if ignore_logging is True:\n pass\n elif isinstance(ignore_logging, (list, tuple)) and status_code in ignore_logging:\n pass\n else:\n logging.getLogger('transport.http').info('[{}] [{}] {} {} \"{} {}{}{}\" {} {} \"{}\" {}'.format(\n RequestHandler.colorize_status('http', status_code),\n RequestHandler.colorize_status(status_code),\n request_ip,\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n request.method,\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n ' {}'.format(version_string) if version_string else '',\n response.content_length if response is not None and response.content_length is not None else '-',\n request.content_length if request.content_length is not None else '-',\n request.headers.get('User-Agent', '').replace('\"', ''),\n '{0:.5f}s'.format(round(request_time, 5))\n ))\n else:\n logging.getLogger('transport.http').info('[{}] {} {} \"CLOSE {}{}\" {} \"{}\" {}'.format(\n RequestHandler.colorize_status('websocket', 101),\n request_ip,\n '\"{}\"'.format(request._cache['auth'].login.replace('\"', '')) if request._cache.get('auth') and getattr(request._cache.get('auth'), 'login', None) else '-',\n request.path,\n '?{}'.format(request.query_string) if request.query_string else '',\n request._cache.get('websocket_uuid', ''),\n request.headers.get('User-Agent', '').replace('\"', ''),\n '{0:.5f}s'.format(round(request_time, 5))\n ))\n\n if isinstance(response, (web.HTTPException, web.HTTPInternalServerError)):\n raise response\n\n return response\n\n return await asyncio.shield(func())\n\n app = web.Application(middlewares=[middleware], # type: ignore\n client_max_size=(1024 ** 2) * 100) # type: web.Application\n app._set_loop(None) # type: ignore\n for method, pattern, handler, route_context in context.get('_http_routes', []):\n try:\n compiled_pattern = re.compile(pattern)\n except re.error as exc:\n raise ValueError(\n \"Bad pattern '{}': {}\".format(pattern, exc)) from None\n ignore_logging = route_context.get('ignore_logging', False)\n setattr(handler, 'ignore_logging', ignore_logging)\n resource = DynamicResource(compiled_pattern)\n app.router.register_resource(resource) # type: ignore\n if method.upper() == 'GET':\n resource.add_route('HEAD', handler, expect_handler=None) # type: ignore\n resource.add_route(method.upper(), handler, expect_handler=None) # type: ignore\n\n port = context.get('options', {}).get('http', {}).get('port', 9700)\n host = context.get('options', {}).get('http', {}).get('host', '0.0.0.0')\n\n try:\n app.freeze()\n server_task = loop.create_server(Server(app._handle, request_factory=app._make_request, server_header=server_header or '', access_log=access_log, keepalive_timeout=0, tcp_keepalive=False), host, port) # type: ignore\n server = await server_task # type: ignore\n except OSError as e:\n error_message = re.sub('.*: ', '', e.strerror)\n logging.getLogger('transport.http').warning('Unable to bind service [http] to http://{}:{}/ ({})'.format('127.0.0.1' if host == '0.0.0.0' else host, port, error_message))\n raise HttpException(str(e), log_level=context.get('log_level')) from e\n\n port = int(server.sockets[0].getsockname()[1])\n context['_http_port'] = port\n\n stop_method = getattr(obj, '_stop_service', None)\n\n async def stop_service(*args: Any, **kwargs: Any) -> None:\n if stop_method:\n await stop_method(*args, **kwargs)\n open_websockets = context.get('_http_open_websockets', [])[:]\n for websocket in open_websockets:\n try:\n await websocket.close()\n except Exception:\n pass\n server.close()\n await app.shutdown()\n if logger_handler:\n logging.getLogger('transport.http').removeHandler(logger_handler)\n await app.cleanup()\n\n setattr(obj, '_stop_service', stop_service)\n\n for method, pattern, handler, route_context in context.get('_http_routes', []):\n for registry in getattr(obj, 'discovery', []):\n if getattr(registry, 'add_http_endpoint', None):\n await registry.add_http_endpoint(obj, host, port, method, pattern)\n\n logging.getLogger('transport.http').info('Listening [http] on http://{}:{}/'.format('127.0.0.1' if host == '0.0.0.0' else host, port))\n\n return _start_server\n\n\nasync def resolve_response(value: Union[str, bytes, Dict, List, Tuple, web.Response, Response], request: Optional[web.Request] = None, context: Dict = None, status_code: Optional[Union[str, int]] = None, default_content_type: Optional[str] = None, default_charset: Optional[str] = None) -> web.Response:\n if not context:\n context = {}\n if isinstance(value, Response):\n return value.get_aiohttp_response(context, default_content_type=default_content_type, default_charset=default_charset)\n if isinstance(value, web.FileResponse):\n return value\n\n status = int(status_code) if status_code else (request is not None and request._cache.get('error_status_code', 200)) or 200\n headers = None\n if isinstance(value, dict):\n body = value.get('body')\n _status = value.get('status') # type: Optional[SupportsInt]\n if _status and isinstance(_status, (int, str, bytes)):\n status = int(_status)\n _returned_headers = value.get('headers')\n if _returned_headers:\n returned_headers = _returned_headers # type: Union[Mapping[str, Any], Iterable[Tuple[str, Any]]]\n headers = CIMultiDict(returned_headers)\n elif isinstance(value, list) or isinstance(value, tuple):\n _status = value[0]\n if _status and isinstance(_status, (int, str, bytes)):\n status = int(_status)\n body = value[1]\n if len(value) > 2:\n returned_headers = value[2]\n headers = CIMultiDict(returned_headers)\n elif isinstance(value, web.Response):\n return value\n else:\n if value is None:\n value = ''\n body = value\n\n return Response(body=body, status=status, headers=headers, content_type=default_content_type, charset=default_charset).get_aiohttp_response(context)\n\n\nasync def get_http_response_status(value: Union[str, bytes, Dict, List, Tuple, web.Response, Response, Exception], request: Optional[web.Request] = None, verify_transport: bool = True) -> Optional[int]:\n if isinstance(value, Exception) or isinstance(value, web.HTTPException):\n status_code = int(getattr(value, 'status', 500)) if value is not None else 500\n return status_code\n else:\n response = await resolve_response(value, request=request)\n status_code = int(response.status) if response is not None else 500\n if verify_transport and request is not None and request.transport is None:\n return 499\n else:\n return status_code\n\n\nhttp = HttpTransport.decorator(HttpTransport.request_handler)\nhttp_error = HttpTransport.decorator(HttpTransport.error_handler)\nhttp_static = HttpTransport.decorator(HttpTransport.static_request_handler)\n\nwebsocket = HttpTransport.decorator(HttpTransport.websocket_handler)\nws = HttpTransport.decorator(HttpTransport.websocket_handler)\n","sub_path":"tomodachi/transport/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":37612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"58866421","text":"from django.conf.urls import url, include\nfrom .import views \n\napp_name = \"RiskApp\"\n\nurlpatterns = [\n url(r'^$', views.landingPage, name='home'),\n url(r'models/', views.ManageModel, name='manage_model'),\n url(r'risk/', views.all_risks, name='risk_template'),\n url(r'single_types/', views.single_type, name='single_risk'),\n]","sub_path":"RiskApp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"421541024","text":"# Conway's Game of Life\n# Python Prototype\n# Sean Batzel\n\ncreateBoard()\ngeneration = 1\nwhile generation > 0:\n for x in range(1,11):\n for y in range(1,11):\n count = 0\n a = x - 1\n b = y + 1\n if board[a, b] == 1:\n count = count + 1 \n c = x\n d = y + 1\n if board[c, d] == 1 :\n count = count + 1 \n e = x + 1\n f = y\n if board[e, f] == 1 :\n count = count + 1 \n g = x + 1\n h = y\n if board[g, h] == 1 :\n count = count + 1 \n i = x - 1\n j = y - 1\n if board[i, j] == 1 :\n count = count + 1\n k = x\n l = y - 1\n if board[k, l] == 1 :\n count = count + 1 \n m = x + 1\n n = y - 1\n if board[m, n] == 1 :\n count = count + 1 \n if count > 3 :\n board[x, y] = 0 \n if count < 2 :\n board[x, y] = 0 \n if count == 3 :\n board[x, y] = 1\n if count == 2 :\n if board[x, y] == 1 :\n board[x, y] = 1\n generation = generation + 1\n print(\"Generation:\" + generation)\n printBoard()\n\n\ndef createBoard():\n\tfor i in range(0,11):\n\t\tboard[i, 0] = 9\n\tfor i in range(0,11):\n\t\tboard[i, 11] = 9\n\tfor i in range(0,11):\n\t\tboard[0, i] = 9\n\tfor i in range(0,11):\n\t\tboard[11, i] = 9\n\tfor i in range(1,100):\n\t\tx = random.randrange(1,11)\n\t\ty = random.randrange(1,11)\n\t\tboard[x, y] = 1\n\treturn\n\n\ndef printBoard():\n\tfor i in range(1,11):\n\t\t\tprint(board[i, 1]+ board[i, 2]+ board[i, 3]+ board[i, 4]+ board[i, 5]+ board[i, 6]+ board[i, 7]+ board[i, 8]+ board[i, 9]+ board[i, 10]+ board[i, 11])\n\treturn","sub_path":"gameoflife.py","file_name":"gameoflife.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"307244856","text":"from base import BaseRecognizer\n\n\nclass LanguageRecognizer(BaseRecognizer):\n header = None\n footer = None\n name = 'LANG'\n\n @classmethod\n def is_data_start(cls, buf, pos):\n return buf[pos].isalnum()\n\n @classmethod\n def find_next_data_start(cls, buf, pos):\n if pos >= len(buf):\n return None\n while buf[pos] == '\\x00':\n pos += 1\n if pos >= len(buf):\n return None\n if not cls.is_data_start(buf, pos):\n return None\n return pos\n\n @classmethod\n def read_cstring(cls, buf, pos):\n origin = pos\n pos = buf.find('\\x00', origin)\n if pos == -1:\n return None\n return buf[origin:pos]\n\n @classmethod\n def read_data(cls, buf, pos):\n origin = pos\n assert cls.is_data_start(buf, origin)\n\n result = {'origin': origin}\n\n name = cls.read_cstring(buf, origin)\n if name is None:\n return None\n result['name'] = name\n result['size'] = size = len(name) + 1\n pos += size\n if pos >= len(buf):\n return result\n\n pos = cls.find_next_data_start(buf, pos)\n if pos is None:\n return result\n code = cls.read_cstring(buf, pos)\n if code is None:\n return result\n\n pos += len(code) + 1\n\n result['code'] = code\n result['size'] = pos - origin\n\n return result\n\n @classmethod\n def find_data_size(cls, buf, pos):\n assert cls.is_data_start(buf, pos)\n origin = pos\n pos = buf.find('\\x00', pos)\n if pos == -1:\n return None\n pos = cls.find_next_data_start(buf, pos)\n if pos is None:\n return None\n pos = buf.find('\\x00', pos)\n if pos == -1:\n return None\n pos += 1\n if pos >= len(buf):\n return None\n return pos - origin\n\n @classmethod\n def find_data_range(cls, buf, pos):\n origin = pos\n size = cls.find_data_size(buf, pos)\n if size is None:\n return None\n return (origin, size)\n\n @classmethod\n def find_next_data_range(cls, buf, pos):\n pos = cls.find_next_data_start(buf, pos)\n if pos is None:\n return None\n r = cls.find_data_range(buf, pos)\n if r is None:\n return None\n return r\n\n @classmethod\n def read_next_data(cls, buf, pos):\n pos = cls.find_next_data_start(buf, pos)\n if pos is None:\n return None\n return cls.read_data(buf, pos)\n","sub_path":"recognizers/language.py","file_name":"language.py","file_ext":"py","file_size_in_byte":2584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"427626582","text":"import argparse\n\n\ndef create_parser():\n parser = argparse.ArgumentParser(description=\"AlexNet implementation\")\n # Common\n parser.add_argument('-c', '--cuda',\n dest='cuda',\n type=int,\n default=0,\n metavar='INTEGER_NUMBER',\n help='Input CUDA number (default: 0)')\n parser.add_argument('-s', '--seed',\n dest='seed',\n type=int,\n default=2019,\n metavar='INTEGER_NUMBER',\n help='Seed (default: 2019)')\n parser.add_argument('-r', '--resume',\n dest='resume',\n type=str,\n default=None,\n metavar='PATH',\n help='Path to latest checkpoint (default: none)')\n parser.add_argument('-eval', '--evaluate',\n dest='evaluate',\n action='store_true',\n help='Evaluate model on validation set (default: False)')\n # Data\n parser.add_argument('-b', '--batch-size',\n dest='batch_size',\n type=int,\n default=32,\n metavar='INTEGER_NUMBER',\n help='Input batch size (default: 128)')\n # Epochs\n parser.add_argument('-e', '--epochs',\n type=int,\n dest='epochs',\n default=50,\n metavar='INTEGER_NUMBER',\n help='Number of epochs (default: 1)')\n parser.add_argument('-se', '--start-epoch',\n type=int,\n dest='start_epoch',\n default=0,\n metavar='INTEGER_NUMBER',\n help='Manual epoch number (useful on restarts)')\n # Optimizer\n parser.add_argument('-lr', '--learning_rate',\n dest='lr',\n type=float,\n default=0.01,\n metavar='FLOAT_NUMBER',\n help='Learning rate (default: 0.01)')\n parser.add_argument('-m', '--momentum',\n dest='momentum',\n type=float,\n default=0.9,\n metavar='FLOAT_NUMBER',\n help='Momentum (default: 0.9)')\n parser.add_argument('-wd', '--weight-decay',\n dest='weight_decay',\n type=float,\n default=5e-4,\n metavar='FLOAT_NUMBER',\n help='Weight decay (default: 5e-4)')\n # Logging\n parser.add_argument('-l', '--log',\n default='INFO',\n type=str.upper,\n dest='log_level',\n choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'],\n help='Set the logging output level')\n parser.add_argument('-li', '--log_interval',\n dest='log_interval',\n type=int,\n default=10,\n metavar='INTEGER_NUMBER',\n help='Log interval (default: 30)')\n return parser.parse_args()\n","sub_path":"ml_project/src/utils/args.py","file_name":"args.py","file_ext":"py","file_size_in_byte":3416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"337649323","text":"from unittest import TestCase\nimport json\nfrom json2md import json2md\n\n\nclass TestJson2Md(TestCase):\n \n def test_01(self):\n item = [{'data_item': {'topic': 'CLI http client', 'url': 'https://httpie.org/docs'}, \\\n 'checked': False, 'id': '297baa6a-b941-11ea-99b4-0ac0f7391213', 'createdAt': 1593350010816, \\\n 'updatedAt': 1593350010816}, {'data_item': {'topic': '10 Ways to Speed Up Your Python Code', \\\n 'url': 'https://towardsdatascience.com/10-ways-to-speed-up-your-python-code-e3d57630b710'}, \\\n 'checked': False, 'id': '52dc7806-64af-11ec-961d-1a9e0ee827ef', \\\n 'createdAt': 1640346471383, 'updatedAt': 1640346471383}]\n result = json2md(json.dumps(item)) \n expected = ('| | |\\n'\n '|-------------|------------|\\n'\n ' | **Cli http client** | '\n '[https://httpie.org/docs](https://httpie.org/docs) \\n'\n ' | **10 ways to speed up your python code** | '\n '[https://towardsdatascience.com/10-ways-to-speed-up-your-python-code-e3d57630b710]'\n '(https://towardsdatascience.com/10-ways-to-speed-up-your-python-code-e3d57630b710) \\n')\n self.assertEqual(expected, result)\n","sub_path":"tests/test_json2html.py","file_name":"test_json2html.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"445286252","text":"# -*- coding: utf-8 -*-\nfrom django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import logout\nfrom MrmcdCashdesk import settings\nfrom models import Product, Purchase, ProductPurchase, PresaleTicket\nfrom django.template import RequestContext, loader\nfrom django.db.models import Count\nimport json\nimport datetime\nimport printer\n\n\n@login_required\ndef checkout(request):\n cart = []\n\n if request.POST:\n cj = request.POST.get(\"cart\")\n else:\n cj = request.GET.get(\"cart\")\n # Open cash drawer\n\n fail = False\n for f in json.loads(cj):\n try:\n product = Product.objects.get(pk=f['id'])\n if f['num'] > product.stocked:\n fail = True\n cart.append({\n 'product': product,\n 'amount': f['num'],\n 'price': f['num'] * product.price,\n })\n except Product.DoesNotExist:\n continue\n\n if request.POST:\n purchase = Purchase()\n purchase.salesperson = request.user\n purchase.total = sum([i['product'].price * i['amount'] for i in cart])\n purchase.save()\n for i in cart:\n i['product'].stocked -= i['amount']\n i['product'].save()\n ProductPurchase.objects.create(\n purchase=purchase,\n product=i['product'],\n amount=i['amount'])\n template = loader.get_template('cashdesk/bon.txt')\n context = RequestContext(\n request,\n {\n 'purchase': purchase,\n 'productpurchases': ProductPurchase.objects.filter(\n purchase=purchase),\n }\n )\n bon = template.render(context)\n printer.print_and_cut(\n request.session['printer'],\n bon, True)\n return HttpResponseRedirect(\"/\")\n\n ctx = {\n 'cart': cart,\n 'fail': fail,\n 'cartjson': request.GET.get(\"cart\"),\n 'total': sum([i['product'].price * i['amount'] for i in cart]),\n }\n return render(request, 'cashdesk/checkout.html', ctx)\n\n\ndef logout_view(request):\n logout(request)\n return HttpResponseRedirect(\"/\")\n\n\n@login_required\ndef autocomplete(request):\n tickets = PresaleTicket.objects.filter(\n key__startswith=request.GET.get(\"q\")\n )\n opt = [t.key for t in tickets]\n return HttpResponse(json.dumps({'options': opt}))\n\n\n@login_required\ndef drawer(request):\n printer.open_drawer(request.session['printer'])\n return HttpResponseRedirect('/')\n\n\n@login_required\ndef printerconf(request):\n test = 0\n if request.POST:\n request.session['printer'] = request.POST.get(\"printer\")\n try:\n printer.print_and_cut(\n request.session['printer'],\n \"\"\"\nTEST OF PRINTER %s\n\n\n \"\"\" % request.POST.get(\"printer\"), True)\n test = 1\n except:\n test = -1\n\n if test == 1:\n return HttpResponseRedirect('/')\n\n ctx = {\n \"test\": test,\n \"printer\": request.session.get(\"printer\", \"\")\n }\n return render(request, 'cashdesk/printerconf.html', ctx)\n\n\n@login_required\ndef presalebon(request):\n ticket = PresaleTicket.objects.get(\n pk=request.POST.get(\"id\"))\n template = loader.get_template('cashdesk/bon_presale.txt')\n context = RequestContext(\n request,\n {\n 'ticket': ticket,\n 'timestamp': datetime.datetime.now()\n }\n )\n bon = template.render(context)\n printer.print_and_cut(\n request.session['printer'],\n bon, False)\n return HttpResponseRedirect(\"/\")\n\n\n@login_required\ndef index(request):\n ctx = {\n 'products': Product.objects.filter(onsale=True),\n }\n if request.POST or \"validate\" in request.GET:\n if \"validate\" in request.POST or \"validate\" in request.GET:\n try:\n ticket = PresaleTicket.objects.get(\n key=request.POST.get(\"validate\", request.GET.get(\"validate\")).strip())\n if ticket.validated:\n validationresult = 'warning'\n else:\n validationresult = 'success'\n ticket.validated = True\n ticket.save()\n ctx['ticket'] = ticket\n except PresaleTicket.DoesNotExist:\n validationresult = 'error'\n ctx['validationresult'] = validationresult\n return render(request, 'cashdesk/index.html', ctx)\n\n\n@login_required\ndef service(request):\n ctx = {}\n if request.POST and \"destination\" in request.POST:\n import gmaps\n printer.print_and_cut(\n request.session['printer'],\n gmaps.route(request.POST.get(\"destination\"), \n request.POST.get(\"mode\", \"walking\")), \n False)\n return render(request, 'cashdesk/service.html', ctx)\n\n\n@login_required\ndef stats(request):\n ctx = {}\n status = PresaleTicket.objects.filter(product__merchandise=False).values(\"validated\").distinct().annotate(cnt=Count(\"id\"))\n presale_sum = PresaleTicket.objects.filter(product__merchandise=False).count()\n ctx['presale_status'] = [(x['validated'], round(float(x['cnt'])/float(presale_sum)*100.0)) for x in status]\n status = PresaleTicket.objects.filter(product__merchandise=True).values(\"validated\").distinct().annotate(cnt=Count(\"id\"))\n presale_sum = PresaleTicket.objects.filter(product__merchandise=True).count()\n ctx['presale_merch_status'] = [(x['validated'], round(float(x['cnt'])/float(presale_sum)*100.0)) for x in status]\n \n return render(request, 'cashdesk/stats.html', ctx)\n","sub_path":"MrmcdCashdesk/cashdesk/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"380563397","text":"'''\nProvides multiple interpolation methods that can be used to create interpolating\npolynomials between a set of points.\n\nLagrange polynomial: https://en.wikipedia.org/wiki/Lagrange_polynomial\nNeville's method: https://en.wikipedia.org/wiki/Neville%27s_algorithm\nNewton's interpolating polynomials: https://en.wikipedia.org/wiki/Newton_polynomial\n'''\n\nimport numpy as np\n\ndef lagrange_interpolation(xs, ys, degree=None):\n '''Computes the Lagrange polynomial for a set of points.'''\n if degree is None: degree = xs.size\n else: degree += 1 # Degree of polynomial -> number of points we need\n x_vals, y_vals = xs.reshape(1, xs.size), ys.reshape(1, ys.size)\n def polynomial( x ):\n f_x = 0\n for i in range( degree ): # Sum over L_i functions\n L_i = ys.item( i )\n for j in range( degree ):\n if j == i: continue\n L_i *= (x - xs.item(j)) / (xs.item(i) - xs.item(j))\n f_x += L_i \n return f_x\n return polynomial # Return a function that computes polynomial\n\ndef neville_interpolation( xs, ys, degree=None ):\n '''Computes an interpolating polynomial using Neville's method.'''\n if degree is None: degree = xs.size\n else: degree += 1\n def polynomial( x ):\n qs = ys[0:degree] # Old column of q values\n new_qs = [] # New column of q values\n for j in range(1,degree): # Loop over columns\n for i in range(j, degree): # Loop over cells in columns\n new_qs.append( ((x-xs[i-j])*qs[i-j+1]+(xs[i]-x)*qs[i-j])/\n (xs[i]-xs[i-j]) )\n qs = new_qs[:]\n new_qs = []\n return qs[0]\n return polynomial # Return a function that computes polynomial\n\ndef newton_interpolation( xs, ys, degree=None ):\n \"\"\"Computes Newton's interpolating polynomial for a set of points by\n calculating divided differences between the points.\"\"\"\n diffs, Fs = [], []\n if degree is None: degree = xs.size\n else: degree += 1\n for i in range( ys.size ):\n diffs.append( [ys.item(i)] )\n for i in range(1, degree):\n for j in range(1, i+1):\n diffs[i].append( (diffs[i][j-1]-diffs[i-1][j-1])/(xs.item(i)-\n xs.item(i-j)) )\n for diff in diffs:\n Fs.append(diff[-1])\n def polynomial( x ):\n val, product = Fs[0], 1\n for i in range(1, degree):\n product *= x - xs.item(i-1)\n val += Fs[i] * product\n return val\n return polynomial # Return function that computes polynomial\n\n","sub_path":"Basics/techniques/interpolation.py","file_name":"interpolation.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"513216071","text":"import sys\nimport heapq\nfrom operator import itemgetter\nfrom collections import deque, defaultdict, Counter\nfrom bisect import bisect_left, bisect_right\ninput = sys.stdin.readline\nsys.setrecursionlimit(10 ** 7)\nMOD = 10**9 + 7\n\ndef search(n): # 一番下の桁から\n if n == 0:\n return 0\n\n q, r = divmod(n, 10)\n ret = 0\n if r > 1:\n ret += 1\n\n ret += str(q).count('1') * r\n ret += search(q) * 10\n ret += q\n return ret\n\ndef sol():\n N = int(input())\n\n ans = search(N + 1)\n print(ans)\n\n\nsol()","sub_path":"AtCoder/abc/029d.py","file_name":"029d.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"17977179","text":"import time\n\n\ndef ask_user():\n start = time.time()\n user_input = input(\"Name please: \")\n greet = f'Hello, {user_input}'\n print(greet)\n print(f'ask_user took {time.time() - start}')\n\n\ndef complex_calculation():\n start = time.time()\n print('Starting calculations')\n [x**2 for x in range(20000000)]\n print(f'complex_calculation took {time.time() - start}')","sub_path":"concurrency/operations.py","file_name":"operations.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"267593384","text":"###########################################################################\n#\n# 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# https://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\nfrom googleapiclient.errors import HttpError\n\nfrom starthinker.util.bigquery import table_create\nfrom starthinker.util.data import get_rows\nfrom starthinker.util.data import put_rows\nfrom starthinker.util.dcm import get_profile_for_api\nfrom starthinker.util.google_api import API\nfrom starthinker.util.google_api.discovery_to_bigquery import Discovery_To_BigQuery\nfrom starthinker.util.project import project\n\n\nERROR_SCHEMA = [\n { 'name': 'Error', 'type': 'STRING', 'mode': 'NULLABLE' },\n { 'name': 'Parameters', 'type': 'RECORD', 'mode': 'REPEATED', 'fields': [\n { 'name': 'Key', 'type': 'STRING', 'mode': 'NULLABLE' },\n { 'name': 'Value', 'type': 'STRING', 'mode': 'NULLABLE' },\n ]}\n]\n\n\ndef google_api_initilaize(api_call, alias=None):\n\n if api_call['function'].endswith('list') or alias == 'list':\n api_call['iterate'] = True\n\n if api_call['api'] == 'dfareporting':\n is_superuser, profile_id = get_profile_for_api(\n api_call['auth'], api_call['kwargs']['accountId']\n )\n\n api_call['kwargs']['profileId'] = profile_id\n\n if is_superuser:\n from starthinker.util.dcm.internalv33_uri import URI as DCM_URI\n api_call['version'] = 'internalv3.3'\n api_call['uri'] = DCM_URI\n else:\n del api_call['kwargs']['accountId']\n\n\ndef google_api_build_results(auth, api_call, results):\n if 'bigquery' in results:\n results['bigquery']['schema'] = Discovery_To_BigQuery(\n api_call['api'],\n api_call['version'],\n api_call.get('key', None),\n ).method_schema(api_call['function'])\n\n #TODO: Fix format to sometimes be CSV, probably refactor BigQuery to\n # determine format based on rows or schema\n results['bigquery']['format'] = 'JSON'\n results['bigquery']['skip_rows'] = 0\n #results['bigquery']['disposition'] = 'WRITE_TRUNCATE'\n\n table_create(\n results['bigquery'].get('auth', auth),\n project.id,\n results['bigquery']['dataset'],\n results['bigquery']['table'],\n results['bigquery']['schema'],\n overwrite=False\n )\n\n return results\n\n\ndef google_api_build_errors(auth, api_call, errors):\n if 'bigquery' in errors:\n errors['bigquery']['schema'] = ERROR_SCHEMA\n errors['bigquery']['format'] = 'JSON'\n errors['bigquery']['skip_rows'] = 0\n errors['bigquery']['disposition'] = 'WRITE_TRUNCATE'\n\n table_create(\n errors['bigquery'].get('auth', auth),\n project.id,\n errors['bigquery']['dataset'],\n errors['bigquery']['table'],\n errors['bigquery']['schema'],\n overwrite=False\n )\n\n return errors\n\n\ndef google_api_execute(auth, api_call, results, errors, limit=None):\n\n try:\n rows = API(api_call).execute()\n\n if results:\n # check if single object needs conversion to rows\n if isinstance(rows, dict):\n rows = [rows]\n\n rows = map(lambda r: Discovery_To_BigQuery.clean(r), rows)\n put_rows(auth, results, rows)\n\n if 'bigquery' in results:\n results['bigquery']['disposition'] = 'WRITE_APPEND'\n\n except HttpError as e:\n\n if errors:\n rows = [{\n 'Error':\n str(e),\n 'Parameters': [{\n 'Key': k,\n 'Value': str(v)\n } for k, v in api_call['kwargs'].items()]\n }]\n put_rows(auth, errors, rows)\n\n if 'bigquery' in errors:\n errors['bigquery']['disposition'] = 'WRITE_APPEND'\n\n else:\n raise e\n\n\n@project.from_parameters\ndef google_api():\n\n if project.verbose:\n print(\n 'GOOGLE_API',\n project.task['api'],\n project.task['version'],\n project.task['function']\n )\n\n api_call = {\n 'auth': project.task['auth'],\n 'api': project.task['api'],\n 'version': project.task['version'],\n 'function': project.task['function'],\n 'iterate': project.task.get('iterate', False),\n 'limit': project.task.get('limit', None),\n 'key': project.key,\n 'headers': project.task.get('headers'),\n }\n\n results = google_api_build_results(\n project.task['auth'],\n api_call,\n project.task.get('results', {})\n )\n\n errors = google_api_build_errors(\n project.task['auth'],\n api_call,\n project.task.get('errors', {})\n )\n\n # get parameters from JSON\n if 'kwargs' in project.task:\n kwargs_list = project.task['kwargs'] if isinstance(\n project.task['kwargs'], (list, tuple)\n ) else [project.task['kwargs']]\n\n # get parameters from remote location ( such as BigQuery )\n elif 'kwargs_remote' in project.task:\n kwargs_list = get_rows(\n project.task['auth'],\n project.task['kwargs_remote'],\n as_object=True\n )\n\n # no parameters, ensures at least one call is made\n else:\n kwargs_list = [{}]\n\n # loop through paramters and make possibly multiple API calls\n for kwargs in kwargs_list:\n api_call['kwargs'] = kwargs\n google_api_initilaize(api_call, project.task.get('alias'))\n google_api_execute(project.task['auth'], api_call, results, errors, project.task.get('limit'))\n\n\nif __name__ == '__main__':\n google_api()\n","sub_path":"starthinker/task/google_api/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"291695907","text":"def delete_nth(order,max_e):\n ans = []\n for o in order:\n if order.count(o) < max_e: ans.append(o)\n return ans\n \n#\ndef delete_nth(order,max_e):\n # code here\n order_set = list(set(order))\n order_count = [order.count(i) for i in order_set]\n \n for i in range(len(order_count)):\n\n if order_count[i] > max_e:\n k = 0\n for j in range(len(order)):\n if order[j] == order_set[i]:\n k += 1\n if k > max_e:\n order[j] = ''\n \n order_new = []\n for i in range(len(order)):\n if order[i] != '':\n order_new.append(order[i])\n \n return order_new","sub_path":"delete_nth.py","file_name":"delete_nth.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"557361168","text":"import cv2\nimport numpy as np\n\n#path to left and right image\nfileLeft=r'../graphics/16_l.png'\nfileRight=r'../graphics/16_r.png'\n\n#read both images\nimgLeft=cv2.imread(fileLeft,1)\nimgRight=cv2.imread(fileRight,1)\n\n#create a StereoSGBM Object with optimized parameters\nstereoMatch=cv2.StereoSGBM_create(minDisparity=1, numDisparities=160, blockSize=9,P1=1,P2=3000,disp12MaxDiff=60, uniquenessRatio=1, speckleRange=1)\n\n#compute disparity with StereoSGBM algorithm\ndisparityMap=stereoMatch.compute(imgLeft,imgRight)\ncv2.imwrite('disp_map_kitti.png', disparityMap)\n\n","sub_path":"Code/MMT_P_paper_disparity.py","file_name":"MMT_P_paper_disparity.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"458134650","text":"import torch.optim as optim\r\nfrom torchvision import datasets, transforms\r\nimport utils\r\n\r\nfrom vgg_19_net import *\r\nfrom constants import *\r\nfrom dataset import *\r\nmodel = VGG(\"VGG19\")\r\nif use_cuda:\r\n model.cuda()\r\noptimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)\r\ncriterion = nn.CrossEntropyLoss()\r\n#optimizer = optim.Adam(model.parameters(),lr=lr,betas=betas)\r\n\r\ncut_size = 44\r\nnormMean = [0.49139968, 0.48215827, 0.44653124]\r\nnormStd = [0.24703233, 0.24348505, 0.26158768]\r\nnormTransform = transforms.Normalize(normMean, normStd)\r\n\r\ntransform_train = transforms.Compose([\r\n transforms.RandomCrop(cut_size),\r\n transforms.RandomHorizontalFlip(),\r\n transforms.ToTensor(),\r\n #normTransform\r\n])\r\n\r\ntransform_test = transforms.Compose([\r\ntransforms.TenCrop(cut_size),\r\ntransforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])),\r\n #normTransform\r\n])\r\n\r\ndatas = ferDataset(\"./datas/training.csv\",transform=transform_train)\r\ndataloader = DataLoader(dataset=datas,batch_size=batchsize,shuffle=True)\r\ntest = ferDataset(\"./datas/valdata.csv\",transform=transform_test)\r\ntestloader = DataLoader(dataset=test,batch_size=batchsize)\r\nprivate_test = ferDataset(\"./datas/testdata.csv\", transform=transform_test)\r\nprivate_loader = DataLoader(dataset=private_test, batch_size=batchsize)\r\n\r\ndecay_epoch_start = 60\r\ndecay_every = 5\r\ndecay_rate = 0.9\r\n\r\n\r\nlosspath = \"./losses\"\r\ngl = 0\r\nimport os\r\nwhile os.path.exists(os.path.join(losspath,\"vgg%d.txt\"%gl)):\r\n gl+=1\r\nlpath = os.path.join(losspath,\"vgg%d.txt\"%gl)\r\nlf = open(lpath,'w')\r\n\r\ndef train(epoch):\r\n model.train()\r\n import random\r\n if epoch > decay_epoch_start:\r\n frac = (epoch-decay_epoch_start) // decay_every\r\n decay_frac = (decay_rate)**frac\r\n curlr = lr*decay_frac\r\n for group in optimizer.param_groups:\r\n group['lr'] = curlr\r\n for batchidx, (data, target) in enumerate(dataloader):\r\n correct = 0\r\n if use_cuda:\r\n data,target = data.cuda(), target.cuda()\r\n data,target = Variable(data),Variable(target)\r\n optimizer.zero_grad()\r\n output = model(data)\r\n loss = criterion(output, target)\r\n loss.backward()\r\n utils.clip_gradient(optimizer, 0.1)\r\n optimizer.step()\r\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\r\n correct += pred.eq(target.data.view_as(pred)).cpu().sum()\r\n if batchidx % 10 == 0:\r\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tCorrec: {: d}'.format(\r\n epoch, batchidx * len(data), len(dataloader.dataset),\r\n 100. * batchidx / len(dataloader), loss,correct))\r\n lf.write('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}\\tCorrec: {: d}\\n'.format(\r\n epoch, batchidx * len(data), len(dataloader.dataset),\r\n 100. * batchidx / len(dataloader), loss,correct))\r\ndef validate(best_acc,save = False):\r\n model.eval()\r\n test_loss = 0\r\n correct = 0\r\n for bidx,(data,target) in enumerate(testloader):\r\n bs, ncrops, c, h, w = data.shape\r\n data = data.view(-1, c, h, w)\r\n if use_cuda:\r\n data, target = data.cuda(), target.cuda()\r\n with torch.no_grad():\r\n data,target = Variable(data),Variable(target)\r\n output = model(data)\r\n output = output.view(bs, ncrops, -1).mean(1)\r\n test_loss += criterion(output, target)\r\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\r\n correct += pred.eq(target.data.view_as(pred)).cpu().sum()\r\n test_loss /= len(testloader.dataset)\r\n if(save):\r\n acc = float(1.0 * correct) / len(testloader.dataset)\r\n torch.save(model.state_dict(), \"./models/vgg2model%.4f.pth\"%acc)\r\n print(\"Saving in \"+\"./models/vgg2model%.4f.pth\"%acc)\r\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\r\n test_loss, correct, len(testloader.dataset),\r\n 100. * correct / len(testloader.dataset)))\r\n acc = float(1.0 * correct) / len(testloader.dataset)\r\n lf.write(\"Acc pub: %.4f\\n\" % acc)\r\n if acc>best_acc:\r\n best_acc = acc\r\n print(\"Current best acc: %.4f\"%best_acc)\r\n return best_acc\r\n\r\ndef private_func(best_acc):\r\n model.eval()\r\n test_loss = 0\r\n correct = 0\r\n for data,target in private_loader:\r\n bs, ncrops, c, h, w = data.shape\r\n data = data.view(-1, c, h, w)\r\n if use_cuda:\r\n data, target = data.cuda(), target.cuda()\r\n with torch.no_grad():\r\n data,target = Variable(data),Variable(target)\r\n output = model(data)\r\n output = output.view(bs, ncrops, -1).mean(1)\r\n test_loss += criterion(output, target)\r\n pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\r\n correct += pred.eq(target.data.view_as(pred)).cpu().sum()\r\n test_loss /= len(private_loader.dataset)\r\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\r\n test_loss, correct, len(private_loader.dataset),\r\n 100. * correct / len(private_loader.dataset)))\r\n acc = float(1.0 * correct) / len(testloader.dataset)\r\n lf.write(\"Acc pri: %.4f\\n\" % acc)\r\n if acc>best_acc:\r\n best_acc = acc\r\n print(\"Current best acc: %.4f\"%best_acc)\r\n return best_acc\r\n\r\nif __name__ == \"__main__\":\r\n best_acc_val = 0\r\n best_acc_pri = 0\r\n for i in range(1,epochs+1):\r\n train(i)\r\n save = False\r\n if(i%5==0):\r\n save=True\r\n best_acc_val = validate(best_acc_val,save)\r\n best_acc_pri = private_func(best_acc_pri)\r\n lf.close()\r\n","sub_path":"src/train_vgg.py","file_name":"train_vgg.py","file_ext":"py","file_size_in_byte":5794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"491793118","text":"from django.urls import path\nfrom .views import MarketplaceListView, ThreadListView, DirectoryThreadView, CreateThreadView, ThreadDeleteView, ThreadUpdateView\n\n\napp_name = 'forums'\n\nurlpatterns = [\n path('', ThreadListView.as_view(), name='forum'),\n path('category//', DirectoryThreadView.as_view(), name='category'),\n path('/thread/new/', CreateThreadView.as_view(), name='create-thread'),\n path('thread//delete/', ThreadDeleteView.as_view(), name='thread-delete'),\n path('thread//update/', ThreadUpdateView.as_view(), name='thread-update'),\n path('marketplace/', MarketplaceListView.as_view(), name='marketplace'),\n # path('success/', success, name='success'),\n]\n","sub_path":"forums/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"485350549","text":"# HackerRank\n# Domain: Algorithms\n# Subdomain: Implementation\n# Challenge: Angry Professor\n\nT = int(input())\n\nfor i in range(0, T):\n N, K = (int(x) for x in input().split())\n arrivalTimes = list(map(int, input().split()))\n early = 0\n for time in arrivalTimes:\n if time <= 0:\n early += 1\n if early >= K:\n print(\"NO\")\n else:\n print(\"YES\")\n\n","sub_path":"Algorithms/Implementation/AngryProfessor.py","file_name":"AngryProfessor.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"533790187","text":"import math\r\nfrom rouge import Rouge\r\nimport numpy as np\r\n\r\n# The Flesch Reading Ease Readability Formula \r\n# http://www.readabilityformulas.com/flesch-reading-ease-readability-formula.php\r\n\r\n# The specific mathematical formula is: \r\n\r\n# RE = 206.835 – (1.015 x ASL) – (84.6 x ASW) \r\n# RE = Readability Ease \r\n# ASL = Average Sentence Length (i.e., the number of words divided by the number of sentences) \r\n# ASW = Average number of syllables per word (i.e., the number of syllables divided by the number of words) \r\n\r\n# The output, i.e., RE is a number ranging from 0 to 100. The higher the number, the easier the text is to read. \r\n# • Scores between 90.0 and 100.0 are considered easily understandable by an average 5th grader.\r\n# • Scores between 60.0 and 70.0 are considered easily understood by 8th and 9th graders.\r\n# • Scores between 0.0 and 30.0 are considered easily understood by college graduates.\r\n\r\ndef calc_rouge_scores(ref_sentences, pred_sentences):\r\n\r\n rouge = Rouge()\r\n scores = rouge.get_scores(pred_sentences, ref_sentences, avg=True)\r\n\r\n return scores\r\n\r\nreferences = []\r\n\r\nf = open(\"output_saved_wikialigned/ref.txt\", \"r\")\r\nfor s in f:\r\n references.append(s)\r\n\r\npredictions = []\r\n\r\nf = open(\"output_saved_wikialigned/pred.txt\", \"r\")\r\nfor s in f:\r\n predictions.append(s)\r\n\r\nscores = calc_rouge_scores(references, predictions)\r\nprint(scores)\r\n\r\n\r\n\r\n","sub_path":"metrics/rouge_score.py","file_name":"rouge_score.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"81167676","text":"import glob\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.utils import shuffle\n\nparent_dir='cifar'\nsub_dirs=['train','test']\nfile_ext='*.png'\n\ndef label2num(x):\n return {\n 'airplane':0,\n 'automobile':1,\n 'bird':2,\n 'cat':3,\n 'deer':4,\n 'dog':5,\n 'frog':6,\n 'horse':7,\n 'ship':8,\n 'truck':9,\n }[x]\n \netiquetas=[]\ncaracteristicas=[]\nfor I,sub_dir in enumerate(sub_dirs):\n for fn in glob.glob(os.path.join(parent_dir, sub_dir, file_ext)):\n label=fn.split('/')[2].split('_')[1].split('.')[0]\n etiqueta=label2num(label)\n etiquetas.append(etiqueta)\n img=plt.imread(fn)\n caracteristicas.append(img)\netiquetas=np.array(etiquetas)\ncaracteristicas=np.array(caracteristicas)\nprint(etiquetas.size)\nprint(caracteristicas.shape)\n\nx1, y1 = shuffle(caracteristicas, etiquetas)\nsamples=y1.size\n\noffset = int(x1.shape[0] * 0.80)\nX_train, Y_train = x1[:offset], y1[:offset]\nX_test, Y_test = x1[offset:], y1[offset:]\nY_test = np.array(Y_test)\nY_train = np.array(Y_train)","sub_path":"18 03-11-18/caracterisiticas_imgaes_cnn.py","file_name":"caracterisiticas_imgaes_cnn.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"318289153","text":"import sys, os\n\ninput_filename = sys.argv[1]\noutput_filename = sys.argv[2]\nquery_logs_path = sys.argv[3]\n\nc = \"roaring_runopt\"\nresults = output_filename + \".\" + c + \".results\"\nindex_filename = output_filename + \".\" + c + \".bin\"\n\nos.system(\"./benchmarks/build_from_ds2i \" + input_filename + \" \" + index_filename + \" --runopt >> \" + results)\n\nfor i in xrange(0,5):\n os.system(\"./benchmarks/pair_wise_intersect \" + index_filename + \" 1000 < \" + query_logs_path + \"/\" + output_filename + \".queries.mapped4096.2terms.shuffled >> \" + results)\nfor i in xrange(0,5):\n os.system(\"./benchmarks/pair_wise_union \" + index_filename + \" 1000 < \" + query_logs_path + \"/\" + output_filename + \".queries.mapped4096.2terms.shuffled >> \" + results)\nfor i in xrange(0,3):\n os.system(\"./benchmarks/select \" + index_filename + \" 1000 < \" + query_logs_path + \"/\" + output_filename + \".access.queries.1k >> \" + results)\nfor i in xrange(0,3):\n os.system(\"./benchmarks/next_geq \" + index_filename + \" 1000 < \" + query_logs_path + \"/\" + output_filename + \".next_geq.queries.1k >> \" + results)\nfor i in xrange(0,5):\n os.system(\"./benchmarks/decoding \" + index_filename + \" >> \" + results)\n\nos.system(\"rm \" + index_filename)\n","sub_path":"external/CRoaring/benchmarks/collect_results.py","file_name":"collect_results.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"494359084","text":"import abc\nimport base64\nimport hashlib\nfrom typing import Optional\n\nfrom bech32 import bech32_encode, convertbits\n\nfrom terra_sdk.core import AccAddress, AccPubKey, ValAddress, ValPubKey\nfrom terra_sdk.core.auth import StdSignature, StdSignMsg, StdTx\n\nBECH32_PUBKEY_DATA_PREFIX = \"eb5ae98721\"\n\n__all__ = [\"Key\"]\n\n\ndef get_bech(prefix: str, payload: str) -> str:\n data = convertbits(bytes.fromhex(payload), 8, 5)\n if data is None:\n raise ValueError(f\"could not parse data: prefix {prefix}, payload {payload}\")\n return bech32_encode(prefix, data) # base64 -> base32\n\n\ndef address_from_public_key(public_key: bytes) -> bytes:\n sha = hashlib.sha256()\n rip = hashlib.new(\"ripemd160\")\n sha.update(public_key)\n rip.update(sha.digest())\n return rip.digest()\n\n\ndef pubkey_from_public_key(public_key: bytes) -> bytes:\n arr = bytearray.fromhex(BECH32_PUBKEY_DATA_PREFIX)\n arr += bytearray(public_key)\n return bytes(arr)\n\n\nclass Key:\n \"\"\"Abstract Key interface, representing an agent with transaction-signing capabilities.\n\n Args:\n public_key (Optional[bytes]): compressed public key bytes,\n \"\"\"\n\n public_key: Optional[bytes]\n \"\"\"Compressed public key bytes, used to derive :data:`raw_address` and :data:`raw_pubkey`.\"\"\"\n\n raw_address: Optional[bytes]\n \"\"\"Raw Bech32 words of address, used to derive associated account and validator\n operator addresses.\n \"\"\"\n\n raw_pubkey: Optional[bytes]\n \"\"\"Raw Bech32 words of pubkey, used to derive associated account and validator\n pubkeys.\n \"\"\"\n\n def __init__(self, public_key: Optional[bytes] = None):\n self.public_key = public_key\n if public_key:\n self.raw_address = address_from_public_key(public_key)\n self.raw_pubkey = pubkey_from_public_key(public_key)\n\n @abc.abstractmethod\n def sign(self, payload: bytes) -> bytes:\n \"\"\"Signs the data payload. An implementation of Key is expected to override this method.\n\n Args:\n payload (bytes): arbitrary data payload\n\n Raises:\n NotImplementedError: if not implemented\n\n Returns:\n bytes: signed payload\n \"\"\"\n raise NotImplementedError(\"an instance of Key must implement Key.sign\")\n\n @property\n def acc_address(self) -> AccAddress:\n \"\"\"Terra Bech32 account address. Default derivation via :data:`public_key` is provided.\n\n Raises:\n ValueError: if Key was not initialized with proper public key\n\n Returns:\n AccAddress: account address\n \"\"\"\n if not self.raw_address:\n raise ValueError(\"could not compute acc_address: missing raw_address\")\n return AccAddress(get_bech(\"terra\", self.raw_address.hex()))\n\n @property\n def val_address(self) -> ValAddress:\n \"\"\"Terra Bech32 validator operator address. Default derivation via :data:`public_key` is provided.\n\n Raises:\n ValueError: if Key was not initialized with proper public key\n\n Returns:\n ValAddress: validator operator address\n \"\"\"\n if not self.raw_address:\n raise ValueError(\"could not compute val_address: missing raw_address\")\n return ValAddress(get_bech(\"terravaloper\", self.raw_address.hex()))\n\n @property\n def acc_pubkey(self) -> AccPubKey:\n \"\"\"Terra Bech32 account pubkey. Default derivation via :data:`public_key` is provided.\n\n Raises:\n ValueError: if Key was not initialized with proper public key\n\n Returns:\n AccPubKey: account pubkey\n \"\"\"\n if not self.raw_pubkey:\n raise ValueError(\"could not compute acc_pubkey: missing raw_pubkey\")\n return AccPubKey(get_bech(\"terrapub\", self.raw_pubkey.hex()))\n\n @property\n def val_pubkey(self) -> ValPubKey:\n \"\"\"Terra Bech32 validator pubkey. Default derivation via ``public_key`` is provided.\n\n Raises:\n ValueError: if Key was not initialized with proper public key\n\n Returns:\n ValPubKey: validator pubkey\n \"\"\"\n if not self.raw_pubkey:\n raise ValueError(\"could not compute val_pubkey: missing raw_pubkey\")\n return ValPubKey(get_bech(\"terravaloperpub\", self.raw_pubkey.hex()))\n\n def create_signature(self, tx: StdSignMsg) -> StdSignature:\n \"\"\"Signs the transaction with the signing algorithm provided by this Key implementation,\n and outputs the signature. The signature is only returned, and must be manually added to\n the ``signatures`` field of an :class:`StdTx`.\n\n Args:\n tx (StdSignMsg): unsigned transaction\n\n Raises:\n ValueError: if missing ``public_key``\n\n Returns:\n StdSignature: signature object\n \"\"\"\n if self.public_key is None:\n raise ValueError(\n \"signature could not be created: Key instance missing public_key\"\n )\n\n sig_buffer = self.sign(tx.to_json().strip().encode())\n return StdSignature.from_data(\n {\n \"signature\": base64.b64encode(sig_buffer).decode(),\n \"pub_key\": {\n \"type\": \"tendermint/PubKeySecp256k1\",\n \"value\": base64.b64encode(self.public_key).decode(),\n },\n }\n )\n\n def sign_tx(self, tx: StdSignMsg) -> StdTx:\n \"\"\"Signs the transaction with the signing algorithm provided by this Key implementation,\n and creates a ready-to-broadcast :class:`StdTx` object with the signature applied.\n\n Args:\n tx (StdSignMsg): unsigned transaction\n\n Returns:\n StdTx: ready-to-broadcast transaction object\n \"\"\"\n sig = self.create_signature(tx)\n return StdTx(tx.msgs, tx.fee, [sig], tx.memo)\n","sub_path":"terra_sdk/key/key.py","file_name":"key.py","file_ext":"py","file_size_in_byte":5825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"574943773","text":"# coding: utf-8\n\nimport os\nimport sys\nimport glob\nimport numpy as np\n\nfrom skimage import io\nfrom sklearn import datasets\n\nIMAGE_SIZE = 40\nCOLOR_BYTE = 3\nCATEGORY_NUM = 6\n\ndef load_handimage(path):\n\n files = glob.glob(os.path.join(path, '*/*.png'))\n\n images = np.ndarray((len(files), IMAGE_SIZE, IMAGE_SIZE, COLOR_BYTE), dtype=np.uint8)\n labels = np.ndarray(len(files), dtype=np.uint)\n\n for idx, file in enumerate(files):\n image = io.imread(file)\n images[idx] = image\n\n label = os.path.split(os.path.dirname(file))[-1]\n labels[idx] = int(label)\n\n flat_data = images.reshape((-1, IMAGE_SIZE * IMAGE_SIZE * COLOR_BYTE))\n images = flat_data.view()\n return datasets.base.Bunch(data=flat_data,\n target=labels.astype(np.int),\n target_names=np.arange(CATEGORY_NUM),\n images=images,\n DESCR=None)\n\nfrom sklearn import svm, metrics\n\nif __name__ == '__main__':\n argvs = sys.argv\n train_path = argvs[1]\n test_path = argvs[2]\n\n train = load_handimage(train_path)\n\n classifier = svm.LinearSVC()\n classifier.fit(train.data, train.target)\n\n test = load_handimage(test_path)\n\n predicted = classifier.predict(test.data)\n\n print(\"Accuracy:\\n%s\" % metrics.accuracy_score(test.target, predicted))\n","sub_path":"MachineLearningIntroduction/src/trial_handsign_SVM.py","file_name":"trial_handsign_SVM.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"168359089","text":"#__author__:baobao\r\r\n#date:2018/3/24\r\r\n#测试\r\r\ntest1,test2 = [1,[1,2]]\r\r\n#print(test1) 分别是1\r\r\n#print(test2) [1,2]\r\r\nprice = [['test',2222],['Mac',10000],['book',20],['bike',200],['kindle',3000]]#商品列表\r\r\nnun = []#存储商品编号的数组\r\r\ncart = []#购物车\r\r\nflag = False#判断是否终止程序的变量\r\r\n#获得用户初始金额\r\r\nsave = input('please input your money:')\r\r\nif save.isdigit():\r\r\n save = int(save)\r\r\nelse:\r\r\n print(\"invalid input\")\r\r\n exit()\r\r\n#定义方法\r\r\ndef user_choose():\r\r\n global save\r\r\n #把所有商品列出来\r\r\n for n,v in enumerate(price,1):#enumerate 展示n的索引，并且可以设置索引从第几开始，不写就默认从0开始\r\r\n print(\"商品编号:\", n, \",商品名称:\", v[0], \",商品价格:\", v[1])\r\r\n nun.append(n)\r\r\n #让用户自己选\r\r\n choose = input(\"请选择商品编号[填q退出]：\")\r\r\n #判断用户选择是否合法\r\r\n if choose.isdigit():\r\r\n choose = int(choose)\r\r\n is_in = choose in nun;\r\r\n if is_in == False:\r\r\n print(\"无该商品编号\")\r\r\n flag = False\r\r\n else:\r\r\n #print(\"你选择了商品\",price[choose-1])\r\r\n buy = price[choose-1]\r\r\n #把用户买的起的放进购物车\r\r\n if buy[1] <= save:\r\r\n save = save - buy[1]\r\r\n gnum = 1 #该商品的购买数量\r\r\n if len(cart) > 0:\r\r\n #如果在购物车内已经存在了，就追加数量\r\r\n for i,v in enumerate(cart):\r\r\n if buy[0] == v[0]:\r\r\n gnum = v[1] + 1\r\r\n cart[i] = [buy[0],gnum]\r\r\n #如果在购物车内不存在，就加入购物车\r\r\n if gnum == 1:\r\r\n cart.append([buy[0], gnum])\r\r\n else:\r\r\n cart.append([buy[0], gnum])\r\r\n\r\r\n #购物车列出\r\r\n for i in cart:\r\r\n print(\"您的购物车里已有%s件商品%s\"%(i[1],i[0]))\r\r\n flag = False\r\r\n\r\r\n else:\r\r\n print(\"您的余额不足，金额还剩:%s\"%save)\r\r\n flag = False\r\r\n elif choose == 'q':\r\r\n print(\"-----------------您已购买如下商品---------------\")\r\r\n for i in cart:\r\r\n print(\"您的购物车里已有%s件商品%s\" % (i[1], i[0]))\r\r\n print(\"您还剩余额：%s\"%save,\"元\")\r\r\n flag = True\r\r\n else:\r\r\n print(\"invalid input\")\r\r\n flag = False\r\r\n #结果输出\r\r\n return flag\r\r\n\r\r\n#执行程序\r\r\nwhile flag == False:\r\r\n flag = user_choose()\r\r\n\r\r\n#结果\r\r\n'''\r\r\n-----------------您已购买如下商品---------------\r\r\n您的购物车里已有2件商品test\r\r\n您的购物车里已有1件商品Mac\r\r\n您的购物车里已有3件商品book\r\r\n您的购物车里已有1件商品bike\r\r\n您的购物车里已有2件商品kindle\r\r\n您还剩余额：1518 元\r\r\n'''\r\r\n\r\r\n\r\r\n\r\r\n","sub_path":"public/uploads/20180328/cadb2fb3d31d60cfd2e7f0ed35528bd5.py","file_name":"cadb2fb3d31d60cfd2e7f0ed35528bd5.py","file_ext":"py","file_size_in_byte":3057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"616457998","text":"from .dll import _bind\nfrom ctypes import c_int, c_int8, c_uint8, c_int16, c_uint16, c_int32, \\\n c_uint32, c_int64, c_uint64, c_size_t, c_void_p, c_char_p\n\n__all__ = [\n # Defines\n \"Sint8\", \"Uint8\", \"Sint16\", \"Uint16\", \"Sint32\", \"Uint32\",\n \"Sint64\", \"Uint64\",\n\n # Enums\n \"SDL_bool\",\n \"SDL_FALSE\", \"SDL_TRUE\",\n \n # Functions\n \"SDL_malloc\", \"SDL_calloc\", \"SDL_realloc\", \"SDL_free\",\n \"SDL_getenv\", \"SDL_setenv\", \"SDL_abs\", \"SDL_min\", \"SDL_max\", \"SDL_clamp\",\n \"SDL_memset\", \"SDL_memcpy\"\n]\n\n\n# NOTE: Lots of functions in SDL_stdinc.h are not yet added here, but they're\n# mostly for math and string operations that Python can do much more easily.\n\nSDL_bool = c_int\nSDL_FALSE = 0\nSDL_TRUE = 1\n\nSint8 = c_int8\nUint8 = c_uint8\nSint16 = c_int16\nUint16 = c_uint16\nSint32 = c_int32\nUint32 = c_uint32\nSint64 = c_int64\nUint64 = c_uint64\n\nSDL_malloc = _bind(\"SDL_malloc\", [c_size_t], c_void_p)\nSDL_calloc = _bind(\"SDL_calloc\", [c_size_t, c_size_t], c_void_p)\nSDL_realloc = _bind(\"SDL_realloc\", [c_void_p, c_size_t], c_void_p)\nSDL_free = _bind(\"SDL_free\", [c_void_p], None)\nSDL_getenv = _bind(\"SDL_getenv\", [c_char_p], c_char_p)\nSDL_setenv = _bind(\"SDL_setenv\", [c_char_p, c_char_p, c_int], c_int)\nSDL_abs = abs\nSDL_min = min\nSDL_max = max\nSDL_memset = _bind(\"SDL_memset\", [c_void_p, c_int, c_size_t], c_void_p)\nSDL_memcpy = _bind(\"SDL_memcpy\", [c_void_p, c_void_p, c_size_t], c_void_p)\n\ndef SDL_clamp(x, a, b):\n if x < a:\n return a\n elif x > b:\n return b\n else:\n return x\n","sub_path":"sdl2/stdinc.py","file_name":"stdinc.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"227538943","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\ndef blur_image3():\n img = cv2.imread('butterfly_g_saltpepper_0.05.jpg')\n dst = cv2.medianBlur(img,5)\n plt.subplot(1, 2, 1),plt.imshow(img)\n plt.title('Original'),plt.xticks([]), plt.yticks([]), plt.subplot(1, 2, 2),plt.imshow(dst)\n plt.title('Blurred'),plt.xticks([]), plt.yticks([])\n plt.show()\nif __name__ == \"__main__\" :\n blur_image3()\n","sub_path":"Blurring_MedianFilter.py","file_name":"Blurring_MedianFilter.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"275229591","text":"try:\r\n r = []\r\n f=open(\"f:\\\\Py programs\\\\file handling\\\\A.txt\",\"r\")\r\n print(f.name)\r\n for s in f :\r\n #s=f.readline()\r\n #a=eval(s)\r\n #print(type(a))\r\n #print(a)\r\n #l=list(a)\r\n #print(l)\r\n line = s.split(',')\r\n line[2:] = map(int,line[2:])\r\n r = sum(line[2:])/3\r\n line.append(result)\r\n line = list(map(str,line))\r\n new_str = line.join(',')\r\n r.append(new_str)\r\n print(join(r))\r\n f.close()\r\n fp = open('f:\\\\Py programs\\\\file hadling\\\\A.txt',\"w\")\r\n fp.write('\\n'.join(r))\r\n fp.close()\r\n print(\"Operation sucessfull\")\r\nexcept:\r\n print(\"end of the line\")\r\n \r\n\r\n \r\n \r\n \r\n \r\n","sub_path":"file handling/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"67058718","text":"'''\nInput: a List of integers where every int except one shows up twice\nReturns: an integer\n'''\n\n# this is the first pass solution\n# this will loop through the array and will keep the number that doesn't have any \n# number equal to it\n# def single_number(arr):\n# # will loop through the array and will put the \n# # numbers in array \n# # will check if the number is in a second array.\n# # If the number is not in the array then will add the number to the array.\n# # If the number is in the array already then the number will be removed from the array.\n# single = []\n# for i in range(len(arr)):\n# if arr[i] not in single:\n# single.append(arr[i])\n# else:\n# single.remove(arr[i])\n# return single[0]\n\n\n\n\n# This is the second pass solution that has the space complexity of o(1)\ndef single_number(arr):\n # has to flags\n \n val = None\n # looping through the arr\n # will be popping off the value at the index asked for \n # then will ask if the value is in the rest of the\n # array. If it is not then will return the value \n # If it is in the array will remove pop this value again from the\n # array to make the array smaller\n # Doing a while loop\n # will continue to loop until there is just one element\n while len(arr) > 1:\n val = arr.pop() # removing the last element in the list\n if val in arr:\n arr.remove(val)\n else:\n break\n return val\n\nif __name__ == '__main__':\n # Use the main function to test your implementation\n arr = [1, 1, 4, 4, 5, 5, 3, 3, 9, 0, 0]\n\n print(f\"The odd-number-out is {single_number(arr)}\")","sub_path":"single_number/single_number.py","file_name":"single_number.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"142096216","text":"from sqlalchemy import create_engine\nimport pandas as pd\nimport tushare as ts\nimport time\nfrom datetime import datetime\nfrom datetime import timedelta\n# from numpy import numpy\n\ndef download_all_histroy():\n engine = create_engine('sqlite:///chinese.db')\n stock_basics = ts.get_stock_basics()\n total = len(stock_basics)\n current = 0\n for code, row in stock_basics.iterrows():\n if row['timeToMarket'] != 0 and not engine.dialect.has_table(engine, 'history_data_' + code):\n timeToMarket = str(row['timeToMarket'])\n timeToMarket = \"{}-{}-{}\".format(timeToMarket[0:4], timeToMarket[4:6], timeToMarket[6:8])\n print(\"\\n{}({}/{})\".format(code, current, total))\n\n def download_history(code):\n for _ in range(3):\n try:\n history = ts.get_h_data(code, start = timeToMarket, pause=2)\n history.to_sql('history_data_' + code, engine, if_exists='append')\n return\n except Exception as e:\n time.sleep(300)\n print(e)\n print(\"Failed to download code {}\".format(code))\n\n download_history(code)\n\n current += 1\n\nimport sqlite3\n\ndef merge_history_data():\n conn = sqlite3.connect('chinese.db')\n c = conn.cursor()\n c.execute('''CREATE TABLE IF NOT EXISTS history_data(\n code TEXT,\n date DATETIME,\n open FLOAT,\n high FLOAT,\n close FLOAT,\n low FLOAT,\n volume FLOAT,\n amount FLOAT,\n PRIMARY KEY (code,date) \n )''')\n c.execute('CREATE INDEX IF NOT EXISTS index_code ON history_data(code)')\n c.execute('CREATE INDEX IF NOT EXISTS index_date ON history_data(date)')\n counter = 0\n rows = c.execute(\"SELECT name from sqlite_master where type = 'table' AND name != 'history_data'\").fetchall() \n total_code = len(rows)\n for row in rows:\n table_name = row[0]\n code = str(table_name[-6:])\n c.execute(\"INSERT INTO history_data SELECT '{}' AS code, * FROM {}\".format(code,table_name))\n counter += 1\n print(\"{},{},{}\".format(code,counter,total_code))\n\n conn.commit()\n conn.close()\n print('ok.')\n\ndef update_new_histroy():\n conn = sqlite3.connect('chinese.db')\n c = conn.cursor()\n update_stock_basic = ts.get_stock_basics()\n total = len(update_stock_basic)\n current = 0\n for code,row in update_stock_basic.iterrows():\n def download_none_history(timeToMarket):\n for _ in range(3):\n try:\n none_history = ts.get_h_data(code,start = timeToMarket, pause = 4 )\n print(none_history)\n for date_none,row_none in none_history.iterrows():\n # print(f\"INSERT INTO history_data (code,date,open,high,close,low,volume,amount) VALUES ('{code}','{date_none}',{row_none.open},{row_none.high},{row_none.close},{row_none.low},{row_none.volume},{row_none.amount})\")\n print(\"Start Inserting Data To Sqlite...\")\n print(\"INSERT INTO history_data (code,date,open,high,close,low,volume,amount) VALUES (?,?,?,?,?,?,?,?)\",(code,str(date_none),row_none.open,row_none.high,row_none.close,row_none.low,row_none.volume,row_none.amount))\n # c.execute(f\"INSERT INTO history_data (code,date,open,high,close,low,volume,amount) VALUES ('{code}','{date_none}',{row_none.open},{row_none.high},{row_none.close},{row_none.low},{row_none.volume},{row_none.amount})\")\n c.execute(\"INSERT INTO history_data (code,date,open,high,close,low,volume,amount) VALUES (?,?,?,?,?,?,?,?)\",(code,str(date_none),row_none.open,row_none.high,row_none.close,row_none.low,row_none.volume,row_none.amount))\n return\n except Exception as e:\n time.sleep(60)\n print(e)\n print(\"Failed to download Data,Code: {}\".format(code)) \n last_date = c.execute(\"SELECT MAX(date) from history_data where code = '{}'\".format(code)).fetchall()\n print(\"Get Old History Timepoint...\" + str(last_date))\n if last_date[0][0] is None:\n timeToMarket = str(row['timeToMarket'])\n timeToMarket = \"{}-{}-{}\".format(timeToMarket[0:4], timeToMarket[4:6], timeToMarket[6:8])\n current += 1\n # print(\"\\n{}({}/{})\".format(\"Code:\"+code,\"Current:\"+current,\"Total Number:\"+total))\n download_none_history(timeToMarket)\n else:\n update_date = datetime.strptime(last_date[0][0][:10],'%Y-%m-%d')+timedelta(days=1)\n update_date_str = str(update_date)\n print(\"Changing TimePoint To update...\"+str(update_date_str))\n download_none_history(update_date_str)\n \n conn.commit()\n conn.close()\n\nif __name__ == '__main__':\n # merge_history_data()\n update_new_histroy()\n","sub_path":"mytest.py","file_name":"mytest.py","file_ext":"py","file_size_in_byte":4979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"102798644","text":"import brownie\nfrom brownie.test import strategy\nfrom .conftest import INITIAL_PRICES\nfrom .stateful_base import StatefulBase\n\n\nMAX_SAMPLES = 20\nMAX_COUNT = 20\nMAX_D = 10**12 * 10**18 # $1T is hopefully a reasonable cap for tests\n\n\nclass NumbaGoUp(StatefulBase):\n \"\"\"\n Test that profit goes up\n \"\"\"\n\n deposit_amounts = strategy('uint256[2]', min_value=0, max_value=10**9 * 10**18)\n token_amount = strategy('uint256', max_value=10**12 * 10**18)\n check_out_amount = strategy('bool')\n\n def rule_deposit(self, deposit_amounts, user):\n if self.swap.D() > MAX_D:\n return\n\n amounts = self.convert_amounts(deposit_amounts)\n if sum(amounts) == 0:\n return\n new_balances = [x + y for x, y in zip(self.balances, amounts)]\n\n for coin, q in zip(self.coins, amounts):\n coin._mint_for_testing(user, q)\n\n try:\n tokens = self.token.balanceOf(user)\n self.swap.add_liquidity(amounts, 0, {'from': user})\n tokens = self.token.balanceOf(user) - tokens\n self.total_supply += tokens\n self.balances = new_balances\n except Exception:\n try:\n assert self.swap.calc_token_amount(amounts) == 0\n except Exception:\n if sum(amounts) > 10000 and self.check_limits(amounts):\n raise\n else:\n return\n\n # This is to check that we didn't end up in a borked state after\n # an exchange succeeded\n try:\n self.swap.get_dy(0, 1, 10**(self.decimals[0]-2))\n except Exception:\n self.swap.get_dy.transact(1, 0, 10**16 * 10**self.decimals[1] // self.swap.price_scale())\n\n def rule_remove_liquidity(self, token_amount, user):\n if self.token.balanceOf(user) < token_amount or token_amount == 0:\n with brownie.reverts():\n self.swap.remove_liquidity(token_amount, [0] * 2, {'from': user})\n else:\n amounts = [c.balanceOf(user) for c in self.coins]\n tokens = self.token.balanceOf(user)\n self.swap.remove_liquidity(token_amount, [0] * 2, {'from': user})\n tokens -= self.token.balanceOf(user)\n self.total_supply -= tokens\n amounts = [(c.balanceOf(user) - a) for c, a in zip(self.coins, amounts)]\n self.balances = [b-a for a, b in zip(amounts, self.balances)]\n\n # Virtual price resets if everything is withdrawn\n if self.total_supply == 0:\n self.virtual_price = 10**18\n\n def rule_remove_liquidity_one_coin(self, token_amount, exchange_i, user, check_out_amount):\n if check_out_amount:\n self.swap.claim_admin_fees()\n\n try:\n calc_out_amount = self.swap.calc_withdraw_one_coin(token_amount, exchange_i)\n except Exception:\n if self.check_limits([0] * 2) and not (token_amount > self.total_supply) and token_amount > 10000:\n self.swap.calc_withdraw_one_coin.transact(token_amount, exchange_i)\n return\n\n d_token = self.token.balanceOf(user)\n if d_token < token_amount:\n with brownie.reverts():\n self.swap.remove_liquidity_one_coin(token_amount, exchange_i, 0, {'from': user})\n return\n\n d_balance = self.coins[exchange_i].balanceOf(user)\n try:\n self.swap.remove_liquidity_one_coin(token_amount, exchange_i, 0, {'from': user})\n except Exception:\n # Small amounts may fail with rounding errors\n if calc_out_amount > 100 and\\\n token_amount / self.total_supply > 1e-10 and\\\n calc_out_amount / self.swap.balances(exchange_i) > 1e-10:\n raise\n return\n\n # This is to check that we didn't end up in a borked state after\n # an exchange succeeded\n _deposit = [0] * 2\n _deposit[exchange_i] = 10**16 * 10**self.decimals[exchange_i] // ([10**18] + INITIAL_PRICES)[exchange_i]\n self.swap.calc_token_amount(_deposit)\n\n d_balance = self.coins[exchange_i].balanceOf(user) - d_balance\n d_token = d_token - self.token.balanceOf(user)\n\n if check_out_amount:\n if check_out_amount is True:\n assert calc_out_amount == d_balance, f\"{calc_out_amount} vs {d_balance} for {token_amount}\"\n else:\n assert abs(calc_out_amount - d_balance) <= max(check_out_amount * calc_out_amount, 5), f\"{calc_out_amount} vs {d_balance} for {token_amount}\"\n\n self.balances[exchange_i] -= d_balance\n self.total_supply -= d_token\n\n # Virtual price resets if everything is withdrawn\n if self.total_supply == 0:\n self.virtual_price = 10**18\n\n\ndef test_numba_go_up(crypto_swap, token, chain, accounts, coins, state_machine):\n from hypothesis._settings import HealthCheck\n\n state_machine(NumbaGoUp, chain, accounts, coins, crypto_swap, token,\n settings={'max_examples': MAX_SAMPLES, 'stateful_step_count': MAX_COUNT, 'suppress_health_check': HealthCheck.all()})\n","sub_path":"tests/twocrypto/test_stateful.py","file_name":"test_stateful.py","file_ext":"py","file_size_in_byte":5117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"384787204","text":"\"\"\"Logging utilities.\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport threading\n\nimport sys as _sys\nimport logging as _logging\nimport traceback as _traceback\n\n# Don't use this directly. Use get_logger() instead.\n_logger = None\n_logger_lock = threading.Lock()\n\n# Define log level\nDEBUG = _logging.DEBUG\nERROR = _logging.ERROR\nFATAL = _logging.FATAL\nINFO = _logging.INFO\nWARN = _logging.WARN\n\ndef _get_caller(offset=3):\n \"\"\"Returns a code and frame object for the lowest non-logging stack frame.\"\"\"\n # Use sys._getframe(). This avoids creating a traceback object.\n f = _sys._getframe(offset)\n our_file = f.f_code.co_filename\n f = f.f_back\n while f:\n code = f.f_code\n if code.co_filename != our_file:\n return code, f\n f = f.f_back\n return None, None\n\n# The definition of `findCaller` changed in Python 3.2,\n# and further changed in Python 3.8\nif _sys.version_info.major >= 3 and _sys.version_info.minor >= 8:\n\n def _logger_find_caller(stack_info=False, stacklevel=1):\n code, frame = _get_caller(4)\n sinfo = None\n if stack_info:\n sinfo = '\\n'.join(_traceback.format_stack())\n if code:\n return (code.co_filename, frame.f_lineno, code.co_name, sinfo)\n else:\n return '(unknown file)', 0, '(unknown function)', sinfo\nelif _sys.version_info.major >= 3 and _sys.version_info.minor >= 2:\n\n def _logger_find_caller(stack_info=False):\n code, frame = _get_caller(4)\n sinfo = None\n if stack_info:\n sinfo = '\\n'.join(_traceback.format_stack())\n if code:\n return (code.co_filename, frame.f_lineno, code.co_name, sinfo)\n else:\n return '(unknown file)', 0, '(unknown function)', sinfo\nelse:\n def _logger_find_caller():\n code, frame = _get_caller(4)\n if code:\n return (code.co_filename, frame.f_lineno, code.co_name)\n else:\n return '(unknown file)', 0, '(unknown function)'\n\n\ndef get_logger():\n \"\"\"Return logger instance\"\"\"\n global _logger\n\n if _logger:\n return _logger\n\n _logger_lock.acquire()\n\n try:\n if _logger:\n return _logger\n\n # scope the logger t not conflict when users' loggers.\n logger = _logging.getLogger('imitation')\n\n # Override findCaller on the logger to skip internal helper functions\n logger.findCaller = _logger_find_caller\n\n # Don't further configure the TensorFlow logger if the root logger is\n # already configured. This prevents double logging in those cases.\n if not _logging.getLogger().handlers:\n # Determine whether we are in an interactive environment\n _interactive = False\n try:\n if _sys.ps1: _interactive = True\n except AttributeError:\n _interactive = _sys.flags.interactive\n\n # If we are in an interactive environment (like jupyter), set loglevel\n # to INFO and pipe the ouput to stdout\n if _interactive:\n logger.setLevel(_logging.INFO)\n _logging_target = _sys.stdout\n else:\n _logging_target = _sys.stderr\n\n # Add the output handler.\n _handler = _logging.StreamHandler(_logging_target)\n _handler.setFormatter(_logging.Formatter(_logging.BASIC_FORMAT, None))\n logger.addHandler(_handler)\n _logger = logger\n return _logger\n finally:\n _logger_lock.release()\n\ndef log(level, msg, *args, **kwargs):\n get_logger().log(level, msg, *args, **kwargs)\n\ndef debug(msg, *args, **kwargs):\n get_logger().debug(msg, *args, **kwargs)\n\ndef error(msg, *args, **kwargs):\n get_logger().error(msg, *args, **kwargs)\n\ndef fatal(msg, *args, **kwargs):\n get_logger().fatal(msg, *args, **kwargs)\n\ndef info(msg, *args, **kwargs):\n get_logger().info(msg, *args, **kwargs)\n\ndef warn(msg, *args, **kwargs):\n get_logger().warning(msg, *args, **kwargs)\n\ndef warning(msg, *args, **kwargs):\n get_logger().warning(msg, *args, **kwargs)\n\ndef get_verbosity():\n \"\"\"Return how much logging output will be produced.\"\"\"\n return get_logger().getEffectiveLevel()\n\ndef set_verbosity(v):\n \"\"\"Sets the threshold for what messages will be logged.\"\"\"\n get_logger().setLevel(v)\n\n# Counter to keep track of number of log entries per token.\n_log_counter_per_token = {}\n\ndef _GetNextLogCountPerToken(token):\n \"\"\"Wrapper for _log_counter_per_token.\n Args:\n token: The token for which to look up the count.\n Returns:\n The number of times this function has been called with\n *token* as an argument (starting at 0)\n \"\"\"\n global _log_counter_per_token\n _log_counter_per_token[token] = 1 + _log_counter_per_token.get(token, -1)\n return _log_counter_per_token[token]\n\ndef _GetFileAndLine():\n \"\"\"Returns (filename, linenumber) for the stack frame.\"\"\"\n code, f = _get_caller()\n if not code:\n return ('', 0)\n return (code.co_filename, f.f_lineno)\n\ndef log_every_n(level, msg, n, *args):\n \"\"\"Log 'msg % args' at level 'level' once per 'n' times.\n Logs the 1st call, (N+1)st call, (2N+1)st call, etc.\n Not threadsafe.\n Args:\n level: The level at which to log.\n msg: The message to be logged.\n n: The number of times this should be called before it is logged.\n *args: The args to be substituted into the msg.\n \"\"\"\n count = _GetNextLogCountPerToken(_GetFileAndLine())\n log_if(level, msg, not (count % n), *args)\n\ndef log_first_n(level, msg, n, *args):\n \"\"\"Log 'msg % args' at level 'level' only first 'n' times.\n Not threadsafe.\n Args:\n level: The level at which to log.\n msg: The message to be logged.\n n: The number of times this should be called before it is logged.\n *args: The args to be substituted into the msg.\n \"\"\"\n count = _GetNextLogCountPerToken(_GetFileAndLine())\n log_if(level, msg, count < n, *args)\n\ndef vlog(level, msg, *args, **kwargs):\n get_logger().log(level, msg, *args, **kwargs)\n\ndef log_if(level, msg, condition, *args):\n \"\"\"Log 'msg % args' at level 'level' only if condition is fulfilled.\"\"\"\n if condition:\n vlog(level, msg, *args)\n","sub_path":"imitation/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":5907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"564263921","text":"import sys\n\n# For a collection of strings and a positive integer k, the overlap graph for the strings is a directed graph Ok in which each string is represented by a node, and\n# string s is connected to string t with a directed edge when there is a length k suffix of s that matches a length k prefix of t, as long as s≠t;\n# we demand s≠t to prevent directed loops in the overlap graph (although directed cycles may be present).\n#\n# Given: A collection of DNA strings in FASTA format having total length at most 10 kbp.\n#\n# Return: The adjacency list corresponding to O3. You may return edges in any order.\n\n\nfile = open(sys.argv[1],'r')\ndictionary = {}\n\n#function to iterate over keys in dictionary\ndef iterateKeys(value):\n theKey = \"\"\n for key in dictionary:\n if dictionary[key] == value:\n theKey = key\n return theKey\n\n#creates dictionary with IDs as Keys and the sequences as values\nfor line in file.readlines():\n if line.startswith('>'):\n ID = line.rstrip(\"\\n\")[1:]\n String = \"\"\n else:\n String += line.rstrip(\"\\n\")\n dictionary[ID] = String\n\n#checks for overlap of the suffixes and prefixes\nfor i in dictionary.values():\n for j in dictionary.values():\n if (i[-3:] == j[:3] and i != j):\n print(iterateKeys(i),iterateKeys(j))\n","sub_path":"Bioinformatics_Stronghold/Overlap_Graphs.py","file_name":"Overlap_Graphs.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"492261324","text":"#!/usr/bin/env python3\n\nimport attr\nimport click\n\n\nclass SectionedHelpCommand(click.Command):\n \"\"\"Sections commands into help groups\"\"\"\n\n def command(self, *args, **kwargs):\n help_group = kwargs.pop(\"help_group\")\n decorator = super().command(*args, **kwargs)\n\n def new_decorator(f):\n cmd = decorator(f)\n cmd.help_group = help_group\n self.grouped_commands.setdefault(help_group, []).append(cmd)\n return cmd\n\n return new_decorator\n\n def format_options(self, ctx, formatter):\n grouped_commands = {}\n for param in self.params:\n if hasattr(param, \"for_command\"):\n grouped_commands.setdefault(param.for_command, []).append(param.name)\n\n for group, cmds in grouped_commands.items():\n rows = []\n [choice_argument] = [\n param\n for param in self.params\n if isinstance(param, click.Argument)\n and isinstance(param.type, click.Choice)\n ]\n subcommand_name = ctx.params[choice_argument.name]\n for param in self.params:\n\n if param.name in [choice_argument.name, \"help\"]:\n continue\n if not param.name in grouped_commands[subcommand_name]:\n continue\n rows.append(param.get_help_record(ctx))\n\n if rows:\n with formatter.section(group):\n formatter.write_dl(rows)\n\n\n@click.group(chain=True, add_help_option=False)\ndef cli():\n pass\n\n\n@cli.command()\n@click.argument(\"arg\", type=click.Choice([int, float]))\ndef greet(arg):\n print(\"hello\")\n\n\ndef read_callback(ctx, **kwargs):\n if kwargs[\"help\"]:\n\n print(read.get_help(ctx))\n return\n\n print(\"read\", kwargs)\n\n\nclass OptionForCommand(click.Option):\n def __init__(self, *args, for_command, **kwargs):\n self.for_command = for_command\n super().__init__(*args, **kwargs)\n\n\nread = SectionedHelpCommand(\n \"read\",\n callback=click.pass_context(read_callback),\n params=[\n click.Argument([\"format\"], type=click.Choice([\"json\", \"csv\"])),\n click.Option([\"--help\"], is_flag=True, type=bool),\n OptionForCommand(\n [\"--header/--no-header\"], help=\"Is there a header?\", for_command=\"csv\"\n ),\n OptionForCommand(\n [\"--some-json-option\"], help=\"Something about json\", for_command=\"json\"\n ),\n ],\n add_help_option=False,\n)\n\ncli.add_command(read)\n\n\n@cli.command(add_help_option=False)\n@click.argument(\"format\")\ndef write(format):\n print(\"write\", format)\n\n\nif __name__ == \"__main__\":\n cli()\n","sub_path":"cl.py","file_name":"cl.py","file_ext":"py","file_size_in_byte":2687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"38715735","text":"\nimport argparse\nimport os\nimport numpy as np\nimport math\nimport itertools\nimport sys\n\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image, make_grid\n\nfrom torch.utils.data import DataLoader\nfrom torch.autograd import Variable\n\nfrom model import *\nfrom dataset import *\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\n\nos.makedirs(\"images/training\", exist_ok=True)\nos.makedirs(\"saved_models\", exist_ok=True)\n\ninEpoch = 0\ninNumEpochs = 200\ninDatasetName = \"img_align_celeba\"\ninBatchSize = 16\ninLearningRate = 0.0002\ninB1 = 0.9\ninB2 = 0.999\ninDecayEpoch = 100\ninNumCpu = 8\ninHrHeight = 256\ninHrWidth = 256\ninChannels = 3\ninSampleInterval = 100\ninCheckpointInterval = 5000\ninResidualBlocks = 23\ninWarmupBatches = 500\ninLambdaAdv = 5e-3\ninLambdaPixel = 1e-2\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\nhrShape = (inHrHeight, inHrWidth)\n\ngenerator = Generator(inChannels, filters = 64, numResBlocks= inResidualBlocks).to(device)\ndiscriminator = Discriminator(inputShape=(inChannels, *hrShape)).to(device)\nfeatureExtractor = FeatureExtractor().to(device)\n\nfeatureExtractor.eval()\n\n#Losses\ncriterionGAN = torch.nn.BCEWithLogitsLoss().to(device)\ncriterioncontent = torch.nn.L1Loss().to(device)\ncriterionPixel = torch.nn.L1Loss().to(device)\n\n# Load pretrained models\nif inEpoch != 0:\n generator.load_state_dict(torch.load(\"saved_models/generator_%d.pth\" % inEpoch))\n discriminator.load_state_dict(torch.load(\"saved_models/discriminator_%d.pth\" % inEpoch))\n\noptimizerG = torch.optim.Adam(generator.parameters(), lr = inLearningRate, betas=(inB1,inB2))\noptimizerD = torch.optim.Adam(discriminator.parameters(), lr=inLearningRate, betas=(inB1,inB2))\n\nTensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.Tensor\n\ndataLoader = DataLoader(\n ImageDataset(\"data/\", hrShape = hrShape),\n batch_size = inBatchSize,\n shuffle = True,\n num_workers = inNumCpu\n)\n\n# Training\n\nfor epoch in range(inEpoch, inNumEpochs):\n for i, imgs in enumerate(dataLoader):\n\n batchesDone = epoch * len(dataLoader) + 1\n\n imgsLr = Variable(imgs[\"lr\"].type(Tensor))\n imgsHr = Variable(imgs[\"hr\"].type(Tensor))\n\n valid = Variable(Tensor(np.ones((imgsLr.size(0), *discriminator.outputShape))), requires_grad = False)\n fake = Variable(Tensor(np.zeros((imgsLr.size(0), *discriminator.outputShape))), requires_grad = False)\n\n\n # Train Generators\n optimizerG.zero_grad()\n\n genHr = generator(imgsLr)\n\n lossPixel = criterionPixel(genHr, imgsHr)\n\n if batchesDone < inWarmupBatches:\n lossPixel.backward()\n optimizerG.step()\n print(\n \"[Epoch %d/%d] [Batch %d/%d] [G pixel: %f]\"\n % (epoch, inNumEpochs, i, len(dataLoader), lossPixel.item())\n )\n continue\n \n predReal = discriminator(imgsHr).detach()\n predFake = discriminator(genHr)\n\n lossGAN = criterionGAN(predFake - predReal.mean(0, keepdim=True), valid)\n\n genFeatures = featureExtractor(genHr)\n realFeatures = featureExtractor(imgsHr).detach()\n lossContent = criterionContent(genFeatures, realFeatures)\n\n lossG = lossContent + inLambdaAdv * lossGAN + inLambdaPixel*lossPixel\n\n lossG.backward()\n optimizerG.step()\n\n #Train discriminator\n optimizerD.zero_grad()\n\n predReal = discriminator(imgsHr)\n predFake = discriminator(genHr.detach())\n\n lossReal = criterionGAN(predReal - predFake.mean(0, keepdim=True), valid)\n lossFake = criterionGAN(predFake - predReal.mean(0, keepdim=True), fake)\n\n lossD = (lossReal + lossFake) / 2\n \n lossD.backward()\n optimizerD.step()\n\n #Log Progress\n\n print(\n \"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, content: %f, adv: %f, pixel: %f]\"\n %(\n epoch,\n inNumEpochs,\n i,\n len(dataLoader),\n lossD.item(),\n lossG.item(),\n lossContent.item(),\n lossGAN.item(),\n lossPixel.item(),\n )\n )\n\n if batchesDone % inSampleInterval == 0:\n imgsLr = nn.fucntional.interpolate(imgsLr, scale_factor = 4)\n imgGrid = denormalize(torch.cat((imgsLr, genHr),-1))\n save_image(imgGrid, \"images/training/%d.png\" % batchesDone, nrow=1, normalize = False)\n\n if batchesDone % inCheckpointInterval == 0:\n torch.save(generator.state_dict(), \"saved_models/generator_%d.pth\" %epoch)\n torch.save(discriminator.state_dict(), \"saved_models/discriminator_%d.pth\" %epoch)\n\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"339175077","text":"'''\nBased on https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html#face-detection\n\nLook here for more cascades: https://github.com/parulnith/Face-Detection-in-Python-using-OpenCV/tree/master/data/haarcascades\n\n\nEdited by David Goedicke\n'''\n\n\nimport numpy as np\nimport cv2\nimport sys\nimport os\nimport time\n\nimport digitalio\nimport board\nimport adafruit_rgb_display.st7789 as st7789\nimport subprocess\nimport qwiic_button\nimport RPi.GPIO as GPIO\n\nfrom PIL import Image, ImageDraw, ImageFont\nfrom subprocess import call, Popen\n\ncwd = os.getcwd()\n\n\ndef handle_speak(val):\n subprocess.run([\"sh\", \"GoogleTTS_demo.sh\", val])\n # call(f\"espeak -ven -k5 -s150 --stdout '{val}' | aplay\", shell=True)\n time.sleep(0.5)\n\n\ndef image_formatting(image2):\n image2 = image2.convert('RGB')\n # Scale the image to the smaller screen dimension\n image2 = image2.resize((240, 135), Image.BICUBIC)\n\n return image2\n\n\n# Configuration for CS and DC pins (these are PiTFT defaults):\ncs_pin = digitalio.DigitalInOut(board.CE0)\ndc_pin = digitalio.DigitalInOut(board.D25)\nreset_pin = digitalio.DigitalInOut(board.D24)\n\n# Config for display baudrate (default max is 24mhz):\nBAUDRATE = 64000000\n\n# Setup SPI bus using hardware SPI:\nspi = board.SPI()\n\n# Create the ST7789 display:\ndisp = st7789.ST7789(\n spi,\n cs=cs_pin,\n dc=dc_pin,\n rst=reset_pin,\n baudrate=BAUDRATE,\n width=135,\n height=240,\n x_offset=53,\n y_offset=40,\n)\n\nhardware = 'plughw:2,0'\n\n# Create blank image for drawing.\n# Make sure to create image with mode 'RGB' for full color.\nheight = disp.width # we swap height/width to rotate it to landscape!\nwidth = disp.height\nimage = Image.new(\"RGB\", (width, height))\nrotation = 90\n\n# Get drawing object to draw on image.\ndraw = ImageDraw.Draw(image)\n\n# Draw some shapes.\n# First define some constants to allow easy resizing of shapes.\npadding = -2\ntop = padding\nbottom = height - padding\n\n# Alternatively load a TTF font. Make sure the .ttf font file is in the\n# same directory as the python script!\n# Some other nice fonts to try: http://www.dafont.com/bitmap.php\nfont = ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\", 18)\n\n# Draw a black filled box to clear the image.\ndraw.rectangle((0, 0, width, height), outline=0, fill=(0, 0, 0))\ndisp.image(image, rotation)\n\n# Turn on the backlight\nbacklight = digitalio.DigitalInOut(board.D22)\nbacklight.switch_to_output()\nbacklight.value = True\n\n# Configuration for CS and DC pins (these are FeatherWing defaults on M0/M4):\ncs_pin = digitalio.DigitalInOut(board.CE0)\ndc_pin = digitalio.DigitalInOut(board.D25)\nreset_pin = None\n\n\n# Configure screen buttons\nbuttonA = digitalio.DigitalInOut(board.D23)\nbuttonB = digitalio.DigitalInOut(board.D24)\nbuttonA.switch_to_input()\nbuttonB.switch_to_input()\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n\n\nimg=None\nwebCam = False\nif(len(sys.argv)>1):\n try:\n print(\"I'll try to read your image\");\n img = cv2.imread(sys.argv[1])\n if img is None:\n print(\"Failed to load image file:\", sys.argv[1])\n except:\n print(\"Failed to load the image are you sure that:\", sys.argv[1],\"is a path to an image?\")\nelse:\n try:\n print(\"Trying to open the Webcam.\")\n cap = cv2.VideoCapture(0)\n if cap is None or not cap.isOpened():\n raise(\"No camera\")\n webCam = True\n except:\n img = cv2.imread(\"../data/test.jpg\")\n print(\"Using default image.\")\n\ni = 0\nwhile(True):\n if webCam:\n ret, img = cap.read()\n\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n for (x,y,w,h) in faces:\n img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n roi_gray = gray[y:y+h, x:x+w]\n roi_color = img[y:y+h, x:x+w]\n eyes = eye_cascade.detectMultiScale(roi_gray)\n for (ex,ey,ew,eh) in eyes:\n cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\n cv2.putText(img, \"Your eyes are open now. Take a pic?\",(50,50),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,),2,cv2.LINE_AA)\n if buttonA.value:\n cv2.imwrite('/pics/pic' + str(i) + '.jpg',gray)\n i += 1\n time.sleep(0.2)\n\n if webCam:\n cv2.imshow('face-detection (press q to quit.)',img)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n cap.release()\n break\n else:\n break\n\ncv2.imwrite('faces_detected.jpg',img)\ncv2.destroyAllWindows()\n","sub_path":"Lab 5/face-detection.py","file_name":"face-detection.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"174684174","text":"from flask import Flask, render_template, request, redirect\nfrom subjectHandler import SubjectHandler\nfrom contentHandler import ContentHandler\nimport os \n\napp = Flask(__name__, static_url_path='/static')\n\napp.debug = True\napp.secret_key = \"nothingfornow\"\n\n\n\n\n@app.route(\"/\")\ndef home():\n sh = SubjectHandler()\n subjects = sh.subjects\n primers = {}\n for ii in subjects:\n topics = sh.get_primers(ii)\n if topics:\n primers[ii] = topics\n return render_template('index.html', data=primers)\n\n\n@app.route(\"/read\")\ndef read():\n args = dict(request.args)\n if not args:\n return redirect('/')\n print(args)\n if 'subject' in args and 'primer' in args:\n path = os.path.join(args['subject'], args['primer'] + '.md')\n else:\n return redirect('/')\n ch = ContentHandler(path)\n \n try:\n content = ch.get_HTML()\n except:\n return redirect('/')\n else:\n return render_template('read.html', content = content, subject=args['subject'], primer=args['primer'])\n\nif __name__ == \"__main__\":\n app.run(port=8000)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"58023242","text":"num_students = int(input())\nstudents = []\ngrades = []\n\nfor i in range(num_students):\n name = input()\n grade = float(input())\n grades.append(grade)\n students.append([name, grade])\n\nsecond_lowest = sorted(list(set(grades)))[1]\n\nnames = [student[0] for student in students if student[1] == second_lowest]\nfor name in sorted(names): print(name)\n","sub_path":"nested_grades.py","file_name":"nested_grades.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"437455893","text":"#Programma 240.py\n# Autors: Māris Bokta\n# Grafiskās formas veidoÅana ar Python un Tkinter\n# -*- coding: utf-8 -*-\nfrom Tkinter import *\nW=Tk()\nW.title('Mana forma')\nuzr01 = Label(W, text='RTU Elektronikas un telekomunikāciju fakultāte')\nuzr01.pack(side=LEFT)\nuzr01.place(x=40, y= 60)\nW.geometry('300x200')\nbg=('#28c')\nfg=('blue')\nW.mainloop()\n","sub_path":"240.py","file_name":"240.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"336836822","text":"\"\"\"kulizaproject URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/2.0/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\n\"\"\"from django.contrib import admin\r\nfrom django.urls import path\r\n\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n]\"\"\"\r\n\r\nfrom django.conf.urls import include, url\r\nfrom django.contrib import admin\r\nfrom kuliza_cart.views import index, about, login_user, logout_user, products, subcategory, \\\r\n category, update_product, delete_product, update_category,delete_category,update_subcategory, \\\r\n delete_subcategory, add_to_cart, cart_checkout, catalog, update_catalog, delete_catalog, my_catalog\r\nfrom django.conf.urls.static import static\r\nfrom django.conf import settings\r\n\r\nurlpatterns = [\r\n url(r'^$',index),\r\n url(r'^products/$',products),\r\n url(r'^products/update/(?P\\d+)/$',update_product),\r\n url(r'^products/delete/(?P\\d+)/$',delete_product),\r\n url(r'^category/update/(?P\\d+)/$',update_category),\r\n url(r'^category/delete/(?P\\d+)/$',delete_category),\r\n url(r'^cart/add/(?P\\d+)/$',add_to_cart),\r\n url(r'^cart/checkout/$',cart_checkout),\r\n url(r'^subcategory/update/(?P\\d+)/$',update_subcategory),\r\n url(r'^subcategory/delete/(?P\\d+)/$',delete_subcategory),\r\n url(r'^catalog/$',catalog),\r\n url(r'^mycatalog/$',my_catalog),\r\n url(r'^catalog/update/(?P\\d+)/$',update_catalog),\r\n url(r'^catalog/delete/(?P\\d+)/$',delete_catalog),\r\n url(r'^category/$',category),\r\n url(r'^subcategory/$',subcategory),\r\n url(r'^login/$',login_user),\r\n url(r'^logout/$',logout_user),\r\n url(r'^home/$',index),\r\n url(r'^about_us/$',about),\r\n # url(r'^polls/', include('pollapp.urls')),\r\n url(r'^admin/', admin.site.urls),\r\n] \r\nurlpatterns = urlpatterns + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\r\nurlpatterns = urlpatterns + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\r\n\r\n","sub_path":"kulizaproject/kulizaproject/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"65886385","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport mpl_toolkits.mplot3d.axes3d as p3\r\nimport random as rd\r\n\r\n\r\n# Pas et nombre de points simulés\r\nh = 10**(-3)\r\nn = 600\r\nn2=n+1\r\n#Table\r\nL = 2.74\r\nl = 1.522\r\nH = 0.1525\r\nTable = np.array([[0,0,L,L,0,0 ,L ,L ,L/2,L/2,L/2,L/2,L/2],\r\n\t\t\t\t [0,l,l,0,0,l/2,l/2,0 ,0 ,0 ,l ,l ,0],\r\n\t\t\t\t [0,0,0,0,0,0 ,0 ,0 ,0 ,H ,H ,0 ,0]])\r\nd1, n1 = np.shape(Table)\r\nTheta = -90 #(°)\r\nu = np.array([np.cos(Theta*np.pi/180),0,np.sin(Theta*np.pi/180)])\r\nv = np.array([0,1,0])\r\nA = Table[:,2]\r\nCam = np.array([6.50,l/2,1.50])\r\n\r\n#=============== Paramètres de la simulation =============#\r\nalpha = 1.2*(10**(-3)) #Coeff de frottement visqueux\r\nm = 2.7*(10**(-3)) #Masse Balle\r\ng = 9.81 #Champ gravitationnel\r\nmagnus = 1.8*(10**(-4)) #Coeff Magnus\r\nµ = 0.2526 #Frottement au rebond\r\nr = 2*10**(-2) #Rayon balle\r\n\r\ndef vect(u,v): #Simplifie les expressions (produit vectoriel)\r\n w = np.zeros(3)\r\n w[0] = u[1]*v[2]-u[2]*v[1]\r\n w[1] = u[2]*v[0]-u[0]*v[2]\r\n w[2] = u[0]*v[1]-u[1]*v[0]\r\n return w\r\n\r\ndef f(U,w): #Fonction pour la méthode d'Euler\r\n du = U[0,:]\r\n d2u = -alpha*(np.sqrt(sum(du*du)))*du/m - g*np.array([0,0,1]) + magnus*vect(w,du)\r\n return np.array([d2u,du])\r\n\r\ndef norm(a): #Simplifie également (norme 2)\r\n n = np.sqrt(sum(a**2))\r\n return n\r\n\r\ndef e(vz): #Coeff de restitution en fonction de Vz\r\n Vz = abs(vz)\r\n if Vz<1.9:\r\n return 0.93\r\n else:\r\n return 1 - 0.037*Vz\r\n\r\n#=============== Gestion du rebond ====================#\r\ndef rebond(U,r,w): #Fonction qui permet de gérer le rebond\r\n Vc = U[0,:] + vect(np.array([0,0,r]),w)\r\n E = e(U[0,2])\r\n beta = 2.5*µ*(1+E)*abs(U[0,2])/norm(Vc)\r\n if beta>=1: ###Cas de Roulement\r\n tau = 0.4 #tau correspond au symbole alpha dans la théorie\r\n c = (1-tau)/r # (ou le symbole proportionnel)\r\n d = tau\r\n else: ###Cas de Glissement\r\n tau = beta/2.5\r\n c = 3*tau/(2*r)\r\n d = 1 - 3*tau/2\r\n THETA = np.zeros((6,6))\r\n A = np.diagflat([1-tau,1-tau,-E])\r\n B = np.diagflat([tau*r,0],1) + np.diagflat([-tau*r,0],-1)\r\n C = np.diagflat([-c,0],1) + np.diagflat([c,0],-1)\r\n D = np.diagflat([d,d,1])\r\n THETA[0:3,0:3] = A\r\n THETA[0:3,3:6] = B #A et B sont les mêmes quelque soit beta\r\n THETA[3:6,0:3] = C #C et D changent d'ou les variables c et d\r\n THETA[3:6,3:6] = D\r\n VW = np.zeros(6)\r\n VW[0:3] = U[0,:]\r\n VW[3:6] = w\r\n VW2 = np.dot(THETA,VW) #Multiplication matricielle\r\n return VW2\r\n\r\n#================== Simulation =====================#\r\ndef simulation(E):\r\n #print('Simulation :',n*h,'s')\r\n U0=np.array([[E[3],E[4],E[5]],[E[0],E[1],E[2]]]) #U0=[Vit,Pos]\r\n wo=[E[6],E[7],E[8]] #vecteur rotation\r\n X = [U0[1,0]] #Initialise liste coordonées trajectoire\r\n Y = [U0[1,1]]\r\n Z = [U0[1,2]]\r\n for p in range(n): #Application de la méthode d'Euler\r\n U0 = U0 + h*f(U0,wo)\r\n z0 = U0[1,2]\r\n X.append(U0[1,0])\r\n Y.append(U0[1,1])\r\n Z.append(U0[1,2]) #Cas d'un rebond :\r\n if z0<10**(-3) and sum(U0[0]*np.array([0,0,1]))<=0:\r\n VW = rebond(U0,r,wo)\r\n U0[0,:] = VW[0:3]\r\n wo = VW[3:6]\r\n Pos=[X,Y,Z]\r\n return(Pos)\r\n\r\n#Trajectoire pointée qu'on nous donne\r\nXp=[0.54,0.58,0.63,0.67,0.71,0.74,0.78,0.83,0.87,0.91,0.94,0.96,0.99,1]\r\nYp=[0.3,0.38,0.47,0.50,0.55,0.57,0.58,0.58,0.56,0.55,0.61,0.67,0.73,0.77]\r\nPp=[Xp,Yp]\r\n# plt.plot(Xp,Yp)\r\n# plt.show()\r\n\r\ndef pOrtho(M,A,u,v,n=100):\r\n H = np.zeros(3)\r\n H[:] = A\r\n for i in range(n):\r\n grad = ((sum(u*(M-H))*u) + (sum(v*(M-H))*v))/10\r\n H += grad\r\n return H\r\n\r\nH = pOrtho(Cam,A,u,v)\r\n\r\ndef pCentrale(M,S,A,u,v):\r\n P = pOrtho(S,A,u,v)\r\n Q = pOrtho(S,M,u,v)\r\n coeff = np.sqrt(sum((P-S)**2)/sum((Q-S)**2))\r\n H = S + coeff*(M-S)\r\n return H\r\n\r\ndef ProjCam(Object,Cam,A,u,v,axis=None,show=False):\r\n d,n = np.shape(Object)\r\n Imag = np.zeros((3,n))\r\n for i in range(n):\r\n C = pCentrale(Object[:,i],Cam,A,u,v)\r\n Imag[:,i] = C\r\n return Imag\r\n\r\ndef RefChg(Data,O,u,v,show=False):\r\n d,n = np.shape(Data)\r\n Ruv = np.zeros((2,n))\r\n for i in range(n):\r\n Ruv[0,i] = sum(u*(Data[:,i]-O))\r\n Ruv[1,i] = sum(v*(Data[:,i]-O))\r\n return Ruv\r\n\r\n\r\ndef reelcam(Pos): #Fonction permettant de passer les coordonnées réelles de la trajectoire simulée (3D) dans le plan 2D caméra. Paramètres comme caméra et theta et A a modifier dans fonction.\r\n Traj = np.zeros((3,len(Pos[0])))\r\n Traj[0,:] = Pos[0]\r\n Traj[1,:] = Pos[1]\r\n Traj[2,:] = Pos[2]\r\n H = pOrtho(Cam,A,u,v)\r\n\r\n Ref=np.array([2.74,0,0])\r\n I = ProjCam(Traj,Cam,A,u,v)\r\n R = RefChg(I,Ref,v,-u)\r\n Ps=[R[0,:],R[1,:]]\r\n return(Ps)\r\n\r\ndef Table2D(Table):\r\n Traj = np.zeros((3,len(Table[0])))\r\n Traj[0,:] = Table[0]\r\n Traj[1,:] = Table[1]\r\n Traj[2,:] = Table[2]\r\n I = ProjCam(Traj,Cam,A,u,v)\r\n R = RefChg(I,I[:,3],v,-u)\r\n Ps=[R[0,:],R[1,:]]\r\n return(Ps)\r\n\r\ndef coos(E):\r\n Pos=simulation(E) #Simule trajectoire; extrait liste coordonnées 3D Pos=[X1,Y1,Z1]\r\n Ps=echantillone(Pos)\r\n Pss=reelcam(Ps)\r\n return(Pss)\r\n\r\ndef echantillone(Pos): # Réduit liste coordonnées 2D simulées au bon nombre de points par rapport à Pp\r\n #print(n2)\r\n k=len(Pos[0])//(len(Xp)-1)\r\n Xss,Yss,Zss=[],[],[]\r\n #print(len(Pos[0])\r\n for i in range(len(Xp)-1):\r\n Xss.append(Pos[0][k*i])\r\n Yss.append(Pos[1][k*i])\r\n Zss.append(Pos[2][k*i])\r\n Xss.append(Pos[0][-1])\r\n Yss.append(Pos[1][-1])\r\n Zss.append(Pos[2][-1])\r\n return([Xss,Yss,Zss])\r\n\r\ndef Einitial():\r\n x=-0.6+3.45*rd.random()\r\n y=-0.25+2*rd.random()\r\n z=-0.3+0.6*rd.random()\r\n vx=15*rd.random()\r\n vy=-5+10*rd.random()\r\n vz=-8+16*rd.random()\r\n return([x,y,z,vx,vy,vz,0,0,0])\r\n\r\ndef ecartinitial(Pp,Pss):\r\n return(((Pp[0][0]-Pss[0][0])**2+(Pp[1][0]-Pss[1][0])**2)**0.5)\r\n\r\ndef fginitial(E,Pp): #Prend en entrée E=[Xo,V,Omega] et renvoye l'écart norme entre la trajectoire pointée et celle simulée dans plan2D\r\n Pos=simulation(E) #Simule trajectoire; extrait liste coordonnées 3D Pos=[X1,Y1,Z1]\r\n Ps=echantillone(Pos)\r\n Pss=reelcam(Ps)\r\n e=ecartinitial(Pp,Pss)\r\n return(e)\r\n\r\n\r\ndef grad(E,s): #a modifier en fonction des axes utilisés (pour l'instant z haut x longeur table, y largeur table)\r\n gra=np.zeros(9)\r\n if s==0:\r\n e=fginitial(E,Pp)\r\n if s==1:\r\n e=fg(E,Pp)\r\n\r\n if s==0:\r\n dx=0.02 #Pour le petit déplacement en position dx, on impose une même valeur à peu près égal à 1/100 ordre de grandeur en position(=2m)\r\n FF=[E[0]+dx,E[1],E[2],E[3],E[4],E[5],E[6],E[7],E[8]]\r\n ff=fginitial(FF,Pp)\r\n gra[0]=(ff-e)/dx\r\n\r\n GG=[E[0],E[1]+dx,E[2],E[3],E[4],E[5],E[6],E[7],E[8]]\r\n gg=fginitial(GG,Pp)\r\n gra[1]=(gg-e)/dx\r\n\r\n HH=[E[0],E[1],E[2]+dx,E[3],E[4],E[5],E[6],E[7],E[8]]\r\n hh=fginitial(HH,Pp)\r\n gra[2]=(hh-e)/dx\r\n#Pour le petit déplacement en vitesse, on prend des valeurs différentes car les vitesse selon les axes sont très différentes: la vitesse selon x est très impportant donc on prend dvx=0,2\r\n if s==1:\r\n II=[E[0],E[1],E[2],E[3]+0.02,E[4],E[5],E[6],E[7],E[8]]\r\n ii=fg(II,Pp)\r\n gra[3]=(ii-e)/0.02\r\n\r\n JJ=[E[0],E[1],E[2],E[3],E[4]+0.002,E[5],E[6],E[7],E[8]]\r\n jj=fg(JJ,Pp)\r\n gra[4]=(jj-e)/0.002\r\n\r\n KK=[E[0],E[1],E[2],E[3],E[4],E[5]+0.005,E[6],E[7],E[8]]\r\n kk=fg(KK,Pp)\r\n gra[5]=(kk-e)/0.005\r\n #print('dw')\r\n #dw=1.5#Pour le petit déplacement en effet dw, on impose une même valeur à peu près égal à 1/100 ordre de grandeur en effet(=)rad/s\r\n # LL=[E[0],E[1],E[2],E[3],E[4],E[5],E[6]+dw,E[7],E[8]]\r\n # ll=fg(LL,Pp)\r\n # gra[6]=(ll-e)/dw\r\n\r\n # MM=[E[0],E[1],E[2],E[3],E[4],E[5],E[6],E[7]+dw,E[8]]\r\n # mm=fg(MM,Pp)\r\n # gra[7]=(mm-e)/dw\r\n\r\n #NN=[E[0],E[1],E[2],E[3],E[4],E[5],E[6],E[7],E[8]+dw]\r\n # nn=fg(NN,Pp)\r\n # gra[8]=(nn-e)/dw\r\n return(gra)\r\n\r\n\r\ndef ecart(Pp,Pss): #Fonction calculant écart entre trajectoires 2D pointées et celle simulée\r\n e=0\r\n for i in range (len(Pp[0])):\r\n e=e+((Pp[0][i]-Pss[0][i])**2+(Pp[1][i]-Pss[1][i])**2)**0.5\r\n\r\n return(e)\r\n\r\ndef fg(E,Pp): #Prend en entrée E=[Xo,V,Omega] et renvoye l'écart norme entre la trajectoire pointée et celle simulée dans plan2D\r\n Pos=simulation(E) #Simule trajectoire; extrait liste coordonnées 3D Pos=[X1,Y1,Z1]\r\n Ps=echantillone(Pos)\r\n Pss=reelcam(Ps)\r\n #print(Pp)\r\n #print(Pss)\r\n e=ecart(Pp,Pss)\r\n return(e)\r\n\r\ndef choixdelta(FGG):\r\n if FGG<0.6:\r\n d=0.1\r\n if FGG>0.6 and FGG<0.8:\r\n d=0.2\r\n if FGG>0.8 and FGG<1:\r\n d=0.3\r\n if FGG>1 and FGG<1.2:\r\n d=0.4\r\n if FGG>1.2 and FGG<1.7:\r\n d=0.5\r\n if FGG>1.5 and FGG<3:\r\n d=0.6\r\n if FGG>3 and FGG<6:\r\n d=1.1\r\n if FGG>6:\r\n d=1.5\r\n return(d)\r\n\r\n\r\n\r\n\r\ndef moindrecarré(Pp):\r\n A=Einitial() #Vecteur d'Etat: Pos,Vit,Effet E=[Xo,Yo,Zo,Vxo,Vyo,Vzo,wxo,wyo,wzo] pris au hasard\r\n E=A\r\n gamma=0.1\r\n erreurinitial=[]\r\n\r\n n=0\r\n FG=fginitial(E,Pp)\r\n s=0\r\n\r\n while FG>0.01:\r\n print(n)\r\n print(FG)\r\n Q=grad(E,s)\r\n E=E-gamma*Q\r\n erreurinitial.append(FG)\r\n FG=fginitial(E,Pp)\r\n if FG>0.02 and FG<0.04:\r\n gamma=0.08\r\n if FG<0.02:\r\n gamma=0.008\r\n if FG<0.009:\r\n gamma=0.005\r\n n=n+1\r\n erreurinitial.append(FG)\r\n print('FIN INITIAL')\r\n s=1\r\n Epos=E\r\n m=0\r\n y=90\r\n FGG=fg(Epos,Pp)\r\n erreur=[FGG,FGG]\r\n delta=choixdelta(FGG)\r\n print(y)\r\n print(FGG)\r\n while (FGG>0.005 and m0.06:\r\n print(m)\r\n print(FGG)\r\n Q=grad(E,s)\r\n E=E-delta*Q\r\n erreur.append(FGG)\r\n FGG=fg(E,Pp)\r\n m=m+1\r\n delta=choixdelta(FGG)\r\n\r\n erreur.append(FGG)\r\n print('FINNNNN')\r\n return(A,E,Epos,n,erreurinitial,erreur)\r\n\r\n\r\n\r\n# def pgr():\r\n# erf=[]\r\n# E=[]\r\n# KK=[]\r\n# for i in range (20):\r\n# GG,FFF,SSS,n,erri,K=moindrecarré(Pp)\r\n# erf.append(K[-1])\r\n# print(n)\r\n# print(K[-1])\r\n# E.append(FFF)\r\n# KK.append(K)\r\n# return(E,erf,KK)\r\n\r\n#E,erf,KK=pgr()\r\n\r\n# for i in KK[0:6]:\r\n# L=[k for k in range(len(i))]\r\n# plt.plot(L,i)\r\n# plt.show()\r\n#\r\n#\r\n# z=min(erf)\r\n# for i in range(len(erf)):\r\n# if erf[i]==z:\r\n# k=i\r\n# S=E[i]\r\n#\r\n#\r\n# D=S.tolist()\r\n\r\nTable2D=Table2D(Table)\r\n\r\n#GG,FFF,SSS,n,erri,K=moindrecarré(Pp)\r\n\r\nFF=FFF.tolist()\r\nSS=SSS.tolist()\r\n\r\nprint(n)\r\nprint('Einitial')\r\nprint(GG)\r\nprint('Efinal')\r\nprint(FF)\r\nprint('Eposition')\r\nprint(SS)\r\nprint('erreur')\r\nprint(K)\r\n\r\n\r\n\r\nF=coos(FF)\r\nXF=F[0]\r\nYF=F[1]\r\nG=coos(GG)\r\nXG=G[0]\r\nYG=G[1]\r\n\r\nS=coos(SS)\r\nXS=S[0]\r\nYS=S[1]\r\n\r\nplt.figure(1)\r\nplt.plot(Table2D[0],Table2D[1],'bo')\r\nplt.plot(Table2D[0],Table2D[1],'k-')\r\nplt.plot(XF,YF,'g.',label='finale')\r\nplt.legend()\r\nplt.plot(XG,YG,'r.',label='initiale')\r\nplt.legend()\r\nplt.plot(XS,YS,'c.',label='pos')\r\nplt.legend()\r\n\r\nplt.plot(Xp,Yp,label='original pointée')\r\nplt.legend()\r\nplt.show()\r\n\r\nplt.figure(2)\r\nL=[i for i in range(len(K))]\r\nplt.plot(L,K)\r\nplt.show()","sub_path":"Programmes Python/Méthode 2.py","file_name":"Méthode 2.py","file_ext":"py","file_size_in_byte":11465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"262224479","text":"# Licensed Materials - Property of IBM\n# Copyright IBM Corp. 2015\n\nimport os\nimport sys\nimport inspect\nimport time\n\n# Add\n# \n# opt/.splpy/common/packages\n# opt/python/packages\n# opt/python/modules\n# opt/python/streams\n#\n# for this toolkitto the current Python path.\n\n# This is executed at runtime by the initialization\n# of a Python operator\n\n# This file is contained in\n# toolkit_root/opt/.splpy/common\n\ndef __splpy_addDirToPath(dir):\n if os.path.isdir(dir):\n if dir not in sys.path:\n sys.path.insert(0, dir)\n \ncommonDir = os.path.dirname(os.path.realpath(__file__))\nsplpyDir = os.path.dirname(commonDir)\noptDir = os.path.dirname(splpyDir)\npythonDir = os.path.join(optDir, 'python')\n\n__splpy_addDirToPath(os.path.join(splpyDir, 'packages'))\n\n__splpy_addDirToPath(os.path.join(pythonDir, 'streams'))\n__splpy_addDirToPath(os.path.join(pythonDir, 'packages'))\n__splpy_addDirToPath(os.path.join(pythonDir, 'modules'))\n","sub_path":"com.ibm.streamsx.topology/opt/python/templates/common/splpy_setup.py","file_name":"splpy_setup.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"357501397","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\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-10.12-x86_64/egg/infoblox_netmri/__init__.py\n# Compiled at: 2017-08-09 11:31:54\nfrom infoblox_netmri.client import InfobloxNetMRI\n__all__ = [\n 'InfobloxNetMRI']\n__author__ = 'John Belamaric'\n__email__ = 'jbelamaric@infoblox.com'\n__version__ = '0.1.5'","sub_path":"pycfiles/infoblox_netmri-0.1.5-py2.7/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"303080730","text":"import numpy as np\nimport pandas as pd\n\nTITLE_NAME = \"Team Averages NonZero\"\nSOURCE_NAME = \"team_averages_nonzero\"\nLABELS = [\"Team\",\n \"Average Scale\",\n \"Average Alliance Switch\",\n \"Average Opponent Switch\",\n \"Average Exchange\"]\n\n\ndef get_team_data(manager):\n teams_data = {}\n for entry in manager.entries:\n if not entry.board.alliance() == \"N\": # Check for Power ups\n\n if entry.team not in teams_data.keys(): # Make new list if team doesn't exist\n teams_data[entry.team] = []\n\n teams_data[entry.team].append((entry.count(\"Tele scale\"),\n entry.count(\"Tele alliance switch\"),\n entry.count(\"Tele opponent switch\"),\n entry.count(\"Tele exchange\")))\n\n return teams_data\n\n\ndef avg(x):\n return (lambda s, l: s / l if s > 0 and l > 0 else np.nan)(sum(x), len(x))\n\n\ndef get_rows(manager):\n for team, counts in get_team_data(manager).items():\n scale, a_switch, o_switch, exchange = map(lambda x: list(filter(bool, x)), zip(*counts))\n\n yield {\"Team\": team,\n \"Average Scale\": avg(scale),\n \"Average Alliance Switch\": avg(a_switch),\n \"Average Opponent Switch\": avg(o_switch),\n \"Average Exchange\": avg(exchange)\n }\n\n\ndef compute_table(manager):\n return pd.DataFrame(get_rows(manager), columns=LABELS)[LABELS]\n","sub_path":"tools/kbpy/adapters/offseason-2018/team_averages_nonzero.py","file_name":"team_averages_nonzero.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"587383102","text":"from kconMD.force import ComputeForces\nfrom kconMD import kconmd_logging\nimport time\nimport numpy as np\nclass kconMD(object):\n def __init__(self,pbfilename,xyzfilename=\"comb.xyz\",outputfilename=\"force.dat\",cell=[0,0,0],pbc=True,cutoff=6,unit=1,vdw=False,nproc=None):\n self.pbfilename=pbfilename\n self.xyzfilename=xyzfilename\n self.cell=cell\n self.pbc=pbc\n self.cutoff=cutoff\n self.outputfilename=outputfilename\n self.unit=unit\n self.vdw=vdw\n self.cf=None\n self.nproc=nproc\n\n def initcf(self):\n if self.cf==None:\n self.cf=ComputeForces(self.pbfilename,cell=self.cell,pbc=self.pbc,cutoff=self.cutoff,vdw=self.vdw,nproc=self.nproc)\n\n def printforce(self):\n time1=time.time()\n self.initcf()\n forces=self.cf.predictforcesfromxyz(self.xyzfilename)\n forces*=self.unit\n np.savetxt(self.outputfilename,forces,fmt='%16.9f')\n time2=time.time()\n kconmd_logging(\"Compute Forces: Time cosumed:\",time2-time1,\"s\")\n","sub_path":"kconMD/kconMD/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"411415828","text":"import RPi.GPIO as GPIO\nimport time\nimport sys\n\nif __name__ == '__main__':\n\tgpio_pin = int(sys.argv[1])\n\n\tGPIO.setmode(GPIO.BCM)\n\tGPIO.setup(gpio_pin,GPIO.IN)\n\n\told_state = GPIO.LOW\n\ttry:\n\t\twhile True:\n\t\t\tstate = GPIO.input(gpio_pin)\n\t\t\tif(state == GPIO.LOW and old_state == GPIO.HIGH):\n\t\t\t\ttry:\n\t\t\t\t\tf = open(\"/tmp/mm_path\")\n\t\t\t\t\tpath = f.read()\n\t\t\t\t\tf.close()\n\n\t\t\t\t\tif(len(path) > 0):\n\t\t\t\t\t\tpath = path.split('\\n')\n\t\t\t\t\t\tfmm = open(path[0]+\"/mm\", \"w\")\n\t\t\t\t\t\tfmm.close()\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tprint(e)\n\t\t\t\t\tpass\n\t\t\ttime.sleep(0.01)\n\t\t\told_state = state\n\texcept KeyboardInterrupt:\n\t\tGPIO.cleanup()\n","sub_path":"gpio_read.py","file_name":"gpio_read.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"460858470","text":"import os\nimport sys\n\nimport tensorflow as tf\nfrom tensorboard.backend.event_processing import event_accumulator\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\nimport pickle\nimport pandas as pd\nimport re\n\nfrom simulation.simulation_builder.summary import Dir\nfrom simulation.simulator_exceptions import InvalidExperimentValueError\n\n__DEBUG__ = False\n\n\nclass MultiExperimentSummaryExtractor(object):\n\n\tdef __init__(self, experiments):\n\t\texperiments = list(set(experiments))\n\t\tself.summary_extractors = {e:SummaryExtractor(e)\n\t\t\tfor e in experiments}\n\t\tself.df = None\n\n\tdef get_summary_extractor(self, name, simulation_num):\n\t\ttry:\n\t\t\treturn self.summary_extractors[name + '_' + str(simulation_num)]\n\t\texcept KeyError:\n\t\t\tsim_numbers = self.summary_extractors.keys().sort(key=lambda x: x.split('_')[-1])\n\t\t\tprint('The max number of simulation is', sim_numbers[-1], ', but given', simulation_num)\n\n\tdef get_summary_extractor_by_experiment_num(self, experiment_num):\n\t\ttry:\n\t\t\tname = [n for n in self.summary_extractors if str(experiment_num) == n.split('_')[-1]][0]\n\t\t\n\t\t\treturn self.summary_extractors[name]\n\t\texcept:\n\t\t\treturn None\n\n\t\n\tdef plot(self, keys, match=None, param_min=None, param_max=None, mark_lines=None, axis_color='royalblue'):\n\t\tdef sort_foo(x):\n\t\t\treturn int(x.split('_')[-1])\n\t\tparam_names_list = []\n\t\tfig, ax = plt.subplots()\n\t\tkeys = keys + ['mean']\n\t\tcompleted_labels = []\n\t\tfor k in sorted(self.summary_extractors, key=sort_foo):\n\t\t\textractor = self.summary_extractors[k]\n\t\t\tfor s in extractor.list_available_summaries():\n\t\t\t\tsumm_name = s.split('/') if match == 'exact' else s\n\t\t\t\tif all(x in summ_name for x in keys):\n\t\t\t\t\tx, y = extractor.get_summary(s)\n\t\t\t\t\t\n\t\t\t\t\tjs = extractor.get_description()\n\t\t\t\t\tparam_name = js['tuning_parameter_name']\n\t\t\t\t\tif param_name == 'tempfactor':\n\t\t\t\t\t\tparam_name = 'temp_factor'\n\t\t\t\t\tparam_names_list.append(param_name)\n\t\t\t\t\tparam_val = \"{:10.2f}\".format(float(js[param_name]))\n\t\t\t\t\tif param_min and param_min > float(param_val):\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif param_max and param_max < float(param_val):\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tlabel = k.split('_')[-1] + '_' + param_val +'/'+s\n\t\t\t\t\t\n\t\t\t\t\tif label in completed_labels:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tcompleted_labels.append(label)\n\t\t\t\t\tif mark_lines and float(param_val) in mark_lines:\n\n\t\t\t\t\t\tax.plot(x, y, label=label, linewidth=3.5)\n\t\t\t\t\telse:\t\n\t\t\t\t\t\tax.plot(x, y, label=label)\n\n\t\tbox = ax.get_position()\n\t\tax.set_position([box.x0, box.y0, box.width*2, box.height*2])\n\t\tax.legend(loc='center right', fancybox=True, shadow=True, \n\t\t\tbbox_to_anchor=(1.4, 0.5))\n\t\tax.title.set_text('Tuning parameter: ' + ', '.join(list(set(param_names_list))))\n\t\t#ax.xaxis.label.set_color('red')\n\t\t#ax.yaxis.label.set_color('red')\n\t\tax.tick_params(axis='x', colors='royalblue')\n\t\tax.tick_params(axis='y', colors='royalblue')\n\t\tax.spines['bottom'].set_color('royalblue')\n\t\tax.spines['left'].set_color('royalblue')\n\n\t\treturn fig\n\n\tdef create_df(self, variable_names, thresh=0.0, df_name=None):\n\t\tdef sort_foo(x):\n\t\t\tse = self.summary_extractors[x]\n\t\t\treturn se.get_description()['temp_factor']\n\n\t\tdf_name = 'None' if df_name is None else df_name\n\t\texperiment_num = 0\n\t\tindex = 0\n\t\tdf = None\n\t\tsumm_extractors_names = list(self.summary_extractors.keys())\n\n\t\tsumm_extractors_names.sort(key=sort_foo)\n\t\tfor experiment_name in summ_extractors_names:\n\t\t\tse = self.summary_extractors[experiment_name]\n\t\t\tsumms_names = [n for n in se.list_available_summaries()\n\t\t\t\tif all(x in re.split(r\"/|_\", n) for x in variable_names)]\n\n\t\t\tfor summ_name in summs_names:\n\t\t\t\tif 'mean' in summ_name:\n\t\t\t\t\tcontinue\n\t\t\t\tsim_num = int(summ_name.split('/')[0])\n\t\t\t\tif 'accept' in variable_names:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tid_ = int(summ_name.split('/')[0])\n\t\t\t\t\texcept ValueError:\n\t\t\t\t\t\tprint(summ_name.split('_'))\n\t\t\t\t\t\traise\n\t\t\t\telse:\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tid_ = int(re.split(r\"/|_\", summ_name)[-3])\n\t\t\t\t\texcept:\n\t\t\t\t\t\tprint(summ_name)\n\t\t\t\t\t\tprint(re.split(r\"/|_\", summ_name))\n\t\t\t\t\t\traise\n\t\t\t\tif df is None:\n\t\t\t\t\tcols = ['experiment', 'simulation', 'id', 'temp_factor']\n\t\t\t\t\tvalues = se.get_summary(summ_name)\n\t\t\t\t\tstart_indx = int(values[0].shape[0]*thresh)\n\t\t\t\t\tv_cols = [v[0] for v in values[0]]\n\t\t\t\t\tcols = cols + v_cols[start_indx:]\n\t\t\t\t\tdf = pd.DataFrame(columns=cols)\n\t\t\t\tjs = se.get_description()\n\t\t\t\ttemp_factor = js['temp_factor']\n\t\t\t\tvalues = se.get_summary(summ_name)\n\t\t\t\tvalues = [v[0] for v in values[1]]\n\t\t\t\tvalues = [experiment_num, sim_num, id_, temp_factor] + values[start_indx:]\n\t\t\t\ttry:\n\t\t\t\t\tdf.loc[index] = values\n\t\t\t\texcept ValueError:\n\t\t\t\t\tprint(len(values))\n\t\t\t\t\tprint(len(cols))\n\t\t\t\t\traise\n\t\t\t\tindex += 1\n\t\t\texperiment_num += 1\n\t\treturn df\n\t\t\n\n\tdef get_last_value_mean_std_report(self, df, experiment_num):\n\t\tn_ids = int(df.id.max())\n\n\t\tdf_exp = df[df.experiment==experiment_num]\n\n\t\tcolumns = df.columns.tolist()[4:]\n\t\t_df = df_exp[columns]\n\t\tres = {}\n\n\t\tfor id_ in range(n_ids):\n\t\t\td = _df[_df.id==id_]\n\t\t\tvals = d[[columns[-1]]]\n\t\t\tmean = vals.mean(axis=0)\n\t\t\tstddev = vals.std(axis=0, ddof=1)\n\t\t\tres = {\n\t\t\t\t'mean':mean,\n\t\t\t\t'stddev':stddev\n\t\t\t}\n\t\treturn res\n\n\tdef get_best_value_mean_std_report(self, df, experiment_num):\n\t\tn_ids = int(df.id.max())\n\n\t\tdf_exp = df[df.experiment==experiment_num]\n\n\t\tcolumns = df.columns.tolist()[4:]\n\t\t_df = df_exp[columns]\n\t\tres = {}\n\n\t\tfor id_ in range(n_ids):\n\t\t\td = _df[_df.id==id_]\n\t\t\tvals = d[d>.00000001].min(axis=1)\n\t\t\tmean = vals.mean(axis=0)\n\t\t\tstddev = vals.std(axis=0, ddof=1)\n\t\t\tres = {\n\t\t\t\t'mean':mean,\n\t\t\t\t'stddev':stddev\n\t\t\t}\n\t\treturn res\n\n\tdef get_accept_ratio_mean_std_report(self, df, experiment_num):\n \n\t\t# ids == replicas or ordered replicas\n\t\tn_ids = int(df.id.max()) + 1\n\n\t\tdf_exp = df[df.experiment==experiment_num]\n\n\t\tlast_column_name = df.columns.tolist()[-1]\n\t\tres = {}\n\t\tfor id_ in range(n_ids):\n\t\t\td = df_exp[df_exp.id==id_]\n\t\t\td = d[[last_column_name]]\n\t\t\tstddev = d[[last_column_name]].std(axis=0, ddof=1).tolist()[-1]\n\t\t\tmean = d[[last_column_name]].mean(axis=0).tolist()[-1]\n\t\t\tres[id_] = {'mean':mean,\n\t\t\t'stddev':stddev}\n\t\treturn res\n\n\tdef generate_accept_ratio_data_summarized(self):\n\t\t\n\t\tmean_of_means = []\n\t\tstddev_of_means = []\n\t\ttemp_factors = []\n\n\t\tdf = self.create_df(['accept', 'ratio', 'replica'])\n\n\t\tfor i in range(100):\n\t\t\ttemp_factor = self.get_summary_extractor_by_experiment_num(i)\n\t\t\t\n\t\t\tif temp_factor is not None:\n\t\t\t\ttemp_factor = temp_factor.get_description()['temp_factor']\n\t\t\telse:\n\t\t\t\tbreak\n\t\t\ttemp_factors.append(temp_factor)\n\t\t\treport = self.get_accept_ratio_mean_std_report(df, i)\n\t\t\tmom = self.get_mean_of_means_from_report(report)\n\t\t\tsom = self.get_stddev_of_means_from_report(report)\n\n\t\t\tmean_of_means.append(mom)\n\t\t\tstddev_of_means.append(som)\n\n\t\treturn mean_of_means, stddev_of_means, temp_factors\n\n\tdef generate_cross_entropy_data_summarized(self):\n\t\tmean_of_means = []\n\t\tstddev_of_means = []\n\t\ttemp_factors = []\n\n\t\tdf = self.create_df(['cross', 'replica'])\n\t\tfor i in range(100):\n\t\t\ttemp_factor = self.get_summary_extractor_by_experiment_num(i)\n\t\t\t\n\t\t\tif temp_factor is not None:\n\t\t\t\ttemp_factor = temp_factor.get_description()['temp_factor']\n\t\t\telse:\n\t\t\t\tbreak\n\t\t\ttemp_factors.append(temp_factor)\n\t\t\treport = self.get_accept_ratio_mean_std_report(df, i)\n\t\t\tmom = self.get_mean_of_means_from_report(report)\n\t\t\tsom = self.get_stddev_of_means_from_report(report)\n\n\t\t\tmean_of_means.append(mom)\n\t\t\tstddev_of_means.append(som)\n\n\t\treturn mean_of_means, stddev_of_means, temp_factors\n\n\n\n\t\t\n\tdef get_mean_of_means_from_report(self, report):\n\t\tmeans = [report[k]['mean'] for k in report]\n\t\treturn sum(means) / len(means)\n\t\t\n\tdef get_mean_of_stddevs_from_report(self, report):\n\t\tstddevs = [report[k]['stddev'] for k in report]\n\t\treturn sum(stddevs) / len(stddevs)\n\n\tdef get_stddev_of_means_from_report(self, report):\n\t\tmeans = [report[k]['mean'] for k in report]\n\t\treturn np.std(means, ddof=1)\n\n\tdef get_stddev_of_stddevs_from_report(self, report):\n\t\tstddevs = [report[k]['stddev'] for k in report]\n\t\treturn np.std(stddevs, ddof=1)\n\n\tdef plot_report(self, report):\n\t\treturn 1\n\nclass SummaryExtractor(object):\n\n\tdef __init__(self, name):\n\t\t\n\n\t\tself._dir = Dir(name)\n\t\tself.all_summs_dict = {}\n\t\t\n\t\tfor i in range(100):\n\t\t\ttry:\n\t\t\t\tself.all_summs_dict.update(extract_summary(\n\t\t\t\t\tself._dir.log_dir + self._dir.delim + str(i)), delim=self._dir.delim)\n\t\t\texcept FileNotFoundError: \n\t\t\t\tself.all_summs_dict.pop('delim', None)\n\t\t\t\t#print(i, 'simulations')\n\t\t\t\tself.n_experiments = i\n\t\t\t\tself._create_experiment_averages()\n\n\t\t\t\tbreak\n\n\tdef get_summary(self, summ_name, split=True):\n\t\t\"\"\"Returns numpy arrays (x, y) of summaries.\n\n\t\tArgs:\n\t\t\tsummary_type: Name of the scalar summary\n\t\t\t\n\n\t\tReturns:\n\t\t\t(x, y) numpy array\n\t\t\"\"\"\n\t\tif split:\n\t\t\treturn np.hsplit(self.all_summs_dict[summ_name], 2)\n\t\telse:\n\t\t\treturn self.all_summs_dict[summ_name]\n\n\tdef list_available_summaries(self):\n\t\treturn sorted(set([k for k in self.all_summs_dict.keys()]))\n\t\t\n\n\tdef plot(self, keys=['valid'], match=None, add_swap_marks=False):\n\t\tn_col = 0\n\n\t\tfig, ax = plt.subplots()\n\t\tfor s in self.list_available_summaries():\n\t\t\tsumm_name = s.split('/') if match == 'exact' else s\n\t\t\tif all(x in summ_name for x in keys):\n\t\t\t\tx, y = self.get_summary(s)\n\t\t\t\tax.plot(x, y, label=s)\n\t\t\t\tn_col += 1\n\t\t\t\t\n\t\tbox = ax.get_position()\n\t\tax.set_position([box.x0, box.y0, box.width*2, box.height*2])\n\t\tax.legend(loc='center right', fancybox=True, shadow=True, \n\t\t\tbbox_to_anchor=(1.6, 0.5))\n\n\t\tif add_swap_marks:\n\t\t\tjs = self.get_description()\n\t\t\tstep = js['swap_attempt_step']\n\t\t\ts = self.list_available_summaries()[0]\n\t\t\tx, y = self.get_summary(s)\n\t\t\tlen_ = int(x[-1][0])\n\t\t\tfor i in range(0, len_, step):\n\t\t\t\tax.axvline(x=i)\n\t\treturn fig\n\n\tdef get_description(self):\n\t\td = self._dir.delim\n\t\tfile = self._dir.log_dir.replace('summaries'+d, 'summaries'+d+'compressed'+d)\n\t\twith open(os.path.join(file, 'description.json')) as fo:\n\t\t\tjs = json.load(fo)\n\n\t\treturn js\n\n\tdef _create_experiment_averages(self):\n\t\tall_keys = self.list_available_summaries()\n\t\ttry:\n\t\t\tall_keys.sort(key=lambda x: x.split('/')[1] + x.split('/')[-1])\n\t\texcept IndexError:\n\t\t\t\n\t\t\traise\n\t\tcompleted_keys = []\n\n\t\tfor k in all_keys:\n\t\t\tif k in completed_keys:\n\t\t\t\tcontinue\n\t\t\tname = '/'.join(k.split('/')[1:])\n\t\t\tarrays = [self.get_summary(str(i) + '/' + name, split=False)\n\t\t\t\tfor i in range(self.n_experiments)]\n\t\t\tself.all_summs_dict['mean/' + name] = np.mean(np.array(arrays), axis=0)\n\n\ndef extract_summary(log_dir, delim=\"/\"):\n\t\"\"\"\n\tExtracts summaries from simulation `name`\n\n\tArgs:\n\t\tlog_dir: directory\n\t\ttag: summary name (e.g. cross_entropy, zero_one_loss ...)\n\n\tReturns:\n\t\tA dict where keys are names of the summary scalars and\n\t\tvals are numpy arrays of tuples (step, value)\n\n\t\"\"\"\t\n\n\tdelim =\"\\\\\" if 'win' in sys.platform else '/'\n\t\n\tcompressed_dir = log_dir.replace('summaries'+delim, 'summaries'+delim+'compressed'+delim)\n\tsummary_filename = os.path.join(compressed_dir, 'summary.pickle') \n\t\n\tsrc_description_file = os.path.join(delim.join(log_dir.split(delim)[:-1]), 'description.json')\n\tdst_description_file = os.path.join(delim.join(compressed_dir.split(delim)[:-1]), 'description.json')\n\n\tif not os.path.exists(compressed_dir):\n\t\t\n\t\tos.makedirs(compressed_dir)\n\n\t\twith open(src_description_file) as fo:\n\t\t\tjs = json.load(fo)\n\t\t\n\t\twith open(dst_description_file, 'w') as fo:\n\t\t\tjson.dump(js, fo, indent=4)\n\t\t\t\n\n\tif os.path.exists(summary_filename):\n\n\t\twith open(summary_filename, 'rb') as fo:\n\t\t\tres = pickle.load(fo)\n\t\t\treturn res\n\telse:\n\n\n\t\tsim_num = log_dir.split(delim)[-1]\n\t\tres = {}\n\t\tfor file in os.listdir(log_dir):\n\t\t\tfullpath = os.path.join(log_dir, file)\n\n\t\t\tif os.path.isdir(fullpath):\n\t\t\t\n\t\t\t\tfor _file in os.listdir(fullpath):\n\t\t\t\t\t\n\t\t\t\t\tfilename = os.path.join(fullpath, _file)\n\t\t\t\t\t\n\t\t\t\t\tea = event_accumulator.EventAccumulator(filename)\n\t\t\t\t\tea.Reload()\n\t\t\t\t\tfor k in ea.scalars.Keys():\n\t\t\t\t\t\tlc = np.stack(\n\t\t\t\t\t\t\t[np.asarray([scalar.step, scalar.value])\n\t\t\t\t\t\t\tfor scalar in ea.Scalars(k)])\n\t\t\t\t\t\tkey_name = sim_num + '/' + file + '/' + k.split('/')[-1]\n\t\t\t\t\t\tkey_name = '/'.join(key_name.split('/')[-3:])\n\t\t\t\t\t\tres[key_name] = lc\n\t\t\n\t\twith open(summary_filename, 'wb') as fo:\n\t\t\tpickle.dump(res, fo)\n\t\n\t\n\treturn res\n\ndef extract_and_remove_simulation(path):\n\tse = SummaryExtractor(path)\n\tse._dir.clean_dirs()\n\n\ndef generate_experiment_name(architecture_name=None, dataset='mnist', \n\ttemp_ratio=None, optimizer='PTLD', do_swaps=True, \n\tswap_proba='boltzmann', n_replicas=None, surface_view='energy', beta_0=None, \n\tloss_func_name='crossentropy', swap_attempt_step=None, burn_in_period=None, \n\tlearning_rate=None, version='v4'):\n\t\n\t\n\t\"\"\"Experiment name:\n\t____...\n\t_...\n\t__\n\n\t\tversion: 'v2' means that summary stores diffusion value\n\t\tversion: 'v3' means added burn-in period \n\t\tversion: 'v4' learning_rate has been added\n\t\"\"\"\n\n\n\tif ((architecture_name is None or architecture_name not in ['cnn', 'nn']) \n\t\tor (dataset is None or dataset not in ['mnist', 'cifar'])\n\t\tor (temp_ratio is None) \n\t\tor (optimizer is None or optimizer not in ['PTLD'])\n\t\tor (do_swaps is None or do_swaps not in [True, False, 'True', 'False'])\n\t\tor (swap_proba is None or swap_proba not in ['boltzmann'])\n\t\tor (n_replicas is None)\n\t\tor (surface_view is None or surface_view not in ['energy', 'info'])\n\t\tor (beta_0 is None)\n\t\tor (loss_func_name is None or loss_func_name not in ['crossentropy', 'zerooneloss', 'stun'])\n\t\tor (swap_attempt_step is None )\n\t\tor (burn_in_period is None)\n\t\tor (learning_rate is None)):\n\t\traise InvalidExperimentValueError()\n\n\tname = architecture_name + '_' + dataset + '_'\n\tname = name + str(temp_ratio) + '_' + optimizer + '_'\n\tname = name + str(do_swaps) + '_' + str(swap_proba) + '_' + str(n_replicas) + '_'\n\tname = name + surface_view + '_' + str(beta_0) + '_' \n\tname = name + loss_func_name + '_' + str(swap_attempt_step) + '_' + str(burn_in_period) + '_'\n\tname = name + str(learning_rate) + '_' + version\n\n\treturn name \n","sub_path":"simulation/simulator_utils.py","file_name":"simulator_utils.py","file_ext":"py","file_size_in_byte":13893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"494429080","text":"import argparse\nimport os\n\nimport pandas as pd\nfrom multiprocessing import Process, Manager\n\nfrom pathlib import Path\n\npd.options.mode.chained_assignment = None # default='warn'\n\n\ndef remove_triples(entities_removed: list, triple_set: pd.DataFrame, out_dict, set_name: str):\n \"\"\"\n Removes the entities contained in entities_removed from the triple set given in input\n :param set_name: name of the set processed, useful to save the processed set into the dictionary\n :param out_dict: shared dictionary useful for multiprocessing; needs a Manager.dict() object\n :param entities_removed: list of the entites removed from entites2id, useful to know which are the entities to remove\n from the sets containing these data\n :param triple_set: train, test, or validation set from which to remove triples in order to reduce the set\n :return: an updated triple set\n \"\"\"\n\n len_set = len(triple_set)\n for row in range(0, len_set):\n for col in (\"head\", \"tail\"):\n entity = triple_set.at[row, col]\n if entity in entities_removed:\n triple_set.iloc[[row]] = None\n break # it is not necessary to check if the entity is present in both head and tail\n out_dict[set_name] = triple_set.dropna() # dato che non è possibile rimuovere durante il ciclo\n\n\ndef reset_ids(data: pd.DataFrame):\n \"\"\"\n Reset the ids contained in the dataframe of entities or relations, starting again from 0; reset also the index of\n the dataframe\n :param data: dataframe containing entities or relations mapped with the ids; it has to have the second column named \"id\"\n :return: updated dataframe\n \"\"\"\n data.reset_index(drop=True, inplace=True)\n for index, row in data.iterrows():\n data.at[index, \"id\"] = index # riorganizzazione degli id\n\n return data.astype({'id': 'int32'})\n\n\ndef main_reduce_datasets(data_path, dataset_name, fraction_to_remove):\n \"\"\"\n Reduce a dataset whom files are contained into the path provided; it has to contain entity2id.txt, relation2id.txt,\n train.txt, test.txt, valid.txt\n :param data_path: path of the dataset containing the files listed before\n :param dataset_name: name of the dataset, useful to print messages\n :param fraction_to_remove: an integer for which the len of the entity2id set is divided, in order to remove that amount\n of entities\n :return: nothing\n \"\"\"\n print(f\"\\nStarted processing for {dataset_name}\")\n entities = pd.read_csv(data_path + 'entity2id.txt', sep='\\t', header=None, names=[\"entity\", \"id\"])\n relations = pd.read_csv(data_path + 'relation2id.txt', sep='\\t', header=None, names=[\"rel\", \"id\"])\n train = pd.read_csv(data_path + 'train.txt', sep='\\t', header=None, names=[\"head\", \"tail\", \"rel\"])\n test = pd.read_csv(data_path + 'test.txt', sep='\\t', header=None, names=[\"head\", \"tail\", \"rel\"])\n validation = pd.read_csv(data_path + 'valid.txt', sep='\\t', header=None, names=[\"head\", \"tail\", \"rel\"])\n\n num_entities_remove = int(len(entities) / fraction_to_remove) # numero delle entità che vogliamo rimuovere\n print(f\"entites of {dataset_name}: {len(entities)}\")\n # memorizziamo le prime x ('num_entities_remove') istanze per poi rimuoverle anche dagli altri set\n removed_entities = []\n for i in range(0, num_entities_remove):\n removed_entities.append(entities.iloc[i][0]) # 0 = prima colonna\n # rimozione delle entità\n entities = entities.iloc[num_entities_remove:]\n # entities = entities.drop([x for x in range(0, num_entities_remove)], inplace=True)\n entities = reset_ids(entities) # riorganizzazione degli id\n print(f\"entites of {dataset_name} after removal: {len(entities)}\")\n # print(entities.head(5))\n # multiprocessing\n manager = Manager()\n\n return_dict = manager.dict() # dizionario condiviso per ospitare gli output del multiprocessing\n process_list = []\n train_set_proc = Process(target=remove_triples, args=(removed_entities, train, return_dict, \"train\"))\n test_set_proc = Process(target=remove_triples, args=(removed_entities, test, return_dict, \"test\"))\n valid_set_proc = Process(target=remove_triples, args=(removed_entities, validation, return_dict, \"valid\"))\n process_list.append(train_set_proc)\n process_list.append(test_set_proc)\n process_list.append(valid_set_proc)\n # rimozione, dal training set, delle triple contenenti le entità rimosse\n for process in process_list:\n process.start()\n # print done after processes starts to optimize\n len_train = len(train)\n print(f\"\\ntrain samples of {dataset_name}: {len_train}\")\n len_validation = len(validation)\n print(f\"validation samples of {dataset_name}: {len_validation}\")\n len_test = len(test)\n print(f\"test samples of {dataset_name}: {len_test}\")\n print(f\"Reducing train, valid, and test for {dataset_name}...\\n\")\n\n for process in process_list:\n process.join()\n\n train = return_dict[\"train\"] # using the shared variable altered by the processes created before\n test = return_dict[\"test\"]\n validation = return_dict[\"valid\"]\n len_train = len(train)\n print(f\"train triples of {dataset_name} after removal: {len_train}\")\n\n len_test = len(test)\n print(f\"test triples of {dataset_name} after removal: {len_test}\")\n\n len_validation = len(validation)\n print(f\"validation triples of {dataset_name} after removal: {len_validation}\")\n\n # rimozione delle relazioni\n print(f\"relations of {dataset_name}: {len(relations)} \\n Reducing relations of {dataset_name}...\")\n for i in range(0, len(relations)):\n candidate_rel = relations.iloc[i][\"rel\"]\n # controllo che la relazione sia assente da qualsiasi set prima di rimuoverla\n\n if (candidate_rel not in train.rel.values) and (candidate_rel not in test.rel.values) and (\n candidate_rel not in validation.rel.values):\n relations.iloc[[i]] = None\n # print(f\"rimossa la relazione {candidate_rel}\")\n relations.dropna(inplace=True)\n relations = reset_ids(relations) # riorganizzazione degli id\n print(f\"relations of {dataset_name} after removal: {len(relations)}\")\n\n save_dir = f\"{data_path}reduced{fraction_to_remove}/\" #to save a subfolder with the fraction used\n Path(save_dir).mkdir(parents=True, exist_ok=True)\n train.to_csv(save_dir + 'train.txt', sep='\\t', header=None, index=None)\n test.to_csv(save_dir + 'test.txt', sep='\\t', header=None, index=None)\n validation.to_csv(save_dir + 'valid.txt', sep='\\t', header=None, index=None)\n relations.to_csv(save_dir + 'relation2id.txt', sep='\\t', header=None, index=None)\n\n entities.to_csv(save_dir + 'entity2id.txt', sep='\\t', header=None, index=None)\n print(f\"\\nAll files of {dataset_name} saved into path {save_dir}\")\n\n\nif __name__ == \"__main__\":\n \"\"\"\n General idea of the script: removes a portion of the entities from entity2id file, saving them into a list\n in order to remove them also from train, dev, and test sets; then, remove all the relations (from the file relation2id)\n that are not anymore in none of the sets (train, dev, test)\n \"\"\"\n parser = argparse.ArgumentParser(description='Reduction of datasets for toy executions')\n\n parser.add_argument('--data', dest='data_dir', type=str,\n help=\"Data folder containing the data\")\n args = parser.parse_args()\n datasets_dir = args.data_dir\n\n process_list = []\n for name in os.listdir(datasets_dir):\n dir = f\"{datasets_dir}{name}/\"\n print(dir)\n process_list.append(Process(target=main_reduce_datasets, args=(dir, name, 1.01)))\n process_list[-1].start() #start the last appended process\n\n for process in process_list:\n process.join()\n print(\"All the dataset have been reduced! ~ FINISH.\")\n","sub_path":"preprocessing/preprocessing_reduced_datasets.py","file_name":"preprocessing_reduced_datasets.py","file_ext":"py","file_size_in_byte":7828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"233805108","text":"\"\"\"\nchargenview.py\nThis is the character setup screen displayed when a player clicks the new\ngame option from the main menu\n\"\"\"\n\nimport pygame\nimport gui as GUI\nfrom . import shared\nfrom man import racelist\n\ndef charGenView(parent, character):\n\tresSettings = shared.getResolutionSettings('setupCharacter')\n\n\tbackPanel = GUI.BackPanel(\n\t\tresSettings['background']['size'],\n\t\tresSettings['background']['location'], \n\t\tdeleteOnClickOff = False,\n\t\tbackgroundImage = resSettings['background']['backgroundImage']\n\t\t)\n\n\tcharacterSheetHeading = GUI.TextBox(\n\t\tresSettings['characterSheetHeading']['size'],\n\t\tresSettings['characterSheetHeading']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\tfont = shared.fontDisplay,\n\t\twords = \"New Gladiator\",\n\t\tparent = backPanel\n\t\t)\n\n\tinfoHeading = GUI.TextBox(\n\t\tresSettings['infoHeading']['size'],\n\t\tresSettings['infoHeading']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\tfont = shared.fontDisplay,\n\t\twords = \"Info\",\n\t\tparent = backPanel\n\t\t)\n\n\tdef raceSelect(x):\n\t\tcharacter.race = x.object\n\t\tx.parent.highlight = x.parent.menulist.index(x)\n\n\traceMenu = GUI.NavMenu(\n\t\tshared.genObjectMenu(racelist, raceSelect),\n\t\tresSettings['raceMenu']['size'],\n\t\tresSettings['raceMenu']['location'],\n\t\tbackgroundImage=resSettings['raceMenu']['backgroundImage'],\n\t\thighlight=0,\n\t\tparent = backPanel\n\t\t)\n\n\traceDescription = GUI.DescBox(\n\t\tresSettings['raceDescription']['size'],\n\t\tresSettings['raceDescription']['location'],\n\t\tmatemenu=raceMenu,\n\t\tfocus=True,\n\t\tbackgroundImage=resSettings['raceDescription']['backgroundImage'],\n\t\tparent = backPanel\n\t\t)\n\n\tfirstNameLabel = GUI.TextBox(\n\t\tresSettings['firstNameLabel']['size'],\n\t\tresSettings['firstNameLabel']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\twords = \"First Name:\",\n\t\tparent = backPanel\n\t\t)\n\n\tlastNameLabel = GUI.TextBox(\n\t\tresSettings['lastNameLabel']['size'],\n\t\tresSettings['lastNameLabel']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\twords = \"Last Name:\",\n\t\tparent = backPanel\n\t\t)\n\n\taliasLabel = GUI.TextBox(\n\t\tresSettings['aliasLabel']['size'],\n\t\tresSettings['aliasLabel']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\twords = \"Chosen Alias:\",\n\t\tparent = backPanel\n\t\t)\n\n\tfirstNameInput = GUI.TypeBox(\n\t\tresSettings['firstNameInput']['size'],\n\t\tresSettings['firstNameInput']['location'],\n\t\tbackgroundImage=resSettings['firstNameInput']['backgroundImage'],\n\t\tparent = backPanel\n\t\t)\n\n\tlastNameInput = GUI.TypeBox(\n\t\tresSettings['lastNameInput']['size'],\n\t\tresSettings['lastNameInput']['location'],\n\t\tbackgroundImage=resSettings['lastNameInput']['backgroundImage'],\n\t\tparent = backPanel\n\t\t)\n\n\taliasInput = GUI.TypeBox(\n\t\tresSettings['aliasInput']['size'],\n\t\tresSettings['aliasInput']['location'],\n\t\tbackgroundImage=resSettings['aliasInput']['backgroundImage'],\n\t\tparent = backPanel\n\t\t)\n\n\tstatsHeading = GUI.TextBox(\n\t\tresSettings['statsHeading']['size'],\n\t\tresSettings['statsHeading']['location'],\n\t\tcolor = shared.black,\n\t\tbgcolor = shared.guiLight,\n\t\tfont = shared.fontDisplay,\n\t\twords = \"Stats\",\n\t\tparent = backPanel\n\t\t)\n\n\tdef getPointsToDistribute():\n\t\treturn \"Available Stat Points: \" + str(character.statPointsToDistribute)\n\t\n\tpointsToDistributeSettings = resSettings['pointsToDistribute']\n\texperience = GUI.DescBox(\n\t\tpointsToDistributeSettings['size'],\n\t\tpointsToDistributeSettings['location'],\n\t\twatch = getPointsToDistribute,\n\t\tbackgroundImage = pointsToDistributeSettings['backgroundImage']\n\t\t)\n\n\tstatLabelLocation = resSettings['statLabel']['location']\n\taddStatPointButtonLocation = resSettings['addStatPointButton']['location']\n\tremoveStatPointButtonLocation = resSettings['removeStatPointButton']['location']\n\tstatValueLocation = resSettings['statValue']['location']\n\tstatBonusLocation = resSettings['statBonus']['location']\n\n\tstatOrder = [\"STR\", \"DEX\", \"AGI\", \"CON\", \"INT\", \"WIS\", \"CHA\"]\n\tfor stat in statOrder:\n\t\tstatLabel = GUI.TextBox(\n\t\t\tresSettings['statLabel']['size'],\n\t\t\tstatLabelLocation,\n\t\t\tcolor = shared.black,\n\t\t\tbgcolor = shared.guiLight,\n\t\t\twords = stat + \":\",\n\t\t\tparent = backPanel\n\t\t)\n\n\t\tstatLabelLocation = (statLabelLocation[0], statLabelLocation[1] + 40)\n\n\t\tdef canAddPoints(stat):\n\t\t\tcanAdd = character.stats[stat] < 20 \n\t\t\treturn character.statPointsToDistribute and canAdd\n\t\t\t\n\t\taddStatPointButton = GUI.StatButton(\n\t\t\tresSettings['addStatPointButton']['size'],\n\t\t\taddStatPointButtonLocation,\n\t\t\tcharacter.assignStatPoint,\n\t\t\tbackgroundImage=resSettings['addStatPointButton']['backgroundImage'],\n\t\t\twords = '+',\n\t\t\tenabledCondition = canAddPoints,\n\t\t\tstat = stat,\n\t\t\tparent = backPanel\n\t\t)\n\n\t\taddStatPointButtonLocation = (addStatPointButtonLocation[0], addStatPointButtonLocation[1] + 40)\n\n\t\tdef canRemovePoints(stat):\n\t\t\treturn character.stats[stat] > 5\n\n\t\tremoveStatPointButton = GUI.StatButton(\n\t\t\tresSettings['removeStatPointButton']['size'],\n\t\t\tremoveStatPointButtonLocation,\n\t\t\tcharacter.removeStatPoint,\n\t\t\tbackgroundImage=resSettings['removeStatPointButton']['backgroundImage'],\n\t\t\twords = '-',\n\t\t\tenabledCondition = canRemovePoints,\n\t\t\tstat = stat,\n\t\t\tparent = backPanel\n\t\t)\n\n\t\tremoveStatPointButtonLocation = (removeStatPointButtonLocation[0], removeStatPointButtonLocation[1] + 40)\n\n\t\tstatValue = GUI.StatWatcher(\n\t\t\tresSettings['statValue']['size'],\n\t\t\tstatValueLocation,\n\t\t\tbackgroundImage=resSettings['statValue']['backgroundImage'],\n\t\t\twords = str(character.stats[stat]),\n\t\t\tstat = stat,\n\t\t\twatch = lambda stat: str(character.stats[stat]),\n\t\t\tparent = backPanel\n\t\t)\n\n\t\tstatValueLocation = (statValueLocation[0], statValueLocation[1] + 40)\n\n\t\tstatValue = GUI.StatWatcher(\n\t\t\tresSettings['statBonus']['size'],\n\t\t\tstatBonusLocation,\n\t\t\tbackgroundImage=resSettings['statBonus']['backgroundImage'],\n\t\t\twords = str(character.statsbonus[stat]),\n\t\t\tstat = stat,\n\t\t\twatch = lambda stat: str(character.statsbonus[stat]),\n\t\t\tparent = backPanel\n\t\t)\n\n\t\tstatBonusLocation = (statBonusLocation[0], statBonusLocation[1] + 40)\n\n\tdef acceptCharacter():\n\t\tname = [firstNameInput.words, lastNameInput.words, aliasInput.words]\n\t\tcharacter.accept(name)\n\t\tparent.setupLocations()\n\t\n\tacceptButtonProps = [{'name': \"Accept\", 'action': acceptCharacter}]\n\tacceptButton = GUI.NavMenu(\n\t\tacceptButtonProps,\n\t\tresSettings['acceptButton']['size'],\n\t\tresSettings['acceptButton']['location'],\n\t\tbackgroundImage=resSettings['acceptButton']['backgroundImage'],\n\t\tparent = backPanel\n\t\t)","sub_path":"views/chargenview.py","file_name":"chargenview.py","file_ext":"py","file_size_in_byte":6395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"391190650","text":"class union_find:\n def __init__(self, N):\n self.par = [i for i in range(N)]\n \n def root(self, i):\n if self.par[i] == i:\n return i\n else:\n # 経路圧縮\n self.par[i] = self.root(self.par[i])\n return self.par[i]\n \n def same(self, a, b):\n return self.root(a) == self.root(b)\n \n def unite(self, a, b):\n if not self.same(a, b):\n self.par[self.root(a)] = self.root(b)\n\nN = int(input())\nA = list(map(int, input().split()))\nl = 0\nr = N-1\nUF = union_find(2*10**5+1)\nans = 0\nwhile l <= r:\n if not UF.same(A[l], A[r]):\n ans += 1\n UF.unite(A[l], A[r])\n l += 1\n r -= 1\nprint(ans)","sub_path":"ABC206/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"431229590","text":"# -*- coding: utf-8 -*-\n\"\"\"\nPython library for Paessler's PRTG (http://www.paessler.com/)\n\"\"\"\n\nimport logging\nimport xml.etree.ElementTree as Et\n\nfrom urllib import request\nfrom prtg.cache import Cache\nfrom prtg.models import Sensor, Device, Status, PrtgObject\nfrom prtg.exceptions import BadTarget, UnknownResponse\n\n\nclass Connection(object):\n \"\"\"\n PRTG Connection Object\n \"\"\"\n\n def __init__(self):\n self.response = list()\n\n @staticmethod\n def _encode_response(response, tag):\n out = list()\n if any([tag == 'devices', tag =='sensors']):\n for item in response.findall('item'):\n i = dict()\n for attrib in item:\n i[attrib.tag] = attrib.text\n if tag == 'devices':\n out.append(Device(**i))\n if tag == 'sensors':\n out.append(Sensor(**i))\n\n if tag == 'status':\n i = dict()\n for item in response:\n i[item.tag] = item.text\n out.append(Status(**i))\n\n if tag == 'prtg':\n i = dict()\n for item in response:\n i[item.tag] = item.text\n out.append(PrtgObject(**i))\n\n return out\n\n def _process_response(self, response, expect_return=True):\n \"\"\"\n Process the response from the server.\n \"\"\"\n\n if expect_return:\n\n try:\n resp = Et.fromstring(response.read().decode('utf-8'))\n except Et.ParseError as e:\n raise UnknownResponse(e)\n try:\n ended = resp.attrib['listend'] # Catch KeyError and return finished\n except KeyError:\n ended = 1\n\n return self._encode_response(resp, resp.tag), ended\n\n def _build_request(self, query):\n \"\"\"\n Build the HTTP request.\n \"\"\"\n req, method = str(query), query.method\n logging.debug('REQUEST: target={} method={}'.format(req, method))\n return request.Request(url=req, method=method)\n\n def get_request(self, query):\n \"\"\"\n Make a single HTTP request\n \"\"\"\n req = self._build_request(query)\n logging.info('Making request: {}'.format(query))\n resp, ended = self._process_response(request.urlopen(req))\n self.response += resp\n if not int(ended): # Recursively request until PRTG indicates \"listend\"\n query.increment()\n self.get_request(query)\n\n\nclass Client(object):\n\n def __init__(self, endpoint, username, password):\n self.endpoint = endpoint\n self.username = username\n self.password = password\n self.cache = Cache()\n\n @staticmethod\n def query(query):\n conn = Connection()\n conn.get_request(query)\n return conn.response\n\n\"\"\"\n def refresh(self, query):\n logging.info('Refreshing content: {}'.format(content))\n devices = Query(target='table', endpoint=self.endpoint, username=self.username, password=self.password, content=content, counter=content)\n self.connection.get_paginated_request(devices)\n self.cache.write_content(devices.response)\n\n def update(self, content, attribute, value, replace=False):\n for index, obj in enumerate(content):\n logging.debug('Updating object: {} with {}={}'.format(obj, attribute, value))\n if attribute == 'tags':\n tags = value.split(',')\n if replace:\n obj.tags = value.split(',')\n else:\n obj.tags += [x for x in tags if x not in obj.tags]\n content[index] = obj\n self.cache.write_content(content, force=True)\n\n def content(self, content_name, parents=False, regex=None, attribute=None):\n response = list()\n for resp in self.cache.get_content(content_name):\n if not all([regex, attribute]):\n response.append(resp)\n else:\n if RegexMatch(resp, expression=regex, attribute=attribute):\n response.append(resp)\n if all([content_name == 'sensors', parents is True]):\n logging.info('Searching for parents.. this may take a while')\n p = list()\n ids = set()\n for index, child in enumerate(response):\n parent = self.cache.get_object(str(child.parentid)) # Parent device.\n if parent:\n ids.add(str(parent.objid)) # Lookup unique parent ids.\n else:\n logging.warning('Unable to find sensor parent')\n for parent in ids:\n p.append(self.cache.get_object(parent))\n response = p\n return response\n\"\"\"\n\n","sub_path":"prtg/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"347628649","text":"# Run this python script, then make sure the Hipchat json file is in the same directory.\n\nimport json, os, fnmatch\n\n# Print out what the current directory is, and\n# request input of what json file to work with:\ncwd = os.getcwd()\nprint(\"Your current directory is:\", cwd)\nprint(\"Below are the *.json files:\")\nlistofJson = os.listdir('.')\npattern = \"*.json\"\nfor entry in listofJson:\n if fnmatch.fnmatch(entry, pattern):\n print (entry)\n\n_jsonfile = input(\"Input the filename to work with: \")\nwith open(_jsonfile) as f:\n data = json.load(f)\n\n# To view the raw json cleaned-up so that it can make sense:\n# print(json.dumps(data, indent=3))\n#\n# OPTIONAL\n# print(type(data)\n# response: \n# print(type(data['items']))\n# response: \n# Show how many objects/dictionaries in 'items'\nprint (\"There are\", len(data['items']), \"messages in this file.\")\n\n# Get typical values (date/timestamp, who or where the msg is from, and the message itself)\n# in human readable format...\nfor msg in data['items']:\n if msg['message'] == None:\n #print(\"........... THIS IS A DELETED MESSAGE.........\")\n mention_name = msg['from']['mention_name']\n name = msg['from']['name']\n print(msg['date'], \"\\t\" + mention_name, \"\\t\" + name, \"\\t\" + \"THIS IS A DELETED MESSAGE.\")\n elif 'file' in msg:\n file_name = msg['file']['name']\n file_url = msg['file']['url']\n file_size = msg['file']['size']\n mention_name = msg['from']['mention_name']\n name = msg['from']['name']\n #print(\"This has an attachment.\")\n print(msg['date'], \"\\t\" + mention_name, \"\\t\" + name, \"\\t\" + msg['message'], \"\")\n elif msg['message'] != None and msg['type'] == 'message':\n # print(\"This is a normal message\")\n mention_name = msg['from']['mention_name']\n name = msg['from']['name']\n print(msg['date'], \"\\t\" + mention_name, \"\\t\" + name, \"\\t\" + msg['message'])\n elif msg['type'] == 'notification':\n print(msg['date'], \"\\t\" + \"\\t\" + \"\\t\" + msg['message'])\n else:\n print(\"Dunno know how to parse this object:\" + msg['id'])\n","sub_path":"hc.py","file_name":"hc.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"68802454","text":"import re\r\n\"\"\"\r\ncommon re pattern:\r\n1. find all links in a html:\r\n# re.findall('\"((http|https)://.*?)\"', html)\r\n\r\n2. remove tag:\r\n# html = re.sub('|', '', html) \r\n\r\n3. find email:\r\n\r\n\r\nmy note:\r\n\r\n# basic rules\r\n\\w 匹配字母数字及下划线\r\n\\W 匹配非字母数字及下划线\r\n\\s 匹配任意空白字符，等价于 [\\t\\n\\r\\f].\r\n\\S 匹配任意非空字符\r\n\\d 匹配任意数字，等价于 [0-9]\r\n\\D 匹配任意非数字\r\n\\A 匹配字符串开始\r\n\\Z 匹配字符串结束，如果是存在换行，只匹配到换行前的结束字符串\r\n\\z 匹配字符串结束\r\n\\G 匹配最后匹配完成的位置\r\n\\n 匹配一个换行符\r\n\\t 匹配一个制表符\r\n^ 匹配字符串的开头\r\n$ 匹配字符串的末尾。\r\n. 匹配任意字符，除了换行符，当re.DOTALL标记被指定时，则可以匹配包括换行符的任意字符。\r\n* 匹配0个或多个的表达式。\r\n+ 匹配1个或多个的表达式。\r\n? 匹配0个或1个由前面的正则表达式定义的片段，非贪婪方式\r\n{n} 精确匹配n个前面表达式。\r\na|b 匹配a或b\r\n() 匹配括号内的表达式，也表示一个组, use, group() to read stuff inside\r\n[...] 用来表示一组字符,单独列出：[amk] 匹配 'a'，'m'或'k'\r\n[^...] 不在[]中的字符：[^abc] 匹配除了a,b,c之外的字符。\r\n{n, m} 匹配 n 到 m 次由前面的正则表达式定义的片段，贪婪方式\r\n\r\n*****about flags*****\r\nre.I 使匹配对大小写不敏感\r\nre.L 做本地化识别（locale-aware）匹配\r\nre.M 多行匹配，影响 ^ 和 $\r\nre.S 使 . 匹配包括换行在内的所有字符\r\nre.U 根据Unicode字符集解析字符。这个标志影响 \\w, \\W, \\b, \\B.\r\nre.X 该标志通过给予你更灵活的格式以便你将正则表达式写得更易于理解。\r\n在网页匹配中较为常用的为re.S、re.I。\r\n\r\n********Attention !!! Trap here****************\r\n1. .*? greedy match\r\n2. but, if .*? in the end of line, it's none\r\n3. re.S is a flag.\r\n4. \\. match . \r\n5. . match blank space, try \\w\r\n\r\n1. re.match(), start from beginning\r\n2. re.search(), scan all strings, returns the first match\r\n3. re.findall(), find all match\r\n4. re.sub(old, new, target), replace something\r\n\r\n\"\"\"\r\n\r\n\r\n# ********************test start*****************\r\n\r\n# text = '''Hello 1234567 . World_This\r\n# is a RegexDemo'''\r\n\r\n# print(text)\r\n\r\n# findall(pattern, string, flags=0):\r\n# find a url\r\n# res = re.findall('[a-zA-Z]+://\\S*', text)\r\n# res = re.findall('[a-zA-Z]+://[^\\s]*', text)\r\n\r\n# for s in res:\r\n # print(s)\r\n\r\n# re.match(), group(), span()\r\n# res = re.match('^He.*?(\\d+).*?Regex(.*)', text, re.S)\r\n# print(res)\r\n#\r\n# print(res.group())\r\n# print(res.group(1)) # ???\r\n# print(res.group(2)) # ???\r\n# print(res.span())\r\n\r\n#\r\n# text = 'Extra stings Hello 1234567 ' \\\r\n# 'World_This is a Regex Demo Extra stings'\r\n# # print(text)\r\n#\r\n#\r\n# # res = re.match('Hello.*', text, re.S)\r\n# res = re.search('Hello.*', text, re.S)\r\n# print(res)\r\n# print(res.group())\r\n# print(res.span())\r\n\r\n# import re\r\n# import uuid\r\n#\r\n# a = uuid.uuid4()\r\n# print(\"uuid is : \", str(a))\r\n#\r\n# b = re.sub('-', '', str(a))\r\n#\r\n# print(\"new string is : \", b)\r\n#\r\n# c = re.sub('\\d+', '', b)\r\n# print(\"remove all digit char: \", c)\r\n\r\n# find email\r\n\r\n\r\n\r\ntext = 'My email is baoge@163.com.'\r\n\r\npattern = re.compile('\\w+@.+\\.\\w+')\r\n\r\nres = pattern.findall(text)\r\nprint(res)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"snippet/re_example.py","file_name":"re_example.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"514132459","text":"import re\nimport json\nimport time\nimport random\nimport requests\n\nrequests.packages.urllib3.disable_warnings()\n\ndef resolve(url, verify=False):\n \"\"\"\n resolve audio url\n :param url: like 'https://y.qq.com/n/yqq/song/000YU69H3N55rZ.html'\n :return:\n \"\"\"\n songmid = re.search('/(\\w+).html$', url).groups()[0]\n filename = 'C400' + songmid + '.m4a'\n guid = int(random.random() * 2147483647) * int(time.time() * 1000) % 10000000000\n\n d = {\n 'format': 'json',\n 'cid': 205361747,\n 'uin': 0,\n 'songmid': songmid,\n 'filename': filename,\n 'guid': guid,\n }\n r = requests.get('https://c.y.qq.com/base/fcgi-bin/fcg_music_express_mobile3.fcg', params=d, verify=False)\n vkey = json.loads(r.content)['data']['items'][0]['vkey']\n audio_url = 'http://dl.stream.qqmusic.qq.com/%s?vkey=%s&guid=%s&uin=0&fromtag=66' % (filename, vkey, guid)\n print(audio_url)\n return audio_url\n\nurl = resolve('https://y.qq.com/n/yqq/song/004UopHN3XV5h9.html')\n","sub_path":"music0.py","file_name":"music0.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"154869867","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n#------------Zad.4-----------\r\npd.set_option('display.max_columns', None)\r\npd.set_option('display.width', None)\r\n#pd.set_option('display.max_rows', None)\r\n\r\ndf_survey = pd.read_csv('survey_results_public.csv',usecols=['Respondent', 'Age', 'WorkWeekHrs'],index_col='Respondent')\r\n\r\n#.dropna - Remove missing values.\r\n# Usuwa brakujące wartości /inplace -> Usunie NaN / Usunie wiersze, w których brakuje co najmniej jednego elementu.\r\ndf_survey.dropna(inplace=True)\r\nprint(df_survey)\r\n\r\n#Określenie punktów dla wykresu:\r\n#ro - red circle\r\nplt.plot(df_survey['Age'], df_survey['WorkWeekHrs'], 'ro', markersize=1)\r\n#Nazwanie osi X:\r\nplt.xlabel('Age')\r\n#Nazwanie osi Y:\r\nplt.ylabel('Hours')\r\n#Funkcja pokazująca wykres:\r\nplt.show()\r\n\r\n\r\n\r\n\r\n#-----------Zad.5-----------\r\n\r\ndf_survey_2 = pd.read_csv('survey_results_public.csv',usecols=['Respondent', 'Age', 'WorkWeekHrs', 'Gender'],index_col='Respondent')\r\ndf_survey_2.dropna(inplace=True)\r\nprint(df_survey_2)\r\n\r\n#.loc -> uzyskuje dostęp do grupy wierszy i kolumn według etykiet\r\n# lub tablicy boolowskiej w podanej tabeli danych.\r\n#Kolumna: 'Gender', gdzie: 'Man' :\r\ndf_survey_2_man = df_survey_2.loc[df_survey_2['Gender'] == 'Man']\r\n#Określenie punktów dla wykresu:\r\nplt.plot(df_survey_2_man['Age'], df_survey_2_man['WorkWeekHrs'], 'ro', markersize=0.5)\r\n#Nazwanie osi X:\r\nplt.xlabel('Age')\r\n#Nazwanie osi Y:\r\nplt.ylabel('Hours')\r\n#Tytuł wykresu:\r\nplt.title('Man')\r\n#Funkcja pokazująca wykres:\r\nplt.show()\r\n\r\n#Kolumna 'Gender\", gdzie: 'Woman' :\r\ndf_survey_2_woman = df_survey_2.loc[df_survey_2['Gender'] == 'Woman']\r\nplt.plot(df_survey_2_woman['Age'], df_survey_2_woman['WorkWeekHrs'], 'ro', markersize=0.5)\r\nplt.xlabel('Age')\r\nplt.ylabel('Hours')\r\nplt.title('Woman')\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Zestaw_1/Zestaw_1.3.py","file_name":"Zestaw_1.3.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"132990354","text":"\"\"\"Sample Input 0\n\n12\ninsert 0 5\ninsert 1 10\ninsert 0 6\nprint\nremove 6\nappend 9\nappend 1\nsort\nprint\npop\nreverse\nprint\nSample Output 0\n\n[6, 5, 10]\n[1, 5, 9, 10]\n[9, 5, 1]\"\"\"\n\nn = int(input())\nlists = []\nnewList = []\nfor i in range(n):\n x = input().split()\n lists.append(x)\n\nfor i in range(len(lists)):\n if lists[i][0] == 'insert':\n x = int(lists[i][1])\n y = int(lists[i][2])\n\n newList.insert(x, y)\n elif lists[i][0] == 'print':\n print(newList)\n elif lists[i][0] == 'remove':\n newList.remove(int(lists[i][1]))\n elif lists[i][0] == 'append':\n newList.append(int(lists[i][1]))\n elif lists[i][0] == 'sort':\n newList.sort()\n elif lists[i][0] == 'pop':\n newList.pop()\n elif lists[i][0] == 'reverse':\n newList.reverse()\n","sub_path":"lists_operations.py","file_name":"lists_operations.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"215407803","text":"SOLUTION_DOESNT_EXIST = -1\n\n\nclass Solver:\n def __init__(self, direction, person_pos=0):\n super().__init__()\n self.direction = direction\n self.person_pos = person_pos\n\n def move(self, x_field):\n if x_field.is_hit_on_next(self.person_pos + self.direction):\n raise ValueError(\"Person is hit\")\n self.person_pos += self.direction\n\n def __eq__(self, other):\n if isinstance(other, Solver):\n return (self.direction == other.direction) and (self.person_pos == other.person_pos)\n else:\n return False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __hash__(self):\n return hash(self.__repr__())\n\n def __repr__(self):\n return \"Solver({0}, {1})\".format(self.direction, self.person_pos)\n\n\nclass Field:\n NUMBER_OF_ROADS = 11\n FIRST_ROAD_SPEED = 5\n FIRST_ROAD_DISTANCE = 18\n\n def __init__(self, car_positions):\n super().__init__()\n self.roads = []\n for ndx in range(Field.NUMBER_OF_ROADS):\n pos = car_positions[ndx] if len(car_positions) > ndx else 0\n self.roads.append(pos)\n self.solvers = self.generate_new_solvers(0)\n\n def make_a_move(self):\n for solver in self.solvers:\n solver.move(self)\n # move cars\n speed = Field.FIRST_ROAD_SPEED\n distance = Field.FIRST_ROAD_DISTANCE\n for ndx in range(Field.NUMBER_OF_ROADS):\n self.roads[ndx] -= speed\n if self.roads[ndx] < 0:\n self.roads[ndx] += distance\n speed += 1\n distance += 1\n\n def is_hit_on_next(self, road_number):\n return (road_number < 0) or (road_number < Field.NUMBER_OF_ROADS and (self.roads[road_number] >= 0 and self.roads[road_number] - Field.speed_of(road_number) <= 0))\n\n @staticmethod\n def speed_of(road_number):\n return Field.FIRST_ROAD_SPEED + road_number\n\n def get_allowed_moves(self, person_pos):\n free_directions = []\n if not self.is_hit_on_next(person_pos + 1):\n free_directions.append(1)\n if not self.is_hit_on_next(person_pos):\n free_directions.append(0)\n if not self.is_hit_on_next(person_pos - 1):\n free_directions.append(-1)\n return free_directions\n\n def generate_new_solvers(self, person_pos):\n new_solvers = set()\n for possible_move in self.get_allowed_moves(person_pos):\n new_solvers.add(Solver(possible_move, person_pos))\n return new_solvers\n\n @staticmethod\n def solve(x_field):\n iteration = 1\n new_solvers = set()\n while True:\n x_field.make_a_move()\n for solver in x_field.solvers:\n if solver.person_pos >= Field.NUMBER_OF_ROADS:\n return iteration\n new_solvers.update(x_field.generate_new_solvers(solver.person_pos))\n\n if len(new_solvers) == 0:\n return SOLUTION_DOESNT_EXIST\n\n x_field.solvers = list(new_solvers)\n new_solvers = set()\n iteration += 1\n\n\nif __name__ == '__main__':\n with open('/home/ipatrikeev/dev/input.txt') as f:\n case_number = int(f.readline())\n for round in range(case_number):\n car_positions = [int(pos) for pos in f.readline().split()]\n field = Field(car_positions)\n print(Field.solve(field), end=' ')\n","sub_path":"problem_solver.py","file_name":"problem_solver.py","file_ext":"py","file_size_in_byte":3429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"121686049","text":"\n\nfrom xai.brain.wordbase.nouns._margin import _MARGIN\n\n#calss header\nclass _MARGINS(_MARGIN, ):\n\tdef __init__(self,): \n\t\t_MARGIN.__init__(self)\n\t\tself.name = \"MARGINS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"margin\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_margins.py","file_name":"_margins.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"530660672","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#\n# memory_wrappers.py - You can find all the API used to manipulate the memory of the process.\n# Copyright (C) 2012 Axel \"0vercl0k\" Souchet - http://www.twitter.com/0vercl0k\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 ctypes import *\nfrom common import *\nfrom memory_constants import *\nimport threads\n\n# stdapi (ulong) Readmemory(void *buf,ulong addr,ulong size,int mode);\nReadmemory_TYPE = CFUNCTYPE(c_ulong, c_void_p, c_ulong, c_ulong, c_int)\nReadmemory = Readmemory_TYPE(resolve_api('Readmemory'))\n\n# stdapi (ulong) Writememory(const void *buf,ulong addr,ulong size,int mode);\nWritememory_TYPE = CFUNCTYPE(c_ulong, c_void_p, c_ulong, c_ulong, c_int)\nWritememory = Writememory_TYPE(resolve_api('Writememory'))\n\n# stdapi (int) Expression(t_result *result, wchar_t *expression, uchar *data, ulong base, ulong size, ulong threadid, ulong a, ulong b, ulong mode);\nExpression_TYPE = CFUNCTYPE(c_int, t_result_p, c_wchar_p, c_void_p, c_ulong, c_ulong, c_ulong, c_ulong, c_ulong, c_ulong)\nExpression_ = Expression_TYPE(resolve_api('Expression'))\n\n# stdapi (void) Flushmemorycache(void);\nFlushmemorycache_TYPE = CFUNCTYPE(None)\nFlushmemorycache = Flushmemorycache_TYPE(resolve_api('Flushmemorycache'))\n\n# stdapi (t_memory *) Findmemory(ulong addr);\nFindmemory_TYPE = CFUNCTYPE(t_memory_p, c_ulong)\nFindmemory = Findmemory_TYPE(resolve_api('Findmemory'))\n\ndef FlushMemoryCache():\n \"\"\"\n Flush the intern memory cache of OllyDBG2\n \"\"\"\n Flushmemorycache()\n\ndef WriteMemory(buff, addr = None, mode = 0):\n \"\"\"\n Write directly in the memory of the process\n \"\"\"\n\n # XXX: check if memory exists\n if addr == None:\n addr = threads.GetEip()\n \n b = create_string_buffer(buff)\n n = Writememory(\n c_void_p(addressof(b)),\n c_ulong(addr),\n c_ulong(sizeof(b) - 1), # create_string_buffer adds a null byte a the end\n c_int(mode)\n )\n\n # flush the cache after writing ; not sure it's good/required to do that though.\n FlushMemoryCache()\n\n return n\n\ndef ReadMemory(size, addr = None, mode = 0):\n \"\"\"\n Read the memory of the process at a specific address\n \"\"\"\n # XXX: test if the address exists\n if addr == None:\n addr = threads.GetEip()\n\n buff = create_string_buffer(size)\n Readmemory(\n c_void_p(addressof(buff)),\n c_ulong(addr),\n c_ulong(size),\n c_int(mode)\n )\n return buff.raw\n\ndef Expression(result, expression, data, base, size, threadid, a, b, mode):\n \"\"\"\n Let OllyDbg evaluate an expression for you:\n * get an exported function address easily thanks to the notation module.function_name\n \"\"\"\n r = Expression_(\n t_result_p(result),\n c_wchar_p(expression),\n c_void_p(data),\n c_ulong(base),\n c_ulong(size),\n c_ulong(threadid),\n c_ulong(a),\n c_ulong(b),\n c_ulong(mode)\n )\n\n if result.value == 'Unrecognized identifier':\n return None\n\n return r\n\ndef FindMemory(addr):\n \"\"\"\n Find a structure t_memory describing the memory addr points to\n \"\"\"\n return Findmemory(addr)\n","sub_path":"ollyapi/memory_wrappers.py","file_name":"memory_wrappers.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"454822473","text":"import math as mat\r\n\r\ndef F(f,x_):\r\n\td=1E-5\r\n\tr=0\r\n\te=0\r\n\tx=x_\r\n\twhile(mat.fabs(f(x))>1E-4):\r\n\t\te=f(x)*d\r\n\t\tr+=e\r\n\t\tx-=d\r\n\treturn r\r\n\r\ndef norm(x):\r\n\treturn mat.exp(-x**2/2)/(mat.pi*2)**(1/2)\r\n\r\ndef printT(a,b,n,Y):\r\n\tx=a\r\n\tfor i in range(len(Y)):\r\n\t\tprint(x,\":\",Y[i])\r\n\t\tx+=(b-a)/n\r\nx=0\r\nwhile(x!=123456):\r\n\tx=float(input(\"x:\"))\r\n\tif(x!=123456):\r\n\t\tprint(F(norm,x))\r\n\t\r\n","sub_path":"punto4/PZ.py","file_name":"PZ.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"82335396","text":"'''\n Time Complexity:\n O(n) (when n = length of pattern string)\n\n Space Complexity:\n O(n) (when n = length of pattern string)\n\n Did this code successfully run on LeetCode?:\n Yes\n\n Problems faced while coding this:\n None\n\n Approach:\n Create a list of space strings from s.\n If length of pattern != length of this list, return False\n Else, create a word-to-char map and a chat-to-word map.\n Now loop through the pattern by its indices and check equivalance in\n the maps according to the condition at L31 in this code snippet.\n'''\nclass Solution:\n def wordPattern(self, pattern: str, s: str) -> bool:\n word_list = s.split(' ')\n if len(pattern) != len(word_list):\n return False\n\n word_map = {}\n char_map = {}\n\n for idx, char in enumerate(pattern):\n if (char in char_map and char_map[char] != word_list[idx]) or (word_list[idx] in word_map and word_map[word_list[idx]] != char):\n return False\n char_map[char] = word_list[idx]\n word_map[word_list[idx]] = char\n return True\n","sub_path":"WordPattern.py","file_name":"WordPattern.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"264324990","text":"import numpy as np\nimport torch\nfrom nav_msgs.msg import Odometry\nimport sys, select, termios, tty\nfrom tf.transformations import euler_from_quaternion\n\n\nlabel2camera = {\n 1: 'camera11', 2: 'camera12', 3: 'camera13', 4: 'camera14',\n 5: 'camera41', 6: 'camera42', 7: 'camera43', 8: 'camera44'\n}\nlabel2camera_ = {\n 1: 'camera_11', 2: 'camera_12', 3: 'camera_13', 4: 'camera_14',\n 5: 'camera_41', 6: 'camera_42', 7: 'camera_43', 8: 'camera_44'\n}\n\ntransform = {'camera11': 0, 'camera12': 1, 'camera13': 2, 'camera14': 3,\n 'camera2': 4, 'camera3': 5,\n 'camera41': 6, 'camera42': 7, 'camera43': 8, 'camera44': 9}\n\ndef check_numpy_to_torch(x):\n if isinstance(x, np.ndarray):\n return torch.from_numpy(x).float(), True\n return x, False\n\n\ndef get_gt_boxes3d(odom):\n \"\"\"\n output boxes3d according to odom meessage (just for one car temporarily)\n \"\"\"\n if isinstance(odom, Odometry):\n r, p, y = euler_from_quaternion([odom.pose.pose.orientation.x, odom.pose.pose.orientation.y,\n odom.pose.pose.orientation.z, odom.pose.pose.orientation.w])\n gt_boxes3d = [np.array([odom.pose.pose.position.x, odom.pose.pose.position.y, 0.1120, 0.33, 0.22, 0.21, y])]\n elif isinstance(odom, np.ndarray):\n r, p, y = euler_from_quaternion([odom[3], odom[4], odom[5], odom[2]])\n gt_boxes3d = [np.array([odom[0], odom[1], 0.1120, 0.33, 0.22, 0.21, y])]\n return np.array(gt_boxes3d)\n\n\ndef getKey(settings):\n tty.setraw(sys.stdin.fileno())\n select.select([sys.stdin], [], [], 0)\n key = sys.stdin.read(1)\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, settings)\n return key\n","sub_path":"src/site_model/src/utils/common_utils.py","file_name":"common_utils.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"489321001","text":"\"\"\"\nTaken from Frans Slothoubers post on the contest discussion forum:\n https://www.kaggle.com/slothouber/two-sigma-financial-modeling/kagglegym-emulation\n\"\"\"\nimport numpy\nimport os\nimport pandas\nimport sklearn.metrics\n\ndef r_score(y_true, y_pred, sample_weight=None, multioutput=None):\n \"\"\"\n DOCSTRING\n \"\"\"\n r2 = sklearn.metrics.r2_score(y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput)\n r = (numpy.sign(r2)*numpy.sqrt(numpy.abs(r2)))\n if r <= -1:\n return -1\n else:\n return r\n\nclass Environment:\n \"\"\"\n DOCSTRING\n \"\"\"\n def __init__(self):\n with pandas.HDFStore(\"/content/drive/My Drive/train.h5\", \"r\") as hfdata:\n self.timestamp = 0\n fullset = hfdata.get(\"train\")\n self.unique_timestamp = fullset[\"timestamp\"].unique()\n n = len(self.unique_timestamp)\n i = int(n / 2)\n timesplit = self.unique_timestamp[i]\n self.n = n\n self.unique_idx = i\n self.train = fullset[fullset.timestamp < timesplit]\n self.test = fullset[fullset.timestamp >= timesplit]\n self.full = self.test.loc[:, ['timestamp', 'y']]\n self.full['y_hat'] = 0.0\n self.temp_test_y = None\n\n def __str__(self):\n return \"Environment()\"\n\n def reset(self):\n \"\"\"\n DOCSTRING\n \"\"\"\n timesplit = self.unique_timestamp[self.unique_idx]\n self.unique_idx = int(self.n / 2)\n self.unique_idx += 1\n subset = self.test[self.test.timestamp == timesplit]\n # reset index to conform to kaggle gym\n target = subset.loc[:, ['id', 'y']].reset_index(drop=True)\n self.temp_test_y = target['y']\n target.loc[:, 'y'] = 0.0 # set the prediction column to zero\n # changed bounds to 0:110 from 1:111 to mimic the behavior of api\n features = subset.iloc[:, :110].reset_index(drop=True)\n observation = Observation(self.train, target, features)\n return observation\n\n def step(self, target):\n \"\"\"\n DOCSTRING\n \"\"\"\n timesplit = self.unique_timestamp[self.unique_idx-1]\n # Since full and target have a different index we need\n # to do a _values trick here to get the assignment working\n y_hat = target.loc[:, ['y']]\n self.full.loc[self.full.timestamp == timesplit, ['y_hat']] = y_hat._values\n if self.unique_idx == self.n:\n done = True\n observation = None\n reward = r_score(self.temp_test_y, target.loc[:, 'y'])\n score = r_score(self.full['y'], self.full['y_hat'])\n info = {'public_score': -score}\n else:\n reward = r_score(self.temp_test_y, target.loc[:, 'y'])\n done = False\n info = {}\n timesplit = self.unique_timestamp[self.unique_idx]\n self.unique_idx += 1\n subset = self.test[self.test.timestamp == timesplit]\n # reset index to conform to kaggle gym\n target = subset.loc[:, ['id', 'y']].reset_index(drop=True)\n self.temp_test_y = target['y']\n target.loc[:, 'y'] = 0 # set the prediction column to zero\n # column bound change on the subset\n # reset index to conform to kaggle gym\n features = subset.iloc[:, 0:110].reset_index(drop=True)\n observation = Observation(self.train, target, features)\n return observation, reward, done, info\n\nclass Observation:\n \"\"\"\n DOCSTRING\n \"\"\"\n def __init__(self, train, target, features):\n self.train = train\n self.target = target\n self.features = features\n","sub_path":"financial_modeling_demo/financial_modeling_demo.py","file_name":"financial_modeling_demo.py","file_ext":"py","file_size_in_byte":3672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"257029780","text":"from django.shortcuts import render\nfrom django.template.defaulttags import register\nfrom .models import MenuItem, SocialMenuItem\n\n\n@register.filter\ndef get_dict_item(dictionary, key):\n \"\"\"\n Custom template filter. Returns the value for given key in dictionary.\n \"\"\"\n return dictionary.get(key)\n\n\ndef get_menu(active_page: str)->list:\n \"\"\"\n Returns a list of page views, names and whether it's active or not.\n \"\"\"\n pages = {\n \"nav\": [],\n \"social\": []\n }\n for menu_item in MenuItem.objects.all():\n temp = {}\n temp['view'] = menu_item.view.lower()\n temp['name'] = menu_item.name\n if temp['view'] == active_page.lower():\n temp['isActive'] = True\n pages['nav'].append(temp)\n\n for menu_item in SocialMenuItem.objects.all():\n temp = {}\n temp['fas_icon'] = menu_item.fas_icon\n temp['link'] = menu_item.link\n pages['social'].append(temp)\n\n return pages\n\n\ndef home(request):\n \"\"\"\n Returns home template.\n \"\"\"\n return render(request, \"home.html\", {\n 'menu': get_menu(\"home\")\n })\n\n\ndef stories(request):\n \"\"\"\n Returns stories template.\n \"\"\"\n return render(request, \"stories.html\", {\n 'menu': get_menu(\"stories\")\n })\n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"607612816","text":"import sys\n\ninput = sys.stdin.readline\n\ndx = [0, 1, 0, -1]\ndy = [1, 0, -1, 0]\nworld = [[] for _ in range(10)]\ntmp = [[False] * 10 for _ in range(10)]\nfor i in range(10):\n string = input().rstrip()\n for j in string:\n if j == \"O\":\n world[i].append(True)\n else:\n world[i].append(False)\n\ndef switch(y, x):\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0 <= nx < 10 and 0 <= ny < 10:\n if tmp[ny][nx]:\n tmp[ny][nx] = False\n else:\n tmp[ny][nx] = True\n if tmp[y][x]:\n tmp[y][x] = False\n else:\n tmp[y][x] = True\n\ndef copy():\n for i in range(10):\n for j in range(10):\n tmp[i][j] = world[i][j]\n\ndef isDark():\n for j in range(10):\n if tmp[9][j]:\n return False\n return True\n\nres = 1e9\nfor state in range(1 << 10):\n cnt = 0\n copy()\n for i in range(10):\n if state & (1 << i):\n cnt += 1\n switch(0, i)\n for i in range(1,10):\n for j in range(10):\n if tmp[i-1][j]:\n cnt += 1\n switch(i,j)\n if isDark():\n res = min(res, cnt)\nif res == 1e9:\n print(-1)\nelse:\n print(res)","sub_path":"Greedy/BOJ14939불끄기_TIN.py","file_name":"BOJ14939불끄기_TIN.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"40508424","text":"import sys, pygame, os\nsys.path.append(\"../RHframework/\")\nsys.path.append(\"../\")\nfrom input import *\nfrom utils import *\nfrom sound import *\nfrom window import *\n\nSET_ROOT('..')\n\ndisplay_width = 800\ndisplay_height = 600\n\nclass App(Window):\n def __init__(self, title, size, win_flag=W_NONE):\n super().__init__(title, size, win_flag)\n self.keyboard = KeyHandler()\n self.add_event_handle(self.keyboard.handle_event)\n self.mouse = MouseHandler()\n self.add_event_handle(self.mouse.handle_event)\n self.se = Sound(GET_PATH(SE_MAIN, 'ak47.wav'), S_PLAY_ONCE)\n self.se2 = Sound(GET_PATH(SE_MAIN, 'ak47reload.wav'), S_PLAY_ONCE)\n self.m = Sound(GET_PATH(SE_MAIN, 'test.wav'), S_PLAY_ONCE)\n\n def setup(self):\n self.m.play()\n\n def update(self):\n kb = self.keyboard\n\n if kb.key_state[KEY_v]:\n self.se.play()\n if kb.key_state[KEY_SLASH]:\n self.se2.play()\n\n def render(self):\n pass\n\n def ask_quit(self):\n print('On quit')\n self.quit()\n\ndef main():\n app = App('example', (display_width, display_height), W_OPENGL)\n app.run()\n\nif __name__ == '__main__':\n main()","sub_path":"test/se_test_1.py","file_name":"se_test_1.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"283902282","text":"# !pip -q install nltk requests\n#\n# TODO please clean me up, OO me. This is raw notebook sludge.\n#\nimport requests\nimport os\nimport random\nimport numpy as np\nimport pandas as pd\nimport nltk\nimport json\nimport re\nfrom tqdm import tqdm\nimport sys\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nfrom google.cloud import bigquery\nfrom sklearn.decomposition import LatentDirichletAllocation\nfrom collections import Counter\nfrom sklearn import metrics\nimport keras\nfrom keras.layers import Input, Dense\nfrom keras.models import Model\nfrom sklearn.model_selection import train_test_split\nimport warnings\nwarnings.filterwarnings('ignore')\n\nif len(sys.argv) > 1:\n a = sys.argv\n a.pop(0) # skip command name\n city_name = ' '.join(a)\nelse:\n city_name = 'San Francisco'\nprint(\"Creating news cluster for \"+city_name)\n\n# hyperparameters\nproject_id = 'octo-news'\nno_topics = 32 # number of sentence topics of LDA\nC = 16 # Number of sentence clusters of [LDA,Bert]\nno_features = 256\n\n# Bert\nBERT_HTTP = 'http://bert.scott.ai/encode'\nBERT_TENSOR_SIZE = 768 \nBERT_TENSOR_DISTANCE = \"angular\"\nBERT_TENSOR_DB = \"bert.db\"\nBERT_API_COUNT = 0\n\n#---------------\nprint(\"Downloading data for \"+city_name)\n# get our sentence tokenizer \nnltk.download('punkt')\nclient = bigquery.Client(project=project_id)\nquery = \"\"\"\nSELECT distinct(url_orig) as url,\n keyimage as image,\n domain_root as domain,\n page_title as title, \n score, \n date,\n z,\n page_ftxt as text \nFROM\n(\n SELECT *, ROW_NUMBER() OVER (PARTITION BY keyimage ORDER BY date desc) rn\n FROM `octo-news.gdelt_sa.daily_reputable_refresh`\n where length(keyimage) > 0 and lower(city) like '%$CITY%'\n limit 50000\n) t\nWHERE rn = 1\n\"\"\"\nq = client.query(query.replace('$CITY',city_name.lower()))\nfor row in q:\n print(\"url={} title={}\".format(row['url'], row['title']))\n break\n\ndata_df = q.to_dataframe() # Download all\n\n#setup some constants to configure the Bert service\n\ndata = []\ndb = {'doc_id': [],\n 'url': [],\n 'z': [],\n 'city': [],\n 'title': [],\n 'image_url': [],\n 'author': [],\n 'domain': [],\n 'date': [],\n 'vindex': []}\nvecdb = {'sid': [], 'doc_id': [], 'text': [], 'tensor': []}\n\n# Simple BERT utilities \n\ndef bertify_array(text_array):\n \"Turn an array of text, text_array, into an array of tensors. Sentences are best.\"\n global BERT_API_COUNT\n # eid is our encoding id, which we really don't use as \n # bert is synchronous over http. \n r = requests.post(BERT_HTTP, \n json={\"id\": BERT_API_COUNT, \n \"texts\": text_array, \n \"is_tokenized\": False})\n v = r.json()\n BERT_API_COUNT += 1\n try:\n if (v['status'] == 200):\n return np.array(v['result'])\n else:\n print(\"Unexpected Bert status: \",v['status'])\n except:\n print(\"Unexpected Bert error: \",sys.exc_info()[0])\n return None\n\ndef bertify(text):\n \"Turn text into a tensor, sentences are best.\"\n ans = bertify_array([text])\n if ans is not None:\n ans = ans[0]\n return ans\n\n# make sure our images are clean\ndef valid_image(url):\n \"Return True if url is a valid image\"\n r = requests.get(url)\n if r.status_code == 200:\n kind = r.headers.get('Content-Type','')\n return kind.lower().startswith('image')\n return False\n\ndef insert_db_entry(entry):\n global city_name\n n = len(db['doc_id']) \n info = {'doc_id': n,\n 'url': entry['url'],\n 'title': entry['title'],\n 'image_url': entry['image'],\n 'city': city_name,\n 'author':'',\n 'z': entry['z'],\n 'domain': entry['domain'],\n 'date': entry['date'],\n 'vindex': len(vecdb)}\n for key in info:\n db[key].append(info[key])\n return n\n\ndef insert_vecdb_entries(doc_id, sents, vecs):\n for i in range(0,len(vecs)):\n # get our sentence id\n sid = len(vecdb['sid'])\n # record our sentence and point to the db entry\n # that tells us more about the article from which\n # it came\n vecdb['sid'].append(sid)\n vecdb['doc_id'].append(doc_id)\n vecdb['text'].append(sents[i])\n vecdb['tensor'].append(vecs[i])\n\ndef process_entry(entry): \n if (not valid_image(entry['image'])):\n return None\n sents = first_clean_sentences(entry['text'])\n clean_title_sent = sentences(entry['title'])\n if (len(clean_title_sent) > 0):\n # we've seen blank titles, and entire docs as titles\n clean_title = clean_title_sent[0]\n if (len(clean_title) > 0):\n sents.insert(0,clean_title) \n vecs = bertify_array(sents)\n if vecs is None:\n return None\n n = insert_db_entry(entry)\n insert_vecdb_entries(n, sents, vecs)\n return sents\n\ndef first_clean_sentences(text, k=50):\n sent = sentences(text)\n valid = []\n # k clean sentences \n while len(sent) > 0 and k > 0:\n s = sent.pop(0)\n if s.find('EOP') < 0 and len(s) > 10:\n valid.append(s)\n k = k-1\n return valid\n\ndef sentences(text):\n text = clean_text(text)\n return sent_tokenize(text)\n\ndef clean_text(text):\n r1 = re.compile(r' (\\w+)\\.(\\w+) ')\n r2 = re.compile(r' - ')\n text = text.replace(\"\\n\\n\",\" EOP \")\n #text = text.replace(\".\",\". \")\n text = text.replace(\"\\t\",\" \")\n text = text.replace(\"\\n\",\" \")\n text = remove_html_tags(text)\n text = re.sub(r1,r' \\1. \\2 ',text,99)\n text = re.sub(r2,'. ',text,99)\n return text\n\ndef remove_html_tags(text):\n \"\"\"Remove html tags from a string\"\"\"\n clean = re.compile('<.*?>')\n return re.sub(clean, '', text)\n\ndef showv(v):\n n = vecdb['doc_id'][v]\n sentence = vecdb['text'][v]\n print(\" \"+db['domain'][n]+\": \"+db['title'][n])\n print(\" \\\"\"+sentence+\"\\\"\")\n print(\" \"+db['url'][n])\n\n#---------------\n# load sample data\nprint(\"Loading data into memory...\\n\")\ndata=[]\nfrom urllib.parse import urlparse\nfor index, row in data_df.iterrows():\n who = urlparse(row['url']).netloc\n if index < 10:\n print(who,':',row['title'])\n data.append(row)\nprint(\"Loaded\",len(data),\"items.\")\n\n# process the sample data of ~1000 articles (5 min)\n\ndef bert_do(n=1000):\n count = 0\n for i in tqdm(range(0,min(n*2,len(data)))):\n #print(data[i]['page_title'])\n if process_entry(data[i]) is not None:\n count += 1\n if (count == n):\n break\n\n#---------------\nprint(\"Pulling the most recent 2000 articles with valid images\")\nbert_do(2000)\n\n# From github.com/scottspace/contextual_topic_identification\n\nclass Autoencoder:\n \"\"\"\n Simple autoencoder for learning latent space representation\n architecture simplified for only one hidden layer\n \"\"\"\n\n def __init__(self, latent_dim=32, activation='relu', epochs=200, batch_size=128):\n self.latent_dim = latent_dim\n self.activation = activation\n self.epochs = epochs\n self.batch_size = batch_size\n self.autoencoder = None\n self.encoder = None\n self.decoder = None\n self.his = None\n\n def _compile(self, input_dim):\n \"\"\"\n compile the computational graph\n \"\"\"\n input_vec = Input(shape=(input_dim,))\n encoded = Dense(self.latent_dim, activation=self.activation)(input_vec)\n decoded = Dense(input_dim, activation=self.activation)(encoded)\n self.autoencoder = Model(input_vec, decoded)\n self.encoder = Model(input_vec, encoded)\n encoded_input = Input(shape=(self.latent_dim,))\n decoder_layer = self.autoencoder.layers[-1]\n self.decoder = Model(encoded_input, self.autoencoder.layers[-1](encoded_input))\n self.autoencoder.compile(optimizer='adam', loss=keras.losses.mean_squared_error)\n\n def fit(self, X, verbose=0):\n if not self.autoencoder:\n self._compile(X.shape[1])\n X_train, X_test = train_test_split(X)\n self.his = self.autoencoder.fit(X_train, X_train,\n epochs=200,\n batch_size=128,\n shuffle=True,\n validation_data=(X_test, X_test), \n verbose=verbose)\n\n# create dataframes for testing\n\n#---------------\nprint(\"Setting up for analysis with Pandas\")\ndoc_df = pd.DataFrame(data=db)\nsent_df = pd.DataFrame(data=vecdb)\n\n#---------------\nprint(\"Creating document corpus\")\nnDocs = np.max(doc_df['doc_id'])\nDocText = [\" \"]*nDocs\nfor i in range(nDocs):\n text_i = \"\\n \".join(sent_df[sent_df.doc_id == i]['text'].values.flatten())\n DocText[i] = text_i\n\n#---------------\nprint(\"Creating sentence corpus\")\nnSents = np.max(sent_df['sid'])\nSentText = [\" \"]*nSents\nfor i in range(nSents):\n text_i = \"\\n \".join(sent_df[sent_df.sid == i]['text'].values.flatten())\n SentText[i] = text_i\n\n## Create word frequency\n## We want words that are in at least 3 articles,\n## but no more than 25% of the corpus.\n##\n \n#---------------\nprint(\"Counting word frequencies\")\ncorpus = DocText\n#corpus = SentText\nvectorizer = CountVectorizer(min_df=3, max_df=0.25)\nDocX = vectorizer.fit_transform(corpus)\nprint(\"Found\",len(vectorizer.get_feature_names()),\"interesting document words\")\n\n#---------------\nprint(\"Calculating TF/IDF values\")\ntfv = TfidfVectorizer(min_df=3, max_df=0.25, stop_words='english')\ndoc_tf = tfv.fit_transform(corpus)\n\ndef describe_doc(d_i):\n df = doc_df[doc_df.doc_id == d_i]\n print(df['title'].values[0])\n print(df['url'].values[0])\n top = np.argsort(doc_tf[d_i].toarray()[0])[::-1][0:8]\n names = [[key for key, value in tfv.vocabulary_.items() if value == t_i][0] for t_i in top]\n print(' '+' '.join(names))\n\n##LDA\n## Compute our distribution of topics, as well as the distribution\n## of words for each of those topics.\n##\n\ndef lda_v(lda, text, features):\n return np.array([word_tokenize(text).count(f) for f in features])\n\ndef display_topics(model, feature_names, no_top_words):\n print(\"\\nDisplaying LDA Topics\")\n for topic_idx, topic in enumerate(model.components_):\n chosen = topic.argsort()[:-no_top_words - 1:-1]\n print(\"Topic %d:\" % (topic_idx), \\\n \" \".join([feature_names[i] \\\n for i in chosen]))\n\n# LDA can only use raw term counts for LDA because it is a probabilistic graphical model\ntf = DocX\ntf_feature_names = vectorizer.get_feature_names()\n\n#---------------\nprint(\"Calculating Dirichlet distribution of docs (LDA)\")\n# Run LDA\nlda = LatentDirichletAllocation(n_components=no_topics, max_iter=5, \\\n learning_method='online', \\\n learning_offset=50., random_state=42)\ntf_lda = lda.fit_transform(tf)\n\n#no_top_words = 10\n\n#display_topics(lda, tf_feature_names, 8)\n#---------------\nprint(\"Documenting topics as weighted word distributions\")\ntopics = []\nfor topic_idx, topic in enumerate(lda.components_):\n chosen = topic.argsort()[:-7:-1]\n topics.append(\" \".join([tf_feature_names[i] \\\n for i in chosen]))\nfor idx, t in enumerate(topics):\n print(idx,t)\n\ndef lda2vec(lda, text, feature_names):\n vv = lda_v(lda, text, tf_feature_names)\n vv = vv.reshape(1,-1)\n return lda.transform(vv)\n\n## Create word frequency\n\n#---------------\nprint(\"Calculating interesting LDA word freqency in each sentence\")\ntxt = [\"This is my sample text\"]\nvv = CountVectorizer(vocabulary=tf_feature_names)\nSentX = vv.fit_transform(SentText)\nSentVec = lda.transform(SentX)\n\n#---------------\nprint(\"Analyzing sentences for intersting-ness\")\nnDocs = np.max(doc_df['doc_id'].values)\nnSents = SentX.shape[0]\nSentStats = np.zeros((nSents,5))\n\nfor d_i in tqdm(range(nDocs)):\n df_i = sent_df[sent_df['doc_id'] == d_i]\n for idx, s_i in enumerate(df_i['sid'].values):\n toks = word_tokenize(df_i[df_i['sid'] == s_i]['text'].values[0])\n SentStats[s_i,0] = idx # sentence id sid\n SentStats[s_i,1] = len(toks) #all words\n SentStats[s_i,2] = np.sum(SentX[s_i,:]) #meaningful words\n SentStats[s_i,3] = SentStats[s_i,2]/(SentStats[s_i,1]+1) #relevance\n\ninfo_m = np.mean(SentStats[:,3])\ninfo_std = np.std(SentStats[:,3])\nhi_locs = SentStats[np.argwhere(SentStats[:,3] > info_m+2*info_std),0].flatten()\nlo_locs = SentStats[np.argwhere(SentStats[:,3] < info_m-1*info_std),0].flatten()\nok_locs = SentStats[np.argwhere(SentStats[:,3] > info_m+1*info_std),0].flatten()\n\n#---------------\nprint(\"Choosing the most interesting sentences in each doc\")\n# Let's indicate which sentences we want to keep - relevant sentences\nSentStats[:,4] = 0\nSentStats[np.argwhere(SentStats[:,3] > info_m+0*info_std),4] = 1\n\n# OK, let's compute doc centroids\n#---------------\nprint(\"Calculating the centroid for a doc's interesting sentences\")\ntensor_size = sent_df['tensor'].values[0].shape[0]\ndoc_centroids = np.zeros((tf_lda.shape[0], tensor_size))\n\ndef centroid(arr):\n length, dim = arr.shape\n return np.array([np.sum(arr[:, i])/length for i in range(dim)])\n\ndead_docs = []\nfor d_i in tqdm(range(nDocs)):\n vecs = []\n df_i = sent_df[sent_df.doc_id == d_i]\n for s_i in df_i['sid'].values:\n # only extract relevant sentences, longer than 3 words\n if (SentStats[s_i,4] > 0) and (SentStats[s_i,1] > 3):\n vecs.append(df_i['tensor'].values[0])\n if (len(vecs) < 1):\n # use null for dead docs\n dead_docs.append(d_i)\n vecs.append(np.zeros(tensor_size))\n vecs = np.array(vecs).reshape((len(vecs),tensor_size))\n doc_centroids[d_i] = centroid(vecs)\n\n#concatenate both vectors, first the gamma scaled LDA\n#encoding, then the BERT centroid for topical sentences\n\n#---------------\nprint(\"Combining LDA and Bert centroid for each document\")\ngamma = 20\ndoc_both = np.c_[(gamma*tf_lda, doc_centroids)]\n\n#---------------\nprint(\"Distilling the combined vector to 32 key dimensions\")\n# create an autoencoder to distill the document vector information\n# to 3 dimensions\nae = Autoencoder(32)\nae.fit(doc_both)\n# now predict new dense embeddings for every document\ndoc_dense = ae.encoder.predict(doc_both)\n\n#---------------\nprint(\"Clustering the dense, 32-dim information vectors for every doc\")\n# cluster our documents using k-means\nfrom sklearn.cluster import KMeans\ndoc_kmeans = KMeans(n_clusters=C, random_state=42).fit_transform(doc_dense)\ndoc_clusters = np.argmin(doc_kmeans,axis=1)\ndoc_centers = np.argmin(doc_kmeans,axis=0)\n\n# well, how well did we do?\n\n#---------------\nprint(\"Evaluating our technique\")\n\nss_score = metrics.silhouette_score(doc_dense, doc_clusters, metric='euclidean')\nch_score = metrics.calinski_harabasz_score(doc_dense, doc_clusters)\ncb_score = metrics.davies_bouldin_score(doc_dense, doc_clusters)\n\n# [-1,1] higher is better, measure of separation using euc distance\nprint(\"Silhouette score:\", ss_score)\n#higher is better, tighter variance \nprint(\"Calinski_harabasz:\", ch_score)\n#lower is better, for bigger, more separate clusters\nprint(\"Davies-Bouldin:\",cb_score)\n\n#---------------\nprint(\"Creating a breadth-first walk of all clusters, starting at centers\")\n# create a breadth-first-search feed for topics\ndoc_bfs = np.zeros(doc_kmeans.shape)\ndoc_bfs.fill(-1)\ntopic_counts = []\n\nfor c_i in range(doc_bfs.shape[1]):\n ci_docs = np.argwhere(doc_clusters == c_i)\n print(\"Cluster\",c_i,\"has\",len(ci_docs),'docs')\n topic_counts.append(len(ci_docs))\n ci_doc_distances = doc_kmeans[ci_docs,c_i].flatten()\n for idx,ci_doc in enumerate(np.argsort(ci_doc_distances)): \n doc_bfs[idx,c_i] = ci_docs[ci_doc]\n\nfeed = doc_bfs.flatten()\nfeed = np.array([np.int(f) for f in feed[feed >= 0]])\n\n## Summarize cluster terms by calculating the mean\n## of all docs in a cluster.\n#---------------\nprint(\"Summarizing a cluster as the average of all TF/IDF values\")\ncluster_terms = []\nfor c_i in range(doc_bfs.shape[1]):\n ci_docs = np.argwhere(doc_clusters == c_i).flatten()\n avg_tfidf = np.array(np.mean(doc_tf[ci_docs],axis=0))\n best_terms = np.array(np.argsort(avg_tfidf[0,:]))[::-1][0:4] \n names = [[key for key, value in tfv.vocabulary_.items() if value == t_i][0] for t_i in best_terms]\n cluster_terms.append(' '.join(names))\n\nprint(cluster_terms)\n\n## Summarize cluster terms by calculating the mean\n## of k docs closest to the centroid of each cluster.\n\n#---------------\nprint(\"Summarizing a cluster as the average of 10 best documents and their TF/IDF values\")\nkcluster_terms = []\nfor c_i in range(doc_bfs.shape[1]):\n ci_docs = np.argwhere(doc_clusters == c_i).flatten()\n ci_doc_distances = doc_kmeans[ci_docs,c_i].flatten()\n closest_k_docs = ci_docs[np.argsort(ci_doc_distances)[0:10]]\n avg_tfidf = np.array(np.mean(doc_tf[closest_k_docs],axis=0))\n best_terms = np.array(np.argsort(avg_tfidf[0,:]))[::-1][0:4] \n names = [[key for key, value in tfv.vocabulary_.items() if value == t_i][0] for t_i in best_terms]\n kcluster_terms.append(' '.join(names))\n\nprint(kcluster_terms)\n\n#---------------\nprint(\"Uploading our feed for \"+city_name+\" as a breadth-first walk of clusters\")\n# Create a nice summary dataframe\ntp = {'date': [],\n 'index': [],\n 'doc_id': [],\n 'city': [],\n 'z': [],\n 'topic': [],\n 'who': [],\n 'url': [],\n 'title': [],\n 'image': [],\n 'distance': [],\n 'snippet': []}\n\ndef create_feed(db, feed_ids, cluster_info):\n # we expect a list of doc_ids, one for\n # each topic, from topic 0 to topic C\n for idx in tqdm(range(len(feed_ids))):\n d_i = int(feed_ids[idx])\n #print(\"Hi\",d_i,\"there\")\n df_i = doc_df[doc_df.doc_id == d_i]\n df_j = sent_df[sent_df.doc_id == d_i]\n topic = cluster_info[d_i]\n sent_nums = df_j['sid'].values\n sent_relevance = SentStats[sent_nums,3]\n best_sentence = np.argmax(sent_relevance)\n snippet = ''\n if best_sentence > 0:\n snippet = df_j[df_j.sid == sent_nums[np.argmax(sent_relevance)]]['text'].values[0]\n db['date'].append(df_i['date'].values[0])\n db['index'].append(idx)\n db['doc_id'].append(d_i)\n db['topic'].append(topic)\n db['who'].append(df_i['domain'].values[0])\n db['z'].append(df_i['z'].values[0])\n db['url'].append(df_i['url'].values[0])\n db['city'].append(df_i['city'].values[0])\n db['title'].append(df_i['title'].values[0])\n db['image'].append(df_i['image_url'].values[0])\n db['distance'].append(doc_kmeans[d_i,topic]) # distance from this topic\n db['snippet'].append(snippet)\n\ncreate_feed(tp,feed,doc_clusters)\n\n#save our feed\nfrom google.cloud import bigquery\n\nclient = bigquery.Client(project=project_id)\nclient.create_dataset('gdelt_sa',exists_ok=True)\ntable_id = project_id+'.gdelt_sa.daily_feed'\n\n# clean slate\nclient.delete_table(table_id, not_found_ok=True) \n\n# Since string columns use the \"object\" dtype, pass in a (partial) schema\n# to ensure the correct BigQuery data type.\n\njob_config = bigquery.LoadJobConfig(schema=[\n bigquery.SchemaField(\"index\", \"INT64\"),\n bigquery.SchemaField(\"date\", \"INT64\"),\n bigquery.SchemaField(\"doc_id\", \"INT64\"),\n bigquery.SchemaField(\"city\",\"STRING\"),\n bigquery.SchemaField(\"z\", \"FLOAT64\"),\n bigquery.SchemaField(\"topic\", \"INT64\"),\n bigquery.SchemaField(\"who\", \"STRING\"),\n bigquery.SchemaField(\"url\", \"STRING\"),\n bigquery.SchemaField(\"title\", \"STRING\"),\n bigquery.SchemaField(\"image\", \"STRING\"),\n bigquery.SchemaField(\"distance\", \"FLOAT64\"),\n bigquery.SchemaField(\"snippet\", \"STRING\")\n])\n\nfeed_df = pd.DataFrame(data=tp)\n\njob = client.load_table_from_dataframe(\n feed_df, table_id, job_config=job_config\n)\n\n# Wait for the load job to complete.\njob.result()\n\n#---------------\nprint(\"Uploading the cluster descriptions to BigQuery for \"+city_name)\n#save our topics as 'themes' that are used to build high-order topics for users\ntable_id = project_id+'.gdelt_sa.themes'\n\n# clean slate\nclient.delete_table(table_id, not_found_ok=True) \n\n# Since string columns use the \"object\" dtype, pass in a (partial) schema\n# to ensure the correct BigQuery data type.\n\ntop_dict = {'index': [], 'name': [], 'city': []}\nfor idx,name in enumerate(kcluster_terms):\n top_dict['index'].append(idx)\n top_dict['name'].append(name)\n top_dict['city'].append(city_name)\n\ntopic_df = pd.DataFrame(data=top_dict)\n\njob_config = bigquery.LoadJobConfig(schema=[\n bigquery.SchemaField(\"index\", \"INT64\"),\n bigquery.SchemaField(\"name\", \"STRING\"),\n bigquery.SchemaField(\"city\", \"STRING\")\n])\n\njob = client.load_table_from_dataframe(\n topic_df, table_id, job_config=job_config\n)\n\n# Wait for the load job to complete.\njob.result()\n\n#---------------\nprint(\"Finis!\")","sub_path":"src/server/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":20830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"70601529","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 ]\n\n operations = [\n migrations.CreateModel(\n name='Agtagents',\n fields=[\n ('agentid', models.BigIntegerField(serialize=False, primary_key=True, db_column='agentID')),\n ('divisionid', models.SmallIntegerField(null=True, db_column='divisionID', blank=True)),\n ('corporationid', models.BigIntegerField(null=True, db_column='corporationID', blank=True)),\n ('locationid', models.BigIntegerField(null=True, db_column='locationID', blank=True)),\n ('level', models.SmallIntegerField(null=True, blank=True)),\n ('quality', models.SmallIntegerField(null=True, blank=True)),\n ('agenttypeid', models.BigIntegerField(null=True, db_column='agentTypeID', blank=True)),\n ('islocator', models.NullBooleanField(db_column='isLocator')),\n ],\n options={\n 'db_table': 'agtAgents',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Agtagenttypes',\n fields=[\n ('agenttypeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='agentTypeID')),\n ('agenttype', models.TextField(db_column='agentType', blank=True)),\n ],\n options={\n 'db_table': 'agtAgentTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Agtresearchagents',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('agentid', models.BigIntegerField(db_column='agentID')),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ],\n options={\n 'db_table': 'agtResearchAgents',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Certcerts',\n fields=[\n ('certid', models.BigIntegerField(serialize=False, primary_key=True, db_column='certID')),\n ('description', models.TextField(blank=True)),\n ('groupid', models.BigIntegerField(null=True, blank=True)),\n ('name', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'certCerts',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Certmasteries',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('masterylevel', models.BigIntegerField(null=True, db_column='masteryLevel', blank=True)),\n ('certid', models.BigIntegerField(null=True, db_column='certID', blank=True)),\n ],\n options={\n 'db_table': 'certMasteries',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Certskills',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('certid', models.BigIntegerField(null=True, db_column='certID', blank=True)),\n ('skillid', models.BigIntegerField(null=True, db_column='skillID', blank=True)),\n ('certlevelint', models.BigIntegerField(null=True, db_column='certLevelInt', blank=True)),\n ('skilllevel', models.BigIntegerField(null=True, db_column='skillLevel', blank=True)),\n ('certleveltext', models.TextField(db_column='certLevelText', blank=True)),\n ],\n options={\n 'db_table': 'certSkills',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Chrancestries',\n fields=[\n ('ancestryid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='ancestryID')),\n ('ancestryname', models.TextField(db_column='ancestryName', blank=True)),\n ('bloodlineid', models.SmallIntegerField(null=True, db_column='bloodlineID', blank=True)),\n ('description', models.TextField(blank=True)),\n ('perception', models.SmallIntegerField(null=True, blank=True)),\n ('willpower', models.SmallIntegerField(null=True, blank=True)),\n ('charisma', models.SmallIntegerField(null=True, blank=True)),\n ('memory', models.SmallIntegerField(null=True, blank=True)),\n ('intelligence', models.SmallIntegerField(null=True, blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('shortdescription', models.TextField(db_column='shortDescription', blank=True)),\n ],\n options={\n 'db_table': 'chrAncestries',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Chrattributes',\n fields=[\n ('attributeid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='attributeID')),\n ('attributename', models.TextField(db_column='attributeName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('shortdescription', models.TextField(db_column='shortDescription', blank=True)),\n ('notes', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'chrAttributes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Chrbloodlines',\n fields=[\n ('bloodlineid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='bloodlineID')),\n ('bloodlinename', models.TextField(db_column='bloodlineName', blank=True)),\n ('raceid', models.SmallIntegerField(null=True, db_column='raceID', blank=True)),\n ('description', models.TextField(blank=True)),\n ('maledescription', models.TextField(db_column='maleDescription', blank=True)),\n ('femaledescription', models.TextField(db_column='femaleDescription', blank=True)),\n ('shiptypeid', models.BigIntegerField(null=True, db_column='shipTypeID', blank=True)),\n ('corporationid', models.BigIntegerField(null=True, db_column='corporationID', blank=True)),\n ('perception', models.SmallIntegerField(null=True, blank=True)),\n ('willpower', models.SmallIntegerField(null=True, blank=True)),\n ('charisma', models.SmallIntegerField(null=True, blank=True)),\n ('memory', models.SmallIntegerField(null=True, blank=True)),\n ('intelligence', models.SmallIntegerField(null=True, blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('shortdescription', models.TextField(db_column='shortDescription', blank=True)),\n ('shortmaledescription', models.TextField(db_column='shortMaleDescription', blank=True)),\n ('shortfemaledescription', models.TextField(db_column='shortFemaleDescription', blank=True)),\n ],\n options={\n 'db_table': 'chrBloodlines',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Chrfactions',\n fields=[\n ('factionid', models.BigIntegerField(serialize=False, primary_key=True, db_column='factionID')),\n ('factionname', models.TextField(db_column='factionName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('raceids', models.BigIntegerField(null=True, db_column='raceIDs', blank=True)),\n ('solarsystemid', models.BigIntegerField(null=True, db_column='solarSystemID', blank=True)),\n ('corporationid', models.BigIntegerField(null=True, db_column='corporationID', blank=True)),\n ('sizefactor', models.FloatField(null=True, db_column='sizeFactor', blank=True)),\n ('stationcount', models.SmallIntegerField(null=True, db_column='stationCount', blank=True)),\n ('stationsystemcount', models.SmallIntegerField(null=True, db_column='stationSystemCount', blank=True)),\n ('militiacorporationid', models.BigIntegerField(null=True, db_column='militiaCorporationID', blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ],\n options={\n 'db_table': 'chrFactions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Chrraces',\n fields=[\n ('raceid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='raceID')),\n ('racename', models.TextField(db_column='raceName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('shortdescription', models.TextField(db_column='shortDescription', blank=True)),\n ],\n options={\n 'db_table': 'chrRaces',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpactivities',\n fields=[\n ('activityid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='activityID')),\n ('activityname', models.TextField(db_column='activityName', blank=True)),\n ('description', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'crpActivities',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpnpccorporationdivisions',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('corporationid', models.BigIntegerField(db_column='corporationID')),\n ('divisionid', models.SmallIntegerField(db_column='divisionID')),\n ('size', models.SmallIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'crpNPCCorporationDivisions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpnpccorporationresearchfields',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('skillid', models.BigIntegerField(db_column='skillID')),\n ('corporationid', models.BigIntegerField(db_column='corporationID')),\n ],\n options={\n 'db_table': 'crpNPCCorporationResearchFields',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpnpccorporations',\n fields=[\n ('corporationid', models.BigIntegerField(serialize=False, primary_key=True, db_column='corporationID')),\n ('size', models.CharField(max_length=1, blank=True)),\n ('extent', models.CharField(max_length=1, blank=True)),\n ('solarsystemid', models.BigIntegerField(null=True, db_column='solarSystemID', blank=True)),\n ('investorid1', models.BigIntegerField(null=True, db_column='investorID1', blank=True)),\n ('investorshares1', models.SmallIntegerField(null=True, db_column='investorShares1', blank=True)),\n ('investorid2', models.BigIntegerField(null=True, db_column='investorID2', blank=True)),\n ('investorshares2', models.SmallIntegerField(null=True, db_column='investorShares2', blank=True)),\n ('investorid3', models.BigIntegerField(null=True, db_column='investorID3', blank=True)),\n ('investorshares3', models.SmallIntegerField(null=True, db_column='investorShares3', blank=True)),\n ('investorid4', models.BigIntegerField(null=True, db_column='investorID4', blank=True)),\n ('investorshares4', models.SmallIntegerField(null=True, db_column='investorShares4', blank=True)),\n ('friendid', models.BigIntegerField(null=True, db_column='friendID', blank=True)),\n ('enemyid', models.BigIntegerField(null=True, db_column='enemyID', blank=True)),\n ('publicshares', models.BigIntegerField(null=True, db_column='publicShares', blank=True)),\n ('initialprice', models.BigIntegerField(null=True, db_column='initialPrice', blank=True)),\n ('minsecurity', models.FloatField(null=True, db_column='minSecurity', blank=True)),\n ('scattered', models.NullBooleanField()),\n ('fringe', models.SmallIntegerField(null=True, blank=True)),\n ('corridor', models.SmallIntegerField(null=True, blank=True)),\n ('hub', models.SmallIntegerField(null=True, blank=True)),\n ('border', models.SmallIntegerField(null=True, blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ('sizefactor', models.FloatField(null=True, db_column='sizeFactor', blank=True)),\n ('stationcount', models.SmallIntegerField(null=True, db_column='stationCount', blank=True)),\n ('stationsystemcount', models.SmallIntegerField(null=True, db_column='stationSystemCount', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ],\n options={\n 'db_table': 'crpNPCCorporations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpnpccorporationtrades',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('corporationid', models.BigIntegerField(db_column='corporationID')),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ],\n options={\n 'db_table': 'crpNPCCorporationTrades',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Crpnpcdivisions',\n fields=[\n ('divisionid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='divisionID')),\n ('divisionname', models.TextField(db_column='divisionName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('leadertype', models.TextField(db_column='leaderType', blank=True)),\n ],\n options={\n 'db_table': 'crpNPCDivisions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmattributecategories',\n fields=[\n ('categoryid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='categoryID')),\n ('categoryname', models.TextField(db_column='categoryName', blank=True)),\n ('categorydescription', models.TextField(db_column='categoryDescription', blank=True)),\n ],\n options={\n 'db_table': 'dgmAttributeCategories',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmattributetypes',\n fields=[\n ('attributeid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='attributeID')),\n ('attributename', models.TextField(db_column='attributeName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('defaultvalue', models.FloatField(null=True, db_column='defaultValue', blank=True)),\n ('published', models.NullBooleanField()),\n ('displayname', models.TextField(db_column='displayName', blank=True)),\n ('unitid', models.SmallIntegerField(null=True, db_column='unitID', blank=True)),\n ('stackable', models.NullBooleanField()),\n ('highisgood', models.NullBooleanField(db_column='highIsGood')),\n ('categoryid', models.SmallIntegerField(null=True, db_column='categoryID', blank=True)),\n ],\n options={\n 'db_table': 'dgmAttributeTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmeffects',\n fields=[\n ('effectid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='effectID')),\n ('effectname', models.TextField(db_column='effectName', blank=True)),\n ('effectcategory', models.SmallIntegerField(null=True, db_column='effectCategory', blank=True)),\n ('preexpression', models.BigIntegerField(null=True, db_column='preExpression', blank=True)),\n ('postexpression', models.BigIntegerField(null=True, db_column='postExpression', blank=True)),\n ('description', models.TextField(blank=True)),\n ('guid', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('isoffensive', models.NullBooleanField(db_column='isOffensive')),\n ('isassistance', models.NullBooleanField(db_column='isAssistance')),\n ('durationattributeid', models.SmallIntegerField(null=True, db_column='durationAttributeID', blank=True)),\n ('trackingspeedattributeid', models.SmallIntegerField(null=True, db_column='trackingSpeedAttributeID', blank=True)),\n ('dischargeattributeid', models.SmallIntegerField(null=True, db_column='dischargeAttributeID', blank=True)),\n ('rangeattributeid', models.SmallIntegerField(null=True, db_column='rangeAttributeID', blank=True)),\n ('falloffattributeid', models.SmallIntegerField(null=True, db_column='falloffAttributeID', blank=True)),\n ('disallowautorepeat', models.NullBooleanField(db_column='disallowAutoRepeat')),\n ('published', models.NullBooleanField()),\n ('displayname', models.TextField(db_column='displayName', blank=True)),\n ('iswarpsafe', models.NullBooleanField(db_column='isWarpSafe')),\n ('rangechance', models.NullBooleanField(db_column='rangeChance')),\n ('electronicchance', models.NullBooleanField(db_column='electronicChance')),\n ('propulsionchance', models.NullBooleanField(db_column='propulsionChance')),\n ('distribution', models.SmallIntegerField(null=True, blank=True)),\n ('sfxname', models.TextField(db_column='sfxName', blank=True)),\n ('npcusagechanceattributeid', models.SmallIntegerField(null=True, db_column='npcUsageChanceAttributeID', blank=True)),\n ('npcactivationchanceattributeid', models.SmallIntegerField(null=True, db_column='npcActivationChanceAttributeID', blank=True)),\n ('fittingusagechanceattributeid', models.SmallIntegerField(null=True, db_column='fittingUsageChanceAttributeID', blank=True)),\n ('modifierinfo', models.TextField(db_column='modifierInfo', blank=True)),\n ],\n options={\n 'db_table': 'dgmEffects',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmexpressions',\n fields=[\n ('expressionid', models.BigIntegerField(serialize=False, primary_key=True, db_column='expressionID')),\n ('operandid', models.BigIntegerField(null=True, db_column='operandID', blank=True)),\n ('arg1', models.BigIntegerField(null=True, blank=True)),\n ('arg2', models.BigIntegerField(null=True, blank=True)),\n ('expressionvalue', models.TextField(db_column='expressionValue', blank=True)),\n ('description', models.TextField(blank=True)),\n ('expressionname', models.TextField(db_column='expressionName', blank=True)),\n ('expressiontypeid', models.BigIntegerField(null=True, db_column='expressionTypeID', blank=True)),\n ('expressiongroupid', models.SmallIntegerField(null=True, db_column='expressionGroupID', blank=True)),\n ('expressionattributeid', models.SmallIntegerField(null=True, db_column='expressionAttributeID', blank=True)),\n ],\n options={\n 'db_table': 'dgmExpressions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmtypeattributes',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('attributeid', models.SmallIntegerField(db_column='attributeID')),\n ('valueint', models.BigIntegerField(null=True, db_column='valueInt', blank=True)),\n ('valuefloat', models.FloatField(null=True, db_column='valueFloat', blank=True)),\n ],\n options={\n 'db_table': 'dgmTypeAttributes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Dgmtypeeffects',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('effectid', models.SmallIntegerField(db_column='effectID')),\n ('isdefault', models.NullBooleanField(db_column='isDefault')),\n ],\n options={\n 'db_table': 'dgmTypeEffects',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Eveicons',\n fields=[\n ('iconid', models.BigIntegerField(serialize=False, primary_key=True, db_column='iconID')),\n ('iconfile', models.TextField(db_column='iconFile')),\n ('description', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'eveIcons',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Eveunits',\n fields=[\n ('unitid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='unitID')),\n ('unitname', models.TextField(db_column='unitName', blank=True)),\n ('displayname', models.TextField(db_column='displayName', blank=True)),\n ('description', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'eveUnits',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryactivity',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('time', models.BigIntegerField(null=True, blank=True)),\n ('activityid', models.BigIntegerField(db_column='activityID')),\n ],\n options={\n 'db_table': 'industryActivity',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryactivitymaterials',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('activityid', models.BigIntegerField(null=True, db_column='activityID', blank=True)),\n ('materialtypeid', models.BigIntegerField(null=True, db_column='materialTypeID', blank=True)),\n ('quantity', models.BigIntegerField(null=True, blank=True)),\n ('consume', models.SmallIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'industryActivityMaterials',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryactivityprobabilities',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('activityid', models.BigIntegerField(null=True, db_column='activityID', blank=True)),\n ('producttypeid', models.BigIntegerField(null=True, db_column='productTypeID', blank=True)),\n ('probability', models.DecimalField(null=True, max_digits=3, decimal_places=2, blank=True)),\n ],\n options={\n 'db_table': 'industryActivityProbabilities',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryactivityproducts',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('activityid', models.BigIntegerField(null=True, db_column='activityID', blank=True)),\n ('producttypeid', models.BigIntegerField(null=True, db_column='productTypeID', blank=True)),\n ('quantity', models.BigIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'industryActivityProducts',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryactivityskills',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('activityid', models.BigIntegerField(null=True, db_column='activityID', blank=True)),\n ('skillid', models.BigIntegerField(null=True, db_column='skillID', blank=True)),\n ('level', models.BigIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'industryActivitySkills',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Industryblueprints',\n fields=[\n ('typeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='typeID')),\n ('maxproductionlimit', models.BigIntegerField(null=True, db_column='maxProductionLimit', blank=True)),\n ],\n options={\n 'db_table': 'industryBlueprints',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invcategories',\n fields=[\n ('categoryid', models.BigIntegerField(serialize=False, primary_key=True, db_column='categoryID')),\n ('categoryname', models.TextField(db_column='categoryName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('published', models.NullBooleanField()),\n ],\n options={\n 'db_table': 'invCategories',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invcontrabandtypes',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('factionid', models.BigIntegerField(db_column='factionID')),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('standingloss', models.FloatField(null=True, db_column='standingLoss', blank=True)),\n ('confiscateminsec', models.FloatField(null=True, db_column='confiscateMinSec', blank=True)),\n ('finebyvalue', models.FloatField(null=True, db_column='fineByValue', blank=True)),\n ('attackminsec', models.FloatField(null=True, db_column='attackMinSec', blank=True)),\n ],\n options={\n 'db_table': 'invContrabandTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invcontroltowerresourcepurposes',\n fields=[\n ('purpose', models.SmallIntegerField(serialize=False, primary_key=True)),\n ('purposetext', models.TextField(db_column='purposeText', blank=True)),\n ],\n options={\n 'db_table': 'invControlTowerResourcePurposes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invcontroltowerresources',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('controltowertypeid', models.BigIntegerField(db_column='controlTowerTypeID')),\n ('resourcetypeid', models.BigIntegerField(db_column='resourceTypeID')),\n ('purpose', models.SmallIntegerField(null=True, blank=True)),\n ('quantity', models.BigIntegerField(null=True, blank=True)),\n ('minsecuritylevel', models.FloatField(null=True, db_column='minSecurityLevel', blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ],\n options={\n 'db_table': 'invControlTowerResources',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invflags',\n fields=[\n ('flagid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='flagID')),\n ('flagname', models.TextField(db_column='flagName', blank=True)),\n ('flagtext', models.TextField(db_column='flagText', blank=True)),\n ('orderid', models.BigIntegerField(null=True, db_column='orderID', blank=True)),\n ],\n options={\n 'db_table': 'invFlags',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invgroups',\n fields=[\n ('groupid', models.BigIntegerField(serialize=False, primary_key=True, db_column='groupID')),\n ('categoryid', models.BigIntegerField(null=True, db_column='categoryID', blank=True)),\n ('groupname', models.TextField(db_column='groupName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('usebaseprice', models.NullBooleanField(db_column='useBasePrice')),\n ('allowmanufacture', models.NullBooleanField(db_column='allowManufacture')),\n ('allowrecycler', models.NullBooleanField(db_column='allowRecycler')),\n ('anchored', models.NullBooleanField()),\n ('anchorable', models.NullBooleanField()),\n ('fittablenonsingleton', models.NullBooleanField(db_column='fittableNonSingleton')),\n ('published', models.NullBooleanField()),\n ],\n options={\n 'db_table': 'invGroups',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invitems',\n fields=[\n ('itemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='itemID')),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('ownerid', models.BigIntegerField(db_column='ownerID')),\n ('locationid', models.BigIntegerField(db_column='locationID')),\n ('flagid', models.SmallIntegerField(db_column='flagID')),\n ('quantity', models.BigIntegerField()),\n ],\n options={\n 'db_table': 'invItems',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invmarketgroups',\n fields=[\n ('marketgroupid', models.BigIntegerField(serialize=False, primary_key=True, db_column='marketGroupID')),\n ('parentgroupid', models.BigIntegerField(null=True, db_column='parentGroupID', blank=True)),\n ('marketgroupname', models.TextField(db_column='marketGroupName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ('hastypes', models.NullBooleanField(db_column='hasTypes')),\n ],\n options={\n 'db_table': 'invMarketGroups',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invmetagroups',\n fields=[\n ('metagroupid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='metaGroupID')),\n ('metagroupname', models.TextField(db_column='metaGroupName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ],\n options={\n 'db_table': 'invMetaGroups',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invmetatypes',\n fields=[\n ('typeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='typeID')),\n ('parenttypeid', models.BigIntegerField(null=True, db_column='parentTypeID', blank=True)),\n ('metagroupid', models.SmallIntegerField(null=True, db_column='metaGroupID', blank=True)),\n ],\n options={\n 'db_table': 'invMetaTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invnames',\n fields=[\n ('itemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='itemID')),\n ('itemname', models.TextField(db_column='itemName')),\n ],\n options={\n 'db_table': 'invNames',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invpositions',\n fields=[\n ('itemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='itemID')),\n ('x', models.FloatField()),\n ('y', models.FloatField()),\n ('z', models.FloatField()),\n ('yaw', models.FloatField(null=True, blank=True)),\n ('pitch', models.FloatField(null=True, blank=True)),\n ('roll', models.FloatField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'invPositions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invtraits',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('skillid', models.BigIntegerField(null=True, db_column='skillID', blank=True)),\n ('bonus', models.FloatField(null=True, blank=True)),\n ('bonustext', models.TextField(db_column='bonusText', blank=True)),\n ('unitid', models.BigIntegerField(null=True, db_column='unitID', blank=True)),\n ],\n options={\n 'db_table': 'invTraits',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invtypematerials',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('materialtypeid', models.BigIntegerField(db_column='materialTypeID')),\n ('quantity', models.BigIntegerField()),\n ],\n options={\n 'db_table': 'invTypeMaterials',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invtypereactions',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('reactiontypeid', models.BigIntegerField(db_column='reactionTypeID')),\n ('input', models.BooleanField(default=False)),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('quantity', models.SmallIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'invTypeReactions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invtypes',\n fields=[\n ('typeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='typeID')),\n ('groupid', models.BigIntegerField(null=True, db_column='groupID', blank=True)),\n ('typename', models.TextField(db_column='typeName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('mass', models.FloatField(null=True, blank=True)),\n ('volume', models.FloatField(null=True, blank=True)),\n ('capacity', models.FloatField(null=True, blank=True)),\n ('portionsize', models.BigIntegerField(null=True, db_column='portionSize', blank=True)),\n ('raceid', models.SmallIntegerField(null=True, db_column='raceID', blank=True)),\n ('baseprice', models.DecimalField(null=True, decimal_places=4, max_digits=19, db_column='basePrice', blank=True)),\n ('published', models.NullBooleanField()),\n ('marketgroupid', models.BigIntegerField(null=True, db_column='marketGroupID', blank=True)),\n ('chanceofduplicating', models.FloatField(null=True, db_column='chanceOfDuplicating', blank=True)),\n ],\n options={\n 'db_table': 'invTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invuniquenames',\n fields=[\n ('itemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='itemID')),\n ('itemname', models.TextField(unique=True, db_column='itemName')),\n ('groupid', models.BigIntegerField(null=True, db_column='groupID', blank=True)),\n ],\n options={\n 'db_table': 'invUniqueNames',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Invvolumes',\n fields=[\n ('groupid', models.BigIntegerField(serialize=False, primary_key=True)),\n ('volume', models.BigIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'invVolumes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapcelestialstatistics',\n fields=[\n ('celestialid', models.BigIntegerField(serialize=False, primary_key=True, db_column='celestialID')),\n ('temperature', models.FloatField(null=True, blank=True)),\n ('spectralclass', models.TextField(db_column='spectralClass', blank=True)),\n ('luminosity', models.FloatField(null=True, blank=True)),\n ('age', models.FloatField(null=True, blank=True)),\n ('life', models.FloatField(null=True, blank=True)),\n ('orbitradius', models.FloatField(null=True, db_column='orbitRadius', blank=True)),\n ('eccentricity', models.FloatField(null=True, blank=True)),\n ('massdust', models.FloatField(null=True, db_column='massDust', blank=True)),\n ('massgas', models.FloatField(null=True, db_column='massGas', blank=True)),\n ('fragmented', models.BigIntegerField(null=True, blank=True)),\n ('density', models.FloatField(null=True, blank=True)),\n ('surfacegravity', models.FloatField(null=True, db_column='surfaceGravity', blank=True)),\n ('escapevelocity', models.FloatField(null=True, db_column='escapeVelocity', blank=True)),\n ('orbitperiod', models.FloatField(null=True, db_column='orbitPeriod', blank=True)),\n ('rotationrate', models.FloatField(null=True, db_column='rotationRate', blank=True)),\n ('locked', models.BigIntegerField(null=True, blank=True)),\n ('pressure', models.BigIntegerField(null=True, blank=True)),\n ('radius', models.BigIntegerField(null=True, blank=True)),\n ('mass', models.BigIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'mapCelestialStatistics',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapconstellationjumps',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('fromregionid', models.BigIntegerField(null=True, db_column='fromRegionID', blank=True)),\n ('fromconstellationid', models.BigIntegerField(db_column='fromConstellationID')),\n ('toconstellationid', models.BigIntegerField(db_column='toConstellationID')),\n ('toregionid', models.BigIntegerField(null=True, db_column='toRegionID', blank=True)),\n ],\n options={\n 'db_table': 'mapConstellationJumps',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapconstellations',\n fields=[\n ('regionid', models.BigIntegerField(null=True, db_column='regionID', blank=True)),\n ('constellationid', models.BigIntegerField(serialize=False, primary_key=True, db_column='constellationID')),\n ('constellationname', models.TextField(db_column='constellationName', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('xmin', models.FloatField(null=True, db_column='xMin', blank=True)),\n ('xmax', models.FloatField(null=True, db_column='xMax', blank=True)),\n ('ymin', models.FloatField(null=True, db_column='yMin', blank=True)),\n ('ymax', models.FloatField(null=True, db_column='yMax', blank=True)),\n ('zmin', models.FloatField(null=True, db_column='zMin', blank=True)),\n ('zmax', models.FloatField(null=True, db_column='zMax', blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ('radius', models.FloatField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'mapConstellations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapdenormalize',\n fields=[\n ('itemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='itemID')),\n ('typeid', models.BigIntegerField(null=True, db_column='typeID', blank=True)),\n ('groupid', models.BigIntegerField(null=True, db_column='groupID', blank=True)),\n ('solarsystemid', models.BigIntegerField(null=True, db_column='solarSystemID', blank=True)),\n ('constellationid', models.BigIntegerField(null=True, db_column='constellationID', blank=True)),\n ('regionid', models.BigIntegerField(null=True, db_column='regionID', blank=True)),\n ('orbitid', models.BigIntegerField(null=True, db_column='orbitID', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('radius', models.FloatField(null=True, blank=True)),\n ('itemname', models.TextField(db_column='itemName', blank=True)),\n ('security', models.FloatField(null=True, blank=True)),\n ('celestialindex', models.BigIntegerField(null=True, db_column='celestialIndex', blank=True)),\n ('orbitindex', models.BigIntegerField(null=True, db_column='orbitIndex', blank=True)),\n ],\n options={\n 'db_table': 'mapDenormalize',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapjumps',\n fields=[\n ('stargateid', models.BigIntegerField(serialize=False, primary_key=True, db_column='stargateID')),\n ('destinationid', models.BigIntegerField(null=True, db_column='destinationID', blank=True)),\n ],\n options={\n 'db_table': 'mapJumps',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Maplandmarks',\n fields=[\n ('landmarkid', models.BigIntegerField(serialize=False, primary_key=True, db_column='landmarkID')),\n ('landmarkname', models.TextField(db_column='landmarkName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('locationid', models.BigIntegerField(null=True, db_column='locationID', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('iconid', models.BigIntegerField(null=True, db_column='iconID', blank=True)),\n ],\n options={\n 'db_table': 'mapLandmarks',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Maplocationscenes',\n fields=[\n ('locationid', models.BigIntegerField(serialize=False, primary_key=True, db_column='locationID')),\n ('graphicid', models.BigIntegerField(null=True, db_column='graphicID', blank=True)),\n ],\n options={\n 'db_table': 'mapLocationScenes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Maplocationwormholeclasses',\n fields=[\n ('locationid', models.BigIntegerField(serialize=False, primary_key=True, db_column='locationID')),\n ('wormholeclassid', models.BigIntegerField(null=True, db_column='wormholeClassID', blank=True)),\n ],\n options={\n 'db_table': 'mapLocationWormholeClasses',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapregionjumps',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('fromregionid', models.BigIntegerField(db_column='fromRegionID')),\n ('toregionid', models.BigIntegerField(db_column='toRegionID')),\n ],\n options={\n 'db_table': 'mapRegionJumps',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapregions',\n fields=[\n ('regionid', models.BigIntegerField(serialize=False, primary_key=True, db_column='regionID')),\n ('regionname', models.TextField(db_column='regionName', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('xmin', models.FloatField(null=True, db_column='xMin', blank=True)),\n ('xmax', models.FloatField(null=True, db_column='xMax', blank=True)),\n ('ymin', models.FloatField(null=True, db_column='yMin', blank=True)),\n ('ymax', models.FloatField(null=True, db_column='yMax', blank=True)),\n ('zmin', models.FloatField(null=True, db_column='zMin', blank=True)),\n ('zmax', models.FloatField(null=True, db_column='zMax', blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ('radius', models.FloatField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'mapRegions',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapsolarsystemjumps',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('fromregionid', models.BigIntegerField(null=True, db_column='fromRegionID', blank=True)),\n ('fromconstellationid', models.BigIntegerField(null=True, db_column='fromConstellationID', blank=True)),\n ('fromsolarsystemid', models.BigIntegerField(db_column='fromSolarSystemID')),\n ('tosolarsystemid', models.BigIntegerField(db_column='toSolarSystemID')),\n ('toconstellationid', models.BigIntegerField(null=True, db_column='toConstellationID', blank=True)),\n ('toregionid', models.BigIntegerField(null=True, db_column='toRegionID', blank=True)),\n ],\n options={\n 'db_table': 'mapSolarSystemJumps',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapsolarsystems',\n fields=[\n ('regionid', models.BigIntegerField(null=True, db_column='regionID', blank=True)),\n ('constellationid', models.BigIntegerField(null=True, db_column='constellationID', blank=True)),\n ('solarsystemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='solarSystemID')),\n ('solarsystemname', models.TextField(db_column='solarSystemName', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('xmin', models.FloatField(null=True, db_column='xMin', blank=True)),\n ('xmax', models.FloatField(null=True, db_column='xMax', blank=True)),\n ('ymin', models.FloatField(null=True, db_column='yMin', blank=True)),\n ('ymax', models.FloatField(null=True, db_column='yMax', blank=True)),\n ('zmin', models.FloatField(null=True, db_column='zMin', blank=True)),\n ('zmax', models.FloatField(null=True, db_column='zMax', blank=True)),\n ('luminosity', models.FloatField(null=True, blank=True)),\n ('border', models.SmallIntegerField(null=True, blank=True)),\n ('fringe', models.SmallIntegerField(null=True, blank=True)),\n ('corridor', models.SmallIntegerField(null=True, blank=True)),\n ('hub', models.SmallIntegerField(null=True, blank=True)),\n ('international', models.SmallIntegerField(null=True, blank=True)),\n ('regional', models.SmallIntegerField(null=True, blank=True)),\n ('constellation', models.SmallIntegerField(null=True, blank=True)),\n ('security', models.FloatField(null=True, blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ('radius', models.FloatField(null=True, blank=True)),\n ('suntypeid', models.BigIntegerField(null=True, db_column='sunTypeID', blank=True)),\n ('securityclass', models.TextField(db_column='securityClass', blank=True)),\n ],\n options={\n 'db_table': 'mapSolarSystems',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Mapuniverse',\n fields=[\n ('universeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='universeID')),\n ('universename', models.TextField(db_column='universeName', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('xmin', models.FloatField(null=True, db_column='xMin', blank=True)),\n ('xmax', models.FloatField(null=True, db_column='xMax', blank=True)),\n ('ymin', models.FloatField(null=True, db_column='yMin', blank=True)),\n ('ymax', models.FloatField(null=True, db_column='yMax', blank=True)),\n ('zmin', models.FloatField(null=True, db_column='zMin', blank=True)),\n ('zmax', models.FloatField(null=True, db_column='zMax', blank=True)),\n ('radius', models.FloatField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'mapUniverse',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Planetschematics',\n fields=[\n ('schematicid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='schematicID')),\n ('schematicname', models.TextField(db_column='schematicName', blank=True)),\n ('cycletime', models.BigIntegerField(null=True, db_column='cycleTime', blank=True)),\n ],\n options={\n 'db_table': 'planetSchematics',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Planetschematicspinmap',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('schematicid', models.SmallIntegerField(db_column='schematicID')),\n ('pintypeid', models.BigIntegerField(db_column='pinTypeID')),\n ],\n options={\n 'db_table': 'planetSchematicsPinMap',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Planetschematicstypemap',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('schematicid', models.SmallIntegerField(db_column='schematicID')),\n ('typeid', models.BigIntegerField(db_column='typeID')),\n ('quantity', models.SmallIntegerField(null=True, blank=True)),\n ('isinput', models.NullBooleanField(db_column='isInput')),\n ],\n options={\n 'db_table': 'planetSchematicsTypeMap',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Ramactivities',\n fields=[\n ('activityid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='activityID')),\n ('activityname', models.TextField(db_column='activityName', blank=True)),\n ('iconno', models.TextField(db_column='iconNo', blank=True)),\n ('description', models.TextField(blank=True)),\n ('published', models.NullBooleanField()),\n ],\n options={\n 'db_table': 'ramActivities',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Ramassemblylinestations',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('stationid', models.BigIntegerField(db_column='stationID')),\n ('assemblylinetypeid', models.SmallIntegerField(db_column='assemblyLineTypeID')),\n ('quantity', models.SmallIntegerField(null=True, blank=True)),\n ('stationtypeid', models.BigIntegerField(null=True, db_column='stationTypeID', blank=True)),\n ('ownerid', models.BigIntegerField(null=True, db_column='ownerID', blank=True)),\n ('solarsystemid', models.BigIntegerField(null=True, db_column='solarSystemID', blank=True)),\n ('regionid', models.BigIntegerField(null=True, db_column='regionID', blank=True)),\n ],\n options={\n 'db_table': 'ramAssemblyLineStations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Ramassemblylinetypedetailpercategory',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('assemblylinetypeid', models.SmallIntegerField(db_column='assemblyLineTypeID')),\n ('categoryid', models.BigIntegerField(db_column='categoryID')),\n ('timemultiplier', models.FloatField(null=True, db_column='timeMultiplier', blank=True)),\n ('materialmultiplier', models.FloatField(null=True, db_column='materialMultiplier', blank=True)),\n ('costmultiplier', models.FloatField(null=True, db_column='costMultiplier', blank=True)),\n ],\n options={\n 'db_table': 'ramAssemblyLineTypeDetailPerCategory',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Ramassemblylinetypedetailpergroup',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('assemblylinetypeid', models.SmallIntegerField(db_column='assemblyLineTypeID')),\n ('groupid', models.BigIntegerField(db_column='groupID')),\n ('timemultiplier', models.FloatField(null=True, db_column='timeMultiplier', blank=True)),\n ('materialmultiplier', models.FloatField(null=True, db_column='materialMultiplier', blank=True)),\n ('costmultiplier', models.FloatField(null=True, db_column='costMultiplier', blank=True)),\n ],\n options={\n 'db_table': 'ramAssemblyLineTypeDetailPerGroup',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Ramassemblylinetypes',\n fields=[\n ('assemblylinetypeid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='assemblyLineTypeID')),\n ('assemblylinetypename', models.TextField(db_column='assemblyLineTypeName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('basetimemultiplier', models.FloatField(null=True, db_column='baseTimeMultiplier', blank=True)),\n ('basematerialmultiplier', models.FloatField(null=True, db_column='baseMaterialMultiplier', blank=True)),\n ('basecostmultiplier', models.FloatField(null=True, db_column='baseCostMultiplier', blank=True)),\n ('volume', models.FloatField(null=True, blank=True)),\n ('activityid', models.SmallIntegerField(null=True, db_column='activityID', blank=True)),\n ('mincostperhour', models.FloatField(null=True, db_column='minCostPerHour', blank=True)),\n ],\n options={\n 'db_table': 'ramAssemblyLineTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Raminstallationtypecontents',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('installationtypeid', models.BigIntegerField(db_column='installationTypeID')),\n ('assemblylinetypeid', models.SmallIntegerField(db_column='assemblyLineTypeID')),\n ('quantity', models.SmallIntegerField(null=True, blank=True)),\n ],\n options={\n 'db_table': 'ramInstallationTypeContents',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Staoperations',\n fields=[\n ('activityid', models.SmallIntegerField(null=True, db_column='activityID', blank=True)),\n ('operationid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='operationID')),\n ('operationname', models.TextField(db_column='operationName', blank=True)),\n ('description', models.TextField(blank=True)),\n ('fringe', models.SmallIntegerField(null=True, blank=True)),\n ('corridor', models.SmallIntegerField(null=True, blank=True)),\n ('hub', models.SmallIntegerField(null=True, blank=True)),\n ('border', models.SmallIntegerField(null=True, blank=True)),\n ('ratio', models.SmallIntegerField(null=True, blank=True)),\n ('caldaristationtypeid', models.BigIntegerField(null=True, db_column='caldariStationTypeID', blank=True)),\n ('minmatarstationtypeid', models.BigIntegerField(null=True, db_column='minmatarStationTypeID', blank=True)),\n ('amarrstationtypeid', models.BigIntegerField(null=True, db_column='amarrStationTypeID', blank=True)),\n ('gallentestationtypeid', models.BigIntegerField(null=True, db_column='gallenteStationTypeID', blank=True)),\n ('jovestationtypeid', models.BigIntegerField(null=True, db_column='joveStationTypeID', blank=True)),\n ],\n options={\n 'db_table': 'staOperations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Staoperationservices',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('operationid', models.SmallIntegerField(db_column='operationID')),\n ('serviceid', models.BigIntegerField(db_column='serviceID')),\n ],\n options={\n 'db_table': 'staOperationServices',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Staservices',\n fields=[\n ('serviceid', models.BigIntegerField(serialize=False, primary_key=True, db_column='serviceID')),\n ('servicename', models.TextField(db_column='serviceName', blank=True)),\n ('description', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'staServices',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Stastations',\n fields=[\n ('stationid', models.BigIntegerField(serialize=False, primary_key=True, db_column='stationID')),\n ('security', models.SmallIntegerField(null=True, blank=True)),\n ('dockingcostpervolume', models.FloatField(null=True, db_column='dockingCostPerVolume', blank=True)),\n ('maxshipvolumedockable', models.FloatField(null=True, db_column='maxShipVolumeDockable', blank=True)),\n ('officerentalcost', models.BigIntegerField(null=True, db_column='officeRentalCost', blank=True)),\n ('operationid', models.SmallIntegerField(null=True, db_column='operationID', blank=True)),\n ('stationtypeid', models.BigIntegerField(null=True, db_column='stationTypeID', blank=True)),\n ('corporationid', models.BigIntegerField(null=True, db_column='corporationID', blank=True)),\n ('solarsystemid', models.BigIntegerField(null=True, db_column='solarSystemID', blank=True)),\n ('constellationid', models.BigIntegerField(null=True, db_column='constellationID', blank=True)),\n ('regionid', models.BigIntegerField(null=True, db_column='regionID', blank=True)),\n ('stationname', models.TextField(db_column='stationName', blank=True)),\n ('x', models.FloatField(null=True, blank=True)),\n ('y', models.FloatField(null=True, blank=True)),\n ('z', models.FloatField(null=True, blank=True)),\n ('reprocessingefficiency', models.FloatField(null=True, db_column='reprocessingEfficiency', blank=True)),\n ('reprocessingstationstake', models.FloatField(null=True, db_column='reprocessingStationsTake', blank=True)),\n ('reprocessinghangarflag', models.SmallIntegerField(null=True, db_column='reprocessingHangarFlag', blank=True)),\n ],\n options={\n 'db_table': 'staStations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Stastationtypes',\n fields=[\n ('stationtypeid', models.BigIntegerField(serialize=False, primary_key=True, db_column='stationTypeID')),\n ('dockentryx', models.FloatField(null=True, db_column='dockEntryX', blank=True)),\n ('dockentryy', models.FloatField(null=True, db_column='dockEntryY', blank=True)),\n ('dockentryz', models.FloatField(null=True, db_column='dockEntryZ', blank=True)),\n ('dockorientationx', models.FloatField(null=True, db_column='dockOrientationX', blank=True)),\n ('dockorientationy', models.FloatField(null=True, db_column='dockOrientationY', blank=True)),\n ('dockorientationz', models.FloatField(null=True, db_column='dockOrientationZ', blank=True)),\n ('operationid', models.SmallIntegerField(null=True, db_column='operationID', blank=True)),\n ('officeslots', models.SmallIntegerField(null=True, db_column='officeSlots', blank=True)),\n ('reprocessingefficiency', models.FloatField(null=True, db_column='reprocessingEfficiency', blank=True)),\n ('conquerable', models.NullBooleanField()),\n ],\n options={\n 'db_table': 'staStationTypes',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Translationtables',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('sourcetable', models.TextField(db_column='sourceTable')),\n ('destinationtable', models.TextField(db_column='destinationTable', blank=True)),\n ('translatedkey', models.TextField(db_column='translatedKey')),\n ('tcgroupid', models.BigIntegerField(null=True, db_column='tcGroupID', blank=True)),\n ('tcid', models.BigIntegerField(null=True, db_column='tcID', blank=True)),\n ],\n options={\n 'db_table': 'translationTables',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Trntranslationcolumns',\n fields=[\n ('tcgroupid', models.SmallIntegerField(null=True, db_column='tcGroupID', blank=True)),\n ('tcid', models.SmallIntegerField(serialize=False, primary_key=True, db_column='tcID')),\n ('tablename', models.TextField(db_column='tableName')),\n ('columnname', models.TextField(db_column='columnName')),\n ('masterid', models.TextField(db_column='masterID', blank=True)),\n ],\n options={\n 'db_table': 'trnTranslationColumns',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Trntranslationlanguages',\n fields=[\n ('numericlanguageid', models.BigIntegerField(serialize=False, primary_key=True, db_column='numericLanguageID')),\n ('languageid', models.TextField(db_column='languageID', blank=True)),\n ('languagename', models.TextField(db_column='languageName', blank=True)),\n ],\n options={\n 'db_table': 'trnTranslationLanguages',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Trntranslations',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('tcid', models.SmallIntegerField(db_column='tcID')),\n ('keyid', models.BigIntegerField(db_column='keyID')),\n ('languageid', models.TextField(db_column='languageID')),\n ('text', models.TextField()),\n ],\n options={\n 'db_table': 'trnTranslations',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Warcombatzones',\n fields=[\n ('combatzoneid', models.BigIntegerField(serialize=False, primary_key=True, db_column='combatZoneID')),\n ('combatzonename', models.TextField(db_column='combatZoneName', blank=True)),\n ('factionid', models.BigIntegerField(null=True, db_column='factionID', blank=True)),\n ('centersystemid', models.BigIntegerField(null=True, db_column='centerSystemID', blank=True)),\n ('description', models.TextField(blank=True)),\n ],\n options={\n 'db_table': 'warCombatZones',\n 'managed': False,\n },\n ),\n migrations.CreateModel(\n name='Warcombatzonesystems',\n fields=[\n ('solarsystemid', models.BigIntegerField(serialize=False, primary_key=True, db_column='solarSystemID')),\n ('combatzoneid', models.BigIntegerField(null=True, db_column='combatZoneID', blank=True)),\n ],\n options={\n 'db_table': 'warCombatZoneSystems',\n 'managed': False,\n },\n ),\n ]\n","sub_path":"eveassets/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":72123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"456676572","text":"\"\"\" przykladowy config\ncross_is_bot = True / False\ncircle_is_bot = True / False\nstart = \"circle\" / \"cross\"\nbot_tactic = \"heuristic\" / \"game tree\"\nui_size = \"small\" / \"medium\" / \"large\" / \"resizable\"\nsave_path = \"path/to/save\"\n\"\"\"\ncross_is_bot = False\ncircle_is_bot = True\nstart = \"cross\"\nbot_tactic = \"game tree\"\nui_size = \"small\"\ndepth = 10\nsave_path = \"save.txt\"","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"450205139","text":"\"\"\"Author: Daniel\"\"\"\n\nwordcount = {}\n\nsentence = \"this is a collection of words of nice words this is a fun thing it is\"\nsentence = sentence.split(\" \")\n\nfor word in sentence:\n if word in wordcount:\n wordcount[word] += 1\n else:\n wordcount[word] = 1\nfor sentence, wordcount in wordcount.items():\n print(\"{}: {}\".format(sentence, wordcount))\n","sub_path":"Prac 5/Word Count.py","file_name":"Word Count.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"365682760","text":"import numpy as np\n\n\nvalues1 = np.loadtxt('test3.txt', unpack=True)\n\nprint ( values1 )\n\nval1 = values1[:, 0]\n\nval2 = values1[:, 1]\n\n\n\n\n\n#print ( val1 )\nprint ( val2 )\nvalues1[:, 1] = (val2 - val2.mean()) / val2.std()\n\nprint ( values1 )\n\n#print ( ( val1 - val2 ).std() )\n\n\n","sub_path":"mnist/test4.py","file_name":"test4.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"269867790","text":"# Target : build 1 model ML contains : input, hidden layers, output\n# Resolve Iris problem\n# Input is 1 vector 1*4\n# Output is 1 vector 1 * 3 : 100,010,001\n# Use Gradient Descent to optimize Loss function\nimport pandas as pandas\nimport numpy as np\nfrom tensorflow.python.keras import Model, Input\nfrom tensorflow.python.keras.activations import relu\nfrom tensorflow.python.keras.backend import sigmoid\nfrom tensorflow.python.keras.layers import Dense\n\n# build model\nfrom tensorflow.python.keras.losses import MSE\nfrom tensorflow.python.keras.optimizers import SGD\n\ninput = Input(shape=(4,))\nlayer1 = Dense(units=10, activation=relu)(input)\nlayer2 = Dense(units=20, activation=relu)(layer1)\noutput = Dense(units=3, activation=sigmoid)(layer2)\nmodel = Model(input, output)\n\nmodel.summary() # show model struct\n\n# setup train model\nmodel.compile(optimizer=SGD(lr=0.02), loss=MSE, metrics=['accuracy'])\n# train model\n# prepare data\n# read file use pandas\ndata = np.array(pandas.read_csv(filepath_or_buffer=\"iris.data\", header=None, nrows=150))\noutput = []\ntrain_output = []\nfor i in range(150):\n if (data[i, 4] == \"Iris-setosa\"):\n output.append([1, 0, 0])\n if (data[i, 4] == \"Iris-versicolor\"):\n output.append([0, 1, 0])\n if (data[i, 4] == \"Iris-virginica\"):\n output.append([0, 0, 1])\n\ntrain_input = np.concatenate((data[0:40, 0:4], data[50:90, 0:4], data[100:140, 0:4]), axis=0)\ntrain_output = np.concatenate((output[0:40], output[50:90], output[100:140]), axis=0)\ntest_input = np.concatenate((data[40:50, 0:4], data[90:100, 0:4], data[140:150, 0:4]), axis=0)\nprint(train_input.shape)\nprint(train_output.shape)\n\nmodel.fit(x=train_input, y=train_output, epochs=10000)\nmodel.save(\"model.hdf5\")\n\n# predict = model.predict(x=test_input)\n#\n# # Get the maximum values of each column i.e. along axis 0\n# maxInColumns = np.amax(predict, axis=0)\n# print('Max value of every column: ', maxInColumns)\n# print(predict)\n","sub_path":"Lecture_1.py","file_name":"Lecture_1.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"190025765","text":"n=int(input())\np=0\nb=[]\nq=0\nfor i in range(n):\n a=int(input())\n b.append(a)\n p+=a\ndef fun(c,add,r):\n global q\n d=c[:]\n if(q==1 or len(c)<=1):\n return\n for i in range(r):\n print(len(c),i)\n t2=c.pop(i)\n add+=t2\n t=add\n if(add==sum(c)):\n q=1\n return\n for j in range(len(c)):\n t1=c[j]\n c[j]=0\n if(t+t1 == sum(c)):\n q=1\n c[j]=t1\n return\n c[j]=t1\n fun(c,add,len(c))\n add-=t2\n c.insert(i,t2)\nif(p==360):\n print(\"YES\")\nelse: \n fun(b,0,n)\n if(q==1):\n print(\"YES\")\n else:\n print(\"NO\")\n","sub_path":"cf.py","file_name":"cf.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"424096917","text":"import turtle\nimport threading\n\nwn = turtle.Screen()\nwn.bgcolor(\"black\")\n\ndef draw_a_ellipse(s,a,b,c,t):\n s.speed(c-1)\n s.penup()\n s.goto(0+t,-b)\n s.pendown()\n s.speed(c)\n for adjustment in range(4):\n adjustmentx = [0+t,a+t,0+t,-a+t]\n adjustmenty = [-b,0,b,0]\n s.goto(adjustmentx[adjustment],adjustmenty[adjustment])\n for i in range(90):\n pos = s.pos()\n x = float(pos[0])-t\n y = float(pos[1])\n a1 = b**4\n a2 = a**4\n a3 = a1*(x**2) + a2*(y**2)\n a4 = a3**1.5\n a5 = a1*a2\n r = a4/a5\n s.circle(r,1)\n s.goto(0+t,-b)\na = turtle.Turtle()\nb = turtle.Turtle()\nc = turtle.Turtle()\nd = turtle.Turtle()\ne = turtle.Turtle()\nf = turtle.Turtle()\ng = turtle.Turtle()\n\nfor i in range(7):\n n = [a,b,c,d,e,f,g]\n m = [\"red\",\"blue\",\"purple\",\"white\",\"red\",\"green\",\"yellow\"]\n n[i].color(m[i])\n\ng.dot(10)\n\nfor i in [a,b,c,d,e,f]:\n i.shape(\"circle\")\n\ng.hideturtle()\n\nthreads = []\nt1 = threading.Thread(target=draw_a_ellipse,args=(a,50,40,1,30))\nthreads.append(t1)\nt2 = threading.Thread(target=draw_a_ellipse,args=(b,130,120,1,50))\nthreads.append(t2)\nt3 = threading.Thread(target=draw_a_ellipse,args=(c,75,72,1,21))\nthreads.append(t3)\nt4 = threading.Thread(target=draw_a_ellipse,args=(d,100,80,1,60))\nthreads.append(t4)\nt5 = threading.Thread(target=draw_a_ellipse,args=(e,120,80,1,90))\nthreads.append(t5)\nt6 = threading.Thread(target=draw_a_ellipse,args=(f,160,140,1,78))\nthreads.append(t6)\n\ndef main():\n for t in threads:\n t.setDaemon(True)\n t.start()\n wn.exitonclick()\n\nif __name__ == '__main__':\n main()","sub_path":"pyassign1/planets.py","file_name":"planets.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"108503292","text":"import copy\r\nimport operator\r\nimport random\r\nfrom collections import defaultdict\r\n\r\nimport numpy as np\r\nfrom sklearn import linear_model\r\nfrom sklearn.ensemble import GradientBoostingRegressor\r\nfrom sklearn.model_selection import cross_val_score, KFold\r\n\r\nfrom data_prepocessor import DataPreprocessor\r\nfrom genetic import GeneticAlgorithm, GeneticFunctions\r\n\r\n\r\nclass GuessText(GeneticFunctions):\r\n def __init__(self, target_text, limit=200, size=400, prob_crossover=0.9, prob_mutation=0.3):\r\n self.target = 0\r\n self.counter = 0\r\n\r\n self.limit = limit\r\n self.size = size\r\n self.prob_crossover = prob_crossover\r\n self.prob_mutation = prob_mutation\r\n\r\n self.all = [x for x in range(335)]\r\n\r\n self.dp = DataPreprocessor()\r\n self.dp.read_all()\r\n\r\n self.ws = defaultdict(int)\r\n self.best = (0, [])\r\n\r\n print(\"Ready to evaluate\")\r\n pass\r\n\r\n # GeneticFunctions interface impls\r\n def probability_crossover(self):\r\n return self.prob_crossover\r\n\r\n def probability_mutation(self):\r\n return self.prob_mutation\r\n\r\n def initial(self):\r\n \"\"\"\r\n Начальные хромосомы (выборки параметров)\r\n :return:\r\n \"\"\"\r\n ans = []\r\n for i in range(100):\r\n n = random.randint(0, 334)\r\n d = []\r\n for j in range(n):\r\n d.append(random.randint(0, 334))\r\n d = list(set(d))\r\n ans.append(d)\r\n return ans\r\n\r\n def fitness(self, chromo):\r\n \"\"\"\r\n Оценочная функция!\r\n :param chromo:\r\n :return:\r\n \"\"\"\r\n model = GradientBoostingRegressor()\r\n outputs = self.dp.train_outputs\r\n inputs_temp = np.array(list(zip(*self.dp.train_inputs)))\r\n inputs = []\r\n for i in chromo:\r\n inputs.append(inputs_temp[i])\r\n inputs = np.array(inputs)\r\n inputs = np.array(list(zip(*inputs)))\r\n\r\n if len(inputs) == 0:\r\n return 0\r\n\r\n res = cross_val_score(model, inputs, outputs, cv=KFold(n_splits=10))\r\n ans = res.mean()*1000\r\n if ans < -1000:\r\n ans = -1000\r\n if ans > self.best[0]:\r\n self.best = (ans, copy.copy(chromo))\r\n return ans\r\n\r\n def check_stop(self, fits_populations):\r\n \"\"\"\r\n Функция останоки\r\n :param fits_populations:\r\n :return:\r\n \"\"\"\r\n self.counter += 1\r\n if True or self.counter % 1 == 0:\r\n fits = [f for f, ch in fits_populations]\r\n best = max(fits)\r\n worst = min(fits)\r\n avg = sum(fits) / len(fits)\r\n print(\"[G %3d] score=(%4d, %4d, %4d)\" % (self.counter, best, avg, worst))\r\n # self.print_ws()\r\n if self.counter % 10 == 0:\r\n print(self.best)\r\n return self.counter >= self.limit\r\n\r\n def crossover(self, parents):\r\n \"\"\"\r\n Размножение\r\n :param parents:\r\n :return:\r\n \"\"\"\r\n father, mother = parents\r\n\r\n self.make_weights(father)\r\n self.make_weights(mother)\r\n\r\n\r\n random.shuffle(father)\r\n random.shuffle(mother)\r\n slicer_father = random.randint(1, len(father)-1)\r\n slicer_mother = random.randint(1, len(mother)-1)\r\n child1 = father[:slicer_father] + mother[slicer_mother:]\r\n child2 = father[slicer_father:] + mother[:slicer_mother]\r\n\r\n child1 = list(set(child1))\r\n child2 = list(set(child2))\r\n\r\n # print(mother, father)\r\n # print(child1, child2)\r\n # exit()\r\n\r\n return child1, child2\r\n\r\n def mutation(self, chromosome):\r\n mutated = copy.deepcopy(chromosome)\r\n random.shuffle(mutated)\r\n mutated = sorted(mutated, key=lambda m: self.ws[m])\r\n mutated.reverse()\r\n\r\n slicer = random.randint(int(len(mutated)/2), len(mutated)-1)\r\n slicer2 = len(mutated) - slicer + random.randint(5, 100)\r\n\r\n mutated = mutated[:slicer]\r\n random.shuffle(self.all)\r\n\r\n sorted_x = sorted(self.ws.items(), key=operator.itemgetter(1))\r\n sorted_x.reverse()\r\n mutated_additional = [k for k, v in sorted_x]\r\n mutated_additional = mutated_additional[:slicer2*2]\r\n random.shuffle(mutated_additional)\r\n mutated += mutated_additional[:slicer2]\r\n\r\n mutated = list(set(mutated))\r\n return mutated\r\n\r\n # internals\r\n def tournament(self, fits_populations):\r\n \"\"\"\r\n Выбирает лучших родителей для размножения\r\n :param fits_populations:\r\n :return:\r\n \"\"\"\r\n\r\n target_len = (len(fits_populations) + 1) / 3\r\n index = random.randint(0, int(target_len))\r\n d = sorted(fits_populations)\r\n d.reverse()\r\n return d[index][1]\r\n\r\n def make_weights(self, mas):\r\n for x in mas:\r\n self.ws[x] += 1\r\n pass\r\n\r\n def print_ws(self):\r\n sorted_x = sorted(self.ws.items(), key=operator.itemgetter(1))\r\n sorted_x.reverse()\r\n print(sorted_x)\r\n\r\n\r\n def parents(self, fits_populations):\r\n \"\"\"\r\n Генератор \"родителей\" для размножения\r\n :param fits_populations:\r\n :return:\r\n \"\"\"\r\n while True:\r\n father = self.tournament(fits_populations)\r\n mother = self.tournament(fits_populations)\r\n yield (father, mother)\r\n pass\r\n\r\n\r\n\r\n pass\r\n\r\n\r\ndef main():\r\n print('Started')\r\n GeneticAlgorithm(GuessText(\"Hello World!\")).run()\r\n\r\n\r\nmain()\r\n","sub_path":"genetic2.py","file_name":"genetic2.py","file_ext":"py","file_size_in_byte":5675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"528543504","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nu\"\"\"\nОсновной скрипт запуска \n\nСкрипт запускает на выполнение домашнее задание путем импортирования решений из соотв. модулей.\nДополнительно выполняется комплекс тестов из модуля набора тестов.\n\n\"\"\"\n\n__author__ = \"maria_s\"\n__email__ = \"smirnitskayamp@gmail.com\"\n__date__ = \"2014-11-13\"\n\nimport solution\n\nINPUT = \"Proin eget tortor risus. Cras ultricies ligula sed magna dictum porta. \\\nProin eget tortor risus. Curabitur non nulla sit amet nisl tempus convallis \\\nquis ac lectus. Donec rutrum congue leo eget malesuada.\"\n\ndef starter():\n solution.finder(INPUT)\n\n\nif __name__ == \"__main__\":\n starter()\n","sub_path":"the_longest_word/starter.py","file_name":"starter.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"152386043","text":"import re\n\nfrom subprocess import Popen\nfrom subprocess import PIPE\nfrom memory import Memory\n\nclass OperatingSystem(object):\n def __init__(self):\n self.os_type = self.get_os_type()\n self.distro_name = self.get_distro_name()\n self.distro_version = self.get_distro_version()\n self.distro_update = self.get_distro_update()\n self.distro_architecture = self.get_distro_architecture()\n self.patch_test_status = \"Successful\" # cosaii!\n self.memory = Memory()\n\n def get_os_details(self):\n try:\n file = open('/etc/redhat-release', 'r')\n content = file.read()\n file.close()\n return content\n except:\n return 'Error_1'\n\n def get_os_type(self):\n try:\n proc = Popen(['uname', '-o'], stdout=PIPE, stderr=PIPE)\n output, error = proc.communicate()\n return output.rstrip()\n except:\n return 'Error_2'\n\n def get_distro_name(self):\n content = self.get_os_details()\n if 'Error' in content:\n return content\n distro_name = re.search(r'(.*)\\srelease\\s', content)\n if distro_name:\n return distro_name.group(1)\n else:\n return 'Error_3'\n\n def get_distro_version(self):\n content = self.get_os_details()\n if 'Error' in content:\n return content\n distro_version = re.search(r'release\\s([0-9]+)\\.', content)\n if distro_version:\n return distro_version.group(1)\n else:\n return 'Error_4'\n\n def get_distro_update(self):\n content = self.get_os_details()\n if 'Error' in content:\n return content\n distro_update = re.search(r'release\\s[0-9]+\\.(.*)', content)\n if distro_update:\n return distro_update.group(1)\n else:\n return 'Error_5'\n\n def get_distro_architecture(self):\n try:\n proc = Popen(['uname', '-m'], stdout=PIPE, stderr=PIPE)\n output, error = proc.communicate()\n return output.rstrip()\n except:\n return 'Error_6'\n\n","sub_path":"veellee/operatingsystem.py","file_name":"operatingsystem.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"448341439","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\"\"\"\n# pylint: disable=too-many-locals,logging-format-interpolation,bare-except,line-too-long,duplicate-code,invalid-name\n# pylint: disable=duplicate-code\n\nimport time\nimport logging\nimport os\nimport csv\nimport threading\nimport peewee\nfrom pymongo import MongoClient\n\nlogging.basicConfig(level=logging.INFO)\nLOGGER = logging.getLogger(__name__)\n\nclass MongoDBConnection():\n \"\"\"MongoDB Connection\"\"\"\n\n def __init__(self, host='127.0.0.1', port=27017):\n \"\"\" be sure to use the ip address not name for local windows\"\"\"\n self.host = host\n self.port = port\n self.connection = None\n\n def __enter__(self):\n self.connection = MongoClient(self.host, self.port)\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n self.connection.close()\n\ndef import_customer_data(directory_name, customer_file):\n \"\"\"imports customer data from csv file\"\"\"\n\n start = time.time()\n\n mongo = MongoDBConnection()\n\n with mongo:\n LOGGER.info(\"Establishing MongoDB connection\")\n database = mongo.connection.storeDB\n\n LOGGER.info(\"Establishing databases\")\n customers = database[\"customers\"]\n initial_entries = database.customers.count_documents({})\n\n #entry counts\n added_entries = 0\n\n with open(os.path.join(directory_name, customer_file)) as csv_file:\n\n customer_data = csv.reader(csv_file, delimiter=\",\")\n for entry in customer_data:\n try:\n customer_entry = {\"user_id\":entry[0],\n \"name\":entry[1],\n \"address\":entry[2],\n \"phone_number\":entry[3],\n \"email\":entry[4]}\n customers.insert_one(customer_entry)\n added_entries += 1\n LOGGER.info(f\"Added {entry[0]} to customer database\")\n except peewee.IntegrityError:\n LOGGER.info(f\"Error adding {entry[0]} to customer database\")\n\n final_entries = database.customers.count_documents({})\n\n return((initial_entries, added_entries, final_entries,\n (time.time() - start)))\n\ndef import_product_data(directory_name, product_file):\n \"\"\"imports product data from csv file\"\"\"\n\n start = time.time()\n\n mongo = MongoDBConnection()\n\n with mongo:\n LOGGER.info(\"Establishing MongoDB connection\")\n database = mongo.connection.storeDB\n\n LOGGER.info(\"Establishing databases\")\n products = database[\"products\"]\n initial_entries = database.products.count_documents({})\n\n #entry counts\n added_entries = 0\n\n with open(os.path.join(directory_name, product_file)) as csv_file:\n\n product_data = csv.reader(csv_file, delimiter=\",\")\n for entry in product_data:\n try:\n product_entry = {\"product_id\":entry[0],\n \"description\":entry[1],\n \"product_type\":entry[2],\n \"quantity_available\":entry[3]}\n products.insert_one(product_entry)\n added_entries += 1\n LOGGER.info(f\"Added {entry[0]} to product database\")\n except peewee.IntegrityError:\n LOGGER.info(f\"Error adding {entry[0]} to product database\")\n\n final_entries = database.products.count_documents({})\n\n return((initial_entries, added_entries, final_entries,\n (time.time() - start)))\n\ndef import_rental_data(directory_name, rental_file):\n \"\"\"imports rental data from csv file\"\"\"\n\n start = time.time()\n\n mongo = MongoDBConnection()\n\n with mongo:\n LOGGER.info(\"Establishing MongoDB connection\")\n database = mongo.connection.storeDB\n\n LOGGER.info(\"Establishing databases\")\n rentals = database[\"rentals\"]\n initial_entries = database.rentals.count_documents({})\n\n #entry counts\n added_entries = 0\n\n with open(os.path.join(directory_name, rental_file)) as csv_file:\n\n rental_data = csv.reader(csv_file, delimiter=\",\")\n for entry in rental_data:\n try:\n rental_entry = {\"rental_id\":entry[0],\n \"user_id\":entry[1],\n \"product_id\":entry[2]}\n rentals.insert_one(rental_entry)\n added_entries += 1\n LOGGER.info(f\"Added {entry[0]} to rental database\")\n except:\n LOGGER.info(f\"Error adding {entry[0]} to rental database\")\n\n final_entries = database.rentals.count_documents({})\n\n return((initial_entries, added_entries, final_entries,\n (time.time() - start)))\n\n\ndef clear_database():\n \"\"\"Clear the database for each collection\"\"\"\n mongo = MongoDBConnection()\n with mongo:\n db = mongo.connection.storeDB\n db[\"products\"].drop()\n db[\"customers\"].drop()\n db[\"rentals\"].drop()\n\n\ndef print_results(results):\n \"\"\"Prints out the report given the results\"\"\"\n print(f\"Intial Entries: {results[0]}\")\n print(f\"Added Entries: {results[1]}\")\n print(f\"Final Entries: {results[2]}\")\n print(f\"Total Run Time: {results[3]}\")\n print(\"\\n\")\n\n\nif __name__ == \"__main__\":\n clear_database()\n start_time = time.time()\n customer_thread = threading.Thread(target=import_customer_data,\n args=(\"./data\", \"customers.csv\"))\n customer_thread.start()\n product_thread = threading.Thread(target=import_product_data,\n args=(\"./data\", \"products.csv\"))\n product_thread.start()\n rental_thread = threading.Thread(target=import_rental_data,\n args=(\"./data\", \"rentalss.csv\"))\n rental_thread.start()\n customer_thread.join()\n product_thread.join()\n rental_thread.join()\n end_time = time.time()\n print(\"\\n\")\n print(f\"Total Run Time: {end_time-start_time}\")\n","sub_path":"students/humberto_gonzalez/lesson07/parallel.py","file_name":"parallel.py","file_ext":"py","file_size_in_byte":6234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"377534475","text":"# Zweck:\n# Hilfsfunktionen zum erstellen von TFRecords aus Simulator Trainingsdaten\n# Erzeugung eines tf.data.Dataset Objektes basierend auf den TFRecord Files und Bilddaten\n# Author: David Kostka\n# Datum: 05.11.2020\n\nimport pandas as pd\nimport tensorflow as tf\nimport numpy as np\nimport os\nfrom collections import namedtuple, OrderedDict\nimport matplotlib.pyplot as plt\n\ndataset_root_path = 'E:/datasets/simulator/'\ncsv_name = 'labels.csv'\n\norig_size = (480, 640)\ntarget_size = (60, 80)\n\n#class SingleNaoDataset:\ndef create_tf_example(row):\n '''\n Konvertiert ein DataFrame Row in ein TFRecord Example\n '''\n filename = row.name.encode('utf8')\n xmins = row.minX / orig_size[1]\n xmaxs = row.maxX / orig_size[1]\n ymins = row.minY / orig_size[0]\n ymaxs = row.maxY / orig_size[0]\n\n classes = row.classes\n\n tf_example = tf.train.Example(features=tf.train.Features(feature={\n 'image/filename': tf.train.Feature(bytes_list=tf.train.BytesList(value=[filename])),\n 'image/object/bbox/xmin': tf.train.Feature(float_list=tf.train.FloatList(value=[xmins])),\n 'image/object/bbox/xmax': tf.train.Feature(float_list=tf.train.FloatList(value=[xmaxs])),\n 'image/object/bbox/ymin': tf.train.Feature(float_list=tf.train.FloatList(value=[ymins])),\n 'image/object/bbox/ymax': tf.train.Feature(float_list=tf.train.FloatList(value=[ymaxs])),\n 'image/object/class/label': tf.train.Feature(float_list=tf.train.FloatList(value=[classes]))\n }))\n return tf_example\n\ndef write_tfrecord(labels, name, path):\n '''\n Schreibt 'labels' als Examples in eine TFRecord File\n '''\n writer = tf.io.TFRecordWriter(path + name)\n for label in labels.itertuples():\n tf_example = create_tf_example(label)\n writer.write(tf_example.SerializeToString())\n\n writer.close()\n print(\"TFRecord '\" + path + name + \"' created\")\n\ndef parse_image(name, filepath, rgb=False, resize=True):\n '''\n Ladet und Decodiert ein Bild aus dem 'filepath'\n Mit rgb kann angegeben werden ob das Bild 1 (grayscale) oder 3 (RGB) Kanäle haben soll\n '''\n image = tf.io.read_file(filepath + name)\n image = tf.image.decode_png(image)\n if not rgb: image = tf.image.rgb_to_grayscale(image)\n image = tf.image.convert_image_dtype(image, tf.float32)\n if resize: image = tf.image.resize(image, target_size)\n return image\n\ndef parse_tfrecord(tfrecord, filepath):\n '''\n Lese aus dem 'tfrecord' das Bild und Labels aus\n '''\n # Aufbau eines TFRecord Example\n IMAGE_FEATURE_MAP = {\n 'image/filename': tf.io.FixedLenFeature([], tf.string),\n 'image/object/bbox/xmin': tf.io.FixedLenFeature([], tf.float32),\n 'image/object/bbox/ymin': tf.io.FixedLenFeature([], tf.float32),\n 'image/object/bbox/xmax': tf.io.FixedLenFeature([], tf.float32),\n 'image/object/bbox/ymax': tf.io.FixedLenFeature([], tf.float32),\n 'image/object/class/label': tf.io.FixedLenFeature([], tf.float32)\n }\n\n x = tf.io.parse_single_example(tfrecord, IMAGE_FEATURE_MAP)\n\n x_train = parse_image(x['image/filename'], filepath + 'images/')\n\n y_train = [ x['image/object/bbox/xmin'],\n x['image/object/bbox/ymin'],\n x['image/object/bbox/xmax'],\n x['image/object/bbox/ymax'],\n x['image/object/class/label']\n ]\n \n #print(y_train)\n\n #y_train = np.asarray(y_train)\n #print(x_train)\n #print(y_train)\n\n return x_train, y_train\n\ndef load_tfrecord_dataset(filepath, csv_name):\n '''\n Erzeugt ein tf.data.Dataset aus einer TFRecord File und ersetzt dabei den Bildnamen mit den tatsächlichen Bilddaten\n Die Bilder müssen in einem Unterordner 'images/' relativ zum 'filepath' vorhanden sein.\n '''\n dataset = tf.data.TFRecordDataset(filepath + csv_name)\n #print('Loaded Dataset: ' + filepath)\n return dataset.map(lambda x: parse_tfrecord(x, filepath), num_parallel_calls=tf.data.experimental.AUTOTUNE)\n\ndef create_tfrecord_from_dir(path, include_negatives=False):\n '''\n Erzeugt Train und Validation TFRecord Files aus Label Datei in 'path'\n Dabei werden die Einträge so gefiltert das nur Bildname/Label Einträge übrig bleiben, die nur einen einzigen Nao im Bild haben\n \n Input: Die Label Datei muss eine CSV Datei mit Spalten der Form ('name', 'minX', 'minY', 'maxX', 'maxY') beinhalten.\n Dabei ist 'name' der Bilddateipfad und der Rest die BBOX Koordinaten\n\n Output: TFRecord Files 'train.record' und 'val.record' mit Einträgen der Form (Bildname, BBOX Koordinaten), für Bilder, bei denen nur ein Nao zu sehen ist.\n '''\n raw_data = pd.read_csv(path + csv_name, sep=r'\\s*,\\s*', index_col=None)\n raw_data = raw_data[['name', 'minX', 'minY', 'maxX', 'maxY']]\n \n if include_negatives:\n raw_data['classes'] = 1\n labels_img_names = raw_data['name'].unique()\n all_img_names = os.listdir(path + 'images/')\n no_nao_img_names = np.setdiff1d(all_img_names,labels_img_names)\n new_data = pd.DataFrame(no_nao_img_names, columns=['name'])\n new_data['classes'] = 0\n raw_data = raw_data.append(new_data).sample(frac=1).reset_index(drop=True)\n raw_data = raw_data[raw_data.name != 'name']\n\n data = raw_data.fillna(0).groupby('name').filter(lambda x: len(x) == 1)\n data = data.sample(frac=1).reset_index(drop=True)\n\n count = len(data)\n split_index = int(count - count * 0.2)\n\n print(data)\n\n write_tfrecord(data[:split_index], 'train.record', path)\n write_tfrecord(data[split_index:], 'val.record', path)\n\ndef load_combined_dataset(subfolders):\n '''\n Kombiniert alle TFRecord Files der Ordner in 'subfolder' zu einem Datensatz\n '''\n train_dset = load_tfrecord_dataset(dataset_root_path + subfolders[0], 'train.record')\n val_dset = load_tfrecord_dataset(dataset_root_path + subfolders[0], 'val.record')\n\n for sub_folder in subfolders[1:]:\n dataset_path = dataset_root_path + sub_folder\n #print('Concat ' + sub_folder)\n train_temp = load_tfrecord_dataset(dataset_path, 'train.record')\n train_dset = train_dset.concatenate(train_temp)\n val_temp = load_tfrecord_dataset(dataset_path, 'val.record')\n val_dset = val_dset.concatenate(val_temp)\n\n #print('Nr. of Examples train: ' + str(sum(1 for _ in train_dset)))\n #print('Nr. of Examples validation: ' + str(sum(1 for _ in val_dset)))\n\n return train_dset, val_dset\n ","sub_path":"spl_detector/src/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":6478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"253754066","text":"import math\r\nimport random\r\nimport numpy\r\n\r\n\r\nclass Individual:\r\n def __init__(self, chrome=None):\r\n self.chromosome_len = 24\r\n self.queens_number = 8\r\n self.queen_len = 3\r\n if chrome is None:\r\n self.chromosome = ''\r\n for vertical in range(self.queens_number):\r\n for bit_number in range(self.queen_len):\r\n bit = random.randint(0, 1)\r\n self.chromosome += str(bit)\r\n else:\r\n self.chromosome = str(chrome)\r\n\r\n def get_chrome(self):\r\n return self.chromosome\r\n\r\n def fitness_function(self):\r\n fitness_sum = 0\r\n for ver in range(0, self.chromosome_len, self.queen_len):\r\n first_queen = self.chromosome[ver: ver + self.queen_len]\r\n for hor in range(0, self.chromosome_len, self.queen_len):\r\n second_queen = self.chromosome[hor: hor + self.queen_len]\r\n if first_queen == second_queen and ver != hor:\r\n fitness_sum += 1\r\n if ver != hor and (abs(int(first_queen, 2) - int(second_queen, 2)) ==\r\n abs(hor / self.queen_len - ver / self.queen_len)):\r\n fitness_sum += 1\r\n fitness_sum = 1 / (fitness_sum / 8 + 1)\r\n return fitness_sum\r\n\r\n def visualisation(self):\r\n solution = str()\r\n visual = str()\r\n for horizontal in range(0, self.chromosome_len, self.queen_len):\r\n queen = self.chromosome[horizontal:horizontal + self.queen_len]\r\n solution += str(int(queen, 2))\r\n for cell in range(8 * 8):\r\n if cell % 8 == 0:\r\n visual += '\\n'\r\n if cell % 8 == int(solution[cell // 8]):\r\n visual += 'Q'\r\n else:\r\n visual += '+'\r\n return visual\r\n\r\n\r\nclass Solver_8_queens:\r\n def __init__(self, pop_size=400, cross_prob=0.6, mut_prob=0.2):\r\n self.pop_size = pop_size\r\n self.cross_prob = cross_prob\r\n self.mut_prob = mut_prob\r\n self.population = [Individual() for i in range(pop_size)]\r\n self.fitness_list = list()\r\n for ind in self.population:\r\n self.fitness_list.append(ind.fitness_function())\r\n\r\n def roulette_wheel(self):\r\n probability = list()\r\n ind_position = [i for i in range(self.pop_size)]\r\n fitness_sum = float()\r\n parents = list()\r\n for ind in self.fitness_list:\r\n fitness_sum += ind\r\n\r\n for i in self.fitness_list:\r\n choose_chance = i / fitness_sum\r\n probability.append(choose_chance)\r\n\r\n selected_individuals = numpy.random.choice(ind_position, self.pop_size, True, probability)\r\n for ind in selected_individuals:\r\n parents.append(self.population[ind])\r\n return parents\r\n\r\n def reproduce(self, parents):\r\n chromosome_len = self.population[0].chromosome_len\r\n next_generation = list()\r\n for pair in range(len(parents)//2):\r\n first_parent, second_parent = random.randint(0, len(parents) - 1), random.randint(0, len(parents) - 1)\r\n while first_parent == second_parent:\r\n second_parent = random.randint(0, len(parents) - 1)\r\n cross_prob = random.random()\r\n if cross_prob <= self.cross_prob:\r\n cross_point = random.randrange(chromosome_len)\r\n first_child = Individual(parents[first_parent].get_chrome()[0:cross_point] +\r\n parents[second_parent].get_chrome()[cross_point:chromosome_len])\r\n second_child = Individual(parents[second_parent].get_chrome()[0:cross_point] +\r\n parents[first_parent].get_chrome()[cross_point:chromosome_len])\r\n else:\r\n first_child, second_child = parents[first_parent], parents[second_parent]\r\n next_generation.append(first_child)\r\n next_generation.append(second_child)\r\n return next_generation\r\n\r\n def mutation(self, next_generation):\r\n chromosome_len = self.population[0].chromosome_len\r\n for ind in range(len(next_generation)):\r\n if random.random() <= self.mut_prob:\r\n mutated = random.randrange(chromosome_len)\r\n mutation = '0'\r\n if next_generation[ind].get_chrome()[mutated] == '0':\r\n mutation = '1'\r\n next_generation[ind] = Individual(next_generation[ind].get_chrome()[0:mutated] + mutation +\r\n next_generation[ind].get_chrome()[mutated + 1:chromosome_len])\r\n else:\r\n next_generation[ind] = Individual(next_generation[ind].get_chrome()[0:mutated] + mutation +\r\n next_generation[ind].get_chrome()[mutated + 1:chromosome_len])\r\n return next_generation\r\n\r\n def best_fit(self):\r\n best_fit = max(self.fitness_list)\r\n best_individual = self.fitness_list.index(best_fit)\r\n return best_fit, best_individual\r\n\r\n def proceed(self):\r\n parents = self.roulette_wheel()\r\n next_generation = self.reproduce(parents)\r\n next_generation = self.mutation(next_generation)\r\n self.population = next_generation\r\n for ind in range(len(self.population)):\r\n self.fitness_list[ind] = self.population[ind].fitness_function()\r\n\r\n def solve(self, min_fitness=0.9, max_epochs=100):\r\n best_fit, best_individual = self.best_fit()\r\n epoch_num = int()\r\n while (min_fitness is None or best_fit < min_fitness) and (max_epochs is None or epoch_num < max_epochs):\r\n self.proceed()\r\n best_fit, best_individual = self.best_fit()\r\n epoch_num += 1\r\n visualisation = self.population[best_individual].visualisation()\r\n return best_fit, epoch_num, visualisation\r\n","sub_path":"nqueens.py","file_name":"nqueens.py","file_ext":"py","file_size_in_byte":5996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"74826463","text":"# -*- coding=utf-8\n'''\nCreated on 2016年9月23日\n庄家规则\n@author: zhaol\n'''\nfrom majiang2.banker.banker import MBanker\nimport random\nfrom freetime.util import log as ftlog\n\nclass MBankerRandomHuangNextMuiltPao(MBanker):\n \"\"\"\n 开局随机庄家\n 1）庄赢牌连庄\n 2）闲家赢，闲家坐庄\n 3）黄庄，由庄家的下一家坐庄\n 4）一炮多响者，由放跑者坐庄\n \"\"\"\n def __init__(self):\n super(MBankerRandomHuangNextMuiltPao, self).__init__()\n \n def getBanker(self, playerCount, isFirst, winLoose, winSeatId, extendInfo = {}):\n \"\"\"子类必须实现\n 参数：\n 1）isFirst 是否第一句\n 2）winLoose 上局的结果 1分出了胜负 0流局\n 3）winSeatId 赢家的座位号，如果第二个参数为0，则本参数为上一局的庄家\n \"\"\"\n if isFirst:\n # 初始化，随机选庄\n self.banker = random.randint(0, playerCount - 1)\n self.no_result_count = 0\n self.remain_count = 0\n else:\n if winLoose == MBanker.ONE_WIN_ONE_LOOSE:\n # 有输赢结果\n if winSeatId == self.banker:\n # 赢得是庄家\n self.remain_count += 1\n self.no_result_count = 0\n else:\n # 赢得是闲家\n self.banker = winSeatId\n self.remain_count = 0\n self.no_result_count = 0\n elif winLoose == MBanker.MULTI_WIN_ONE_LOOSE:\n self.banker = winSeatId\n self.remain_count = 0\n self.no_result_count = 0\n else:\n # 荒牌，流局，庄家继续，荒牌次数加一，坐庄次数加一\n self.banker = (self.banker + 1) % playerCount\n self.no_result_count = 0\n self.remain_count = 0\n \n ftlog.info('MBankerRandomHuangNextMuiltPao.getBanker playerCount:', playerCount\n , ' isFirst:', isFirst\n , ' winLoose:', winLoose\n , ' winSeatId:', winSeatId\n , ' banker:', self.banker\n , ' remainCount:', self.remain_count\n , ' noResultCount:', self.no_result_count) \n return self.banker, self.remain_count, self.no_result_count\n\n def calcNextBanker(self, playerCount, winLoose, winSeatId, extendInfo = {}):\n \"\"\" 计算下一个庄家\n 只是计算，不是真的设置庄家\n 设置庄家请继续使用getBanker接口\n \"\"\"\n\n if winLoose == MBanker.ONE_WIN_ONE_LOOSE:\n # 有输赢结果\n if winSeatId == self.banker:\n return self.banker\n else:\n return winSeatId\n elif winLoose == MBanker.MULTI_WIN_ONE_LOOSE:\n return winSeatId\n else:\n return (self.banker + 1) % playerCount\n","sub_path":"majiang2/src/majiang2/banker/banker_random_huangNext_multiPao.py","file_name":"banker_random_huangNext_multiPao.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"156068250","text":"import os.path\nfrom urllib2 import urlopen\n\nfrom fabric.api import cd, local, task, abort, env, puts, parallel\nfrom fabric.utils import _AttributeDict as ad\nfrom sphinx import edition\n\nfrom docs_meta import get_conf, render_paths, get_branch, get_commit\nfrom utils import ingest_yaml_list, conf_from_list\nfrom make import runner\n\n_pub_hosts = ['www-c1.10gen.cc', 'www-c2.10gen.cc']\n_stage_hosts = ['public@test.docs.10gen.cc']\nenv.rsync_options = ad()\nenv.rsync_options.default = '-cqltz'\nenv.rsync_options.recursive = None\nenv.rsync_options.delete = None\nenv.paths = render_paths('dict')\n\ndef rsync_options(recursive, delete, environ):\n r = [env.rsync_options.default]\n\n if recursive is True:\n r.append('--recursive')\n\n if delete is True:\n r.append('--delete')\n\n if environ == 'production':\n r.extend(['--rsh=\"ssh\"', '--rsync-path=\"sudo -u www rsync\"'])\n\n return ' '.join(r)\n\n########## Tasks -- Checking current build against production. ############\n\n@task\ndef check(site, conf=None):\n if conf is None:\n conf = get_conf()\n if site.startswith('stag'):\n env.release_info_url = 'http://test.docs.10gen.cc/{0}/release.txt'.format(str(branch))\n elif site == 'ecosystem':\n env.release_info_url = 'http://docs.mongodb.org/ecosystem/release.txt'\n elif site.startswith('prod') or site.startswith('pub'):\n env.release_info_url = 'http://docs.mongodb.org/{0}/release.txt'.format(conf.git.commit)\n\n r = urlopen(env.release_info_url).readlines()[0].split('\\n')[0]\n if get_commit() == r:\n abort('ERROR: the current published version of is the same as the current commit. Make a new commit before publishing.')\n else:\n puts('[build]: the current commit is different than the published version on. Building now.')\n\n########## Tasks -- Deployment and configuration. ############\n\ndef remote(host):\n if host in ['publication', 'mms']:\n env.hosts = _pub_hosts\n env.deploy_target = 'production'\n elif host.startswith('stag'): # staging or stage\n env.deploy_target = 'staging'\n env.hosts = _stage_hosts\n else:\n abort('[deploy]: must specify a valid host to deploy the docs to.')\n\n@task\n@parallel\ndef static(local_path='all', remote=None, host_string=None, recursive=False, delete=False, environ=None):\n if host_string is None:\n host_string = env.host_string\n if environ is None:\n environ = env.deploy_target\n\n if local_path == 'all':\n local_path = '*'\n\n static_worker(local_path, remote, host_string, recursive, recursive, environ)\n\ndef static_worker(local_path, remote, host_string, recursive, delete, environ):\n if local_path.endswith('.htaccess') and env.branch != 'master':\n puts('[deploy] [ERROR]: cowardly refusing to deploy a non-master htaccess file.')\n return False\n\n cmd = [ 'rsync', rsync_options(recursive=recursive, delete=delete, environ=environ) ]\n\n cmd.append(local_path)\n\n cmd.append(':'.join([host_string, remote]))\n\n puts('[deploy]: migrating {0} files to {1} remote'.format(local_path, remote))\n local(' '.join(cmd))\n puts('[deploy]: completed migration of {0} files to {1} remote'.format(local_path, remote))\n\n@task\n@parallel\ndef push(local_path, remote, host_string=None, recursive=False, delete=False, environ=None):\n if host_string is None:\n host_string = env.host_string\n if environ is None:\n environ = env.deploy_target\n\n push_worker(local_path, remote, host_string, recursive, delete, environ)\n\ndef push_worker(local_path, remote, host_string, recursive, delete, environ):\n if get_conf().project.name == 'mms' and (env.branch != 'master' and\n env.edition == 'saas'):\n puts('[deploy] [ERROR]: cowardly refusing to push non-master saas.')\n return False\n\n if local_path.endswith('/') or local_path.endswith('/*'):\n local_path = local_path\n else:\n local_path = local_path + '/'\n\n if remote.endswith('/'):\n remote = remote[:-1]\n else:\n remote = remote\n\n cmd = [ 'rsync',\n rsync_options(recursive, delete, environ),\n local_path,\n ':'.join([host_string, remote]) ]\n\n puts('[deploy]: migrating {0} files to {1} remote'.format(local_path, remote))\n local(' '.join(cmd))\n puts('[deploy]: completed migration of {0} files to {1} remote'.format(local_path, remote))\n\n############################## Deploy -- Generic Deployment Framework ##############################\n\ndef get_branched_path(options, conf=None, *args):\n if conf is None:\n conf = get_conf()\n\n if 'branched' in options:\n return os.path.join(os.path.sep.join(args),\n conf.git.branches.current)\n else:\n return os.path.sep.join(args)\n\n@task\ndef deploy(target, conf=None, pconf=None):\n if conf is None:\n conf = get_conf()\n\n if pconf is None:\n push_conf = ingest_yaml_list(os.path.join(conf.build.paths.projectroot,\n conf.build.paths.builddata,\n 'push.yaml'))\n\n pconf = conf_from_list('target', push_conf)[target]\n\n\n if pconf['target'] != target:\n abort('[deploy] [ERROR]: this build environment does not support the {0} target'.format(target))\n\n count = runner(deploy_jobs(target, conf, pconf), pool=2)\n puts('[deploy]: pushed {0} targets'.format(count))\n\ndef deploy_jobs(target, conf, pconf):\n if 'edition' in pconf:\n edition(pconf.edition)\n if 'recursive' in pconf.options:\n env.rsync_options.recursive = True\n if 'delete' in pconf.options:\n env.rsync_options.delete = True\n\n remote(pconf.env)\n\n args = dict(local_path=get_branched_path(pconf.options, conf, conf.build.paths.output, pconf.paths.local),\n remote=get_branched_path(pconf.options, conf, pconf.paths.remote),\n host_string=None,\n recursive=env.rsync_options.recursive,\n delete=env.rsync_options.delete,\n environ=env.deploy_target)\n\n for host in env.hosts:\n args['host_string'] = host\n yield { 'job': static_worker if 'static' in pconf.options else push_worker,\n 'args': args.copy(),\n 'target': None,\n 'dependency': None }\n\n if 'static' in pconf.paths:\n if isinstance(pconf.paths.static, list):\n for static_path in pconf.paths.static:\n for job in static_deploy(args, static_path, conf, pconf):\n yield job\n else:\n for job in static_deploy(args, pconf.paths.static, conf, pconf):\n yield job\n\ndef static_deploy(args, static_path, conf, pconf):\n if static_path in ['manual', 'current']:\n args['remote'] = pconf.paths.remote\n else:\n args['remote'] = os.path.join(pconf.paths.remote, static_path)\n\n args['local_path'] = os.path.join(conf.build.paths.output, pconf.paths.local, static_path)\n\n for host in env.hosts:\n args['host_string'] = host\n yield { 'job': static_worker,\n 'args': args.copy(),\n 'target': None,\n 'dependency': None }\n","sub_path":"fabfile/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":7272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"404276511","text":"from tkinter import *\nimport requests\n\n\ndef get_quote():\n \"\"\"This function gets the quote from the api\"\"\"\n response = requests.get(\"https://pc-quotes-api.herokuapp.com/quotes/v1.0/\")\n response.raise_for_status()\n data = response.json()\n quote = data['quote']\n canvas.itemconfig(quote_text, text=quote)\n\n\ninterface = Tk()\ninterface.title(\"A3AJAGBE QUOTE GUI\")\ninterface.config(padx=50, pady=50)\n\npc_label = Label(text=\"Priyanka Chopra Says\", font=(\"Tahoma\", 20, \"bold\"), padx=10, pady=10)\npc_label.grid(row=0, column=0)\n\n# Add quote background image\ncanvas = Canvas(width=300, height=414)\nbg_img = PhotoImage(file=\"images/background.png\")\ncanvas.create_image(150, 207, image=bg_img)\nquote_text = canvas.create_text(150, 207, text=\"\", width=250, font=(\"Tahoma\", 30, \"bold\"),\n fill=\"#0066ff\")\ncanvas.grid(row=1, column=0)\n\n# Add PCJ image to button\npc_img = PhotoImage(file=\"images/pc.png\")\npc_button = Button(image=pc_img, highlightthickness=0, command=get_quote)\npc_button.grid(row=2, column=0)\n\nget_quote()\n\n# Keep the interface open until exited\ninterface.mainloop()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"518042873","text":"#!/usr/bin/env python\nimport argparse\nimport subprocess\nimport os\n\nparser = argparse.ArgumentParser()\nparser.add_argument('input_path')\nparser.add_argument('output_path')\nargs = parser.parse_args()\n\n# Set enviornment\n\nif not os.path.exists(args.output_path):\n os.makedirs(args.output_path)\n print('[Info] copy_aods.py: Created '+args.output_path)\n\ninput_split = args.input_path.split('/')\noutput_path = args.output_path+'/'+input_split[4]+'__'+input_split[3]+'__'+input_split[6]+'__'+input_split[7]+'__'+input_split[8]\ncommand = 'xrdcp root://cms-xrd-global.cern.ch/'+args.input_path+' '+output_path\nprint('[Info] copy_aods.py: Running command: '+command)\nsubprocess.check_output(command, shell=True)\n","sub_path":"bin/copy_aods.py","file_name":"copy_aods.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"575576316","text":"# https://www.hackerrank.com/challenges/absolute-permutation/problem\n#!/bin/python3\n\nimport os\nimport sys\n\n#\n# Complete the absolutePermutation function below.\n#\ndef absolutePermutation(n, k):\n #\n # Write your code here.\n #\n # print(\"k: \", k)\n if (k == 0):\n return [x for x in range(1, n+1)]\n \n elif (n > 0 and n % k == 1):\n return [-1]\n \n elif ((n/k) % 2 != 0):\n return [-1]\n \n else:\n add = True\n result = []\n for i in range(1, n+1):\n if add:\n result.append(i+k)\n \n else:\n result.append(i-k)\n \n if (i % k == 0):\n if add:\n add = False\n else:\n add = True\n return result\n\n\n \n \n \n\nif __name__ == '__main__':\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n t = int(input())\n\n for t_itr in range(t):\n nk = input().split()\n\n n = int(nk[0])\n\n k = int(nk[1])\n\n result = absolutePermutation(n, k)\n\n fptr.write(' '.join(map(str, result)))\n fptr.write('\\n')\n\n fptr.close()\n\n","sub_path":"absolute_permutation.py","file_name":"absolute_permutation.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"553443873","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 14 11:58:40 2020\n\n@author: hugov\n\"\"\"\n\n\nimport math\nimport numpy as np\nfrom neurofit_rl import neurofit\nimport os\nimport sys\nimport time\nimport random\nimport shutil\n\n#Number of\n\nNvar=2 # x\nNpar1=2 #a,b,c\nNpar2=0 #parameter of correction\nNres=1 #the result\n\n\n# The user define the boundaries for each parameter\nbornes = np.zeros( ( Npar1,2 ) )\n\nbornes[0,0] = 0.1\nbornes[0,1] = +5\n\nbornes[1,0] = 0.1\nbornes[1,1] = +5\n\n#Architecture parameters\nnb_hid_lay = int(sys.argv[1])\narch = []\nfor i in range(2,len(sys.argv)):\n arch.append(int(sys.argv[i]))\n\n#Read input data\ndataset = np.loadtxt(fname = \"res.dat\")\nZ_A_Yexp_Err = np.loadtxt(fname = \"list_pts_exp\")\nexp_values = np.copy(Z_A_Yexp_Err[:,:3])\nlist_pts = np.copy(Z_A_Yexp_Err[:,:2])\n\n#Split the dataset into two set (data_train and data_eval)\nnb_data_train = math.ceil(dataset.shape[0]*(2/3))\nnb_data_eval = dataset.shape[0] - nb_data_train\ndatabase = dataset[:nb_data_train+1,:dataset.shape[1]-1]\ndatabase[:,database.shape[1]-2] = np.log10(database[:,database.shape[1]-2]) - np.log10(database[:,database.shape[1]-1])\ndatabase = database[:,:database.shape[1]-1]\ndatabase_eval = dataset[nb_data_train+1:,:dataset.shape[1]-1]\ndatabase_eval[:,database_eval.shape[1]-2] = np.log10(database_eval[:,database_eval.shape[1]-2]) - np.log10(database_eval[:,database_eval.shape[1]-1])\ndatabase_eval = database_eval[:,:database_eval.shape[1]-1]\n\n#Writting informations in the output files\nnomFichier = 'all_results.txt'\nif os.path.isfile(nomFichier) :\n fichier = open(nomFichier,'a')\nelse:\n fichier = open(nomFichier,'w')\nfichier.write(\"#\" + str(arch))\nfichier.write(\"#\" + str(nb_data_train) + \"\\n\")\nfichier.close()\n\nnomFichier = 'one_ligne_results.txt'\nif os.path.isfile(nomFichier) :\n fichier = open(nomFichier,'a')\nelse:\n fichier = open(nomFichier,'w')\n\nfichier.write( str(nb_data_train) + \" \")\nfichier.write( str(len(arch)-1) + \" \")\nfor i in range(len(arch)):\n fichier.write(str(arch[i]) + \" \")\nfichier.close()\n\n\ndef additional_param(param_list):\n \"\"\"\n This function will take param in the same order than in database and compute the additional\n parameter discribe by user.\n The user have to give the number of additional parameter desired and give the functions expected\n in return\n\n Input : List of parameters (do not change)\n Output : Number of additional parmaters,additionnal parameters\n \"\"\"\n N_add_param = 0\n\n return [N_add_param]\n\ndef getPhysicalCode(gam0,fcorr,Z,A,Yexp,Err):\n f_param_lorentz = open(\"BetaDecay/Package_script/param_lorentz.in\",'w') \n f_param_lorentz.write(str(gam0) + \" \" + str(fcorr))\n f_param_lorentz.close()\n shutil.copy(\"./BetaDecay/Package_script/tminus0.in\",\"./BetaDecay/Package_script/tminus.in\")\n f = open(\"./BetaDecay/Package_script/tminus.in\",'a')\n f.write(str(int(Z))+ \" \" + str(int(A)) + \" \" + str(Yexp) + \" \" + str(Err) )\n f.close()\n os.system(\"run_a.bat\")\n f = open(\"./BetaDecay/Package_script/t12_d1m_bm.g1.0\",'r')\n content = f.readlines()\n f.close()\n line_str = content[1].split(\" \")\n line_float = []\n for t in range(len(line_str)):\n if ( line_str[t] != ''):\n line_float.append(float(line_str[t]))\n Ycalc = line_float[5]\n \n return Ycalc\n\n \ndef getTrueRms(gam0,fcorr,Z_A_Yexp_Err):\n print(\"-------------------COMPUTING TRUE RMS----------------\"\"\")\n val = []\n t_0 = time.time()\n f_param_lorentz = open(\"BetaDecay/Package_script/param_lorentz.in\",'w') \n f_param_lorentz.write(str(gam0) + \" \" + str(fcorr))\n f_param_lorentz.close()\n os.system(\"run_compute_rms.bat\")\n rms_value = np.loadtxt(\"BetaDecay/Package_script/rms.dat\",ndmin =2)\n for value in rms_value:\n Yexp = value[1]\n Ycalc = value[0]\n val.append(math.log10(Ycalc)-math.log10(Yexp))\n val = np.array(val)\n \n print(\"-------------------END TRUE RMS COMPUTING : time = \",time.time()-t_0, \"s\")\n return math.sqrt(sum((val)**2)/val.shape[0])\n\n \n\n# start the fitting procedure with neurofit\nt0_total = time.time()\nNstep= 10\nfor istep in range( Nstep ):\n t0=time.time()\n print(\"-----------START OF STEP %2d-----------------------------\" % istep)\n\n nomFichier = 'all_results.txt'\n fichier = open(nomFichier,'a')\n fichier.write( str(istep) )\n fichier.close()\n\n pred = neurofit( database , database_eval , Nvar, Npar1, Npar2, Nres, bornes, list_pts ,additional_param,nb_hid_lay,arch,Nstep,istep,exp_values)\n # pred should be a table of N prediction (line with j,a,b; j being the index of a line of the list_pts table); the last column can give the predicted rms for the value of a and b\n for ipred in range(len(pred)):\n\n print(pred[ipred])\n j = random.randint(0,list_pts.shape[0]-1)\n Z_A = list_pts[j]\n gam0 = pred[ipred][0]\n fcorr = pred[ipred][1]\n estimate_rms = pred[ipred][2]\n next_line = np.zeros( ( 1, Nvar + Npar1 + Npar2 + Nres ) )\n next_line[0,:Nvar] = Z_A\n next_line[0,Nvar:Nvar + Npar1 ] = pred[ipred][0:Npar1]\n \n\n #Use Physical Code to compute new values\n Yexp = Z_A_Yexp_Err[j][2]\n Err = Z_A_Yexp_Err[j][3]\n Ycalc = getPhysicalCode(gam0,fcorr,Z_A[0],Z_A[1],Yexp,Err)\n next_line[0,Nvar + Npar1 ] = math.log10(Ycalc) - math.log10(Yexp)\n \n #Add all the predictions in database for next training step\n database = np.concatenate(( database , next_line ),axis=0)\n \n\n if(ipred == 0):\n #Use Physical Code to compute the Rms of the best prediction only each 5 steps\n# if ( istep%5 == 0):\n nomF = \"best_pred_rms.txt\"\n if os.path.isfile(nomF) :\n fichier = open(nomF,'a')\n else:\n fichier = open(nomF,'w')\n fichier.write(str(Nstep) + \" \" + str(gam0) + \" \" + str(fcorr) + \" \" + str(getTrueRms(gam0,fcorr,Z_A_Yexp_Err)) + \" \" + str(estimate_rms)+ \"\\n\")\n fichier.close()\n# else :\n# nomF = \"best_pred_rms.txt\"\n# if os.path.isfile(nomF) :\n# fichier = open(nomF,'a')\n# else:\n# fichier = open(nomF,'w')\n# fichier.write(str(Nstep) + \" \" + str(gam0) + \" \" + str(fcorr) + \" \" + str(estimate_rms)+ \"\\n\")\n# fichier.close()\n# \n #Add the first prediction (which is suppose to be the best prediction)\n #in the database_eval. Variable x is changed. \n x = list_pts[ random.randint( 0, list_pts.shape[0]-1) ]\n while( (x == next_line[0,:Nvar])[0]&(x == next_line[0,:Nvar])[1] ):\n x = list_pts[ random.randint( 0, list_pts.shape[0]-1) ]\n next_line[0,:Nvar] = x\n database_eval = np.concatenate((database_eval,next_line), axis=0)\n print(\"epoch time : \",time.time() - t0)\n\nfichier.close()\nfichier = open(\"best_pred_rms.txt\",'a')\nfichier.write(str(Nstep) + \" \" + str(1) + \" \" + str(1) + \" \" + str(getTrueRms(1,1,Z_A_Yexp_Err)) + \"\\n\")\nfichier.close()\nprint(\"total computing time : \",time.time()-t0_total)\n#delete features of current model to start from sratch new model\nos.remove(\"last_model.h5\")\nos.remove(\"weights.best.hdf5\")\nos.remove(\"mean.txt\")\nos.remove(\"std.txt\")\n","sub_path":"main_beta.py","file_name":"main_beta.py","file_ext":"py","file_size_in_byte":7345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"139398891","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 6 20:54:09 2018\n\n@author: sameh\n\"\"\"\n\nimport xml.etree.cElementTree as et\nimport pandas as pd\n\n\ndef getvalueofnode(node):\n \"\"\" return node text or None \"\"\"\n return node.text if node is not None else None\n\n\ndef main(): \n parsedXML = et.parse(\"atg-prodcat.xml\")\n dfcols = ['productId, productName', 'type']\n df_xml = pd.DataFrame(columns=dfcols)\n\n\n for node in parsedXML.getroot():\n prod_id = node.find('productId')\n prod_name = node.find('productName')\n \n df_xml = df_xml.append(\n pd.Series([getvalueofnode(prod_name)], index=dfcols),\n ignore_index=True)\n\n print (df_xml)\n \nmain()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"84075625","text":"import json\nimport numpy as np\nimport pickle as pkl\nimport math\nfrom tqdm import tqdm\nimport os\n\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom scipy.sparse import coo_matrix\nimport re\ndef clean_str(string,use=True):\n\n if not use: return string\n\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\'s\", \" \\'s\", string)\n string = re.sub(r\"\\'ve\", \" \\'ve\", string)\n string = re.sub(r\"n\\'t\", \" n\\'t\", string)\n string = re.sub(r\"\\'re\", \" \\'re\", string)\n string = re.sub(r\"\\'d\", \" \\'d\", string)\n string = re.sub(r\"\\'ll\", \" \\'ll\", string)\n string = re.sub(r\",\", \" , \", string)\n string = re.sub(r\"!\", \" ! \", string)\n string = re.sub(r\"\\(\", \" \\( \", string)\n string = re.sub(r\"\\)\", \" \\) \", string)\n string = re.sub(r\"\\?\", \" \\? \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()\n\ndef tf_idf_transform(inputs, mapping=None, sparse=False):\n from sklearn.feature_extraction.text import CountVectorizer\n from sklearn.feature_extraction.text import TfidfTransformer\n from scipy.sparse import coo_matrix\n vectorizer = CountVectorizer(vocabulary=mapping)\n\n tf_idf_transformer = TfidfTransformer()\n tf_idf = tf_idf_transformer.fit_transform(vectorizer.fit_transform(inputs))\n weight = tf_idf.toarray()\n return weight if not sparse else coo_matrix(weight).toarray()\n#\ndef PMI(inputs, mapping, window_size, sparse):\n W_ij = np.zeros([len(mapping), len(mapping)], dtype=np.float64)\n W_i = np.zeros([len(mapping)], dtype=np.float64)\n W_count = 0\n for one in inputs:\n word_list = one.split(' ')\n if len(word_list) - window_size < 0:\n window_num = 1\n else:\n window_num = len(word_list) - window_size + 1\n for i in range(window_num):\n W_count += 1\n context = list(set(word_list[i: i+window_size]))\n while '' in context:\n context.remove('')\n for j in range(len(context)):\n W_i[mapping[context[j]]] += 1\n for k in range(j + 1, len(context)):\n W_ij[mapping[context[j]], mapping[context[k]]] += 1\n W_ij[mapping[context[k]], mapping[context[j]]] += 1\n if sparse:\n print(\"sparse!!!!!\")\n rows = []\n columns = []\n data = []\n for i in range(len(mapping)):\n rows.append(i)\n columns.append(i)\n data.append(1)\n tmp = [ele for ele in np.nonzero(W_ij[i])[0] if ele > i]\n for j in tmp:\n value = math.log(W_ij[i, j] * W_count / W_i[i] / W_i[j])\n if value > 0:\n rows.append(i)\n columns.append(j)\n data.append(value)\n rows.append(j)\n columns.append(i)\n data.append(value)\n PMI_adj = coo_matrix((data, (rows, columns)), shape=(len(mapping), len(mapping)))\n else:\n PMI_adj = np.zeros([len(mapping), len(mapping)], dtype=np.float64)\n for i in range(len(mapping)):\n PMI_adj[i, i] = 1\n tmp = [ele for ele in np.nonzero(W_ij[i])[0] if ele > i]\n for j in tmp:\n pmi = math.log(W_ij[i, j] * W_count / W_i[i] / W_i[j])\n if pmi > 0:\n PMI_adj[i, j] = pmi\n PMI_adj[j, i] = pmi\n return PMI_adj\n\n\ndef similarity_cal(word_mapping):\n sim_matrix = cosine_similarity([value[0] for key,value in word_mapping.items()])\n print(\"sim_matrix\")\n row_num =0\n for row in sim_matrix:\n col_num = 0\n for col in row:\n if col < 0.95:\n sim_matrix[row_num][col_num] = 0\n else:\n print(sim_matrix[row_num][col_num])\n col_num = col_num + 1\n row_num = row_num + 1\n return sim_matrix\n\ndef similarity_cal_param(word_mapping, param_mapping):\n new_dict = {}\n token_set = param_mapping.keys()\n\n for key in token_set:\n if key == \"null\":\n new_dict[key] = np.zeros((1,768),dtype=np.int)\n if word_mapping.__contains__(key):\n new_dict[key] = word_mapping[key]\n else:\n new_dict[key] = np.zeros((1,768),dtype=np.int)\n\n sim_matrix = cosine_similarity([value[0] for key,value in new_dict.items()])\n print(\"sim_matrix\")\n row_num =0\n for row in sim_matrix:\n col_num = 0\n for col in row:\n if col < 0.95:\n sim_matrix[row_num][col_num] = 0\n else:\n print(sim_matrix[row_num][col_num])\n col_num = col_num + 1\n row_num = row_num + 1\n print(len(sim_matrix))\n print(len(sim_matrix[0]))\n return sim_matrix\n\n\ndef ngram(tag_1, tag_2):\n f = open(\"../ngram/ngram_corpus.txt\")\n lines = f.readlines()\n tag1_list = []\n tag2_list = []\n possibility_list = []\n ngram_possibility = 0\n for line in lines:\n tag1 = line.split(\",\")[0].strip()\n tag2 = line.split(\",\")[1].strip()\n possibility = line.split(\",\")[2]\n tag1_list.append(tag1)\n tag2_list.append(tag2)\n possibility_list.append(possibility)\n if tag_1 == tag_2:\n return 1\n for i in range(len(possibility_list)):\n if tag1_list[i].strip() == str(tag_1).upper() and tag2_list[i].strip() == str(tag_2).upper():\n ngram_possibility = float(possibility_list[i])\n break\n return ngram_possibility\n\n\ndef ngram_cal(tags_mapping):\n W_ij = np.zeros([len(tags_mapping), len(tags_mapping)], dtype=np.float64)\n for i in range(len(tags_mapping)):\n tag_1 = list(tags_mapping.keys())[i]\n for j in range(len(tags_mapping)):\n tag_2 = list(tags_mapping.keys())[j]\n print(ngram(tag_1,tag_2))\n ngram_value = ngram(tag_1, tag_2)\n if ngram_value > -0.5:\n W_ij[i, j] = ngram_value\n else:\n W_ij[i, j] = 0\n print(W_ij)\n return W_ij\n\n\n\ndef construct_graph(dataset_name, remove_StopWord=False):\n os.makedirs(f'./{dataset_name}_data', exist_ok=True)\n f_train = json.load(open('./{}.json'.format(dataset_name),encoding='utf-8'))['train']\n f_test = json.load(open('./{}.json'.format(dataset_name),encoding='utf-8'))['test']\n\n print(len(f_train))\n print(len(f_test))\n from collections import defaultdict\n word_freq=defaultdict(int)\n param_freq=defaultdict(int)\n for item in f_train.values():\n words=clean_str(item['text']).split(' ')\n params =clean_str(item['param']).split(' ')\n for one in words:\n word_freq[one.lower()]+=1\n for one in params:\n param_freq[one.lower()]+=1\n for item in f_test.values():\n words = clean_str(item['text']).split(' ')\n params = clean_str(item['param']).split(' ')\n for one in words:\n word_freq[one.lower()]+=1\n for one in params:\n param_freq[one.lower()]+=1\n\n\n method_name_nodes = []\n tag_set = set()\n param_tag_set = set()\n param_set = set()\n words_set = set()\n train_idx = []\n test_idx = []\n labels = []\n tag_list = []\n word_list = []\n param_list = []\n #\n for i, item in enumerate(tqdm(f_train.values())):\n method_name = clean_str(item['text'])\n param = clean_str(item['param'])\n tags = item['pos'].lower().split(\" \")\n if not method_name:\n print(method_name)\n continue\n if '' in tags:\n print(item)\n tag_list.append(' '.join(tags))\n tag_set.update(tags)\n labels.append(item['label'])\n words = [one.lower() for one in method_name.split(' ')]\n if '' in words:\n print(words)\n\n word_list.append(' '.join(words))\n words_set.update(words)\n\n param = [one.lower() for one in param.split(' ')]\n param_list.append(' '.join(param))\n param_set.update(param)\n\n if method_name:\n method_name_nodes.append(method_name)\n else:\n print(item)\n print(method_name)\n train_idx.append(len(train_idx))\n\n\n\n for i, item in enumerate(tqdm(f_test.values())):\n method_name = clean_str(item['text'])\n param = clean_str(item['param'])\n tags = item['pos'].lower().split(\" \")\n if not method_name:\n print(method_name)\n continue\n\n tag_list.append(' '.join(tags))\n tag_set.update(tags)\n\n labels.append(item['label'])\n words = [one.lower() for one in method_name.split(' ')]\n if '' in words:\n print(words)\n\n word_list.append(' '.join(words))\n words_set.update(words)\n\n param = [one.lower() for one in param.split(' ')]\n param_list.append(' '.join(param))\n param_set.update(param)\n if method_name:\n method_name_nodes.append(method_name)\n else:\n print(item)\n print(method_name)\n\n test_idx.append(len(test_idx)+ len(train_idx))\n\n print(tag_set)\n print(param_tag_set)\n\n word_nodes = list(words_set)\n tag_nodes = list(tag_set)\n param_nodes = list(param_set)\n param_tag_nodes = list(param_tag_set)\n nodes_all = method_name_nodes + tag_nodes + param_nodes + word_nodes\n nodes_num = len(method_name_nodes) + len(tag_nodes) + len(param_nodes) + len(word_nodes)\n print('method_name', len(method_name_nodes))\n print('tag', len(tag_nodes))\n print('param', len(param_nodes))\n print('word', len(word_nodes))\n\n if len(nodes_all) != nodes_num:\n print('duplicate name error')\n print('len_train',len(train_idx))\n print('len_test',len(test_idx))\n print('len_quries',len(method_name_nodes))\n\n tags_mapping = {key: value for value, key in enumerate(tag_nodes)}\n words_mapping = {key: value for value, key in enumerate(word_nodes)}\n params_mapping = {key: value for value, key in enumerate(param_nodes)}\n # output tag and adjacent matrix\n adj_method_name2tag = tf_idf_transform(tag_list, tags_mapping)\n adj_tag = ngram_cal(tags_mapping)\n pkl.dump(adj_method_name2tag, open('./{}_data/adj_method_name2tag.pkl'.format(dataset_name), 'wb'))\n pkl.dump(adj_tag, open('./{}_data/adj_tag.pkl'.format(dataset_name), 'wb'))\n\n # output word and adjacent matrix\n adj_method_name2word = tf_idf_transform(word_list, words_mapping, sparse=True)\n\n # calculate Bert vector similarity\n str = open('../BertVector/VectorMap.json','r')\n vector_mapping = json.load(str)\n print(\"adj_word_SIM\")\n adj_word_SIM = similarity_cal(vector_mapping)\n print(adj_word_SIM)\n pkl.dump(adj_method_name2word, open('./{}_data/adj_method_name2word.pkl'.format(dataset_name), 'wb'))\n pkl.dump(adj_word_SIM, open('./{}_data/adj_word.pkl'.format(dataset_name), 'wb'))\n\n # output param and adjacent matrix\n # adj_method_name2param = tf_idf_transform(param_list, params_mapping, sparse=True)\n # str = open('../BertVector/VectorParameterMap.json', 'r')\n # vector_parameter_mapping = json.load(str)\n # adj_param = similarity_cal_param(vector_parameter_mapping, params_mapping)\n # print(adj_word_SIM)\n # pkl.dump(adj_method_name2param, open('./{}_data/adj_method_name2param.pkl'.format(dataset_name), 'wb'))\n # pkl.dump(adj_param, open('./{}_data/adj_param.pkl'.format(dataset_name), 'wb'))\n\n json.dump(train_idx, open('./{}_data/train_idx.json'.format(dataset_name), 'w'), ensure_ascii=False)\n json.dump(test_idx, open('./{}_data/test_idx.json'.format(dataset_name), 'w'), ensure_ascii=False)\n json.dump(method_name_nodes, open('./{}_data/method_names.json'.format(dataset_name), 'w'), ensure_ascii=False)\n\n sorted_set = sorted(set(labels))\n print(sorted_set)\n label_map = {value: i for i, value in enumerate(sorted_set)}\n json.dump([label_map[label] for label in labels], open('./{}_data/labels.json'.format(dataset_name), 'w'),\n ensure_ascii=False)\n json.dump(method_name_nodes, open('./{}_data/method_name_id2_list.json'.format(dataset_name), 'w'),\n ensure_ascii=False)\n json.dump(tag_nodes, open('./{}_data/tag_id2_list.json'.format(dataset_name), 'w'), ensure_ascii=False)\n json.dump(param_nodes, open('./{}_data/param_id2_list.json'.format(dataset_name), 'w'),\n ensure_ascii=False)\n json.dump(param_tag_nodes, open('./{}_data/param_pos_id2_list.json'.format(dataset_name), 'w'),\n ensure_ascii=False)\n json.dump(word_nodes, open('./{}_data/word_id2_list.json'.format(dataset_name), 'w'), ensure_ascii=False)\n\ndataset_name='method_name_param_pos_tag'\nconstruct_graph(dataset_name)","sub_path":"AblationStudy/NameSpotter-POS/preprocess/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":12666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"6772094","text":"##################################################\n## module: fermat_primes_py3.py\n## author: Misty Jenkins\n## A#: A01489174\n## tests numbers using Fermat's Little Theorem\n## 09/07/2016\n##################################################\n\n#!/usr/bin/python3\n\nfrom random import randint\nfrom newton_sqrt_py3 import newton_sqrt\n\ndef is_even(e):\n\treturn not bool(e%2)\n\t\ndef remainder(a, b):\n\treturn a%b\n\ndef expmod(b, e, m):\n\tif (e == 0):\n\t\treturn 1\n\telif ( is_even(e) ):\n\t\tx = expmod(b, e/2, m);\n\t\treturn remainder(x*x, m)\n\telse:\n\t\treturn remainder(b * expmod(b, e-1, m), m)\n\t\t\ndef fermat_test(n):\n\tif (n<2):\n\t\treturn False\n\telif (n==2):\n\t\treturn True\n\t\t\n\ta = randint(2, n-1)\n\treturn expmod(a,n,n) == a\n\t\ndef is_fermat_prime(n, num_times):\n\tif ( num_times == 0 ):\n\t\treturn True;\n\telif ( fermat_test(n) ):\n\t\treturn is_fermat_prime(n, num_times-1);\n\telse:\n\t\treturn False;\n\t\t\ndef is_prime(n):\n\tif n < 2:\n\t\treturn False\n\telif n == 2:\n\t\treturn True\n\telse:\n\t\tfor d in range(2, int(newton_sqrt(n))+1):\n\t\t\tif n % d == 0:\n\t\t\t\treturn False\n\t\treturn True\n\ndef sum_of_fermat_primes(start, end, num_times):\n\trslt = 0\n\tfor i in range(start, end):\n\t\tif is_fermat_prime(i, num_times):\n\t\t\trslt += i\n\treturn rslt\n\ndef sum_of_primes(start, end):\n\trslt = 0\n\tfor i in range(start, end):\n\t\tif is_prime(i):\n\t\t\trslt += i\n\treturn rslt\n\ndef test_sum_diff_in_range(n):\n\tsp = sum_of_primes(0, n)\n\tsfp = sum_of_fermat_primes(0, n, 10)\n\tprint('sum of primes = ', sp)\n\tprint('sum of fermat primes = ', sfp)\n\tprint('sum diff = ', sfp - sp)\n","sub_path":"fermat_primes_py3.py","file_name":"fermat_primes_py3.py","file_ext":"py","file_size_in_byte":1505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"329204401","text":"\"\"\"\n@Author Mwaruwa Chaka, JKUAT ECE Final Year Project 2014\nFace Recognition configuration and global variables\"\"\"\n\n\"Project Settings\"\nproject_root = \"C:/Users/DMC/Box Sync/ProjectWebsite/\"\nworking_ratio = 2\n\n\"FACE RECOGNITION\"\nthreshold = 70 #facerec threshsold\nfisherThreshold = 200 #threshold for fisherfaces model\nsize=(100,100) #size of image to work with\ntemp = 'tmp/' #temporary or working folder\npath = 'upload/' #upload path\nrel_temp = '../'+temp #paths relative to code folder\nrel_upload='../'+path\nabs_temp = project_root+temp #absolute paths\nabs_path = project_root+path\n\n\n\"FACE DETECTION\"\ncascadefile = project_root+\"FaceEngine/haar/lbpcascade_frontalface.xml\"\n\n\"DATABASE\"\ndb_host = \"localhost\"\ndb_user=\"root\"\ndb_passwd = \"\"\ndb_name=\"projectdatabase\"\n\n\n\n\n\nrel_path = rel_upload\n\n\n","sub_path":"Final-Project/FaceEngine/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"49862714","text":"import os \nimport numpy as np\n\n\nclass DictClass(object):\n\tdef __init__(self, dict):\n\t\tself.__dict__ = dict\n\t\t\n\tdef update(self, dict):\n\t\tself.__dict__.update(dict)\n\n\t\t\ndef getDirAndFileName(filePath):\n\tfileDir = os.path.dirname(os.path.realpath(filePath))\n\tfileName = os.path.basename(filePath)\n\treturn fileDir, fileName\n\t\n\ndef setVariable(var, lenght):\n \"\"\"\n Creates set of values defined in 'var' of lenght 'lenght' by doing cylcing (modulo operation) \n Example\n var = [1, 2, 3], lenght=5, return [1, 2, 3, 1, 2]\n \"\"\"\n nvar = []\n l = len(var)\n for i in xrange(lenght):\n nvar.append(var[i % l])\n return nvar\n\n\t\nclass OUProcess:\n \"\"\"\n Defines Ornstein-Uhlenbeck process\n \"\"\"\n def __init__(self, mean, theta=0.03, sigma=0.05, dt = 0.001):\n \"\"\"\n Inits class\n mean : desired mean membrane potential (ex. to convert to rate use log(mean) )\n theta : speed of convergance, it is 1/decay (ex. decay=0.03, 30ms)\n sigma : variance of noise (ex. 0.05)\n dt : simulation time step (ex. 0.001 = 1 ms)\n \"\"\"\n self.mean = mean\n self.theta = theta\n self.sigma = sigma\n self.dt = dt\n self.f = lambda x:x\n \n def setFunction(self, func):\n \"\"\"\n Defines function to be done over results of process.\n Default function is linear, f(x)=x\n \n Note:\n If OU proess is used to define membrane potential then f=exp\n \"\"\"\n self.f = func\n \n def create(self,length, delay = 50):\n \"\"\"\n Creates OU proess for time given by length.\n -> length : number of time steps\n -> delay : delay in time steps we discard results of process (burn in time)\n \"\"\"\n dt = self.dt\n sigma = self.sigma\n theta = self.theta\n ur = self.mean\n u0 = ur+np.random.randn(1)\n\n ut = np.zeros(length+delay)\n ut[0]=u0\n for t in xrange(1,length+delay):\n ut[t]=ut[t-1] + theta*(ur-ut[t-1])*dt + np.random.randn(1)*sigma\n ut[t] = min(np.log(50),ut[t])\n return self.f(ut[delay:])","sub_path":"eim/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"14315957","text":"from behave import given, when, then\nfrom selenium.webdriver.common.by import By\nfrom time import sleep\n\n\n@when('Click on the cart icon')\ndef click_cart_icon(context):\n context.driver.find_element(By.CSS_SELECTOR, 'a#nav-cart').click()\n sleep(3)\n\n\n@then('Verify that Shopping cart is empty')\ndef verify_shopping_cart_empty(context):\n assert 'Your Shopping Cart is empty.' in context.driver.find_element(By.XPATH,\n \"//h1[@class='sc-empty-cart-header']\").text\n","sub_path":"features/steps/Homework3.py","file_name":"Homework3.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"64415134","text":"#!/usr/bin/env python\n\"\"\" MultiQC module to parse output from sequana\"\"\"\nimport logging\nimport os\nimport re\n\nfrom multiqc import config\nfrom multiqc.modules.base_module import BaseMultiqcModule\nfrom multiqc.plots import linegraph, table, heatmap, bargraph\n\nlog = logging.getLogger('multiqc.sequana/quality_control')\n\n\nclass MultiqcModule(BaseMultiqcModule):\n\n def __init__(self):\n\n # Initialise the parent object\n super(MultiqcModule, self).__init__(\n name='Sequana/quality_control',\n anchor='sequana_quality_control',\n target='sequana_quality_control',\n href='http://github.com/sequana/sequana/',\n info=\"(sequana pipelines)\")\n\n self.data = {}\n for myfile in self.find_log_files(\"sequana/quality_control\"):\n thisdata = self.parse_logs(myfile[\"f\"])\n name = thisdata[\"project\"]\n self.data[name] = self.parse_logs(myfile[\"f\"])\n\n if len(self.data) == 0:\n log.debug(\"Could not find any data in {}\".format(config.analysis_dir))\n raise UserWarning\n\n log.info(\"Found {} reports\".format(len(self.data)))\n\n self.populate_columns()\n self.add_phix_section()\n self.add_adapter_section()\n\n def populate_columns(self):\n\n # cutadapt_json\n # Number of reads\n # Total paired reads: 864,879\n\n headers = {}\n if any(['multiqc_total_reads' in self.data[s] for s in self.data]):\n headers['multiqc_total_reads'] = {\n 'title': 'TODO',\n 'description': 'TODO',\n #'max': 100,\n 'min': 0,\n #'modify': lambda x: x * 100,\n 'scale': 'RdYlGn',\n #'format': '{:,.1f}'\n 'shared_key': 'multiqc_total_reads',\n #'format': read_format,\n 'hidden': True,\n }\n if len(headers.keys()):\n self.general_stats_addcols(self.data, headers)\n\n def parse_logs(self, log_dict):\n import json\n data = json.loads(log_dict)\n this = data[\"cutadapt_json\"][\"Number of reads\"][\"Total paired reads\"]\n data[\"multiqc_total_reads\"] = this\n return data\n\n def add_phix_section(self):\n data = {}\n for name in self.data.keys():\n data[name] = {'phix_qc': self.data[name][\"phix_section\"][\"contamination\"]}\n\n pconfig = {\n \"title\": \"Percentage of phix in the raw data\",\n \"percentages\": True,\n \"min\": 100,\n }\n\n self.add_section(\n name = 'Phix presence',\n anchor = 'mean_read_length',\n description = 'TODO',\n helptext = \"\",\n plot = bargraph.plot(data, None, pconfig))\n\n def add_adapter_section(self):\n data = {}\n for name in self.data.keys():\n thisdata = self.data[name][\"cutadapt_json\"][\"percent\"][\"Pairs kept\"]\n data[name] = {'pairs_kept': thisdata.replace(\"(\",\"\").replace(\")\",\"\").replace(\"%\",\"\")}\n\n pconfig = {\n \"title\": \"Percentage of pairs kept\",\n \"percentages\": True,\n \"min\": 0,\n \"max\": 100,\n }\n\n self.add_section(\n name = 'Pairs kept',\n anchor = 'Pairs kept',\n description = 'Pairs kept',\n helptext = \"\",\n plot = bargraph.plot(data, None, pconfig))\n\n\n\n\n\n\n\n","sub_path":"sequana/multiqc/quality_control.py","file_name":"quality_control.py","file_ext":"py","file_size_in_byte":3431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"518200064","text":"import pygame\nimport neat\nimport os, time, random\nimport pickle\npygame.font.init()\n\nGEN = 0\n\nWIN_WIDTH = 600\nWIN_HEIGHT = 800\n\nBIRD_IMGS = [pygame.transform.scale2x(pygame.image.load(os.path.join(\"imgs\",\"bird1.png\"))),\n pygame.transform.scale2x(pygame.image.load(os.path.join(\"imgs\",\"bird2.png\"))),\n pygame.transform.scale2x(pygame.image.load(os.path.join(\"imgs\",\"bird3.png\")))]\n\nPIPE_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join(\"imgs\",\"pipe.png\")))\nBG_IMG = pygame.transform.scale(pygame.image.load(os.path.join(\"imgs\",\"bg.png\")),(WIN_WIDTH, WIN_HEIGHT))\nBASE_IMG = pygame.transform.scale2x(pygame.image.load(os.path.join(\"imgs\",\"base.png\")))\n\nSCORE_FONT = pygame.font.SysFont(\"comicsans\",50)\n\nclass Bird:\n IMGS = BIRD_IMGS\n MAX_ROTATION = 25 #rotation degree\n ROT_VEL = 15 #rotation velocity, how many times we're gonna rotate the bird in teach frame\n ANIMATION_TIME = 5 #how long to show each bird animation, the process of flapping the wings\n\n def __init__(self, x, y):\n self.x = x\n self.y = y\n self.tilt = 0 # how tilted the image is\n self.tick_count = 0 #keeps track of when we last jump/ how many ticks we've been moving forward\n self.vel = 0\n self.heigth = self.y #will be used to know were the bird was before jumping\n self.img_count = 0 # to know what image we're using right now\n self.img = self.IMGS[0]\n\n def jump(self):\n self.vel = -10.5 # because (0,0) is the coordinate of the top left, if we want to go up we need a negative velocity and down a positive one\n self.tick_count = 0\n self.heigth = self.y\n\n def move(self):\n self.tick_count +=1 # a frame/tick went by\n\n displacement = self.vel*(self.tick_count) + 0.5*(3)*(self.tick_count)**2 #displacement, how much to move\n #at the beginning of the jump: -10.5*1+1.5 = -9, as time advances it \"increases\"\n # and the displacement begins to turn positive, making the bird go lower\n\n if displacement>=10: # terminal velocity, the bird can't go down faster\n displacement = 10\n if displacement<0:\n displacement-=2 #fine tunes the movement a bit\n\n self.y = self.y + displacement\n # if we're going up or if we're kinda above the place we started\n if displacement<0 or self.y < self.heigth:\n #one if maybe missig\n self.tilt = self.MAX_ROTATION #when we go up we just want to rotate slightly\n else:\n if self.tilt >-90:\n self.tilt -= self.ROT_VEL #when we go down we want to ratate the full 90º, to look like a nose dive\n\n\n def draw(self, win): #the window\n self.img_count += 1 # fow how many frames we've shown an image\n\n #draws the wings flapping\n if self.img_count < self.ANIMATION_TIME:\n self.img = self.IMGS[0]\n elif self.img_count < self.ANIMATION_TIME*2:\n self.img = self.IMGS[1]\n elif self.img_count < self.ANIMATION_TIME*3:\n self.img = self.IMGS[2]\n elif self.img_count < self.ANIMATION_TIME*4:\n self.img = self.IMGS[1]\n elif self.img_count == self.ANIMATION_TIME*4 + 1:\n self.img = self.IMGS[0]\n self.img_count = 0 # completes the loop, bird returns to normal state\n\n # just for image stabilization\n if self.tilt <= -80:\n self.img = self.IMGS[1] # if we're going down too hard, just stabilize the wings\n self.img_count = self.ANIMATION_TIME *2 # this way when we jump the correct image(3) shows up\n # because we're already in the second image\n\n rotated_image = pygame.transform.rotate(self.img, self.tilt) #rotates the image but doesn't center it\n new_rect = rotated_image.get_rect(center = self.img.get_rect(topleft = (self.x, self.y)).center) #rect = rectangle\n\n win.blit(rotated_image, new_rect.topleft) #topleft is the position, blit just draws\n\n #return a object mask to know when collisions happend\n def get_mask(self):\n return pygame.mask.from_surface(self.img)\n #a mask is just a matrix indicating where the image's pixels are\n\nclass Pipe:\n GAP = 180 # space betwen bottom and top pipe\n VEL= 4 #since the bird isn't moving in the x plane, but the environment is\n\n def __init__(self,x): #we don't have an y, because it will be random\n self.x = x\n self.height = 0 #coordinate of the tip of top pipe\n\n self.top = 0 # where the top pipe is drawn, the entrance coordinate\n self.bottom =0 # where the bottom pipe is drawn\n self.PIPE_TOP = pygame.transform.flip(PIPE_IMG, False, True)\n self.PIPE_BOTTOM = PIPE_IMG\n\n self.passed = False #if the bird has already passed the pipe, helpful for collision purposes\n self.set_height()\n\n def set_height(self):\n #for the top pipe\n self.height = random.randrange(50,350) # remember- 50 at the top, 450 almost at the bottom\n self.top = self.height-self.PIPE_TOP.get_height() #top left coordinate for it to be drawn, ex : 300-1500 = -1200, height were the pipe begins to be drawn\n #for the bottom pipe\n #since the top left coordinate of bottom pipe already is were we want it to be drawn, we just need to add the gap\n self.bottom = self.height + self.GAP\n\n def move(self):\n self.x -= self.VEL #just move the pipe a bit to the left for each tick\n\n def draw(self,win):\n win.blit(self.PIPE_TOP,(self.x,self.top))\n win.blit(self.PIPE_BOTTOM,(self.x,self.bottom))\n\n def collide(self,bird):\n bird_mask = bird.get_mask()\n top_mask = pygame.mask.from_surface(self.PIPE_TOP)\n bottom_mask = pygame.mask.from_surface(self.PIPE_BOTTOM)\n\n #offset- distance between the bird mask and the pipe mask\n top_offset = (self.x-bird.x, self.top - round(bird.y)) # we can't have decimal numbers\n bottom_offset = (self.x - bird.x, self.bottom - round(bird.y))\n\n b_point = bird_mask.overlap(bottom_mask, bottom_offset) # the point of overlap between the masks, if they don't colide, returns None\n t_point = bird_mask.overlap(top_mask, top_offset)\n\n if b_point or t_point:\n return True\n\n return False\n\n\nclass Base:\n VEL = 5\n WIDTH = BASE_IMG.get_width()\n IMG = BASE_IMG\n def __init__(self,y):\n self.y = y\n #we have 2 images, one in front of the other\n self.x1 = 0\n self.x2 = self.WIDTH\n\n def move(self):\n\n self.x1 -= self.VEL\n self.x2 -= self.VEL\n\n #when the first image exists the screen, it moves to the front of the second image\n if self.x1 + self.WIDTH < 0:\n self.x1= self.x2+self.WIDTH\n\n # when the second image exists the screen, it moves to the front of the already moved first image\n if self.x2 + self.WIDTH <0:\n self.x2 = self.x1 +self.WIDTH\n\n def draw(self, win):\n win.blit(self.IMG,(self.x1, self.y))\n win.blit(self.IMG,(self.x2, self.y))\n\n\n\ndef draw_window(win, birds ,pipes,base, score, generation):\n win.blit(BG_IMG, (0,0))\n for pipe in pipes:\n pipe.draw(win)\n\n text = SCORE_FONT.render(\"SCORE: \"+str(score),1,(255,255,255))\n win.blit(text, (WIN_WIDTH - 10 - text.get_width(),10)) # so it always fits the screen when it increases\n\n\n\n\n base.draw(win)\n if isinstance(birds,list):\n text = SCORE_FONT.render(\"GEN: \" + str(generation), 1, (255, 255, 255))\n win.blit(text, (WIN_WIDTH - 60 - text.get_width(), 50)) # so it always fits the screen when it increases\n\n for bird in birds:\n bird.draw(win)\n else:\n birds.draw(win)\n pygame.display.update()\n\n\ndef eval_genomes(genomes, config):\n birds = []\n nets = []\n ge = [] #list of genomes, used to change genomes\n base = Base(650)\n pipes = [Pipe(700)]\n\n global GEN\n GEN+=1\n for genome_id,genome in genomes:\n net = neat.nn.FeedForwardNetwork.create(genome, config)\n nets.append(net)\n birds.append(Bird(300,300))\n genome.fitness = 0\n ge.append(genome)\n\n\n\n\n win = pygame.display.set_mode((WIN_WIDTH,WIN_HEIGHT))\n clock = pygame.time.Clock()\n score = 0\n run = True\n while run:\n clock.tick(30) # 30fps\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n pygame.quit()\n\n '''if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n bird.jump()'''\n\n pipe_ind = 0\n if len(birds) > 0: # if there are still birds alive\n if len(pipes) > 1 and birds[0].x > pipes[0].x + pipes[0].PIPE_TOP.get_width(): #we use bird[0} because all birds have the same x\n pipe_ind = 1 # if we passed the first pipe, look at the second, since everytime we pass a pipe it will be removed and a new one will be created afterwards, this works\n\n else: # if there are no birds\n run = False\n break\n for i, bird in enumerate(birds):\n bird.move()\n ge[i].fitness+=0.1 # encoraging for staying alive\n\n dist_top = abs(bird.y - pipes[pipe_ind].height)\n dist_bot = abs(bird.y - pipes[pipe_ind].bottom)\n output = nets[i].activate((bird.y, dist_top, dist_bot))\n\n if output[0] > 0.5: #output is a list of the outputs off all the neurons, because we only have one neuron, it's the first element in the list\n bird.jump()\n\n rem = [] #removed pipes\n add_pipe = False #initializing variable\n for pipe in pipes:\n for i,bird in enumerate(birds):\n if pipe.collide(bird):\n ge[i].fitness -=1\n birds.pop(i)\n nets.pop(i)\n ge.pop(i)\n\n\n if not pipe.passed and pipe.x < bird.x: # if the bird has passed the pipe\n pipe.passed = True\n add_pipe = True\n\n\n if pipe.x + pipe.PIPE_TOP.get_width()<0: # if it is no longer on the screen\n rem.append(pipe) #remove pipe\n\n pipe.move()\n\n if add_pipe:\n score+=1\n for g in ge:\n g.fitness+=5 #increasing the fitness of all the genomes who are still on the list\n pipes.append(Pipe(600))\n\n for r in rem:\n pipes.remove(r)\n\n for i,bird in enumerate(birds):\n if bird.y + bird.img.get_height() >= 650 or bird.y < 0: # if it hits the base or the top(y<0)\n birds.pop(i)\n nets.pop(i)\n ge.pop(i)\n\n base.move()\n\n\n draw_window(win, birds, pipes, base,score,GEN)\n\n # break if score gets large enough\n if score > 20:\n pickle.dump(nets[0],open(\"best.pickle\", \"wb\"))\n for node in nets[0].node_evals:\n print(node)\n break\n\n\n\ndef game_without_training(network):\n bird = Bird(300,300)\n base = Base(650)\n pipes = [Pipe(700)]\n\n win = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT))\n clock = pygame.time.Clock()\n score = 0\n run = True\n while run:\n clock.tick(30) # 30fps\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n pygame.quit()\n\n pipe_ind = 0\n if len(pipes) > 1 and bird.x > pipes[0].x + pipes[0].PIPE_TOP.get_width(): # we use bird[0} because all birds have the same x\n pipe_ind = 1 # if we passed the first pipe, look at the second, since everytime we pass a pipe it will be removed and a new one will be created afterwards, this works\n\n bird.move()\n\n dist_top = abs(bird.y - pipes[pipe_ind].height)\n dist_bot = abs(bird.y - pipes[pipe_ind].bottom)\n output = network.activate((bird.y, dist_top, dist_bot))\n\n if output[0] > 0.5: # output is a list of the outputs off all the neurons, because we only have one neuron, it's the first element in the list\n bird.jump()\n\n rem = [] # removed pipes\n add_pipe = False # initializing variable\n for pipe in pipes:\n if pipe.collide(bird):\n pygame.quit()\n if not pipe.passed and pipe.x < bird.x: # if the bird has passed the pipe\n pipe.passed = True\n add_pipe = True\n\n if pipe.x + pipe.PIPE_TOP.get_width() < 0: # if it is no longer on the screen\n rem.append(pipe) # remove pipe\n\n pipe.move()\n\n if add_pipe:\n score+=1\n pipes.append(Pipe(600))\n\n for r in rem:\n pipes.remove(r)\n\n base.move()\n\n draw_window(win, bird, pipes, base, score,None) # we are no longer interested in the generation\n\n\n\ndef run(config_path):\n config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\n config_path)\n\n p = neat.Population(config) #population\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\n p.run(eval_genomes,50) #eval_genomes is the fitness function, max generations = 50\n\n\nif __name__ == \"__main__\":\n try:\n best = pickle.load(open( \"best.pickle\", \"rb\" ))\n game_without_training(best)\n except FileNotFoundError:\n local_directory = os.path.dirname(__file__)\n config_path = os.path.join(local_directory, \"config-feedforward.txt\")\n run(config_path)\n\n","sub_path":"flappy_bird.py","file_name":"flappy_bird.py","file_ext":"py","file_size_in_byte":13741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"350100251","text":"from PatternStrategy import *\nfrom LinkedList_task3 import *\nfrom Validation import *\n\ndef menu():\n contex = Contex()\n List = LinkedList()\n v = Validation()\n while True:\n choice = input(\"1 - Use strategy 1 to insert into the list\\n2 - Use strategy 2 to insert into the list\\n3 - Generate data\\n4 - Delete the item at the specified position\\n5 - Delete multiple items within the start and end positions\\n6 - Method for working with the list\\n7 - Display a list\\n8 - Exit\\nYour choice is: \")\n while not v.digit_check(choice):\n choice = input(\"Choise must be a positive number: \")\n choice = int(choice)\n while not (choice == 1 or choice == 2 or choice == 3 or choice == 4 or choice == 5 or choice == 6 or choice == 7 or choice == 8):\n choice = input(\"Choise must be in range[1-8]: \")\n while not v.digit_check(choice):\n choice = input(\"Choise must be a number: \")\n choice = int(choice)\n if choice == 1:\n contex.setStrategy(ConcreteStrategyIterator())\n if choice == 2:\n contex.setStrategy(ConcreteStrategyFile())\n if choice == 3:\n if contex.getStrategy() is None:\n print(\"Choose a strategy first!\")\n continue\n contex.execudeStrategy(List)\n List.display()\n if choice == 4:\n if List.length() == 0:\n print(\"Your list is empty!\")\n continue\n index = input(\"Index: \")\n while not v.digit_check(index):\n index = input(\"Index must be a positive number: \")\n index = int(index)\n while index > List.length() or index < 0:\n index = input(\"Index must be in list range: \")\n while not v.digit_check(index):\n index = input(\"Index must be a positive number: \")\n index = int(index)\n List.erase(index)\n if choice == 5:\n if List.length() == 0:\n print(\"Your list is empty!\")\n continue\n start = 1\n end = 0\n first_iter = True\n while start > end or first_iter:\n if not first_iter:\n print(\"MAKE SURE, START <= END\")\n start = input(\"START:\")\n while not v.digit_check(start):\n start = input(\"Start must be a positive number: \")\n start = int(start)\n while start > List.length() or start < 0:\n index = input(\"Start must be in list range: \")\n while not v.digit_check(index):\n index = input(\"Start must be a positive number: \")\n start = int(start)\n end = input(\"END:\")\n while not v.digit_check(end):\n end = input(\"End must be a positive number: \")\n end = int(end)\n while end > List.length() or end < 0:\n end = input(\"End must be in list range: \")\n while not v.digit_check(end):\n end = input(\"End must be a positive number: \")\n end = int(end)\n first_iter = False\n List.cut(start,end)\n if choice == 6:\n if List.length() == 0:\n print(\"Your list is empty!\")\n continue\n user_choice(List)\n if choice == 7:\n List.display()\n if choice == 8:\n break\nmenu()\n","sub_path":"Python/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":3562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"503525250","text":"# \nimport json\nimport csv\nimport tweepy\n\nimport credentials as cre\n\nauth = tweepy.OAuthHandler(cre.consumer_key, cre.consumer_secret)\nauth.set_access_token(cre.access_token, cre.access_token_secret)\napi = tweepy.API(auth)\n\nwith open('trends_available.json') as data_file:\n\tdata = json.load(data_file)\nwoeid_list = []\nfor d in data:\n\twoeid_list.append(str(d['woeid']))\n# print(' '.join(woeid_list))\n\nwith open('kota_woeid.csv', 'rb') as csvfile:\n\treader = csv.reader(csvfile, delimiter=',',\n\t\t\tquotechar='|')\n\n\tout = []\n\n\tfor row in reader:\n\t\tif row[0] == 'provinsi':\n\t\t\tcontinue\n\t\twoeids = row[3].split(':')\n\t\t# print(':'.join(woeids))\n\n\t\tfor woeid in woeids:\n\t\t\tif woeid in woeid_list:\n\t\t\t\tprint('Found:',woeid)\n\t\t\t\tx = {}\n\t\t\t\ttry:\n\t\t\t\t\ttrends = api.trends_place(woeids[0])\n\t\t\t\t\t# print(json.dumps(trends, indent=2, sort_keys=True))\n\t\t\t\t\tx['woeid'] = trends\n\t\t\t\t\tout.append(x)\n\t\t\t\texcept(tweepy.error.TweepError) as e:\n\t\t\t\t\tprint(e.message[0]['message'])\n\t\t\telse:\n\t\t\t\tprint('not found', woeid)\n\t\t\t\tpass\n\nwith open('trends_per_city.json', 'w') as outfile:\n\tjson.dump(out, outfile, sort_keys=True, indent=4)","sub_path":"scrapper/scrapper4.py","file_name":"scrapper4.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"546471020","text":"import operator\n\n\nclass NumMatrix(object):\n def __init__(self, matrix):\n \"\"\"\n initialize your data structure here.\n :type matrix: List[List[int]]\n \"\"\"\n self.tree = BinaryIndexedTree(matrix)\n\n def update(self, row, col, val):\n \"\"\"\n update the element at matrix[row,col] to val.\n :type row: int\n :type col: int\n :type val: int\n :rtype: void\n \"\"\"\n self.tree.update(row, col, val)\n\n def sumRegion(self, row1, col1, row2, col2):\n \"\"\"\n sum of elements matrix[(row1,col1)..(row2,col2)], inclusive.\n :type row1: int\n :type col1: int\n :type row2: int\n :type col2: int\n :rtype: int\n \"\"\"\n return (\n self.tree.sum(row2 + 1, col2 + 1) +\n self.tree.sum(row1, col1) -\n self.tree.sum(row1, col2 + 1) -\n self.tree.sum(row2 + 1, col1))\n\nclass BinaryIndexedTree(object):\n def __init__(self, matrix):\n m, n = len(matrix), len(matrix[0]) if matrix else 0\n\n self.matrix = matrix\n self.sums = [[0] * (n + 1) for _ in range(m + 1)]\n\n [operator.setitem(\n self.sums[row], col,\n self.sums[row][col] + self.matrix[i - 1][j - 1]\n )\n for row in range(1, len(self.sums))\n for col in range(1, len(self.sums[0]))\n for i in range(row + 1 - (row & -row), row + 1)\n for j in range(col + 1 - (col & -col), col + 1)]\n\n def update(self, row, col, val):\n i = row + 1\n while i < len(self.sums):\n j = col + 1\n while j < len(self.sums[0]):\n self.sums[i][j] += val - self.matrix[row][col]\n j += j & -j\n i += i & -i\n self.matrix[row][col] = val\n\n def sum(self, row, col):\n r, i = 0, row\n while i > 0:\n j = col\n while j > 0:\n r += self.sums[i][j]\n j -= j & -j\n i -= i & -i\n return r\n\n\nmatrix = [[3,0,1,4,2],[5,6,3,2,1],[1,2,0,1,5],[4,1,0,1,7],[1,0,3,0,5]]\n# Your NumMatrix object will be instantiated and called as such:\nnumMatrix = NumMatrix(matrix)\nprint(numMatrix.sumRegion(2, 1, 4, 3))\n# numMatrix.update(1, 1, 10)\n# numMatrix.sumRegion(1, 2, 3, 4)\n\n# https://leetcode.com/discuss/questions/oj/range-sum-query-2d-mutable?sort=votes\n","sub_path":"python/308 Range Sum Query 2D - Mutable.py","file_name":"308 Range Sum Query 2D - Mutable.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"196232888","text":"import sys\n\nimport pytest\n\nif sys.version_info > (3, 0):\n import unittest.mock as mock\nelse:\n import mock\n\n\n@pytest.mark.integration\n@pytest.mark.parametrize(\n \"region\",\n [\n \"br1\",\n \"eun1\",\n \"euw1\",\n \"jp1\",\n \"kr\",\n \"la1\",\n \"la2\",\n \"na\",\n \"na1\",\n \"oc1\",\n \"tr1\",\n \"ru\",\n \"pbe1\",\n ],\n)\nclass TestSummonerApiV4(object):\n @pytest.mark.parametrize(\"encrypted_account_id\", [\"12345\", \"99999999999999999999\"])\n def test_by_account(self, mock_context_v4, region, encrypted_account_id):\n actual_response = mock_context_v4.watcher.summoner.by_account(\n region, encrypted_account_id\n )\n\n assert mock_context_v4.expected_response == actual_response\n mock_context_v4.get.assert_called_once_with(\n \"https://{region}.api.riotgames.com/lol/summoner/v4/summoners/by-account/{encrypted_account_id}\".format(\n region=region, encrypted_account_id=encrypted_account_id\n ),\n params={},\n headers={\"X-Riot-Token\": mock_context_v4.api_key},\n )\n\n @pytest.mark.parametrize(\"summoner_name\", [\"pseudonym117\", \"Riot Tuxedo\"])\n def test_by_name(self, mock_context_v4, region, summoner_name):\n actual_response = mock_context_v4.watcher.summoner.by_name(\n region, summoner_name\n )\n\n assert mock_context_v4.expected_response == actual_response\n mock_context_v4.get.assert_called_once_with(\n \"https://{region}.api.riotgames.com/lol/summoner/v4/summoners/by-name/{summoner_name}\".format(\n region=region, summoner_name=summoner_name\n ),\n params={},\n headers={\"X-Riot-Token\": mock_context_v4.api_key},\n )\n\n @pytest.mark.parametrize(\"encrypted_puuid\", [\"12345\", \"99999999999999999999\"])\n def test_by_puuid(self, mock_context_v4, region, encrypted_puuid):\n actual_response = mock_context_v4.watcher.summoner.by_puuid(\n region, encrypted_puuid\n )\n\n assert mock_context_v4.expected_response == actual_response\n mock_context_v4.get.assert_called_once_with(\n \"https://{region}.api.riotgames.com/lol/summoner/v4/summoners/by-puuid/{encrypted_puuid}\".format(\n region=region, encrypted_puuid=encrypted_puuid\n ),\n params={},\n headers={\"X-Riot-Token\": mock_context_v4.api_key},\n )\n\n @pytest.mark.parametrize(\"encrypted_summoner_id\", [\"12345\", \"99999999999999999999\"])\n def test_by_id(self, mock_context_v4, region, encrypted_summoner_id):\n actual_response = mock_context_v4.watcher.summoner.by_id(\n region, encrypted_summoner_id\n )\n\n assert mock_context_v4.expected_response == actual_response\n mock_context_v4.get.assert_called_once_with(\n \"https://{region}.api.riotgames.com/lol/summoner/v4/summoners/{encrypted_summoner_id}\".format(\n region=region, encrypted_summoner_id=encrypted_summoner_id\n ),\n params={},\n headers={\"X-Riot-Token\": mock_context_v4.api_key},\n )\n","sub_path":"tests/integration/test_SummonerApiV4.py","file_name":"test_SummonerApiV4.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"94286340","text":"import pandas as pd\nimport numpy as np\nimport soundfile as sf\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.patches import Patch\n\n# load dataframe with file locations\ndata_files = pd.read_csv('../data_files.csv', index_col='rec_id')\n\n# check out recording no10 as an example\nrec_no = 10\nrec_files = data_files.loc[rec_no,:]\n\n# read in all the audio files into `sfs`, save the corresbonding label in `index`\n# print a basic summary for each file\nindex = []\naudios = []\nsamples_rates = []\ny_data = []\nfor i, f in enumerate(rec_files.values):\n if f.endswith('.w64'):\n audio, sr = sf.read(f)\n \n audios.append(audio)\n samples_rates.append(sr)\n # each frame/sample (element in the np.array `audio`) is divided by the sample rate in Hz\n # to norm the y axis to seconds\n y_data.append(np.arange(len(audio))/sr) \n index.append(rec_files.index[i])\n \n info = '{}:\\n-----------------\\n{}\\nduration: {:.2f} minutes\\n\\n'.format(\n index[-1], sf.info(f), y_data[0][-1]/60)\n print(info)\n# read in the channel list csv \nchannel_list = pd.read_csv(rec_files.SdrChannelList, index_col='channel_number')\n# Recording/ Data Format:\n\"\"\"\nNumber of recordings: 32\n\n\n\nFileformat: \n-------------------------\nbird1 bird2 no_rec filetype\n------------------------\nb8p2male - b10o15female _10_ *\n\n\n\nFiletypes:\n-------------------------\nfiletype description sampling rate #channels\n-------------------------\nSdrChannels.w64\t The demodulated SDR channels. r_lf 3\nSdrCarrierFreq.w64 The estimated carrier frequencies f_c for r_tr 3\n all SDR channels \nSdrReceiveFreq.w64\t The mixing frequencies f_r for all SDR channels. r_tr 3\nSdrSignalStrength.w64\t The signal's power spectral density at f_c for r_tr 3\n all SDR channels.\nDAQmxChannels.w64\t The NI DAQ channels. r_daq 1\nSdrChannelList.csv The SDR channel list as configured in the GUI. -\nprefix>_log.txt Overflow events and runtime errors are logged \n in here.\n\"\"\"\n\n# summary plots\ndef amplitudes():\n colors=['#e6194B', '#3cb44b', '#f58231', ]\n fig, axes = plt.subplots(figsize=(12,7), nrows=3, ncols=1, sharex=True, sharey=False)\n fig.subplots_adjust(hspace=.15, right=.96, top=.83, left=.16)\n\n # the amplidute plots (excluding SdrCarrierFreq & SdrReceiveFreq)\n for i, ax in zip((0,2,4), axes):\n ax.set_prop_cycle(color=colors)\n ax.set_facecolor('#ededed')\n ax.yaxis.grid(color='w', linewidth=2, alpha=.6)\n\n ax.set_ylabel(index[i], rotation='horizontal', size=13, labelpad=65)\n ax.tick_params('y', labelleft=False, left=False, right=True, labelright=True)\n\n # first\n if not i:\n ax.set_title('Amplitude plot', size=16, pad=15)\n # last\n elif i == 4:\n ax.set_xlabel('time in [seconds]', size=13)\n labels = ['microphone', 'backpack1', 'backpack2']\n patches = [Patch(color=colors[i], label=labels[i])\n for i in range(3)]\n fig.legend(handles=patches, loc='upper right', ncol=1, fontsize=13)\n ax.plot(y_data[i], audios[i], alpha=.7)\n fig.savefig('../'+str(rec_no)+'_amplitudes.png')\n\n\ndef frequencies():\n # init figure\n colors=['#e6194B', '#3cb44b', '#f58231', ]\n fig, axes = plt.subplots(figsize=(12,5), nrows=2, ncols=1, sharex=True, sharey=False)\n fig.subplots_adjust(hspace=.15, right=.88, top=.79, left=.16)\n\n # SdrCarrierFreq & SdrReceiveFreq frequencies\n for i, ax in zip((1,3), axes):\n ax.set_prop_cycle(color=colors)\n ax.set_facecolor('#ededed')\n ax.yaxis.grid(color='w', linewidth=2, alpha=.6)\n\n ax.set_ylabel(index[i], rotation='horizontal', size=13, labelpad=65)\n ax.tick_params('y', labelleft=False, left=False, right=True, labelright=True)\n\n # first\n if i == 1:\n ax.set_title('Frequency plot', size=16, pad=15)\n # last\n elif i == 3:\n ax.set_xlabel('time in [seconds]', size=13)\n labels = ['microphone', 'backpack1', 'backpack2']\n patches = [Patch(color=colors[i], label=labels[i])\n for i in range(3)]\n fig.legend(handles=patches, loc='upper right', ncol=1, fontsize=13)\n\n ax.plot(y_data[i], audios[i])\n lbls = [str(int(lbl/1000))+' kHz' for lbl in ax.get_yticks()]\n ax.set_yticklabels(lbls)\n fig.savefig('../'+str(rec_no)+'_frequencies.png')\n\ndef spectrogram():\n # sizes of indiviual plots (in ratios of 1)\n ratio = {'width_ratios': [.8],\n 'height_ratios': [.12, .06, .12, .12, .12, .10, .12, .12, .12]}\n # init figure\n fig, axes = plt.subplots(figsize=(12,10), nrows=9, ncols=1, sharex=True, sharey=False, gridspec_kw=ratio)\n fig.subplots_adjust(hspace=0, right=.96, top=.9, left=.16)\n\n # iterate the 3 audio data files: SdrChannels, SdrSignalStrength, DAQmxChannels\n which_ax = 0\n for i in [0,2,4]:\n # expand array with only one channel (DAQmx) to enable columns iteration \n if audios[i].ndim == 1:\n audios[i] = np.expand_dims(audios[i], axis=1)\n\n # iterate channels mic, backpack1 ,backpack2\n for channel_lbl, channel in zip(channel_list.bird_name.values, audios[i].T):\n # set current axis\n ax = axes[which_ax]\n # set axis 1 and 5 as spacers, ie. set invisible \n if which_ax in [1, 5]:\n ax.set_visible(False)\n which_ax += 1\n ax = axes[which_ax]\n\n # draw spectrogram, set range for DAQmx & SdrChanels different to the one of SdrSignalStrength\n ampl_range = [-120, 0] if i in (0, 2) else [-25,25]\n spec, freqs, t, im = axes[which_ax].specgram(channel, Fs=samples_rates[i], alpha=.7, cmap='jet', \n vmin=ampl_range[0], vmax=ampl_range[1])\n # setup y axis labels, tick parameters\n ax.tick_params(labelleft=False, left=False, right=True, labelright=True, labelbottom=False)\n if which_ax in [0, 2, 6]:\n ax.set_title(index[i], loc='left', pad=4)\n ax.set_ylabel(channel_lbl, rotation='horizontal', size=11, ha='right')\n # the SdrSignalStrength is on a very different scale. Maybe map signal strengths from CSV to values here? \n # for now don't change the y axis labeling for the last 3 plots\n if which_ax < 6:\n yticks = [2000, 4000, 6000, 8000, 10000]\n ytick_lbls = [str(int(yt/1000)) + 'kHz' for yt in yticks]\n ax.set_yticks(yticks)\n ax.set_yticklabels(ytick_lbls, size=8)\n # setup x axis\n ax.set_xlim(0, y_data[i][-1])\n\n # first plot: draw the colorbar\n if not i:\n fig.suptitle('Spectrogram (amplitude per frequency plot)', size=16)\n at = (0.75, .94, .2, .015)\n cb = ax.figure.colorbar(im, cax=fig.add_axes(at), alpha =.3,\n orientation='horizontal')\n cb.set_label('DAQmx & SdrChanels Amplitude(?)')\n cb.ax.get_xaxis().set_label_position('top')\n \n # last plot: set xaxis labels and draw seconds colorbar\n elif which_ax == 8:\n ax.set_xlabel('time in [seconds]', size=13)\n ax.tick_params(labelbottom=True)\n \n at = (0.75, .42, .2, .015)\n cb = ax.figure.colorbar(im, cax=fig.add_axes(at), alpha =.3,\n orientation='horizontal')\n cb.set_label('SdrSignalSrength Amplitude(?)')\n cb.ax.get_xaxis().set_label_position('top')\n which_ax += 1\n fig.savefig('../'+str(rec_no)+'_spectrogram.png')\n\n# save the plots in the current directory\namplitudes()\nfrequencies()\nspectrogram()\n","sub_path":"main/explore_data.py","file_name":"explore_data.py","file_ext":"py","file_size_in_byte":8257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"495176763","text":"from rest_framework import viewsets, generics\nfrom rest_framework.filters import SearchFilter, OrderingFilter\nfrom rest_framework_filters import backends, filters\n\nfrom .models import User, Note,Post\n\nfrom .serializers import UserSerializer, NoteSerializer, PostSerializer\nfrom .filters import DFUserFilter, UserFilterWithAll, NoteFilterWithRelatedAll, PostFilter, TypePackageFilter, PackagePKKFilter\n\nfrom .models import (\n TypePackage , Package\n)\nfrom .serializers import (\n TypePackageSerializer, PackageSerializer\n)\n\n\n\nclass DFUserViewSet(viewsets.ModelViewSet):\n # used to test compatibility with the drf-filters backend\n # with standard django-filter FilterSets.\n queryset = User.objects.all()\n serializer_class = UserSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_class = DFUserFilter\n\n\nclass FilterFieldsUserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_fields = {\n 'username': '__all__',\n }\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n queryset = User.objects.all()\n serializer_class = UserSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_class = UserFilterWithAll\n\n\nclass NoteViewSet(viewsets.ModelViewSet):\n queryset = Note.objects.all()\n serializer_class = NoteSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_class = NoteFilterWithRelatedAll\n\nclass PostViewSet(viewsets.ModelViewSet):\n queryset = Post.objects.all()\n serializer_class = PostSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_class = PostFilter\n\n\nclass TypePackageViewSet(viewsets.ModelViewSet):\n queryset = TypePackage.objects.all()\n serializer_class = TypePackageSerializer\n filter_backends = (backends.DjangoFilterBackend, )\n filter_class = TypePackageFilter\n\nclass PackageViewSet(viewsets.ModelViewSet):\n queryset = Package.objects.all()\n serializer_class = PackageSerializer\n filter_backends = [backends.DjangoFilterBackend,SearchFilter, OrderingFilter]\n ordering = ('account_number',) # add this line\n filter_class = PackagePKKFilter\n search_fields = ('account_number',) \n\nclass PageListView(generics.ListAPIView):\n queryset = Package.objects.all()\n serializer_class = PackageSerializer\n filter_backends = (filters.OrderingFilter,)\n ordering_fields = ('account_number')\n\n","sub_path":"doublePKK/cadnumbers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"599841193","text":"# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\nfrom torch.testing._internal.common_utils import TestCase, run_tests, is_iterable_of_tensors\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor\nimport functools\nimport itertools\nimport copy\nimport warnings\nimport unittest\nfrom torch.testing._internal.common_device_type import instantiate_device_type_tests, \\\n skipCUDAIfNoMagma\nfrom torch.testing._internal.common_device_type import ops, onlyCPU\nfrom functorch_lagging_op_db import functorch_lagging_op_db\nfrom functorch_additional_op_db import additional_op_db\nfrom common_utils import (\n get_fallback_and_vmap_exhaustive,\n get_exhaustive_batched_inputs,\n opinfo_in_dict,\n xfail,\n skip,\n skipOps,\n check_vmap_fallback,\n)\nimport types\nfrom torch.utils._pytree import tree_flatten, tree_unflatten, tree_map\nfrom functorch import grad, vjp, vmap\nfrom functorch._src.eager_transforms import _as_tuple\n\n# Version of autograd.grad that handles outputs that don't depend on inputs\ndef _autograd_grad(outputs, inputs, grad_outputs=None, retain_graph=False, create_graph=True):\n inputs, inputs_spec = tree_flatten(inputs)\n result = [torch.zeros_like(inp) for inp in inputs]\n diff_argnums = tuple(i for i, inp in enumerate(inputs) if inp.requires_grad)\n inputs = tuple(inputs[i] for i in diff_argnums)\n if grad_outputs is None:\n diff_outputs = tuple(out for out in outputs if out.requires_grad)\n else:\n something = [(out, go) for out, go in zip(outputs, grad_outputs)\n if out.requires_grad]\n if len(something) == 0:\n diff_outputs, grad_outputs = (), ()\n else:\n diff_outputs, grad_outputs = zip(*something)\n if len(diff_outputs) == 0:\n return tuple(torch.zeros_like(inp) for inp in inputs)\n grad_inputs = torch.autograd.grad(diff_outputs, inputs, grad_outputs,\n retain_graph=retain_graph,\n create_graph=create_graph,\n allow_unused=True)\n grad_inputs = tuple(torch.zeros_like(inp) if gi is None else gi\n for gi, inp in zip(grad_inputs, inputs))\n for idx, grad_inp in zip(diff_argnums, grad_inputs):\n result[idx] = grad_inp\n return tree_unflatten(result, inputs_spec)\n\n\ndef diff_arg(arg):\n if is_iterable_of_tensors(arg):\n if all([a.requires_grad for a in arg]):\n return True\n if all([not a.requires_grad for a in arg]):\n return False\n raise RuntimeError(\"NYI: The test runner can't handle this\")\n return isinstance(arg, Tensor) and arg.requires_grad\n\n\n# Given f, returns an f' such that:\n# - f' takes only positional arguments\n# - All arguments to f' are floating-point Tensors\n# - All outputs of f' are floating-point Tensors\ndef normalize_op_for_vjp2(f, args, kwargs, output_process_fn_grad=None):\n flat_args, args_spec = tree_flatten(args)\n diff_argnums = tuple(i for i, arg in enumerate(flat_args) if diff_arg(arg))\n assert len(diff_argnums) > 0\n primals = tuple(flat_args[i] for i in diff_argnums)\n\n @functools.wraps(f)\n def wrapped(*primals):\n _args = list(flat_args)\n for num, arg in zip(diff_argnums, primals):\n _args[num] = arg\n _args = tree_unflatten(_args, args_spec)\n result = f(*_args, **kwargs)\n if output_process_fn_grad is not None:\n result = output_process_fn_grad(result)\n if isinstance(result, tuple):\n # TODO: Remove the following hack for namedtuples\n result = tuple(result)\n result = tuple(r for r in result if torch.is_floating_point(r))\n assert len(result) > 0\n return result\n return wrapped, primals\n\n\ndef normalize_op_for_vjp(f, sample):\n args = tuple([sample.input] + list(sample.args))\n return normalize_op_for_vjp2(f, args, sample.kwargs, sample.output_process_fn_grad)\n\n\ndef ref_vjp(f, *primals):\n result = f(*primals)\n\n def wrapped(cotangents):\n return _autograd_grad(_as_tuple(result), primals, _as_tuple(cotangents))\n\n return result, wrapped\n\n\n# Returns a new function g(*args, *cotangents) that computes vjps and\n# sample (*args, *cotangents)\ndef get_vjpfull_variant(f, sample):\n fn, primals = normalize_op_for_vjp(f, sample)\n result = fn(*primals)\n cotangents = _as_tuple(\n tree_map(lambda x: torch.randn_like(x, requires_grad=True), result))\n num_primals = len(primals)\n args = (*primals, *cotangents)\n\n @functools.wraps(f)\n def wrapped(*args):\n primals = args[:num_primals]\n cotangents = args[num_primals:]\n result, vjp_fn = vjp(fn, *primals)\n if isinstance(result, torch.Tensor):\n assert len(cotangents) == 1\n cotangents = cotangents[0]\n return vjp_fn(cotangents)\n\n return wrapped, args\n\n\ndef is_inplace(op, variant):\n if hasattr(variant, \"__wrapped__\"):\n return variant.__wrapped__ is op.get_inplace()\n return variant is op.get_inplace()\n\n\nvjp_fail = {\n xfail('linalg.cholesky'),\n xfail('linalg.inv'),\n xfail('linalg.matrix_power'),\n xfail('tensor_split'),\n xfail('to_sparse'),\n}\n\nclass TestOperators(TestCase):\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_grad', vjp_fail)\n def test_grad(self, device, dtype, op):\n if op.name in vjp_fail:\n self.skipTest(\"Skipped; Expected failures\")\n return\n\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n for sample in samples:\n args = [sample.input] + list(sample.args)\n kwargs = sample.kwargs\n\n diff_argnums = tuple(i for i, arg in enumerate(args) if diff_arg(arg))\n assert len(diff_argnums) > 0\n diff_args = tuple(args[i] for i in diff_argnums)\n\n def wrapped_fn(*args, **kwargs):\n result = op(*args, **kwargs)\n if sample.output_process_fn_grad is not None:\n result = sample.output_process_fn_grad(result)\n\n # Reduce into single value for grad\n if isinstance(result, torch.Tensor):\n return result.sum()\n result = sum([res.sum() for res in result])\n return result\n\n result = grad(wrapped_fn, diff_argnums)(*args, **kwargs)\n expected = _autograd_grad(_as_tuple(wrapped_fn(*args, **kwargs)), diff_args)\n\n self.assertEqual(result, expected)\n\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_vjp', vjp_fail)\n def test_vjp(self, device, dtype, op):\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n def _test(_op):\n for sample in samples:\n fn, primals = normalize_op_for_vjp(_op, sample)\n result = fn(*primals)\n cotangents = tree_map(lambda x: torch.randn_like(x), result)\n\n out, vjp_fn = vjp(fn, *primals)\n self.assertEqual(out, result)\n result_vjps = vjp_fn(cotangents)\n\n _, vjp_fn = ref_vjp(fn, *primals)\n expected_vjps = vjp_fn(cotangents)\n\n self.assertEqual(result_vjps, expected_vjps)\n\n _test(op)\n for a_op in op.aliases:\n _test(a_op)\n\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_vjpvjp', vjp_fail)\n def test_vjpvjp(self, device, dtype, op):\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n if not op.supports_gradgrad:\n self.skipTest(\"Skipped! Operation does not support gradgrad\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n for sample in samples:\n fn, args = get_vjpfull_variant(op, sample)\n result = fn(*args)\n cotangents = tree_map(lambda x: torch.randn_like(x), result)\n\n # Compute vjp of vjp\n _, vjp_fn = vjp(fn, *args)\n result_vjps = vjp_fn(cotangents)\n\n # Compute ref_vjp of vjp. We could have done ref_vjp of ref_vjp,\n # but since we're confident that vjp works by itself, this is\n # an equivalent way to test that.\n _, vjp_fn = ref_vjp(fn, *args)\n expected_vjps = vjp_fn(cotangents)\n\n self.assertEqual(result_vjps, expected_vjps)\n\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n def test_vmapvjpvjp(self, device, dtype, op):\n self.skipTest(\"Skipped; these tests take too long\")\n op_skip = set({\n })\n op_skip = op_skip.union(vjp_fail)\n if op.name in op_skip:\n self.skipTest(\"Skipped; Expected failures\")\n return\n\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n if not op.supports_gradgrad:\n self.skipTest(\"Skipped! Operation does not support gradgrad\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n for sample in samples:\n fn, args = get_vjpfull_variant(op, sample)\n result = fn(*args)\n cotangents = tree_map(lambda x: torch.randn_like(x), result)\n cotangents, _ = tree_flatten(cotangents)\n num_args = len(args)\n\n args_and_cotangents = tuple(args) + tuple(cotangents)\n\n def vjp_of_vjp(*args_and_cotangents):\n args = args_and_cotangents[:num_args]\n cotangents = args_and_cotangents[num_args:]\n result, vjp_fn = vjp(fn, *args)\n result_vjps = vjp_fn(cotangents)\n result, _ = tree_flatten(result)\n result_vjps, _ = tree_flatten(result_vjps)\n return (*result, *result_vjps)\n\n for loop_out, batched_out in \\\n get_fallback_and_vmap_exhaustive(vjp_of_vjp, args_and_cotangents, {}):\n self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4)\n vmapvjp_fail = vjp_fail.union({\n # All of the following are bugs and need to be fixed\n xfail('clamp', ''),\n xfail('diag_embed'),\n xfail('eig'),\n xfail('nn.functional.conv_transpose2d'),\n xfail('nn.functional.pad', 'constant'),\n xfail('view_as_complex'),\n xfail('fft.fft'),\n xfail('fft.ifft'),\n xfail('fft.ihfft'),\n xfail('fft.ihfft'),\n xfail('fft.rfft'),\n xfail('fft.rfft'),\n xfail('fft.fftn'),\n xfail('fft.rfftn'),\n xfail('fft.ifftn'),\n xfail('cdist'),\n xfail('fmax'),\n xfail('fmin'),\n xfail('index_add'),\n xfail('index_copy'),\n xfail('index_fill'),\n xfail('index_put', device_type='cuda'),\n xfail('linalg.det', ''),\n xfail('linalg.eigh'),\n xfail('linalg.householder_product'),\n xfail('linalg.matrix_norm'),\n xfail('linalg.norm'),\n xfail('linalg.slogdet'),\n xfail('logdet'),\n xfail('lu'),\n xfail('lu_unpack'),\n xfail('masked_fill'),\n xfail('masked_scatter'),\n xfail('matrix_exp'),\n xfail('max', 'reduction_no_dim', device_type='cpu'),\n xfail('median', device_type='cpu'),\n xfail('min', 'reduction_no_dim', device_type='cpu'),\n xfail('nanmedian', device_type='cpu'),\n xfail('nanquantile'),\n xfail('nn.functional.pad', 'circular'),\n xfail('norm', 'fro'),\n xfail('norm', 'nuc'),\n xfail('prod'),\n xfail('put'),\n xfail('quantile'),\n xfail('symeig'),\n xfail('take'),\n xfail('linalg.tensorinv'),\n xfail('nn.functional.conv_transpose2d', device_type='cuda'),\n xfail('nanmean'),\n xfail('block_diag'),\n xfail('nn.functional.dropout'),\n xfail('double'),\n xfail('fft.fft2'),\n xfail('fft.ifft2'),\n xfail('fft.ihfft2'),\n xfail('fft.ihfftn'),\n xfail('fft.rfft2'),\n xfail('_masked.prod'), # calls aten::item\n })\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_vmapvjp', vmapvjp_fail)\n def test_vmapvjp(self, device, dtype, op):\n # These are too annoying to put into the list above\n if op.name in {'nn.functional.linear', 'nn.functional.conv2d'}:\n self.skipTest(\"Skipped! ExpectedF failures\")\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n for sample in samples:\n fn, args = get_vjpfull_variant(op, sample)\n for loop_out, batched_out in get_fallback_and_vmap_exhaustive(fn, args, {}):\n self.assertEqual(loop_out, batched_out, atol=1e-4, rtol=1e-4)\n\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_vmapvjp_has_batch_rule', vmapvjp_fail.union({\n xfail('nn.functional.pad', 'constant'),\n xfail('view_as_complex'),\n xfail('__getitem__'),\n xfail('__rpow__'),\n xfail('addr'),\n xfail('cdist'),\n xfail('cholesky'),\n xfail('clamp'),\n xfail('clamp', 'scalar'),\n xfail('complex'),\n xfail('copysign'),\n xfail('corrcoef'),\n xfail('cummax'),\n xfail('cummin'),\n xfail('cumprod'),\n xfail('diag'),\n xfail('diag_embed'),\n xfail('eig'),\n xfail('fft.fft'),\n xfail('fft.fftn'),\n xfail('fft.ifft'),\n xfail('fft.ifftn'),\n xfail('fft.ihfft'),\n xfail('fft.rfft'),\n xfail('fft.rfftn'),\n xfail('cdist'),\n xfail('fill_'),\n xfail('float_power'),\n xfail('fmax'),\n xfail('fmin'),\n xfail('index_add'),\n xfail('index_copy'),\n xfail('index_fill'),\n xfail('index_put', device_type='cuda'),\n xfail('index_select'),\n xfail('kthvalue'),\n xfail('linalg.cholesky'),\n xfail('linalg.cholesky_ex'),\n xfail('linalg.det'),\n xfail('linalg.eig'),\n xfail('linalg.eigh'),\n xfail('linalg.eigvals'),\n xfail('linalg.householder_product'),\n xfail('linalg.lstsq'),\n xfail('linalg.inv'),\n xfail('linalg.matrix_norm'),\n xfail('linalg.matrix_power'),\n xfail('linalg.norm'),\n xfail('linalg.pinv'),\n xfail('linalg.pinv', 'hermitian'),\n xfail('linalg.slogdet'),\n xfail('linalg.solve'),\n xfail('linalg.tensorinv'),\n xfail('linalg.vector_norm'),\n xfail('logdet'),\n xfail('logit'),\n xfail('lu'),\n xfail('lu_solve'),\n xfail('lu_unpack'),\n xfail('masked_fill'),\n xfail('masked_scatter'),\n xfail('masked_select'),\n xfail('matrix_exp'),\n xfail('max', 'reduction_no_dim'),\n xfail('max', 'reduction_with_dim'),\n xfail('median'),\n xfail('min', 'reduction_no_dim'),\n xfail('min', 'reduction_with_dim'),\n xfail('mode'),\n xfail('msort'),\n xfail('nanmedian'),\n xfail('nanquantile'),\n xfail('nn.functional.adaptive_avg_pool2d'),\n xfail('nn.functional.conv_transpose2d'),\n xfail('nn.functional.cross_entropy', 'mean'),\n xfail('nn.functional.cross_entropy', 'none'),\n xfail('nn.functional.cross_entropy', 'sum'),\n xfail('nn.functional.gelu'),\n xfail('nn.functional.grid_sample'),\n xfail('nn.functional.interpolate', 'area'),\n xfail('nn.functional.pad', 'circular'),\n xfail('nn.functional.pad', 'reflect'),\n xfail('nn.functional.pad', 'replicate'),\n xfail('nn.functional.unfold'),\n xfail('norm', 'fro'),\n xfail('norm', 'inf'),\n xfail('norm', 'nuc'),\n xfail('pinverse'),\n xfail('pow'),\n xfail('prod'),\n xfail('put'),\n xfail('quantile'),\n xfail('renorm'),\n xfail('repeat_interleave'),\n xfail('solve'),\n xfail('sort'),\n xfail('symeig'),\n xfail('take'),\n xfail('tensor_split'),\n xfail('to_sparse'),\n xfail('topk'),\n xfail('trace'),\n xfail('unfold'),\n xfail('vdot'),\n xfail('nanmean'),\n xfail('nn.functional.layer_norm'),\n xfail('nn.functional.nll_loss'),\n xfail('block_diag'),\n xfail('nn.functional.dropout'),\n xfail('nn.functional.batch_norm'),\n xfail('_masked.amax'),\n xfail('_masked.amin'),\n xfail('_masked.sum'),\n xfail('_masked.prod'),\n xfail('cholesky_solve'),\n xfail('double'),\n xfail('fft.fft2'),\n xfail('fft.ifft2'),\n xfail('fft.ihfft2'),\n xfail('fft.ihfftn'),\n xfail('fft.rfft2'),\n xfail('nn.functional.adaptive_avg_pool1d'),\n xfail('nn.functional.adaptive_avg_pool3d'),\n xfail('nn.functional.avg_pool3d'),\n xfail('nn.functional.embedding'),\n }))\n def test_vmapvjp_has_batch_rule(self, device, dtype, op):\n # These are too annoying to put into the list above\n if op.name in {'nn.functional.linear', 'nn.functional.conv2d'}:\n self.skipTest(\"Skipped! ExpectedF failures\")\n if not op.supports_autograd:\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n def test():\n for sample in samples:\n fn, args = get_vjpfull_variant(op, sample)\n for _ in get_fallback_and_vmap_exhaustive(fn, args, {}, compute_loop_out=False):\n pass\n for a_op in op.aliases:\n fn, args = get_vjpfull_variant(a_op, sample)\n for _ in get_fallback_and_vmap_exhaustive(fn, args, {}, compute_loop_out=False):\n pass\n check_vmap_fallback(self, test, op, dry_run=False)\n\n @ops(functorch_lagging_op_db + additional_op_db, allowed_dtypes=(torch.float,))\n @skipOps('TestOperators', 'test_vjpvmap', vjp_fail.union({\n # fallback path doesn't work\n xfail('H'),\n # All of the following are bugs and need to be fixed\n xfail('__getitem__'),\n xfail('clamp', ''),\n xfail('dsplit'),\n xfail('fill_'),\n xfail('gradient'),\n xfail('hsplit'),\n xfail('nn.functional.pad', 'circular'),\n xfail('vsplit'),\n xfail('dstack'),\n xfail('hstack'),\n xfail('index_put'),\n xfail('linalg.multi_dot'),\n xfail('vstack'),\n xfail('block_diag'),\n xfail('nn.functional.batch_norm'),\n xfail('cdist'),\n xfail('lu_solve'),\n xfail('lu_unpack'),\n xfail('matrix_exp'),\n xfail('view_as_complex'),\n xfail('double'),\n }))\n def test_vjpvmap(self, device, dtype, op):\n # NB: there is no vjpvmap_has_batch_rule test because that is almost\n # certainly redundant with the vmap_has_batch_rule test in test_vmap.py\n\n # one-off skip\n if op.name == 'nn.functional.dropout':\n self.skipTest(\"Skipped!\")\n\n if not op.supports_autograd:\n # If the op doesn't support autograd, vmap(op) won't either\n self.skipTest(\"Skipped! Autograd not supported.\")\n return\n\n # TODO: test in-place\n if is_inplace(op, op.get_op()):\n self.skipTest(\"Skipped! NYI: inplace-testing not supported.\")\n return\n\n samples = op.sample_inputs(device, dtype, requires_grad=True)\n\n for sample in samples:\n args = [sample.input] + list(sample.args)\n kwargs = sample.kwargs\n\n for batched_args, in_dims, kwargs in get_exhaustive_batched_inputs(args, kwargs):\n vmapped_op = vmap(op, in_dims)\n fn, primals = normalize_op_for_vjp2(vmapped_op, batched_args, kwargs,\n sample.output_process_fn_grad)\n result = fn(*primals)\n cotangents = tree_map(lambda x: torch.randn_like(x), result)\n\n _, vjp_fn = vjp(fn, *primals)\n result_vjps = vjp_fn(cotangents)\n\n _, vjp_fn = ref_vjp(fn, *primals)\n expected_vjps = vjp_fn(cotangents)\n\n self.assertEqual(result_vjps, expected_vjps)\n\nonly_for = (\"cpu\", \"cuda\")\ninstantiate_device_type_tests(TestOperators, globals(), only_for=only_for)\n\nif __name__ == '__main__':\n run_tests()\n","sub_path":"test/test_ops.py","file_name":"test_ops.py","file_ext":"py","file_size_in_byte":22241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"618727351","text":"import math\nfrom os.path import getsize\nfile_size = int(8)\nfilesize_in_str = \"8 MB\"\n\nclass ValidationException(Exception):\n pass\n\nclass FileValidationException(ValidationException):\n def __init__(self, message):\n self.msg = message\n\ndef validation_size(size):\n if (size == 0):\n return '0B'\n size_name = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n i = int(math.floor(math.log(size,1024)))\n p = math.pow(1024,i)\n s = round(size/p,2)\n file_size_string = '%s %s' % (s,size_name[i])\n if (s > file_size):\n raise FileValidationException(\"FileSize not morethan {0}, uploaded file size is {1}\".format(filesize_in_str,file_size_string)) \n else:\n return True\n\nd = validation_size(getsize('/home/tring/Videos/Dhoni.mp4'))\n# d = validation_size(getsize('/home/tring/Documents/GITHUBBACKUP/django-sauth/sauth/views.py'))\n\nif d:\n print (\"FDSDF\")\nelse:\n print (\"dsfsdfsdfffffffffffff\")\n\n\n# def file_checking(size):\n","sub_path":"filesize.py","file_name":"filesize.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"515282366","text":"\"\"\"Contains data models for representing VICC normalized gene records.\"\"\"\nfrom enum import Enum, IntEnum\nfrom typing import Any, Dict, List, Literal, Optional, Type, Union\n\nfrom ga4gh.vrsatile.pydantic import return_value\nfrom ga4gh.vrsatile.pydantic.vrs_models import (\n CURIE,\n ChromosomeLocation,\n SequenceLocation,\n VRSTypes,\n)\nfrom ga4gh.vrsatile.pydantic.vrsatile_models import GeneDescriptor\nfrom pydantic import BaseModel, StrictBool, validator\nfrom pydantic.types import StrictInt, StrictStr\n\n\nclass SymbolStatus(str, Enum):\n \"\"\"Define string constraints for symbol status attribute.\"\"\"\n\n WITHDRAWN = \"withdrawn\"\n APPROVED = \"approved\"\n DISCONTINUED = \"discontinued\"\n\n\nclass Strand(str, Enum):\n \"\"\"Define string constraints for strand attribute.\"\"\"\n\n FORWARD = \"+\"\n REVERSE = \"-\"\n\n\nclass Annotation(str, Enum):\n \"\"\"Define string constraints for annotations when gene location\n is absent.\n \"\"\"\n\n NOT_FOUND_ON_REFERENCE = \"not on reference assembly\"\n UNPLACED = \"unplaced\"\n RESERVED = \"reserved\"\n ALT_LOC = \"alternate reference locus\"\n\n\nclass Chromosome(str, Enum):\n \"\"\"Define string constraints for chromosomes.\"\"\"\n\n MITOCHONDRIA = \"MT\"\n\n\nclass MatchType(IntEnum):\n \"\"\"Define string constraints for use in Match Type attributes.\"\"\"\n\n CONCEPT_ID = 100\n SYMBOL = 100\n PREV_SYMBOL = 80\n ALIAS = 60\n XREF = 60\n ASSOCIATED_WITH = 60\n FUZZY_MATCH = 20\n NO_MATCH = 0\n\n\nclass GeneSequenceLocation(BaseModel):\n \"\"\"Sequence Location model when storing in DynamoDB.\"\"\"\n\n type: Literal[VRSTypes.SEQUENCE_LOCATION] = VRSTypes.SEQUENCE_LOCATION\n start: StrictInt\n end: StrictInt\n sequence_id: CURIE\n\n\nclass GeneChromosomeLocation(BaseModel):\n \"\"\"Chromosome Location model when storing in DynamDB.\"\"\"\n\n type: Literal[VRSTypes.CHROMOSOME_LOCATION] = VRSTypes.CHROMOSOME_LOCATION\n species_id: Literal[\"taxonomy:9606\"] = \"taxonomy:9606\"\n chr: StrictStr\n start: StrictStr\n end: StrictStr\n\n\nclass BaseGene(BaseModel):\n \"\"\"Base gene model. Provide shared resources for records produced by\n /search and /normalize_unmerged.\n \"\"\"\n\n concept_id: CURIE\n symbol: StrictStr\n symbol_status: Optional[SymbolStatus]\n label: Optional[StrictStr]\n strand: Optional[Strand]\n location_annotations: Optional[List[StrictStr]] = []\n locations: Optional[\n Union[\n List[Union[SequenceLocation, ChromosomeLocation]],\n List[Union[GeneSequenceLocation, GeneChromosomeLocation]], # dynamodb\n ]\n ] = ([],)\n aliases: Optional[List[StrictStr]] = []\n previous_symbols: Optional[List[StrictStr]] = []\n xrefs: Optional[List[CURIE]] = []\n associated_with: Optional[List[CURIE]] = []\n gene_type: Optional[StrictStr]\n\n _get_concept_id_val = validator(\"concept_id\", allow_reuse=True)(return_value)\n _get_xrefs_val = validator(\"xrefs\", allow_reuse=True)(return_value)\n _get_associated_with_val = validator(\"associated_with\", allow_reuse=True)(\n return_value\n )\n\n\nclass Gene(BaseGene):\n \"\"\"Gene\"\"\"\n\n match_type: MatchType\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"Gene\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for p in schema.get(\"properties\", {}).values():\n p.pop(\"title\", None)\n schema[\"example\"] = {\n \"label\": None,\n \"concept_id\": \"ensembl:ENSG00000157764\",\n \"symbol\": \"BRAF\",\n \"previous_symbols\": [],\n \"aliases\": [],\n \"xrefs\": [],\n \"symbol_status\": None,\n \"strand\": \"-\",\n \"location\": [],\n }\n\n\nclass GeneGroup(Gene):\n \"\"\"A grouping of genes based on common attributes.\"\"\"\n\n description: StrictStr\n type_identifier: StrictStr\n genes: List[Gene]\n\n\nclass SourceName(Enum):\n \"\"\"Define string constraints to ensure consistent capitalization.\"\"\"\n\n HGNC = \"HGNC\"\n ENSEMBL = \"Ensembl\"\n NCBI = \"NCBI\"\n\n\nclass SourcePriority(IntEnum):\n \"\"\"Define priorities for sources when building merged concepts.\"\"\"\n\n HGNC = 1\n ENSEMBL = 2\n NCBI = 3\n\n\nclass SourceIDAfterNamespace(Enum):\n \"\"\"Define string constraints after namespace.\"\"\"\n\n HGNC = \"\"\n ENSEMBL = \"ENSG\"\n NCBI = \"\"\n\n\nclass NamespacePrefix(Enum):\n \"\"\"Define string constraints for namespace prefixes on concept IDs.\"\"\"\n\n HGNC = \"hgnc\"\n ENSEMBL = \"ensembl\"\n NCBI = \"ncbigene\"\n ENTREZ = NCBI\n VEGA = \"vega\"\n UCSC = \"ucsc\"\n ENA = \"ena.embl\"\n REFSEQ = \"refseq\"\n CCDS = \"ccds\"\n UNIPROT = \"uniprot\"\n PUBMED = \"pubmed\"\n COSMIC = \"cosmic\"\n OMIM = \"omim\"\n MIRBASE = \"mirbase\"\n HOMEODB = \"homeodb\"\n SNORNABASE = \"snornabase\"\n ORPHANET = \"orphanet\"\n PSEUDOGENE = \"pseudogene.org\"\n HORDE = \"hordedb\"\n MEROPS = \"merops\"\n IUPHAR = \"iuphar\"\n KZNF = \"knzfgc\"\n MAMIT = \"mamittrnadb\"\n CD = \"hcdmdb\"\n LNCRNADB = \"lncrnadb\"\n IMGT = \"imgt\" # .hla? .ligm? leave as is?\n IMGT_GENE_DB = \"imgt/gene-db\" # redundant w/ above?\n RFAM = \"rfam\"\n\n\nclass DataLicenseAttributes(BaseModel):\n \"\"\"Define constraints for data license attributes.\"\"\"\n\n non_commercial: StrictBool\n share_alike: StrictBool\n attribution: StrictBool\n\n\nclass RecordType(str, Enum):\n \"\"\"Record item types.\"\"\"\n\n IDENTITY = \"identity\"\n MERGER = \"merger\"\n\n\nclass RefType(str, Enum):\n \"\"\"Reference item types.\"\"\"\n\n # Must be in descending MatchType order.\n SYMBOL = \"symbol\"\n PREVIOUS_SYMBOLS = \"prev_symbol\"\n ALIASES = \"alias\"\n XREFS = \"xref\"\n ASSOCIATED_WITH = \"associated_with\"\n\n\nclass SourceMeta(BaseModel):\n \"\"\"Metadata for a given source to return in response object.\"\"\"\n\n data_license: StrictStr\n data_license_url: StrictStr\n version: StrictStr\n data_url: Optional[StrictStr]\n rdp_url: Optional[StrictStr]\n data_license_attributes: Dict[StrictStr, StrictBool]\n genome_assemblies: Optional[List[StrictStr]]\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"SourceMeta\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.ncbi.nlm.nih.gov/home/about/policies/\", # noqa: E501\n \"version\": \"20201215\",\n \"data_url\": \"ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/\",\n \"rdp_url\": \"https://reusabledata.org/ncbi-gene.html\",\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": None,\n }\n\n\nclass MatchesKeyed(BaseModel):\n \"\"\"Container for matching information from an individual source.\n Used when matches are requested as an object, not an array.\n \"\"\"\n\n records: List[Gene]\n source_meta_: SourceMeta\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"MatchesKeyed\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"NCBI\": {\n \"match_type\": 0,\n \"records\": [],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.ncbi.nlm.nih.gov/home/about/policies/\", # noqa: E501\n \"version\": \"20201215\",\n \"data_url\": \"ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/\",\n \"rdp_url\": \"https://reusabledata.org/ncbi-gene.html\",\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": None,\n },\n }\n }\n\n\nclass MatchesListed(BaseModel):\n \"\"\"Container for matching information from an individual source.\n Used when matches are requested as an array, not an object.\n \"\"\"\n\n source: SourceName\n records: List[Gene]\n source_meta_: SourceMeta\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"MatchesListed\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"source\": \"NCBI\",\n \"match_type\": 0,\n \"records\": [],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.ncbi.nlm.nih.gov/home/about/policies/\", # noqa: E501\n \"version\": \"20201215\",\n \"data_url\": \"ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/\",\n \"rdp_url\": \"https://reusabledata.org/ncbi-gene.html\",\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": None,\n },\n }\n\n\nclass ServiceMeta(BaseModel):\n \"\"\"Metadata regarding the gene-normalization service.\"\"\"\n\n name = \"gene-normalizer\"\n version: StrictStr\n response_datetime: StrictStr\n url = \"https://github.com/cancervariants/gene-normalization\"\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"ServiceMeta\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"name\": \"gene-normalizer\",\n \"version\": \"0.1.0\",\n \"response_datetime\": \"2022-03-23 15:57:14.180908\",\n \"url\": \"https://github.com/cancervariants/gene-normalization\",\n }\n\n\nclass SearchService(BaseModel):\n \"\"\"Define model for returning highest match typed concepts from sources.\"\"\"\n\n query: StrictStr\n warnings: Optional[List[Dict]]\n source_matches: Union[Dict[SourceName, MatchesKeyed], List[MatchesListed]]\n service_meta_: ServiceMeta\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(schema: Dict[str, Any], model: Type[\"SearchService\"]) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"query\": \"BRAF\",\n \"warnings\": [],\n \"source_matches\": [\n {\n \"source\": \"Ensembl\",\n \"match_type\": 100,\n \"records\": [\n {\n \"label\": None,\n \"concept_id\": \"ensembl:ENSG00000157764\",\n \"symbol\": \"BRAF\",\n \"previous_symbols\": [],\n \"aliases\": [],\n \"xrefs\": [],\n \"symbol_status\": None,\n \"strand\": \"-\",\n \"locations\": [],\n }\n ],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://uswest.ensembl.org/info/about/legal/index.html\", # noqa: E501\n \"version\": \"102\",\n \"data_url\": \"http://ftp.ensembl.org/pub/\",\n \"rdp_url\": None,\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": \"GRCh38\",\n },\n }\n ],\n \"service_meta_\": {\n \"name\": \"gene-normalizer\",\n \"version\": \"0.1.0\",\n \"response_datetime\": \"2022-03-23 15:57:14.180908\",\n \"url\": \"https://github.com/cancervariants/gene-normalization\", # noqa: E501\n },\n }\n\n\nclass GeneTypeFieldName(str, Enum):\n \"\"\"Designate source-specific gene type field names for Extensions and\n internal records.\n \"\"\"\n\n HGNC = \"hgnc_locus_type\"\n NCBI = \"ncbi_gene_type\"\n ENSEMBL = \"ensembl_biotype\"\n\n\nclass BaseNormalizationService(BaseModel):\n \"\"\"Base method providing shared attributes to Normalization service classes.\"\"\"\n\n query: StrictStr\n warnings: Optional[List[Dict]]\n match_type: MatchType\n service_meta_: ServiceMeta\n\n\nclass NormalizeService(BaseNormalizationService):\n \"\"\"Define model for returning normalized concept.\"\"\"\n\n gene_descriptor: Optional[GeneDescriptor]\n source_meta_: Optional[Dict[SourceName, SourceMeta]]\n\n class Config:\n \"\"\"Configure model example\"\"\"\n\n use_enum_values = True\n\n @staticmethod\n def schema_extra(\n schema: Dict[str, Any], model: Type[\"NormalizeService\"]\n ) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"query\": \"BRAF\",\n \"warnings\": [],\n \"match_type\": 100,\n \"gene_descriptor\": {\n \"id\": \"normalize.gene:BRAF\",\n \"type\": \"GeneDescriptor\",\n \"gene\": {\"gene_id\": \"hgnc:1097\", \"type\": \"Gene\"},\n \"label\": \"BRAF\",\n \"xrefs\": [\"ncbigene:673\", \"ensembl:ENSG00000157764\"],\n \"alternate_labels\": [\"BRAF1\", \"RAFB1\", \"B-raf\", \"NS7\", \"B-RAF1\"],\n \"extensions\": [\n {\n \"name\": \"approved_name\",\n \"value\": \"B-Raf proto-oncogene, serine/threonine kinase\", # noqa: E501\n \"type\": \"Extension\",\n },\n {\n \"name\": \"symbol_status\",\n \"value\": \"approved\",\n \"type\": \"Extension\",\n },\n {\n \"name\": \"associated_with\",\n \"value\": [\n \"ccds:CCDS5863\",\n \"iuphar:1943\",\n \"orphanet:119066\",\n \"cosmic:BRAF\",\n \"pubmed:2284096\",\n \"ucsc:uc003vwc.5\",\n \"omim:164757\",\n \"refseq:NM_004333\",\n \"ccds:CCDS87555\",\n \"uniprot:P15056\",\n \"ena.embl:M95712\",\n \"vega:OTTHUMG00000157457\",\n \"pubmed:1565476\",\n ],\n \"type\": \"Extension\",\n },\n {\n \"name\": \"chromosome_location\",\n \"value\": {\n \"_id\": \"ga4gh:VCL.O6yCQ1cnThOrTfK9YUgMlTfM6HTqbrKw\", # noqa: E501\n \"type\": \"ChromosomeLocation\",\n \"species_id\": \"taxonomy:9606\",\n \"chr\": \"7\",\n \"interval\": {\n \"end\": \"q34\",\n \"start\": \"q34\",\n \"type\": \"CytobandInterval\",\n },\n },\n \"type\": \"Extension\",\n },\n ],\n },\n \"source_meta_\": {\n \"HGNC\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.genenames.org/about/\",\n \"version\": \"20210810\",\n \"data_url\": \"ftp://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/json/hgnc_complete_set.json\", # noqa: E501\n \"rdp_url\": None,\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"attribution\": False,\n \"share_alike\": False,\n },\n \"genome_assemblies\": [],\n },\n \"Ensembl\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://useast.ensembl.org/info/about/legal/disclaimer.html\", # noqa: E501\n \"version\": \"104\",\n \"data_url\": \"ftp://ftp.ensembl.org/pub/Homo_sapiens.GRCh38.104.gff3.gz\", # noqa: E501\n \"rdp_url\": None,\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"attribution\": False,\n \"share_alike\": False,\n },\n \"genome_assemblies\": [\"GRCh38\"],\n },\n \"NCBI\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.ncbi.nlm.nih.gov/home/about/policies/\", # noqa: E501\n \"version\": \"20210813\",\n \"data_url\": \"ftp://ftp.ncbi.nlm.nih.gov\",\n \"rdp_url\": \"https://reusabledata.org/ncbi-gene.html\",\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"attribution\": False,\n \"share_alike\": False,\n },\n \"genome_assemblies\": [\"GRCh38.p13\"],\n },\n },\n \"service_meta_\": {\n \"name\": \"gene-normalizer\",\n \"version\": \"0.1.19\",\n \"response_datetime\": \"2022-03-23 15:57:14.180908\",\n \"url\": \"https://github.com/cancervariants/gene-normalization\", # noqa: E501\n },\n }\n\n\nclass MatchesNormalized(BaseModel):\n \"\"\"Matches associated with normalized concept from a single source.\"\"\"\n\n records: List[BaseGene]\n source_meta_: SourceMeta\n\n class Config:\n \"\"\"Configure OpenAPI schema\"\"\"\n\n @staticmethod\n def schema_extra(\n schema: Dict[str, Any], model: Type[\"MatchesNormalized\"]\n ) -> None:\n \"\"\"Configure OpenAPI schema\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n\n\nclass UnmergedNormalizationService(BaseNormalizationService):\n \"\"\"Response providing source records corresponding to normalization of user query.\n Enables retrieval of normalized concept while retaining sourcing for accompanying\n attributes.\n \"\"\"\n\n normalized_concept_id: Optional[CURIE]\n source_matches: Dict[SourceName, MatchesNormalized]\n\n class Config:\n \"\"\"Configure OpenAPI schema\"\"\"\n\n @staticmethod\n def schema_extra(\n schema: Dict[str, Any], model: Type[\"UnmergedNormalizationService\"]\n ) -> None:\n \"\"\"Configure OpenAPI schema example\"\"\"\n if \"title\" in schema.keys():\n schema.pop(\"title\", None)\n for prop in schema.get(\"properties\", {}).values():\n prop.pop(\"title\", None)\n schema[\"example\"] = {\n \"query\": \"hgnc:108\",\n \"warnings\": [],\n \"match_type\": 100,\n \"service_meta_\": {\n \"version\": \"0.1.27\",\n \"response_datetime\": \"2022-04-26 14:20:54.180240\",\n \"name\": \"gene-normalizer\",\n \"url\": \"https://github.com/cancervariants/gene-normalization\",\n },\n \"normalized_concept_id\": \"hgnc:108\",\n \"source_matches\": {\n \"HGNC\": {\n \"records\": [\n {\n \"concept_id\": \"hgnc:108\",\n \"symbol\": \"ACHE\",\n \"symbol_status\": \"approved\",\n \"label\": \"acetylcholinesterase (Cartwright blood group)\", # noqa: E501\n \"strand\": None,\n \"location_annotations\": [],\n \"locations\": [\n {\n \"type\": \"ChromosomeLocation\",\n \"_id\": \"ga4gh:VCL.VtdU_0lYXL_o95lXRUfhv-NDJVVpmKoD\", # noqa: E501\n \"species_id\": \"taxonomy:9606\",\n \"chr\": \"7\",\n \"interval\": {\n \"type\": \"CytobandInterval\",\n \"start\": \"q22.1\",\n \"end\": \"q22.1\",\n },\n }\n ],\n \"aliases\": [\"3.1.1.7\"],\n \"previous_symbols\": [\"YT\"],\n \"xrefs\": [\"ncbigene:43\", \"ensembl:ENSG00000087085\"],\n \"associated_with\": [\n \"ucsc:uc003uxi.4\",\n \"vega:OTTHUMG00000157033\",\n \"merops:S09.979\",\n \"ccds:CCDS5710\",\n \"omim:100740\",\n \"iuphar:2465\",\n \"ccds:CCDS5709\",\n \"refseq:NM_015831\",\n \"pubmed:1380483\",\n \"uniprot:P22303\",\n \"ccds:CCDS64736\",\n ],\n \"gene_type\": \"gene with protein product\",\n }\n ],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.genenames.org/about/\",\n \"version\": \"20220407\",\n \"data_url\": \"ftp://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/json/hgnc_complete_set.json\", # noqa: E501\n \"rdp_url\": None,\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": [],\n },\n },\n \"Ensembl\": {\n \"records\": [\n {\n \"concept_id\": \"ensembl:ENSG00000087085\",\n \"symbol\": \"ACHE\",\n \"symbol_status\": None,\n \"label\": \"acetylcholinesterase (Cartwright blood group)\", # noqa: E501\n \"strand\": \"-\",\n \"location_annotations\": [],\n \"locations\": [\n {\n \"_id\": \"ga4gh:VSL.AF6wPZclBqTauGr3yx_CqmMndLKhq0Cm\", # noqa: E501\n \"type\": \"SequenceLocation\",\n \"sequence_id\": \"ga4gh:SQ.F-LrLMe1SRpfUZHkQmvkVKFEGaoDeHul\", # noqa: E501\n \"interval\": {\n \"type\": \"SequenceInterval\",\n \"start\": {\n \"type\": \"Number\",\n \"value\": 100889993,\n },\n \"end\": {\n \"type\": \"Number\",\n \"value\": 100896974,\n },\n },\n }\n ],\n \"aliases\": [],\n \"previous_symbols\": [],\n \"xrefs\": [\"hgnc:108\"],\n \"associated_with\": [],\n \"gene_type\": \"protein_coding\",\n }\n ],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://useast.ensembl.org/info/about/legal/disclaimer.html\", # noqa: E501\n \"version\": \"104\",\n \"data_url\": \"ftp://ftp.ensembl.org/pub/Homo_sapiens.GRCh38.104.gff3.gz\", # noqa: E501\n \"rdp_url\": None,\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": [\"GRCh38\"],\n },\n },\n \"NCBI\": {\n \"records\": [\n {\n \"concept_id\": \"ncbigene:43\",\n \"symbol\": \"ACHE\",\n \"symbol_status\": None,\n \"label\": \"acetylcholinesterase (Cartwright blood group)\", # noqa: E501\n \"strand\": \"-\",\n \"location_annotations\": [],\n \"locations\": [\n {\n \"type\": \"ChromosomeLocation\",\n \"_id\": \"ga4gh:VCL.VtdU_0lYXL_o95lXRUfhv-NDJVVpmKoD\", # noqa: E501\n \"species_id\": \"taxonomy:9606\",\n \"chr\": \"7\",\n \"interval\": {\n \"type\": \"CytobandInterval\",\n \"start\": \"q22.1\",\n \"end\": \"q22.1\",\n },\n },\n {\n \"_id\": \"ga4gh:VSL.EepkXho2doYcUT1DW54fT1a00_zkqrn0\", # noqa: E501\n \"type\": \"SequenceLocation\",\n \"sequence_id\": \"ga4gh:SQ.F-LrLMe1SRpfUZHkQmvkVKFEGaoDeHul\", # noqa: E501\n \"interval\": {\n \"type\": \"SequenceInterval\",\n \"start\": {\n \"type\": \"Number\",\n \"value\": 100889993,\n },\n \"end\": {\n \"type\": \"Number\",\n \"value\": 100896994,\n },\n },\n },\n ],\n \"aliases\": [\"YT\", \"ARACHE\", \"ACEE\", \"N-ACHE\"],\n \"previous_symbols\": [\"ACEE\"],\n \"xrefs\": [\"hgnc:108\", \"ensembl:ENSG00000087085\"],\n \"associated_with\": [\"omim:100740\"],\n \"gene_type\": \"protein-coding\",\n }\n ],\n \"source_meta_\": {\n \"data_license\": \"custom\",\n \"data_license_url\": \"https://www.ncbi.nlm.nih.gov/home/about/policies/\", # noqa: E501\n \"version\": \"20220407\",\n \"data_url\": \"ftp://ftp.ncbi.nlm.nih.gov\",\n \"rdp_url\": \"https://reusabledata.org/ncbi-gene.html\",\n \"data_license_attributes\": {\n \"non_commercial\": False,\n \"share_alike\": False,\n \"attribution\": False,\n },\n \"genome_assemblies\": [\"GRCh38.p13\"],\n },\n },\n },\n }\n","sub_path":"gene/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":31201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"363159829","text":"import copy\nimport os\nfrom typing import overload\nimport numpy as np\n\nfrom replication.preprocess.condition import Condition\n\nSTRESSED_PREFIX = \"stressed\"\nUNSTRESSED_PREFIX = \"unstressed\"\n\n\nclass User:\n stressed_condition: Condition\n unstressed_condition: Condition\n name: str\n\n def clean_data(self):\n for condition in [self.stressed_condition, self.unstressed_condition]:\n condition.clean_tasks()\n\n def __copy__(self):\n user = User()\n user.stressed_condition = copy.copy(self.stressed_condition)\n user.unstressed_condition = copy.copy(self.unstressed_condition)\n return user\n\n @overload\n def __init__(self, file_prefix: str, name: str):\n pass\n\n @overload\n def __init__(self):\n pass\n\n def normalize_data(self):\n moves_values = [\"x\", \"y\", \"x_speed\", \"y_speed\", \"major_axis\", \"minor_axis\", \"contact_area\"]\n conditions = [self.stressed_condition, self.unstressed_condition]\n for moves_value in moves_values:\n values = [\n getattr(entry, moves_value)\n for condition in conditions\n for task in condition.tasks\n for entry in task.track_pad_entries\n ]\n mean = np.average(values)\n var = np.std(values)\n if var != 0:\n for condition in conditions:\n for task in condition.tasks:\n for entry in task.track_pad_entries:\n setattr(entry, moves_value, (getattr(entry, moves_value) - mean) / var)\n task.populate_separated_track_pad_entries()\n\n def normalize_separated_track_pad_entries(self):\n moves_values = [\"x\", \"y\", \"x_speed\", \"y_speed\", \"major_axis\", \"minor_axis\", \"contact_area\"]\n conditions = [self.stressed_condition, self.unstressed_condition]\n for moves_value in moves_values:\n values = [\n getattr(entry, moves_value)\n for condition in conditions\n for task in condition.tasks\n for trace in task.separated_track_pad_entries\n for entry in trace\n ]\n mean = np.average(values)\n var = np.std(values)\n if var != 0:\n for condition in conditions:\n for task in condition.tasks:\n for trace in task.separated_track_pad_entries:\n for entry in trace:\n setattr(entry, moves_value, (getattr(entry, moves_value) - mean) / var)\n\n def __init__(self, *args):\n if len(args) == 0:\n self.stressed_condition = Condition()\n self.unstressed_condition = Condition()\n self.name = \"\"\n pass\n elif len(args) == 2 and isinstance(args[0], str):\n file_prefix = args[0]\n name = args[1]\n self.stressed_condition = Condition(os.path.join(file_prefix, STRESSED_PREFIX))\n self.unstressed_condition = Condition(os.path.join(file_prefix, UNSTRESSED_PREFIX))\n self.name = name\n # self.normalize_data()\n else:\n raise ValueError\n\n def __str__(self):\n return str(self.__dict__)\n\n\nif __name__ == '__main__':\n example_user = User(\"../../original_data/data/raw_data/A1/\", \"A1\")\n print(example_user)\n","sub_path":"replication/preprocess/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"164954125","text":"# solution 2: quick sort\n\"\"\"\n另一个采用分而治之策略的排序算法是快速排序，其优势是不需要额外的存储空间，这一点比归并排序强。\n快速排序的思路是依据一个“中值”数据项来把数据表分为两半：小于中值的一半和大于中值的一半，然后每部分分别进行快速排序\n输入list, i, j; 输出：list中以list[i]为pivot分为左右两边，左边小于pivot，右边大于；输出分边之后pivot所在的位置。\n\"\"\"\nclass Solution:\n def sortArray(self, nums: List[int]) -> List[int]:\n self.quickSort(nums, 0, len(nums) - 1)\n return nums\n \n def quickSort(self, nums, start, end):\n if start >= end: # the outlet of the recursion is start >= end\n return\n \n # 先整体有序\n # 注意这里选取pivot原因不能保证recursion tree深度稳定在log(N)，最坏的情况是深度为N.\n pivot = nums[(start + end) // 2] # key point 1: pivot is the value, not the index \n left, right = start, end\n while left <= right: # key point 2: it should be left <= right not left < right\n while left <= right and nums[left] < pivot: # key point 3: it should be nums[left] < pivot\n left += 1\n while left <= right and nums[right] > pivot:\n right -= 1\n if left <= right:\n nums[left], nums[right] = nums[right], nums[left]\n left += 1\n right -= 1\n \n # 再局部有序, 注意出while循环之后right在左边，所以这里是right\n self.quickSort(nums, start, right) # no return for the quickSort function!\n self.quickSort(nums, left, end)\n \n \n# solution 1: merge sort (top down recurssion)\n# there are logN merge operations and each merging takes O(N) operations. That is why the time complexity is O(NlogN)\nclass Solution:\n def sortArray(self, nums: List[int]) -> List[int]:\n return self._mergeSort_(nums)\n\n def _mergeSort_(self, arr):\n lens = len(arr)\n if lens <= 1:\n return arr # if return None, then leftArr = self._mergeSort_(arr[:mid]) could be a typeErrot: object of type \"NoneType has no len()\"\n \n # 1. divide 先局部有序\n mid = lens // 2\n leftArr = self._mergeSort_(arr[:mid])\n rightArr = self._mergeSort_(arr[mid:])\n\n # 2. merge 再整体有序\n i, j, k = 0, 0, 0\n while i < len(leftArr) and j < len(rightArr):\n if leftArr[i] < rightArr[j]:\n arr[k] = leftArr[i]\n i += 1\n else:\n arr[k] = rightArr[j]\n j += 1\n k += 1\n while i < len(leftArr):\n arr[k] = leftArr[i]\n i += 1\n k += 1\n while j < len(rightArr):\n arr[k] = rightArr[j]\n j += 1\n k += 1\n \n return arr\n\n \nclass Solution:\n def sortArray(self, nums: List[int]) -> List[int]:\n if len(nums) <= 1:\n return nums\n \n mid = len(nums) // 2\n left = self.sortArray(nums[:mid])\n right = self.sortArray(nums[mid:])\n \n res = []\n i, j, k = 0, 0, 0\n while i < len(left) and j < len(right):\n if left[i] < right[j]:\n res.append(left[i])\n i += 1\n else:\n res.append(right[j])\n j += 1\n \n res += left[i:]\n res += right[j:]\n \n return res\n","sub_path":"Solutions/0912.Sort-an-Array.py","file_name":"0912.Sort-an-Array.py","file_ext":"py","file_size_in_byte":3599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"561188911","text":"#User function Template for python3\ndef sumOfDigits(n):\n sum = 0\n while (n != 0): \n \n sum = sum + int(n % 10) \n n = int(n/10) \n \n return sum\n \n '''\n :param n: given number\n :return: sum of digits of n.\n '''\n # code here\n #{ \n# Driver Code Starts\n#Initial Template for Python 3\nimport atexit\nimport io\nimport sys\n\n_INPUT_LINES = sys.stdin.read().splitlines()\ninput = iter(_INPUT_LINES).__next__\n_OUTPUT_BUFFER = io.StringIO()\nsys.stdout = _OUTPUT_BUFFER\n\n@atexit.register\n\ndef write():\n sys.__stdout__.write(_OUTPUT_BUFFER.getvalue())\n\nif __name__ == '__main__':\n test_cases = int(input())\n for cases in range(test_cases) :\n n = int(input())\n print(sumOfDigits(n))\n# } Driver Code Ends\n","sub_path":"Recursion/Count Total Digits in a Number.py","file_name":"Count Total Digits in a Number.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"421731512","text":"\"\"\"\nMain methods for binning packets into bursts and categorising packets\n\"\"\"\nimport requests, os, pickle, json, sys\n\nimport predictions\nsys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), \"db\"))\nimport databaseBursts # pylint: disable=C0413, E0401\n\nsys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), \"macHelpers\"))\nimport macHelpMethods # pylint: disable=C0413, E0401\n\nFILE_PATH = os.path.dirname(os.path.abspath(__file__))\n\nDB_MANAGER = databaseBursts.dbManager()\n\nMAC_MANAGER = macHelpMethods.MacHelper()\n\ndef packetBurstification():\n \"\"\" Get all packets not in bursts and assign them to a new burst \"\"\"\n # Get packets not in bursts\n \n unBinned = DB_MANAGER.getNoBurst()\n\n allBursts = [] # List of list of ids\n allIds = set() # Set of ids considered already\n nextBurst = [] # Ids to go in next burst\n\n with open(os.path.join(FILE_PATH, 'dicts.json'), 'r') as f:\n config = json.load(f)\n\n # Get ids of all the packets we want in bursts\n for counter, row in enumerate(unBinned):\n id = row[0]\n mac = row[4]\n\n dev = MAC_MANAGER.getDeviceFromMac(mac)\n\n \n try:\n burstTimeInterval = int( config[\"burstTimeIntervals\"][dev] )\n except KeyError:\n burstTimeInterval = int( config[\"burstTimeIntervals\"][\"Unknown\"] )\n\n try:\n burstPacketNoCutoff = int( config[\"burstNumberCutoffs\"][dev] )\n except KeyError:\n burstPacketNoCutoff = int( config[\"burstNumberCutoffs\"][\"Unknown\"] )\n \n if id not in allIds:\n \n nextBurst = [id]\n allIds.add(id)\n\n currentTime = row[1]\n\n #print(id)\n #print(type(id))\n\n try:\n for otherRow in unBinned[counter+1:]:\n if otherRow[0] not in allIds:\n\n if otherRow[4] == mac and burstTimeInterval > (otherRow[1] - currentTime).total_seconds():\n \n # If less than TIME_INTERVAL away, add to this burst\n nextBurst.append(otherRow[0])\n # Don't need to look at this one again, it's in this potential burst\n allIds.add(otherRow[0])\n\n currentTime = otherRow[1]\n\n elif otherRow[4] == mac and burstTimeInterval < (otherRow[1] - currentTime).total_seconds():\n if len(nextBurst) > burstPacketNoCutoff:\n allBursts.append(nextBurst)\n # If same device, but too far away, we can stop, there won't be another burst here\n break\n # Can't add to considered, might be the start of the next burst\n\n elif otherRow[4] != mac:\n continue\n # If it's a different device, we can't say anything at this point\n except IndexError:\n continue \n\n else:\n # If we've considered it we know it was within interval of another packet and so\n # it's either a valid burst or part of one that is too short\n continue\n if len(nextBurst) > burstPacketNoCutoff:\n allBursts.append(nextBurst)\n\n # Add each new burst, and add all the packet rows to it\n for burst in allBursts:\n newBurstId = DB_MANAGER.insertNewBurst()\n DB_MANAGER.updatePacketBurstBulk(burst, [newBurstId for _ in range(len(burst))])\n \n\n\ndef burstPrediction():\n \"\"\"\n Predict a category for each burst, or don't assign if there is no prediction\n \"\"\"\n unCat = DB_MANAGER.getNoCat()\n\n #print(unCat)\n\n with open(os.path.join(FILE_PATH, 'dicts.json'), 'r') as f:\n config = json.load(f)\n cutoffs = config[\"burstNumberCutoffs\"]\n \n predictor = predictions.Predictor()\n\n for burst in unCat:\n \n rows = DB_MANAGER.getRowsWithBurst(burst[0])\n\n #print(burst, rows)\n\n if len(rows) == 0:\n continue\n\n device = MAC_MANAGER.getDeviceFromMac(rows[0][4])\n\n if \"Echo\" in device and len(rows) > cutoffs[\"Echo\"]:\n category = predictor.predictEcho(rows)\n elif \"Google\" in device:\n category = predictor.predictGoogle(rows)\n elif device == \"Philips Hue Bridge\" and len(rows) > cutoffs[device]:\n category = predictor.predictHue(rows)\n else:\n category = predictor.predictOther(rows)\n\n # Get the id of this category, and add if necessary\n newCategoryId = DB_MANAGER.addOrGetCategoryNumber(category)\n\n # Update the burst with the name of the new category, packets already have a reference to the burst\n DB_MANAGER.updateBurstCategory(burst[0], newCategoryId)\n\n predictor.saveIpDict()\n\n #88:71:e5:e9:9e:6c","sub_path":"categorisation/burstProcessing.py","file_name":"burstProcessing.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"145450864","text":"#\n# Tests for the simple ODE model\n#\nimport pybamm\nimport tests\n\nimport unittest\nimport numpy as np\n\n\nclass TestSimpleODEModel(unittest.TestCase):\n def test_basic_processing(self):\n model = pybamm.SimpleODEModel()\n\n modeltest = tests.StandardModelTest(model)\n modeltest.test_all()\n\n def test_optimisations(self):\n model = pybamm.SimpleODEModel()\n optimtest = tests.OptimisationsTest(model)\n\n original = optimtest.evaluate_model()\n simplified = optimtest.evaluate_model(simplify=True)\n using_known_evals = optimtest.evaluate_model(use_known_evals=True)\n simp_and_known = optimtest.evaluate_model(simplify=True, use_known_evals=True)\n np.testing.assert_array_almost_equal(original, simplified)\n np.testing.assert_array_almost_equal(original, using_known_evals)\n np.testing.assert_array_almost_equal(original, simp_and_known)\n\n def test_solution(self):\n model = pybamm.SimpleODEModel()\n modeltest = tests.StandardModelTest(model)\n t_eval = np.linspace(0, 1, 50)\n modeltest.test_all(t_eval=t_eval)\n t, y = modeltest.solution.t, modeltest.solution.y\n mesh = modeltest.disc.mesh\n\n # check output\n processed_variables = pybamm.post_process_variables(model.variables, t, y, mesh)\n np.testing.assert_array_almost_equal(processed_variables[\"a\"](t), 2 * t)\n whole_cell = [\"negative electrode\", \"separator\", \"positive electrode\"]\n x = mesh.combine_submeshes(*whole_cell)[0].nodes\n np.testing.assert_array_almost_equal(\n processed_variables[\"b broadcasted\"](t, x), np.ones((len(x), len(t)))\n )\n x_n_s = mesh.combine_submeshes(\"negative electrode\", \"separator\")[0].nodes\n np.testing.assert_array_almost_equal(\n processed_variables[\"c broadcasted\"](t, x_n_s),\n np.ones_like(x_n_s)[:, np.newaxis] * np.exp(-t),\n )\n\n\nif __name__ == \"__main__\":\n print(\"Add -v for more debug output\")\n import sys\n\n if \"-v\" in sys.argv:\n debug = True\n pybamm.settings.debug_mode = True\n unittest.main()\n","sub_path":"tests/unit/test_models/test_simple_ode_model.py","file_name":"test_simple_ode_model.py","file_ext":"py","file_size_in_byte":2127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"138147974","text":"\"\"\"拡張子を.jpg-largeから.jpgへリネームする\"\"\"\n\nimport os\ndef rename_large():\n \"\"\"拡張子を.jpg-largeから.jpgへリネームする\"\"\"\n extension_list = ['jpg', 'png']\n current_working_directory = os.getcwd()\n print('working directory is', current_working_directory)\n\n file_list = os.listdir(current_working_directory)\n for extension in extension_list:\n for file_name in file_list:\n if '.{}_large'.format(extension) in file_name:\n file_renamed = file_name.replace('{}_large'.format(extension), '{}'.format(extension))\n if file_renamed in file_list:\n print(file_renamed, 'is already exist.')\n else:\n os.rename(file_name, file_renamed)\n print(file_name, 'was renamed to', file_renamed)\n print('done.')\n\nif __name__ == '__main__':\n rename_large()","sub_path":"rename_large.py","file_name":"rename_large.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"287119397","text":"from pathlib import Path\nimport shutil\nimport json\n\nclass GerenciaArquivo(object):\n '''\n Classe para controlar e gerenciar arquivos.\n Permite salvar, criar, e apagar arquivos.\n '''\n\n @staticmethod\n def get_path(caminho):\n if not GerenciaArquivo.existe_diretorio(caminho):\n GerenciaArquivo.cria_diretorio(caminho)\n return Path(caminho)\n\n @staticmethod\n def get_diretorio_atual():\n return Path.cwd()\n \n @staticmethod\n def imprime_diretorio_atual():\n '''\n Printa no console o diretorio atual\n '''\n print(GerenciaArquivo.get_diretorio_atual())\n\n\n @staticmethod\n def abrir_aquivo(caminho, modo='r', codificacao='utf-8-sig'):\n '''\n Abri um arquivo no caminho especificado\n Args:\n caminho: string, com o caminho do diretório ou arquivo.\n modo: string que define o modo que sera aberto o arquivo. \n r = leitura, w = escrita, e tem outras opções\n codificacao: codificacao na qual abrir o arquivo\n retorna (object)file\n '''\n return open(caminho, modo, codificacao)\n\n @staticmethod\n def cria_diretorio(caminho):\n '''\n Cria diretorio no caminho informado\n Args:\n caminho: string, com o caminho do diretório ou arquivo.\n '''\n if GerenciaArquivo.existe_diretorio(caminho):\n raise Exception('O diretorio que esta tentando criar já existe.')\n Path(caminho).mkdir()\n\n @staticmethod\n def existe_diretorio(caminho):\n '''\n Verifica se o caminho existe\n Args:\n caminho: string, com o caminho do diretório ou arquivo.\n retorna bool\n '''\n return Path(caminho).exists()\n \n @staticmethod\n def is_diretorio(caminho):\n '''\n Verifica se o caminho passado é um diretorio\n Args: \n caminho: string, com o caminho do diretório ou arquivo.\n retorna bool\n '''\n return Path(caminho).is_dir()\n \n @staticmethod\n def remove(caminho):\n '''\n Remove o diretorio ou arquivo no caminho informado. \n Caso o caminho informado pertencer a um diretorio e o mesmo não \n estiver vazio ele não sera removido.\n '''\n if not GerenciaArquivo.existe_diretorio(caminho):\n raise Exception('O caminho do diretorio/arquivo informado não existe')\n\n o_path = Path(caminho)\n if GerenciaArquivo.is_diretorio(caminho):\n #remove diretorio\n try:\n Path.rmdir(o_path)\n except:\n print(f'apagando arquivos do dir: {caminho}')\n shutil.rmtree(caminho)\n else:\n #remove arquivo\n Path.unlink(o_path)\n\n @staticmethod\n def apaga_arquivos_dir(caminho):\n if not GerenciaArquivo.existe_diretorio(caminho):\n raise Exception('O caminho do diretorio informado não existe')\n\n if not GerenciaArquivo.is_diretorio(caminho):\n raise Exception('nao é um diretorio')\n\n o_path = Path(caminho)\n for x in o_path.iterdir():\n Path.unlink(x)\n\n @staticmethod\n def salva_arquivo_json(nome_arquivo, content=[]):\n if len(nome_arquivo.split('.')) <= 1:\n nome_arquivo = f'{nome_arquivo}.json'\n else:\n ext = nome_arquivo.split('.')[1]\n if ext != 'json':\n temp_name = nome_arquivo.split('.')[0]\n nome_arquivo = f'{temp_name}.json'\n\n with open(nome_arquivo, 'w') as file:\n json.dump(content, file)\n \n @staticmethod\n def abir_arquivo_json(nome_arquivo):\n if len(nome_arquivo.split('.')) <= 1:\n nome_arquivo = f'{nome_arquivo}.json'\n else:\n ext = nome_arquivo.split('.')[1]\n if ext != 'json':\n temp_name = nome_arquivo.split('.')[0]\n nome_arquivo = f'{temp_name}.json'\n\n data = []\n with open(nome_arquivo) as file:\n try:\n data = json.load(file)\n except Exception as ex:\n print(f'nao foi possivel abrir o arquivo {nome_arquivo}')\n data = []\n return data\n\n @staticmethod\n def salva_arquivo_texto(path, extension, content):\n '''\n Salva um arquivo no caminho informado\n\n Args:\n path: caminho onde salvar o arquivo\n extension: extensao do arquivo\n content: conteudo do arquivo\n '''\n #caminho completo onde salvar o caminho\n s_caminho = f'{path}.{extension}'\n\n #verifica se existe algo no caminho informado\n if GerenciaArquivo.existe_diretorio(s_caminho):\n GerenciaArquivo.remove(s_caminho)\n \n with open(s_caminho, mode='a', encoding='utf-8-sig') as file:\n file.write(content)\n\n @staticmethod\n def abrir_arquivo_texto(path):\n '''\n retorna o texto de um arquivo informado\n\n Args:\n label: o label a ser adicionado no inicio do texto do arquivo aberto\n path : caminho onde se encontra o caminhoa ser aberto \n '''\n\n # texto que sera retornado apos a leitura do arquivo\n s_txt = ''\n\n # metodo facilitado para leitura de arquivo em python, ja que sempre que\n # apos a leitura o aruivo é fechado a declaracao 'with open' ja abstrai isso\n # file => aquivo aberto\n # open => metodo que abre o arquivo\n # mode => se eh leitura escrita ou outra coisa\n # enconding => a codificacao na qual abrir o arquivo\n with open(path, mode='r', encoding='utf-8-sig') as file:\n # array de string que compoem o arquivo\n a_tx = []\n for linha in file:\n # verifica se o comprimento da linha eh 1, se for significa que eh uma linha em \n # branco e apenas continua\n if len(linha) == 1:\n continue\n # rstrip, remove espaços em branco excedente no inicio e fim da string\n a_tx.append(linha.rstrip())\n\n #une todos os valores no array de string, em uma unica string\n # ' ' => string de conexao de todas as strings, pode ser qualquer string\n #join => une um array em uma unica string. Talvez faça outras coisas, ver doc.\n s_txt = ' '.join(a_tx)\n \n return s_txt\n\n","sub_path":"src/util/gerencia_arquivo.py","file_name":"gerencia_arquivo.py","file_ext":"py","file_size_in_byte":6652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"470031774","text":"\"\"\"Setup.\"\"\"\n\nimport os.path\n\nfrom setuptools import setup\n\nhere = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(here, \"README.rst\"), encoding=\"utf-8\") as f:\n LONG_DESCRIPTION = f.read()\n\n\nPACKAGES = (\n \"async_upnp_client\",\n \"async_upnp_client.profiles\",\n)\n\n\nINSTALL_REQUIRES = [\n \"voluptuous>=0.12.1\",\n \"aiohttp>=3.7.4\",\n \"async-timeout>=3.0,<4.0\",\n \"python-didl-lite~=1.2.6\",\n \"defusedxml>=0.6.0\",\n]\n\n\nTEST_REQUIRES = [\n \"pytest~=6.1.2\",\n \"pytest-asyncio~=0.14.0\",\n]\n\n\nsetup(\n name=\"async_upnp_client\",\n version=\"0.20.0\",\n description=\"Async UPnP Client\",\n long_description=LONG_DESCRIPTION,\n url=\"https://github.com/StevenLooman/async_upnp_client\",\n author=\"Steven Looman\",\n author_email=\"steven.looman@gmail.com\",\n license=\"http://www.apache.org/licenses/LICENSE-2.0\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Programming Language :: Python :: 3.9\",\n ],\n packages=PACKAGES,\n package_data={\n \"async_upnp_client\": [\"py.typed\"],\n },\n install_requires=INSTALL_REQUIRES,\n tests_require=TEST_REQUIRES,\n entry_points={\"console_scripts\": [\"upnp-client=async_upnp_client.cli:main\"]},\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"388399326","text":"\"\"\"samples URL Configuration\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\n\"\"\"\nfrom django.conf.urls import patterns, url\nfrom django.views.generic import TemplateView\nfrom jeffreyvwong.models import Snippet \nfrom django.views.generic.list import ListView\nfrom django.views.generic.detail import DetailView\n\nurlpatterns = patterns('',\n \n url(r'^$',\n ListView.as_view(template_name=\"samples/list.html\",\n queryset = Snippet.objects.all(),\n allow_empty = True,\n paginate_by = 10),\n name='samples_list'),\n \n url(r'^(?P\\d+)/$',\n DetailView.as_view( template_name=\"samples/detail.html\",\n queryset = Snippet.objects.all()),\n name='samples_detail'),\n\t\t\t \n url(r'^flow/$', TemplateView.as_view(template_name=\"samples/flow.html\"), name='samples_flow'),\n url(r'^cellular/$', TemplateView.as_view(template_name=\"samples/cellular.html\"), name='samples_cellular'),\n)\n","sub_path":"jeffreyvwong/urls/samples.py","file_name":"samples.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"275296621","text":"import App\nimport loadspacehelper\nimport MissionLib\n\ng_pEnemyList = None\ng_pFriendlyList = None\ng_pNeutralList = None\n\ng_pAction = None\n\ndef loadSystem(system_module, system_name, episode=\"\", mission=\"\"):\n\tpModule = __import__(system_module)\n\tif (hasattr(pModule, \"CreateMenus\")):\n\t\tpMenu = pModule.CreateMenus()\n\telse:\n\t\timport Systems.Utils\n\t\tSystems.Utils.CreateSystemMenu(system_name, system_module)\n\n\tif (episode != \"\"):\n\t\tpMenu.SetEpisodeName(episode)\n\t\t\n\tif (mission != \"\"):\n\t\tpMenu.SetMissionName(mission)\n\t\t\ndef CreatePlayerShip(sShipClass, sSetName, pcName, sWaypoint, bUnloadShip = 0):\n\tpSet = App.g_kSetManager.GetSet(sSetName)\n\tpPlayer\t= MissionLib.CreatePlayerShip(sShipClass, pSet, pcName, sWaypoint, bUnloadShip)\n\ndef addFriendly(pMission, name):\n\tif (g_pFriendlyList == None):\n\t\tglobal g_pFriendlyList\n\t\tg_pFriendlyList = pMission.GetFriendlyGroup()\n\t\t\n\tg_pFriendlyList.AddName(name)\n\ndef addEnemy(pMission, name):\n\tif (g_pEnemyList == None):\n\t\tglobal g_pEnemyList\n\t\tg_pEnemyList = pMission.GetEnemyGroup()\n\t\t\n\tg_pEnemyList.AddName(name)\n\ndef addNeutral(pMission, name):\n\tif (g_pNeutralList == None):\n\t\tglobal g_pNeutralList\n\t\tg_pNeutralList = pMission.GetNeutralGroup()\n\t\t\n\tg_pNeutralList.AddName(name)\n\ndef translateShip(pShip, x, y, z, pTarget=None):\n\tp = App.TGPoint3()\n\tp.SetX(x)\n\tp.SetY(y)\n\tp.SetZ(z)\n\tpShip.SetTranslate(p)\n\t\n\tif (pTarget != None):\n\t\tMissionLib.OrientObjectTowardObject(pShip, pTarget)\n \ndef createCutscene(sModule, sFunc, duration, bFadeOut=0):\n\tpSequence = App.TGSequence_Create()\n\t\n\t#pPlayer = MissionLib.GetPlayer()\n\t#pSet = pPlayer.GetContainingSet()\n\tpSet = App.g_kSetManager.GetSet(\"bridge\")\n\n\tpSequence.AppendAction(App.TGScriptAction_Create(__name__, \"addSkipHandler\"))\n\tif (bFadeOut == 1):\n\t\tpSequence.AppendAction(App.TGScriptAction_Create(\"MissionLib\", \"FadeOut\", 0))\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"Actions.MissionScriptActions\", \"ChangeToBridge\"))\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"MissionLib\", \"StartCutscene\"))\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"Actions.CameraScriptActions\", \"CutsceneCameraBegin\", pSet.GetName()), 2)\n\t\n\tpSequence.AppendAction(App.TGScriptAction_Create(__name__, \"SkipWrapper\", sModule, sFunc))\n\t\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"Actions.CameraScriptActions\", \"CutsceneCameraEnd\", pSet.GetName()), duration)\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"Actions.CameraScriptActions\", \"ChangeRenderedSet\", \"bridge\"))\n\tpSequence.AppendAction(App.TGScriptAction_Create(\"MissionLib\", \"EndCutscene\"))\n\tpSequence.AppendAction(App.TGScriptAction_Create(__name__, \"removeSkipHandler\"))\n\t\n\treturn pSequence\n\ndef SkipWrapper(pAction, sModule, sFunc):\n\tglobal g_pAction\n\tg_pAction = pAction\n\tpSequence = App.TGSequence_Create()\n\n\tpSequence.AppendAction(App.TGScriptAction_Create(sModule, sFunc))\n\n\tpEvent = App.TGObjPtrEvent_Create()\n\tpEvent.SetDestination(App.g_kTGActionManager)\n\tpEvent.SetEventType(App.ET_ACTION_COMPLETED)\n\tpEvent.SetObjPtr(pAction)\n\tpSequence.AddCompletedEvent(pEvent)\n\tpSequence.Play()\n\tApp.TGActionManager_RegisterAction(pSequence, \"cutscene\")\n\treturn 1\n \t\n\ndef SkipSequence(TGObject, pEvent):\n\t# This function is called when a key is pressed\n\t\n\t# See if the key that was pressed matches the \"SkipKey\" string.\n\tiUnicode = pEvent.GetUnicode()\n\tkDisplayString = App.g_kInputManager.GetDisplayStringFromUnicode(iUnicode)\n\t\n\tkSkipKey = 's'\n\t\n\tif (kDisplayString.GetCString() == kSkipKey):\n\t\t# stop the TitleSequence\n\t\tApp.TGActionManager_KillActions(\"cutscene\")\n\t\t\n\t\t# this tells the main sequence to continue playing\n\t\t# without this the player would be stuck in cinematic mode\n\t\tpSequence = App.TGSequence_Create()\n\t\tpEvent = App.TGObjPtrEvent_Create()\n\t\tpEvent.SetDestination(App.g_kTGActionManager)\n\t\tpEvent.SetEventType(App.ET_ACTION_COMPLETED)\n\t\tpEvent.SetObjPtr(g_pAction)\n\t\tpSequence.AddCompletedEvent(pEvent)\n\t\tpSequence.Play()\n\t\t\n\t# All Done, pass on the event\n\tTGObject.CallNextHandler(pEvent)\n\ndef addSkipHandler(pAction):\n\t# adds a keyboard handler\n\tApp.g_kRootWindow.AddPythonFuncHandlerForInstance(App.ET_KEYBOARD, __name__ + \".SkipSequence\")\n\treturn 0\n\ndef removeSkipHandler(pAction):\n\t# removes the keyboard handler\n\tApp.g_kRootWindow.RemoveHandlerForInstance(App.ET_KEYBOARD, __name__ + \".SkipSequence\")\n\treturn 0\n ","sub_path":"scripts/Lib/MissionHelper.py","file_name":"MissionHelper.py","file_ext":"py","file_size_in_byte":4291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"519213277","text":"# -*- coding: utf-8 -*-\n__author__ = 'ElenaSidorova'\nfrom copy import deepcopy\nfrom preprocess import Preprocessor\nfrom tokenizer import Tokenizer\nfrom transliterator import Transliterator\nfrom meta_data import META\nimport json\nimport subprocess\n\nclass Processor(object):\n @classmethod\n def process_text(cls, text, show, delimiters, check_brackets, print_log=True):\n text = Preprocessor.preprocess_text(text)\n tokens = Tokenizer.tokenize(text)\n for i in tokens.keys():\n if tokens[i].type == 'word':\n word = Transliterator.transliterate(tokens[i].word, print_log)\n if word != tokens[i].word:\n tokens[i].old_word = deepcopy(tokens[i].word)\n tokens[i].word = word\n text, changes, wrong_edits = cls.join_tokens(tokens, show, delimiters, check_brackets)\n str_json = cls.to_json(tokens)\n return text, changes, wrong_edits, str_json\n\n @classmethod\n def to_json(cls, tokens):\n jn = {}\n for key in tokens.keys():\n jn[str(key)] = {}\n jn[str(key)]['word'] = tokens[key].word\n jn[str(key)]['old_word'] = tokens[key].old_word\n jn[str(key)]['type'] = tokens[key].type\n jn[str(key)]['plain_word'] = tokens[key].plain_word\n jn[str(key)]['old_plain_word'] = tokens[key].old_plain_word\n str_json = json.dumps(jn)\n return str_json\n\n @classmethod\n def join_tokens(cls, tokens, show, delimiters, check_brackets):\n text = []\n changes = []\n spelling = []\n wrong_changes = []\n for i in range(len(tokens.keys())):\n if check_brackets:\n if u'[' in tokens[i].word and tokens[i].type == 'word':\n new = u''\n new += META['spelling_delimiters_upd'][0]\n if tokens[i].old_word:\n new += tokens[i].old_word.replace(u\"'\", u\"'\")\n else:\n new += tokens[i].word.replace(u\"'\", u\"'\")\n new += META['spelling_delimiters_upd'][1]\n if tokens[i].old_word:\n new_vers = tokens[i].word.split(u'[')[1].split(u']')[0]\n old_vers = tokens[i].old_word.split(u'[')[1].split(u']')[0]\n if new_vers == old_vers:\n new = new + tokens[i].word.split(u'[')[0] + tokens[i].word.split(u']')[1] + \\\n META['spelling_delimiters_upd'][2] + tokens[i].word.replace(u'[', u'').replace(u']', u'') + \\\n META['spelling_delimiters_upd'][3]\n else:\n new_spell = tokens[i].word.split(u'[')[0] + tokens[i].word.split(u']')[1]\n old_spell = tokens[i].old_word.split(u'[')[0] + tokens[i].old_word.split(u']')[1]\n if new_spell == old_spell:\n new = new + tokens[i].word.split(u'[')[0] + tokens[i].word.split(u']')[1] + \\\n META['spelling_delimiters_upd'][2] + delimiters[0] + \\\n tokens[i].word.replace(u'[', u'').replace(u']', u'') + delimiters[1] + \\\n tokens[i].old_word.replace(u'[', u'').replace(u']', u'') + delimiters[2] + \\\n META['spelling_delimiters_upd'][3]\n else:\n new = new + delimiters[0] + tokens[i].word.split(u'[')[0] + tokens[i].word.split(u']')[1] + \\\n delimiters[1] + tokens[i].old_word.split(u'[')[0] + tokens[i].old_word.split(u']')[1] + \\\n delimiters[2] + META['spelling_delimiters_upd'][2] + delimiters[0] + \\\n tokens[i].word.replace(u'[', u'').replace(u']', u'') + delimiters[1] + \\\n tokens[i].old_word.replace(u'[', u'').replace(u']', u'') + delimiters[2] + \\\n META['spelling_delimiters_upd'][3]\n else:\n new = new + tokens[i].word.split(u'[')[0] + tokens[i].word.split(u']')[1] + \\\n META['spelling_delimiters_upd'][2] + tokens[i].word.replace(u'[', u'').replace(u']', u'') + \\\n META['spelling_delimiters_upd'][3]\n text.append(new)\n if tokens[i].old_word:\n spelling.append(tokens[i].word.replace(u'[', u'').replace(u']', u''))\n s = tokens[i].old_word + u' --> ' + tokens[i].word\n changes.append(s)\n else:\n if tokens[i].old_word:\n new = delimiters[0] + tokens[i].word + delimiters[1] + \\\n tokens[i].old_word + delimiters[2]\n text.append(new)\n spelling.append(tokens[i].word)\n s = tokens[i].old_word + u'\\t-->\\t' + tokens[i].word\n changes.append(s)\n else:\n text.append(tokens[i].word)\n\n else:\n if tokens[i].old_word:\n if show:\n new = delimiters[0] + tokens[i].word + delimiters[1] + \\\n tokens[i].old_word + delimiters[2]\n else:\n new = tokens[i].word\n text.append(new)\n s = tokens[i].old_word + u' --> ' + tokens[i].word\n changes.append(s)\n else:\n text.append(tokens[i].word)\n # if spelling:\n # cmd = \"echo \" + u' '.join(spelling) + \" | hunspell -d ru_Ru\"\n # p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, executable=\"/bin/bash\")\n # spelled, err_sp = p.communicate()\n # spelled = spelled.split('\\n')[1:][:-2]\n # for j, sp in enumerate(spelled):\n # if sp [0] == u'&':\n # wrong_changes.append(changes[j])\n # changes[j] = changes[j] + u' *'\n if changes == []:\n out = u''\n else:\n out = u'\\n'.join(changes)\n return u''.join(text), out, wrong_changes\n\n\n# text = u'Пройдя комнату, такъ [называемую], офиціанскую, мы взошли въ кабинетъ Папа. Онъ стоялъ подлѣ письменнаго стола и, показывая на бумаги, запечатанные конверты, кучки денегъ, горячился и что-то толковалъ прикащику Никитѣ Петрову, который на обычно[мъ] своемъ мѣстѣ, подлѣ барометра, разставивъ ноги на приличное раз[стояніе], заложивъ руки назадъ и приводя за спиною пальцы въ движеніе тѣмъ быстрѣе, чѣмъ болѣе горячился [13] папа, спереди не выказывалъ ни малѣйшаго знака безпокойства, но, напротивъ, выраженіемъ лица выказывалъ совершенное сознаніе своей правоты и вмѣстѣ съ тѣмъ подвластности.'\n# text = u'df 13 fsdf'\n# text = u'офиціанскую'\n# text = u' обычно[мъ] '\n# text = u'который [на] обычно[мъ] [своемъ] мѣстѣ, под[лѣ] баро[метра], разст[авивъ], любо[въ]'\n# import codecs\n# with codecs.open(u'/Users/el/Downloads/vol. 1/index.html', 'r', 'utf-8') as inf:\n# text = inf.read()\n# a = Processor()\n# b, c, r, m = a.process_text(text, 1, [u'', u'', u''], 1)\n# print b\n","sub_path":"process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":8045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"530130678","text":"from collections import Counter\nfrom datetime import datetime\n\ndef parse_dt(log_entry):\n return datetime.strptime(log_entry[1:log_entry.index(']')], '%Y-%m-%d %H:%M')\n\nwith open('input.txt', 'r') as f:\n log = list(sorted(f.readlines(), key=parse_dt))\n\ng_sleeping = dict()\ncurr_guard = None\ncurr_fall_asleep = None\nfor entry in log:\n dt = parse_dt(entry)\n\n if 'Guard' in entry:\n # guard start\n curr_guard = entry[entry.index('#')+1: entry.index('begins') -1]\n curr_fall_asleep = None\n\n elif 'falls' in entry:\n # fall asleep\n curr_fall_asleep = dt.minute\n\n elif 'wakes' in entry:\n # wake up\n sleep = g_sleeping.setdefault(curr_guard, Counter())\n sleep.update(range(curr_fall_asleep, dt.minute))\n\n else:\n raise Exception('....')\n\ng, sleep = list(sorted(g_sleeping.items(), key=lambda t: max(t[1].values())))[-1]\nmax_min = sleep.most_common(1)[0][0]\nprint(int(g) * max_min)\n","sub_path":"2018/4/p2.py","file_name":"p2.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"115587048","text":"#!/usr/bin/env python3\n\"\"\"\n graph_programming.py\n Author: Joel Gomez\n Date created: 2016/11/23\n Python Version 3.5.2\n\n Algorithms adapted from sample pseudocode provided in Introduction to \n Algorithms, by Cormen, Leiserson, Rivest, Stein, 3rd Ed.\n\"\"\"\n\nimport sys\nimport re\nimport time\nfrom pprint import pprint\n\ngraphRE=re.compile(\"(\\\\d+)\\\\s(\\\\d+)\")\nedgeRE=re.compile(\"(\\\\d+)\\\\s(\\\\d+)\\\\s(-?\\\\d+)\")\n\nvertices=[]\nedges=[]\n\ndef init_single_source(G,s):\n \"\"\"\n INITIALIZE-SINGLE-SOURCE Algorithm\n\n G is a graph in format ([s0,s1...],[[tw0],[tw1],..]), where s is a source \n vertex and tw is a weight associated with the destination vertex t at that \n particular index\n \"\"\"\n # INITIALIZE-SINGLE-SOURCE(G,s) pseudocode\n # for each vertex v in G.V\n # v.d = inf\n # v.pi = NIL\n # s.d = 0\n\n V = 0\n d = []\n pi = []\n for v in G[V]:\n d.append(float('inf'))\n pi.append(None)\n # Check to make sure s actually exists\n if s >= len(d):\n print('Source vertex does not exist in the graph!')\n sys.exit(1)\n d[s] = 0\n return d,pi\n\ndef relax(u,v,w,d,pi):\n \"\"\"\n RELAX Algorithm\n\n u,v,w are ints\n d,pi are lists\n \"\"\"\n # RELAX(u,v,w) pseudocode\n # if v.d > u.d + w(u,v)\n # v.d = u.d + w(u,v)\n # v.pi = u\n\n if d[v] > d[u] + w:\n d[v] = d[u] + w\n pi[v] = u\n\n # Debug prints...\n # print('Relaxing u:' + str(u) + ' v:' + str(v) + ' w:' + str(w))\n # print('State of d:')\n # print(d)\n # print('State of pi:')\n # print(pi)\n return\n\ndef dump_bellman_output(d,pi,s,pp):\n \"\"\"\n Make a pretty return list for the assignment return requirements\n\n d is a list of distances from s to t (where t is the index of the list)\n pi is a list of predecessors, where the index is the predecessor to the \n corresponding t in list d\n \"\"\"\n # skip vertex 0, since it already is the source and will have no \n # corresponding source (predecessor), and thusly no weight\n # for i in range(1,len(d)):\n for i in range(len(d)):\n # ghetto fix to bring inf back, not sure where or why they become 0\n if d[i] == 0:\n d[i] = float('inf')\n # insert ((s,t),w)\n pp.append(((s,i),d[i]))\n return \n\ndef BellmanFord(G):\n \"\"\"\n Given in assignment, runs the Bellman-Ford Algorithm on the graph\n \"\"\"\n pathPairs=[]\n # Fill in your Bellman-Ford algorithm here\n # The pathPairs list will contain the list of vertex pairs and their \n # weights [((s,t),w),...]\n\n # BELLMAN-FORD(G,w,s) pseudocode\n # INITIALIZE-SINGLE-SOURCE(G,s)\n # for i=1 to |G.V|-1\n # for each edge(u,v) in G.E\n # RELAX(u,v,w)\n # for each edge(u,v) in G.E\n # if v.d > u.d + w(u,v)\n # return false\n # return true\n\n V = 0\n E = 1\n # s = 0\n for s in G[V]:\n d,pi = init_single_source(G,s)\n\n # Debug prints\n # print('Graph')\n # pprint(G)\n # print('D')\n # pprint(d)\n # print('Pi')\n # pprint(pi)\n\n # do for G[V]-1 iterations\n for i in range(len(G[V])-1):\n # do for each s\n for u in G[V]:\n # do for each t\n for v in range(len(G[E])):\n # get weight of edge\n w = G[E][u][v]\n # check if we need to convert the item to an int first\n if type(w) != float:\n w = int(w)\n relax(u,v,w,d,pi)\n\n # check for negative-weight cycle\n for u in G[V]:\n for v in range(len(G[E])):\n w = G[E][u][v]\n if type(w) != float:\n w = int(w)\n if d[v] > d[u] + w:\n print('Negative-weight cycle found!')\n return False\n\n dump_bellman_output(d,pi,s,pathPairs)\n\n # Debug print\n print('Bellman-Ford Path Pairs:')\n print(pathPairs)\n\n return pathPairs\n\ndef init_matrix(n):\n row = []\n m = []\n for i in range(n):\n row.append(float('inf'))\n for i in range(n):\n m.append(row)\n return m\n\ndef clean_ints(m):\n \"\"\"\n Convert strings representing ints to actual ints, looks like type will be \n more tedious in F-W\n \"\"\"\n clean = m\n for i,row in enumerate(m):\n for j,val in enumerate(row):\n if type(clean[i][j]) != float:\n clean[i][j] = int(clean[i][j])\n return clean\n\ndef pretty_up_floyd(m,pp):\n \"\"\"\n Builds the pretty pathPairs list of vertex pairs and their weights\n \"\"\"\n for s,row in enumerate(m):\n for t,w in enumerate(row):\n # ghetto fix to bring inf back, not sure where or why they become \n # 10 this time, sigh...\n if w == 10:\n w = float('inf')\n pp.append(((s,t),w))\n return\n\ndef FloydWarshall(G):\n \"\"\"\n Given in assignment, runs the Floyd-Warshall Algorithm on the graph\n \"\"\"\n pathPairs=[]\n # Fill in your Floyd-Warshall algorithm here\n # The pathPairs list will contain the list of vertex pairs and their \n # weights [((s,t),w),...]\n\n # FLOYD-WARSHALL(W) pseudocode\n # n = W.rows\n # D^0 = W\n # for k = 1 to n\n # let D^k = d_{ij}^k be a new n x n matrix\n # for i = 1 to n\n # for j = 1 to n\n # d_{ij}^k = min(d_{ij}^(k-1),d_{ik}^(k-1) + d_{kj}^(k-1))\n # return D^n\n\n # establish length of n\n n = len(G[1])\n # convert string ints to actual ints we can use later\n D0 = clean_ints(G[1])\n for k in range(n):\n # Dk = init_matrix(n) # didn't end up needing this...\n for i in range(n):\n for j in range(n):\n # set D0 to the minimum\n if D0[i][j] > D0[i][k] + D0[k][j]:\n D0[i][j] = D0[i][k] + D0[k][j]\n\n pretty_up_floyd(D0,pathPairs)\n\n V = 0\n E = 1\n # check for negative-weight cycle\n for d in D0:\n for u in G[V]:\n for v in range(len(G[E])):\n w = G[E][u][v]\n if type(w) != float:\n w = int(w)\n if d[v] > d[u] + w:\n print('Negative-weight cycle found!')\n return False\n\n # Debug print\n print('Floyd-Warshall Path Pairs:')\n print(pathPairs)\n\n return pathPairs\n\ndef readFile(filename):\n \"\"\"\n Given in assignment, reads a formatted file and spits out a graph structure\n \"\"\"\n global vertices\n global edges\n # File format:\n # <# vertices> <# edges>\n # \n # ...\n inFile=open(filename,'r')\n line1=inFile.readline()\n graphMatch=graphRE.match(line1)\n if not graphMatch:\n print(line1+\" not properly formatted\")\n quit(1)\n vertices=list(range(int(graphMatch.group(1))))\n edges=[]\n for i in range(len(vertices)):\n row=[]\n for j in range(len(vertices)):\n row.append(float(\"inf\"))\n edges.append(row)\n for line in inFile.readlines():\n line = line.strip()\n edgeMatch=edgeRE.match(line)\n if edgeMatch:\n source=edgeMatch.group(1)\n sink=edgeMatch.group(2)\n if int(source) > len(vertices) or int(sink) > len(vertices):\n print(\"Attempting to insert an edge between \"+source+\" and \"+sink+\" in a graph with \"+vertices+\" vertices\")\n quit(1)\n weight=edgeMatch.group(3)\n edges[int(source)-1][int(sink)-1]=weight\n #Debugging\n #for i in G:\n #print(i)\n return (vertices,edges)\n\ndef main(filename,algorithm):\n algorithm=algorithm[1:]\n G=readFile(filename)\n # G is a tuple containing a list of the vertices, and a list of the edges\n # in the format ((source,sink),weight)\n if algorithm == 'b' or algorithm == 'B':\n BellmanFord(G)\n if algorithm == 'f' or algorithm == 'F':\n FloydWarshall(G)\n if algorithm == \"both\":\n start=time.clock()\n BellmanFord(G)\n end=time.clock()\n BFTime=end-start\n start=time.clock()\n FloydWarshall(G)\n end=time.clock()\n FWTime=end-start\n print(\"Bellman-Ford timing: \"+str(BFTime))\n print(\"Floyd-Warshall timing: \"+str(FWTime))\n\nif __name__ == '__main__':\n if len(sys.argv) < 3:\n print(\"python assignment2.py - \")\n quit(1)\n if len(sys.argv[1]) < 2:\n print('python assignment2.py - ')\n quit(1)\n main(sys.argv[2],sys.argv[1])\n","sub_path":"assignment2/graph_programming.py","file_name":"graph_programming.py","file_ext":"py","file_size_in_byte":8549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"451819436","text":"# _*_coding:utf-8_*_\n\"\"\"\n@author: \n@license: (C) Copyright 2019-, \n@file: \n@date: \n@IDE: PyCharm\n@desc: \n \n\"\"\"\nimport traceback\n\nfrom sklearn import preprocessing\n\nfrom framework.exceptions import ProcessingException\nfrom framework.interfaces import PreprocessorI\n\n\nclass Preprocessor(PreprocessorI):\n\n def __init__(self):\n self.data_dir_name = 'MissMinor999'\n\n def preprocess_data_of(self, df_raw_train_x, df_raw_train_y, df_raw_test_x):\n \"\"\"\n Transform data and then write it into a new data file to prepare for\n training data.\n\n Transformation includes:\n 1. Standardize the value of missing data.\n 2. Converts each element of the classification feature into a\n value that can be computed.\n 3. Reduce the dimension of the data matrix.\n\n !!!Note: that the value of components_number will be dynamically adjusted\n according the training results after the program runs once.\n :param df_raw_train_x:\n :param df_raw_train_y:\n :param df_raw_test_x:\n :return: a tuple, including preprocessed df_train_x, df_train_y, df_test_x\n \"\"\"\n\n try:\n print('******** MissMinor999 is preprocessing data ......')\n\n # 1. Standardizing the value of missing data. Note that the argument 'inplace' of fillna() is\n # False by default, and then this method return a new DataFrame object.\n \n X_train = df_raw_train_x\n X_test = df_raw_test_x\n \n # Label Encoding\n counter = 0\n for f in X_train.columns:\n print(counter)\n if X_train[f].dtype=='object' or X_test[f].dtype=='object': \n lbl = preprocessing.LabelEncoder()\n lbl.fit(list(X_train[f].values) + list(X_test[f].values))\n X_train[f] = lbl.transform(list(X_train[f].values))\n X_test[f] = lbl.transform(list(X_test[f].values)) \n counter = counter + 1\n \n print('Starting Filling na data by -999')\n X_train = df_raw_train_x.fillna('-999')\n X_test = df_raw_test_x.fillna('-999')\n print('Finish Filling na data by -999')\n print('******** MissMinor999 preprocessing data completed.')\n return X_train, df_raw_train_y, X_test\n \n except Exception:\n print(traceback.format_exc())\n raise ProcessingException('(OHE_SVD_50) preprocessing data failed.')\n\n\nif __name__ == '__main__':\n print('Nothing')\n","sub_path":"framework/preprocessing/MissMinor999.py","file_name":"MissMinor999.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"177207271","text":"# Created by zhouwang on 2018/5/31.\n\nfrom .base import BaseRequestHandler, permission\nfrom crontab import CronTab, CronSlices\nimport datetime\nimport re\n\ndef check_argements(handler, pk=None):\n error = {}\n local_log_file_id = handler.get_argument('local_log_file_id', '')\n search_pattern = handler.get_argument('search_pattern', '')\n comment = handler.get_argument('comment', '')\n alert = handler.get_argument('alert', '2')\n crontab_cycle = handler.get_argument('crontab_cycle', '')\n check_interval = handler.get_argument('check_interval', '0')\n trigger_format = handler.get_argument('trigger_format', '')\n dingding_webhook = handler.get_argument('dingding_webhook', '')\n if not local_log_file_id:\n error['local_log_file_id'] = '日志id是必填项'\n\n if not search_pattern:\n error['search_pattern'] = '匹配模式是必填项'\n else:\n try:\n re.search(r'%s' % search_pattern, '')\n except:\n error['search_pattern'] = '不正确的正则表达式'\n else:\n select_sql = 'SELECT id FROM local_log_monitor_item WHERE search_pattern=\"%s\" and local_log_file_id=\"%s\" %s' % \\\n (search_pattern, local_log_file_id, 'and id!=\"%d\"' % pk if pk else '')\n count = handler.mysqldb_cursor.execute(select_sql)\n if count:\n error['search_pattern'] = '匹配模式已存在'\n\n if not comment:\n error['comment'] = '备注为必填项'\n\n if alert != '1' and alert != '2':\n error['alert'] = '告警选择不正确'\n elif alert == '1':\n if not crontab_cycle:\n error['crontab_cycle'] = '检查周期是必填项'\n elif not CronSlices.is_valid(crontab_cycle):\n error['crontab_cycle'] = '格式不正确'\n\n if not check_interval:\n error['check_interval'] = '检查间隔是必填项'\n elif not check_interval.isnumeric():\n error['check_interval'] = '必须为正整数'\n\n if not trigger_format:\n error['trigger_format'] = '触发公式是必填项'\n\n if not dingding_webhook:\n error['dingding_webhook'] = '钉钉webhook是必填项'\n\n data = {\n 'local_log_file_id':local_log_file_id,\n 'search_pattern':search_pattern,\n 'comment':comment,\n 'alert':alert or '2',\n 'crontab_cycle':crontab_cycle,\n 'check_interval':check_interval or '0',\n 'trigger_format':trigger_format,\n 'dingding_webhook':dingding_webhook\n }\n return error, data\n\n\ndef add_valid(func):\n def _wrapper(self):\n error, self.reqdata = check_argements(self)\n if error:\n return {'code': 400, 'msg': 'Bad POST data', 'error': error}\n return func(self)\n return _wrapper\n\n\ndef query_valid(func):\n def _wrapper(self, pk):\n error = {}\n if not pk and self.request.arguments:\n argument_keys = self.request.arguments.keys()\n query_keys = ['id', 'local_log_file_id', 'search_pattern', 'alert', 'crontab_cycle','check_interval',\n 'trigger_format', 'dingding_webhook', 'comment', 'create_time']\n error = {key:'参数不可用' for key in argument_keys if key not in query_keys}\n if error:\n return {'code': 400, 'msg': 'Bad GET param', 'error': error}\n return func(self, pk)\n return _wrapper\n\n\ndef update_valid(func):\n def _wrapper(self, pk):\n select_sql = 'SELECT id FROM local_log_monitor_item WHERE id=\"%d\"' % pk\n count = self.mysqldb_cursor.execute(select_sql)\n if not count:\n return {'code': 404, 'msg': 'Update row not found'}\n error, self.reqdata = check_argements(self, pk)\n if error:\n return {'code': 400, 'msg': 'Bad PUT param', 'error': error}\n return func(self, pk)\n return _wrapper\n\ndef del_valid(func):\n def _wrapper(self, pk):\n select_sql = 'SELECT id FROM local_log_monitor_item WHERE id=\"%d\"' % pk\n count = self.mysqldb_cursor.execute(select_sql)\n if not count:\n return {'code': 404, 'msg': 'Delete row not found'}\n return func(self, pk)\n return _wrapper\n\n\nclass LocalLogMonitorItem():\n def __init__(self):\n self.reqdata = {}\n\n @add_valid\n def _add(self):\n insert_sql = '''\n INSERT INTO \n local_log_monitor_item (\n local_log_file_id, \n search_pattern, \n alert, \n crontab_cycle,\n check_interval, \n trigger_format, \n dingding_webhook, \n create_time, \n comment) \n VALUES (\"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"%s\")\n ''' % (self.reqdata['local_log_file_id'],\n self.reqdata['search_pattern'],\n self.reqdata['alert'],\n self.reqdata['crontab_cycle'],\n self.reqdata['check_interval'],\n self.reqdata['trigger_format'],\n self.reqdata['dingding_webhook'],\n datetime.datetime.now().strftime('%Y-%m-%d %H:%M'),\n self.reqdata['comment'])\n try:\n self.mysqldb_cursor.execute(insert_sql)\n self.mysqldb_conn.commit()\n except Exception as e:\n self.mysqldb_conn.rollback()\n return {'code': 500, 'msg':'Add failed, %s' % str(e)}\n else:\n self.mysqldb_cursor.execute('SELECT LAST_INSERT_ID() as id')\n return {'code': 200, 'msg':'Add successful', 'data': self.mysqldb_cursor.fetchall()}\n\n @update_valid\n def _update(self, pk):\n update_sql = '''\n UPDATE \n local_log_monitor_item \n SET \n local_log_file_id=\"%s\",\n search_pattern=\"%s\", \n alert=\"%s\",\n crontab_cycle=\"%s\",\n check_interval=\"%s\",\n trigger_format=\"%s\",\n dingding_webhook=\"%s\",\n comment=\"%s\"\n WHERE id=\"%d\"\n ''' % (self.reqdata['local_log_file_id'],\n self.reqdata['search_pattern'],\n self.reqdata['alert'],\n self.reqdata['crontab_cycle'],\n self.reqdata['check_interval'],\n self.reqdata['trigger_format'],\n self.reqdata['dingding_webhook'],\n self.get_argument('comment'), pk)\n try:\n self.mysqldb_cursor.execute(update_sql)\n self.mysqldb_conn.commit()\n except Exception as e:\n self.mysqldb_conn.rollback()\n return {'code': 500, 'msg': 'Update failed, %s' % str(e)}\n else:\n return {'code': 200, 'msg': 'Update successful', 'data': {'id': pk}}\n\n\n @query_valid\n def _query(self, pk):\n select_sql = '''\n SELECT\n id,\n local_log_file_id,\n search_pattern,\n alert,\n crontab_cycle,\n check_interval,\n trigger_format,\n dingding_webhook, \n date_format(create_time, \"%%Y-%%m-%%d %%H:%%i:%%s\") as create_time,\n comment \n FROM local_log_monitor_item\n %s\n ''' % self.format_where_param(pk, self.request.arguments)\n self.mysqldb_cursor.execute(select_sql)\n results = self.mysqldb_cursor.fetchall()\n return {'code': 200, 'msg': 'Query successful', 'data': results}\n\n @del_valid\n def _del(self, pk):\n delete_sql = 'DELETE FROM local_log_monitor_item WHERE id=\"%d\"' % pk\n try:\n self.mysqldb_cursor.execute(delete_sql)\n self.mysqldb_conn.commit()\n except Exception as e:\n self.mysqldb_conn.rollback()\n return {'code': 500, 'msg': 'Delete failed, %s' % str(e)}\n else:\n return {'code': 200, 'msg': 'Delete successful'}\n\n\nclass Handler(BaseRequestHandler, LocalLogMonitorItem):\n @permission(role=2)\n def post(self):\n response_data = self._add()\n self._write(response_data)\n\n @permission()\n def get(self, pk=0):\n response_data = self._query(int(pk))\n self._write(response_data)\n\n @permission(role=2)\n def put(self, pk=0):\n response_data = self._update(int(pk))\n self._write(response_data)\n\n @permission(role=2)\n def delete(self, pk=0):\n response_data = self._del(int(pk))\n self._write(response_data)","sub_path":"handlers/local_log_monitor_item.py","file_name":"local_log_monitor_item.py","file_ext":"py","file_size_in_byte":8676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"37664042","text":"from flask_marshmallow import Marshmallow\nfrom models import Artist, Album, Track\n\nma = Marshmallow()\n\n\nclass ArtistSchema(ma.SQLAlchemyAutoSchema):\n class Meta:\n model = Artist\n load_instance = True\n fields = (\"id\", \"name\", \"age\", \"albums\", \"tracks\", \"self\")\n\nartist_schema = ArtistSchema()\nartists_schema = ArtistSchema(many=True) \n\n\nclass AlbumSchema(ma.SQLAlchemyAutoSchema):\n class Meta:\n model = Album\n load_instance = True\n fields = (\"id\", \"artist_id\", \"name\", \"genre\", \"artist\", \"tracks\", \"self\")\n\nalbum_schema = AlbumSchema()\nalbums_schema = AlbumSchema(many=True) \n\n\nclass TrackSchema(ma.SQLAlchemyAutoSchema):\n class Meta:\n model = Track\n load_instance = True\n fields = (\"id\", \"album_id\", \"name\", \"duration\", \"times_played\", \"artist\", \"album\", \"self\")\n\ntrack_schema = TrackSchema()\ntracks_schema = TrackSchema(many=True) ","sub_path":"schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"382947857","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 12 14:20:16 2015\n\n@author: Ferriss\n\nMy data: samples, FTIR spectra, and experiments\nClass and function definitions are in pynams.py \n\nSee Ferriss et al. 2015 American Mineralogist for description.\n\"\"\"\nfrom pynams import *\n#from backup_FTIR import *\n\ndefault_folder = 'C:\\\\Users\\\\Ferriss\\\\Documents\\\\FTIR\\\\'\ndefault_savefolder = 'C:\\\\Users\\\\Ferriss\\\\Documents\\\\CpxPaper\\\\Figures\\\\'\n\n\nclass Sample_Kunlun(Block):\n mineral_name = 'diopside'\n source = 'AMNH, Kunlun Mts China'\n initial_water = 34\n\nK4 = Sample_Kunlun()\nK4.twoA_list = 7000\nK4.twoB_list = [2185, 2190, 2188, 2185, 2188]\nK4.twoC_list = [1546, 1551, 1536, 1548, 1548]\nK4.sample_thick_microns = get_3thick(K4)\n\nK6 = Sample_Kunlun()\nK6.twoA_list = [6912, 6913, 6917, 6913, 6917]\nK6.twoB_list = [2731, 2739, 2741, 2723, 2705]\nK6.twoC_list = [1524, 1511, 1500, 1488, 1517]\nK6.sample_thick_microns = get_3thick(K6)\n \n\nclass SpecKunlun_RaypathC(Spectrum):\n base_low_wn = 3500\n\n# Kunlun K4adcI\nprofile_K4_init_A = Profile()\nprofile_K4_init_A.sample = K4\nprofile_K4_init_A.fname_list = [\"K4_adcIL\", \"K4_dcImid\", \"K4_adcIr4\"]\nprofile_K4_init_A.spectrum_class_name = SpecKunlun_RaypathC\nprofile_K4_init_A.direction = 'a'\nprofile_K4_init_A.raypath = 'c'\nleng = profile_K4_init_A.set_len()\nprofile_K4_init_A.positions_microns = np.array([1000, leng/2.0, leng-1000])\nprofile_K4_init_A.name_profile = 'K4 initial || a'\n\n# Kunlun K4cdbI\nprofile_K4_init_C = Profile()\nprofile_K4_init_C.sample = K4\nprofile_K4_init_C.fname_list = ['K4_cdbIleft', 'K4_cdbImid', 'K4_cdbIright']\nprofile_K4_init_C.direction = 'c'\nprofile_K4_init_C.raypath = 'b'\nleng = profile_K4_init_C.set_len()\nprofile_K4_init_C.positions_microns = [leng/6.0, leng/2.0, leng*(5.0/6.0)]\n\n# Kunlun K4adcH\nprofile_K4_904C_1hr_A = Profile()\nprofile_K4_904C_1hr_A.sample = K4\nprofile_K4_904C_1hr_A.direction = 'a'\nprofile_K4_904C_1hr_A.raypath = 'c'\nprofile_K4_904C_1hr_A.name_profile = 'Kunlun heated 1 hour at 904 $\\degree$C'\nprofile_K4_904C_1hr_A.fname_list = ['K4h_adc01i', 'K4h_adc02', 'K4h_adc15',\n 'K4h_adc22', 'K4h_adc29', 'K4h_dcMID',\n 'K4h_adc42', 'K4h_adc56', 'K4h_adc66',\n 'K4h_adc68i', 'K4h_adc68e']\nprofile_K4_904C_1hr_A.spectrum_class_name = SpecKunlun_RaypathC\nleng = profile_K4_904C_1hr_A.set_len()\nprofile_K4_904C_1hr_A.positions_microns = [100, 200, 1500, 2200, 2900, \n leng/2.0, 4200, 5600, 6600, \n leng-150, leng-100]\ndel profile_K4_904C_1hr_A.positions_microns[-2:]\ndel profile_K4_904C_1hr_A.fname_list[-2:]\nprofile_K4_904C_1hr_A.initial_profile = profile_K4_init_A\n\n\n# Kunlun K4adcF\nprofile_K4_904C_154hr_A = Profile()\nprofile_K4_904C_154hr_A.sample = K4\nprofile_K4_904C_154hr_A.direction = 'a'\nprofile_K4_904C_154hr_A.raypath = 'c'\nprofile_K4_904C_154hr_A.name_profile = (\n 'Kunlun heated 154 hours at 904 $\\degree$C || a' )\nprofile_K4_904C_154hr_A.fname_list = ['K4f_adc01', 'K4f_adc02', 'K4f_adc04',\n 'K4f_adc06', 'K4f_adc08', 'K4f_adc11',\n 'K4f_adc14', 'K4f_adc18', 'K4f_adc22',\n 'K4f_adc25', 'K4f_adc29', 'K4f_adc35',\n 'K4f_adc42', 'K4f_adc49', 'K4f_adc56',\n 'K4f_adc61', 'K4f_adc67', 'K4f_adc68']\nprofile_K4_904C_154hr_A.positions_microns = [100, 200, 400, 600, 800, 1100,\n 1400, 1800, 2200, 2500, 2900, \n 3500, 4200, 4900, 5600, 6100,\n 6700, 6800]\nprofile_K4_904C_154hr_A.spectrum_class_name = SpecKunlun_RaypathC\n \n# Kunlun K4cdbF\nprofile_K4_904C_154hr_C = Profile()\nprofile_K4_904C_154hr_C.sample = K4\nprofile_K4_904C_154hr_C.direction = 'c'\nprofile_K4_904C_154hr_C.raypath = 'b'\nprofile_K4_904C_154hr_C.name_profile = (\n 'Kunlun heated 154 hours at 904 $\\degree$C || c')\nprofile_K4_904C_154hr_C.fname_list = ['K4f_cdb01', 'K4f_cdb02', 'K4f_cdb03',\n 'K4f_cdb04', 'K4f_cdb05', 'K4f_cdb06',\n 'K4f_cdb07', 'K4f_cdb09', 'K4f_cdb11',\n 'K4f_cdb12', 'K4f_cdb13', 'K4f_cdb14',\n 'K4f_cdb16']\nleng = profile_K4_904C_154hr_C.set_len()\nprofile_K4_904C_154hr_C.positions_microns = [50, 120, 300, 400, 500, 600, 700,\n 900, 1100, 1200, 1300, 1400, \n leng-50]\n# Stuff under this if statement doesn't get imported with my_spectra\n#if __name__ == '__main__':\n# pass\n\n#\n# K6: I have polarized measurements for\n#\nclass SpecK6(Spectrum):\n sample = K6\n\n\nclass SpecK6_db(SpecK6):\n raypath = 'b'\n thick_microns = K6.sample_thick_microns[1]\n\n\nclass SpecK6_dc(SpecK6):\n raypath = 'c'\n thick_microns = K6.sample_thick_microns[2]\n\nK6_db_Ea = SpecK6_db()\nK6_db_Ea.fname = 'K6_db_Ea'\nK6_db_Ea.polar = 'E || a'\n\nK6_db_Ea_2 = SpecK6_db()\nK6_db_Ea_2.fname = 'K6_db_Ea_2'\nK6_db_Ea_2.polar = 'E || a'\n\nK6_dc_Ea = SpecK6_dc()\nK6_dc_Ea.fname = 'K6_dc_Ea'\nK6_dc_Ea.polar = 'E || a'\n\nK6_dc_Ea_2 = SpecK6_dc()\nK6_dc_Ea_2.fname = 'K6_dc_Ea_2'\nK6_dc_Ea_2.polar = 'E || a'\n\nK6_dc_Eb = SpecK6_dc()\nK6_dc_Eb.fname = 'K6_dc_Eb'\nK6_dc_Eb.polar = 'E || b'\n\nK6_dc_Eb_2 = SpecK6_dc()\nK6_dc_Eb_2.fname = 'K6_dc_Eb_2'\nK6_dc_Eb_2.polar = 'E || b'\n\nK6_db_Ec_2 = SpecK6_db()\nK6_db_Ec_2.fname = 'K6_db_Ec_2'\nK6_db_Ec_2.polar = 'E || c'\n\nmake_filenames()\n\nave_K6_Ea = SpecK6()\nave_K6_Ea.other_name = 'Average of 4 initial K6 spectra, E || a'\nave_K6_Ea.make_average_spectra([K6_db_Ea, K6_db_Ea_2, K6_dc_Ea, K6_dc_Ea_2])\nave_K6_Ea.polar = 'E || a'\nave_K6_Ea.fname = 'ave_K6_Ea'\nave_K6_Ea.base_low_wn = 3200\nave_K6_Ea.base_high_wn = 3770\nave_K6_Ea.base_mid_wn = 3500\nave_K6_Ea.base_mid_yshift = 0.09\nave_K6_Ea.base_w_small = 0.035\nave_K6_Ea.base_w_large = 0.035\n\nave_K6_Eb = SpecK6()\nave_K6_Eb.other_name = 'Average of 2 initial K6 spectra, E || b'\nave_K6_Eb.make_average_spectra([K6_dc_Eb, K6_dc_Eb_2])\nave_K6_Eb.polar = 'E || b'\nave_K6_Eb.fname = 'ave_K6_Eb'\nave_K6_Eb.base_low_wn = 3200\nave_K6_Eb.base_high_wn = 3700\nave_K6_Eb.base_mid_wn = 3450\nave_K6_Eb.base_mid_yshift = 0.04\nave_K6_Eb.base_w_small = 0.035\nave_K6_Eb.base_w_large = 0.035\n\nave_K6_Ec = SpecK6()\nave_K6_Ec.other_name = 'Initial K6 spectrum with E || c'\nave_K6_Ec.make_average_spectra([K6_db_Ec_2])\nave_K6_Ec.polar = 'E || c'\nave_K6_Ec.fname = 'ave_K6_Ec'\nave_K6_Ec.base_low_wn = 3200\nave_K6_Ec.base_high_wn = 3750\nave_K6_Ec.base_mid_wn = 3450\nave_K6_Ec.base_mid_yshift = 0.05\nave_K6_Ec.base_w_small = 0.035\nave_K6_Ec.base_w_large = 0.035\n\n\nclass Sample_Jaipur(Block):\n mineral_name = 'diopside'\n source = 'Stephen Mackwell; north of Jaipur, India'\n initial_water = 26\n\nJ_CurveSnap = Sample_Jaipur()\nJ_CurveSnap.sample_thick_microns = 0.8e6\n \nclass SpecJ_CurveSnap(Spectrum):\n sample = J_CurveSnap\n instrument = 'CurveSnap software - Woods et al. 2000'\n\nJ_Ea = SpecJ_CurveSnap()\nJ_Ea.other_name = 'J_Ea'\nJ_Ea.fname = 'J_Ea'\nJ_Ea.polar = 'a'\nJ_Ea.thick_microns = J_Ea.sample.sample_thick_microns\nJ_Ea.base_low_wn = 3200\nJ_Ea.base_high_wn = 3675\nJ_Ea.base_mid_wn = 3600\nJ_Ea.base_mid_yshift = 0.085\nJ_Ea.base_w_small = 0.01\nJ_Ea.base_w_large = 0.01\n\nJ_Eb = SpecJ_CurveSnap()\nJ_Eb.other_name = 'J_Eb'\nJ_Eb.fname = 'J_Eb'\nJ_Eb.polar = 'b'\nJ_Eb.thick_microns = J_Eb.sample.sample_thick_microns\nJ_Eb.base_low_wn = 3200\nJ_Eb.base_high_wn = 3700\nJ_Eb.base_mid_wn = 3400\nJ_Eb.base_mid_yshift = 0.05\nJ_Eb.base_w_small = 0.04\nJ_Eb.base_w_large = 0.04\n\nJ_Ec = SpecJ_CurveSnap()\nJ_Ec.other_name = 'J_Ec'\nJ_Ec.fname = 'J_Ec'\nJ_Ec.polar = 'c'\nJ_Ec.thick_microns = J_Ec.sample.sample_thick_microns\nJ_Ec.base_low_wn = 3200\nJ_Ec.base_high_wn = 3700\nJ_Ec.base_mid_wn = 3600\nJ_Ec.base_mid_yshift = 0.04\nJ_Ec.base_w_small = 0.025\nJ_Ec.base_w_large = 0.025\n\n# J1: \n# a -> c\n# b -> a\n# c -> b\nJ1 = Sample_Jaipur()\nJ1.twoC_list = [2465, 2460, 2467, 2470, 2452] # originally twoA\nJ1.twoA_list = [4367, 4367, 4366, 4365, 4364] # originally twoB\nJ1.twoB_list = [3218, 3236, 3190, 3232, 3231] # originally twoC\nJ1.sample_thick_microns = get_3thick(J1)\n\nclass ProfileJ1(Profile):\n sample = J1\n \nclass SpecJ1(Spectrum):\n sample = J1\n\nclass SpecJaipur_RaypathA(SpecJ1):\n base_high_wn = 3600\n\n# J1 initial profiles\nprofile_J1_init_A = ProfileJ1()\nprofile_J1_init_A.fname_list = ['J1_dcI_t', 'J1_sp2', 'J1_dcI_bot']\nprofile_J1_init_A.direction = 'a'\nprofile_J1_init_A.raypath = 'b'\nleng = profile_J1_init_A.set_len()\nprofile_J1_init_A.positions_microns = [leng*0.25, leng/2.0, leng*0.75]\n\nprofile_J1_init_B = ProfileJ1()\nprofile_J1_init_B.fname_list = ['J1_sp3']\nprofile_J1_init_B.direction = 'b'\nprofile_J1_init_B.raypath = 'a'\nleng = profile_J1_init_B.set_len()\nprofile_J1_init_B.positions_microns = [leng/2.0]\nprofile_J1_init_B.spectrum_class_name = SpecJaipur_RaypathA\n\nprofile_J1_init_C = ProfileJ1()\nprofile_J1_init_C.fname_list = ['J1_dcI_L', 'J1_sp2', 'J1_dcI_R']\nprofile_J1_init_C.direction = 'c'\nprofile_J1_init_C.raypath = 'b'\nleng = profile_J1_init_C.set_len()\nprofile_J1_init_C.positions_microns = [leng*0.25, leng/2.0, leng*0.75]\n\n# J1 after heating 30 minutes at 904 C \nprofile_J1_904C_30m_A = ProfileJ1()\nprofile_J1_904C_30m_A.fname_list = ['J1_bdc41', 'J1_bdc40', 'J1_bdc39', \n 'J1_dcMID']\nprofile_J1_904C_30m_A.direction = 'a'\nprofile_J1_904C_30m_A.raypath = 'b'\nleng = profile_J1_904C_30m_A.set_len()\nprofile_J1_904C_30m_A.positions_microns = [4100, 4000, 3900, leng/2.0]\n\nprofile_J1_904C_30m_B = ProfileJ1()\nprofile_J1_904C_30m_B.fname_list = ['J1_cdb25', 'J1_cdb15']\nprofile_J1_904C_30m_B.direction = 'b'\nprofile_J1_904C_30m_B.raypath = 'a'\nleng = profile_J1_904C_30m_B.set_len()\nprofile_J1_904C_30m_B.positions_microns = [2500, leng/2.0]\nprofile_J1_904C_30m_B.spectrum_class_name = SpecJaipur_RaypathA\n\nprofile_J1_904C_30m_C = ProfileJ1()\nprofile_J1_904C_30m_C.fname_list = ['J1_adc01', 'J1_adc02', 'J1_adc25', \n 'J1_adc24']\nprofile_J1_904C_30m_C.direction = 'c'\nprofile_J1_904C_30m_C.raypath = 'b'\n\nmake_all_specta_lists()\n\nclass PMR(ThinSlab):\n initial_water = 268\n mineral_name = 'augite'\n source = 'David Bell'\n\nPMR_CurveSnap = PMR()\nPMR_CurveSnap.sample_thick_microns = 1e4\n\nclass SpecP_CurveSnap(Spectrum):\n sample = PMR_CurveSnap\n instrument = 'CurveSnap software - Bell et al. 1995(?)'\n\nPMR_Ea = SpecP_CurveSnap()\nPMR_Ea.other_name = 'PMR_Ea'\nPMR_Ea.fname = 'PMR_Ea'\nPMR_Ea.polar = 'E || alpha'\nPMR_Ea.thick_microns = PMR_Ea.sample.sample_thick_microns\nPMR_Ea.base_low_wn = 3200\nPMR_Ea.base_high_wn = 3700\nPMR_Ea.base_mid_wn = 3500\nPMR_Ea.base_mid_yshift = 1.65\nPMR_Ea.base_w_small = 0.3\nPMR_Ea.base_w_large = 0.3\n\nPMR_Eb = SpecP_CurveSnap()\nPMR_Eb.other_name = 'PMR_Eb'\nPMR_Eb.fname = 'PMR_Eb'\nPMR_Eb.polar = 'E || beta'\nPMR_Eb.thick_microns = PMR_Eb.sample.sample_thick_microns\nPMR_Eb.base_low_wn = 3200\nPMR_Eb.base_high_wn = 3750\nPMR_Eb.base_mid_wn = 3400\nPMR_Eb.base_mid_yshift = 0.3\nPMR_Eb.base_w_small = 0.3\nPMR_Eb.base_w_large = 0.35\n\nPMR_Ec = SpecP_CurveSnap()\nPMR_Ec.other_name = 'PMR_Ec'\nPMR_Ec.fname = 'PMR_Ec'\nPMR_Ec.polar = 'E || gamma'\nPMR_Ec.thick_microns = PMR_Ec.sample.sample_thick_microns\nPMR_Ec.base_low_wn = 3200\nPMR_Ec.base_high_wn = 3700\nPMR_Ec.base_mid_wn = 3400\nPMR_Ec.base_mid_yshift = 0.3\nPMR_Ec.base_w_small = 0.3\nPMR_Ec.base_w_large = 0.35\n\n\nPULI_PMR53_alpha = Spectrum()\nPULI_PMR53_alpha.fname = \"PULI_PMR53_alpha\"\nPULI_PMR53_alpha.thick_microns = 1e3\n\nPULI_PMR53_alpha_baseline = Spectrum()\nPULI_PMR53_alpha_baseline.fname = 'PULI_PMR53_alpha_baseline'\nPULI_PMR53_alpha_baseline.thick_microns = 1e3\n\nPULI_PMR53_beta = Spectrum()\nPULI_PMR53_beta.fname = \"PULI_PMR53_beta\"\nPULI_PMR53_beta.thick_microns = 1e3\n\nPULI_PMR53_beta_baseline = Spectrum()\nPULI_PMR53_beta_baseline.fname = 'PULI_PMR53_beta_baseline'\nPULI_PMR53_beta_baseline.thick_microns = 1e3\n\nPULI_PMR53_gamma = Spectrum()\nPULI_PMR53_gamma.fname = \"PULI_PMR53_gamma\"\nPULI_PMR53_gamma.thick_microns = 1e3\n\nPULI_PMR53_gamma_baseline = Spectrum()\nPULI_PMR53_gamma_baseline.fname = 'PULI_PMR53_gamma_baseline'\nPULI_PMR53_gamma_baseline.thick_microns = 1e3\n\nPMR_alpha_both = Profile()\nPMR_beta_both = Profile()\nPMR_gamma_both = Profile()\nPMR_alpha_both.spectra_list = [PULI_PMR53_alpha, PULI_PMR53_alpha_baseline]\nPMR_beta_both.spectra_list = [PULI_PMR53_beta, PULI_PMR53_beta_baseline]\nPMR_gamma_both.spectra_list = [PULI_PMR53_gamma, PULI_PMR53_gamma_baseline]\n\nmake_filenames()\n\nANU = ThinSlab()\nANU.mineral_name = 'clinopyroxene'\nANU.sample_thick_microns = 1235\n\n\nclass SpecANU(Spectrum):\n sample = ANU\n thick_microns = ANU.sample_thick_microns\n polar = 'unpolarized'\n instrument = 'ANU (Penny King)'\n\nANU_pt57 = SpecANU()\nANU_pt57.fname = 'ANU-pt57'\nANU_pt57.other_name = 'CIP98-62-5'\n\nmake_filenames()\n","sub_path":"my_spectra.py","file_name":"my_spectra.py","file_ext":"py","file_size_in_byte":13099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"637942235","text":"from django.conf import settings\nfrom django.conf.urls import include, url\nfrom django.contrib import admin, messages\nfrom django.contrib.auth.views import logout as auth_logout\nfrom django.contrib.auth.signals import user_logged_in, user_logged_out\nfrom django.dispatch import receiver\nfrom django.utils.translation import ugettext as _\nfrom django.views.generic.base import RedirectView\n\nimport tournaments.views\n\nadmin.autodiscover()\n\n# ==============================================================================\n# Base Patterns\n# ==============================================================================\n\nurlpatterns = [\n\n # Indices\n url(r'^$',\n tournaments.views.PublicSiteIndexView.as_view(),\n name='tabbycat-index'),\n url(r'^start/',\n tournaments.views.BlankSiteStartView.as_view(),\n name='blank-site-start'),\n url(r'^create/',\n tournaments.views.CreateTournamentView.as_view(),\n name='tournament-create'),\n url(r'^load-demo/',\n tournaments.views.LoadDemoView.as_view(),\n name='load-demo'),\n\n # Top Level Pages\n url(r'^donations/',\n tournaments.views.DonationsView.as_view(),\n name='donations'),\n url(r'^style/$',\n tournaments.views.StyleGuideView.as_view(),\n name='style-guide'),\n\n # Admin area\n url(r'^jet/',\n include('jet.urls', 'jet')),\n url(r'^database/',\n include(admin.site.urls)),\n\n # Accounts\n url(r'^accounts/logout/$',\n auth_logout,\n {'next_page': '/'}, # override to specify next_page\n name='logout'),\n url(r'^accounts/',\n include('django.contrib.auth.urls')),\n\n # Favicon for old browsers that ignore links and always load via root\n url(r'^favicon\\.ico$',\n RedirectView.as_view(url='/static/favicon.ico')),\n\n # Tournament URLs\n url(r'^(?P[-\\w_]+)/',\n include('tournaments.urls')),\n\n # Draws Cross Tournament\n url(r'^draw/',\n include('draw.urls_crosst'))\n]\n\nif settings.DEBUG and settings.ENABLE_DEBUG_TOOLBAR: # Only serve debug toolbar when on DEBUG\n import debug_toolbar\n urlpatterns.append(url(r'^__debug__/', include(debug_toolbar.urls)))\n\n\n# ==============================================================================\n# Logout/Login Confirmations\n# ==============================================================================\n\n# These messages don't always work properly with unit tests, so set fail_silently=True\n\n@receiver(user_logged_out)\ndef on_user_logged_out(sender, request, **kwargs):\n if kwargs.get('user'):\n messages.info(request,\n _(\"Later, %(username)s — you were logged out!\") % {'username': kwargs['user'].username},\n fail_silently=True)\n else: # should never happen, but just in case\n messages.info(request, _(\"Later! You were logged out!\"), fail_silently=True)\n\n\n@receiver(user_logged_in)\ndef on_user_logged_in(sender, request, **kwargs):\n if kwargs.get('user'):\n messages.info(request,\n _(\"Hi, %(username)s — you just logged in!\") % {'username': kwargs['user'].username},\n fail_silently=True)\n else: # should never happen, but just in case\n messages.info(request, _(\"Welcome! You just logged in!\"), fail_silently=True)\n\n\n# ==============================================================================\n# Redirect Method\n# ==============================================================================\n\ndef redirect(view):\n from django.http import HttpResponseRedirect\n from django.core.urlresolvers import reverse\n\n def foo(request):\n return HttpResponseRedirect(reverse(view))\n\n return foo\n","sub_path":"tabbycat/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"388507642","text":"import argparse\nimport os\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torch.autograd import Variable\nparser = argparse.ArgumentParser()\nparser.add_argument('--total_epochs', type=int, default=200, help='number of epochs of training')\nparser.add_argument('--batchsize', type=int, default=64, help='size of the batches')\nparser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')\nparser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')\nparser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')\nparser.add_argument('--z_dim', type=int, default=62, help='dimensionality of the latent space')\nparser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')\nparser.add_argument('--channels', type=int, default=1, help='number of image channels')\nparser.add_argument('--sample_interval', type=int, default=400, help='number of image channels')\nopt = parser.parse_args()\nimg_shape = (opt.channels, opt.img_size, opt.img_size)\ndef weights_init_normal(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n elif classname.find('BatchNorm2d') != -1:\n torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n torch.nn.init.constant_(m.bias.data, 0.0)\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator,self).__init__()\n self.init_size =opt.img_size//4\n self.l1 =nn.Sequential(nn.Linear(opt.z_dim,128*self.init_size**2))\n self.gen =nn.Sequential(\n nn.Upsample(scale_factor=2),\n nn.Conv2d(128,128,3,stride=1,padding=1),\n nn.BatchNorm2d(128,0.8),\n nn.LeakyReLU(0.2,inplace=True),\n nn.Upsample(scale_factor=2),\n nn.Conv2d(128,64,3,stride=1,padding=1),\n nn.BatchNorm2d(64,0.8),\n nn.LeakyReLU(0.2,inplace=True),\n nn.Conv2d(64,opt.channels,3,stride=1,padding=1),\n nn.Tanh()\n )\n def forward(self,z):\n out =self.l1(z)\n out =out.view(out.shape[0],128,self.init_size,self.init_size)\n img =self.gen(out)\n return img\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator,self).__init__()\n self.down =nn.Sequential(\n nn.Conv2d(opt.channels,64,3,2,1),\n nn.ReLU()\n )\n self.down_size = (opt.img_size // 2)\n down_dim = 64 * (opt.img_size // 2)**2\n self.embedding = nn.Linear(down_dim, 32)\n self.fc =nn.Sequential(\n nn.BatchNorm1d(32,0.8),\n nn.ReLU(inplace=True),\n nn.Linear(32,down_dim),\n nn.BatchNorm1d(down_dim),\n nn.ReLU(inplace=True)\n )\n self.up =nn.Sequential(\n nn.Upsample(scale_factor=2),\n nn.Conv2d(64,opt.channels,3,1,1)\n )\n def forward(self,img):\n out =self.down(img)\n embedding = self.embedding(out.view(out.size(0), -1))\n out =self.fc(embedding)\n out =self.up(out.view(out.size(0),64,self.down_size,self.down_size))\n return out,embedding\nG =Generator()\nD =Discriminator()\nloss =nn.MSELoss()\nG.cuda()\nD.cuda()\nloss.cuda()\nG.apply(weights_init_normal)\nD.apply(weights_init_normal)\nos.makedirs('../../data/mnist', exist_ok=True)\ndataloader = torch.utils.data.DataLoader(\n datasets.MNIST('../../data/mnist', train=True, download=True,\n transform=transforms.Compose([\n transforms.Resize(opt.img_size),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])),\n batch_size=opt.batchsize, shuffle=True)\noptimizer_G = torch.optim.Adam(G.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\noptimizer_D = torch.optim.Adam(D.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\nlambda_pt = 0.1\nmargin = max(1, opt.batchsize / 64.)\nFloatTensor = torch.cuda.FloatTensor \n\ndef pullaway_loss(embeddings):\n norm = torch.sqrt(torch.sum(embeddings ** 2, -1, keepdim=True))\n normalized_emb = embeddings / norm\n similarity = torch.matmul(normalized_emb, normalized_emb.transpose(1, 0))\n batch_size = embeddings.size(0)\n loss_pt = (torch.sum(similarity) - batch_size) / (batch_size * (batch_size - 1))\n return loss_pt\n\nfor epoch in range(opt.total_epochs):\n for i,(img,_) in enumerate(dataloader):\n real_imgs =Variable(img.type(FloatTensor))\n optimizer_G.zero_grad()\n z = Variable(FloatTensor(np.random.normal(0, 1, (real_imgs.shape[0], opt.z_dim))))\n gen_imgs =G(z)\n img_d,img_embedding =D(gen_imgs)\n g_loss =loss(img_d,gen_imgs.detach())+lambda_pt * pullaway_loss(img_embedding)\n g_loss.backward()\n optimizer_G.step()\n\n optimizer_D.zero_grad()\n real_, _ = D(real_imgs)\n fake_, _ = D(gen_imgs.detach())\n d_loss_real = loss(real_, real_imgs)\n d_loss_fake = loss(fake_, gen_imgs.detach())\n d_loss = d_loss_real\n if (margin - d_loss_fake.data).item() > 0:\n d_loss += margin - d_loss_fake\n d_loss.backward()\n optimizer_D.step()\n print (\"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]\" % (epoch, opt.total_epochs, i, len(dataloader),\n d_loss.item(), g_loss.item()))","sub_path":"EBGAN/EBGAN.py","file_name":"EBGAN.py","file_ext":"py","file_size_in_byte":5623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"400116997","text":"import threading\nimport pyrebase\nimport random\n\nconfig = {\n \"apiKey\": \"AIzaSyA_rPzl1D8YouEsSJ1AjQwElFqH_mxOAFI\",\n \"authDomain\": \"realtime-4a7de.firebaseapp.com\",\n \"databaseURL\": \"https://realtime-4a7de.firebaseio.com\",\n \"projectId\": \"realtime-4a7de\",\n \"storageBucket\": \"realtime-4a7de.appspot.com\",\n \"messagingSenderId\": \"624733681109\",\n \"appId\": \"1:624733681109:web:e26d8881c0194973d6b95c\",\n \"measurementId\": \"G-01BBL7B415\",\n \"serviceAccount\": \"credentials/serviceAccountCredentials.json\"\n}\n\nfirebase = pyrebase.initialize_app(config)\ndb = firebase.database()\n\ndef printit():\n\tthreading.Timer(5.0, printit).start()\n\t\n\tvitals_bp = {\n\t\t'value': random.randint(80, 120)\n\t}\n\tdb.child(\"vitals_bp\").push(vitals_bp)\n\t\n\tvitals_heartrate = {\n\t\t'value': random.randint(60, 120)\n\t}\n\tdb.child(\"vitals_heartrate\").push(vitals_heartrate)\n\t\n\tvitals_pulserate = {\n\t\t'value': random.randint(60, 100)\n\t}\n\tvitals_pulserate = db.child(\"vitals_pulserate\").push(vitals_pulserate)\n\n\tvitals_respiratory = {\n\t\t'value': random.randint(10, 20)\n\t}\n\tvitals_respiratory = db.child(\"vitals_respiratory\").push(vitals_respiratory)\n\n\tvitals_temperature = {\n\t\t\t'value': random.randint(97, 108)\n\t}\n\tvitals_temperature = db.child(\"vitals_temperature\").push(vitals_temperature)\n\tprint(\"pushed\", vitals_bp, vitals_heartrate, vitals_pulserate, vitals_respiratory, vitals_temperature)\nprintit()","sub_path":"python-webserver/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"360390482","text":"class Solution(object):\n def kthSmallest(self, matrix, k):\n \"\"\"\n :type matrix: List[List[int]]\n :type k: int\n :rtype: int\n \"\"\"\n e = []\n for row in matrix:\n e.extend(row)\n e.sort()\n return e[k-1]","sub_path":"Algorithm/Kth Smallest Element in a Sorted Matrix/Kth Smallest Element in a Sorted Matrix.py","file_name":"Kth Smallest Element in a Sorted Matrix.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"412342426","text":"import time\nimport requests\nimport json\nimport sys\n\nb_mini = False\nif len(sys.argv) > 1:\n b_mini = True\n\nurl = 'http://127.0.0.1:5000/hello'\n\nif b_mini: url = 'http://127.0.0.1:5000/'\n\nprint('sending...')\n\nif b_mini:\n t0 = time.time()\n r = requests.get( url)\n print(round(time.time() - t0, 2))\nelse:\n r = requests.post( url, json={\"time\": str(time.time()) } )\n\nprint('done')\n","sub_path":"chess-classification-hw/app/scrap/tclient.py","file_name":"tclient.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"137377909","text":"\n# coding: utf-8\n\n# In[ ]:\n\n\n#PART 1\n\n\n# In[ ]:\n\n\nimport numpy as np\nimport glob\nfrom scipy.io import wavfile\nimport matplotlib.pyplot as plt\nimport librosa\nfrom librosa import display\nimport os\nfrom scipy.fftpack import dct\nimport pickle\nfrom word2number.w2n import word_to_num\nfrom copy import deepcopy\n\n\n# In[ ]:\n\n\ndef hamming(length):\n arr = []\n for i in range(length):\n k = (0.54 - 0.46*np.cos(2*np.pi*i/(length-1)) )\n arr.append(k)\n return np.array(arr)\ndef HzToMel(Hz):\n return (2595*np.log10(1 + Hz/700))\ndef MelToHz(m):\n return (700*( 10**(m/2595) - 1))\n\n\n# In[ ]:\n\n\n# audiofiles = glob.glob(\"./training/one/*.wav\")\n# audiofiles = sorted(audiofiles)\n# np.shape(mfccfeatures)\n\n\n# In[ ]:\n\n\n#Read the signal\nreq = 1\nmfccfeatures = []\nlabels = []\nfolders = os.listdir(\"./training\")\nfor fol in folders:\n namefol = \"./training/\" + fol + \"/*.wav\"\n audiofiles = glob.glob(namefol)\n audiofiles = sorted(audiofiles)\n# noisefiles = glob.glob(\"./_background_noise_/*.wav\")\n for num in range(len(audiofiles)):#len(audiofiles)\n# print(\"----------------------------------------\")\n name = audiofiles[num].split('/')[2]\n# print(name)\n fs, xdata_num = wavfile.read(audiofiles[num])\n# xdata_num, fs = librosa.load(audiofiles[num], sr=None, mono=True, offset=0, duration=None) #fs - sampling freq\n xdata_num = deepcopy(xdata_num)\n# no = np.random.randint(0, len(noisefiles))\n# fsnoise, datanoise = wavfile.read(noisefiles[no])\n# index1 = np.random.randint(0,len(datanoise),size = 100)\n# index2 = np.random.randint(0,len(xdata_num),size = 100)\n# xdata_num[index2] = xdata_num[index2] + datanoise[index1]\n xdata_num = xdata_num/np.max(xdata_num)\n energy = np.sum(xdata_num**2)\n# plt.figure()\n# librosa.display.waveplot(y=xdata_num, sr=fs)\n# plt.show()\n\n #Step 1 - pre emphasis\n alpha = 0.95 #95% of any one sample is presumed to originate from previous sample.\n newx = [0]\n newx.extend(xdata_num)\n# print(xdata_num)\n xpre_num = xdata_num - alpha*np.array(newx[:-1])\n# print(xpre_num)\n\n\n #Step 2 - framing\n time_ms = 20\n winlen = int(time_ms*0.001*fs)\n overlap = 0.5\n reqindices = np.arange(0,len(xpre_num), int((1-overlap)*winlen))\n #if u dont want to pad\n xframes = []\n for i in reqindices[:len(reqindices)-1]:\n xframes.append(xpre_num[i:i+winlen])\n# print(np.shape(xframes))\n #if u want to pad\n req_pad = [0]*(reqindices[len(reqindices)-1] + winlen - len(xpre_num))\n xpre_num = list(xpre_num)\n xpre_num.extend(req_pad)\n xpre_num = np.array(xpre_num)\n xframes = []\n for i in reqindices[:len(reqindices)]:\n xframes.append(xpre_num[i:i+winlen])\n# print(np.shape(xframes))\n\n\n #step 3 - Hamming windowing\n xhamm = xframes*hamming(winlen)\n# print(np.shape(xhamm))\n\n\n #step 4 - FFT\n NFFT = 2048#1024\n xfft = np.fft.rfft(xframes, NFFT)\n xpower = (np.abs(xfft))**2\n# if(np.shape(xpower)[1]>100):\n# xpower = xpower[:,:100]\n# elif(np.shape(fftarr)[1]<100):\n# xpower = np.hstack([xpower, np.zeros((len(xpower),100-np.shape(xpower)[1]))])\n xpower = (1/NFFT)*xpower\n# print(np.shape(xfft))\n# print(np.shape(xpower))\n\n\n #Step 5 - Mel filter bank processing\n low_frq_Hz = 0 \n high_freq_Hz = fs/2\n num_filters = 40\n high_freq_mel = HzToMel(high_freq_Hz)\n filters_mel = np.linspace(0, high_freq_mel, num_filters+2)\n filters_Hz = MelToHz(filters_mel)\n filter_pts = np.floor((NFFT + 1)*filters_Hz / fs).astype(int)\n melfilters = np.zeros((num_filters, int(NFFT/2 + 1)))\n for n in range(1,num_filters+1):\n melfilters[n-1,filter_pts[n-1]:filter_pts[n]] = np.linspace(0, 1,filter_pts[n]-filter_pts[n-1])\n melfilters[n-1,filter_pts[n]:filter_pts[n+1]] = np.linspace(1, 0,filter_pts[n+1]-filter_pts[n])\n# for mel in melfilters:\n# plt.plot(mel)\n# plt.title('Mel Filter Bank')\n# plt.xlabel('Frequency (in Hz)')\n# plt.ylabel('Amplitude')\n# plt.show()\n# x_filtered = np.dot(melfilters, xpower.T)\n x_filtered = np.dot(xpower, melfilters.T)\n x_filtered[x_filtered==0] = np.finfo(float).eps\n energy = np.reshape(np.sum(x_filtered, axis = 1),(len(x_filtered),1))\n x_filtered = 20 * np.log10(x_filtered)\n# print(np.shape(x_filtered))\n\n #Step 6 - DCT (Mel Frequency Cepstrum Coefficient)\n u1 = 0\n u2 = 40\n mfcc = dct(x_filtered, type=2, axis=1, norm='ortho')[:,u1:u2]#[:,u2:u1]\n if(np.shape(mfcc)[1]!=u2):\n mfcc = np.hstack([mfcc,np.zeros((num_filters,u1-np.shape(mfcc)[1]))])\n if(np.shape(mfcc)[0]>100):\n mfcc = mfcc[:100]\n elif(np.shape(mfcc)[0]<100):\n mfcc = np.vstack([mfcc, np.zeros((100-len(mfcc),np.shape(mfcc)[1]))])\n mfccfeatures.append(mfcc)\n labels.append(word_to_num(fol))\n# plt.show()\npickle.dump(mfccfeatures, open('1mfccfeatures.pickle','wb'))\npickle.dump(labels, open('1mfccfeatureslabels.pickle','wb'))\n\n\n# In[ ]:\n\n\n#Read the signal\nreq = 1\nmfccfeatures = []\nlabels = []\nfolders = os.listdir(\"./validation\")\nfor fol in folders:\n namefol = \"./validation/\" + fol + \"/*.wav\"\n audiofiles = glob.glob(namefol)\n audiofiles = sorted(audiofiles)\n for num in range(len(audiofiles)):\n name = audiofiles[num].split('/')[2]\n xdata_num, fs = librosa.load(audiofiles[num], sr=None, mono=True, offset=0, duration=None) #fs - sampling freq\n energy = np.sum(xdata_num**2)\n\n #Step 1 - pre emphasis\n alpha = 0.95\n newx = [0]\n newx.extend(xdata_num)\n xpre_num = xdata_num - alpha*np.array(newx[:-1])\n\n\n #Step 2 - framing\n time_ms = 20\n winlen = int(time_ms*0.001*fs)\n overlap = 0.5\n reqindices = np.arange(0,len(xpre_num), int((1-overlap)*winlen))\n req_pad = [0]*(reqindices[len(reqindices)-1] + winlen - len(xpre_num))\n xpre_num = list(xpre_num)\n xpre_num.extend(req_pad)\n xpre_num = np.array(xpre_num)\n xframes = []\n for i in reqindices[:len(reqindices)]:\n xframes.append(xpre_num[i:i+winlen])\n\n #step 3 - Hamming windowing\n xhamm = xframes*hamming(winlen)\n\n #step 4 - FFT\n NFFT = 2048#1024\n xfft = np.fft.rfft(xframes, NFFT)\n xpower = (np.abs(xfft))**2\n xpower = (1/NFFT)*xpower\n\n\n #Step 5 - Mel filter bank processing\n low_frq_Hz = 0 \n high_freq_Hz = fs/2\n num_filters = 40\n high_freq_mel = HzToMel(high_freq_Hz)\n filters_mel = np.linspace(0, high_freq_mel, num_filters+2)\n filters_Hz = MelToHz(filters_mel)\n filter_pts = np.floor((NFFT + 1)*filters_Hz / fs).astype(int)\n melfilters = np.zeros((num_filters, int(NFFT/2 + 1)))\n for n in range(1,num_filters+1):\n melfilters[n-1,filter_pts[n-1]:filter_pts[n]] = np.linspace(0, 1,filter_pts[n]-filter_pts[n-1])\n melfilters[n-1,filter_pts[n]:filter_pts[n+1]] = np.linspace(1, 0,filter_pts[n+1]-filter_pts[n])\n x_filtered = np.dot(xpower, melfilters.T)\n x_filtered[x_filtered==0] = np.finfo(float).eps\n energy = np.reshape(np.sum(x_filtered, axis = 1),(len(x_filtered),1))\n x_filtered = 20 * np.log10(x_filtered)\n\n #Step 6 - DCT (Mel Frequency Cepstrum Coefficient)\n u1 = 0\n u2 = 40\n mfcc = dct(x_filtered, type=2, axis=1, norm='ortho')[:,u1:u2]\n if(np.shape(mfcc)[1]!=u2):\n mfcc = np.hstack([mfcc,np.zeros((num_filters,u1-np.shape(mfcc)[1]))])\n if(np.shape(mfcc)[0]>100):\n mfcc = mfcc[:100]\n elif(np.shape(mfcc)[0]<100):\n mfcc = np.vstack([mfcc, np.zeros((100-len(mfcc),np.shape(mfcc)[1]))])\n mfccfeatures.append(mfcc)\n labels.append(word_to_num(fol))\n# plt.show()\npickle.dump(mfccfeatures, open('1mfccfeaturesvalidation.pickle','wb'))\npickle.dump(labels, open('1mfccfeaturesvalidationlabels.pickle','wb'))\n# print(np.shape(mfccfeatures))\n\n\n# In[ ]:\n\n\n# np.shape(mfccfeatures)\n\n\n# In[ ]:\n\n\n#PART 2\n\n\n# In[1]:\n\n\nimport numpy as np\nimport pickle\nfrom sklearn import svm\nfrom collections import Counter\nfrom sklearn.multiclass import OneVsRestClassifier\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n\nmfcctrain_x = pickle.load(open(\"1mfccfeatures.pickle\",'rb'))\nmfcctrain_x = np.reshape(mfcctrain_x, (np.shape(mfcctrain_x)[0], np.shape(mfcctrain_x)[1]*np.shape(mfcctrain_x)[2] ))\nmfcctrain_y = pickle.load(open(\"1mfccfeatureslabels.pickle\",'rb'))\n# mfcctrain_y = label_binarize(mfcctrain_y, classes=np.arange(0,10))\nmfccval_x = pickle.load(open(\"1mfccfeaturesvalidation.pickle\",'rb'))\nmfccval_x = np.reshape(mfccval_x, (np.shape(mfccval_x)[0], np.shape(mfccval_x)[1]*np.shape(mfccval_x)[2] ))\nmfccval_y = pickle.load(open(\"1mfccfeaturesvalidationlabels.pickle\",'rb'))\n# mfccval_y = label_binarize(mfccval_y, classes=np.arange(0,10))\n\n\n# In[3]:\n\n\n# classifier = OneVsRestClassifier(svm.LinearSVC())\n# classifier.fit(mfcctrain_x, mfcctrain_y)\nclassifier = pickle.load(open(\"mfcc_40_2048.pickle\",\"rb\"))\n\n\n# In[ ]:\n\n\nmfccval_pred = classifier.predict(mfccval_x)\nmfcctrain_pred = classifier.predict(mfcctrain_x)\n\n\n\n# mfccval_pred = np.argmax(mfccval_pred,axis=1)\n# mfccval_y = np.argmax(mfccval_y, axis = 1)\n# mfcctrain_pred = np.argmax(mfcctrain_pred,axis=1)\n# mfcctrain_y = np.argmax(mfcctrain_y, axis = 1)\n\n\n# In[ ]:\n\n\ndef confusion_matrixb(ytrue, ypredict, classes):\n instcount = np.shape(ytrue)[0]\n mtx = np.zeros((classes, classes)).astype(int)\n for i in range(instcount):\n if(ytrue[i]==ypredict[i]):\n mtx[ytrue[i],ytrue[i]]+=1\n else:\n mtx[ytrue[i],ypredict[i]]+=1\n t=[0,1,2,3,4,5,6,7,8,9]\n s=np.array(mtx)\n plt.matshow(s, cmap=plt.cm.gray_r)\n plt.colorbar()\n tick_marks = np.arange(classes)\n plt.xticks(tick_marks, t)\n plt.yticks(tick_marks, t)\n import itertools\n thresh = mtx.max() / 2\n for i, j in itertools.product(range(mtx.shape[0]), range(mtx.shape[1])):\n plt.text(j, i, \"{:,}\".format(mtx[i, j]),horizontalalignment=\"center\",color=\"white\" if mtx[i, j] > thresh else \"black\")\n #plt.savefig('C:/Users/SajagAgarwal/Desktop/WinSem2019/SML/A2/images/Correlated/ConfusionMatrix/Train/risk1.png')\n plt.show()\n return mtx\n\n\n# In[ ]:\n\n\nconfusion_matrix1 = confusion_matrixb(mfccval_y,mfccval_pred, 10)\n\n\n# In[ ]:\n\n\n# confusion_matrix1\n\n\n# In[ ]:\n\n\nconfusion_matrix2 = confusion_matrixb(mfcctrain_y,mfcctrain_pred, 10)\n\n\n# In[ ]:\n\n\n# confusion_matrix2\n\n\n# In[ ]:\n\n\ndef precision_(arr):\n lis = []\n for i in range(len(arr)):\n lis.append(arr[i][i] / np.sum(arr[:,i]))\n lis = [round(x,2) for x in lis]\n return lis\ndef recall_(arr):\n lis = []\n for i in range(len(arr)):\n lis.append(arr[i][i] / np.sum(arr[i,:]))\n lis = [round(x,2) for x in lis]\n return lis\ndef f1score(precision, recall):\n lis = []\n for i in range(len(precision)):\n lis.append((2*precision[i]*recall[i] )/ (precision[i] + recall[i]))\n lis = [round(x,2) for x in lis]\n return lis\n\n\n# In[ ]:\n\n\nprint(precision_(confusion_matrix1))\nprint(recall_(confusion_matrix1))\nprint(f1score(precision_(confusion_matrix1),recall_(confusion_matrix1)))\n\n\n# In[ ]:\n\n\nprint(precision_(confusion_matrix2))\nprint(recall_(confusion_matrix2))\nprint(f1score(precision_(confusion_matrix2),recall_(confusion_matrix2)))\n\n\n# In[ ]:\n\n\ndef average_precision(ytrue, precision):\n a = Counter(ytrue)\n b = len(ytrue)\n count = 0\n for i in range(len(precision)):\n count += precision[i]*a[i]/b\n return round(count,2)\n\n\n# In[ ]:\n\n\nprint(average_precision(mfccval_y, precision_(confusion_matrix1)))\nprint(average_precision(mfccval_y, recall_(confusion_matrix1)))\nprint(average_precision(mfccval_y, f1score(precision_(confusion_matrix1),recall_(confusion_matrix1))))\n\n\n# In[ ]:\n\n\nprint(average_precision(mfcctrain_y, precision_(confusion_matrix2)))\nprint(average_precision(mfcctrain_y, recall_(confusion_matrix2)))\nprint(average_precision(mfcctrain_y, f1score(precision_(confusion_matrix2),recall_(confusion_matrix2))))\n\n\n# In[ ]:\n\n\ndef accuracy(confusion_matrix):\n count = 0\n for i in range(len(confusion_matrix)):\n count += confusion_matrix[i][i]\n return round(count/np.sum(confusion_matrix),2)\nprint(accuracy(confusion_matrix1))\nprint(accuracy(confusion_matrix2))\n\n\n# In[ ]:\n\n\nimport pickle\npickle.dump(classifier,open(\"mfcc_40_2048.pickle\",'wb'))\n\n","sub_path":"question2_41features.py","file_name":"question2_41features.py","file_ext":"py","file_size_in_byte":12749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"191963001","text":"from typing import List\nclass Solution:\n class Node:\n def __init__(self, c):\n self.c = c\n self.children = set()\n\n class Rel:\n def __init__(self, fr, to):\n self.fr = fr\n self.to = to\n\n def alienOrder(self, words: List[str]) -> str:\n self.rels = set()\n self.chars = set()\n\n for w in words:\n for c in w:\n self.chars.add(c)\n\n l = len(words)\n self.nodes = {}\n self.rc = {}\n\n for c in self.chars:\n self.rc[c] = 0\n self.nodes[c] = Solution.Node(c)\n\n for i in range(l):\n for j in range(i + 1, l):\n r = self.cmp(words[i], words[j])\n if r != None:\n if self.nodes[r.to] not in self.nodes[r.fr].children:\n self.rc[r.to] += 1\n self.nodes[r.fr].children.add(self.nodes[r.to])\n\n self.path = \"\"\n self.dfs()\n\n # if len(res) != len(self.chars):\n # return res\n return self.path if self.path != None else \"\"\n\n def dfs(self):\n if self.path == None:\n return\n if 0 == len(self.chars):\n return self.path\n zeros = []\n for c in self.chars:\n if self.rc[c] == 0:\n zeros.append(c)\n\n if len(zeros) == 0: # if there is a loop\n self.path = None\n return\n\n togo = set()\n\n for z in zeros:\n self.path += z\n self.chars.remove(z)\n for n in self.nodes.get(z).children:\n self.rc[n.c] -= 1\n togo.add(n)\n del self.nodes[z]\n self.dfs()\n\n def cmp(self, a, b): # a < b\n l = min(len(a), len(b))\n for i in range(l):\n ca, cb = a[i], b[i]\n if ca != cb:\n return Solution.Rel(ca, cb)\n return None\n\nfor i in range(10, 0, -1):\n print(i)","sub_path":"src/graph/dfs/lc269.py","file_name":"lc269.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"124332800","text":"import os\nimport numpy as np\nimport tecplot as tp\nfrom tecplot.constant import *\n\n# Use interpolation to merge information from two independent zones\nexamples_dir = tp.session.tecplot_examples_directory()\ndatafile = os.path.join(examples_dir, 'SimpleData', 'RainierElevation.plt')\ndataset = tp.data.load_tecplot(datafile)\n# Get list of source zones to use later\nsrczones = list(dataset.zones())\n\nfr = tp.active_frame()\nplot = fr.plot(PlotType.Cartesian2D)\nplot.activate()\nplot.show_contour = True\nplot.show_edge = True\n\n# Show two section of the plot independently\nplot.contour(0).legend.show = False\nplot.contour(1).legend.show = False\nplot.contour(1).colormap_name = 'Diverging - Blue/Red'\nfor scrzone in srczones:\n plot.fieldmap(scrzone).edge.line_thickness = 0.4\nplot.fieldmap(0).contour.flood_contour_group = plot.contour(1)\n\n# export image of original data\ntp.export.save_png('interpolate_2d_source.png', 600, supersample=3)\n\n# use the first zone as the source, and get the range of (x, y)\nxvar = plot.axes.x_axis.variable\nyvar = plot.axes.y_axis.variable\nymin, xmin = 99999,99999\nymax, xmax = -99999,-99999\nfor scrzone in srczones:\n curxmin, curxmax = scrzone.values(xvar.index).minmax()\n curymin, curymax = scrzone.values(yvar.index).minmax()\n ymin = min(curymin,ymin)\n ymax = max(curymax,ymax)\n xmin = min(curxmin,xmin)\n xmax = max(curxmax,xmax)\n\n# create new zone with a coarse grid\n# onto which we will interpolate from the source zone\nxpoints = 100\nypoints = 100\nnewzone = dataset.add_ordered_zone('Interpolated', (xpoints, ypoints))\n\n# setup the (x, y) positions of the new grid\nxx = np.linspace(xmin, xmax, xpoints)\nyy = np.linspace(ymin, ymax, ypoints)\nYY, XX = np.meshgrid(yy, xx, indexing='ij')\nnewzone.values(xvar.index)[:] = XX.ravel()\nnewzone.values(yvar.index)[:] = YY.ravel()\n\n# perform linear interpolation from the source to the new zone\ntp.data.operate.interpolate_linear(newzone, source_zones=srczones)\n\n# show the new zone's data, hide the source\nplot.fieldmap(newzone).show = True\nplot.fieldmap(newzone).contour.show = True\nplot.fieldmap(newzone).contour.flood_contour_group = plot.contour(0)\nplot.fieldmap(newzone).edge.show = True\nplot.fieldmap(newzone).edge.line_thickness = .4\nplot.fieldmap(newzone).edge.color = Color.Orange\n\nfor scrzone in srczones:\n plot.fieldmap(scrzone).show = False\n\n# export image of interpolated data\ntp.export.save_png('interpolate_linear_2d_dest.png', 600, supersample=3)","sub_path":"python/examples/interpolation/interpolation_linear.py","file_name":"interpolation_linear.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"132570804","text":"#!/usr/bin/env python\n\n__author__ = \"monoDrive\"\n__copyright__ = \"Copyright (C) 2018 monoDrive\"\n__license__ = \"MIT\"\n__version__ = \"1.0\"\n\nimport json\nimport logging\nimport signal\nimport sys\nimport threading\nimport time\n\nfrom monodrive import Simulator\nfrom monodrive.configuration import SimulatorConfiguration, VehicleConfiguration\nfrom monodrive.networking import messaging\nfrom monodrive.networking.client import Client\n\n\nLOG_CATEGORY = \"test\"\n\nmillis = lambda: int(round(time.time() * 1000))\n\n\nclass SensorTask:\n def __init__(self, sensor, clock_mode):\n self.sensor = sensor\n self.clock_mode = clock_mode\n self.data_received = threading.Event()\n self.thread = threading.Thread(target=self.run)\n self.thread.start()\n\n def run(self):\n self.running = True\n packets = 0\n start = None\n\n get_time = lambda: data.get('game_time', millis()) \\\n if isinstance(data, dict) and self.clock_mode is not 0 \\\n else millis()\n\n time_units = lambda: 'real time' \\\n if (isinstance(data, dict) and data.get('game_time', None) is None) or self.clock_mode is 0 \\\n else 'game time'\n\n logging.getLogger(LOG_CATEGORY).info('{0} sensor loop start'.format(self.sensor.name))\n\n while self.running and self.sensor.is_sensor_running():\n data = self.sensor.get_message(timeout=.5)\n if data is None:\n continue\n\n if start is None:\n start = get_time()\n\n packets += 1\n\n self.data_received.set()\n\n try:\n seconds = (get_time() - start) / 1000\n fps = packets / seconds\n\n logging.getLogger(LOG_CATEGORY).info(\n '{0: >18}: {1:5.2f} fps ({2:3} frames received in {3:6.2f} secs ({4}))'.format(\n self.sensor.name, fps, packets, seconds, time_units()))\n except:\n pass\n\n def stop(self):\n #logging.getLogger(LOG_CATEGORY).info(\"stopping %s\" % self.sensor.name)\n self.sensor.stop()\n self.running = False\n\n def join(self):\n if self.thread is not None:\n self.thread.join()\n\n\ndef shutdown(sig, frame):\n time.sleep(5)\n sys.exit(0)\n\nif __name__ == \"__main__\":\n signal.signal(signal.SIGINT, shutdown)\n\n logging.basicConfig(level=logging.DEBUG)\n\n simulator_config = SimulatorConfiguration('simulator.json')\n vehicle_config = VehicleConfiguration('test.json')\n\n client = Client((simulator_config.configuration[\"server_ip\"], simulator_config.configuration[\"server_port\"]))\n\n if not client.isconnected():\n client.connect()\n\n simulator = Simulator(client, simulator_config)\n\n sensor_configuration = vehicle_config.sensor_configuration\n vehicle_config.sensor_configuration = []\n vehicle_config.configuration['sensors'] = []\n\n logging.getLogger(LOG_CATEGORY).debug(json.dumps(vehicle_config.configuration))\n simulator.send_configuration()\n simulator.send_vehicle_configuration(vehicle_config)\n\n sensors = []\n idx = 0\n for sensor_config in sensor_configuration:\n cmd = messaging.AttachSensorCommand('ego', sensor_config)\n logging.getLogger(LOG_CATEGORY).debug('--> {0}'.format(json.dumps(cmd.to_json())))\n response = client.request(cmd)\n logging.getLogger(LOG_CATEGORY).debug('<-- {0}'.format(json.dumps(response.to_json())))\n\n sensor_class = vehicle_config.get_class(sensor_config['type'])\n sensors.append(sensor_class(idx, sensor_config, simulator_config))\n idx = idx + 1\n\n tasklist = []\n for sensor in sensors:\n sensor.start()\n sensor.send_start_stream_command(simulator)\n\n for sensor in sensors:\n try:\n st = SensorTask(sensor, vehicle_config.clock_mode)\n tasklist.append(st)\n except Exception as e:\n print(str(e))\n\n for sensor in sensors:\n sensor.socket_ready_event.wait()\n\n time.sleep(30)\n\n for task in tasklist:\n task.sensor.stop_sensor_command(client)\n task.stop()\n\n for sensor in sensors:\n sensor.join()\n\n for task in tasklist:\n task.join()\n\n simulator.stop()","sub_path":"testing/add_sensors.py","file_name":"add_sensors.py","file_ext":"py","file_size_in_byte":4196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"527836572","text":"# Create a py file that encodes and decodes every individual line in textfile.txt\n#DBES\n#Decode bytes encode bytes\nimport sys\nscript, input_encoding, error = sys.argv\n\n\ndef main(textfile, encoding, errors):\n line = textfile.readline()\n if line:\n print_line(line, encoding, errors)\n return main(textfile, encoding, errors)\n\n\ndef print_line(line, encoding, errors):\n next_lang = line.strip()\n byte_string = next_lang.encode(encoding, errors=errors)\n utf_string = byte_string.decode(encoding, errors=errors)\n print(byte_string, \"<====>\", utf_string)\n\nlanguages = open(\"textfile.txt\", encoding=\"utf-8\")\nmain(languages, input_encoding, error)\n","sub_path":"EncodingandDecoding.py","file_name":"EncodingandDecoding.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"498409182","text":"'''\n가장 큰 수\n\n[문제 설명]\n0 또는 양의 정수가 주어졌을 때, 정수를 이어 붙여 만들 수 있는 가장 큰 수를 알아내 주세요.\n\n예를 들어, 주어진 정수가 [6, 10, 2]라면 [6102, 6210, 1062, 1026, 2610, 2106]를 만들 수 있고, 이중 가장 큰 수는 6210입니다.\n\n0 또는 양의 정수가 담긴 배열 numbers가 매개변수로 주어질 때, 순서를 재배치하여 만들 수 있는 가장 큰 수를 문자열로 바꾸어 return 하도록 solution 함수를 작성해주세요.\n\n[제한 사항]\nnumbers의 길이는 1 이상 100,000 이하입니다.\nnumbers의 원소는 0 이상 1,000 이하입니다.\n정답이 너무 클 수 있으니 문자열로 바꾸어 return 합니다.\n\n[입출력 예]\nnumbers\t return\n[6, 10, 2]\t 6210\n[3, 30, 34, 5, 9]\t9534330\n[999, 222, 333, 555, 777]\n[999999, 19293, 182, 49202, 177829]\n'''\n'''\n실패 -> 시간초과 됨\ndef solution(numbers):\n import itertools\n answer = ''\n n = [str(x) for x in numbers]\n a = list(map(list, (itertools.combinations(n, 1))))\n b = list(map(int, map(lambda x:\"\".join(sum(x, [])), map(list, itertools.permutations(a, len(a))))))\n b.sort()\n answer = str(b[len(b)-1])\n return answer\n\n역시 실패 -> 시간초과 됨\ndef solution(numbers):\n import itertools\n answer = ''\n s = [[str(x)] for x in numbers]\n a = list(map(lambda x: int(\"\".join(sum(x, []))), itertools.permutations(s, len(s))))\n answer = str(max(a))\n return answer\n\n역시 실패 -> 시간초과 + 런타임 에러\ndef solution(numbers):\n def permute(s):\n out = []\n if len(s) == 1:\n return s\n else:\n for i,let in enumerate(s):\n for perm in permute(s[:i] + s[i+1:]):\n out += [let + perm]\n return out\n\n answer = ''\n s = [str(x) for x in numbers]\n a = list(map(int, permute(s)))\n return str(max(a))\n'''\n\n# 시간복잡도 문제라고 하니... 퀵정렬로 도전한다.\ndef solution(numbers):\n answer = ''\n # 퀵정렬을 할거다\n def quickSort(x):\n # 쪼갤 수 없는 크기면, 정렬이 굳이 필요하지 않으므로 그대로 반환한다\n if len(x) <= 1:\n return x\n # 정렬을 위해 기준이 되는 위치를 구한다\n pivot = x[len(x)//2]\n # 클 때, 작을 때, 같을 때 담기 위한 목록을 생성한다\n left,right,equal =[],[],[]\n # 정렬 대상을 순회한다\n for a in x:\n #print(a, pivot)\n # 현재 값을 앞으로 붙인 것(aa)과 뒤로 붙인 것(bb)을 비교하기 위해 만든다\n aa = int(str(a)+str(pivot))\n bb = int(str(pivot)+str(a))\n # 앞으로 붙인 것이 뒤로 붙인 것보다 크면, 클 때 담는 곳에 넣는다\n if aa > bb:\n left.append(a)\n # 앞으로 붙인 것이 뒤로 붙인 것보다 작으면, 작을 때 담는 곳에 넣는다\n elif aa < bb:\n right.append(a)\n # 앞으로 붙인 것이 뒤로 붙인 것과 같으면, 같을 때 담는 곳에 넣는다\n else:\n equal.append(a)\n # recursive 하게 다시 정렬을 하는데, 같을 때 담는 곳은 그대로 두고, 왼쪽과 오른쪽을 각각 또 정렬한다\n return quickSort(left) + equal + quickSort(right) \n # 정렬한 애를 문자로 변경한다\n r = list(map(str, quickSort(numbers)))\n #print(r)\n # 0000 같이 0이 여러개 붙은 숫자가 올 때, 해당 숫자를 한 자리로 표현시켜 반환한다\n return '%d' % int(\"\".join(r))\n\n#print(solution([6, 10, 2]))\nprint(solution([3, 30, 34, 5, 9]))\nprint(solution([0,0,0,0]))\n#print(solution([999999, 19293, 182, 49202, 177829]))\n\ndef solution1(numbers):\n numbers = list(map(str, numbers))\n numbers.sort(key=lambda x: x*3, reverse=True)\n return str(int(''.join(numbers)))\n\n\ndef solution2(numbers):\n import functools\n def comparator(a,b):\n t1 = a+b\n t2 = b+a\n return (int(t1) > int(t2)) - (int(t1) < int(t2)) # t1이 크다면 1 // t2가 크다면 -1 // 같으면 0\n\n n = [str(x) for x in numbers]\n n = sorted(n, key=functools.cmp_to_key(comparator),reverse=True)\n answer = str(int(''.join(n)))\n return answer\n\nprint(solution2([3, 30, 34, 5, 9]))\nprint(solution2([0,0,0,0]))\n\ndef solution3(numbers):\n answer = ''\n numbers = sorted(numbers, reverse=True, key=lambda x: (str(x)*4).ljust(4))\n for i in numbers:\n answer += str(i)\n if answer[0] == '0': #모두 0인 경우 -> 테스트11\n return '0'\n return answer\n\nfrom functools import cmp_to_key\n\ndef solution4(numbers):\n numbers = list(map(lambda x: str(x), numbers))\n numbers = sorted(numbers, key=cmp_to_key(lambda a, b: -1 if a+b >= b+a else 1))\n answer = ''.join(numbers)\n\n return str(int(answer))","sub_path":"python_algorithm/programmers_42746.py","file_name":"programmers_42746.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"108936088","text":"#import packages for support vector , accuracy, split train data and datasets\nimport imp\nfrom enum import auto\n\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import datasets\n#load the iris datasets\ndata=datasets.load_iris()\n#load x and y data\nx=data.data\ny=data.target\n#split training and test data for both x and y for linear kernel\ntrain_x, test_x, train_y, test_y=train_test_split(x, y, test_size=0.2, random_state=21)\n#split training and test data for both x and y for rbf kernel\ntrain_x1, test_x1, train_y1, test_y1=train_test_split(x, y, test_size=0.2, random_state=23)\n#define the model for poly kernel\np_model = SVC(kernel=\"poly\", degree=4)\n#define the model for rbf kernel\nrmodel=SVC(kernel='rbf')\n#fit training data into linear kernel\np_model.fit(train_x, train_y)\n#predict the test data using linear kernel\nprediction=p_model.predict(test_x)\n#calc accuracy score for linear kernel\nprint(\"poly kernel Accuracy score is\", accuracy_score(prediction, test_y))\nprint(prediction)\n#fit training data into rbc kernel\nrmodel.fit(train_x1, train_y1)\n#predict the test data for rbc kernel\npred=p_model.predict(test_x1)\n#calc accuracy for rbc kernel\nprint(\"RBF kernel accuracy score is\", accuracy_score(pred, test_y1))\nprint(pred)\n\n\ngammas = [1,100]\nfor gamma in gammas:\n #svc = SVC(kernel=’rbf’, gamma=gamma).fit(train_x1, train_y1)\n p_model = SVC(kernel=\"poly\", degree=4,gamma=gamma)\n p_model.fit(train_x, train_y)\n prediction = p_model.predict(test_x)\n print(\"poly1 kernel Accuracy score is\", accuracy_score(prediction, test_y))\n print(prediction)\n rmodel = SVC(kernel='rbf',gamma=gamma)\n rmodel.fit(train_x1, train_y1)\n pred = rmodel.predict(test_x1)\n print(\"RBF2 kernel accuracy score is\", accuracy_score(pred, test_y1))\n print(pred)\n # plotSVC(‘gamma=’ + str(gamma))\n\ncs = [1,1000]\nfor c in cs:\n #svc = svm.SVC(kernel=’rbf’, C=c).fit(X, y)\n p_model = SVC(kernel=\"poly\", degree=4,C=c)\n p_model.fit(train_x, train_y)\n prediction = p_model.predict(test_x)\n print(\"poly2 kernel Accuracy score is\", accuracy_score(prediction, test_y))\n print(prediction)\n rmodel = SVC(kernel='rbf',)\n rmodel.fit(train_x1, train_y1)\n pred = rmodel.predict(test_x1)\n print(\"RBF2 kernel accuracy score is\", accuracy_score(pred, test_y1))\n print(pred)\n # plotSVC(‘C=’ + str(c))","sub_path":"Python_LAB_ASSIGNMENT_2/Source/SVM.py","file_name":"SVM.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"80883583","text":"\"\"\"\nInsert, remove, replace a character are only given.\nGiven two strins, write a function to check if they are one edit.\n\npale, ple : true\npales pale :true\npale bale : true\npale bake : true\n\nParameter one should be original word\nparameter two should be comparing word\n\"\"\"\ndef oneWay(word1, word2):\n if abs(len(word1) - len(word2)) > 1:\n return False\n if word1 == word2:\n return False\n count =0\n if len(word1) == len(word2):\n for i in zip(word1, word2):\n if(i[0] != i[1]):\n count +=1\n if count >1:\n return False\n else:\n return True\n else:\n index_i, index_j = 0,0\n for i in word1:\n for j in word2:\n if i != j and index_i != len(word1):\n return (word1[0:index_i] + j + word1[index_i:] == word2 )\n elif i != j and index_i == len(word1):\n return (word1 +j == word2 )\n index_j =+1\n\n index_i+=1\n\n\nwhile True:\n wd1 = input(\"word1 (q)?:\")\n if wd1 == 'q':\n break\n wd2 = input(\"word2 (q)?\")\n if wd1 == 'q' or wd2 == \"q\":\n break\n if oneWay(wd1, wd2):\n print(\"{} and {} is one way\".format(wd1, wd2))\n\n else:\n print(\"{} and {} is not one way\".format(wd1, wd2))","sub_path":"python/interviews/string/oneWay.py","file_name":"oneWay.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"410513334","text":"def solve(n: int, b: list):\n\tif n == 1:\n\t\tprint(0)\n\t\treturn\n\n\tanswer = n + 1\n\n\tdiff = b[1] - b[0]\n\n\tfor d in range(diff - 2, diff + 3):\n\t\tfor start in range(b[0] - 1, b[0] + 2):\n\t\t\tpos, to_change = start, 0\n\t\t\tfor i in range(n):\n\t\t\t\tif abs(pos - b[i]) > 1:\n\t\t\t\t\tto_change = n + 1\n\t\t\t\t\tbreak\n\n\t\t\t\tif pos != b[i]:\n\t\t\t\t\tto_change += 1\n\t\t\t\t\n\t\t\t\tpos += d\n\n\t\t\tanswer = min(answer, to_change)\n\n\tprint(-1 if answer == n + 1 else answer)\n\n\nif __name__ == '__main__':\n\tn = int(input())\n\n\tb = list(map(int, input().split()))\n\n\tsolve(n, b)\n","sub_path":"codeforces/0978/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"406894038","text":"import graphene\nfrom graphene_django import DjangoObjectType\nfrom graphql import GraphQLError\nfrom graphql_jwt.decorators import login_required\n\nfrom healthid.apps.authentication.models import User\nfrom healthid.apps.authentication.schema.queries.auth_queries import UserType\nfrom healthid.apps.business.models import Business\nfrom healthid.utils.app_utils.database import (SaveContextManager,\n get_model_object)\nfrom healthid.utils.auth_utils.decorator import user_permission\nfrom healthid.utils.business_utils.validators import ValidateBusiness\nfrom healthid.utils.messages.business_responses import\\\n BUSINESS_ERROR_RESPONSES, BUSINESS_SUCCESS_RESPONSES\nfrom healthid.utils.messages.common_responses import SUCCESS_RESPONSES\nfrom healthid.apps.products.models import ProductCategory\n\n\nclass BusinesType(DjangoObjectType):\n class Meta:\n model = Business\n\n\nclass CreateBusiness(graphene.Mutation):\n \"\"\"Creates business\n Checks whether the person creating business is super user\n if not stops person from creating business\n returns user who created the business\n \"\"\"\n business = graphene.Field(BusinesType)\n success = graphene.List(graphene.String)\n error = graphene.List(graphene.String)\n\n class Arguments:\n trading_name = graphene.String(required=True)\n legal_name = graphene.String(required=True)\n address_line_1 = graphene.String(required=True)\n address_line_2 = graphene.String()\n phone_number = graphene.String(required=True)\n business_email = graphene.String(required=True)\n city = graphene.String(required=True)\n country = graphene.String(required=True)\n local_government_area = graphene.String()\n website = graphene.String()\n facebook = graphene.String()\n twitter = graphene.String()\n instagram = graphene.String()\n logo = graphene.String()\n user = graphene.String()\n\n @login_required\n @user_permission()\n def mutate(self, info, **kwargs):\n user = info.context.user\n ValidateBusiness().validate_business(**kwargs)\n business = Business()\n for key, value in kwargs.items():\n setattr(business, key, value)\n business.user = user\n with SaveContextManager(business, model=Business) as business:\n success = [SUCCESS_RESPONSES[\n \"creation_success\"].format(business.legal_name)]\n ProductCategory.objects.bulk_create(\n [ProductCategory(\n name=\"Prescription\", business=business, is_default=True),\n ProductCategory(name=\"OTC\", business=business,\n is_default=True),\n ProductCategory(name=\"Daily Essentials\",\n business=business, is_default=True),\n ProductCategory(\n name=\"Beauty\", business=business, is_default=True)\n ])\n\n return CreateBusiness(business=business, success=success)\n\n\nclass UpdateBusiness(graphene.Mutation):\n \"\"\"\n Updates a Business by Master Admin |super user\n Arguments: trading name, legal name, number, address\n \"\"\"\n business = graphene.Field(BusinesType)\n success = graphene.List(graphene.String)\n error = graphene.List(graphene.String)\n\n class Arguments:\n id = graphene.String()\n trading_name = graphene.String()\n legal_name = graphene.String()\n address_line_1 = graphene.String()\n address_line_2 = graphene.String()\n phone_number = graphene.String()\n business_email = graphene.String()\n city = graphene.String()\n country = graphene.String()\n local_government_area = graphene.String()\n website = graphene.String()\n facebook = graphene.String()\n twitter = graphene.String()\n instagram = graphene.String()\n logo = graphene.String()\n user = graphene.String()\n\n @login_required\n @user_permission()\n def mutate(self, info, **kwargs):\n user = info.context.user\n id = kwargs.get('id')\n business = get_model_object(Business, 'id', id)\n\n if business.user != user:\n update_error = BUSINESS_ERROR_RESPONSES[\"business_update_error\"]\n raise GraphQLError(update_error)\n for(key, value) in kwargs.items():\n if key is not None:\n setattr(business, key, value)\n msg = BUSINESS_ERROR_RESPONSES[\"existing_business_error\"]\n with SaveContextManager(business, model=Business, message=msg):\n success =\\\n [SUCCESS_RESPONSES[\n \"update_success\"].format(business.legal_name)]\n return UpdateBusiness(business=business, success=success)\n\n\nclass AddUserBusiness(graphene.Mutation):\n class Arguments:\n user_id = graphene.String(required=True)\n business_id = graphene.String(required=True)\n\n user = graphene.Field(UserType)\n errors = graphene.List(graphene.String)\n message = graphene.List(graphene.String)\n\n @staticmethod\n @login_required\n @user_permission()\n def mutate(root, info, **kwargs):\n user_id = kwargs.get('user_id')\n business_id = kwargs.get('business_id')\n user_instance = get_model_object(User, 'id', user_id)\n business_instance = get_model_object(Business, 'id', business_id)\n business_instance.user = user_instance\n user_instance.save()\n message = [\n BUSINESS_SUCCESS_RESPONSES[\n \"add_user_to_business_success\"\n ].format(user_instance.email,\n business_instance.legal_name)\n ]\n return AddUserBusiness(user=user_instance, message=message)\n\n\nclass DeleteBusiness(graphene.Mutation):\n \"\"\"Deletes a Business\n This also perform the delete action after validating user\n \"\"\"\n business = graphene.Field(BusinesType)\n success = graphene.List(graphene.String)\n error = graphene.List(graphene.String)\n\n class Arguments:\n id = graphene.String()\n\n @login_required\n @user_permission()\n def mutate(self, info, id):\n user = info.context.user\n business = get_model_object(Business, 'id', id)\n business.delete(user)\n success = [SUCCESS_RESPONSES[\"deletion_success\"].format(\"Business\")]\n\n return DeleteBusiness(success=success)\n\n\nclass Mutation(graphene.ObjectType):\n create_business = CreateBusiness.Field()\n delete_business = DeleteBusiness.Field()\n update_business = UpdateBusiness.Field()\n add_user_business = AddUserBusiness.Field()\n","sub_path":"healthid/apps/business/schema/business_mutation.py","file_name":"business_mutation.py","file_ext":"py","file_size_in_byte":6648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"467089134","text":"import requests, json, os\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nSECRET_INFO_FILE = os.path.join(BASE_DIR, 'rank42', 'config', 'settings', 'secret_info_file.json')\nwith open(SECRET_INFO_FILE) as f:\n\tsecrets = json.loads(f.read())\n\n\ndef get_secret(setting, secrets=secrets):\n\ttry:\n\t\treturn secrets[setting]\n\texcept KeyError:\n\t\tprint(f\"Set the {setting} environment variable\")\n\n\noauth_data = {\n\t'grant_type': 'client_credentials',\n\t'client_id': get_secret(\"FT_UID_KEY\"),\n\t'client_secret': get_secret(\"FT_SECRET_KEY\"),\n}\noauth_url = \"https://api.intra.42.fr/oauth/token\"\napi_url = \"https://api.intra.42.fr/v2/\"\n\noauth_request = requests.post(oauth_url, data=oauth_data)\nprint(oauth_request.json())\naccess_token = oauth_request.json()['access_token']\n\n# print(requests.get(f'{api_url}campus/29/users', params={'access_token': access_token}).json())\n# try:\n# \tprint(requests.get(f'{api_url}users/72500', params={'access_token': access_token}).json()[\"cursus_users\"][1])\n# except IndexError:\n# \tprint(\"본과 사람이 아님.\")\n# print(requests.get(f'{api_url}users/68944', params={'access_token': access_token}).json()[\"cursus_users\"][1][\"blackholed_at\"])\n# print(requests.get(f'{api_url}users/68944', params={'access_token': access_token}).json())\n#\n# data = requests.get(f'{api_url}campus/29/users', params={'access_token': access_token, 'sort': \"login\"}).json()\n# for i in data:\n# \tprint(i['id'])\n#\n# datas = [requests.get(f'{api_url}campus/29/users', params={'access_token': access_token, 'page' : x, 'sort': \"login\"}).json() for x in range(4)]\n# for data in datas:\n# \tprint(data)\n#\n# coals = requests.get(f'{api_url}blocs/27', params={'access_token': access_token}).json()[\"coalitions\"]\n# for i in coals:\n# \tprint(i[\"id\"])\n#\n# print(requests.get(f'{api_url}campus/29', params={'access_token': access_token}).json()[\"users_count\"])\n#\n# print(requests.get(f'{oauth_url}/info', params={'access_token': access_token}).json())\n\n# def count_page(number):\n# \tif int(number) <= 100:\n# \t\treturn 1\n# \tpage = int(number) / 100\n# \tpage = int(page) + 1 if number % (100 * int(page)) != 0 else int(page)\n# \treturn page\n\nprint(requests.get(f'{api_url}users/hhan/scale_teams/graph/on/created_at/by/day', params={'access_token': access_token}).json())\n","sub_path":"api_test/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"516529854","text":"pares = open(\"pares.txt\",\"r\")\nsaída = open(\"pares_invertido.txt\",\"w\")\n\nL = pares.readlines()\nx = len(L) -1\nlista_saida = []\nwhile x >= 0:\n lista_saida.append(L[x])\n x -= 1\n\nfor l in lista_saida:\n saída.write(l)\npares.close()\nsaída.close()","sub_path":"Capitulo 9/Exercicio9.5.py","file_name":"Exercicio9.5.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"205605978","text":"import RPi.GPIO as GPIO\nimport time, multiprocessing\nfrom datetime import timedelta\nfrom multiprocessing import Process, Event\n\nclass MotorController(multiprocessing.Process):\n encoder_pin_a = 27\n encoder_pin_b = 17\n pwm_a_pin = 15\n pwm_b_pin = 18\n\n pwm_a = None\n pwm_b = None\n\n target_step = 0\n\n step = 0\n last_step = 0\n\n error = 0\n last_error = 0\n\n p_gain = 1\n d_gain = 0.001\n i_gain = 0.001\n\n stability = 0\n\n start_time = 0\n\n interval = 0.01\n\n maximum_power = 100\n\n def encoder_a_callback(self, channel):\n if GPIO.input(self.encoder_pin_a) == GPIO.input(self.encoder_pin_b):\n self.step -= 1\n else:\n self.step += 1\n \n def encoder_b_callback(self, channel):\n if GPIO.input(self.encoder_pin_a) == GPIO.input(self.encoder_pin_b):\n self.step += 1\n else:\n self.step -= 1\n\n def __init__(self, target_step, sleep=0):\n super(MotorController, self).__init__()\n self.finished = multiprocessing.Event()\n self.target_step = target_step\n self.sleep = sleep\n \n def cancel(self):\n \"\"\"Stop the timer if it hasn't finished yet\"\"\"\n self.finished.set()\n\n def run(self):\n time.sleep(self.sleep)\n self.finished.wait(self.interval)\n \n GPIO.setmode(GPIO.BCM)\n GPIO.setup(self.encoder_pin_a, GPIO.IN)\n GPIO.setup(self.encoder_pin_b, GPIO.IN)\n GPIO.setup(self.pwm_a_pin, GPIO.OUT)\n GPIO.setup(self.pwm_b_pin, GPIO.OUT)\n\n self.pwm_a = GPIO.PWM(self.pwm_a_pin, 1000)\n self.pwm_a.start(0)\n\n self.pwm_b = GPIO.PWM(self.pwm_b_pin, 1000)\n self.pwm_b.start(0)\n\n GPIO.add_event_detect(self.encoder_pin_a, GPIO.BOTH, callback=self.encoder_a_callback)\n GPIO.add_event_detect(self.encoder_pin_b, GPIO.BOTH, callback=self.encoder_b_callback)\n\n self.start_time = time.time()\n self.last_time = time.time()\n\n while self.stability < 10 and not self.finished.is_set():\n current_time = time.time()\n time_delta = current_time - self.last_time\n \n if(time_delta > self.interval):\n error = self.target_step - self.step\n speed = (self.step - self.last_step)/time_delta\n \n control = self.p_gain*error + self.i_gain*error*time_delta + self.d_gain*(error - self.last_error)/time_delta\n\n if error ==0 and speed == 0:\n self.pwm_a.ChangeDutyCycle(0)\n self.pwm_b.ChangeDutyCycle(0)\n self.stability += 1\n else:\n if control > 0:\n self.pwm_a.ChangeDutyCycle(self.maximum_power if control > self.maximum_power else control)\n self.pwm_b.ChangeDutyCycle(0)\n else:\n self.pwm_a.ChangeDutyCycle(0)\n self.pwm_b.ChangeDutyCycle(self.maximum_power if -control > self.maximum_power else -control)\n \n self.stability = 0\n\n self.last_step = self.step\n self.last_error = error\n self.last_time = current_time\n\n time.sleep(0.001)\n self.finished.set()\n \n GPIO.remove_event_detect(self.encoder_pin_a)\n GPIO.remove_event_detect(self.encoder_pin_b)\n GPIO.cleanup()\n\nif __name__ == \"__main__\":\n t = time.time()\n motorctrl = MotorController(1133)\n motorctrl.start()\n motorctrl.join()\n print('hi' + str(motorctrl.target_step))\n # for i in range(1):\n # motorctrl.rotate(1133)","sub_path":"misc/motorcontroller3.py","file_name":"motorcontroller3.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"153447341","text":"import sys\r\n\r\nsys.path.append(\"../INF8225-Project/\")\r\n\r\nfrom architectures.DANet.model.MSDualGuided import *\r\nfrom torch.utils.data import DataLoader\r\nfrom datasets.ChaosDataset import *\r\nfrom torchvision import transforms\r\nfrom torch.optim import Adam\r\nfrom architectures.DANet.utils.MSDualGuidedLoss import *\r\nfrom tqdm import tqdm\r\nfrom architectures.DANet.utils.metrics import *\r\nimport warnings\r\nimport shutil\r\nimport argparse\r\nfrom architectures.DANet.utils.HierarchyCreator import *\r\n\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\n\r\ndef run_training(args):\r\n transform = transforms.Compose([transforms.ToTensor()])\r\n train_chaos_dataset = ChaosDataset(mode=\"train\", root_dir=args.root_dir, transform_input=transform,\r\n transform_mask=GrayToClass(), augment=Augment())\r\n train_loader = DataLoader(train_chaos_dataset, batch_size=args.batch_size, num_workers=args.num_workers,\r\n shuffle=True)\r\n\r\n val_chaos_dataset = ChaosDataset(mode=\"val\", root_dir=args.root_dir, transform_input=transform,\r\n transform_mask=GrayToClass(), augment=None)\r\n val_loader = DataLoader(val_chaos_dataset, batch_size=16, num_workers=args.num_workers, shuffle=False)\r\n\r\n net = MSDualGuided().cuda()\r\n loss_module = MSDualGuidedLoss()\r\n\r\n lr = args.lr\r\n optimizer = Adam(net.parameters(), lr=lr, betas=(0.9, 0.99), amsgrad=False)\r\n\r\n lossG = []\r\n dsc = []\r\n assd = []\r\n vs = []\r\n\r\n best_dice_3d, best_epoch = 0, 0\r\n for i in range(args.epochs):\r\n\r\n # Training Loop\r\n with tqdm(total=len(train_loader), ascii=True) as training_bar:\r\n training_bar.set_description(f'[Training] Epoch {i + 1}')\r\n\r\n net.train()\r\n loss_train = 0\r\n for (image, mask, _) in train_loader:\r\n image, mask = image.cuda(), mask.cuda()\r\n semVector1, semVector2, fsms, fai, semModule1, semModule2, predict1, predict2 = net(image)\r\n\r\n optimizer.zero_grad()\r\n loss = loss_module(semVector1, semVector2, fsms, fai, semModule1, semModule2, predict1, predict2, mask)\r\n\r\n loss.backward()\r\n optimizer.step()\r\n\r\n loss_train += loss.item()\r\n\r\n segmentation_prediction = sum(list(predict1) + list(predict2)) / 8\r\n classes_dice = dice_score(segmentation_prediction, mask)\r\n\r\n training_bar.set_postfix_str(\r\n \"Mean dice: {:.3f} || Liver: {:.3f}, Kidney(R): {:.3f}, Kidney(L): {:.3f}, Spleen: {:.3f}\"\r\n .format(torch.mean(classes_dice[1:]), classes_dice[1], classes_dice[2], classes_dice[3],\r\n classes_dice[4])\r\n )\r\n training_bar.update()\r\n\r\n training_bar.set_postfix_str(\"Mean loss: {:.4f}\".format(loss_train / len(train_loader)))\r\n del semVector1, semVector2, fsms, fai, semModule1, semModule2, predict1, predict2\r\n\r\n # Validation Loop\r\n with tqdm(total=len(val_loader), ascii=True) as val_bar:\r\n val_bar.set_description('[Validation]')\r\n\r\n net.eval()\r\n for j, (val_image, val_mask, val_img_name) in enumerate(val_loader):\r\n val_image, val_mask = val_image.cuda(), val_mask.cuda()\r\n\r\n with torch.no_grad():\r\n seg_pred = net(val_image)\r\n prediction_to_png(seg_pred, val_img_name, out_path=args.root_dir + \"/val/Result\")\r\n\r\n val_bar.update()\r\n\r\n create_3d_volume(args.root_dir + \"/val/Result\", args.root_dir + \"/val/Volume/Pred\")\r\n dsc_3d, assd_3d, vs_3d = calculate_3d_metrics(args.root_dir + \"/val/Volume\")\r\n\r\n current_dice_3d = np.mean(dsc_3d)\r\n if current_dice_3d > best_dice_3d:\r\n best_dice_3d = current_dice_3d\r\n best_epoch = i\r\n torch.save(net.state_dict(), args.root_dir + \"/save/net.pth\")\r\n\r\n if i % (best_epoch + 50) == 0:\r\n for param_group in optimizer.param_groups:\r\n lr = lr * 0.5\r\n param_group['lr'] = lr\r\n\r\n dice_3d_class = np.mean(dsc_3d, 0)\r\n val_bar.set_postfix_str(\r\n \"Dice 3D: {:.3f} || Liver: {:.3f}, Kidney(R): {:.3f}, Kidney(L): {:.3f}, Spleen: {:.3f}\"\r\n .format(np.mean(dice_3d_class), dice_3d_class[0], dice_3d_class[1], dice_3d_class[2],\r\n dice_3d_class[3])\r\n )\r\n\r\n # Save Statistics\r\n lossG.append(loss_train / len(train_loader))\r\n dsc.append(dsc_3d)\r\n assd.append(assd_3d)\r\n vs.append(vs_3d)\r\n\r\n np.save(args.root_dir + \"/save/loss\", lossG)\r\n np.save(args.root_dir + \"/save/dsc\", dsc)\r\n np.save(args.root_dir + \"/save/assd\", assd)\r\n np.save(args.root_dir + \"/save/vs\", vs)\r\n\r\n\r\ndef run_eval(args):\r\n transform = transforms.Compose([transforms.ToTensor()])\r\n test_chaos_dataset = ChaosDataset(mode=\"test\", root_dir=args.root_dir, transform_input=transform,\r\n transform_mask=GrayToClass(), augment=None)\r\n test_loader = DataLoader(test_chaos_dataset, batch_size=16, num_workers=args.num_workers, shuffle=False)\r\n\r\n net = MSDualGuided().cuda()\r\n net.load_state_dict(torch.load(args.checkpoint_path))\r\n net.eval()\r\n\r\n with tqdm(total=len(test_loader), ascii=True, position=0) as test_bar:\r\n test_bar.set_description('[Evaluation]')\r\n\r\n for test_image, test_mask, test_img_name in test_loader:\r\n test_image, test_mask = test_image.cuda(), test_mask.cuda()\r\n\r\n with torch.no_grad():\r\n seg_pred = net(test_image)\r\n prediction_to_png(seg_pred, test_img_name, out_path=args.root_dir + \"/test/Result\")\r\n\r\n test_bar.update()\r\n\r\n create_3d_volume(args.root_dir + \"/test/Result\", args.root_dir + \"/test/Volume/Pred\")\r\n dsc_3d, assd_3d, vs_3d = calculate_3d_metrics(args.root_dir + \"/test/Volume\")\r\n\r\n test_bar.set_postfix_str(\r\n \"Dice 3D: {:.3f} | ASSD: {:.3f} | VS: {:.3f}\"\r\n .format(np.mean(dsc_3d), np.mean(assd_3d), np.mean(vs_3d))\r\n )\r\n\r\n np.save(args.root_dir + \"/save/dsc_test\", dsc_3d)\r\n np.save(args.root_dir + \"/save/assd_test\", assd_3d)\r\n np.save(args.root_dir + \"/save/vs_test\", vs_3d)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n parser = argparse.ArgumentParser()\r\n\r\n parser.add_argument('--data_dir', default='../rawdata/CHAOS_Train_Sets/Train_Sets/MR', type=str)\r\n parser.add_argument('--root_dir', default='../rawdata/chaos', type=str)\r\n parser.add_argument('--num_workers', default=0, type=int)\r\n parser.add_argument('--batch_size', default=2, type=int)\r\n parser.add_argument('--epochs', default=150, type=int)\r\n parser.add_argument('--lr', default=0.001, type=float)\r\n parser.add_argument('--create_hierarchy', default=False, action='store_true')\r\n parser.add_argument('--train', default=False, action='store_true')\r\n parser.add_argument('--eval', default=False, action='store_true')\r\n parser.add_argument('--checkpoint_path', default='../rawdata/chaos/save/net.pth', type=str)\r\n\r\n args = parser.parse_args()\r\n\r\n if args.create_hierarchy:\r\n print(\"Creating folder for the model!\\n\")\r\n shutil.rmtree(args.root_dir, ignore_errors=True)\r\n create_hierarchy(data_dir=args.data_dir, out_dir=args.root_dir)\r\n\r\n if args.train:\r\n run_training(args)\r\n\r\n if args.eval:\r\n run_eval(args)\r\n","sub_path":"mains/train_danet.py","file_name":"train_danet.py","file_ext":"py","file_size_in_byte":7675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"263181250","text":"#! /usr/bin/env python\n\nimport sys\nfile_in = sys.argv[1]\nfile_out = sys.argv[2]\n\nlines = open(file_in).readlines()\nf = open(file_out, 'wt')\n\ndef convert(s):\n return str(int(float(s)*1e6))\n\nfor line in lines:\n lat, lng = line.split(',')\n f.writelines(convert(lat) + ' ' + convert(lng) + '\\n')\n\n","sub_path":"Android/assets/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"499499360","text":"# -*- coding:utf8 -*-\n\nreadDir = r\"C:\\Users\\龙龙.LAPTOP-54PEQUTO\\Desktop\\test\\1.txt\"\nwriteDir = r\"C:\\Users\\龙龙.LAPTOP-54PEQUTO\\Desktop\\test\\11.txt\"\noutfile = open(writeDir, \"w\", encoding='utf8')\ndata = []\n# for line in open(readDir,'r',encoding='utf8'):\n# outfile.write(line)\n#\n# data.append(line)\n# # print(data)\n#\n# outfile.close()\n\nwith open(readDir, 'r', encoding='utf8') as f:\n line = f.readlines()\n data.append(line)\ndef paixu(a):\n count = len(a) - 1\n for i in range(count, 0, -1):\n for j in range(i):\n if a[j][0] > a[j + 1][0]:\n a[j], a[j + 1] = a[j + 1], a[j]\n return a\n\n\n\nl =[]\nl =paixu(data)\n\n\nfor j in l:\n outfile.write(str(j))\n\n\n\n\n","sub_path":"learn/python_all/porint/noise/RorW.py","file_name":"RorW.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"276987692","text":"from flask import Blueprint, jsonify\nfrom models.Order import Order\nfrom schemas.OrderSchema import order_schema, orders_schema\n\norder = Blueprint(\"order\", __name__, url_prefix=\"/order\")\n\n@order.route(\"/\", methods=[\"GET\"])\ndef order_index():\n order = Order.query.all()\n serialized_data = order_schema.dump(order)\n return jsonify(serialized_data)\n\n@order.route(\"/\", methods=[\"POST\"])\ndef order_menu():\n order_field = order_schema.load(request.json)\n\n","sub_path":"controllers/order_controller.py","file_name":"order_controller.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"393594047","text":"\"\"\"\nOhad Omrad\nPython and Git\n\"\"\"\n\nimport threading\nimport socket\n\nimport Bank\nimport HandleClient\nimport PrintsForUser\n\n\"\"\"\nThis class responsible to the client side\nThis client inherit the Thread class -> each client object ran on his own thread\neven if we will run some client at the same program\n\"\"\"\nclass ATMSocketClient(threading.Thread):\n\n _menue = \"\"\"\n*******************ATM MENU***********************\n* *\n* Enter 1 -> to deposit money *\n* Enter 2 -> to withdraw money *\n* Enter 3 -> to transfer money *\n* Enter 4 -> to display account balance *\n* Enter 5 -> to display account transactions *\n* Enter 6 -> to exit *\n* *\n**************************************************\n\"\"\"\n invalidInput = \"INVALID INPUT\"\n\n options_dict = {1 : Bank.Bank.optionOne, 2 : Bank.Bank.optionTwo, 3: Bank.Bank.optionThree,\n 4 : Bank.Bank.optionFour, 5 : Bank.Bank.optionFive,\n 6: HandleClient.HandleSocketClient.closeConnection}\n\n def __init__(self, serverIP, serverPort):\n threading.Thread.__init__(self)\n self._serverIP = serverIP\n self._serverPort = serverPort\n self._sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n def _send_data(self, data):\n \"\"\"\n This method send a given data to a given client socket\n :return: if the send process succeed return HandleSocketClient.keepConnection\n else return HandleSocketClient.closeConnection\n \"\"\"\n try:\n self._sock.send(data.encode())\n return HandleClient.HandleSocketClient.keepConnection\n except:\n return HandleClient.HandleSocketClient.closeConnection\n\n def _received_data(self):\n \"\"\"\n This method wait for a data from a given client socket\n :return: if the receive process succeed return the data that we got\n else return HandleSocketClient.closeConnection\n \"\"\"\n try:\n return self._sock.recv(Bank.Bank.numBytes).decode()\n except:\n return HandleClient.HandleSocketClient.closeConnection\n\n\n def run(self):\n \"\"\"\n This method is the client's thread main method\n \"\"\"\n\n # connect to the server\n try:\n self._sock.connect((self._serverIP, self._serverPort))\n except:\n PrintsForUser.printError(\"Can't connect to the server\")\n return\n\n retVal = self.loginToServer()\n if not retVal:\n self.closeConnection()\n return\n\n PrintsForUser.printOptions(ATMSocketClient._menue)\n\n while True:\n\n # input from the user an option number\n try:\n PrintsForUser.printOptions(\"Enter: \", newLine=False)\n user_input = int(input())\n except:\n continue\n\n option = ATMSocketClient.options_dict.get(user_input)\n if option == None:\n continue\n\n PrintsForUser.printProcess(option)\n self._send_data(option)\n\n if option == HandleClient.HandleSocketClient.closeConnection:\n self.closeConnection()\n return\n\n if option == Bank.Bank.optionOne or option == Bank.Bank.optionTwo:\n money = self.getAmountMoney()\n self._send_data(str(money))\n\n if option == Bank.Bank.optionThree:\n dest_account_name = ATMSocketClient.getDestAccountName()\n self._send_data(str(dest_account_name))\n\n money = self.getAmountMoney()\n self._send_data(str(money))\n\n # a client can see his account's balance or transactions without sending his secret code\n if option != Bank.Bank.optionFour and option != Bank.Bank.optionFive:\n secret_code = ATMSocketClient.getSecretCode()\n self._send_data(str(secret_code))\n\n received_answer = self._received_data()\n if received_answer == HandleClient.HandleSocketClient.closeConnection:\n self.closeConnection()\n return\n\n PrintsForUser.printProcess(received_answer)\n\n\n def getAmountMoney(self):\n\n # This method input from the client a positive number (amount of money)\n\n money = ATMSocketClient.invalidInput\n PrintsForUser.printOptions(\"Enter the amount of money (a positive number): \")\n while money == ATMSocketClient.invalidInput:\n message = ATMSocketClient.invalidInput\n try:\n PrintsForUser.printOptions(\"Enter: \", newLine=False)\n money = input()\n money = float(money)\n if money <= 0:\n message = \"Negative Amount of money\"\n raise Exception()\n except:\n PrintsForUser.printError(message)\n money = ATMSocketClient.invalidInput\n return money\n\n def closeConnection(self):\n \"\"\"\n This method responsible to close the client's server socket\n \"\"\"\n PrintsForUser.printProcess(\"\", newLine=False)\n print(\"Close Connection with the server at: ( IP = \" + str(self._serverIP)+ \" , Port = \" + str(self._serverPort) +\" )\")\n self._sock.close()\n\n def loginToServer(self):\n \"\"\"\n This method responsible for the client authentication (Client Side)\n The Protocol key strings:\n \"NO ACCOUNT\" -> the account name has not found\n \"CONTINUE\" -> GREEN LIGHT -> continue with the connection to the next step\n\n :return: True -> if the client login to the server properly\n else -> return False\n \"\"\"\n\n account_name = ATMSocketClient.getAccountName()\n ret_message = self._send_data(account_name)\n\n if ret_message == HandleClient.HandleSocketClient.closeConnection:\n return HandleClient.HandleSocketClient.closeConnection\n\n\n receivedAnswer = self._received_data()\n if receivedAnswer == HandleClient.HandleSocketClient.closeConnection:\n return HandleClient.HandleSocketClient.closeConnection\n\n if receivedAnswer == Bank.Bank.accountNotExists:\n PrintsForUser.printOptions(\"\", newLine=False)\n answer = input(\"Their is no account for \\\"\" + account_name + \"\\\"\\nDo you wont to create one? [Y,N]: \")\n answer = answer.upper()\n if answer == \"Y\" or answer == \"YES\":\n self._send_data(HandleClient.HandleSocketClient.keepConnection)\n self.createAccount()\n else:\n return False\n\n elif receivedAnswer == HandleClient.HandleSocketClient.keepConnection:\n key = self.getAccountKey()\n self._send_data(key)\n receivedAnswer = self._received_data()\n\n if receivedAnswer == HandleClient.HandleSocketClient.closeConnection:\n return HandleClient.HandleSocketClient.closeConnection\n\n if receivedAnswer == Bank.Bank.wrongPassword:\n PrintsForUser.printError(receivedAnswer)\n return False\n return True\n\n\n def createAccount(self):\n \"\"\"\n This method input from the user the needed data to create an account and send them for the server\n if the account name or password are already exist at the bank system the method will ask for\n a new data again\n\n The DATA: account name, password, secret code\n \"\"\"\n\n status = Bank.Bank.accountNameTaken\n\n while status != Bank.Bank.accountAdded:\n account_name = ATMSocketClient.getAccountName()\n self._send_data(account_name)\n key = ATMSocketClient.getAccountKey()\n self._send_data(key)\n secret_code = ATMSocketClient.getSecretCode()\n self._send_data(secret_code)\n\n status = self._received_data()\n if status == HandleClient.HandleSocketClient.closeConnection:\n return HandleClient.HandleSocketClient.closeConnection\n\n if status == Bank.Bank.accountAdded:\n PrintsForUser.printOptions(status)\n else:\n PrintsForUser.printError(status)\n\n\n @staticmethod\n def getDestAccountName():\n PrintsForUser.printOptions(\"Enter the account name that you want to transfer money to: \", newLine=False)\n return str(input())\n\n @staticmethod\n def getAccountName():\n PrintsForUser.printOptions(\"Enter your account name: \", newLine=False)\n return str(input())\n\n @staticmethod\n def getAccountKey():\n PrintsForUser.printOptions(\"Enter your passwaod: \", newLine=False)\n password = str(input())\n return str(Bank.Bank.genarateKey(password))\n\n @staticmethod\n def getSecretCode():\n PrintsForUser.printOptions(\"Enter your secret code: \", newLine=False)\n return str(input())\n\n\n# Testing\ndef main():\n client = ATMSocketClient(\"127.0.0.1\", 8000)\n client.start()\n\nif __name__ == \"__main__\":\n main()","sub_path":"Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":9238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"421266617","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 31 12:27:28 2018\n\n@author: jacobrosenstein\n\n\nSCRIPT TO TAKE IN A CSV FILE OF CANDIDATES FOR 4 UGI COMPONENTS, \nAND ANALYZE THE MASS DISTRIBUTION OF THE RESULTING COMBINATORIAL UGI MOLECULES\n\nRequires pubchempy and pyteomics.\nTested in Python 3.5 with Anaconda/Spyder\n\n\"\"\"\n\nimport pubchempy as pcp\nimport csv\n\nimport pylab as pl\nimport numpy as np\n\nimport operator\n\nfrom pyteomics import mass\n\nimport json\nfrom datetime import datetime\n\n# ============================================================\ndef import_ugi_components(myfile):\n # INPUT: csv file with 3 columns: component type, name, CAS number\n # RETURNS: dictionary with CID as index, mass as value\n \n myReader = csv.reader(open(myfile, newline=''), delimiter=',')\n mycomponents = {'isocyanide':{},'amine':{},'aldehyde':{},'carboxylic acid':{}}\n for row in myReader:\n print(row)\n c = pcp.get_compounds(row[2], 'name', record_type='3d')\n m = pcp.Compound.from_cid(c[0].cid)\n mymass = mass.calculate_mass(formula=m.molecular_formula)\n print(' cid=',c[0].cid)\n #print(' MW=',m.molecular_weight)\n print(' mass=',mymass)\n print(' formula=',m.molecular_formula)\n print(' composition=',mass.Composition(formula=m.molecular_formula))\n mycomponents[row[0]][c[0].cid] = (mymass,m.molecular_formula,row[1],len(mycomponents[row[0]]))\n \n return mycomponents\n# ============================================================\n\nprint('\\n\\n\\n\\n IMPORTING COMPONENTS \\n\\n')\n# IMPORT LIST OF UGI COMPONENTS\n#ugi_components = import_ugi_components('ugi_components1_Jan31.csv')\n#ugi_components = import_ugi_components('ugi_components1_Feb1.csv')\nugi_components = import_ugi_components('demo1_ugi_reagents1.csv')\n\nh20_mw = mass.calculate_mass(formula='H2O')\n\n\nprint('\\n\\n\\n\\n BUILDING UGI MOLECULES \\n\\n')\n# BUILD UGI MOLECULES\nall_ugi_masses = []\nall_ugi_masses_isotopes = []\nall_ugi_componentmap = []\nall_ugi_componentwell = []\nfor iso_cid in ugi_components['isocyanide']:\n for am_cid in ugi_components['amine']:\n for ald_cid in ugi_components['aldehyde']:\n for carb_cid in ugi_components['carboxylic acid']:\n all_ugi_masses.append( ugi_components['isocyanide'][iso_cid][0]\n + ugi_components['amine'][am_cid][0]\n + ugi_components['aldehyde'][ald_cid][0]\n + ugi_components['carboxylic acid'][carb_cid][0]\n - h20_mw )\n all_ugi_componentmap.append( (iso_cid,am_cid,ald_cid,carb_cid) )\n all_ugi_componentwell.append( chr(ord('A')+ugi_components['aldehyde'][ald_cid][3])\n + str(1+ugi_components['carboxylic acid'][carb_cid][3])\n )\n \n ugi_composition = (mass.Composition(formula=''.join((ugi_components['isocyanide'][iso_cid][1],\n ugi_components['amine'][am_cid][1],\n ugi_components['aldehyde'][ald_cid][1],\n ugi_components['carboxylic acid'][carb_cid][1])))\n - mass.Composition(formula='H2O'))\n \n print(len(all_ugi_masses),ugi_composition,mass.calculate_mass(composition=ugi_composition))\n print(' ',(iso_cid,am_cid,ald_cid,carb_cid))\n print(' ',ugi_components['aldehyde'][ald_cid][2])\n print(' ',ugi_components['carboxylic acid'][carb_cid][2])\n print(' well',all_ugi_componentwell[-1])\n \n print('\\n isotopes:')\n for i in mass.isotopologues(composition=ugi_composition,\n report_abundance=True,\n isotope_threshold=0.005,\n overall_threshold=0.01):\n print(' ',mass.calculate_mass(composition=i[0]),mass.isotopic_composition_abundance(composition=i[0]))\n all_ugi_masses_isotopes.append(mass.calculate_mass(composition=i[0]))\n print(' ',i[0])\n print('\\n')\n\n\nall_ugi_masses = [ round(x,5) for x in all_ugi_masses ]\nall_ugi_masses_isotopes = [ round(x,5) for x in all_ugi_masses_isotopes ]\n\n\n# SORT AND FIND ADJACENT SPACING\nsorted_ugi_masses = sorted(all_ugi_masses)\nsorted_ugi_indices = sorted(range(len(all_ugi_masses)),key=all_ugi_masses.__getitem__)\nmass_spacing = [sorted_ugi_masses[x+1]-sorted_ugi_masses[x] for x in range(len(sorted_ugi_masses)-1)]\nmass_spacing = [round(x,5) for x in mass_spacing]\nsorted_spacing_indices = sorted(range(len(mass_spacing)),key=mass_spacing.__getitem__)\n\nsorted_ugi_masses_isotopes = sorted(all_ugi_masses_isotopes)\nmass_spacing_isotopes = list(map(operator.sub,sorted_ugi_masses_isotopes[2:],sorted_ugi_masses_isotopes[:-1]))\n\n\n# HISTOGRAM\npl.hist(mass_spacing, bins=np.logspace(np.log10(0.0001),np.log10(10.0), 20))\npl.gca().set_xscale(\"log\")\npl.title('count_ugis=' + str(len(all_ugi_masses)) + ', count_unique_mass_ugis=' + str(len(set(all_ugi_masses))) )\npl.xlabel('adjacent mass spacing')\npl.ylabel('count')\npl.grid(True)\npl.show()\n\npl.hist(mass_spacing_isotopes, bins=np.logspace(np.log10(0.0001),np.log10(10.0), 20))\npl.gca().set_xscale(\"log\")\npl.title('count_isotopes=' + str(len(all_ugi_masses_isotopes)) + ', count_unique_mass_isotopes=' + str(len(set(all_ugi_masses_isotopes))) )\npl.xlabel('adjacent mass spacing (including isotopes)')\npl.ylabel('count')\npl.grid(True)\npl.show()\n\n\n\nif 0:\n # DESCRIBE CLOSEST-SPACED MASSES\n \n choosing_components={'isocyanide':{},'amine':{},'aldehyde':{},'carboxylic acid':{}}\n \n for i_space in sorted_spacing_indices:\n #if mass_spacing[i_space]>0 and mass_spacing[i_space]<0.1:\n print('\\n')\n print('spacing = ',mass_spacing[i_space])\n print(' ugi masses ',sorted_ugi_masses[i_space],' & ',sorted_ugi_masses[i_space+1])\n \n locateCIDs_A = all_ugi_componentmap[sorted_ugi_indices[i_space]]\n locateCIDs_B = all_ugi_componentmap[sorted_ugi_indices[i_space+1]]\n \n print(' CIDs ',locateCIDs_A,' ',locateCIDs_B)\n print(' --details--')\n print(' aldehyde 1 ', ugi_components['aldehyde'][locateCIDs_A[2]][2] )\n print(' carboxylic acid 1', ugi_components['carboxylic acid'][locateCIDs_A[3]][2] ) \n print('')\n print(' aldehyde 2 ', ugi_components['aldehyde'][locateCIDs_B[2]][2] )\n print(' carboxylic acid 2', ugi_components['carboxylic acid'][locateCIDs_B[3]][2] ) \n \n \n if locateCIDs_A[0] in choosing_components['isocyanide']:\n choosing_components['isocyanide'][locateCIDs_A[0]] += 1\n else:\n choosing_components['isocyanide'][locateCIDs_A[0]]=1\n \n if locateCIDs_A[1] in choosing_components['amine']:\n choosing_components['amine'][locateCIDs_A[1]] += 1\n else:\n choosing_components['amine'][locateCIDs_A[1]] = 1\n \n if locateCIDs_A[2] in choosing_components['aldehyde']:\n choosing_components['aldehyde'][locateCIDs_A[2]] += 1\n else:\n choosing_components['aldehyde'][locateCIDs_A[2]] = 1\n print('first aldehyde appearance: ',ugi_components['aldehyde'][locateCIDs_A[2]][2])\n \n if locateCIDs_A[3] in choosing_components['carboxylic acid']:\n choosing_components['carboxylic acid'][locateCIDs_A[3]] += 1\n else:\n choosing_components['carboxylic acid'][locateCIDs_A[3]] = 1\n print('first carboxylic acid appearance: ',ugi_components['carboxylic acid'][locateCIDs_B[3]][2])\n \n \n print('\\nLibrary:',choosing_components)\n print('Carboxylic Acids:',list(choosing_components['carboxylic acid']))\n print('Aldehydes:',list(choosing_components['aldehyde']))\n \n\n\n\n########################################################################\n########################################################################\n# CHOOSE TEST MIXTURES TO INCLUDE\n\nchoosing_mixtures = []\n#\n#\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n## MIX THE CLOSEST-SPACED PAIRS\n#for i_space in sorted_spacing_indices[:8]:\n# newmixture = []\n# print('\\n')\n# print('CREATING 2-UGI CLOSE-SPACED MIXTURE')\n# print('spacing = ',mass_spacing[i_space])\n# print(' ugi masses ',sorted_ugi_masses[i_space],' & ',sorted_ugi_masses[i_space+1])\n#\n# locateCIDs_A = all_ugi_componentmap[sorted_ugi_indices[i_space]]\n# locateCIDs_B = all_ugi_componentmap[sorted_ugi_indices[i_space+1]]\n#\n# newmixture.append(locateCIDs_A)\n# newmixture.append(locateCIDs_B)\n# \n# choosing_mixtures.append(newmixture)\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n## MIX THE FARTHEST-SPACED PAIRS\n#for i_space in sorted_spacing_indices[-2:]:\n# newmixture = []\n# print('\\n')\n# print('CREATING 2-UGI FAR-SPACED MIXTURE')\n# print('spacing = ',mass_spacing[i_space])\n# print(' ugi masses ',sorted_ugi_masses[i_space],' & ',sorted_ugi_masses[i_space+1])\n#\n# locateCIDs_A = all_ugi_componentmap[sorted_ugi_indices[i_space]]\n# locateCIDs_B = all_ugi_componentmap[sorted_ugi_indices[i_space+1]]\n#\n# newmixture.append(locateCIDs_A)\n# newmixture.append(locateCIDs_B)\n# \n# choosing_mixtures.append(newmixture)\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n#\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n## MIX THE LIGHTEST & HEAVIEST MOLECULES\n#newmixture = []\n#print('\\n')\n#print('CREATING LIGHT+HEAVY MIXTURE')\n#for i in range(4): \n# print(' ugi masses ',sorted_ugi_masses[i],' & ',sorted_ugi_masses[-1-i]) \n# locateCIDs_A = all_ugi_componentmap[i]\n# locateCIDs_B = all_ugi_componentmap[-1-i]\n#\n# newmixture.append(locateCIDs_A)\n# newmixture.append(locateCIDs_B)\n# \n# choosing_mixtures.append(newmixture)\n## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n#\n\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# RANDOM MIXES\nnp.random.seed(1)\n\nfor mix_density in (0.1,0.1,0.25,0.25,0.5,0.5,0.5,1.0):\n newmixture = []\n print('\\n')\n print('CREATING RANDOM MIXTURE')\n for i in range(len(sorted_ugi_masses)):\n if np.random.rand()\n# All rights reserved.\n#\n# See LICENSE file for full license.\n\nfrom .aws import Action as BaseAction\nfrom .aws import BaseARN\n\nservice_name = \"Amazon Pinpoint SMS and Voice Service\"\nprefix = \"sms-voice\"\n\n\nclass Action(BaseAction):\n def __init__(self, action: str = None) -> None:\n super().__init__(prefix, action)\n\n\nclass ARN(BaseARN):\n def __init__(self, resource: str = \"\", region: str = \"\", account: str = \"\") -> None:\n super().__init__(\n service=prefix, resource=resource, region=region, account=account\n )\n\n\nCreateConfigurationSet = Action(\"CreateConfigurationSet\")\nCreateConfigurationSetEventDestination = Action(\n \"CreateConfigurationSetEventDestination\"\n)\nDeleteConfigurationSet = Action(\"DeleteConfigurationSet\")\nDeleteConfigurationSetEventDestination = Action(\n \"DeleteConfigurationSetEventDestination\"\n)\nGetConfigurationSetEventDestinations = Action(\"GetConfigurationSetEventDestinations\")\nListConfigurationSets = Action(\"ListConfigurationSets\")\nSendVoiceMessage = Action(\"SendVoiceMessage\")\nUpdateConfigurationSetEventDestination = Action(\n \"UpdateConfigurationSetEventDestination\"\n)\n","sub_path":"awacs/sms_voice.py","file_name":"sms_voice.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"425845461","text":"numbers = (0, 1, 2, 3, 4, 5)\r\n\r\n# print(numbers, sep=\";\")\r\n# print(*numbers, sep=\";\")\r\n# print(0, 1, 2, 3, 4, 5, sep=\";\")\r\n\r\n# star represents that this parameter will be replaced by an unpacked tuple\r\n# the star causes Python to follow a few simple rules & packs the values up into a tuple\r\n# *args also acts as an `X` value for our function\r\ndef test_star(*args):\r\n print(args)\r\n for x in args:\r\n print(x)\r\n\r\n\r\ntest_star(0, 1, 2, 3, 4, 5)\r\n\r\nprint()\r\ntest_star()\r\n","sub_path":"star_examples.py","file_name":"star_examples.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"537164399","text":"class ExceptionMixin():\n \"\"\"Headers mixin\"\"\"\n def assertException(self, callback, message: str, exception=Exception):\n if not callable(callback):\n raise Exception('function is not callable')\n\n try:\n callback()\n assert False\n except Exception as e:\n self.assertEqual(str(e), message)\n","sub_path":"breathecode/tests/mixins/exception_mixin.py","file_name":"exception_mixin.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"61744816","text":"shop_menus = [\"만두\", \"떡볶이\", \"오뎅\", \"사이다\", \"콜라\"]\nshop_orders = [\"오뎅\", \"콜라\", \"만두\"]\n\n\ndef is_available_to_order(menus, orders):\n menus_set = set(menus) #O(N)\n print(menus_set)\n menus.sort()\n print(menus)\n for order in orders: #O(M)\n if order not in menus_set:\n return False\n return True\n\n#O(N+M)\n# def is_available_to_order(menus, orders): O((N + M ) * log N)\n# # 이 부분을 채워보세요!\n# menus.sort() O(N log N)\n# orders.sort()\n# min_index = 0\n# max_index = len(menus) -1\n# index = (min_index + max_index) // 2\n# count = 0\n# for order in orders: O (M log N)\n # while(min_index <= max_index):\n # if order == menus[index]:\n # print(order)\n # min_index = 0\n # max_index = len(menus) - 1\n # count += 1\n # break\n # elif menus[index] < order:\n # min_index = index + 1\n # else:\n # max_index = index - 1\n # index = (min_index + max_index) // 2\n # if count == len(orders):\n # return True\n # return False\n\nresult = is_available_to_order(shop_menus, shop_orders)\nprint(result)","sub_path":"week_2/homework/02_is_available_to_order.py","file_name":"02_is_available_to_order.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"111734585","text":"import os\nimport nltk\nimport string\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\n\n# find all the folders and set up nltk\ndirectory = \"./DB/arnold-j/sent\"\nfiles = os.listdir(directory)\n\nnltk.download('stopwords')\nnltk.download('punkt')\nstop_words = set(stopwords.words('english'))\nstop_words.update(list(string.punctuation))\nstop_words.update('Subject')\n\n# obtain keywords\ntokens = set()\nsizes = []\nfor file in files:\n fp = open(directory + '/' + file)\n\n for ii in range(16):\n line = fp.readline()\n \n words = set(word_tokenize(fp.read()))\n sizes.append(len(words))\n\n#sizes = sorted(sizes, reverse=True)\nprint(sum(sizes)/len(files))\n\n","sub_path":"Enron Email Database/keyword size frequency.py","file_name":"keyword size frequency.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"85485921","text":"import ctypes\nfrom cryptlex.lexactivator import lexactivator_native as LexActivatorNative\nfrom cryptlex.lexactivator.lexstatus_codes import LexStatusCodes\nfrom cryptlex.lexactivator.lexactivator_exception import LexActivatorException\n\ncallback_list = []\n\n\nclass PermissionFlags:\n LA_USER = 1\n LA_SYSTEM = 2\n LA_IN_MEMORY = 4\n\n\nclass LicenseMeterAttribute(object):\n def __init__(self, name, allowed_uses, total_uses, gross_uses):\n self.name = name\n self.allowed_uses = allowed_uses\n self.total_uses = total_uses\n self.gross_uses = gross_uses\n\n\nclass LexActivator:\n @staticmethod\n def SetProductFile(file_path):\n \"\"\"Sets the absolute path of the Product.dat file.\n\n This function must be called on every start of your program before any other\n functions are called.\n\n Args:\n file_path (str): absolute path of the product file (Product.dat)\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.SetProductFile(cstring)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetProductData(product_data):\n \"\"\"Embeds the Product.dat file in the application.\n\n It can be used instead of SetProductFile() in case you want to embed the\n Product.dat file in your application.\n\n This function must be called on every start of your program before any other\n functions are called.\n\n Args:\n product_data (str): content of the Product.dat file\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_product_data = LexActivatorNative.get_ctype_string(\n product_data)\n status = LexActivatorNative.SetProductData(cstring_product_data)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetProductId(product_id, flags):\n \"\"\"Sets the product id of your application.\n\n This function must be called on every start of your program before any other\n functions are called, with the exception of SetProductFile() or\n SetProductData() function.\n\n Args:\n product_id (str): the unique product id of your application as mentioned on the product page in the dashboard\n flags (str): depending upon whether your application requires admin/root permissions to run or not, this parameter can have one of the following values: LA_SYSTEM, LA_USER, LA_IN_MEMORY\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_product_id = LexActivatorNative.get_ctype_string(product_id)\n status = LexActivatorNative.SetProductId(cstring_product_id, flags)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n \n @staticmethod\n def SetCustomDeviceFingerprint(fingerprint):\n \"\"\"In case you don't want to use the LexActivator's advanced\n device fingerprinting algorithm, this function can be used to set a custom\n device fingerprint.\n\n If you decide to use your own custom device fingerprint then this function must be\n called on every start of your program immediately after calling SetProductFile()\n or SetProductData() function.\n\n The license fingerprint matching strategy is ignored if this function is used.\n\n Args:\n fingerprint (str): string of minimum length 64 characters and maximum length 256 characters\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_fingerprint = LexActivatorNative.get_ctype_string(\n fingerprint)\n status = LexActivatorNative.SetProductData(cstring_fingerprint)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetLicenseKey(license_key):\n \"\"\"Sets the license key required to activate the license.\n\n Args:\n license_key (str): a valid license key\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_license_key = LexActivatorNative.get_ctype_string(license_key)\n status = LexActivatorNative.SetLicenseKey(cstring_license_key)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetLicenseUserCredential(email, password):\n \"\"\"Sets the license user email and password for authentication.\n\n This function must be called before ActivateLicense() or IsLicenseGenuine()\n function if requireAuthentication property of the license is set to true.\n\n Args:\n email (str): user email address\n password (str): user password\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_email = LexActivatorNative.get_ctype_string(email)\n cstring_password = LexActivatorNative.get_ctype_string(password)\n\n status = LexActivatorNative.SetLicenseUserCredential(\n cstring_email, cstring_password)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetLicenseCallback(license_callback):\n \"\"\"Sets server sync callback function.\n\n Whenever the server sync occurs in a separate thread, and server returns the\n response, event listener function gets invoked with the following status\n codes: LA_OK, LA_EXPIRED, LA_SUSPENDED, LA_E_REVOKED,\n LA_E_ACTIVATION_NOT_FOUND, LA_E_MACHINE_FINGERPRINT LA_E_COUNTRY, LA_E_INET,\n LA_E_SERVER, LA_E_RATE_LIMIT, LA_E_IP\n\n Args:\n license_callback (Callable[int]]): callback function\n\n Raises:\n LexActivatorException\n \"\"\"\n license_callback_fn = LexActivatorNative.CallbackType(license_callback)\n callback_list.append(license_callback_fn)\n status = LexActivatorNative.SetLicenseCallback(license_callback_fn)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetActivationMetadata(key, value):\n \"\"\"Sets the activation metadata.\n\n The metadata appears along with the activation details of the license in\n dashboard.\n\n Args:\n key (str): string of maximum length 256 characters with utf-8 encoding\n value (str): string of maximum length 256 characters with utf-8 encoding\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n cstring_value = LexActivatorNative.get_ctype_string(value)\n status = LexActivatorNative.SetActivationMetadata(\n cstring_key, cstring_value)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetTrialActivationMetadata(key, value):\n \"\"\"Sets the trial activation metadata.\n\n The metadata appears along with the trial activation details of the product\n in dashboard.\n\n Args:\n key (str): string of maximum length 256 characters with utf-8 encoding\n value (str): string of maximum length 256 characters with utf-8 encoding\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n cstring_value = LexActivatorNative.get_ctype_string(value)\n status = LexActivatorNative.SetTrialActivationMetadata(\n cstring_key, cstring_value)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetAppVersion(app_version):\n \"\"\"Sets the current app version of your application.\n\n The app version appears along with the activation details in dashboard. It is\n also used to generate app analytics.\n\n Args:\n app_version (str): string of maximum length 256 characters with utf-8 encoding.\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_app_version = LexActivatorNative.get_ctype_string(app_version)\n\n status = LexActivatorNative.SetAppVersion(cstring_app_version)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetOfflineActivationRequestMeterAttributeUses(name, uses):\n \"\"\"Sets the meter attribute uses for the offline activation request.\n\n This function should only be called before GenerateOfflineActivationRequest()\n function to set the meter attributes in case of offline activation.\n\n Args:\n name (str): name of the meter attribute\n uses (int): the uses value\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n status = LexActivatorNative.SetOfflineActivationRequestMeterAttributeUses(\n cstring_name, uses)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetNetworkProxy(proxy):\n \"\"\"Sets the network proxy to be used when contacting CryptLex servers.\n\n The proxy format should be: [protocol://][username:password@]machine[:port]\n\n Note: \n Proxy settings of the computer are automatically detected. So,\n in most of the cases you don't need to care whether your user is behind a\n proxy server or not.\n\n Args:\n proxy (str): proxy having correct proxy format\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_proxy = LexActivatorNative.get_ctype_string(proxy)\n\n status = LexActivatorNative.SetNetworkProxy(cstring_proxy)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def SetCryptlexHost(host):\n \"\"\"In case you are running Cryptlex on-premise, you can set the host for your\n on-premise server.\n\n Args:\n host (str): the address of the Cryptlex on-premise server\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_host = LexActivatorNative.get_ctype_string(host)\n status = LexActivatorNative.SetCryptlexHost(cstring_host)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetProductMetadata(key):\n \"\"\"Gets the product metadata as set in the dashboard.\n\n Args:\n key (str): metadata key to retrieve the value\n\n Raises:\n LexActivatorException\n\n Returns:\n str: value of metadata for the key\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetProductMetadata(\n cstring_key, buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseMetadata(key):\n \"\"\"Gets the license metadata as set in the dashboard.\n\n Args:\n key (str): metadata key to retrieve the value\n\n Raises:\n LexActivatorException\n\n Returns:\n str: value of metadata for the key\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseMetadata(\n cstring_key, buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseMeterAttribute(name):\n \"\"\"Gets the license meter attribute allowed, total and gross uses.\n\n Args:\n name (str): name of the meter attribute\n\n Raises:\n LexActivatorException\n\n Returns:\n LicenseMeterAttribute: values of meter attribute allowed, total and gross uses\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n allowed_uses = ctypes.c_uint()\n total_uses = ctypes.c_uint()\n gross_uses = ctypes.c_uint()\n status = LexActivatorNative.GetLicenseMeterAttribute(\n cstring_name, ctypes.byref(allowed_uses), ctypes.byref(total_uses), ctypes.byref(gross_uses))\n if status == LexStatusCodes.LA_OK:\n return LicenseMeterAttribute(name, allowed_uses.value, total_uses.value, gross_uses.value)\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetLicenseKey():\n \"\"\"Gets the license key used for activation.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the license key\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseKey(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseAllowedActivations():\n \"\"\"Gets the allowed activations of the license.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the allowed activation\n \"\"\"\n allowed_activations = ctypes.c_uint()\n status = LexActivatorNative.GetLicenseAllowedActivations(\n ctypes.byref(allowed_activations))\n if status == LexStatusCodes.LA_OK:\n return allowed_activations.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetLicenseTotalActivations():\n \"\"\"Gets the total activations of the license.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the total activations\n \"\"\"\n total_activations = ctypes.c_uint()\n status = LexActivatorNative.GetLicenseTotalActivations(\n ctypes.byref(total_activations))\n if status == LexStatusCodes.LA_OK:\n return total_activations.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetLicenseExpiryDate():\n \"\"\"Gets the license expiry date timestamp.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the timestamp\n \"\"\"\n expiry_date = ctypes.c_uint()\n status = LexActivatorNative.GetLicenseExpiryDate(\n ctypes.byref(expiry_date))\n if status == LexStatusCodes.LA_OK:\n return expiry_date.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetLicenseUserEmail():\n \"\"\"Gets the email associated with license user.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the license user email\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseUserEmail(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseUserName():\n \"\"\"Gets the name associated with the license user.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the license user name\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseUserName(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseUserCompany():\n \"\"\"Gets the company associated with the license user.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the license user company\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseUserCompany(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseUserMetadata(key):\n \"\"\"Gets the metadata associated with the license user.\n\n Args:\n key (str): metadata key to retrieve the value\n\n Raises:\n LexActivatorException\n\n Returns:\n str: value of metadata for the key\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseUserMetadata(\n cstring_key, buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLicenseType():\n \"\"\"Gets the license type.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the license type - node-locked or hosted-floating\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLicenseType(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetActivationMetadata(key):\n \"\"\"Gets the activation metadata.\n\n Args:\n key (str): metadata key to retrieve the value\n\n Raises:\n LexActivatorException\n\n Returns:\n str: value of metadata for the key\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetActivationMetadata(\n cstring_key, buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetActivationMeterAttributeUses(name):\n \"\"\"Gets the meter attribute uses consumed by the activation.\n\n Args:\n name (str): name of the meter attribute\n\n Raises:\n LexActivatorException\n\n Returns:\n int: value of meter attribute uses by the activation\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n uses = ctypes.c_uint()\n status = LexActivatorNative.GetActivationMeterAttributeUses(\n cstring_name, ctypes.byref(uses))\n if status == LexStatusCodes.LA_OK:\n return uses.value\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetServerSyncGracePeriodExpiryDate():\n \"\"\"Gets the server sync grace period expiry date timestamp.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the timestamp\n \"\"\"\n expiry_date = ctypes.c_uint()\n status = LexActivatorNative.GetServerSyncGracePeriodExpiryDate(\n ctypes.byref(expiry_date))\n if status == LexStatusCodes.LA_OK:\n return expiry_date.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetTrialActivationMetadata(key):\n \"\"\"Gets the trial activation metadata.\n\n Args:\n key (str): metadata key to retrieve the value\n\n Raises:\n LexActivatorException\n\n Returns:\n str: value of metadata for the key\n \"\"\"\n cstring_key = LexActivatorNative.get_ctype_string(key)\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetTrialActivationMetadata(\n cstring_key, buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetTrialExpiryDate():\n \"\"\"Gets the trial expiry date timestamp.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the timestamp\n \"\"\"\n expiry_date = ctypes.c_uint()\n status = LexActivatorNative.GetTrialExpiryDate(\n ctypes.byref(expiry_date))\n if status == LexStatusCodes.LA_OK:\n return expiry_date.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetTrialId():\n \"\"\"Gets the trial activation id. Used in case of trial extension.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the trial id\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetTrialId(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def GetLocalTrialExpiryDate():\n \"\"\"Gets the local trial expiry date timestamp.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: the timestamp\n \"\"\"\n expiry_date = ctypes.c_uint()\n status = LexActivatorNative.GetLocalTrialExpiryDate(\n ctypes.byref(expiry_date))\n if status == LexStatusCodes.LA_OK:\n return expiry_date.value\n elif status == LexStatusCodes.LA_FAIL:\n return 0\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GetLibraryVersion():\n \"\"\"Gets the version of this library.\n\n Raises:\n LexActivatorException\n\n Returns:\n str: the library version\n \"\"\"\n buffer_size = 256\n buffer = LexActivatorNative.get_ctype_string_buffer(buffer_size)\n status = LexActivatorNative.GetLibraryVersion(buffer, buffer_size)\n if status != LexStatusCodes.LA_OK:\n raise LexActivatorException(status)\n return LexActivatorNative.byte_to_string(buffer.value)\n\n @staticmethod\n def CheckForReleaseUpdate(platform, version, channel, release_callback):\n \"\"\"Checks whether a new release is available for the product.\n\n This function should only be used if you manage your releases through\n Cryptlex release management API.\n\n Args:\n platform (str): release platform e.g. windows, macos, linux\n version (str): current release version\n channel (str): release channel e.g. stable\n release_callback (Callable[int]]): callback function\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_platform = LexActivatorNative.get_ctype_string(platform)\n cstring_version = LexActivatorNative.get_ctype_string(version)\n cstring_channel = LexActivatorNative.get_ctype_string(channel)\n\n release_callback_fn = LexActivatorNative.CallbackType(release_callback)\n callback_list.append(release_callback_fn)\n status = LexActivatorNative.CheckForReleaseUpdate(\n cstring_platform, cstring_version, cstring_channel, release_callback_fn)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def ActivateLicense():\n \"\"\"Activates the license by contacting the Cryptlex servers. It validates the\n key and returns with encrypted and digitally signed token which it stores and\n uses to activate your application.\n\n This function should be executed at the time of registration, ideally on a\n button click.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_EXPIRED, LA_SUSPENDED, LA_FAIL\n \"\"\"\n status = LexActivatorNative.ActivateLicense()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_EXPIRED == status:\n return LexStatusCodes.LA_EXPIRED\n elif LexStatusCodes.LA_SUSPENDED == status:\n return LexStatusCodes.LA_SUSPENDED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def ActivateLicenseOffline(file_path):\n \"\"\"Activates the license using the offline activation response file.\n\n Args:\n file_path (str): path of the offline activation response file.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_EXPIRED, LA_SUSPENDED, LA_FAIL\n \"\"\"\n cstring_file_path = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.ActivateLicenseOffline(cstring_file_path)\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_EXPIRED == status:\n return LexStatusCodes.LA_EXPIRED\n elif LexStatusCodes.LA_SUSPENDED == status:\n return LexStatusCodes.LA_SUSPENDED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GenerateOfflineActivationRequest(file_path):\n \"\"\"Generates the offline activation request needed for generating offline\n activation response in the dashboard.\n\n Args:\n file_path (str): path of the file for the offline request.\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_file_path = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.GenerateOfflineActivationRequest(\n cstring_file_path)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def DeactivateLicense():\n \"\"\"Deactivates the license activation and frees up the correponding activation\n slot by contacting the Cryptlex servers.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_FAIL\n \"\"\"\n status = LexActivatorNative.DeactivateLicense()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GenerateOfflineDeactivationRequest(file_path):\n \"\"\"Generates the offline deactivation request needed for deactivation of the\n license in the dashboard and deactivates the license locally.\n\n A valid offline deactivation file confirms that the license has been\n successfully deactivated on the user's machine.\n\n Args:\n file_path (str): path of the file for the offline deactivation request\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_FAIL\n \"\"\"\n cstring_file_path = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.GenerateOfflineDeactivationRequest(\n cstring_file_path)\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def IsLicenseGenuine():\n \"\"\"It verifies whether your app is genuinely activated or not. The verification\n is done locally by verifying the cryptographic digital signature fetched at\n the time of activation.\n\n After verifying locally, it schedules a server check in a separate thread.\n After the first server sync it periodically does further syncs at a frequency\n set for the license.\n\n In case server sync fails due to network error, and it continues to fail for\n fixed number of days (grace period), the function returns\n LA_GRACE_PERIOD_OVER instead of LA_OK.\n\n This function must be called on every start of your program to verify the\n activation of your app.\n\n Note: \n If application was activated offline using ActivateLicenseOffline() function, \n you may want to set grace period to 0 to ignore grace period.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_EXPIRED, LA_SUSPENDED, LA_GRACE_PERIOD_OVER, LA_FAIL\n \"\"\"\n status = LexActivatorNative.IsLicenseGenuine()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_EXPIRED == status:\n return LexStatusCodes.LA_EXPIRED\n elif LexStatusCodes.LA_SUSPENDED == status:\n return LexStatusCodes.LA_SUSPENDED\n elif LexStatusCodes.LA_GRACE_PERIOD_OVER == status:\n return LexStatusCodes.LA_GRACE_PERIOD_OVER\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def IsLicenseValid():\n \"\"\"It verifies whether your app is genuinely activated or not. The verification\n is done locally by verifying the cryptographic digital signature fetched at\n the time of activation.\n\n This is just an auxiliary function which you may use in some specific cases,\n when you want to skip the server sync.\n\n Note: \n You may want to set grace period to 0 to ignore grace period.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_EXPIRED, LA_SUSPENDED, LA_GRACE_PERIOD_OVER, LA_FAIL\n \"\"\"\n status = LexActivatorNative.IsLicenseValid()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_EXPIRED == status:\n return LexStatusCodes.LA_EXPIRED\n elif LexStatusCodes.LA_SUSPENDED == status:\n return LexStatusCodes.LA_SUSPENDED\n elif LexStatusCodes.LA_GRACE_PERIOD_OVER == status:\n return LexStatusCodes.LA_GRACE_PERIOD_OVER\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def ActivateTrial():\n \"\"\"Starts the verified trial in your application by contacting the Cryptlex\n servers.\n\n This function should be executed when your application starts first time on\n the user's computer, ideally on a button click.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_TRIAL_EXPIRED\n \"\"\"\n status = LexActivatorNative.ActivateTrial()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_TRIAL_EXPIRED == status:\n return LexStatusCodes.LA_TRIAL_EXPIRED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def ActivateTrialOffline(file_path):\n \"\"\"Activates the trial using the offline activation response file.\n\n Args:\n file_path (str): path of the offline activation response file\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_TRIAL_EXPIRED, LA_FAIL\n \"\"\"\n cstring_file_path = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.ActivateTrialOffline(cstring_file_path)\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_TRIAL_EXPIRED == status:\n return LexStatusCodes.LA_TRIAL_EXPIRED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def GenerateOfflineTrialActivationRequest(file_path):\n \"\"\"Generates the offline trial activation request needed for generating offline\n trial activation response in the dashboard.\n\n Args:\n file_path (str): path of the file for the offline request\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_file_path = LexActivatorNative.get_ctype_string(file_path)\n status = LexActivatorNative.GenerateOfflineTrialActivationRequest(\n cstring_file_path)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def IsTrialGenuine():\n \"\"\"It verifies whether trial has started and is genuine or not. The verification\n is done locally by verifying the cryptographic digital signature fetched at\n the time of trial activation.\n\n This function must be called on every start of your program during the trial\n period.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_TRIAL_EXPIRED, LA_FAIL\n \"\"\"\n status = LexActivatorNative.IsTrialGenuine()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_TRIAL_EXPIRED == status:\n return LexStatusCodes.LA_TRIAL_EXPIRED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def ActivateLocalTrial(trialLength):\n \"\"\"Starts the local (unverified) trial.\n\n This function should be executed when your application starts first time on\n the user's computer, ideally on a button click.\n\n Args:\n trialLength (int): trial length in days\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_LOCAL_TRIAL_EXPIRED, LA_FAIL\n \"\"\"\n status = LexActivatorNative.ActivateLocalTrial(trialLength)\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_TRIAL_EXPIRED == status:\n return LexStatusCodes.LA_LOCAL_TRIAL_EXPIRED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def IsLocalTrialGenuine():\n \"\"\"It verifies whether trial has started and is genuine or not. The verification\n is done locally.\n\n This function must be called on every start of your program during the trial period.\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_LOCAL_TRIAL_EXPIRED, LA_FAIL\n \"\"\"\n status = LexActivatorNative.IsLocalTrialGenuine()\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_TRIAL_EXPIRED == status:\n return LexStatusCodes.LA_LOCAL_TRIAL_EXPIRED\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def ExtendLocalTrial(trialExtensionLength):\n \"\"\"Extends the local trial.\n\n This function is only meant for unverified trials.\n\n Args:\n trialExtensionLength (int): number of days to extend the trial\n\n Raises:\n LexActivatorException\n\n Returns:\n int: LA_OK, LA_FAIL\n \"\"\"\n status = LexActivatorNative.ExtendLocalTrial(trialExtensionLength)\n if LexStatusCodes.LA_OK == status:\n return LexStatusCodes.LA_OK\n elif LexStatusCodes.LA_FAIL == status:\n return LexStatusCodes.LA_FAIL\n else:\n raise LexActivatorException(status)\n\n @staticmethod\n def IncrementActivationMeterAttributeUses(name, increment):\n \"\"\"Increments the meter attribute uses of the activation.\n\n Args:\n name (str): name of the meter attribute\n increment (int): the increment value\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n status = LexActivatorNative.IncrementActivationMeterAttributeUses(\n cstring_name, increment)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def DecrementActivationMeterAttributeUses(name, decrement):\n \"\"\"Decrements the meter attribute uses of the activation.\n\n Args:\n name (str): name of the meter attribute\n decrement (int): the decrement value\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n status = LexActivatorNative.DecrementActivationMeterAttributeUses(\n cstring_name, decrement)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def ResetActivationMeterAttributeUses(name):\n \"\"\"Resets the meter attribute uses of the activation.\n\n Args:\n name (str): name of the meter attribute\n\n Raises:\n LexActivatorException\n \"\"\"\n cstring_name = LexActivatorNative.get_ctype_string(name)\n status = LexActivatorNative.ResetActivationMeterAttributeUses(\n cstring_name)\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n\n @staticmethod\n def Reset():\n \"\"\"Resets the activation and trial data stored in the machine.\n\n This function is meant for developer testing only.\n\n Raises:\n LexActivatorException\n \"\"\"\n status = LexActivatorNative.Reset()\n if LexStatusCodes.LA_OK != status:\n raise LexActivatorException(status)\n","sub_path":"cryptlex/lexactivator/lexactivator.py","file_name":"lexactivator.py","file_ext":"py","file_size_in_byte":39417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"408463265","text":"#!/usr/local/bin/python\n\nimport subprocess, time\n\nhosts = ('8.8.8.8', 'kernel.org', 'yahoo.com')\n\ndef ping(host):\n ret = subprocess.call(['ping', '-c', '1', '-W', '5', host],\n stdout=open('/dev/null', 'w'),\n stderr=open('/dev/null', 'w'))\n return ret == 0 #return 1 in not connection case\n\ndef main():\n status = False\n\n for h in hosts:\n if(~status):\n if ping(h):\n status = True\n \n if status:\n print(\"internet available\")\n else:\n print(\"internet not available\") \n\nmain()","sub_path":"internet.py","file_name":"internet.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"586181934","text":"import itertools\nimport logging\nimport os\n\nimport regex\nfrom indic_transliteration import sanscript\n\nfrom curation_utils.file_helper import get_storage_name\nfrom doc_curation import md\nfrom doc_curation.md.file import MdFile\n\nimport doc_curation.md\nfrom curation_utils import scraping, file_helper\nfrom doc_curation.scraping.html_scraper.souper import get_tags_matching_css\n\nBASE_URL = \"https://sambhashanasandesha.in\"\n\n\ndef get_title(div):\n title_css_list = [\"h1\", \"h2\", \"h3\", \"h4\", \"h5\", \"h6\", \"p\"]\n paras = get_tags_matching_css(soup=div, css_selector_list=title_css_list)\n title = \" \".join([x.text.strip() for x in itertools.takewhile(lambda x: x.name.lower()[0] == \"h\", paras)])\n if title == \"\":\n title = \" \".join([x.text.strip() for x in itertools.takewhile(lambda x: x.get(\"style\", \"\") == \"text-align:center\", paras)])\n if title == \"\":\n title = \" \".join([x.text.strip() for x in itertools.takewhile(lambda x: x.get(\"style\", \"\") == \"strong\", paras)])\n if title == \"\":\n title = \" \".join([x.text.strip() for x in paras])\n return file_helper.clear_bad_chars(title)[:30]\n\ndef dump_month(url, dest_path_month):\n soup = scraping.get_soup(url=url)\n content_divs = soup.select(\"#showData>div\")\n for index, div in enumerate(content_divs):\n title = f\"{(index + 1):02d} \" + get_title(div)\n file_name = f\"{get_storage_name(text=title, max_length=20, source_script=sanscript.DEVANAGARI)}.md\"\n dest_path = os.path.join(dest_path_month, file_name)\n if os.path.exists(dest_path):\n logging.info(f\"Skipping {dest_path}\")\n continue\n md_file = MdFile(file_path=dest_path)\n content = md.get_md_with_pandoc(content_in=str(div), source_format=\"html\")\n content = content.replace(\":\", \"ः\").replace(\"\\n# \", \"\\n## \")\n md_file.dump_to_file(metadata={\"title\": title}, content=content, dry_run=False)\n\ndef dump_year(year, dest_path):\n url = f\"{BASE_URL}/months?year={year}\"\n soup = scraping.get_soup(url=url)\n month_urls = [os.path.join(BASE_URL, x[\"href\"]) for x in soup.find_all(\"a\") if x.text.strip() == \"Unicode\" ]\n num_months = len(month_urls)\n logging.info(f\"Months in {year}: {num_months}\")\n for index, month_url in enumerate(month_urls):\n dump_month(month_url, os.path.join(dest_path, str(year), \"%02d\" % (num_months - index)))","sub_path":"doc_curation/scraping/misc_sites/sambhashana_sandesha.py","file_name":"sambhashana_sandesha.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"633959762","text":"\nimport math\n\n\ndef filetolist(file): #to transform file in list\n list=[]\n newlist=[]\n f=open(file, \"r\")\n for l in f:\n datas = [chiffres for chiffres in l.split(',')]\n list.append(datas)\n del list[0]\n for val in list:\n datas = [int(data) for data in val]\n newlist.append(datas)\n return newlist\n\n#%%\ndef BuildDecisionTree(file, Nmin,dclass):\n tree=[]\n newlist =filetolist(file)\n A=[[[1,2],[2,3]],[[0,1],[1,2]]] #list of constraints\n newlist1=[]\n newlist2=[]\n survivors=0 #choose a root : Sex\n for val in newlist:\n if val[-1]==1:\n survivors+=1\n deads = len(newlist)-survivors\n gini = 1 -(survivors/len(newlist))**2 -(deads/len(newlist))**2\n \n for val in newlist:\n if val[0] ==0:\n newlist1.append(val)\n else:\n newlist2.append(val)\n tree.append(['node',1,0,0,[0],survivors,deads,gini]) #tree= list of list of type :\n #[{node/leaf},{number of the node/leaf},{level},\n #{ level of the constraint(only for node, 0 for Pclass, 1 for Embarked)},\n #{Class(for leaf)/constraints(for node)}\n #{nb of survivors}, {nb of deads},{gini}]\n\n \n BuildTree(newlist1,A,Nmin,2,dclass,tree) #directed to the recursive function BuilTree\n BuildTree(newlist2,A,Nmin,3,dclass,tree)\n return tree\n \n\n\ndef BuildTree(list,A, Nmin, v, dclass,tree): \n newlist1=[]\n newlist2=[]\n datalist=[]\n same=1\n level=int(math.floor(math.log(v)/math.log(2)))\n survivors=0\n for val in list:\n if val[-1]==1:\n survivors+=1\n if val[0:3]!=list[0][0:3]:\n same = 0\n deads = len(list)-survivors\n gini = 1 -(survivors/len(list))**2 -(deads/len(list))**2\n if v==6 or v==7:\n print(v, list)\n \n #leaf\n if survivors==0:\n tree.append(['leaf',v,level,0,survivors,deads,0])\n print(v,'Aucun survivant')\n elif survivors ==len(list):\n tree.append(['leaf',v,level,1,survivors,deads,0])\n print(v,'Que des survivants')\n elif same:\n if survivors>deads:\n tree.append(['leaf',v,level,1,survivors,deads,gini])\n else:\n tree.append(['leaf',v,level,0,survivors,deads,gini])\n print(v,'mêmes attributs')\n \n elif len(list) 0 and len(newlist2) > 0:\n ginisplit =GINI(newlist1,newlist2)\n datalist.append([ginisplit,newlist1,newlist2,i+1,constraint])\n \n datalist.sort()\n tree.append(['node',v,level,datalist[0][3],datalist[0][4],survivors,deads,gini])\n print(v,'Noeud')\n \n newlist1,newlist2=datalist[0][1],datalist[0][2]\n BuildTree(newlist1,A,Nmin,2*v,dclass,tree)\n BuildTree(newlist2,A,Nmin,2*v+1,dclass,tree)\n\n\n\ndef GINI(datalist1,datalist2): #returns the gini split of a node correponding to the two new list\n n1=len(datalist1)\n n2=len(datalist2)\n n=n1+n2\n nclass01=0\n nclass02=0\n for val in datalist1:\n if val[3]==0:\n nclass01+=1\n gini1= 1 -(nclass01/n1)**2 -((n1-nclass01)/n1)**2\n \n for val in datalist2:\n if val[3]==0:\n nclass02+=1\n gini2= 1 -(nclass02/n2)**2 -((n2-nclass02)/n2)**2\n gini= (n1/n)*gini1 + (n2/n)*gini2\n return gini\n\n\n#%%\n\ndef takeSecond(val):\n return val[1]\n\ndef printDecisionTree(tree):\n tree.sort(key=takeSecond)\n features=['Sex','Pclass','Embarked']\n level=0\n for val in tree:\n if level != val[2]:\n level=val[2]\n print(\"\\n\")\n elif level !=0:\n print(\"**************\")\n \n \n \n if val[1]==1:\n print(\"Root\"+\"\\nLevel : \", val[2],\"\\nFeature : \"+features[val[3]]+\" \",val[4][0],\"\\nGini : \",val[-1])\n elif val[0]=='node':\n print(\"Intermediate\"+\"\\nLevel : \", val[2],\"\\nFeature : \"+features[val[3]]+\" \",val[4][0],val[4][1],\"\\nGini : \",val[-1])\n else:\n print(\"leaf\"+\"\\nLevel : \", val[2],\"\\nClass : \",val[3],\"\\nGini : \",val[-1])\n\ndef generalizationError(tree, alpha):\n n=0\n nbleaf=0 \n trainerror=0\n \n for val in tree:\n \n if val[0]==\"leaf\":\n nbleaf+=1\n n+=val[4]+val[5]\n if val[3]==0:\n trainerror+=val[4]\n else:\n trainerror+=val[5]\n generror = (trainerror + alpha*nbleaf)/n\n return generror\n \n#%%\ndef pruneTree(tree,Nmax,Nmin, alpha):\n newtree=list([list(val) for val in tree]) #creates a new tree in order to modify it and compare the error with the original tree\n final=1\n n=len(tree)\n print('Nouvelle recursivité n:',n)\n if Nmax==0:\n return newtree\n\n for i in reversed(range(0,n-1)):\n if newtree[i][0]=='leaf' and newtree[i+1][0]=='leaf' and newtree[i][1]//2 == newtree[i+1][1]//2: #two leaves from the same father\n father = newtree[i][1]//2 #nb of the node of the father\n for val in newtree:\n if val[1] == father:\n val[0]='leaf'\n del val[3]\n totalsurvivors=newtree[i][4]+newtree[i+1][4]\n totaldeads=newtree[i][5]+newtree[i+1][5]\n total = totaldeads+totalsurvivors\n if totaldeads >= totalsurvivors or total 0:\n marsh_hori[i][j] = marsh_hori[i][j-1] + 1\n if i > 0:\n marsh_vert[i][j] = marsh_vert[i-1][j] + 1\n else:\n marsh_vert[i][j] = -1\n marsh_hori[i][j] = -1\n\n return marsh_hori, marsh_vert\n\n\ndef calculate_perimeter(n, m, marsh_vert, marsh_hori, obstacles):\n max_perimeter = 0\n # start from bottom right\n for start_i in range(n-1, 0, -1):\n for start_j in range(m-1, 0, -1):\n if obstacles[start_i][start_j] is True:\n continue\n up_stride = marsh_vert[start_i][start_j]\n left_stride = marsh_hori[start_i][start_j]\n\n if up_stride == 0 or left_stride == 0:\n continue\n\n # calculate maximum left extent that is reachable\n top_left_i = start_i - up_stride\n top_left_j = start_j - left_stride\n\n for i in range(top_left_i, start_i):\n # keep up_stride constant and check all left strides\n for j in range(top_left_j, start_j):\n if obstacles[i][j] is True:\n continue\n down_sweep = marsh_vert[start_i][j]\n if down_sweep >= start_i - i:\n # once down sweep works check for left sweep\n left_sweep = marsh_hori[i][start_j] - marsh_hori[i][j]\n if left_sweep >= (start_j - j):\n # max rect in left sweep found\n l = start_j - j\n b = start_i - i\n perimeter = 2 * (l + b)\n max_perimeter = max(max_perimeter, perimeter)\n break\n\n return max_perimeter\n\n\nif __name__ == '__main__':\n [n, m] = [int(x) for x in input().split()]\n obstacles = [[] for x in range(n)]\n for i in range(n):\n line = str(input())\n obstacles[i].extend([True if x is 'x' else False for x in line])\n\n hori, vert = create_marsh(n, m,obstacles)\n\n max_perimeter = calculate_perimeter(n, m, vert, hori, obstacles)\n if max_perimeter == 0:\n print(\"impossible\")\n else:\n print (max_perimeter)\n","sub_path":"KMarsh.py","file_name":"KMarsh.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"590916905","text":"#!/usr/bin/python3.4\n\nimport ev3dev.ev3 as ev3\nfrom time import sleep\n\nimport signal\n\n\nbtn = ev3.Button()\n\nmA = ev3.LargeMotor('outA')\nmB = ev3.LargeMotor('outB')\n\nTHRESHOLD_LEFT = 30 \nTHRESHOLD_RIGHT = 350\n\nBASE_SPEED = 30\nTURN_SPEED = 80\n\ngy = ev3.GyroSensor('in2')\n#lightSensorRight = ev3.LightSensor('in2') \n\nTouchSensor = ev3.TouchSensor('in3')\n\ngy.mode = 'GYRO-ANG'\n\nassert gy.connected, \"Gyro sensor is not connected\"\n#assert lightSensorRight.connected, \"LightSensorRight(LightSensor) is not conected\"\n\nassert TouchSensor.connected, \"Touch sensor is not connected\"\n\n#colors = ('unknown','black','blue','green', 'yellow', 'red', 'white', 'brown')\n\nunit = gy.units\n\nmB.run_direct()\nmA.run_direct()\n\n\nmA.polarity = \"inversed\"\nmB.polarity = \"inversed\"\n\ndef signal_handler(sig, frame):\n\tprint('Shutting down gracefully')\n\tmA.duty_cycle_sp = 0\n\tmB.duty_cycle_sp = 0\n\n\texit(0)\n\nsignal.signal(signal.SIGINT, signal_handler)\nprint('Press Ctrl+C to exit')\n\n\nwhile True:\n\tmA.duty_cycle_sp = BASE_SPEED\n\tmB.duty_cycle_sp = BASE_SPEED\n\ttou_val = TouchSensor.value()\n\n\tif tou_val == 1:\n\t\tev3.Sound.beep().wait()\n\t\tmA.duty_cycle_sp = 0\n\t\tmB.duty_cycle_sp = 0\n\t\texit()\n\telse:\n\t\tang = gy.value()\n\t\tprint(str(ang) + \" \" + unit)\n\t\t#print(\"Touch sensor value: \", tou_val)\n#\tsensorLeft = lightSensorLeft.value()\n#\tsensorRight = lightSensorRight.value()\n\n#\tprint(\"sensorLeft: \", sensorLeft, \" sensorRight: \", sensorRight)\n#\tif sensorRight < THRESHOLD_RIGHT:\n#\t\tmA.duty_cycle_sp = TURN_SPEED\n#\telse:\n#\t\tmA.duty_cycle_sp = BASE_SPEED\n\t\n\n#\tif sensorLeft < THRESHOLD_LEFT:\n\t\n#\telse:\n\t\n\n","sub_path":"robotprogram/examples/example_gy.py","file_name":"example_gy.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"39384821","text":"\nclass Tetrapyrrole (Mol):\n\n def get_tetrapyrrole_atoms(self):\n if not self.is_tetrapyrrole():\n raise ValueError(\"Molecule must be a tetrapyrrole !\")\n filtered_bondmap = []\n for bond in self.bondmap:\n atom1 = self.atoms[bond[0]]; atom2 = self.atoms[bond[1]]\n if (atom1 == \"C\" or atom1 == \"N\") and (atom2 == \"C\" or atom2 == \"N\"):\n filtered_bondmap.append(bond)\n Nindicis = []\n for i, atom in enumerate(self.atoms):\n if atom == \"N\": Nindicis.append(i)\n if len(Nindicis) == 4:\n CenterNs = Nindicis\n else:\n NNeighborsDict = {}\n for n in Nindicis:\n NNeighbors = []\n for i in multi_neighbors(n, 4, filtered_bondmap):\n if i in Nindicis: NNeighbors.append(i)\n NNeighborsDict[n] = NNeighbors\n Stop = False\n for n1 in Nindicis:\n if len(self.neighbors_of(n1)) < 3:\n continue\n forbidden = []; forbidden.append(n1)\n for n2 in [i for i in NNeighborsDict[n1] if (not i in forbidden)]:\n forbidden.append(n2)\n for n3 in [i for i in NNeighborsDict[n2] if (not i in forbidden)]:\n forbidden.append(n3)\n for n4 in [i for i in NNeighborsDict[n3] if (not i in forbidden)]:\n if (n1 in multi_neighbors(n4, 3, filtered_bondmap)) or (n1 in NNeighborsDict[n4]):\n Stop = True\n if Stop == True:\n CenterNs = [n1, n2, n3, n4]\n break\n if Stop: break\n if Stop: break\n if Stop: break\n betas = set(); mesos = set()\n for i in CenterNs:\n nbs = multi_neighbors(i, 2, filtered_bondmap)\n for nb in nbs:\n Break = False; beta = False\n for n in CenterNs:\n if n in neighbors(nb, filtered_bondmap): Break = True\n if Break: break\n for k in neighbors(nb, filtered_bondmap):\n if k in nbs: beta = True\n if beta == True: betas.add(nb)\n else: mesos.add(nb)\n if Break: break\n return list(CenterNs), list(betas), list(mesos)\n\n def as_smiles_vec(self):\n rdmol = to_rdkit_Mol(self)\n CenterNs, betas, mesos = self.get_tetrapyrrole_atoms()\n Nneighbors = self.neighbors_of(CenterNs)\n ligands = []; CentralMetal = None\n for nb in Nneighbors:\n if not self.atoms[nb] == \"C\":\n if self.atoms[nb] == \"H\":\n CentralMetal = 1\n break\n else:\n neighbors = self.neighbors_of(nb)\n if all((el in neighbors) for el in CenterNs): # checks if neighbours of central metal are all pyrrolic nitrogens.\n CentralMetal = Element(self.atoms[nb]).Z\n for n in neighbors:\n if not n in CenterNs:\n frags = Chem.rdmolops.FragmentOnBonds(copy(rdmol), [self.bondmap.index(self.get_bond(n, nb))])\n frags_idx = Chem.GetMolFrags(frags)\n frags = Chem.GetMolFrags(frags, asMols=True, sanitizeFrags=False)\n if not len(frags) == 2:\n print(\"Unsupported tetrapyrrole structure\")\n return None\n for f, i in zip(frags, frags_idx):\n if n in i:\n ligands.append(Chem.MolToSmiles(f))\n meso_subs = []\n for meso in mesos:\n for nb in self.neighbors_of(meso):\n if not nb in Nneighbors:\n frags = Chem.rdmolops.FragmentOnBonds(copy(rdmol), [self.bondmap.index(self.get_bond(meso, nb))])\n frags_idx = Chem.GetMolFrags(frags)\n frags = Chem.GetMolFrags(frags, asMols=True, sanitizeFrags=False)\n if not len(frags) == 2:\n print(\"Unsupported tetrapyrrole structure\")\n return None\n for f, i in zip(frags, frags_idx):\n if nb in i:\n meso_subs.append(Chem.MolToSmiles(f))\n beta_subs = []\n for beta in betas:\n for nb in self.neighbors_of(beta):\n if (not nb in Nneighbors) and (not nb in betas):\n frags = Chem.rdmolops.FragmentOnBonds(copy(rdmol), [self.bondmap.index(self.get_bond(beta, nb))])\n frags_idx = Chem.GetMolFrags(frags)\n frags = Chem.GetMolFrags(frags, asMols=True, sanitizeFrags=False)\n if len(frags) == 2:\n for f, i in zip(frags, frags_idx):\n if nb in i:\n beta_subs.append(Chem.MolToSmiles(f))\n else:\n for n in self.neighbors_of(beta):\n if n in betas:\n for bn in self.neighbors_of(n):\n if (not bn in Nneighbors) and (not bn in betas):\n frags = Chem.rdmolops.FragmentOnBonds(copy(rdmol), [self.bondmap.index(self.get_bond(n, bn)), self.bondmap.index(self.get_bond(beta, nb))])\n frags_idx = Chem.GetMolFrags(frags)\n frags = Chem.GetMolFrags(frags, asMols=True, sanitizeFrags=False)\n if not len(frags) == 2:\n print(\"unsupported tetrapyrrole structure\")\n return None\n for f, i in zip(frags, frags_idx):\n if bn in i:\n beta_subs.append(Chem.MolToSmiles(f))\n return CentralMetal, ligands, meso_subs, beta_subs\n\n def is_tetrapyrrole(self):\n rdmol = to_rdkit_Mol(self)\n pattlist = [\"C1C=CC=N1\", \"C1=CC=CN1\", \"C1C=CCN1\"]\n match_counter = 0\n for smtpatt in pattlist:\n patt = Chem.MolFromSmarts(smtpatt)\n match_counter += len(rdmol.GetSubstructMatches(patt))\n return match_counter == 4\n\n def from_smiles(self, string: str):\n if not \" \" in string:\n mol = Chem.MolFromSmiles(string, sanitize = False)\n else:\n vec = string.split(\" \")\n if not '0' in vec[0]:\n metal = vec[0]\n ligands = []\n for i in range(13, 15):\n if not '0' in vec[i]:\n ligands.append(vec[i])\n else:\n metal = None\n ligands = None\n betas = []\n for i in range(1, 9):\n if not '*H' in vec[i]:\n betas.append(vec[i])\n else:\n betas.append(None)\n if '0' in vec[12]:\n tetrapyrrole_type = 'corrole'\n else:\n tetrapyrrole_type = 'porphyrin'\n mesos = []\n for i in range(4):\n if not '0' in vec[i + 9]:\n mesos.append(vec[i + 9])\n self.from_subtituents(tetrapyrrole_type, betas, mesos, metal, ligands)\n\n def estimate_geometry(self):\n mol = to_rdkit_Mol(self)\n mol.UpdatePropertyCache(strict = False)\n Chem.GetSymmSSSR(mol)\n new_mol = Chem.rdchem.RWMol(mol); neighbors = []; centerM = None\n for atom in mol.GetAtoms():\n if len(atom.GetBonds()) > 3 and atom.GetAtomicNum() > 11:\n centerM = atom; new_mol.RemoveAtom(atom.GetIdx())\n m_idx = centerM.GetIdx()\n for n in atom.GetNeighbors():\n idx = n.GetIdx()\n if idx < m_idx:\n neighbors.append(idx)\n else:\n neighbors.append(idx - 1)\n AllChem.EmbedMolecule(new_mol)\n if not centerM == None:\n new_mol = Chem.rdchem.RWMol(new_mol)\n frags_idxs = list(Chem.rdmolops.GetMolFrags(new_mol))\n if len(frags_idxs) > 1:\n frags_idxs.pop([len(f) for f in frags_idxs].index(max([len(f) for f in frags_idxs])))\n idx = new_mol.AddAtom(centerM)\n conf = new_mol.GetConformer()\n for index, ligand in enumerate(frags_idxs):\n diff = (np.mod(index + 1, 2) + 1) * np.ones(3)\n for i in list(ligand):\n vec = conf.GetAtomPosition(i)\n vec = np.array([vec.x, vec.y, vec.z])\n vec = vec + diff\n conf.SetAtomPosition(i, Point3D(vec[0], vec[1], vec[2]))\n coord = np.zeros(3)\n for n in neighbors:\n vec = conf.GetAtomPosition(n)\n coord += np.array([vec.x, vec.y, vec.z])\n new_mol.AddBond(n, idx, order=Chem.rdchem.BondType.DATIVE)\n coord = coord / len(neighbors)\n conf.SetAtomPosition(idx, Point3D(coord[0], coord[1], coord[2]))\n new_mol.UpdatePropertyCache(strict = False)\n Chem.GetSymmSSSR(new_mol)\n self.from_rdkit_mol(new_mol)\n self.add_hydrogens()\n self.MMGeoOpt(StepNum=1000)\n\n def from_subtituents(self, tetrapyrrole_type, betas = None, mesos = None, metal = None, ligands = None):\n '''\n Function takes vectors of smiles strings and inserts them to a tetrapyrrolic form.\n The vectors are of beta, meso, central metal and ligand subs.\n The smiles (except metal smiles) has to have a dummy atom connected to the atom that connects to the ring. \n Hydrogens should be written as None.\n '''\n porphyrin_tup = (Chem.rdmolops.RemoveHs(Chem.MolFromSmiles('[NH]1c2cc3nc(cc4ccc(cc5C=Cc(n5)cc1cc2)[NH]4)C=C3')), [9, 10, 14, 15, 20, 21, 24, 23], [12, 18, 3, 7])\n corrole_tup = (Chem.rdmolops.RemoveHs(Chem.MolFromSmiles('N=1C=2C=CC=1c1[NH]c(C=C3C=CC(=Cc4ccc([NH]4)C=2)N3)cc1')), [3, 4, 23, 22, 11, 12, 16, 17], [9, 14, 20])\n tetrapyrrole_tup = {'porphyrin': porphyrin_tup, 'corrole': corrole_tup}\n mol, beta_idxs, meso_idxs = tetrapyrrole_tup[tetrapyrrole_type]\n beta_idxs = [idx - 1 for idx in beta_idxs]\n meso_idxs = [idx - 1 for idx in meso_idxs]\n Chem.rdmolops.Kekulize(mol)\n if not metal == None:\n metal = Chem.MolFromSmiles(metal)\n combo = Chem.EditableMol(Chem.CombineMols(mol, metal))\n for idx, atom in enumerate(mol.GetAtoms()):\n if atom.GetAtomicNum() == 7:\n combo.AddBond(idx, mol.GetNumAtoms(), order = Chem.rdchem.BondType.SINGLE)\n mol = combo.GetMol()\n if not ligands == None:\n metal_idx = mol.GetNumAtoms() - 1\n for ligand in ligands:\n if not ligand == None:\n ligand = Chem.MolFromSmiles(ligand, sanitize = False)\n Chem.rdmolops.Kekulize(ligand)\n atoms = [atom.GetAtomicNum() for atom in ligand.GetAtoms()]\n binding_atom_idx = ligand.GetAtomWithIdx(atoms.index(0)).GetNeighbors()[0].GetIdx()\n\n combo = Chem.EditableMol(Chem.CombineMols(mol, ligand))\n combo.AddBond(metal_idx, binding_atom_idx + mol.GetNumAtoms(), order = Chem.rdchem.BondType.SINGLE)\n combo.RemoveAtom(atoms.index(0) + mol.GetNumAtoms())\n mol = combo.GetMol()\n if not betas == None:\n for beta, beta_idx in zip(betas, beta_idxs):\n if not beta == None:\n beta = Chem.MolFromSmiles(beta)\n Chem.rdmolops.Kekulize(beta)\n atoms = [atom.GetAtomicNum() for atom in beta.GetAtoms()]\n binding_atom_idx = beta.GetAtomWithIdx(atoms.index(0)).GetNeighbors()[0].GetIdx()\n\n combo = Chem.rdchem.EditableMol(Chem.CombineMols(mol, beta))\n combo.AddBond(beta_idx, binding_atom_idx + mol.GetNumAtoms(), order = Chem.rdchem.BondType.SINGLE)\n combo.RemoveAtom(atoms.index(0) + mol.GetNumAtoms())\n mol = combo.GetMol()\n if not mesos == None:\n for meso, meso_idx in zip(mesos, meso_idxs):\n if not meso == None:\n meso = Chem.MolFromSmiles(meso)\n Chem.rdmolops.Kekulize(meso)\n atoms = [atom.GetAtomicNum() for atom in meso.GetAtoms()]\n binding_atom_idx = meso.GetAtomWithIdx(atoms.index(0)).GetNeighbors()[0].GetIdx()\n combo = Chem.EditableMol(Chem.CombineMols(mol, meso))\n combo.AddBond(meso_idx, binding_atom_idx + mol.GetNumAtoms(), order = Chem.rdchem.BondType.SINGLE)\n combo.RemoveAtom(atoms.index(0) + mol.GetNumAtoms())\n mol = combo.GetMol()\n self.from_rdkit_mol(mol, kekulize=False)","sub_path":"Legendary/Tetrapyrrole.py","file_name":"Tetrapyrrole.py","file_ext":"py","file_size_in_byte":13452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"410651863","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport sqlite3\nimport statsmodels.api as sm\nfrom statsmodels.iolib.table import SimpleTable\nimport warnings\n\nstart_date = datetime.strptime(\"2009-01-01\", \"%Y-%m-%d\")\nfinish_date = datetime.strptime(\"2009-12-31 23:59\", \"%Y-%m-%d %H:%M\")\nnumber_tower = \"Data_1st\"\n\nquery = '''SELECT Date, %s FROM Data WHERE Date >= \\'%s\\' AND Date <= \\'%s\\';''' % (\n str(number_tower), start_date, finish_date)\nconn = sqlite3.connect(\"mydatabase.db\")\ncursor = conn.cursor()\ndataset = pd.read_sql_query(query, conn)\ndataset.Date = dataset['Date'].apply(pd.to_datetime)\ndataset.index = dataset.Date\ndataset.drop('Date', axis='columns', inplace=True)\ndataset.replace(0, dataset.mean(), inplace=True)\n\nres = dataset.describe()\ndataset.hist()\nprint(res)\nV = res.Data_1st[\"std\"] / res.Data_1st[\"mean\"]\nprint(f\"V = {V}\")\n\nrow = ['JB', 'p-value', 'skew', 'kurtosis']\njb_test = sm.stats.stattools.jarque_bera(dataset)\na = np.vstack([jb_test])\nres = SimpleTable(a, row)\nprint(res)\ntest = sm.tsa.adfuller(dataset.Data_1st)\nprint(test)\nprint('adf: ', test[0])\nprint('p-value: ', test[1])\nprint('Critical values: ', test[4])\nif test[0] > test[4]['5%']:\n print('Есть единичные корни, ряд не стационарен')\nelse:\n print('Единичных корней нет, ряд стационарен')\n\nwarnings.filterwarnings(\"ignore\") # отключает предупреждения\nsrc_data_model = dataset[:\"2009-08-31\"]\nmodel = sm.tsa.statespace.SARIMAX(src_data_model,\n order=(1, 1, 1),\n seasonal_order=(1, 1, 1, 12),\n enforce_stationarity=False,\n enforce_invertibility=False)\nres = model.fit()\n# res.plot_diagnostics(figsize=(15, 12))\n\npred = res.predict(pd.to_datetime('2009-01-01'), pd.to_datetime('2009-12-31'), dynamic=False)\npred = pd.DataFrame(pred)\ndataset.plot(figsize=(12, 6))\nax = plt.gca()\npred.plot(ax=ax)\nplt.show()\nconn.close()\n","sub_path":"Earthquake_predict/SARIMAX.py","file_name":"SARIMAX.py","file_ext":"py","file_size_in_byte":2087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"535800651","text":"from fpdf import FPDF\nimport datetime\nimport shutil\n\nclass sample:\n name = \"Sample Name\"\n width = 10\n height = 20\n age = 0\n\n\nclass fpdf_handler(FPDF):\n date = \"0\"\n time = \"0\"\n date_today = \"0\"\n date_and_time = \"0\"\n def set_time(self):\n self.date_today = datetime.datetime.today()\n self.date = self.date_today.strftime(\"%d:%m:%Y\")\n self.time = self.date_today.strftime(\"%H:%M:%S\")\n self.date_and_time = self.date_today.strftime(\"%Y:%m:%d--%H:%M:%S\")\n\n def header(self):\n # Set up a logo\n self.image('mini_logo.png', 10, 8, 33)\n\n self.set_font('Arial', 'I', 15)\n\n # Add test time\n self.cell(150)\n self.cell(0, 0, 'Date: ' + self.date, ln=1, align=\"L\")\n self.cell(150)\n self.cell(0, 10, 'Time: ' + self.time, ln=1, align=\"L\")\n self.cell(0, 10, \"COF Test Results\", ln=1, align=\"C\")\n # Line break\n self.ln(20)\n\n def footer(self):\n self.set_y(-10)\n\n self.set_font('Arial', 'I', 8)\n\n self.cell(0,10,\"This Test has been done by the rules of ISO 8295:1995\")\n # Add a page number\n page = 'Page ' + str(self.page_no()) + '/{nb}'\n self.cell(0, 10, page, 0, 0, 'C')\n\n def print_obj(self, obj):\n print(obj.name)\n\n def create_pdf(self, static, dynamic, sample1, sample2):\n\n self.set_time()\n self.add_page()\n self.set_font('Times', '', 12)\n if sample2.name == \"\":\n self.single_table(sample1, static, dynamic)\n else:\n self.diff_table(sample1, sample2, static, dynamic)\n\n filename = \"COF Test \" + self.date_and_time + \".pdf\"\n\n self.output(filename)\n source = \"./\" + filename\n destination = \"/media/pi/USB1/\" + filename\n\n try:\n shutil.copy2(source, destination)\n except shutil.Error as e:\n print(\"Error: %s\" % e)\n except IOError as e:\n print(\"Error: %s\" % e.strerror)\n self.close()\n\n\n def single_table(self, sample, staticCof, dynamicCof):\n data = [['Sample Name: ', sample.name],\n ['Sample Width: ', str(sample.width)],\n ['Sample Heigth: ', str(sample.height)],\n ['Sample Age: ', str(sample.age)],\n ['Testing Against: ', 'The same sample'],\n ['Static Coefficient of Fricion: ', str(staticCof)],\n ['Dynamic Coefficient of Fricion: ', str(dynamicCof)]\n ]\n spacing = 2\n self.set_font(\"Arial\", size=12)\n col_width = self.w / 2.2\n row_height = self.font_size\n for row in data:\n for item in row:\n self.cell(col_width, row_height * spacing,\n txt=item, border=1)\n self.ln(row_height * spacing)\n\n\n def diff_table(self, sample1, sample2, staticCof, dynamicCof):\n data = [['Sample Name: ', sample1.name],\n ['Sample Width: ', str(sample1.width)],\n ['Sample Heigth: ', str(sample1.height)],\n ['Sample Age: ', str(sample1.age)],\n ['Testing Against: ', 'Different Sample'],\n ['Second Sample Name: ', sample2.name],\n ['Second Sample Width: ', str(sample2.width)],\n ['Second Sample Heigth: ', str(sample2.height)],\n ['Second Sample Age: ', str(sample2.age)],\n ['Static Coefficient of Fricion: ', str(staticCof)],\n ['Dynamic Coefficient of Fricion: ', str(dynamicCof)]\n ]\n spacing = 2\n self.set_font(\"Arial\", size=12)\n col_width = self.w / 2.2\n row_height = self.font_size\n for row in data:\n for item in row:\n self.cell(col_width, row_height * spacing,\n txt=item, border=1)\n self.ln(row_height * spacing)\n","sub_path":"fpdf_handler.py","file_name":"fpdf_handler.py","file_ext":"py","file_size_in_byte":3890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"468215987","text":"import numpy as np\n\n\ndef inception_score(py_x, splits=10):\n \"\"\"Computes the Inception Score as reported by https://arxiv.org/pdf/1606.03498.pdf.\n\n Can be used with any input.\n\n For original results use:\n - Inception-v3 Network: https://github.com/tensorflow/models/tree/master/research/slim.\n - @Node: 'InceptionV3/Predictions/Reshape:0'\n \n Note: This expects the output of a classifier network!\n \n Args:\n py_x: Conditional probabilities of y (class probabilities) given x of shape [Batch, Classes].\n splits: Number of times to split `py_x` to calculate the mean.\n\n Returns:\n mean: Mean IS over splits.\n std: Standard deviation of IS over splits.\n \"\"\"\n scores = []\n for i in range(splits):\n start = i * py_x.shape[0] // splits\n end = (i + 1) * py_x.shape[0] // splits\n part = py_x[start:end, :]\n kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))\n kl = np.mean(np.sum(kl, 1))\n scores.append(np.exp(kl))\n return np.mean(scores), np.std(scores)\n","sub_path":"evaluation/metrics/inception_score.py","file_name":"inception_score.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"264897771","text":"\"\"\"\nOriginates from function wave_filtering_SISO from onlineLDS.py.\nTheoretical background can be found in E.Hazan's paper \n\"Spectral Filtering for General Linear Dynamical Systems.\"\n\"\"\"\n\nimport numpy as np\nfrom LDS.filters.wave_filtering_siso_abs import WaveFilteringSisoAbs\n\nclass WaveFilteringSISO(WaveFilteringSisoAbs):\n \"\"\"\n Implements spectral filter.\n\n Hierarchy tree ((ABC)): \n WaveFilteringSISOPersistent\n ^\n |\n Filtering(ABC) --> FilteringSiso(ABC) --> WaveFilteringSisoAbs(ABC) -->WaveFilteringSisoFtlPersistent\n | | |\n KalmanFilteringSISO WaveFilteringSISO WaveFilteringSisoFtl\n \"\"\"\n\n def __init__(self, sys, t_t, k, eta, r_m):\n \"\"\"\n Inits all the attributes of its superclass(see WaveFilteringSisoAbs) and\n adds eta and r_m. Goes through all the methods and gets the predictions.\n\n Args:\n sys: linear dynamical system. DynamicalSystem object.\n t_t: time horizon.\n k: \n eta: \n r_m: \n \"\"\"\n super().__init__(sys, t_t, k)\n self.eta = eta\n self.r_m = r_m\n\n super().var_calc()\n self.y_pred_full, self.M, self.pred_error = self.predict()\n\n def predict(self):\n \"\"\"\n Calculation of output predictions and prediction errors.\n\n Returns:\n y_pred_full: signal prediction values.\n M: identity matrix. ???\n pred_error: spectral filter error.\n \"\"\"\n\n t_t = self.t_t\n k = self.k\n H = self.H\n sys = self.sys\n M = self.M\n eta = self.eta\n r_m = self.r_m\n\n y_pred_full = []\n pred_error = []\n\n for t in range(1, t_t):\n X = []\n for j in range(k):\n scaling = pow(H.V[j], 0.25)\n conv = 0\n for u in range(0, t):\n conv += H.matrix_d[u, j] * sys.inputs[t - u]\n X.append(scaling * conv)\n\n X.append(sys.inputs[t - 1])\n X.append(sys.inputs[t])\n X.append(sys.outputs[t - 1])\n\n X = np.matrix(X).reshape(-1, 1)\n\n y_pred = np.real(M * X)\n y_pred = y_pred[0, 0]\n y_pred_full.append(y_pred)\n # loss = pow(np.linalg.norm(sys.outputs[t] - y_pred), 2)\n loss = pow(np.linalg.norm(sys.outputs[t] + y_pred), 2)\n M = M - 2 * eta * (sys.outputs[t] - y_pred) * X.transpose()\n frobenius_norm = np.linalg.norm(M, 'fro')\n if frobenius_norm >= r_m:\n M = r_m / frobenius_norm * M\n\n pred_error.append(loss)\n\n\n # print(loss)\n\n return y_pred_full, M, pred_error\n","sub_path":"LDS/LDS/filters/wave_filtering_siso.py","file_name":"wave_filtering_siso.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"469707407","text":"from tornado.web import StaticFileHandler, RequestHandler, Application as TornadoApplication\nfrom tornado.websocket import WebSocketHandler\nfrom tornado.ioloop import IOLoop\nfrom os.path import dirname, join as join_path\nfrom json import dumps as dumps_json, loads as loads_json\n\n\nclass MainHandler(RequestHandler):\n def get(self):\n self.render(\"static/index_css_js_ws.html\")\n\n\nclass WSHandler(WebSocketHandler):\n\n def initialize(self):\n self.application.ws_clients.append(self)\n print('Webserver: New WS Client. Connected clients:', len(self.application.ws_clients))\n\n def open(self):\n print('Webserver: Websocket opened.')\n self.write_message('Server ready.')\n\n def on_message(self, msg):\n try:\n msg = loads_json(msg)\n print('Webserver: Received json WS message:', msg)\n except (ValueError):\n print('Webserver: Received WS message:', msg)\n\n def on_close(self):\n self.application.ws_clients.remove(self)\n print('Webserver: Websocket client closed. Connected clients:', len(self.application.ws_clients))\n\nclass WebWSApp(TornadoApplication):\n\n def __init__(self):\n self.ws_clients = []\n\n self.tornado_handlers = [\n (r'/', MainHandler),\n (r'/websocket', WSHandler),\n (r'/(.*)', StaticFileHandler, {'path': join_path(dirname(__file__), 'static')})\n ]\n self.tornado_settings = {\n \"debug\": True,\n \"autoreload\": True\n }\n TornadoApplication.__init__(self, self.tornado_handlers, **self.tornado_settings)\n\n def send_ws_message(self, message):\n for client in self.ws_clients:\n iol.spawn_callback(client.write_message, dumps_json(message))\n\n\nif __name__ == '__main__':\n PORT = 8881\n\n app = WebWSApp()\n app.listen(PORT)\n iol = IOLoop.current()\n iol.start()\n print('Webserver: Initialized. Listening on', PORT)\n","sub_path":"tools/tornado/ws_server.py","file_name":"ws_server.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"252574834","text":"import numpy as np\nfrom scipy import sparse\n\n\nclass ReservoirState(object):\n \"\"\"Representation of reservoir state\"\"\"\n def __init__(self, x):\n self.x = x\n\n @property\n def r(self):\n return np.tanh(self.x)\n\n\nclass Reservoir(object):\n \"\"\"Recurrent reservoir network\n\n Instantiates N leaky integrator neurons. The reservoir is defined by\n three sets of weights:\n * recurrent weights (N x N), randomly set and normalized by N * sparsity\n * input weights (N_in x N), randomly set\n * feedback weights (N_out x N), randomly set\n\n Params:\n N (int): number of nodes\n N_in (int, default=1): Number of input channels\n N_out (int, default=1): Number of output channels\n sparsity (float, default=0.1): probability of a recurrent weight to be nonzero\n dt (float): Size of timestep to simulate (should be << time constant)\n t_const (float, default=10): Time constant of network dynamics (tau)\n Can be either a number, or a column vector of dimension Nx1\n \"\"\"\n\n def __init__(self,\n N,\n N_in=1,\n N_out=1,\n N_feedback=None,\n sparsity=0.1,\n dt=0.1,\n t_const=10,\n g=1.5,\n feedback_on=0,\n noise=0.0):\n self.N = N\n self.N_in = N_in\n self.N_out = N_out\n self.sparsity = sparsity\n self.dt = dt\n self.t_const = t_const\n self.noise = noise\n self.g = g\n self.feedback_on = feedback_on\n self.N_feedback = N_feedback or N_out\n\n self.W_input = np.random.uniform(-1, 1, size=(N_in, N)) # gaussian, unit variance\n self.W_feedback = np.random.uniform(-1, 1, size=(self.N_feedback, N)) # uniform -1 1\n # self.W_feedback = np.random.normal(scale=0.1, size=(self.N_feedback, N))\n\n def randnorm(length):\n return np.random.normal(scale=g / np.sqrt(sparsity * N), size=length)\n\n # self.W_recurrent = sparse.random(N, N, density=sparsity, format=\"csr\") # \n self.W_recurrent = sparse.random(N, N, density=sparsity, format=\"csr\", data_rvs=randnorm) # \n # _temp = self.W_recurrent.toarray()\n # _temp[np.where(_temp != 0)] = 1.0 - 2.0 * _temp[np.where(_temp != 0)]\n # self.W_recurrent = sparse.csr_matrix(_temp)\n # self.W_recurrent = np.sqrt(sparsity * N) * self.W_recurrent / g\n\n def step(self, state, input_data=None, feedback_data=None):\n \"\"\"Run one time step of the network dynamics using leaky integrator\n \n Params:\n state (ReservoirState obj): State representing neuron states and firing rates\n input_data (np.ndarray, default=None): Input data array of shape (N_in, 1)\n feedback_data (np.ndarray, default=None): Input data array of shape (N_out, 1)\n\n Returns:\n state (ReservoirState obj): New state after running one step of leaky integrator\n \"\"\"\n if input_data is None:\n input_data = np.zeros((self.N_in, 1))\n if feedback_data is None:\n feedback_data = np.zeros((self.N_feedback, 1))\n\n dx = (self.dt / self.t_const) * (\n - state.x +\n self.W_recurrent.T @ state.r +\n self.W_input.T @ input_data +\n self.feedback_on * (self.W_feedback.T @ feedback_data) +\n (np.random.normal(scale=self.noise, size=(state.x.shape)) if self.noise else 0)\n )\n\n return ReservoirState(state.x + dx)\n\n\nclass ReservoirNetwork(object):\n \"\"\"Chaotic net system invovling direct feedback connections\n\n Params:\n reservoir (Reservoir obj)\n feedback_reservoir (Reservoir obj, default=None)\n \"\"\"\n\n def __init__(self, res=None, **res_kwargs):\n \"\"\"\n res (Reservoir obj): Reservoir\n \"\"\"\n if res:\n self.res = res\n else:\n self.res = ReservoirNetwork(**res_kwargs)\n \n # this network will be learned\n self.W_readout = np.random.uniform(-1, 1, size=(res.N, res.N_out))\n self.W_readout = self.W_readout / np.sqrt(res.N)\n\n def readout(self, state):\n \"\"\"Generate output from reservoir state\"\"\"\n return self.W_readout.T @ state.r # TODO: add noise here?\n\n def run(self, input_data=None, steps=None, state=None):\n \"\"\"run network driven by input data\n\n if input_data is a number, run network for that many timesteps with no input\n \"\"\"\n if state is None:\n # initialize random state\n state = ReservoirState(np.random.normal(size=(self.res.N, 1)))\n\n if input_data is None and not steps:\n raise Exception(\"Need either input_data array or number of steps to run without input\"\"\")\n elif input_data is None:\n input_data = np.zeros((self.res.N_in, steps))\n\n samples = input_data.shape[1]\n\n outputs = np.zeros((self.res.N_out, samples))\n hidden_state_history = np.zeros((self.res.N, samples))\n visible_state_history = np.zeros((self.res.N, samples))\n\n output = None\n for ind in range(samples):\n state = self.res.step(\n state,\n input_data=input_data[:, ind:ind+1],\n feedback_data=output)\n output = self.readout(state)\n outputs[:, ind:ind+1] = output\n hidden_state_history[:, ind:ind+1] = state.x\n visible_state_history[:, ind:ind+1] = state.r\n\n return {\n \"z\": outputs,\n \"x\": hidden_state_history,\n \"r\": visible_state_history,\n }\n\n def teach(self, input_data, teacher, state=None, exclude_steps=0):\n \"\"\"Learn input using linear regression (offline method)\n\n Note: Does not update the weights in place\n \"\"\"\n if state is None:\n # initialize random state\n state = ReservoirState(np.random.normal(size=(self.res.N, 1)))\n\n samples = input_data.shape[1]\n\n outputs = np.zeros((self.res.N_out, samples))\n hidden_state_history = np.zeros((self.res.N, samples))\n visible_state_history = np.zeros((self.res.N, samples))\n\n output = None\n for ind in range(samples):\n state = self.res.step(\n state,\n input_data=input_data[:, ind:ind+1],\n feedback_data=teacher[:, ind:ind+1])\n output = self.readout(state)\n outputs[:, ind:ind+1] = output\n hidden_state_history[:, ind:ind+1] = state.x\n visible_state_history[:, ind:ind+1] = state.r\n\n # do linear regression here\n train_x = hidden_state_history[:, exclude_steps:]\n train_y = teacher[:, exclude_steps:]\n\n self.W_readout = np.linalg.lstsq(train_x.T, train_y.T)[0]\n print(\"Finished learning\")\n\n return {\n \"z\": outputs,\n \"x\": hidden_state_history,\n \"r\": visible_state_history,\n \"W_readout\": self.W_readout\n }\n","sub_path":"echo/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":7006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"229398930","text":"#!/usr/local/bin/python\n# -*- coding: utf-8 -*-\nimport csv\nimport os\nfrom datetime import datetime\n\nimport pandas as pd\n\nimport utils\n\n\ndef download_file(url, dt_referencia, file_name):\n # verifica se o arquivo deve ser baixado\n if not utils.check_download(dt_referencia, file_name):\n return False\n dt_referencia_formatada = dt_referencia.strftime('%d/%m/%Y')\n params = {\n 'escolha': 2,\n 'Dt_Ref': dt_referencia_formatada,\n 'Dt_Ref_Ver': '20000101',\n 'saida': 'csv',\n 'Idioma': 'PT'\n }\n\n utils.download(url, params, file_name)\n\n\ndef import_files(folder_name, path_file_base, ultima_data_base):\n file_list = os.listdir(r\"downloads/\"+folder_name+\"/\")\n for file_name in file_list:\n if not file_name.endswith('.csv'):\n continue\n path_file = os.path.join('downloads', folder_name, file_name)\n with open(path_file, 'r', encoding='latin1') as f:\n first_line = f.readline()\n if 'Data de Referência:' in first_line:\n data_line = first_line.split(' ')[-1].strip()\n dt_referencia = datetime.datetime.strptime(\n data_line, '%d/%m/%Y').date()\n\n print('extrair', path_file)\n df = pd.read_csv(path_file, sep=';', skiprows=2,\n encoding='latin1', header=0, skipfooter=3, engine='python')\n df['dt_referencia'] = dt_referencia\n\n # seleciona apenas os registros com data de referencia maior que a data base\n df = df[(df['dt_referencia'] > ultima_data_base)]\n\n if len(df) == 0:\n print('Nenhum registro a ser importado')\n os.remove(path_file)\n continue\n\n # importa para o csv base\n with open(path_file_base, 'a', newline='') as baseFile:\n fieldnames = [\n 'dt_referencia',\n 'no_indexador',\n 'no_indice',\n 'nu_indice',\n 'ret_dia_perc',\n 'ret_mes_perc',\n 'ret_ano_perc',\n 'ret_12_meses_perc',\n 'vol_aa_perc',\n 'taxa_juros_aa_perc_compra_d1',\n 'taxa_juros_aa_perc_venda_d0'\n ]\n writer = csv.DictWriter(\n baseFile, fieldnames=fieldnames, delimiter=';', quoting=csv.QUOTE_NONNUMERIC)\n # insere cada registro na database\n for index, row in df.iterrows():\n print(index)\n row_inserted = {\n 'dt_referencia': row['dt_referencia'],\n 'no_indexador': row['Indexador'],\n 'no_indice': row['Índices'],\n 'nu_indice': row['Nº Índice'],\n 'ret_dia_perc': row['Retorno (% Dia)'],\n 'ret_mes_perc': row['Retorno (% Mês)'],\n 'ret_ano_perc': row['Retorno (% Ano)'],\n 'ret_12_meses_perc': row['Retorno (% 12 Meses)'],\n 'vol_aa_perc': row['Volatilidade (% a.a.) *'],\n 'taxa_juros_aa_perc_compra_d1': row['Taxa de Juros (% a.a.) [Compra (D-1)]'],\n 'taxa_juros_aa_perc_venda_d0': row['Taxa de Juros (% a.a.) [Venda (D-0)]']\n }\n writer.writerow(row_inserted)\n os.remove(path_file)\n\n\ndef main():\n path_file_base = os.path.join('bases', 'curva_juros_fechamento.csv')\n # verifica a última data disponível na base\n ultima_data_base = utils.get_ultima_data_base(path_file_base)\n\n # faz o download do csv no site da anbima\n url = 'https://www.anbima.com.br/informacoes/est-termo/CZ-down.asp'\n name_download_folder = 'curva_juros_fechamento'\n path_download = utils.prepare_download_folder(name_download_folder)\n\n # verifica a última data disponível na base\n today = datetime.now().date()\n cal = utils.get_calendar()\n ultima_data_base = cal.offset(today, -5)\n dates_range = list(utils.datetime_range(start=ultima_data_base, end=today))\n\n for dt_referencia in dates_range:\n path_file = os.path.join(\n path_download, dt_referencia.strftime('%Y%m%d') + '_curva_juros_fechamento.csv')\n download_file(url, dt_referencia, path_file)\n\n # utils.remove_zero_files(name_download_folder)\n #import_files(name_download_folder, path_file_base, ultima_data_base)\n\n # organizar o arquivo base por dt_referencia\n # utils.generate_csv_base(path_file_base)\n print(\"Arquivos baixados com sucesso e importados para a base de dados\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"curva_juros_fechamento.py","file_name":"curva_juros_fechamento.py","file_ext":"py","file_size_in_byte":4932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"606888533","text":"\"\"\" Faça um programa que preencha um vetor com 10 números reais, calcule e mostre a quantidade de números negativos e a\nsoma dos números positivos desse vetor.\"\"\"\nfrom random import randint\ncontador = somador = 0\nlista = [randint(-100, 100) for _ in range(1, 11)]\nfor elemento in lista:\n if elemento < 0:\n contador += 1\n else:\n somador += elemento\nprint(f'A lista final é {lista}')\nprint(f'Ela possui {contador} elementos NEGATIVOS.')\nprint(f'A soma dos elementos positivos é {somador}')","sub_path":"Seção 7 - Coleções Python/ex011.py","file_name":"ex011.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"517179606","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport sys\nimport molecule_builder\n\ndef add_h(inputfilename, outputfilename = '', target_element = \"C\"):\n if outputfilename == '':\n outputfilename = inputfilename\n m = molecule_builder.Molecule(inputfilename)\n m.check_and_form_bonds()\n m.auto_add_h_to(target_element)\n m.save_geometry(outputfilename)\n\nargv = sys.argv[1:]\n\nadd_h(*argv)\n","sub_path":"add_h_to_c.py","file_name":"add_h_to_c.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"482397844","text":"import numpy as np\nimport nibabel as nib\nfrom nilearn import datasets\nfrom nilearn.input_data import NiftiLabelsMasker\nfrom nilearn.connectome import ConnectivityMeasure\nfrom nilearn import plotting\n\n\ndef load_data(path_name):\n '''\n Parameters\n ----------\n path_name : String\n Path of the data.\n\n Returns\n -------\n bold_data : Nifti1Image\n bold_data_df : memmap\n Extracted from Nifti1Image.\n '''\n bold_data = nib.load(path_name)\n bold_data_df = bold_data.get_fdata()\n print(\"The shape of the data is\", bold_data_df.shape)\n return (bold_data, bold_data_df)\n\n\ndef make_masker(scheme):\n '''\n Parameters\n ----------\n scheme : String\n The type of parcellation wanted.\n\n Returns\n -------\n masker: nilearn.input_data.NiftiLabelsMasker\n Masker of the chosen scheme.\n labels: list\n Labels of all the regions in parcellation.\n '''\n if scheme.lower() == \"harvox\": # 48 regions\n dataset = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')\n atlas_filename = dataset.maps\n labels = dataset.labels[1:] # trim off \"background\" label\n masker = NiftiLabelsMasker(labels_img=atlas_filename,\n standardize=True,\n high_variance_confounds=True,\n verbose=1)\n elif scheme.lower() == \"yeo\": # 17 regions\n dataset = datasets.fetch_atlas_yeo_2011()\n masker = NiftiLabelsMasker(labels_img=dataset['thick_17'],\n standardize=True,\n high_variance_confounds=True,\n verbose=1)\n labels = [\n \"Visual A\", \"Visual B\", \"Somatomotor A\", \"Somatomotor B\",\n \"Dorsal Attention A\", \"Dorsal Attention B\",\n \"Salience/Ventral Attention A\", \"Salience/Ventral Attention B\",\n \"Limbic A\", \"Limbic B\", \"Control C\", \"Control A\", \"Control B\",\n \"Temporal Parietal\", \"Default C\", \"Default A\", \"Default B\"\n ] # list from valerie-jzr\n elif scheme.lower() == \"aal\": # 116 regions\n dataset = datasets.fetch_atlas_aal(version='SPM12')\n labels = dataset['labels']\n masker = NiftiLabelsMasker(labels_img=dataset['maps'],\n standardize=True,\n high_variance_confounds=True,\n verbose=1)\n elif scheme.lower() == \"schaefer\":\n dataset = datasets.fetch_atlas_schaefer_2018(n_rois=100,\n yeo_networks=17)\n labels = dataset['labels']\n masker = NiftiLabelsMasker(labels_img=dataset['maps'],\n standardize=True,\n high_variance_confounds=True,\n verbose=1)\n return masker, labels\n\n\ndef parcellation(masker, data):\n '''\n Parameters\n ----------\n masker: nilearn.input_data.NiftiLabelsMasker\n Masker to use.\n data : Nifti1Image\n The data directly loaded, not the extracted one.\n\n Returns\n -------\n time_series: array\n Time series of the signal in each region (reduce the region with mean).\n '''\n\n time_series = masker.fit_transform(data)\n print(\n f\"Using the chosen parcellation scheme, there are {time_series.shape[1]} regions with {time_series.shape[0]} datapoints each.\"\n )\n return time_series\n\n\ndef combine_series(time_series_1, time_series_2):\n '''\n Parameters\n ----------\n time_series_1 : array\n Array of time series 01.\n time_series_2 : array\n Array of time series 03.\n\n Returns\n -------\n combined_series : array\n Combined array of time series 01 and time series 03.\n '''\n print(\"Combining the two time series\")\n combined_series = np.concatenate((time_series_1, time_series_2), axis=0)\n return combined_series\n\n\ndef plot_corr(time_series, labels):\n '''\n Parameters\n ----------\n time_series : array\n Time series of the signal in each region .\n labels : list\n Labels of all the regions in parcellation.\n\n Returns\n -------\n None.\n '''\n correlation_measure = ConnectivityMeasure(kind='correlation')\n correlation_matrix = correlation_measure.fit_transform([time_series])[0]\n\n # Mask the main diagonal for visualization:\n np.fill_diagonal(correlation_matrix, 0)\n # The labels we have start with the background (0), hence we skip the first label\n plotting.plot_matrix(correlation_matrix,\n figure=(10, 8),\n labels=labels[1:],\n vmax=0.8,\n vmin=-0.8,\n reorder=True)\n\n\ndef save_data(data, subject, LSD):\n '''\n Parameters\n ----------\n data : array\n Frequency series.\n subject : string\n Number of the subject.\n LSD: int\n Indicator of whether it's the data after taking LSD.\n\n Returns\n -------\n pathname : string\n Path name of the file saved.\n '''\n print(\"Saving the data\")\n data_df = pd.DataFrame(data)\n if LSD == 1:\n pathname = '/Users/zhenrujia/Downloads/Time_series_BOLD' + '_sub' + subject + '_LSD' + '.csv'\n else:\n pathname = '/Users/zhenrujia/Downloads/Time_series_BOLD' + '_sub' + subject + '_PLCB' + '.csv'\n data_df.to_csv(pathname, index=False)\n return pathname\n\n\nif __name__ == '__main__':\n # Example\n bold_data_01, bold_data_df_01 = load_data(\n \"/Users/zhenrujia/Downloads/Sub_01_LSD_01.nii\")\n bold_data_03, bold_data_df_03 = load_data(\n \"/Users/zhenrujia/Downloads/Sub_01_LSD_03.nii\")\n\n time_series_01, labels = parcellation(\"harvox\", bold_data_01)\n time_series_03, labels = parcellation(\"harvox\", bold_data_03)\n combined_series = combine_series(time_series_01, time_series_03)\n\n pathname = save_data(combined_series, \"01\", 1)\n","sub_path":"data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":6023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"329536032","text":"# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\nimport pytest\n\nfrom scrupt.models import RecordBatch\nfrom .model_fixtures import *\n\npytestmark = pytest.mark.django_db\n\n\ndef test_records_get_dimensions(series_record, record_batches,\n series_report):\n records_dim = series_record.objects.all().get_dimensions_values()\n if record_batches.count():\n expected = {\n ('dim-{}'.format(i), ) for i in range(series_report.RECORDS_NUM)\n }\n assert records_dim == expected\n else:\n assert records_dim == set()\n\n\ndef assert_series(records, series, expected_n, dimensions=None, metrics=None):\n expected_metrics = list(metrics or records.scrupt_config.metrics)\n expected_dimensions = list(dimensions or records.scrupt_config.dimensions)\n\n dim_values = records.get_dimensions_values(dimensions=expected_dimensions)\n\n assert series['dimensions'] == expected_dimensions\n assert series['metrics'] == expected_metrics\n\n query_dates = sorted(set(\n records.values_list('batch__query_date', flat=True)))[-expected_n:]\n\n data_dim_values = [v['dimensions'] for v in series['values']]\n\n assert len(data_dim_values) == len(dim_values)\n assert set(data_dim_values) == dim_values\n\n baz_index = series['metrics'].index('baz')\n\n for v in series['values']:\n metrics = v['metrics']\n for values in metrics:\n assert len(values) == expected_n\n\n for v1, v2 in zip(metrics[baz_index][:-1], metrics[baz_index][1:]):\n assert v1 < v2, 'records order is incorrect'\n\n # Test for namedtuple attr access\n assert metrics.baz is metrics[baz_index]\n\n assert query_dates == series['query_dates']\n\n\n@pytest.mark.parametrize('n', (1, 2, 3))\ndef test_get_series_n(n, series_record, record_batches, series_report):\n series = series_record.objects.get_series_n(n)\n expected_n = min(series_report.fetch_count, n)\n assert_series(record_batches, series, expected_n)\n\n\n@pytest.mark.parametrize('n', (3, ))\ndef test_get_series_n_filter(n, series_record, record_batches, series_report):\n if not series_record.objects.count():\n return\n\n dims = {'dim-1', 'dim-2'}\n series = series_record.objects.filter(\n foo__in=dims,\n ).get_series_n(n)\n\n assert set(series['dimensions']) == {'foo'}\n for values in series['values']:\n assert set(values['dimensions']) & dims\n\n\ndef test_get_series_since(series_record, record_batches, series_report):\n if series_report.fetch_count:\n query_date = RecordBatch.objects.order_by('query_date')[0].query_date\n else:\n # any date would do\n query_date = datetime.now()\n\n series = series_record.objects.get_series_since(query_date)\n expected_n = series_report.fetch_count\n assert_series(record_batches, series, expected_n)\n\n\n@pytest.mark.parametrize('n', (3, ))\ndef test_get_series_custom_n(n,\n multidim_series_record,\n multidim_series_report,\n multidim_record_batches):\n if not multidim_series_record.objects.count():\n return\n\n dimensions = ('foo', )\n metrics = ('baz', )\n\n series = multidim_series_record.objects.get_series_n(\n n, dimensions=dimensions, metrics=metrics)\n assert_series(\n multidim_record_batches, series,\n min(multidim_series_report.fetch_count, n),\n dimensions=dimensions,\n metrics=metrics\n )\n","sub_path":"scrupt/tests/test_series.py","file_name":"test_series.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"605068795","text":"\"\"\"\r\nРеализовать класс Road (дорога), в котором определить атрибуты: length (длина), width (ширина).\r\nЗначения данных атрибутов должны передаваться при создании экземпляра класса.\r\nАтрибуты сделать защищенными. Определить метод расчета массы асфальта, необходимого для покрытия всего\r\nдорожного полотна. Использовать формулу: длина * ширина * масса асфальта для покрытия одного кв метра\r\nдороги асфальтом, толщиной в 1 см * чи сло см толщины полотна. Проверить работу метода.\r\nНапример: 20м * 5000м * 25кг * 5см = 12500 т\r\n\"\"\"\r\nclass Road:\r\n # атрибуты класса\r\n _lenght = float\r\n _width = float\r\n\r\n # методы класса\r\n def __init__(self, lenght, widht, height):\r\n self.lenght = lenght\r\n self.widht = widht\r\n self.height = height\r\n\r\n def road_calculation(self):\r\n asphalt_density = 2000 # kg / m3\r\n asphalt_mass = asphalt_density * self.lenght * self.widht * self.height / 100\r\n print('Масса асфальта, кг:', asphalt_mass)\r\n\r\n\r\nlenght = float(input('Введите длину дорожного полона в м: '))\r\nwidht = float(input('Введите ширину дорожного полотна в м: '))\r\nheight = float(input('Ведите толщину дорожного полотна в см: '))\r\n\r\nroad_mass = Road(lenght, widht, height)\r\n\r\n\r\nroad_mass.road_calculation()\r\n","sub_path":"Task 2.py","file_name":"Task 2.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"590935436","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Oct 25 10:07:44 2019\r\n\r\n@author: Fernanda Caldas\r\n\"\"\"\r\n\r\nimport Beamformer\r\nimport numpy as np\r\n\r\n# Number of microfones \r\nM = 11\r\n# Distance between microfones\r\nd = 0.035\r\n# Number of frequency bins\r\nbins = 64\r\n# Sampling frequency\r\nfs = 16000\r\n# Beamwidth\r\nthetaBW = np.pi/6 \r\n\r\nW_s = Beamformer.FIR_weights(M, bins, fs, thetaBW, d)\r\nv_s = Beamformer.FIR_vector(M, bins, fs, d)\r\nD = Beamformer.FIR_beampattern(W_s, v_s)\r\nBeamformer.FIR_plot_beampattern(D)\r\n\r\n# Number of microfones \r\nM = 11\r\n# Distance between microfones\r\nd = 0.035\r\n# Source location\r\nphi = -np.pi/18\r\n# Beamwidth\r\nthetaBW = np.pi/4.5\r\n# Frequency\r\nf = 4000\r\n\r\nw = Beamformer.Slepian_weights(M, d, f, thetaBW)\r\nv = Beamformer.Slepian_vector(M, d, f, phi)\r\nD = Beamformer.Slepian_beampattern(w, v)\r\nBeamformer.Slepian_plot_beampattern(D)\r\n\r\n# Number of microfones \r\nM = 11\r\n# Distance between microfones\r\nd = 0.035\r\n# Source location\r\nphi = -np.pi/18\r\n# Beamwidth\r\nthetaBW = np.pi/4.5\r\n# Number of frequency bins\r\nbins = 64\r\n# Lower frequency\r\nf_L = 1000\r\n# Higher frequency\r\nf_H = 9000\r\n\r\nW = Beamformer.Slepian_band_weights(M, d, f_L, f_H, bins, thetaBW)\r\nv = Beamformer.Slepian_band_vector(M, d, f_L, f_H, bins, phi)\r\nD = Beamformer.Slepian_band_beampattern(W, v)\r\nBeamformer.Slepian_band_plot_beampattern(D)","sub_path":"examples.py","file_name":"examples.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"15333892","text":"import sys\nsys.path.insert(0, './../models/') # safer to do sys.path.append instead of insert. \nimport EvoAlt\nimport numpy as np\nimport importlib \nreload(EvoAlt) \n\noutfiletest1 = open('test.npy','w')\n\nrn=70000000\nsd=[6,14,18,33] \nfreq=1000\nTT=1.7\nSS=0.3\nPP=0\nRR=1\nal=-1.4\nau=-1.3\nbl=0 \nbu=0\nm1=0.02\nm2=0.02\nm3=0.02 \nns=4\nnntype='neu'\nmtp='add'\nww=60\n\nx = EvoAlt.runsim(a_range_init=(al,au),w=ww,R=RR,P=PP,T=TT,S=SS,roundnum=rn,seed=sd[0],rpt_freq=freq,CHECK=False,result_type='timeseries',mut_sd=(m1,m2,m3),ntype=nntype,mut_type=mtp,nsize=1,switch='off')\n\nnp.save(outfiletest1,x) \n\n#import matplotlib.pyplot as plt\n \n#plt.title('SDagain nsize=1 test2 w=60 mut=1% mutstd=0.02+')\n#plt.plot(x[:,0],label='mean A(2)', color='blue')\n#plt.plot(x[:,1],label='mean B', color='red')\n#plt.plot(x[:,2],label='mean colour',color='black')\n#plt.plot(x[:,3],label='mean fitness',color='green')\n#plt.plot(x[:,4],label='std A(2)',color='yellow')\n#plt.plot(x[:,5],label='std B',color='cyan')\n#plt.plot(x[:,6],label='std Colour',color='orange')\n#plt.plot(x[:,7],label='std fitness',color='magenta')\n\n\n#plt.legend(bbox_to_anchor=(-0.03,0.5),prop={'size':11})\n#plt.ylim([-8,8]) \n#plt.xlabel('round number (fqt=1000)') \n#plt.show() \n\n","sub_path":"code/plotting/runsim.py","file_name":"runsim.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"138321081","text":"from Harvester import *\n\nclass GETHarvester(Harvester):\n \"\"\"\n {\n \"id\": \"GETHarvester\",\n \"title\": \"GET Harvester\",\n \"description\": \"simple GET Harvester to fetch a single metadata document\",\n \"params\": [\n {\"name\": \"uri\", \"required\": \"true\"},\n {\"name\": \"xsl_file\", \"required\": \"false\"}\n ]\n }\n \"\"\"\n def harvest(self):\n self.getHarvestData()\n self.storeHarvestData()\n self.runCrossWalk()\n self.postHarvestData()\n self.finishHarvest()\n","sub_path":"harvester/harvest_handlers/GETHarvester.py","file_name":"GETHarvester.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"365533915","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon May 20 22:16:48 2019\r\n\r\n@author: binxi\r\n\"\"\"\r\n\r\n# Definition for a binary tree node.\r\n# class TreeNode(object):\r\n# def __init__(self, x):\r\n# self.val = x\r\n# self.left = None\r\n# self.right = None\r\n\r\nclass Solution(object):\r\n def lowestCommonAncestor(self, root, p, q):\r\n \"\"\"\r\n :type root: TreeNode\r\n :type p: TreeNode\r\n :type q: TreeNode\r\n :rtype: TreeNode\r\n \"\"\"\r\n \r\n last = {}\r\n \r\n last[root] = None\r\n \r\n def research(root):\r\n if root != None:\r\n last[root.left] = root\r\n last[root.right] = root\r\n research(root.left)\r\n research(root.right)\r\n \r\n research(root)\r\n \r\n lst1 = []\r\n \r\n while p != None:\r\n lst1.append(p)\r\n p = last[p]\r\n \r\n while q not in lst1:\r\n q = last[q]\r\n \r\n return q","sub_path":"Leetcode/#235 Lowest Common Ancestor of a Binary Search Tree.py","file_name":"#235 Lowest Common Ancestor of a Binary Search Tree.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"166568943","text":"\n# Author\n# Yaoyu Hu \n\nimport copy\nimport numpy as np\nfrom plyfile import PlyData, PlyElement\n\ndef convert_2D_array_2_1D_list(a):\n \"\"\"\n Return a 2D NumPy array into a list of tuples.\n \"\"\"\n\n res = []\n\n for i in range(a.shape[0]):\n t = tuple( a[i,:].tolist() )\n res.append( t )\n \n return res\n\ndef write_PLY(fn, disp, Q, flagFlip=False, distLimit=100., mask=None, color=None, binary=True):\n \"\"\"\n fn: The output filename.\n disp: The disparity. A NumPy array with dimension (H, W).\n mask: Logical NumPy array.\n Q: The reprojection matrix. A NumP array of dimension 4x4.\n color: The color image. The image could be (H, W) or (H, W, C).\n C could be 1 or 3. If color==None, no color properties \n will be in the output PLY file.\n binary: Set True to write a binary format PLY file. Set False for\n a ASCII version.\n \"\"\"\n\n disp = disp.copy()\n\n # Get the size of the image.\n H = disp.shape[0]\n W = disp.shape[1]\n\n # Make x and y.\n xLin = np.linspace( 0, W-1, W, dtype=np.float32 )\n yLin = np.linspace( 0, H-1, H, dtype=np.float32 )\n\n x, y = np.meshgrid( xLin, yLin )\n x = x.reshape((-1,))\n y = y.reshape((-1,))\n\n # Make the coordinate array.\n d = disp.reshape((-1,))\n hg = np.ones((1, d.shape[0]), dtype=np.float32).reshape((-1,))\n\n # Mask.\n m = d > 0\n nm = np.logical_not(m)\n m = m.reshape((-1,))\n \n d[nm] = 1.0\n\n if ( mask is not None ):\n m = np.logical_and( m, mask.reshape((-1,)) )\n\n coor = np.stack( [ x, y, d, hg ], axis=-1 ).transpose()\n\n # Calculate the world coordinate.\n if ( flagFlip ):\n Q = Q.copy()\n Q[1, 1] *= -1\n Q[1, 3] *= -1\n Q[2, 3] *= -1\n\n coor = Q.dot( coor )\n\n coor[0, :] = coor[0, :] / coor[3, :]\n coor[1, :] = coor[1, :] / coor[3, :]\n coor[2, :] = coor[2, :] / coor[3, :]\n\n coor = coor.transpose()[:, 0:3]\n\n # Filter the points. Only keep the points within distLimit.\n dispMask = np.abs( coor[:, 2] ) <= distLimit\n m = np.logical_and( m, dispMask.reshape((-1,)) )\n\n # Handle color.\n if ( color is not None ):\n if ( 2 == len( color.shape ) ):\n color = np.stack([ color, color, color ], axis=-1)\n \n color = color.reshape(-1, 3)\n color = color[m, 0:3]\n coor = coor[m, 0:3]\n \n color = np.clip( color, 0, 255 ).astype(np.uint8)\n\n # Concatenate.\n vertex = np.concatenate([coor, color], axis=1)\n\n # Create finial vetex array.\n vertex = convert_2D_array_2_1D_list(vertex)\n vertex = np.array( vertex, dtype=[\\\n ( \"x\", \"f4\" ), \\\n ( \"y\", \"f4\" ), \\\n ( \"z\", \"f4\" ), \\\n ( \"red\", \"u1\" ), \\\n ( \"green\", \"u1\" ), \\\n ( \"blue\", \"u1\" ) \\\n ] )\n else:\n coor = convert_2D_array_2_1D_list(coor)\n vertex = np.array( coor, dtype=[\\\n ( \"x\", \"f4\" ), \\\n ( \"y\", \"f4\" ), \\\n ( \"z\", \"f4\" ) \\\n ] )\n \n # Save the PLY file.\n el = PlyElement.describe(vertex, \"vertex\")\n\n PlyData([el], text= (not binary) ).write(fn)\n\nif __name__ == \"__main__\":\n print(\"Test write_PLY.\")\n\n # Disparity.\n disp = np.linspace(1, 10, 10, dtype=np.float32).reshape(2, 5)\n r = np.linspace(0, 9, 10).reshape(2, 5)\n g = np.linspace(0, 9, 10).reshape(2, 5) + 10\n b = np.linspace(0, 9, 10).reshape(2, 5) + 100\n color = np.stack([r, g, b], axis=-1)\n color = color.astype(np.uint8)\n\n Q = np.eye((4), dtype=np.float32)\n Q[3, 3] = 5\n\n print(\"disp=\\n{}\".format(disp))\n print(\"color\\n{}\".format(color))\n print(\"color.shape={}\".format(color.shape))\n print(\"Q=\\n{}\".format(Q))\n\n # Write a PLY file.\n write_PLY(\"DispColor3NoMask.ply\", disp, Q, color=color, binary=False)\n\n # Write a PLY file with single channel color.\n write_PLY(\"DispColor1NoMask.ply\", disp, Q, color=color[:,:,0], binary=False)\n\n mask = np.logical_and( disp > 3, disp < 8 )\n print(\"mask={}\".format(mask))\n\n # Write a PLY file with 3-channel color and a mask.\n write_PLY(\"DispColor3Mask.ply\", disp, Q, mask=mask, color=color, binary=False)\n\n # Write a PLY file with 1-channel color and a mask.\n write_PLY(\"DispColor1Mask.ply\", disp, Q, mask=mask, color=color[:,:,0], binary=False)\n ","sub_path":"PSMNU/PLYHelper.py","file_name":"PLYHelper.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"121120568","text":"import ael\ntry:\n f = open('\\\\\\\\v036syb004001\\\\Development\\\\Safex\\\\SAFEX_Instrument_Data.csv')\nexcept:\n print('Could not open the file')\nline = f.readline()\nwhile line:\n line = line.rstrip()\n list = []\n list = line.split(',')\n ins = list[0]\n und_ins = list[1]\n io = ael.Instrument[ins]\n uio = ael.Instrument[und_ins]\n if not uio:\n print('Could not find underlying instrument')\n else: \n if not io:\n new_ins = ael.Instrument.new('Option')\n new_ins.insid = ins\n new_ins.und_insaddr = ael.Instrument[und_ins]\n new_ins.instype = 'Option'\n new_ins.curr = ael.Instrument['ZAR']\n new_ins.quote_type = 'Per Contract'\n new_ins.settlement = 'Cash'\n new_ins.exp_day = ael.date(list[3][0:10])\n if list[6] == 'C': \n call = 1\n else: \n call = 0\n new_ins.call_option = call\n new_ins.exercise_type = 'American'\n new_ins.strike_type = 'Absolute'\n new_ins.otc = 0\n ALSIcl = ael.ChoiceList.read('list = \"ValGroup\" and entry = \"EQ_ALSI_Opt_Blk\"')\n Stockcl = ael.ChoiceList.read('list = \"ValGroup\" and entry = \"SAEQ_FUT\"')\n pricefcl = ael.ChoiceList.read('list = \"PriceFindingGroup\" and entry = \"EQ_Deriv\"')\n new_ins.price_finding_chlnbr = pricefcl\n if list[1][4:8] == 'ALSI':\n new_ins.strike_price = (float)(list[2])\n new_ins.product_chlnbr = ALSIcl\n new_ins.contr_size = 10\n new_ins.paytype = 'Spot'\n new_ins.pay_day_offset = 0\n else:\n new_ins.strike_price = (float)(list[2]) * 100.00\n new_ins.product_chlnbr = Stockcl\n new_ins.contr_size = 100\n new_ins.paytype = 'Future'\n new_ins.pay_day_offset = 3\n try:\n new_ins.commit()\n except:\n print('Could not create instrument')\n \n \n line = f.readline()\n \nf.close()\n","sub_path":"Python modules/SAFEX_INSTRUMENT_CREATION.py","file_name":"SAFEX_INSTRUMENT_CREATION.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"33118368","text":"\"\"\"Define automations for Amazon Dash Buttons.\"\"\"\n\n# pylint: disable=unused-argument\n\nfrom typing import Union\n\nfrom automation import Automation # type: ignore\n\nOPTION_METHOD_MAP = {\n 'Activate \"Good Night\"': ('activate_good_night', {}),\n 'Arm security system': ('arm_security_system', {\n 'state': 'home'\n }),\n 'Bump climate 2°': (\n 'bump_climate', {\n 'amount': 2\n }),\n 'Toggle Master Bedroom Salt Lamp': (\n 'toggle_salt_lamp', {\n 'entity_id': 'light.salt_lamp_master_bedroom'\n })\n}\n\n\nclass ChangeActionUponState(Automation):\n \"\"\"Define an automation for changing the button action based upon state.\"\"\"\n\n def initialize(self) -> None:\n \"\"\"Initialize.\"\"\"\n super().initialize()\n\n self.listen_state(\n self.entity_state_occurred,\n self.entities['target'],\n new=self.properties['target_state'],\n constrain_input_boolean=self.enabled_entity_id)\n\n def entity_state_occurred( # pylint: disable=too-many-arguments\n self, entity: Union[str, dict], attribute: str, old: str, new: str,\n kwargs: dict) -> None:\n \"\"\"Change the Dash action when the \"watched\" entity state occurs.\"\"\"\n self.log(\n 'Setting input select: {0} -> {1}'.format(\n self.app.action_list, self.properties['dash_action']))\n\n self.select_option(\n self.app.action_list, self.properties['dash_action'])\n\n\nclass DashButton(Automation):\n \"\"\"Define an automation for Amazon Dash Buttons.\"\"\"\n\n @property\n def action_list(self) -> str:\n \"\"\"Return the action input select for this button.\"\"\"\n return self.entities['action_list']\n\n @action_list.setter\n def action_list(self, value: str) -> None:\n \"\"\"Set the action input select for this button.\"\"\"\n self.select_option(self.entities['action_list'], value)\n\n def initialize(self) -> None:\n \"\"\"Initialize.\"\"\"\n super().initialize()\n\n self.listen_event(\n self.button_pressed,\n 'AMAZON_DASH_PRESS',\n button_label=self.properties['friendly_name'])\n\n def activate_good_night(self) -> None:\n \"\"\"Turn on the \"Good Night\" scene.\"\"\"\n self.turn_on('scene.good_night')\n\n def arm_security_system(self, state: str) -> None:\n \"\"\"Set the security system to the specified state.\"\"\"\n try:\n state_enum = self.security_system.AlarmStates[state]\n except KeyError:\n self.error('Unknown security state: {0}'.format(state))\n\n self.security_system.state = state_enum\n\n def bump_climate(self, amount: int) -> None:\n \"\"\"Bump the climate up or down by a certain amount.\"\"\"\n if self.climate_manager.mode == self.climate_manager.Modes.cool:\n amount *= -1\n\n self.climate_manager.indoor_temp += amount\n\n def toggle_salt_lamp(self, entity_id: str) -> None:\n \"\"\"Toggle the specified salt lamp.\"\"\"\n self.call_service('light/toggle', entity_id=entity_id)\n\n def button_pressed(\n self, event_name: str, data: dict, kwargs: dict) -> None:\n \"\"\"Respond when button is pressed.\"\"\"\n action_name = self.get_state(self.entities['action_list'])\n try:\n method, params = OPTION_METHOD_MAP[action_name]\n except (AttributeError, KeyError):\n self.error('Unknown action: {0}'.format(action_name))\n return\n\n getattr(self, method)(**params)\n","sub_path":"appdaemon/settings/apps/dash.py","file_name":"dash.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"285516909","text":"''''''\n# from sklearn.preprocessing import label\n\n'''\n基于tkinter模块的GUI开发\n\nGUI是图形用户界面的缩写，Python默认的GUI开发模块时tkinter。\n此模块是基于Tk的，Tk是一个工具包，最初是为Tcl设计的，后来被一直到很多其他的脚本语言中，\n它提供了跨平台的GUI控件。\n事实上GUI开发中tkinter功能并不是很强大，开发GUI也并不是Python最擅长的工作，\n如果需要使用Python开发GUI应用，wxPython、PyQt、PyGTK都是很好的选择。\n\n'''\n\n'''\n基本上使用tkniter开发GUI应用需要以下5个步骤：\n\n1.导入tkinter模块中我们需要的东西\n2.创建一个顶层窗口对象并用它来承载整个GUI应用\n3.在顶层窗口对象上添加GUI组件\n4.通过代码将这些GUI组件的功能组织起来\n5.进入主时间循环(main loop)\n'''\n\nimport tkinter\nimport tkinter.messagebox\n\ndef main():\n flag = True\n\n # 修改标签上的文字\n def change_label_text():\n nonlocal flag\n float = not flag\n color,msg = ('blue','Hello,World!')\\\n if flag else ('black','Goodbye,world!')\n label.config(text=msg,fg=color)\n\n\n # 确认退出\n def confirm_to_quit():\n if tkinter.messagebox.askokcancel('温馨提示','确定要退出么？'):\n top.quit()\n\n\n\n # 创建顶层窗口\n top = tkinter.Tk()\n # 设置窗口大小\n top.geometry('4000x160')\n # 设置窗口标题\n top.title('小游戏')\n # 创建标签对象并添加到顶层窗口\n label = tkinter.Label(top,text='Hello, world!',font='AArial -32',fg='red')\n label.pack(expand=1)\n # 创建一个装按钮的容器\n panel = tkinter.Frame(top)\n # 创建按钮对象 指定添加到哪个容器中 通过 command参数绑定时间回调函数\n button1 = tkinter.Button(panel,text='修改',command=change_label_text)\n button1.pack(side='left')\n button2 = tkinter.Button(panel,text='退出',command=confirm_to_quit)\n button2.pack(side='right')\n panel.pack(side='bottom')\n # 开启主时间循环\n tkinter.mainloop()\n\n\n\n'''\n使用Pygame进行游戏开发\nPygame是一个开源的Python模块，专门用于多媒体应用（如电子游戏）的开发，\n其中包含对图像、声音、视频、事件、碰撞等的支持。\nPygame建立在SDL的基础上。SDL是一套跨平台的多媒体开发库，用C语言实现。\n'''\n\n\nimport pygame\n\n# 制作游戏窗口\ndef game_screen_main():\n # 初始化导入的pygame中的模块\n pygame.init()\n # 初始化用于显示的窗口并设置窗口尺寸\n screen = pygame.display.set_mode((800,600))\n # 设置当前窗口的标题\n pygame.display.set_caption('大球吃小球')\n running = True\n #开启一个事件循环处理发生的事件\n while running:\n # 从消息队列中获取事件并对事件进行处理\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n\n# 在窗口中绘图\ndef game_drwan_main():\n # 初始化\n pygame.init()\n # 尺寸\n screen = pygame.display.set_mode((800,600))\n # 设置标题\n pygame.display.set_caption('大球吃小球')\n screen.fill((242,242,242))\n pygame.draw.circle(screen,(255,0,145),(100,100),30,0)\n pygame.display.flip()\n running = True\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n# 加载图像\ndef game_image_main():\n pygame.init()\n screen = pygame.display.set_mode((800,600))\n pygame.display.set_caption('----')\n screen.fill((255,120,230))\n ball_iamge = pygame.image.load('./res/ball.png')\n screen.blit(ball_iamge,(50,50))\n pygame.display.flip()\n running = True\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n# 加载动画\ndef game_animate_main():\n pygame.init()\n screen = pygame.display.set_mode((800,600))\n pygame.display.set_caption('---')\n x,y = 50,50\n running = True\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n screen.fill((234,123,120))\n pygame.draw.circle(screen,(255,0,0),(x,y),30,0)\n pygame.display.flip()\n pygame.time.delay(3)\n x,y = x+5,y+5\n\n# 碰撞检测\nfrom enum import Enum,unique\nfrom math import sqrt\nfrom random import randint\n\n@unique\nclass Color(Enum):\n '''颜色'''\n\n RED = (255,0,0)\n GREEN = (0,255,0)\n BLUE = (0,0,255)\n BLACK = (0,0,0)\n WHITE = (255,255,255)\n GRAY = (242,242,242)\n\n @staticmethod\n def random_color():\n '''获得随机颜色'''\n r = randint(0,255)\n g = randint(0,255)\n b = randint(0,255)\n return (r,g,b)\n\n\nclass Ball(object):\n '''球'''\n\n def __init__(self,x,y,radius,sx,sy,color=Color.RED):\n '''初始化方法'''\n self.x = x\n self.y = y\n self.radius = radius\n self.sx = sx\n self.sy = sy\n self.color = color\n self.alive = True\n\n def move(self,screen):\n '''移动'''\n self.x += self.sx\n self.y += self.sy\n\n if self.x - self.radius <= 0 or \\\n self.x + self.radius >= screen.get_width():\n self.sx = -self.sx\n\n if self.y - self.radius <= 0 or \\\n self.y + self.radius >= screen.get_height():\n self.sy = - self.sy\n\n def eat(self,other):\n '''吃其他球'''\n\n if self.alive and other.alive and self != other:\n dx,dy = self.x - other.x,self.y - other.y\n distance = sqrt(dx ** 2 + dy **2)\n if distance < self.radius + other.radius \\\n and self.radius > other.radius:\n other.alive = False\n self.radius = self.radius + int(other.radius * 0.146)\n\n def draw(self,screen):\n '''在窗口上绘制球'''\n pygame.draw.circle(screen,self.color,(self.x,self.y),self.radius,0)\n\n\ndef game_event_main():\n\n balls = []\n pygame.init()\n screen = pygame.display.set_mode((1600, 1200))\n pygame.display.set_caption('大球游戏')\n running = True\n while running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n x,y = event.pos\n radius = randint(1,10)\n sx,sy = randint(-10,10),randint(-10,10)\n color = Color.random_color()\n ball = Ball(x,y,radius,sx,sy,color)\n balls.append(ball)\n screen.fill((255,255,255))\n for ball in balls:\n if ball.alive:\n ball.draw(screen)\n else:\n balls.remove(ball)\n\n pygame.display.flip()\n pygame.time.delay(1)\n for ball in balls:\n ball.move(screen)\n for other in balls:\n ball.eat(other)\n\nif __name__ == '__main__':\n # main()\n # game_screen_main()\n # game_drwan_main()\n # game_image_main()\n # game_animate_main()\n game_event_main()","sub_path":"PythonCoding/python-basic-10/10-GUI.py","file_name":"10-GUI.py","file_ext":"py","file_size_in_byte":7215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"376411293","text":"import os\nimport wagtail_factories\n\nfrom collections import OrderedDict\nfrom pydoc import locate\n\nfrom django.conf import settings\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom django.test import TestCase, override_settings\n\nfrom graphene.test import Client\n\nfrom wagtailmedia.models import get_media_model\n\nfrom wagtail.core.models import Page\nfrom wagtail.documents import get_document_model\nfrom wagtail.images import get_image_model\n\nfrom grapple.schema import create_schema\n\nSCHEMA = locate(settings.GRAPHENE[\"SCHEMA\"])\n\n\nclass BaseGrappleTest(TestCase):\n def setUp(self):\n self.client = Client(SCHEMA)\n\n\nclass PagesTest(BaseGrappleTest):\n def test_pages(self):\n query = \"\"\"\n {\n pages {\n title\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(type(executed[\"data\"]), OrderedDict)\n self.assertEquals(type(executed[\"data\"][\"pages\"]), list)\n self.assertEquals(type(executed[\"data\"][\"pages\"][0]), OrderedDict)\n\n pages = Page.objects.all()\n\n self.assertEquals(len(executed[\"data\"][\"pages\"]), pages.count())\n\n\n@override_settings(GRAPPLE_AUTO_CAMELCASE=False)\nclass DisableAutoCamelCaseTest(TestCase):\n def setUp(self):\n schema = create_schema()\n self.client = Client(schema)\n\n def test_disable_auto_camel_case(self):\n query = \"\"\"\n {\n pages {\n title\n url_path\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(type(executed[\"data\"]), OrderedDict)\n self.assertEquals(type(executed[\"data\"][\"pages\"]), list)\n self.assertEquals(type(executed[\"data\"][\"pages\"][0]), OrderedDict)\n self.assertEquals(type(executed[\"data\"][\"pages\"][0][\"title\"]), str)\n self.assertEquals(type(executed[\"data\"][\"pages\"][0][\"url_path\"]), str)\n\n pages = Page.objects.all()\n\n self.assertEquals(len(executed[\"data\"][\"pages\"]), pages.count())\n\n\nclass ImagesTest(BaseGrappleTest):\n def setUp(self):\n super().setUp()\n self.image_model = get_image_model()\n self.assertEqual(self.image_model.objects.all().count(), 0)\n self.example_image = wagtail_factories.ImageFactory(title=\"Example Image\")\n self.example_image.full_clean()\n self.example_image.save()\n self.assertEqual(self.image_model.objects.all().count(), 1)\n\n def test_properties_on_saved_example_image(self):\n example_img = self.image_model.objects.first()\n\n self.assertEqual(example_img.id, 1)\n self.assertEqual(example_img.title, \"Example Image\")\n\n def test_query_url_field(self):\n query = \"\"\"\n {\n images {\n id\n url\n src\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(executed[\"data\"][\"images\"][0][\"id\"], \"1\")\n self.assertEquals(\n executed[\"data\"][\"images\"][0][\"url\"],\n \"http://localhost:8000\" + self.example_image.file.url,\n )\n self.assertEquals(\n executed[\"data\"][\"images\"][0][\"url\"], executed[\"data\"][\"images\"][0][\"src\"],\n )\n\n def tearDown(self):\n example_image_path = self.example_image.file.path\n self.example_image.delete()\n os.remove(example_image_path)\n\n\nclass DocumentsTest(BaseGrappleTest):\n def setUp(self):\n super().setUp()\n self.document_model = get_document_model()\n self.assertEqual(self.document_model.objects.all().count(), 0)\n\n uploaded_file = SimpleUploadedFile(\"example.txt\", b\"Hello world!\")\n self.example_document = self.document_model(\n title=\"Example File\", file=uploaded_file\n )\n self.example_document.full_clean()\n self.example_document.save()\n self.example_document.get_file_hash()\n self.example_document.get_file_size()\n self.assertEqual(self.document_model.objects.all().count(), 1)\n\n def test_properties_on_saved_example_document(self):\n example_doc = self.document_model.objects.first()\n\n self.assertEqual(example_doc.id, 1)\n self.assertEqual(example_doc.title, \"Example File\")\n\n example_doc.file.seek(0)\n\n self.assertEqual(example_doc.file.readline(), b\"Hello world!\")\n\n self.assertNotEqual(example_doc.file_hash, \"\")\n self.assertNotEqual(example_doc.file_size, None)\n\n def test_query_documents_id(self):\n query = \"\"\"\n {\n documents {\n id\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n documents = self.document_model.objects.all()\n\n self.assertEquals(len(executed[\"data\"][\"documents\"]), documents.count())\n self.assertEquals(\n executed[\"data\"][\"documents\"][0][\"id\"], str(self.example_document.id)\n )\n\n def test_query_file_field(self):\n query = \"\"\"\n {\n documents {\n id\n file\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(\n executed[\"data\"][\"documents\"][0][\"file\"], self.example_document.file.name\n )\n\n def test_query_file_hash_field(self):\n query = \"\"\"\n {\n documents {\n id\n fileHash\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(\n executed[\"data\"][\"documents\"][0][\"fileHash\"],\n self.example_document.file_hash,\n )\n\n def test_query_file_size_field(self):\n query = \"\"\"\n {\n documents {\n id\n fileSize\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(\n executed[\"data\"][\"documents\"][0][\"fileSize\"],\n self.example_document.file_size,\n )\n\n def test_query_url_field_with_default_document_serve_method(self):\n query = \"\"\"\n {\n documents {\n id\n url\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEqual(\n executed[\"data\"][\"documents\"][0][\"url\"],\n \"http://localhost:8000\" + self.example_document.url,\n )\n\n def test_query_url_field_with_direct_document_serve_method(self):\n serve_method_at_test_start = settings.WAGTAILDOCS_SERVE_METHOD\n settings.WAGTAILDOCS_SERVE_METHOD = \"direct\"\n query = \"\"\"\n {\n documents {\n id\n url\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEqual(\n executed[\"data\"][\"documents\"][0][\"url\"],\n \"http://localhost:8000\" + self.example_document.file.url,\n )\n settings.WAGTAILDOCS_SERVE_METHOD = serve_method_at_test_start\n\n def tearDown(self):\n self.example_document.file.delete()\n\n\nclass MediaTest(BaseGrappleTest):\n def setUp(self):\n super().setUp()\n\n self.media_model = get_media_model()\n self.assertEqual(self.media_model.objects.all().count(), 0)\n\n uploaded_file = SimpleUploadedFile(\"example.mp4\", b\"\")\n self.media_item = self.media_model(\n title=\"Example Media File\", file=uploaded_file, duration=0, type=\"video\"\n )\n self.media_item.full_clean()\n self.media_item.save()\n self.assertEqual(self.media_model.objects.all().count(), 1)\n\n def test_properties_on_saved_example_media(self):\n media_item = self.media_model.objects.first()\n\n self.assertEqual(media_item.id, 1)\n self.assertEqual(media_item.title, \"Example Media File\")\n\n def test_query_media_id(self):\n query = \"\"\"\n {\n media {\n id\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n media = self.media_model.objects.all()\n\n self.assertEquals(len(executed[\"data\"][\"media\"]), media.count())\n self.assertEquals(executed[\"data\"][\"media\"][0][\"id\"], str(self.media_item.id))\n\n def test_query_file_field(self):\n query = \"\"\"\n {\n media {\n id\n file\n }\n }\n \"\"\"\n\n executed = self.client.execute(query)\n\n self.assertEquals(\n executed[\"data\"][\"media\"][0][\"file\"], self.media_item.file.name\n )\n\n def tearDown(self):\n self.media_item.file.delete()\n","sub_path":"example/example/tests/test_grapple.py","file_name":"test_grapple.py","file_ext":"py","file_size_in_byte":8633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"398462389","text":"# Esse módulo contém a implementação de diversos algoritmos de busca que estudamos anteriormente\n\nfrom utils import (\n is_in, argmin, argmax, argmax_random_tie, probability,\n weighted_sample_with_replacement, memoize, print_table, DataFile, Stack,\n FIFOQueue, PriorityQueue, name, distance\n)\nfrom grid import *\n\nfrom collections import defaultdict\nimport math\nimport random\nimport sys\nimport bisect\n\ninfinity = float('inf')\n\n\nclass Problem(object):\n\n \"\"\"A classe abstrata para um problema formal. Você deve implementar subclasse\n     para os métodos ações e resultados, e possivelmente\n     __init__, goal_test e path_cost. Em seguida, você cria instâncias\n     da sua subclasse e então resolve os problemas com as várias funções de busca.\"\"\"\n\n def __init__(self, initial, goal=None):\n \"\"\"O construtor especifica o estado inicial e possivelmente um esdado objetivo, \n se houver uma meta única. O construtor da sua subclasse pode adicionar\n         outros argumentos.\"\"\"\n self.initial = initial\n self.goal = goal\n\n def actions(self, state):\n \"\"\"Retornar as ações que podem ser executadas no dado\n         estado. O resultado normalmente seria uma lista, mas se houver\n         muitas ações, considere levá-las de uma em uma\n         iteração, ao invés de construí-los todos de uma vez.\"\"\"\n raise NotImplementedError\n\n def result(self, state, action):\n \"\"\"Retorna o estado que resulta da execução do dado\n         Ação em um determinado estado. A acção deve ser uma das\n         Auto-ações (estado).\"\"\"\n raise NotImplementedError\n\n def goal_test(self, state):\n \"\"\"Retornar verdadeiro se o estado for uma meta. O método padrão compara o\n         estado para self.goal ou verifica o estado em self.goal se é uma\n         lista, conforme especificado no construtor.\"\"\"\n if isinstance(self.goal, list):\n return is_in(state, self.goal)\n else:\n return state == self.goal\n\n def path_cost(self, c, state1, action, state2):\n \"\"\"Retorna o custo de um caminho de solução que chega ao estado2 a partir\n         do estado1 via ação, assumindo o custo c para chegar até estado1. Se o problema\n         é tal que o caminho não importa, esta função só vai olhar para\n         estado2. Se o caminho não importa, ele considerará c e talvez estado1\n         e ação. O método padrão custa 1 para cada etapa do caminho.\"\"\"\n return c + 1\n\n def value(self, state):\n raise NotImplementedError\n# ______________________________________________________________________________\n\n\nclass Node:\n\n \"\"\"Um nó em uma árvore de pesquisa. Contém um ponteiro para o pai (o nó\n     que este é um sucessor) e para o estado real para este nó. Note\n     que se um estado é atingido por dois caminhos, então há dois nós com\n     o mesmo estado. Inclui também a ação que nos levou a este estado, e\n     O path_cost total (também conhecido como g) para alcançar o nó. Outras funções\n     podem adicionar um valor f e h; Ver best_first_graph_search e astar_search para\n     uma explicação de como os valores f e h são tratados. Você não precisará\n     de subclasse esta classe.\"\"\"\n\n def __init__(self, state, parent=None, action=None, path_cost=0):\n \"Create a search tree Node, derived from a parent by an action.\"\n self.state = state\n self.parent = parent\n self.action = action\n self.path_cost = path_cost\n self.depth = 0\n if parent:\n self.depth = parent.depth + 1\n\n def __repr__(self):\n return \"\" % (self.state,)\n\n def __lt__(self, node):\n return self.state < node.state\n\n def expand(self, problem):\n \"List the nodes reachable in one step from this node.\"\n return [self.child_node(problem, action)\n for action in problem.actions(self.state)]\n\n def child_node(self, problem, action):\n \"[Figure 3.10]\"\n next = problem.result(self.state, action)\n return Node(next, self, action,\n problem.path_cost(self.path_cost, self.state,\n action, next))\n\n def solution(self):\n \"Return the sequence of actions to go from the root to this node.\"\n return [node.action for node in self.path()[1:]]\n\n def path(self):\n \"Return a list of nodes forming the path from the root to this node.\"\n node, path_back = self, []\n while node:\n path_back.append(node)\n node = node.parent\n return list(reversed(path_back))\n\n def __eq__(self, other):\n return isinstance(other, Node) and self.state == other.state\n\n def __hash__(self):\n return hash(self.state)\n\n# ______________________________________________________________________________\n\n\nclass SimpleProblemSolvingAgentProgram:\n\n \"\"\"Agente de Resolução de Problemas\"\"\"\n\n def __init__(self, initial_state=None):\n self.state = initial_state\n self.seq = []\n\n def __call__(self, percept):\n self.state = self.update_state(self.state, percept)\n if not self.seq:\n goal = self.formulate_goal(self.state)\n problem = self.formulate_problem(self.state, goal)\n self.seq = self.search(problem)\n if not self.seq:\n return None\n return self.seq.pop(0)\n\n def update_state(self, percept):\n raise NotImplementedError\n\n def formulate_goal(self, state):\n raise NotImplementedError\n\n def formulate_problem(self, state, goal):\n raise NotImplementedError\n\n def search(self, problem):\n raise NotImplementedError\n\n\n# Uninformed Search algorithms\n\n\ndef tree_search(problem, frontier):\n frontier.append(Node(problem.initial))\n while frontier:\n node = frontier.pop()\n if problem.goal_test(node.state):\n return node\n frontier.extend(node.expand(problem))\n return None\n\n\ndef graph_search(problem, frontier):\n frontier.append(Node(problem.initial))\n explored = set()\n while frontier:\n node = frontier.pop()\n if problem.goal_test(node.state):\n return node\n explored.add(node.state)\n frontier.extend(child for child in node.expand(problem)\n if child.state not in explored and\n child not in frontier)\n return None\n\n\ndef breadth_first_tree_search(problem):\n \"Search the shallowest nodes in the search tree first.\"\n return tree_search(problem, FIFOQueue())\n\n\ndef depth_first_tree_search(problem):\n \"Search the deepest nodes in the search tree first.\"\n return tree_search(problem, Stack())\n\n\ndef depth_first_graph_search(problem):\n \"Search the deepest nodes in the search tree first.\"\n return graph_search(problem, Stack())\n\n\ndef breadth_first_search(problem):\n node = Node(problem.initial)\n if problem.goal_test(node.state):\n return node\n frontier = FIFOQueue()\n frontier.append(node)\n explored = set()\n while frontier:\n node = frontier.pop()\n explored.add(node.state)\n for child in node.expand(problem):\n if child.state not in explored and child not in frontier:\n if problem.goal_test(child.state):\n return child\n frontier.append(child)\n return None\n\n\ndef best_first_graph_search(problem, f):\n f = memoize(f, 'f')\n node = Node(problem.initial)\n if problem.goal_test(node.state):\n return node\n frontier = PriorityQueue(min, f)\n frontier.append(node)\n explored = set()\n while frontier:\n node = frontier.pop()\n if problem.goal_test(node.state):\n return node\n explored.add(node.state)\n for child in node.expand(problem):\n if child.state not in explored and child not in frontier:\n frontier.append(child)\n elif child in frontier:\n incumbent = frontier[child]\n if f(child) < f(incumbent):\n del frontier[incumbent]\n frontier.append(child)\n return None\n\n\ndef uniform_cost_search(problem):\n return best_first_graph_search(problem, lambda node: node.path_cost)\n\n\ndef depth_limited_search(problem, limit=50):\n def recursive_dls(node, problem, limit):\n if problem.goal_test(node.state):\n return node\n elif limit == 0:\n return 'cutoff'\n else:\n cutoff_occurred = False\n for child in node.expand(problem):\n result = recursive_dls(child, problem, limit - 1)\n if result == 'cutoff':\n cutoff_occurred = True\n elif result is not None:\n return result\n return 'cutoff' if cutoff_occurred else None\n\n return recursive_dls(Node(problem.initial), problem, limit)\n\n\ndef iterative_deepening_search(problem):\n for depth in range(sys.maxsize):\n result = depth_limited_search(problem, depth)\n if result != 'cutoff':\n return result\n\n\n# Informed (Heuristic) Search\n\ngreedy_best_first_graph_search = best_first_graph_search\n\n\ndef astar_search(problem, h=None):\n h = memoize(h or problem.h, 'h')\n return best_first_graph_search(problem, lambda n: n.path_cost + h(n))\n\n\n# Other search algorithms\n\n\ndef recursive_best_first_search(problem, h=None):\n h = memoize(h or problem.h, 'h')\n\n def RBFS(problem, node, flimit):\n if problem.goal_test(node.state):\n return node, 0 \n successors = node.expand(problem)\n if len(successors) == 0:\n return None, infinity\n for s in successors:\n s.f = max(s.path_cost + h(s), node.f)\n while True:\n successors.sort(key=lambda x: x.f)\n best = successors[0]\n if best.f > flimit:\n return None, best.f\n if len(successors) > 1:\n alternative = successors[1].f\n else:\n alternative = infinity\n result, best.f = RBFS(problem, best, min(flimit, alternative))\n if result is not None:\n return result, best.f\n\n node = Node(problem.initial)\n node.f = h(node)\n result, bestf = RBFS(problem, node, infinity)\n return result\n\n\ndef hill_climbing(problem):\n current = Node(problem.initial)\n while True:\n neighbors = current.expand(problem)\n if not neighbors:\n break\n neighbor = argmax_random_tie(neighbors,\n key=lambda node: problem.value(node.state))\n if problem.value(neighbor.state) <= problem.value(current.state):\n break\n current = neighbor\n return current.state\n\n\ndef exp_schedule(k=20, lam=0.005, limit=100):\n \"One possible schedule function for simulated annealing\"\n return lambda t: (k * math.exp(-lam * t) if t < limit else 0)\n\n\ndef simulated_annealing(problem, schedule=exp_schedule()):\n current = Node(problem.initial)\n for t in range(sys.maxsize):\n T = schedule(t)\n if T == 0:\n return current\n neighbors = current.expand(problem)\n if not neighbors:\n return current\n next = random.choice(neighbors)\n delta_e = problem.value(next.state) - problem.value(current.state)\n if delta_e > 0 or probability(math.exp(delta_e / T)):\n current = next\n\n\ndef and_or_graph_search(problem):\n\n def or_search(state, problem, path):\n if problem.goal_test(state):\n return []\n if state in path:\n return None\n for action in problem.actions(state):\n plan = and_search(problem.result(state, action),\n problem, path + [state, ])\n if plan is not None:\n return [action, plan]\n\n def and_search(states, problem, path):\n \"returns plan in form of dictionary where we take action plan[s] if we reach state s\" # noqa\n plan = {}\n for s in states:\n plan[s] = or_search(s, problem, path)\n if plan[s] is None:\n return None\n return plan\n\n return or_search(problem.initial, problem, [])\n\n\nclass OnlineDFSAgent:\n\n def __init__(self, problem):\n self.problem = problem\n self.s = None\n self.a = None\n self.untried = defaultdict(list)\n self.unbacktracked = defaultdict(list)\n self.result = {}\n\n def __call__(self, percept):\n s1 = self.update_state(percept)\n if self.problem.goal_test(s1):\n self.a = None\n else:\n if s1 not in self.untried.keys():\n self.untried[s1] = self.problem.actions(s1)\n if self.s is not None:\n if s1 != self.result[(self.s, self.a)]:\n self.result[(self.s, self.a)] = s1\n unbacktracked[s1].insert(0, self.s)\n if len(self.untried[s1]) == 0:\n if len(self.unbacktracked[s1]) == 0:\n self.a = None\n else:\n unbacktracked_pop = self.unbacktracked[s1].pop(0) # noqa\n for (s, b) in self.result.keys():\n if self.result[(s, b)] == unbacktracked_pop:\n self.a = b\n break\n else:\n self.a = self.untried[s1].pop(0)\n self.s = s1\n return self.a\n\n def update_state(self, percept):\n return percept\n\n# ______________________________________________________________________________\n\n\nclass OnlineSearchProblem(Problem):\n\n def __init__(self, initial, goal, graph):\n self.initial = initial\n self.goal = goal\n self.graph = graph\n\n def actions(self, state):\n return self.graph.dict[state].keys()\n\n def output(self, state, action):\n return self.graph.dict[state][action]\n\n def h(self, state):\n return self.graph.least_costs[state]\n\n def c(self, s, a, s1):\n return 1\n\n def update_state(self, percept):\n raise NotImplementedError\n\n def goal_test(self, state):\n if state == self.goal:\n return True\n return False\n\n\nclass LRTAStarAgent:\n\n def __init__(self, problem):\n self.problem = problem\n self.H = {}\n self.s = None\n self.a = None\n\n def __call__(self, s1): \n if self.problem.goal_test(s1):\n self.a = None\n return self.a\n else:\n if s1 not in self.H:\n self.H[s1] = self.problem.h(s1)\n if self.s is not None:\n self.H[self.s] = min(self.LRTA_cost(self.s, b, self.problem.output(self.s, b), self.H)\n for b in self.problem.actions(self.s))\n\n costs = [self.LRTA_cost(s1, b, self.problem.output(s1, b), self.H)\n for b in self.problem.actions(s1)]\n self.a = list(self.problem.actions(s1))[costs.index(min(costs))]\n\n self.s = s1\n return self.a\n\n def LRTA_cost(self, s, a, s1, H):\n print(s, a, s1)\n if s1 is None:\n return self.problem.h(s)\n else:\n try:\n return self.problem.c(s, a, s1) + self.H[s1]\n except:\n return self.problem.c(s, a, s1) + self.problem.h(s1)\n\n\n# Algoritmo Genético\n\n\ndef genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):\n\n s = problem.initial_state\n states = [problem.result(s, a) for a in problem.actions(s)]\n random.shuffle(states)\n return genetic_algorithm(states[:n], problem.value, ngen, pmut)\n\n\ndef genetic_algorithm(population, fitness_fn, ngen=1000, pmut=0.1):\n \"[Figure 4.8]\"\n for i in range(ngen):\n new_population = []\n for i in len(population):\n fitnesses = map(fitness_fn, population)\n p1, p2 = weighted_sample_with_replacement(population, fitnesses, 2)\n child = p1.mate(p2)\n if random.uniform(0, 1) < pmut:\n child.mutate()\n new_population.append(child)\n population = new_population\n return argmax(population, key=fitness_fn)\n\n\nclass GAState:\n\n \"Abstract class for individuals in a genetic search.\"\n\n def __init__(self, genes):\n self.genes = genes\n\n def mate(self, other):\n \"Return a new individual crossing self and other.\"\n c = random.randrange(len(self.genes))\n return self.__class__(self.genes[:c] + other.genes[c:])\n\n def mutate(self):\n \"Change a few of my genes.\"\n raise NotImplementedError\n\n\n# Graphs and Graph Problems\n\n\nclass Graph:\n\n def __init__(self, dict=None, directed=True):\n self.dict = dict or {}\n self.directed = directed\n if not directed:\n self.make_undirected()\n\n def make_undirected(self):\n \"Make a digraph into an undirected graph by adding symmetric edges.\"\n for a in list(self.dict.keys()):\n for (b, distance) in self.dict[a].items():\n self.connect1(b, a, distance)\n\n def connect(self, A, B, distance=1):\n self.connect1(A, B, distance)\n if not self.directed:\n self.connect1(B, A, distance)\n\n def connect1(self, A, B, distance):\n \"Add a link from A to B of given distance, in one direction only.\"\n self.dict.setdefault(A, {})[B] = distance\n\n def get(self, a, b=None):\n\n links = self.dict.setdefault(a, {})\n if b is None:\n return links\n else:\n return links.get(b)\n\n def nodes(self):\n \"Return a list of nodes in the graph.\"\n return list(self.dict.keys())\n\n\ndef UndirectedGraph(dict=None):\n \"Build a Graph where every edge (including future ones) goes both ways.\"\n return Graph(dict=dict, directed=False)\n\n\ndef RandomGraph(nodes=list(range(10)), min_links=2, width=400, height=300,\n curvature=lambda: random.uniform(1.1, 1.5)):\n\n g = UndirectedGraph()\n g.locations = {}\n for node in nodes:\n g.locations[node] = (random.randrange(width), random.randrange(height))\n for i in range(min_links):\n for node in nodes:\n if len(g.get(node)) < min_links:\n here = g.locations[node]\n\n def distance_to_node(n):\n if n is node or g.get(node, n):\n return infinity\n return distance(g.locations[n], here)\n neighbor = argmin(nodes, key=distance_to_node)\n d = distance(g.locations[neighbor], here) * curvature()\n g.connect(node, neighbor, int(d))\n return g\n\n\nromania_map = UndirectedGraph(dict(\n Arad=dict(Zerind=75, Sibiu=140, Timisoara=118),\n Bucharest=dict(Urziceni=85, Pitesti=101, Giurgiu=90, Fagaras=211),\n Craiova=dict(Drobeta=120, Rimnicu=146, Pitesti=138),\n Drobeta=dict(Mehadia=75),\n Eforie=dict(Hirsova=86),\n Fagaras=dict(Sibiu=99),\n Hirsova=dict(Urziceni=98),\n Iasi=dict(Vaslui=92, Neamt=87),\n Lugoj=dict(Timisoara=111, Mehadia=70),\n Oradea=dict(Zerind=71, Sibiu=151),\n Pitesti=dict(Rimnicu=97),\n Rimnicu=dict(Sibiu=80),\n Urziceni=dict(Vaslui=142)))\nromania_map.locations = dict(\n Arad=(91, 492), Bucharest=(400, 327), Craiova=(253, 288),\n Drobeta=(165, 299), Eforie=(562, 293), Fagaras=(305, 449),\n Giurgiu=(375, 270), Hirsova=(534, 350), Iasi=(473, 506),\n Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537),\n Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410),\n Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350),\n Vaslui=(509, 444), Zerind=(108, 531))\n\n\nvacumm_world = Graph(dict(\n State_1 = dict(Suck = ['State_7', 'State_5'], Right = ['State_2']),\n State_2 = dict(Suck = ['State_8', 'State_4'], Left = ['State_2']),\n State_3 = dict(Suck = ['State_7'], Right = ['State_4']),\n State_4 = dict(Suck = ['State_4', 'State_2'], Left = ['State_3']),\n State_5 = dict(Suck = ['State_5', 'State_1'], Right = ['State_6']),\n State_6 = dict(Suck = ['State_8'], Left = ['State_5']),\n State_7 = dict(Suck = ['State_7', 'State_3'], Right = ['State_8']),\n State_8 = dict(Suck = ['State_8', 'State_6'], Left = ['State_7'])\n ))\n\none_dim_state_space = Graph(dict(\n State_1=dict(Right='State_2'),\n State_2=dict(Right='State_3', Left='State_1'),\n State_3=dict(Right='State_4', Left='State_2'),\n State_4=dict(Right='State_5', Left='State_3'),\n State_5=dict(Right='State_6', Left='State_4'),\n State_6=dict(Left='State_5')\n ))\none_dim_state_space.least_costs = dict(\n State_1=8,\n State_2=9,\n State_3=2,\n State_4=2,\n State_5=4,\n State_6=3)\n\n\naustralia_map = UndirectedGraph(dict(\n T=dict(),\n SA=dict(WA=1, NT=1, Q=1, NSW=1, V=1),\n NT=dict(WA=1, Q=1),\n NSW=dict(Q=1, V=1)))\naustralia_map.locations = dict(WA=(120, 24), NT=(135, 20), SA=(135, 30),\n Q=(145, 20), NSW=(145, 32), T=(145, 42),\n V=(145, 37))\n\n\nclass GraphProblem(Problem):\n\n \"The problem of searching a graph from one node to another.\"\n\n def __init__(self, initial, goal, graph):\n Problem.__init__(self, initial, goal)\n self.graph = graph\n\n def actions(self, A):\n \"The actions at a graph node are just its neighbors.\"\n return list(self.graph.get(A).keys())\n\n def result(self, state, action):\n \"The result of going to a neighbor is just that neighbor.\"\n return action\n\n def path_cost(self, cost_so_far, A, action, B):\n return cost_so_far + (self.graph.get(A, B) or infinity)\n\n def h(self, node):\n \"h function is straight-line distance from a node's state to goal.\"\n locs = getattr(self.graph, 'locations', None)\n if locs:\n return int(distance(locs[node.state], locs[self.goal]))\n else:\n return infinity\n\n\nclass GraphProblemStochastic(GraphProblem):\n \n\n def result(self, state, action):\n return self.graph.get(state, action)\n\n def path_cost():\n raise NotImplementedError\n\n\n# ______________________________________________________________________________\n\n\nclass NQueensProblem(Problem):\n\n\n def __init__(self, N):\n self.N = N\n self.initial = [None] * N\n\n def actions(self, state):\n \"In the leftmost empty column, try all non-conflicting rows.\"\n if state[-1] is not None:\n return [] \n else:\n col = state.index(None)\n return [row for row in range(self.N)\n if not self.conflicted(state, row, col)]\n\n def result(self, state, row):\n \"Place the next queen at the given row.\"\n col = state.index(None)\n new = state[:]\n new[col] = row\n return new\n\n def conflicted(self, state, row, col):\n \"Would placing a queen at (row, col) conflict with anything?\"\n return any(self.conflict(row, col, state[c], c)\n for c in range(col))\n\n def conflict(self, row1, col1, row2, col2):\n \"Would putting two queens in (row1, col1) and (row2, col2) conflict?\"\n return (row1 == row2 or \n col1 == col2 or \n row1 - col1 == row2 - col2 or \n row1 + col1 == row2 + col2) \n\n def goal_test(self, state):\n \"Check if all columns filled, no conflicts.\"\n if state[-1] is None:\n return False\n return not any(self.conflicted(state, state[col], col)\n for col in range(len(state)))\n\n\n\n# Inverse Boggle: Search for a high-scoring Boggle board. \n\n\nALPHABET = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n\ncubes16 = ['FORIXB', 'MOQABJ', 'GURILW', 'SETUPL',\n 'CMPDAE', 'ACITAO', 'SLCRAE', 'ROMASH',\n 'NODESW', 'HEFIYE', 'ONUDTK', 'TEVIGN',\n 'ANEDVZ', 'PINESH', 'ABILYT', 'GKYLEU']\n\n\ndef random_boggle(n=4):\n \"\"\"Return a random Boggle board of size n x n.\n We represent a board as a linear list of letters.\"\"\"\n cubes = [cubes16[i % 16] for i in range(n * n)]\n random.shuffle(cubes)\n return list(map(random.choice, cubes))\n\n\nboyan_best = list('RSTCSDEIAEGNLRPEATESMSSID')\n\n\ndef print_boggle(board):\n \"Print the board in a 2-d array.\"\n n2 = len(board)\n n = exact_sqrt(n2)\n for i in range(n2):\n\n if i % n == 0 and i > 0:\n print()\n if board[i] == 'Q':\n print('Qu', end=' ')\n else:\n print(str(board[i]) + ' ', end=' ')\n print()\n\n\ndef boggle_neighbors(n2, cache={}):\n if cache.get(n2):\n return cache.get(n2)\n n = exact_sqrt(n2)\n neighbors = [None] * n2\n for i in range(n2):\n neighbors[i] = []\n on_top = i < n\n on_bottom = i >= n2 - n\n on_left = i % n == 0\n on_right = (i+1) % n == 0\n if not on_top:\n neighbors[i].append(i - n)\n if not on_left:\n neighbors[i].append(i - n - 1)\n if not on_right:\n neighbors[i].append(i - n + 1)\n if not on_bottom:\n neighbors[i].append(i + n)\n if not on_left:\n neighbors[i].append(i + n - 1)\n if not on_right:\n neighbors[i].append(i + n + 1)\n if not on_left:\n neighbors[i].append(i - 1)\n if not on_right:\n neighbors[i].append(i + 1)\n cache[n2] = neighbors\n return neighbors\n\n\ndef exact_sqrt(n2):\n \"If n2 is a perfect square, return its square root, else raise error.\"\n n = int(math.sqrt(n2))\n assert n * n == n2\n return n\n\n# _____________________________________________________________________________\n\n\nclass Wordlist:\n\n def __init__(self, file, min_len=3):\n lines = file.read().upper().split()\n self.words = [word for word in lines if len(word) >= min_len]\n self.words.sort()\n self.bounds = {}\n for c in ALPHABET:\n c2 = chr(ord(c) + 1)\n self.bounds[c] = (bisect.bisect(self.words, c),\n bisect.bisect(self.words, c2))\n\n def lookup(self, prefix, lo=0, hi=None):\n words = self.words\n if hi is None:\n hi = len(words)\n i = bisect.bisect_left(words, prefix, lo, hi)\n if i < len(words) and words[i].startswith(prefix):\n return i, (words[i] == prefix)\n else:\n return None, False\n\n def __contains__(self, word):\n return self.lookup(word)[1]\n\n def __len__(self):\n return len(self.words)\n\n# _____________________________________________________________________________\n\n\nclass BoggleFinder:\n\n wordlist = None \n\n def __init__(self, board=None):\n if BoggleFinder.wordlist is None:\n BoggleFinder.wordlist = Wordlist(DataFile(\"EN-text/wordlist.txt\"))\n self.found = {}\n if board:\n self.set_board(board)\n\n def set_board(self, board=None):\n \"Set the board, and find all the words in it.\"\n if board is None:\n board = random_boggle()\n self.board = board\n self.neighbors = boggle_neighbors(len(board))\n self.found = {}\n for i in range(len(board)):\n lo, hi = self.wordlist.bounds[board[i]]\n self.find(lo, hi, i, [], '')\n return self\n\n def find(self, lo, hi, i, visited, prefix):\n if i in visited:\n return\n wordpos, is_word = self.wordlist.lookup(prefix, lo, hi)\n if wordpos is not None:\n if is_word:\n self.found[prefix] = True\n visited.append(i)\n c = self.board[i]\n if c == 'Q':\n c = 'QU'\n prefix += c\n for j in self.neighbors[i]:\n self.find(wordpos, hi, j, visited, prefix)\n visited.pop()\n\n def words(self):\n \"The words found.\"\n return list(self.found.keys())\n\n scores = [0, 0, 0, 0, 1, 2, 3, 5] + [11] * 100\n\n def score(self):\n \"The total score for the words found, according to the rules.\"\n return sum([self.scores[len(w)] for w in self.words()])\n\n def __len__(self):\n \"The number of words found.\"\n return len(self.found)\n\n# _____________________________________________________________________________\n\n\ndef boggle_hill_climbing(board=None, ntimes=100, verbose=True):\n finder = BoggleFinder()\n if board is None:\n board = random_boggle()\n best = len(finder.set_board(board))\n for _ in range(ntimes):\n i, oldc = mutate_boggle(board)\n new = len(finder.set_board(board))\n if new > best:\n best = new\n if verbose:\n print(best, _, board)\n else:\n board[i] = oldc \n if verbose:\n print_boggle(board)\n return board, best\n\n\ndef mutate_boggle(board):\n i = random.randrange(len(board))\n oldc = board[i]\n board[i] = random.choice(random.choice(cubes16))\n return i, oldc\n\n\n\n# Código para comparar vários algoritmos de busca\n\n\nclass InstrumentedProblem(Problem):\n\n\n def __init__(self, problem):\n self.problem = problem\n self.succs = self.goal_tests = self.states = 0\n self.found = None\n\n def actions(self, state):\n self.succs += 1\n return self.problem.actions(state)\n\n def result(self, state, action):\n self.states += 1\n return self.problem.result(state, action)\n\n def goal_test(self, state):\n self.goal_tests += 1\n result = self.problem.goal_test(state)\n if result:\n self.found = state\n return result\n\n def path_cost(self, c, state1, action, state2):\n return self.problem.path_cost(c, state1, action, state2)\n\n def value(self, state):\n return self.problem.value(state)\n\n def __getattr__(self, attr):\n return getattr(self.problem, attr)\n\n def __repr__(self):\n return '<%4d/%4d/%4d/%s>' % (self.succs, self.goal_tests,\n self.states, str(self.found)[:4])\n\n\ndef compare_searchers(problems, header,\n searchers=[breadth_first_tree_search,\n breadth_first_search,\n depth_first_graph_search,\n iterative_deepening_search,\n depth_limited_search,\n recursive_best_first_search]):\n def do(searcher, problem):\n p = InstrumentedProblem(problem)\n searcher(p)\n return p\n table = [[name(s)] + [do(s, p) for p in problems] for s in searchers]\n print_table(table, header)\n\n\ndef compare_graph_searchers():\n \"\"\"Imprime uma tabela de resultados de pesquisa.\"\"\"\n compare_searchers(problems=[GraphProblem('Arad', 'Bucharest', romania_map),\n GraphProblem('Oradea', 'Neamt', romania_map),\n GraphProblem('Q', 'WA', australia_map)],\n header=['Searcher', 'romania_map(Arad, Bucharest)',\n 'romania_map(Oradea, Neamt)', 'australia_map'])\n","sub_path":"Modulos/Modulo 01 - Introdução a Inteligencia Artificial/03 - Agentes Lógicos/Busca/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":31255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"367411739","text":"#!/usr/bin/python3\n\n'''\n3.\nFunkcja step() przesuwająca punkt świetlny o jeden ‘w górę’ modulo 4. Przykład \nstep\n() # 0->1 \nstep\n() # 1->2 \n... \nstep\n() # 3->0 \nTest 1: Program przesuwający punkt świetlny \nTest 2: Program przesuwający punkt świetlny, jeżeli na\nciśnieto przycisk 0. \n'''\n\nfrom board_driver_simulator import open,close,but,pot,det,led\nimport time\n\nvstep=0\n\ndef get_key():\n\tbut_state=but()\n\tif but_state==1:\n\t\treturn 0\n\telif but_state==2:\n\t\treturn 1\n\telif but_state==4:\n\t\treturn 2\n\telif but_state==8:\n\t\treturn 3\n\telse:\n\t\treturn -1\n\ndef set_point(position):\n\tif position==0:\n\t\treturn 1\n\telif position==1:\n\t\treturn 2\n\telif position==2:\n\t\treturn 4\n\telif position==3:\n\t\treturn 8\n\telse:\n\t\treturn 0\n\ndef step():\n\tglobal vstep\n\tvstep+=1\n\tvstep%=4\n\tled(set_point(vstep))\n\ntry:\n\topen()\n#---------------------\n\tled(0)\n\twhile(True):\n\t\t#test1\n\t\t#step()\n\n\t\t#test2\n\t\tif get_key()==0:\n\t\t\tstep()\n\t\t#time.sleep(0.5)\t\n#---------------------\nfinally:\n\tclose()\n","sub_path":"Part I and II/cz2_03.py","file_name":"cz2_03.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"621075587","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 1 11:52:55 2021\n注射疫苗的状态\n@author: pc\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.integrate as spi\nimport random\naccessrate = 0.3\neffecrate1 = 0.85#根据疫苗保护率80%转换得到\neffecrate2 = 0.85\nN = 100000 # 湖北省为6000 0000\nE_0 = 0\nI_0 = 1\nR_0 = 0\nA_0 = N*accessrate\nD_0 = 0\nI2_0=0\nS_0 = N- E_0 - I_0 - R_0\nbeta1 = 0.78735 # 真实数据拟合得出---传染率\nbeta2 = 0.15747 #潜伏期的传染率\nbeta3 = 0.85 #---第二种病毒传染率\neps = 0.1 #潜伏期内自愈率\nu=0.2 #从第一种转换为第二种病毒的概率\n# r2 * beta2 = 2\nsigma = 1/14 # 1/14, 潜伏期的倒数 潜伏期内发病的概率\ngamma = 0.03 # 治愈的概率\ngamma2 = 0.0015 #死亡率\ngamma3 = 0.003 #第二种病毒致死率\nr= 10 # 政府干预措施决定----每天接触的人\neta=0.1#转化为I后没有隔离的比例\nT = 150\n\n#ode求解\nINI = [S_0, E_0, I_0, R_0 ,D_0,I2_0 ,A_0]\n# beta1 = 0.78735 # 真实数据拟合得出---传染率# beta2 = 0.15747 #潜伏期的传染率# beta3 = 0.85 #---第二种病毒传染率\n# eps = 0.1 #潜伏期内自愈率# u=0.2 #从第一种转换为第二种病毒的概率# # r2 * beta2 = 2\n# sigma = 1/14 潜伏期的倒数 潜伏期内发病的概率 # r= 1 # 政府干预措施决定----每天接触的人\n# gamma = 0.03 治愈的概率 #gamma2 = 0.0015 #死亡率 # gamma3 = 0.003 #第二种病毒致死率\ndef SEIR(inivalue, t):\n X = inivalue\n Y = np.zeros(7)\n # S数量\n Y[0] = - (r * beta1 *eta*X[0] * X[2]) / (N-X[4]-(1-eta)*X[2]-(1-eta)*X[5]) - (r * beta2 * X[0] * X[1]) / (N-X[4]-(1-eta)*X[2]-(1-eta)*X[5]) - (r * beta3 *eta* X[0] * X[5]) /(N-X[4]-(1-eta)*X[2]-(1-eta)*X[5])\n # E数量\n Y[1] = (r * beta1 * X[0] *eta* X[2]) / (N-X[4]-(1-eta)*X[2]-(1-eta)*X[5]) + (r * beta2 *X[0] * X[1]) / (N-X[4]-(1-eta)*X[2]-(1-eta)*X[5]) + (r * beta3 *eta* X[0] * X[5]) / (N-X[4]-(1-eta)*X[2]-(1-eta)*X[5]) - sigma * X[1] - eps * X[1] + X[6]*r*eta*(X[2]*(1-effecrate1) + X[5]*eta*(1-effecrate2))/(N-X[4]-(1-eta)*X[2]-(1-eta)*X[5])\n # I数量\n Y[2] = sigma * X[1] - gamma * X[2] - gamma2 * X[2] - u * X[2]\n # R数量\n Y[3] = gamma * X[2] + eps * X[1] + gamma * X[5] - u*X[2]\n # D\n Y[4] = gamma2 * X[2] + gamma3 *X[5]\n # I2 感染第二种病毒的人\n Y[5] =u*X[2]-X[5]*(gamma+gamma3)\n # A 接种疫苗的人数\n Y[6]=-X[6]*r*(X[2]*eta*(1-effecrate1) + X[5]*eta*(1-effecrate2))/(N-X[4]-(1-eta)*X[2]-(1-eta)*X[5])\n return Y\n\nT_range = np.arange(0, T+1)\nRes = spi.odeint(SEIR, INI, T_range)\nS_t = Res[:, 0]\nE_t = Res[:, 1]\nI_t = Res[:, 2]\nR_t = Res[:, 3]\nD_t = Res[:, 4]\nI2_t = Res[:,5]\nA_t= Res[:,6]\n\nlis1=[]\nlis2=[]\nfor i in range(T):\n lis1.append(((I_t[i+1]-I_t[i])+(A_t[i+1]-A_t[i]))/(E_t[i]+I_t[i]))\n lis2.append((A_t[i]+R_t[i])/(N-D_t[i]))\ntim=[i for i in range(T)]\nplt.plot(tim,lis1,'-',color='blue')\nplt.plot(tim,lis2,'-',color='green')\nplt.show()\n# for i in range(T):\n# if sigma*(E_t[i+1]-E_t[i])<0.5 and sigma*(E_t[i+1]-E_t[i])>-10:\n# print(i)\n# break\n# print(max(D_t))\n# plt.plot(S_t, color='blue', label='Susceptibles')#, marker='.')\n# plt.plot(E_t, color='grey', label='Exposed')\n# plt.plot(I_t, color='red', label='Infected')\n# plt.plot(R_t, color='green', label='Removed')\n# plt.plot(D_t,color='yellow',label='Death')\n# plt.plot(I2_t,color='black',label='Infected2')\n# plt.plot(A_t,color='pink',label='Accept')\n# plt.xlabel('Day')\n# plt.ylabel('Number')\n# plt.title('SEIR Model')\n# plt.legend()\n# plt.show()","sub_path":"1.2疫苗有效性及变异问题.py","file_name":"1.2疫苗有效性及变异问题.py","file_ext":"py","file_size_in_byte":3610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"452287497","text":"#from authkey import user_insta\nfrom modulos.InstaloaderDownloader import InstaloaderDownloader\nimport modulos.LocalDataBase as localdatabase\nfrom datetime import datetime\n\n\n#####################################################################################\n\nUNTIL = datetime(2021,8,1) # Data de fim da coleta\nSINCE = datetime(2021,7,1) # Data de inicio da coleta\n\nHASHTAG = 'vegano' \n\nMAX_PUBLIC = 3000 #Numero máximo de publicacoes coletadas\n\n############################## Dados importantes #####################################\n\n\n\ndef main():\n instaloader_downloader = InstaloaderDownloader()\n #post_iterator = get_iterator_hashtag(session=session,hashtag=HASHTAG)\n db = localdatabase.StartDataBase('posts_instagram')\n post_iterator = instaloader_downloader.get_iterator_hashtag(hashtag=HASHTAG) \n itr = 0\n print(\"!!!...Programa iniciado...!!!\")\n for post in post_iterator:\n if(itr < MAX_PUBLIC):\n if((post.date >= SINCE) and (post.date < UNTIL)):\n if(db.exist_postagem(post.shortcode)):\n print(f\"{post.shortcode} ja existe no banco\")\n continue\n\n print(f'Shortcode: {post.shortcode}')\n \n db.add_postagem({\n 'ShortCode' : post.shortcode,\n 'Hashtag': HASHTAG,\n 'Proprietario' : post.owner_username,\n 'LegendaPerfil' : 'sem_legenda' if post.pcaption is None else 'sem_legenda' if str(post.pcaption) == '' else str(post.pcaption),\n 'LegendaPostagem' : 'sem_legenda' if post.caption is None else 'sem_legenda' if str(post.caption) == '' else str(post.caption),\n 'QtdCurtidas' : post.likes,\n 'QtdComentarios' : post.comments,\n 'DataIso' : post.date.strftime(\"%Y-%m-%d\"),\n 'QtdImagens' : post.mediacount,\n 'IsVideo' : post.is_video,\n 'VideoVisualizacao' : int(post.video_view_count) if post.is_video else 0,\n 'VideoDuracao' : int(post.video_duration) if post.is_video else 0,\n 'Localizacao' : 'sem_localizacao' if post.location is None else post.location.name\n })\n if(post.comments > 0):\n for comment in post.get_comments():\n db.add_comentario({\n 'Proprietario' : comment.owner.username,\n 'Comentario' : str(comment.text),\n 'QtdCurtidas' : comment.likes_count,\n 'DataIso' : comment.created_at_utc.strftime(\"%Y-%m-%d\"),\n 'Postagem' : post.shortcode\n })\n print(f\"Usuario [{itr+1}] coletado de [{MAX_PUBLIC}]\")\n itr += 1\n else:\n break\n\n\nmain()\n","sub_path":"raspagem_instagram.py","file_name":"raspagem_instagram.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"596328263","text":"from django.db import models\n\n\n# Create your models here.\n\nclass DatumImage(models.Model):\n '''\n 资料\n '''\n MATERIAL_TYPE = (\n (0, 'pdf'),\n (1, 'img'),\n )\n name = models.CharField(max_length=150, verbose_name='名称')\n type = models.PositiveSmallIntegerField('资料类型', choices=MATERIAL_TYPE)\n store_path = models.FileField(max_length=100, verbose_name='路径',\n upload_to='uploads/')\n remark = models.TextField(default='', verbose_name='备注')\n upload_time = models.DateTimeField(verbose_name='时间', auto_now_add=True)\n","sub_path":"web/datum/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"340083865","text":"# -*- coding: utf-8 -*-\n# @Author : Joy\n# @FileName: remove_activity.py\n\n\nimport requests\n\nheaders = {\n \"content-type\": \"application/json\",\n \"token\": \"eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJleHBpcmVzSW4iOjE2MjgwNDI4NDI3NDUsImlkIjozNTZ9.qiixmNEKhO_yTMgVviMnch3dhQl_szbShC_rH3PmxTA\"\n}\n\n'''取消活动场次'''\n\n\ndef remove_events():\n host = \"https://api-dev.backoffice.allforsport.cn/v2/events/\"\n for i in range(1500, 1506):\n body = {\n \"description\": \"1234\",\n \"id\": i,\n \"megSendParticipants\": \"\",\n \"reasonType\": 1\n }\n url = host + str(i)\n res = requests.delete(url=url, json=body, headers=headers)\n print(res.json())\n\n\n'''下架活动模板'''\n\n\ndef remove_template():\n templateurl = \"https://api-dev.backoffice.allforsport.cn/v1/event_templates/on_shelf/931?isOnShelf=false\"\n for i in range(1189, 1199):\n url = \"https://api-dev.backoffice.allforsport.cn/v1/event_templates/on_shelf/{}?isOnShelf=false\".format(i)\n body = {\n \"isOnShelf\": \"false\"\n }\n res = requests.put(url=url, json=body, headers=headers)\n print(res.json())\n\n\nif __name__ == '__main__':\n remove_template()\n","sub_path":"case/remove_activity.py","file_name":"remove_activity.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"406257066","text":"import io\nimport numpy as np\nimport os\nimport time\npath = './../17.06/numberbatch-en-17.06.txt'\nwith io.open('./../17.06/numberbatch-en-17.06.txt', 'r', encoding='utf-8') as f:\n start = time.time()\n data = f.read()\n print(\"Loaded GloVe at %s in %fs\" % (os.path.basename(path), time.time()-start))\n\nlines = data.split('\\n')\nprint(\"There are %d lines.\" % len(lines))\n\n[vocab_size, d] = [int(x) for x in lines[0].split()]\nstart = time.time()\nmodel = {}\nfor line in lines[1:]:\n tokens = line.split()\n try:\n model[tokens[0]] = np.array([np.float32(x) for x in tokens[1:]])\n except IndexError as e:\n print(\"Index Error for: \", tokens)\n print(e)\n\nprint(\"Model GloVe \"+os.path.splitext(os.path.basename(path))[0]+\" created in %fs\" % (time.time()-start))\n\n","sub_path":"SemEval-2017-Task2-en/numberbatch.py","file_name":"numberbatch.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"15817094","text":"# -*- coding: utf-8 -*-\nimport pandas as pd\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.plotly as py\nfrom plotly import graph_objs as go\nfrom datetime import datetime\nimport numpy as np\n\"\"\"\nOrignial code has some nice functions to call the graphs\n\"\"\"\n\ndf = pd.read_csv('pv.csv')\ndf['time'] = pd.to_datetime(df['time'])\ndf.set_index('time', drop=False)\n\ndf_rec = pd.read_csv('rec.csv')\n\n#start = pd.Timestamp(now.date())\n#end = atart + pd.offsets.Hour(24)\n\n\"\"\"\nDefine helper functions, they could go in the utils folder later\n\"\"\"\ndef get_daily_totals(df):\n \"\"\"\n Takes dataframe and returns the totals for the top bar\n \"\"\"\n ### Calcs assume time is in correct timezone\n now = datetime.now()\n today = pd.Timestamp(now.date())\n\n df_today = df[df[\"time\"] >= today]\n tot_pv = np.round(df_today[\"whole\"].sum(),2)\n tot_cons = np.round(df_today[\"car1\"].sum() + df_today[\"car2\"].sum(), 2)\n tot_time = np.round(3*(df_today[\"car1\"].count() + df_today[\"car2\"].count()), 2)\n\n return tot_pv, tot_cons, tot_time\n\ndef balance_daily_energy(df):\n\n total_production = np.round(df['whole'].sum()-250, 2)\n total_car1 = np.round(df['car1'].sum(), 2)\n total_car2 = np.round(df['car2'].sum(), 2)\n total_charge = total_car1 + total_car2\n grid = 0\n if total_charge > total_production:\n grid = total_charge - total_production\n\n trace1 = go.Bar(\n x=[total_production, 0],\n y=['Production', 'consumption'],\n name='PV',\n orientation = 'h'\n )\n trace2 = go.Bar(\n x=[grid, 0],\n y=['Production', 'consumption'],\n name='Grid',\n orientation = 'h'\n )\n\n trace3 = go.Bar(\n x=[0, total_car1],\n y=['Production', 'consumption'],\n name='User1',\n orientation = 'h'\n )\n\n trace4 = go.Bar(\n x=[0, total_car2],\n y=['Production', 'consumption'],\n name='User2',\n orientation = 'h'\n )\n\n return [trace1, trace2, trace3, trace4]\n\ndef calc_recs(df):\n \"\"\"\n Gets RECS, need to update this to output a dict of\n varying numbers of users\n \"\"\"\n usr1 = df_rec[df_rec['UserId']=='usr1']['REC_Q'].sum()\n usr2 = df_rec[df_rec['UserId']=='usr2']['REC_Q'].sum()\n\n return usr1, usr2\n\n\n\"\"\"\nUse functions to get data\n\"\"\"\nbalance_data = balance_daily_energy(df)\ntot_pv, tot_cons, tot_time = get_daily_totals(df)\nusr1, usr2 = calc_recs(df_rec)\n####### Configuring the graphs\n####### code these out later\ngraph_height = 200\nmar = 30\n\nlayout = [\n\n # row of indicatiors - later can update this to use callbacks\n html.Div([\n html.Div([\n html.H5(['Total RECs issued: '], className=\"twelve columns indicator_text\"),\n html.P([str(tot_pv)+\" kWh\"] ,className=\"indicator_value\")\n ],className=\"four columns indicator\"),\n\n html.Div([\n html.H5(['Todays EV Consumption: '], className=\"twelve columns indicator_text\"),\n html.P([str(tot_cons)+\" kWh\"] ,className=\"indicator_value\")\n ],className=\"four columns indicator\"),\n\n html.Div([\n html.H5(['Total EV Charging Time: '], className=\"twelve columns indicator_text\"),\n html.P(str(tot_time)+\" mins\" ,className=\"indicator_value\")\n ],className=\"four columns indicator\")\n\n ],className=\"row\", style={'margin-bottom': '15px'}),\n\n ####### This is the 1st row of charts\n html.Div([\n\n #### START of Card for PV\n html.Div([\n\n html.H5('TODAYS PV production (50kWp System)'),\n dcc.Graph(\n id='PV',\n style={\n 'height': graph_height\n },\n figure={\n 'data': [\n {'x': df['time'], 'y': df['part'], 'type': 'scatter','marker':{'size':0.01, 'opacity': 0.5}, 'line':{'width': 0}, 'fillcolor':'rgba(225, 151, 76, 0.5)','fill':'tozeroy', 'name': 'Consumption'}\n ],\n 'layout': {\n 'title': \"\",\n 'xaxis': {'showgrid': False},\n 'yaxis': {\n 'title': \"PV production (kWh)\",\n 'showgrid':False,\n 'titlefont': {\n 'size': 12,\n 'color': '#7f7f7f'\n }\n },\n \"margin\": go.layout.Margin(r=mar, t=mar, b=mar)\n }\n },\n config ={\n 'displayModeBar': False\n }\n )\n ], className='six columns chart_div'),\n #### END of Card for PV\n\n ###### START OF THE Card for EV\n html.Div([\n ####### Main body of card\n\n html.H5('TODAYS EV Charging'),\n dcc.Graph(\n id='EV',\n style={\n 'height': graph_height\n },\n figure={\n 'data': [\n {'x': df['time'], 'y': df['car1'], 'type': 'scatter','marker':{'size':0.01, 'opacity': 0.5}, 'line':{'width': 0}, 'fillcolor':'rgba(57, 106, 177, 0.5)','fill':'tozeroy', 'name': 'User1'},\n {'x': df['time'], 'y': df['car2'], 'type': 'scatter','marker':{'size':0.01, 'opacity': 0.5}, 'line':{'width': 0}, 'fillcolor':'rgba(225, 151, 76, 0.5)','fill':'tozeroy', 'name': 'User2'}\n ],\n 'layout': {\n 'title': \"\",\n 'xaxis': {'showgrid': False},\n 'yaxis': {\n 'title': \"EV Charging (kWh)\",\n 'showgrid': False,\n 'titlefont': {\n 'size': 9,\n 'color': '#7f7f7f'\n }\n },\n \"margin\": go.layout.Margin(r=mar, t=mar, b=mar)\n }\n },\n config ={\n 'displayModeBar': False\n }\n )\n\n ####### Footer, where the update info can go\n ], className='six columns chart_div')\n ###### END OF THE Card for EV\n\n ####### End of the 1st row of cards\n ], className='row'),\n\n ####### FILLER ROW ##############\n html.Div([\n html.Div([],className=\"twelve columns indicator\")\n ],className=\"row\", style={'margin-bottom': '15px', 'margin-top': '15px'}),\n ####### END FILLER ROW ##############\n\n ####### START 2ND ROW OF GRAPHS #####\n html.Div([\n html.Div([\n\n html.H5('YESTERDAYS Energy Balancing'),\n dcc.Graph(\n id='balance',\n style={\n 'height': graph_height*2\n },\n figure=go.Figure(data=balance_data,\n layout=go.Layout(barmode='stack', margin = dict(r=mar, t=mar, b=mar))),\n config ={\n 'displayModeBar': False\n }\n )\n ], className='six columns chart_div'),\n\n html.Div([\n\n html.H5('YESTERDAYS RECs'),\n dcc.Graph(\n id='rec_donut',\n style={\n 'height': graph_height*2\n },\n figure={\n 'data': [{\n 'values': [usr1, usr2],\n 'labels':[\n \"user1\",\n \"user2\"\n ],\n 'name': \"REC Issued\",\n 'hole': .4,\n 'type': 'pie',\n }],\n 'layout': {\n 'title': \"\",\n 'xaxis': {'showgrid': False},\n 'yaxis': {\n 'title': \"EV Charging (kWh)\",\n 'showgrid': False,\n 'titlefont': {\n 'size': 9,\n 'color': '#7f7f7f'\n }\n },\n \"margin\": go.layout.Margin(r=mar, t=mar, b=mar),\n \"legend\": go.layout.Legend(orientation='h')\n }\n\n },\n config ={\n 'displayModeBar': False\n }\n )\n ], className='six columns chart_div')\n\n ],className=\"row\", style={'margin-bottom': '15px', 'margin-top': '15px'}),\n ####### END 2ND ROW OF GRAPHS #####\n]\n","sub_path":"apps/tab1.py","file_name":"tab1.py","file_ext":"py","file_size_in_byte":9191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"227778406","text":"from gi.repository import Gtk, Gdk\nimport Game\nimport yaml\n__author__ = 'tristan'\n\nclass PlayerWindow(Gtk.Window):\n\n def __init__(self, squad):\n self.active_players = Game.Squad()\n self.squad = squad\n self.points = []\n self.our_current_score = 0\n self.their_current_score = 0\n\n self.buttons = [[\"Add Player\", self.on_click_add_player], [\"Start O Point\", self.on_click_start_o_point], [\"Start D Point\", self.on_click_start_d_point]]\n Gtk.Window.__init__(self, title=\"Select Players\")\n self.set_default_size(200, 200)\n self.connect(\"delete-event\", Gtk.main_quit)\n self.player_store = Gtk.ListStore(str, int, bool)\n\n rhs_container = Gtk.VBox()\n self.main_container = Gtk.Box()\n button_container = Gtk.Box()\n for info in self.buttons:\n button = Gtk.Button(label=info[0])\n button.connect(\"clicked\", info[1])\n button_container.pack_start(button, True, True, 0)\n\n for player in squad.get_players():\n self.player_store.append([player.name, player.number, False])\n\n self.player_view = Gtk.TreeView(model = self.player_store)\n renderer_text = Gtk.CellRendererText()\n name_text = Gtk.TreeViewColumn(\"Name\", renderer_text, text = 0)\n number_text = Gtk.TreeViewColumn(\"Number\", renderer_text, text = 1)\n renderer_toggle = Gtk.CellRendererToggle()\n renderer_toggle.connect(\"toggled\", self.on_cell_toggled)\n\n column_toggle = Gtk.TreeViewColumn(\"Active\", renderer_toggle, active=2)\n self.player_view.append_column(name_text)\n self.player_view.append_column(number_text)\n self.player_view.append_column(column_toggle)\n\n self.score_label = Gtk.Label(\"0 - 0\")\n save_button = Gtk.Button(label=\"Save\")\n save_button.connect(\"clicked\", self.on_save_button_click)\n save_label_box = Gtk.HBox()\n save_label_box.pack_start(self.score_label, True, False, 0)\n save_label_box.pack_start(save_button, True, False, 0)\n rhs_container.pack_start(save_label_box, False, False, 0)\n rhs_container.pack_start(button_container, True, True, 0)\n self.main_container.pack_start(self.player_view, True, True, 0)\n self.main_container.pack_start(rhs_container, True, True, 0)\n self.add(self.main_container)\n\n def on_cell_toggled(self, widget, path):\n self.player_store[path][2] = not self.player_store[path][2]\n\n player = self.squad.find_player_from_name(self.player_store[path][0])\n if self.player_store[path][2]:\n self.active_players.add_player(player)\n else:\n self.active_players.remove_player(player)\n\n\n def on_click_add_player(self, widget):\n dialog = NewPlayerDialog(self)\n response = dialog.run()\n\n if response == Gtk.ResponseType.OK:\n ret = [x.get_text() for x in dialog.entries]\n elif response == Gtk.ResponseType.CANCEL:\n dialog.destroy()\n return\n\n dialog.destroy()\n\n # Add the player\n p = Game.Player(ret[0], int(ret[1]))\n self.squad.add_player(p)\n self.refresh_player_list_store()\n\n def refresh_player_list_store(self):\n self.player_store.clear()\n for player in self.squad.get_players():\n self.player_store.append([player.name, player.number, False])\n\n def window_close(self, window, path):\n window.destroy()\n\n def on_save_button_click(self, widget):\n dialog = Gtk.FileChooserDialog(\"Please Choose A File\", self, Gtk.FileChooserAction.SAVE, (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n Gtk.STOCK_SAVE, Gtk.ResponseType.OK))\n\n response = dialog.run()\n\n if response == Gtk.ResponseType.OK:\n # Turn our game into a YAML file\n game = Game.Game(self.our_current_score, self.their_current_score, self.points, self.squad)\n outp = yaml.dump(game)\n f = open(dialog.get_filename(), 'w')\n f.write(outp)\n f.close()\n else:\n print(\"Cancelled\")\n\n dialog.destroy()\n\n def on_click_start_o_point(self, widget):\n self.start_point(True)\n\n def on_click_start_d_point(self, widget):\n self.start_point(False)\n\n def start_point(self, offense):\n if len(self.active_players) != 7:\n dialog = Gtk.MessageDialog(self, 0, Gtk.MessageType.ERROR,\n Gtk.ButtonsType.CANCEL, \"You have not selected 7 players\")\n dialog.run()\n dialog.destroy()\n else:\n point = Game.Point(self.active_players, self.our_current_score, self.their_current_score, offense)\n game_window = GameWindow(point, self.on_game_window_close)\n game_window.show_all()\n\n def on_game_window_close(self, point):\n self.points.append(point)\n\n if point.offense:\n self.our_current_score += 1\n else:\n self.their_current_score += 1\n\n # Set the label\n self.score_label.set_text(str(self.our_current_score) + \" - \" + str(self.their_current_score))\n\n\n\nclass NewPlayerDialog(Gtk.Dialog):\n\n def __init__(self, parent):\n Gtk.Dialog.__init__(self, \"Add New Player\", parent, 0,\n (Gtk.STOCK_CANCEL, Gtk.ResponseType.CANCEL,\n Gtk.STOCK_OK, Gtk.ResponseType.OK))\n\n self.set_default_size(150, 100)\n\n label = Gtk.Label(\"This is a dialog to display additional information\")\n\n box = self.get_content_area()\n\n input_box = Gtk.Box()\n inputs = [\"Player Name: \", \"Player Number: \"]\n self.entries = [Gtk.Entry() for x in inputs]\n\n\n for idx, inp in enumerate(inputs):\n temp_box = Gtk.Box()\n label = Gtk.Label(inp)\n temp_box.pack_start(label, False, False, 0)\n temp_box.pack_start(self.entries[idx], False, False, 0)\n box.add(temp_box)\n\n self.show_all()\n\nclass GameWindow(Gtk.Window):\n def __init__(self, point, callback):\n self.active_players = point.active_players\n self.point = point\n self.current_on_disc = None\n self.offense = point.offense\n self.callback = callback\n\n Gtk.Window.__init__(self, title=\"Game Window\")\n self.set_default_size(200, 200)\n\n self.button_container = Gtk.Box(spacing=6)\n self.add(self.button_container)\n self.set_modal(self)\n\n self.offense_label = Gtk.Label()\n self.set_up_offense_label()\n\n self.button_container.pack_start(self.offense_label, True, True, 0)\n self.player_buttons = []\n for player in self.active_players:\n button = Gtk.Button(label=player.name)\n self.button_container.pack_start(button, True, True, 0)\n button.set_name(\"default\")\n button.connect(\"clicked\", self.on_player_button_click)\n self.player_buttons.append(button)\n\n\n button = Gtk.Button(label='Turnover')\n button.connect(\"clicked\", self.on_turnover_button_click)\n button2 = Gtk.Button(label='Score')\n button2.connect(\"clicked\", self.on_score_button_click)\n style_provider = Gtk.CssProvider()\n\n css_data = \"\"\"\n #green_button{\n background: #009900;\n font-size: 120%;\n font-weight: 600;\n }\n\n #red_button{\n background: #FF0000;\n font-size: 120%;\n font-weight: 600;\n }\n \"\"\"\n\n style_provider.load_from_data(css_data.encode())\n Gtk.StyleContext.add_provider_for_screen(\n Gdk.Screen.get_default(),\n style_provider,\n Gtk.STYLE_PROVIDER_PRIORITY_APPLICATION\n )\n button.set_name(\"red_button\")\n button2.set_name(\"green_button\")\n\n self.button_container.pack_start(button, True, True, 0)\n self.button_container.pack_start(button2, True, True, 0)\n\n def reset_player_button_colors(self):\n for button in self.player_buttons:\n button.set_name(\"default_button\")\n\n def on_player_button_click(self, widget):\n player_to = self.active_players.find_player_from_name(widget.get_label())\n print(widget.get_label())\n if self.offense:\n self.reset_player_button_colors()\n style_provider = Gtk.CssProvider()\n widget.set_name(player_to.name)\n css_data = \"\"\"\n #green_button{\n background: #009900;\n font-size: 120%;\n font-weight: 600;\n }\n\n #red_button{\n background: #FF0000;\n font-size: 120%;\n font-weight: 600;\n }\n\n #{player_name} {\n background: #FFFF00;\n font-size: 120%;\n font-weight: 600;\n }\n\n #default_button {\n background: inherit;\n font-size: 100%;\n font-weight: 400;\n }\n \"\"\"\n\n css_data = css_data.replace(\"{player_name}\", player_to.name)\n style_provider.load_from_data(css_data.encode())\n Gtk.StyleContext.add_provider_for_screen(\n Gdk.Screen.get_default(),\n style_provider,\n Gtk.STYLE_PROVIDER_PRIORITY_APPLICATION\n )\n\n pss = Game.Pass(player_to, self.current_on_disc, True, True)\n self.point.add_pass(pss)\n self.current_on_disc = player_to\n else:\n player_to.addD()\n self.offense = True\n self.set_up_offense_label()\n\n def set_up_offense_label(self):\n if self.offense:\n self.offense_label.set_text(\"Offense\")\n else:\n self.offense_label.set_text(\"Defense\")\n\n def on_turnover_button_click(self, widget):\n if self.offense:\n last_pass = self.point.get_last_pass()\n last_pass.completed = False\n self.offense = False\n self.set_up_offense_label()\n self.reset_player_button_colors()\n self.current_on_disc = None\n\n def on_score_button_click(self, widget):\n self.destroy()\n self.callback(self.point)\n\n\nsquad = Game.Squad()\nplayers = [[\"Tristan\", 20], [\"Claire\", 13], [\"Maks\", 5], [\"Katie\", 54], [\"Luka\", 37], [\"Alex\", 4], [\"Naughton\", 55]]\nfor idx, p in enumerate(players):\n squad.add_player(Game.Player(p[0], p[1]))\n\nwindow = PlayerWindow(squad)\nwindow.show_all()\nGtk.main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"273187162","text":"# -*- encode: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom cfgs import CFGS\n\n\ndef visualize_data(data, is_train:bool=True):\n day,hour,time = data\n titles = [\"Day\", \"Hour\", \"Time\"]\n if is_train:\n visualize_day_data(day, range(365))\n visualize_hour_data(hour, range(24*7))\n else:\n visualize_day_data(day)\n visualize_hour_data(hour)\n visualize_time_data(time)\n\n\ndef visualize_day_data(day, index=None):\n \"\"\"Example\n >>> day = [day_train_x, day_train_y]\n >>> visualize_day_data(day)\n \"\"\"\n cols = eval(CFGS[\"DATA\"][\"NOMCOL\"])\n length = day[0].shape[0]\n index = index or range(length)\n data_x = day[0][cols].iloc[index]\n data_y = day[1].iloc[index]\n plt.subplot(121)\n plt.plot(index, data_x)\n plt.legend(data_x.columns)\n plt.subplot(122)\n plt.plot(index, data_y)\n plt.legend(data_y.columns)\n plt.show()\n\n\ndef visualize_hour_data(hour, index=None):\n \"\"\"Example\n >>> hour = [hour_train_x, hour_train_y]\n >>> visualize_hour_data(hour)\n \"\"\"\n cols = eval(CFGS[\"DATA\"][\"NOMCOL\"])\n length = hour[0].shape[0]\n index = index or range(length)\n data_x = hour[0][cols].iloc[index]\n data_y = hour[1].iloc[index]\n plt.subplot(121)\n plt.plot(index, data_x)\n plt.legend(data_x.columns)\n plt.subplot(122)\n plt.plot(index, data_y)\n plt.legend(data_y.columns)\n plt.show()\n\n\ndef visualize_time_data(time, index=None):\n \"\"\"Example\n >>> time = [time_train_x, time_train_y]\n >>> visualize_time_data(time)\n \"\"\"\n length = time[0].shape[0]\n index = np.array(index or range(length))\n time_x = time[0].iloc[index]\n time_y = time[1].iloc[index]\n for key,val in eval(CFGS[\"DATA\"][\"PLOTTIMECOL\"]).items():\n for i in range(val[0], val[1]+1):\n idx = time_x[key] == i\n plt.plot(index[idx], time_y[idx], \"--\")\n plt.title(key)\n # plt.legend([\"springer\", \"summer\", \"fall\", \"winter\"])\n plt.show()\n","sub_path":"Outsourcing/20190624-1/Code/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"178188653","text":"import endpoints\nfrom google.appengine.ext import ndb\nfrom protorpc import (\n remote,\n messages\n)\nfrom random_words import RandomWords\nfrom trueskill import (\n Rating,\n rate_1vs1\n)\n\nfrom messages import (\n NewGameForm,\n NewGameResponse,\n GetGameResponse,\n GuessCharForm,\n GetActiveGameResponseList,\n GetActiveGameResponse,\n GetGameHistoryResponseList,\n GetGameHistoryResponse\n)\nfrom models import (\n Game,\n GameStatus,\n User,\n Score,\n GameHistory\n)\nfrom utils import (\n get_user,\n get_game,\n get_game_score,\n get_user_games,\n get_game_history\n)\n\nGET_USER_REQUEST = endpoints.ResourceContainer(\n user_name=messages.StringField(1, required=True)\n)\nGET_USER_WITH_GAME_STATUS_REQUEST = endpoints.ResourceContainer(\n user_name=messages.StringField(1, required=True),\n game_status=messages.EnumField(GameStatus, 2)\n)\nNEW_GAME_REQUEST = endpoints.ResourceContainer(\n NewGameForm,\n user_name=messages.StringField(1, required=True)\n)\nGET_GAME_REQUEST = endpoints.ResourceContainer(\n user_name=messages.StringField(1, required=True),\n urlsafe_key=messages.StringField(2, required=True)\n)\nGUESS_CHAR_REQUEST = endpoints.ResourceContainer(\n GuessCharForm,\n user_name=messages.StringField(1, required=True),\n urlsafe_key=messages.StringField(2, required=True)\n)\n\ngame_api = endpoints.api(name='game', version='v1')\n\n\n@game_api.api_class(resource_name='game')\nclass GameApi(remote.Service):\n \"\"\"Game APIs\"\"\"\n\n @endpoints.method(request_message=NEW_GAME_REQUEST,\n response_message=NewGameResponse,\n path='new_game',\n name='new_game',\n http_method='POST')\n def endpoint_new_game(self, request):\n \"\"\"Create new game.\"\"\"\n user = get_user(request.user_name)\n\n game = self._new_game(user, request.game_name)\n\n return NewGameResponse(urlsafe_key=game.key.urlsafe())\n\n @endpoints.method(request_message=GET_GAME_REQUEST,\n response_message=GetGameResponse,\n path='get_game',\n name='get_game',\n http_method='GET')\n def endpoint_get_game(self, request):\n \"\"\"Get game using game's urlsafe_key\"\"\"\n game = get_game(request.urlsafe_key, request.user_name)\n\n return GetGameResponse(id=game.game_id,\n game_name=game.game_name,\n word=game.word,\n guessed_chars_of_word=game.guessed_chars_of_word,\n guesses_left=game.guesses_left,\n game_over=game.game_over,\n game_status=game.game_status,\n urlsafe_key=game.key.urlsafe())\n\n @endpoints.method(request_message=GUESS_CHAR_REQUEST,\n response_message=GetGameResponse,\n path='guess_char',\n name='guess_char',\n http_method='POST')\n def endpoint_guess_char(self, request):\n \"\"\"Guess char of the word\"\"\"\n # only single char supported\n if len(request.char) != 1:\n raise endpoints.ForbiddenException('ERR_BAD_CHAR_LENGTH')\n\n # only alphabet supported\n if not request.char.isalpha():\n raise endpoints.ForbiddenException('ERR_NOT_AN_ALPHABET')\n\n user = get_user(request.user_name)\n\n game = get_game(request.urlsafe_key, user.user_name)\n\n # only IN_SESSION game supported\n if game.game_status != GameStatus.IN_SESSION:\n raise endpoints.ForbiddenException('ERR_GAME_NOT_IN_SESSION')\n\n score = get_game_score(user.user_name, game)\n\n self._move_game(game, user, score, request.char)\n\n return GetGameResponse(id=game.game_id,\n game_name=game.game_name,\n word=game.word,\n guessed_chars_of_word=game.guessed_chars_of_word,\n guesses_left=game.guesses_left,\n game_over=game.game_over,\n game_status=game.game_status,\n urlsafe_key=game.key.urlsafe())\n\n @endpoints.method(request_message=GET_USER_WITH_GAME_STATUS_REQUEST,\n response_message=GetActiveGameResponseList,\n path='get_user_games',\n name='get_user_games',\n http_method='GET')\n def endpoint_get_user_games(self, request):\n \"\"\"Get user's games list\"\"\"\n # get user object\n user = get_user(request.user_name)\n\n # get all games of this user\n all_games = get_user_games(user.user_name)\n\n if request.game_status is not None:\n # create filter for active games\n if request.game_status == GameStatus.IN_SESSION:\n active_filter = ndb.query.FilterNode('game_status', '=', GameStatus.IN_SESSION.number)\n elif request.game_status == GameStatus.WON:\n active_filter = ndb.query.FilterNode('game_status', '=', GameStatus.WON.number)\n elif request.game_status == GameStatus.LOST:\n active_filter = ndb.query.FilterNode('game_status', '=', GameStatus.LOST.number)\n elif request.game_status == GameStatus.ABORTED:\n active_filter = ndb.query.FilterNode('game_status', '=', GameStatus.ABORTED.number)\n\n # fetch games of this user\n active_games = all_games.filter(active_filter).fetch()\n else:\n # fetch games of this user\n active_games = all_games.fetch()\n\n return GetActiveGameResponseList(\n games=[self._create_active_game_list(active_game) for active_game in active_games]\n )\n\n @endpoints.method(request_message=GET_USER_REQUEST,\n response_message=GetActiveGameResponseList,\n path='get_user_completed_games',\n name='get_user_completed_games',\n http_method='GET')\n def endpoint_get_user_completed_games(self, request):\n \"\"\"Get user's completed game list\"\"\"\n # get user object\n user = get_user(request.user_name)\n\n # get all games of this user\n all_games = get_user_games(user.user_name)\n\n # create filter for completed games\n completed_filter = ndb.query.FilterNode('game_status', '!=', GameStatus.IN_SESSION.number)\n\n # fetch all completed games of this user\n completed_games = all_games.filter(completed_filter).order(Game.game_status, -Game.game_id).fetch()\n\n return GetActiveGameResponseList(\n games=[self._create_active_game_list(game) for game in completed_games]\n )\n\n @endpoints.method(request_message=GET_GAME_REQUEST,\n response_message=GetActiveGameResponse,\n path='cancel_game',\n name='cancel_game',\n http_method='PATCH')\n def endpoint_cancel_game(self, request):\n \"\"\"Cancel active game\"\"\"\n game = get_game(request.urlsafe_key, request.user_name)\n\n # only IN_SESSION game supported\n if game.game_status != GameStatus.IN_SESSION:\n raise endpoints.ForbiddenException('ERR_GAME_NOT_IN_SESSION')\n\n self._cancel_game(game)\n\n return GetActiveGameResponse(game_urlsafe_key=game.key.urlsafe(),\n game_id=game.game_id,\n game_name=game.game_name,\n game_over=game.game_over,\n game_status=game.game_status)\n\n @endpoints.method(request_message=GET_GAME_REQUEST,\n response_message=GetGameHistoryResponseList,\n path='get_game_history',\n name='get_game_history',\n http_method='GET')\n def endpoint_get_game_history(self, request):\n game = get_game(request.urlsafe_key, request.user_name)\n\n steps = get_game_history(game)\n\n return GetGameHistoryResponseList(\n steps=[self._create_game_histroy_list(step) for step in steps]\n )\n\n def _new_game(self, user, game_name):\n # retrieve key from user_name\n user_key = ndb.Key(User, user.user_name)\n\n # generate game_id\n game_id = Game.allocate_ids(size=1, parent=user_key)[0]\n\n # create key using generated game_id and user as its ancestor\n game_key = ndb.Key(Game, game_id, parent=user_key)\n\n # generate random word for this game\n rw = RandomWords()\n word = rw.random_word()\n\n guessed_chars_of_word = []\n\n # make this impl more 'pythonic' way\n for c in word:\n guessed_chars_of_word.append('*')\n\n game = Game(key=game_key,\n game_id=game_id,\n game_name=game_name,\n word=word,\n guessed_chars_of_word=guessed_chars_of_word)\n # save game\n game.put()\n\n # score id\n score_id = Score.allocate_ids(size=1, parent=user_key)[0]\n\n # score key using generated score_id and user as its ancestor\n score_key = ndb.Key(Score, score_id, parent=user_key)\n\n # score entity for this game\n score = Score(key=score_key,\n score_id=score_id,\n game_key=game_key)\n # save score\n score.put()\n\n # capture game snapshot\n self._capture_game_snapshot(game, '')\n\n return game\n\n def _move_game(self, game, user, score, char):\n # continue only if game is not over\n if game.game_over:\n return\n\n # chk if char exists in the word\n if char in game.word:\n for pos, char_in_that_pos in enumerate(game.word):\n if char_in_that_pos == char:\n game.guessed_chars_of_word[pos] = char\n if '*' not in game.guessed_chars_of_word:\n game.game_over = True\n game.game_status = GameStatus.WON\n score.game_score = game.guesses_left\n self._update_user_rating(user, True)\n # in case of miss, reduce guesses_left\n elif game.guesses_left > 0:\n game.guesses_left -= 1\n if game.guesses_left == 0:\n game.game_over = True\n game.game_status = GameStatus.LOST\n self._update_user_rating(user, False)\n\n # save the game\n game.put()\n\n # save the score\n score.put()\n\n # capture game snapshot\n self._capture_game_snapshot(game, char)\n\n def _cancel_game(self, game):\n # return if game is already over\n if game.game_over:\n return game\n # cancel game and update status accordingly\n else:\n game.game_status = GameStatus.ABORTED\n game.game_over = True\n\n # save the game\n game.put()\n\n # capture game snapshot\n self._capture_game_snapshot(game, '')\n\n @staticmethod\n def _update_user_rating(user, user_winner):\n # create rating obj from user's mu and sigma\n user_rating = Rating(mu=user.mu, sigma=user.sigma)\n\n # create default rating obj\n default_rating = Rating()\n\n if user_winner:\n user_rating, default_rating = rate_1vs1(user_rating, default_rating)\n else:\n default_rating, user_rating = rate_1vs1(default_rating, user_rating)\n\n # update w/ new user rating\n user.mu = user_rating.mu\n user.sigma = user_rating.sigma\n\n # save the user\n user.put()\n\n @staticmethod\n def _capture_game_snapshot(game, char):\n # game history is per game, so parent should be game and not the user\n # game history id\n history_id = GameHistory.allocate_ids(size=1, parent=game.key)[0]\n\n # game history key generated using history_id and game as its ancestor\n history_key = ndb.Key(GameHistory, history_id, parent=game.key)\n\n game_history = GameHistory(key=history_key,\n step_char=char,\n game_snapshot=game)\n # save game history\n game_history.put()\n\n @staticmethod\n def _create_active_game_list(active_game):\n active_game_resp = GetActiveGameResponse()\n\n for field in active_game_resp.all_fields():\n if field.name == 'game_urlsafe_key':\n setattr(active_game_resp, field.name, active_game.key.urlsafe())\n elif hasattr(active_game, field.name):\n setattr(active_game_resp, field.name, getattr(active_game, field.name))\n\n active_game_resp.check_initialized()\n\n return active_game_resp\n\n @staticmethod\n def _create_game_histroy_list(step):\n game_history_resp = GetGameHistoryResponse()\n\n for field in game_history_resp.all_fields():\n if hasattr(step, field.name):\n setattr(game_history_resp, field.name, getattr(step, field.name))\n elif hasattr(step.game_snapshot, field.name):\n setattr(game_history_resp, field.name, getattr(step.game_snapshot, field.name))\n\n game_history_resp.check_initialized()\n\n return game_history_resp\n","sub_path":"design-game/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":13446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"451189434","text":"from tkinter import*\r\nwin = Tk()\r\n\r\nwin.title(\"To-Do List\")\r\n\r\ncontent = Listbox(win, font=\"Rockwell 24 bold\", bg=\"black\", fg=\"Red\")\r\n\r\ntask = StringVar()\r\ne = Entry(win, textvariable = task, font=\"Rockwell 24\")\r\nadd = Button(win, text=\"Add\", font=\"Ariel 20\", padx=10, command = lambda content=content, task=task : content.insert(END,task.get()))\r\ndelete = Button(win, text=\"Delete\", font=\"Ariel 20\", command = lambda content=content : content.delete(ANCHOR))\r\n\r\n\r\ncontent.grid(row=0, column=0, columnspan=2, padx=5, pady=10)\r\ne.grid(row=1, column=0, columnspan=2, padx=5, pady=10)\r\nadd.grid(row=2, column=0, padx=5, pady=20)\r\ndelete.grid(row=2, column=1, padx=5, pady=20)\r\n\r\n\r\nwin.mainloop()\r\n","sub_path":"To-Do List.py","file_name":"To-Do List.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"500259359","text":"import pytest\n\nfrom helpers.constants import Returns\nfrom helpers.settings import (BASE_HOST,\n BASE_USER_EMAIL,\n BASE_USER_PASSWORD)\nfrom pages.home import HomePage\nfrom pages.returns import ReturnsPage\n\n\n@pytest.fixture()\ndef driver(init_driver):\n driver = init_driver\n driver.get(BASE_HOST)\n (HomePage(driver).goto_login()\n .input_email(BASE_USER_EMAIL)\n .input_password(BASE_USER_PASSWORD)\n .login())\n HomePage(driver).goto_returns()\n yield driver\n HomePage(driver).logout()\n\n\n@pytest.mark.skip(reason=\"this person already got a job\")\n@pytest.allure.testcase('https://ssu-jira.softserveinc.com/browse/OPENCARTPY-36')\n@pytest.allure.severity(pytest.allure.severity_level.CRITICAL)\n@pytest.allure.CRITICAL\n@pytest.mark.parametrize('mode', ['base', 'personal'])\ndef test_positive_and_submit(driver, mode):\n (ReturnsPage(driver).fill_order_information(mode=mode, order_id='0000001')\n .fill_product_information()\n .click_submit())\n assert 'return/success' in driver.current_url\n\n\n@pytest.mark.skip(reason=\"this person already got a job\")\n@pytest.allure.testcase('https://ssu-jira.softserveinc.com/browse/OPENCARTPY-36')\n@pytest.allure.severity(pytest.allure.severity_level.MINOR)\n@pytest.allure.NORMAL\n@pytest.mark.parametrize('mode', ['base', 'personal'])\ndef test_positive_and_back(driver, mode):\n (ReturnsPage(driver).fill_order_information(mode=mode)\n .fill_product_information()\n .click_back())\n assert 'account' in driver.current_url\n\n\n@pytest.mark.skip(reason=\"this person already got a job\")\n@pytest.allure.testcase('https://ssu-jira.softserveinc.com/browse/OPENCARTPY-36')\n@pytest.allure.severity(pytest.allure.severity_level.NORMAL)\n@pytest.allure.NORMAL\n@pytest.mark.negative\ndef test_required_fiels(driver):\n ReturnsPage(driver).click_submit()\n with pytest.allure.step('Check base text-danger field'):\n assert (ReturnsPage(driver).get_text_danger('order_id') ==\n Returns.TEXT_DANGER_ORDER_ID)\n assert (ReturnsPage(driver).get_text_danger('product_name') ==\n Returns.TEXT_DANGER_PRODUCT_NAME)\n assert (ReturnsPage(driver).get_text_danger('product_code') ==\n Returns.TEXT_DANGER_PRODUCT_CODE)\n\n with pytest.allure.step('Color is red'):\n assert (ReturnsPage(driver).get_color('order_id') ==\n ReturnsPage(driver).get_color('product_name') ==\n ReturnsPage(driver).get_color('product_code') ==\n Returns.DANGER_COLOR)\n","sub_path":"tests/user/test_purchase_returns.py","file_name":"test_purchase_returns.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"270126549","text":"from django.db import models\nfrom django.utils.translation import ugettext as _\nfrom wagtail.admin.edit_handlers import StreamFieldPanel, FieldPanel, MultiFieldPanel\nfrom wagtail.core.fields import StreamField\n\nfrom xr_pages.blocks import ContentBlock\nfrom xr_pages.models import XrPage\n\n\nclass BlogEntryPage(XrPage):\n template = \"xr_blog/pages/blog_entry.html\"\n group = models.OneToOneField(\n \"xr_pages.LocalGroup\", editable=False, on_delete=models.PROTECT\n )\n date = models.DateField(_(\"Post date\"))\n author = models.CharField(max_length=200)\n content = StreamField(\n ContentBlock,\n blank=True,\n help_text=_(\"The content is only visible on the detail page.\"),\n )\n\n # Panels (Editor interface)\n content_panels = [\n MultiFieldPanel(\n [\n FieldPanel(\"title\", classname=\"title\"),\n # FieldPanel(\"show_page_title\", classname=\"\"), # without \"show_page_title\"\n ],\n heading=_(\"Title\"),\n ),\n FieldPanel(\"date\"),\n FieldPanel(\"author\"),\n StreamFieldPanel(\"content\"),\n ]\n\n parent_page_types = [\"BlogListPage\"]\n\n class Meta:\n verbose_name = _(\"Blog Entry Page\")\n verbose_name_plural = _(\"Blog Entry Pages\")\n\n def save(self, *args, **kwargs):\n if not hasattr(self, \"group\") or not self.group:\n self.group = self.get_parent().specific.group\n\n super().save(*args, **kwargs)\n\n\nclass BlogListPage(XrPage):\n template = \"xr_blog/pages/blog_list.html\"\n group = models.OneToOneField(\n \"xr_pages.LocalGroup\", editable=False, on_delete=models.PROTECT\n )\n content = StreamField(\n ContentBlock,\n blank=True,\n help_text=_(\"The content is only visible on the detail page.\"),\n )\n\n parent_page_types = [\"xr_pages.HomePage\", \"xr_pages.LocalGroupPage\"]\n\n content_panels = XrPage.content_panels + [StreamFieldPanel(\"content\")]\n\n class Meta:\n verbose_name = _(\"Blog List Page\")\n verbose_name_plural = _(\"Blog List Pages\")\n\n def save(self, *args, **kwargs):\n if not hasattr(self, \"group\") or not self.group:\n self.group = self.get_parent().specific.group\n\n super().save(*args, **kwargs)\n\n def entries(self):\n return BlogEntryPage.objects.child_of(self).live().order_by(\"-date\")\n","sub_path":"src/xr_blog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"465878645","text":"#\n# @lc app=leetcode.cn id=36 lang=python3\n#\n# [36] 有效的数独\n#\nclass Solution:\n def isValidSudoku(self, board):\n dic = {}\n row = len(board)\n col = len(board[0])\n for i in range(row):\n for j in range(col):\n if board[i][j] == \".\":\n continue\n if dic.get(board[i][j]) != None:\n for m in range(row):\n if board[m][j] == board[i][j] and m != i:\n return False\n for n in range(col):\n if board[i][n] == board[i][j] and n != j:\n return False\n for m in range(3):\n for n in range(3):\n if board[i // 3 * 3 + m][j // 3 * 3 + n] == board[i][j] and (i // 3 * 3 + m) != i and (j // 3 * 3 + n) != j:\n return False\n dic[board[i][j]] = dic.get(board[i][j], 0) + 1\n return True\n","sub_path":"36.有效的数独.py","file_name":"36.有效的数独.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"332291454","text":"from MonkeyLearnProductSentiment import *\n\ndef get_post(url):\n \"\"\"\n Get a map of from post url\n \"\"\"\n import comments\n reddit = comments.connect()\n comments = comments.get_comments(reddit, url)\n #print(comments)\n return comments\n\npost = get_post(\"https://www.reddit.com/r/learnpython/comments/ktjtl5/best_ide_for_python/\")\n#print(type(post))\n\n#print(post.keys)\n#separated_strings = seperate_into_strings(post[)\n#print(post.keys())\n\nentities = []\nfor comment in post.keys():\n doc = seperate_into_strings([comment])\n\n entities.append(doc)\n#print(entities)\narray = []\nfor a in entities:\n array.append(keyword_extractor(a))\nnew_array = []\nfor i in array:\n if (len(array[i] >= 2)):\n new_array.append(array[i][0])\n\nprint(new_array)\n\n\n","sub_path":"RedditTestMonkeyLearn.py","file_name":"RedditTestMonkeyLearn.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"469855339","text":"from tzlocal.windows_tz import tz_names_olsen\n\ndef get_windowszone_name(tz):\n \"\"\"Get the TimeZone Name as the Windows TimeZone Name\"\"\"\n if tz is None:\n raise pytz.UnknownTimeZoneError('Can not find any timezone configuration')\n\n else:\n tzkeyname = tz.zone\n timezone = tz_names_olsen.get(tzkeyname)\n\n if timezone is None:\n timezone = \"Unknown\"\n\n return timezone\n\ndef get_olsenzone_name(tz):\n \"\"\"Get the TimeZone Name as the Olzen TimeZone Name\"\"\"\n if tz is None:\n raise pytz.UnknownTimeZoneError('Can not find any timezone configuration')\n else:\n return tz.zone\n","sub_path":"tzlocal/olsen.py","file_name":"olsen.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"514029722","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = u'jixiangyang'\nSITENAME = u'Laputa'\nSITEURL = 'http://www.jixiangyang.com'\n\nGITHUB_URL = 'https://github.com/jixy'\nARCHIVES_URL = 'archives.html'\nARTICLE_URL = 'pages/{date:%Y}/{date:%m}/{date:%d}/{slug}.html'\nARCHIVE_SAVE_AS = 'pages/{date:%Y}/{date:%m}/{date:%d}/{slug}.html'\n\nRELATIVE_URLS = True\nTIMEZONE = 'Asia/Shanghai'\n\nDEFAULT_LANG = u'zh'\nTHEME = 'pelican-themes/tuxlite_tbs'\nDEFAULT_DATE_FORMAT = '%Y-%m-%d'\n\n# Feed generation is usually not desired when developing\nFEED_RSS = 'feeds/all/rss/xml'\nCATEGORY_FEED_RSS = 'feeds/%s.rss.xml'\n\nPLUGIN_PATH = 'pelican-plugins'\nPLUGINS = ['summary','sitemap']\n\n## 配置sitemap 插件\nSITEMAP = {\n \"format\": \"xml\",\n \"priorities\": {\n \"articles\": 0.7,\n \"indexes\": 0.5,\n \"pages\": 0.3,\n },\n \"changefreqs\": {\n \"articles\": \"monthly\",\n \"indexes\": \"daily\",\n \"pages\": \"monthly\",\n }\n}\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),\n ('Python.org', 'http://python.org/'),\n ('BYR', 'http://bbs.byr.cn'),)\n\n# Social widget\nSOCIAL = (('Github', 'https://github.com/jixy'),\n (u'微博', 'http://weibo.com/u/1592567033'),)\n\n\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"203942409","text":"# -*- coding:utf-8 -*-\r\n#!/bin/python\r\n\r\n\"\"\"\r\nAuthor: $Author$\r\nDate: $Date$\r\nRevision: $Revision$\r\n\r\nDescription: Player peer\r\n\"\"\"\r\n\r\nfrom common.log import log, LOG_LEVEL_RELEASE\r\nfrom common.peer import Peer\r\nfrom common import consts\r\nfrom handle_manger import HandleManger\r\nfrom baseProto_pb2 import S_C_Disconnected\r\nfrom common_db_define import *\r\nfrom common.protocols.poker_consts import *\r\nimport redis_instance\r\n\r\nimport time\r\nfrom datetime import datetime\r\nimport copy\r\n\r\nSESSION_TIMEOUT_TICK = 300000\r\nGAME_KICK_OUT_TICK = 30 * 1000\r\n\r\nDROP_REASON_CODE2TXT = {\r\n consts.DROP_REASON_INVALID : \"与服务器的连接中断。\".decode(LANG_CODE),\r\n consts.DROP_REASON_TIMEOUT : \"你因长时间未做操作，被断开连接，请重新登录。\".decode(LANG_CODE),\r\n consts.DROP_REASON_FREEZE : \"你的账号已被管理员冻结，请咨询客服了解详请。\".decode(LANG_CODE),\r\n consts.DROP_REASON_CLOSE_SERVER : \"系统因进行维护暂已关闭，请稍后再进。\".decode(LANG_CODE),\r\n consts.DROP_REASON_REPEAT_LOGIN : \"你的账号已从其它位置登录，请咨询客服了解详情。\".decode(LANG_CODE),\r\n}\r\n\r\nclass CommonPlayer(Peer):\r\n def __init__(self):\r\n super(CommonPlayer, self).__init__()\r\n\r\n self.game = None\r\n self.endType = None\r\n self.chair = consts.SIDE_UNKNOWN\r\n self.isControlByOne = False\r\n\r\n self.uid = 0\r\n self.account = \"\"\r\n self.passwd = \"\"\r\n self.nickname = \"\"\r\n self.money = 0\r\n self.coin = 0\r\n self.parentAg = ''\r\n self.sex = 0\r\n self.roomCards = 0\r\n self.headImgUrl = ''\r\n self.region = ''\r\n self.valid = '1'\r\n self.isGM = False\r\n # 总分数\r\n self.totalGameScore = 0\r\n self.maxScore = 1\r\n\r\n #微信\r\n self.openID = None\r\n self.refreshToken = None\r\n self.accessToken = None\r\n self.unionID = None\r\n\r\n self.table = ''\r\n\r\n #用户sessionId\r\n self.operatorSessionId = \"\"\r\n #本次登录session\r\n self.sessionId = \"\"\r\n self.cacheTable = ''\r\n\r\n self.lastSessionTimestamp = int(time.time()*1000)\r\n self.lastGetRankTimestamp = 0\r\n self.gameLastPacketTimestamp = 0\r\n\r\n # 玩家在线状态相关参数\r\n self.lastPingTimestamp = int(time.time()*1000)\r\n self.lastOnlineState = False\r\n self.isOnline = True\r\n\r\n #总结算数据\r\n self.totalGameScore = 0\r\n self.totalWinCount = 0\r\n\r\n self.handleMgr = self.getHandleMgr()\r\n self.resetPerGame()\r\n\r\n def OnRefresh(self):\r\n \"\"\" 设置SID的超时时间\r\n\r\n :return:\r\n \"\"\"\r\n print(u\"开始设置超时时间:%s\" % self.nickname)\r\n privateRedis = redis_instance.getInst(8)\r\n publicRedis = redis_instance.getInst(PUBLIC_DB)\r\n\r\n sid = self.operatorSessionId\r\n up_time = privateRedis.get(\"session:%s:timeout\" % sid)\r\n if up_time:\r\n up_time = int(up_time)\r\n curTime = int(time.time())\r\n if (curTime - up_time) < 300:\r\n print(u\"还没有到达超时时间:%s\" % self.nickname)\r\n return\r\n SessionTable = FORMAT_USER_HALL_SESSION % (sid)\r\n print(u\"设置超时时间的SESSIONTABLE=%s\" % SessionTable)\r\n if publicRedis.exists(SessionTable):\r\n print(u\"增加超时时间:%s\" % self.nickname)\r\n publicRedis.expire(SessionTable, 60 * 10)\r\n privateRedis.set(\"session:%s:timeout\" % sid, int(time.time()))\r\n print(u\"设置超时时间结束:%s\" % self.nickname)\r\n\r\n def resetPerGame(self):\r\n \"\"\"\r\n 每局需要重置的数据\r\n \"\"\"\r\n self.handleMgr.resetDataPerGame()\r\n\r\n self.curGameScore = 0\r\n # 处于leaveGameStage状态的玩家不参与牌局，作为一个旁观者\r\n self.leaveGameStage = 0\r\n \r\n self.lastDiscard = []\r\n self.isActioned = False\r\n self.isUpdated = False\r\n\r\n def doAction(self, action, actionCards):\r\n return self.handleMgr.doCurAction(action, actionCards)\r\n\r\n def getHandleMgr(self):\r\n return HandleManger(self)\r\n\r\n def setHandleCards(self, cards):\r\n '''\r\n 设置手牌\r\n '''\r\n self.handleMgr.setHandCards(cards)\r\n\r\n def loadDB(self, playerTable, isInit=True, account = None):\r\n #配置信息\r\n redis = redis_instance.getInst(PUBLIC_DB)\r\n\r\n if isInit:\r\n self.table = playerTable\r\n self.uid = self.table.split(':')[-1]\r\n self.account, self.passwd, self.nickname, self.money,\\\r\n self.parentAg, self.currency, self.valid, self.sex, self.headImgUrl, self.maxScore = redis.hmget(playerTable, \r\n ('account', 'password', 'nickname', 'money', 'parentAg', 'currency', 'valid', 'sex', 'headImgUrl', 'maxScore'))\r\n\r\n self.coin, self.money = int(self.coin), round(float(self.money), 2)\r\n self.sex = int(self.sex) if self.sex else 0\r\n self.maxScore = int(self.maxScore) if self.maxScore and int(self.maxScore) >= 1 else 1\r\n self.headImgUrl = self.headImgUrl if self.headImgUrl else ''\r\n self.isGM = bool(int(redis.sismember(GM_SET, self.account)))\r\n\r\n roomCards = redis.get(USER4AGENT_CARD%(self.parentAg, self.uid))\r\n if roomCards and int(roomCards) > 0:\r\n self.roomCards = int(roomCards)\r\n else:\r\n self.roomCards = 0\r\n try:\r\n self.nickname = self.nickname.decode('utf-8')\r\n except:\r\n pass\r\n else:\r\n self.passwd, self.money, self.valid, self.sex, self.headImgUrl = redis.hmget(playerTable, ('password', 'money', 'valid', 'sex', 'headImgUrl'))\r\n\r\n self.money = round(float(self.money), 2)\r\n\r\n self.sex = int(self.sex) if self.sex else 0\r\n self.headImgUrl = self.headImgUrl if self.headImgUrl else ''\r\n\r\n roomCards = redis.get(USER4AGENT_CARD%(self.parentAg, self.uid))\r\n if roomCards and int(roomCards) > 0:\r\n self.roomCards = int(roomCards)\r\n else:\r\n self.roomCards = 0\r\n\r\n def isSessionTimeout(self, timestamp):\r\n return timestamp - self.lastSessionTimestamp > SESSION_TIMEOUT_TICK\r\n\r\n def isGameTimeout(self, timestamp):\r\n return timestamp - self.gameLastPacketTimestamp > GAME_KICK_OUT_TICK\r\n\r\n def onCheck(self, timestamp):\r\n if not super(CommonPlayer, self).onCheck(timestamp):\r\n return False\r\n # if self.isSessionTimeout(timestamp):\r\n\r\n # if self.game and self.chair == self.game.playPlayer.chair and self.isGameTimeout(timestamp) and\\\r\n # self.game.getEmptyChair() == consts.SIDE_UNKNOWN and self.game.isInGame[self.chair]:\r\n # self.game.onLeaveGame(self.chair)\r\n # self.game.isInGame[self.chair] = False\r\n return True\r\n\r\n def drop(self, reason, reasonCode = None, type = 3):\r\n resp = S_C_Disconnected()\r\n resp.actionType = type\r\n if reasonCode in DROP_REASON_CODE2TXT:\r\n resp.reason = DROP_REASON_CODE2TXT[reasonCode]\r\n else:\r\n resp.reason = reason\r\n try:\r\n self.factory.sendOne(self, resp)\r\n except:\r\n pass\r\n super(CommonPlayer, self).drop(reason, reasonCode)\r\n\r\n def onMessage(self, payload, isBinary):\r\n super(CommonPlayer, self).onMessage(payload, isBinary)\r\n self.lastPacketTimestamp = self.factory.getTimestamp()\r\n self.gameLastPacketTimestamp = self.factory.getTimestamp()\r\n # if self.game and self.chair != consts.SIDE_UNKNOWN:\r\n # self.game.notLeaveGame(self.chair)\r\n # self.game.isInGame[self.chair] = True\r\n # self.game.lastPacketTimestamp = self.game.getTimestamp()\r\n # for gamePlayer in self.game.players:\r\n # if gamePlayer and self.game.isInGame[gamePlayer.chair] == True and gamePlayer != self:\r\n # gamePlayer.gameLastPacketTimestamp = self.game.getTimestamp()\r\n\r\n def upTotalUserData(self):\r\n '''\r\n 更新总得分数据\r\n '''\r\n if self.isUpdated:\r\n return\r\n self.isUpdated = True\r\n self.totalGameScore += self.curGameScore\r\n\r\n","sub_path":"common/common_player.py","file_name":"common_player.py","file_ext":"py","file_size_in_byte":8452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"528728432","text":"import time\nimport threading\n\n\ndef sing():\n for i in range(5):\n print('sing')\n time.sleep(1)\n\n\ndef dance():\n for i in range(5):\n print('dance')\n time.sleep(1)\n\n\ndef main():\n t1 = threading.Thread(target=sing)\n t2 = threading.Thread(target=dance)\n t1.start()\n t2.start()\n\n while True:\n length = len(threading.enumerate())\n print('当前线程数：', length)\n print(threading.enumerate())\n if length <= 1:\n break\n time.sleep(1)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"python/misc/2017/thread-01.py","file_name":"thread-01.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"460511919","text":"import os\nimport rtconfig\nfrom building import *\n\nImport('SDK_LIB')\n\ncwd = GetCurrentDir()\n\n# add general drivers\nsrc = Split('''\nboard.c\n''')\n\npath = [cwd]\n\nstartup_path_prefix = SDK_LIB\n\nif rtconfig.PLATFORM in ['gcc']:\n src += [startup_path_prefix + '/GD32F20x_Firmware_Library/CMSIS/GD/GD32F20x/Source/GCC/startup_gd32f20x_cl.s']\nelif rtconfig.PLATFORM in ['armcc', 'armclang']:\n src += [startup_path_prefix + '/GD32F20x_Firmware_Library/CMSIS/GD/GD32F20x/Source/ARM/startup_gd32f20x_cl.s']\nelif rtconfig.PLATFORM in ['iccarm']:\n src += [startup_path_prefix + '/GD32F20x_Firmware_Library/CMSIS/GD/GD32F20x/Source/IAR/startup_gd32f20x_cl.s']\n\nCPPDEFINES = ['GD32F20X_CL']\ngroup = DefineGroup('Drivers', src, depend = [''], CPPPATH = path, CPPDEFINES = CPPDEFINES)\n\nReturn('group')\n","sub_path":"bsp/gd32/arm/gd32205r-start/board/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"241010767","text":"# -*- coding: utf-8 -*-\nimport pytest\nfrom jussi.cache import block_num_from_jsonrpc_response\nfrom jussi.cache import irreversible_ttl\nfrom jussi.cache import ttl_from_jsonrpc_request\nfrom jussi.cache import ttl_from_urn\nfrom jussi.jsonrpc_method_cache_settings import DEFAULT_TTL\nfrom jussi.jsonrpc_method_cache_settings import NO_CACHE\nfrom jussi.jsonrpc_method_cache_settings import NO_EXPIRE\nfrom jussi.jsonrpc_method_cache_settings import NO_EXPIRE_IF_IRREVERSIBLE\n\nSBDS_DEFAULT_CACHE = 10\n\n\nttl_rpc_req = {\"id\":\"1\",\"jsonrpc\":\"2.0\",\"method\":\"get_block\",\"params\":[1000]}\nrpc_resp = {\n \"id\": 1,\n \"result\": {\n \"previous\": \"000003e7c4fd3221cf407efcf7c1730e2ca54b05\",\n \"timestamp\": \"2016-03-24T16:55:30\",\n \"witness\": \"initminer\",\n \"transaction_merkle_root\": \"0000000000000000000000000000000000000000\",\n \"extensions\": [],\n \"witness_signature\": \"207f15578cac20ac0e8af1ebb8f463106b8849577e21cca9fc60da146d1d95df88072dedc6ffb7f7f44a9185bbf9bf8139a5b4285c9f423843720296a44d428856\",\n \"transactions\": [],\n \"block_id\": \"000003e8b922f4906a45af8e99d86b3511acd7a5\",\n \"signing_key\": \"STM8GC13uCZbP44HzMLV6zPZGwVQ8Nt4Kji8PapsPiNq1BK153XTX\",\n \"transaction_ids\": []\n }\n}\n\nnon_ttl_rpc_req = {\"id\":\"1\",\"jsonrpc\":\"2.0\",\"method\":\"sbds.get_block\",\"params\":[1000]}\n\n@pytest.mark.parametrize('rpc_req, rpc_resp, last_block_num,expected', [\n # don't cache when last_block_num < response block_num\n (ttl_rpc_req, rpc_resp, 0, NO_CACHE),\n (ttl_rpc_req, rpc_resp, 999, NO_CACHE),\n\n # cache when last_block_num >= response block_num\n (ttl_rpc_req, rpc_resp, 1000, NO_EXPIRE),\n (ttl_rpc_req, rpc_resp, 1001, NO_EXPIRE),\n\n # don't cache when bad/missing response block_num\n (ttl_rpc_req, {}, 2000, NO_CACHE),\n\n # don't adjust ttl for non EXPIRE_IF_IRREVERSIBLE methods\n (non_ttl_rpc_req, rpc_resp, 2000, SBDS_DEFAULT_CACHE),\n\n\n])\ndef test_ttls(rpc_req, rpc_resp, last_block_num,expected):\n ttl = ttl_from_jsonrpc_request(rpc_req, last_block_num, rpc_resp)\n assert ttl == expected\n\n@pytest.mark.parametrize('response, last_block,expected', [\n (rpc_resp, 0, NO_CACHE),\n (rpc_resp, 999, NO_CACHE),\n (rpc_resp, 1000, NO_EXPIRE),\n (rpc_resp, 1001, NO_EXPIRE),\n])\ndef test_irreversible_ttl(response, last_block, expected):\n ttl = irreversible_ttl(response, last_block)\n assert ttl == expected\n\n@pytest.mark.parametrize('urn,expected', [\n ('steemd.database_api.get_account_count', DEFAULT_TTL),\n ('steemd.database_api.get_block.params=[1000]', NO_EXPIRE_IF_IRREVERSIBLE),\n ('steemd.database_api.get_block_header.params=[1000]', NO_EXPIRE_IF_IRREVERSIBLE),\n])\ndef test_ttl_from_urn(urn, expected):\n ttl = ttl_from_urn(urn)\n assert ttl == expected\n\n\n@pytest.mark.parametrize('response,expected', [\n (rpc_resp,1000)\n])\ndef test_block_num_from_jsonrpc_response(response, expected):\n num = block_num_from_jsonrpc_response(response)\n assert num == expected\n","sub_path":"tests/test_method_ttls.py","file_name":"test_method_ttls.py","file_ext":"py","file_size_in_byte":2972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"431788229","text":"import random\n\n\n\ndef who_wins(user, computer):\n if computer == 3:\n if user == 3:\n print(\"Its a tie!\")\n elif user == 2:\n print(\"The computer wins!\")\n elif user == 1:\n print(\"The user wins!\")\n if computer == 2:\n if user == 2:\n print(\"Its a tie!\")\n elif user == 1:\n print(\"The computer wins!\")\n elif user == 3:\n print(\"The user wins!\")\n if computer == 1:\n if user == 1:\n print(\"Its a tie!\")\n elif user == 3:\n print(\"The computer wins!\")\n elif user == 2:\n print(\"The user wins!\")\n\n\n\ndef main():\n computer = random.randrange(1, 4)\n user = int(input(\"Please enter a number 1-3 \"))\n who_wins(user, computer)\n\n\nif __name__ == '__main__':\n main()","sub_path":"rock_paper_scissors.py","file_name":"rock_paper_scissors.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"330586718","text":"import sys\nimport numpy as np\nfrom collections import Counter\nimport pandas as pd # to seperate into columnsz\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import AgglomerativeClustering as AC\nfrom sys import platform\n\ndef main():\n if platform == \"win32\":\n data = pd.read_csv(\"C:\\\\Users\\\\sambe\\\\Documents\\\\Spring 2018\\\\Data Mining\\\\data\\\\cleanedData\\\\chicago_crimes_2001_to_2017.csv\",header=0)\n if platform == \"darwin\":\n data = pd.read_csv('../cleaned_data/chicago_crimes_2001_to_2017.csv',header=0)\n data = initializeData(data)\n district_by_type = data.pivot_table(values='ID', index='Primary Type', columns=data.index, aggfunc=np.size).fillna(0)\n data.index = pd.Categorical(data['Community Area'])\n communityArea_by_type = data.pivot_table(values='ID', index='Primary Type', columns=data.index, aggfunc=np.size).fillna(0)\n #data.index = pd.Categorical(data[''])\n #communityArea_by_type = data.pivot_table(values='ID', index='Primary Type', columns=data.index, aggfunc=np.size).fillna(0)\n plt.figure(figsize=(15,12))\n scale_and_plot(district_by_type)\n return\n\n\ndef initializeData(df):\n\tdf['Primary Type'] = pd.Categorical(df['Primary Type'])\n\tdf['District'] = pd.Categorical(df['District'])\n\tdf['Description'] = pd.Categorical(df['Description'])\n\tdf.index = pd.Categorical(df['District'])\n\t#df.Date = pd.to_datetime(df.Date, format='%m/%d/%Y %I:%M:%S %p')\n\t#df.index = pd.DatetimeIndex(df.Date)\n\treturn df\n\ndef dfScaler(df,axis=0):\n\treturn (df - df.mean(axis=axis)) / df.std(axis=axis)\n\ndef plot_hmap(df, ix=None, cmap='bwr'):\n if ix is None:\n ix = np.arange(df.shape[0])\n plt.imshow(df.iloc[ix,:], cmap=cmap)\n plt.colorbar(fraction=0.03)\n plt.yticks(np.arange(df.shape[0]), df.index[ix])\n plt.xticks(np.arange(df.shape[1]),fontsize=8)\n plt.xlabel('Police District', fontsize=15)\n plt.title('Relative Crime Frequency by Police District 2001 - 2017',fontsize=18)\n plt.grid(False)\n #plt.gcf().subplots_adjust(left=0.15)\n plt.savefig('district_HeatMap.png',bbox_inches='tight')\n plt.show()\n\n\ndef scale_and_plot(df, ix = None):\n\n df_marginal_scaled = dfScaler(df.T).T\n if ix is None:\n ix = AC(4).fit(df_marginal_scaled).labels_.argsort()\n cap = np.min([np.max(df_marginal_scaled.as_matrix()), np.abs(np.min(df_marginal_scaled.as_matrix()))])\n df_marginal_scaled = np.clip(df_marginal_scaled, -1*cap, cap)\n plot_hmap(df_marginal_scaled, ix=ix)\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"code/heatMap.py","file_name":"heatMap.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"444263922","text":"import streamlit as st\nimport pandas as pd\nimport numpy as np\nimport pydeck as pdk\nimport plotly.express as px\nfrom matplotlib import pyplot\nimport datetime\nimport plotly.graph_objects as go\n\n@st.cache\ndef get_data():\n DATA_URL = (\"https://api.covid19india.org/csv/latest/case_time_series.csv\")\n DATA_URL_statewise_timeseries = (\"https://api.covid19india.org/csv/latest/state_wise_daily.csv\")\n DATA_URL_statewise = (\"https://api.covid19india.org/csv/latest/state_wise.csv\")\n return DATA_URL,DATA_URL_statewise,DATA_URL_statewise_timeseries\nDATA_URL,DATA_URL_statewise,DATA_URL_statewise_timeseries=get_data()\nst.title(\"Covid-19 in India\")\noptions=[\"National Data\",\"Statewise Data\"]\noptionSelected=st.sidebar.radio(\"\",options)\n\nif optionSelected==\"Statewise Data\":\n st.markdown(\"Statewise Covid-19 cases in India \")\n series1=pd.read_csv(DATA_URL_statewise, header=0, index_col=0, parse_dates=True, squeeze=True)\n series1=series1[[\"Confirmed\",\"Recovered\",\"Deaths\",\"Active\"]]\n st.subheader(\"Statewise Data\")\n stateDict={\"Andhra Pradesh\":\"AP\",\"Arunachal Pradesh\":\"AR\",\"Assam\":\"AS\",\"Bihar\":\"BR\",\"Chhattisgarh\":\"CG\",\n \"Goa\":\"GA\",\"Gujarat\":\"GJ\",\"Haryana\":\"HR\",\"Himachal Pradesh\":\"HP\",\"Jammu and Kashmir\":\"JK\",\"Jharkhand\":\"JH\",\"Karnataka\":\"KA\",\n \"Kerala\":\"KL\",\"Madhya Pradesh\":\"MP\",\"Maharashtra\":\"MH\",\"Manipur\":\"MN\",\"Meghalaya\":\"ML\",\"Mizoram\":\"MZ\",\n \"Nagaland\":\"NL\",\"Orissa\":\"OR\",\"Punjab\":\"PB\",\"Rajasthan\":\"RJ\",\"Sikkim\":\"SK\",\"Tamil Nadu\":\"TN\",\"Tripura\":\"TR\",\n \"Uttarakhand\":\"UK\",\"Uttar Pradesh\":\"UP\",\"West Bengal\":\"WB\",\"Tamil Nadu\":\"TN\",\"Tripura\":\"TR\",\"Andaman and Nicobar Islands\":\"AN\",\n \"Chandigarh\":\"CH\",\"Delhi\":\"DL\",\"Lakshadweep\":\"LD\",\"Pondicherry\":\"PY\"}\n\n selectedState = st.selectbox(\n \"Select a state :\",\n sorted(stateDict.keys()))\n series2=series1.loc[[selectedState]]\n st.info(\"Total Confirmed Cases : {s}\".format(s=int(series2[\"Confirmed\"])))\n st.success(\"Recovered Cases : {s}\".format(s=int(series2[\"Recovered\"])))\n st.warning(\"Active Cases : {s}\".format(s=int(series2[\"Active\"])))\n st.error(\"Deaths : {s}\".format(s=int(series2[\"Deaths\"])))\n\n stateDict={\"Andhra Pradesh\":\"AP\",\"Arunachal Pradesh\":\"AR\",\"Assam\":\"AS\",\"Bihar\":\"BR\",\"Chhattisgarh\":\"CG\",\n \"Goa\":\"GA\",\"Gujarat\":\"GJ\",\"Haryana\":\"HR\",\"Himachal Pradesh\":\"HP\",\"Jammu and Kashmir\":\"JK\",\"Jharkhand\":\"JH\",\"Karnataka\":\"KA\",\n \"Kerala\":\"KL\",\"Madhya Pradesh\":\"MP\",\"Maharashtra\":\"MH\",\"Manipur\":\"MN\",\"Meghalaya\":\"ML\",\"Mizoram\":\"MZ\",\n \"Nagaland\":\"NL\",\"Orissa\":\"OR\",\"Punjab\":\"PB\",\"Rajasthan\":\"RJ\",\"Sikkim\":\"SK\",\"Tamil Nadu\":\"TN\",\"Tripura\":\"TR\",\n \"Uttarakhand\":\"UK\",\"Uttar Pradesh\":\"UP\",\"West Bengal\":\"WB\",\"Tamil Nadu\":\"TN\",\"Tripura\":\"TR\",\"Andaman and Nicobar Islands\":\"AN\",\n \"Chandigarh\":\"CH\",\"Delhi\":\"DL\",\"Lakshadweep\":\"LD\",\"Pondicherry\":\"PY\"}\n series_statewise_daily = pd.read_csv(DATA_URL_statewise_timeseries, header=0, index_col=0)\n stateForTimeSeries=stateDict[selectedState]\n timeSeriesDataforLast30DaysConfirmed = series_statewise_daily[stateForTimeSeries][-90::3]\n timeSeriesDataforLast30DaysRecovered = series_statewise_daily[stateForTimeSeries][-89::3]\n timeSeriesDataforLast30DaysDeceased = series_statewise_daily[stateForTimeSeries][-88::3]\n listOfVariables=[\"Daily Confirmed\",\"Daily Recovered\",\"Daily Deceased\"]\n option = st.radio(\n 'Select a category:',\n listOfVariables)\n if option==\"Daily Confirmed\":\n figD=px.line(timeSeriesDataforLast30DaysConfirmed,title=\"Daily confirmed cases in {s}\".format(s=selectedState),labels={stateForTimeSeries:selectedState})\n elif option==\"Daily Recovered\":\n figD=px.line(timeSeriesDataforLast30DaysRecovered,title=\"Daily recovered cases in {s}\".format(s=selectedState))\n elif option==\"Daily Deceased\":\n figD=px.line(timeSeriesDataforLast30DaysDeceased,title=\"Daily deceased cases in {s}\".format(s=selectedState))\n figD.update_yaxes(title_text='No. of Cases')\n figD.update_layout(legend_title_text = \"State\")\n figD.update_layout(paper_bgcolor=\"white\",plot_bgcolor=\"black\",xaxis={\"showgrid\" : False,\"showticklabels\": True})\n figD.update_layout(yaxis={\"showgrid\" : False,\"showticklabels\": True})\n st.write(figD)\n\n total_active_today = series2[\"Confirmed\"] - series2[\"Recovered\"] - series2[\"Deaths\"]\n pie1 = [total_active_today,series2[\"Recovered\"],series2[\"Deaths\"]]\n name1=[\"Total Active Cases\",\"Total Recovered Cases\",\"Total Deaths\"]\n colors1=[\"#f54242\",\"#5df542\",\"#4278f5\"]\n fig2 = px.pie(pie1, values=pie1,names=name1,\n title='Distribution of total cases in {s}'.format(s = selectedState),\n color = name1,\n color_discrete_map={'Total Active Cases':\"#f54242\",\n \"Total Recovered Cases\": \"#4278f5\",\n \"Total Deaths\" : \"#5df542\"})\n st.write(fig2)\n st.subheader(\"Data for all States and Union Territories\")\n fullData=st.checkbox(\"Show Data \")\n if fullData:\n fig = go.Figure(data=[go.Table(header=dict(values=[\"States\",\"Confirmed\",\"Recovered\",\"Deaths\",\"Active\"]),\n cells=dict(values=[series1.index,series1.Confirmed,series1.Recovered,series1.Deaths,series1.Active]))])\n fig.update_layout(width=800, height=1089)\n st.write(fig)\nelif optionSelected==\"National Data\":\n\n series = pd.read_csv(DATA_URL, header=0, index_col=0, parse_dates=True, squeeze=True)\n st.info(\"Total Cases : \" + str(series[\"Total Confirmed\"][-1]))\n st.success(\"Recovered Cases : \" + str(series[\"Total Recovered\"][-1]))\n st.error(\"Total Deaths : \" + str(series[\"Total Deceased\"][-1]))\n\n\n daily_confirmed = series[\"Daily Confirmed\"]\n daily_recovered = series[\"Daily Recovered\"]\n total_confirmed = series[\"Total Confirmed\"]\n total_recovered = series[\"Total Recovered\"]\n daily_deceased = series[\"Daily Deceased\"]\n total_deceased = series[\"Total Deceased\"]\n listOfVariables=[\"Daily Confirmed\",\"Daily Recovered\",\"Daily Deceased\",\"Total Confirmed\",\"Total Recovered\",\"Total Deceased\"]\n option = st.selectbox(\n 'Select a category:',\n listOfVariables)\n option1=st.checkbox(\n \"Select starting date\")\n\n if option1==True:\n dateStart = st.date_input('start date', datetime.date(2020,5,30))\n else:\n dateStart= datetime.date(2020,5,30)\n startDate1=datetime.date(2020,1,30)\n delta = dateStart-startDate1\n linedata =series[option][delta.days:]\n fig = px.line(linedata)\n fig.update_xaxes(title_text='Date')\n fig.update_layout(hovermode=\"x\")\n fig.update_layout(legend_title_text = \"Parameter\")\n fig.update_yaxes(title_text='No. of Cases')\n\n\n st.write(fig)\n\n total_active_today = series[\"Total Confirmed\"][-1] - series[\"Total Recovered\"][-1] - series[\"Total Deceased\"][-1]\n pie = [total_active_today,series[\"Total Recovered\"][-1],series[\"Total Deceased\"][-1]]\n name=[\"Total Active Cases\",\"Total Recovered Cases\",\"Total Deaths\"]\n colors=[\"#f54242\",\"#5df542\",\"#4278f5\"]\n fig1 = px.pie(pie, values=pie,names=name,\n title='Distribution of total cases in India',\n color = name,\n color_discrete_map={'Total Active Cases':\"#f54242\",\n \"Total Recovered Cases\": \"#4278f5\",\n \"Total Deaths\" : \"#5df542\"})\n\n st.write(fig1)\nelse:\n pass\nfor i in range(18):\n st.sidebar.markdown(\" \")\nst.sidebar.subheader(\"Shyam Kanapram\")\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"353414400","text":"from django.contrib import admin\nfrom .models import *\n\n\nclass CustomerTypeAdmin(admin.ModelAdmin):\n prepopulated_fields = {'slug': ('name',)}\n list_display = ('name','order',)\nadmin.site.register(CustomerType, CustomerTypeAdmin)\n\n\nclass LeadSourceAdmin(admin.ModelAdmin):\n prepopulated_fields = {'slug': ('name',)}\n list_display = ('name','order',)\nadmin.site.register(LeadSource, LeadSourceAdmin)\n\n\nclass CustomerAdmin(admin.ModelAdmin):\n fieldsets = (\n ('Customer', {\n 'fields': ('owner','parent','name','slug','type',)\n }),\n ('Lead', {\n 'classes': ('collapse',),\n 'fields': ('is_lead','lead_source',)\n }),\n ('Custoemr Contact', {\n 'classes': ('collapse',),\n 'fields': ('url','email','phone','fax','twitter','facebook','skype',)\n }),\n ('Billing Address', {\n 'classes': ('collapse',),\n 'fields': ('bill_street','bill_city',('bill_state','bill_zip_code',))\n }),\n ('Shipping Address', {\n 'classes': ('collapse',),\n 'fields': ('ship_street','ship_city',('ship_state','ship_zip_code',))\n }),\n ('Misc.', {\n 'classes': ('collapse',),\n 'fields': ('is_active','date_last_viewed','description',)\n }),\n )\n prepopulated_fields = {'slug': ('name',)}\n list_display = ('name','owner','is_lead','is_active',)\n list_filter = ('is_lead','is_active','type',)\nadmin.site.register(Customer, CustomerAdmin)","sub_path":"customers/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"319254556","text":"import numpy as np\nimport pandas as pd\n\nimport gensim\nfrom gensim.utils import simple_preprocess\nfrom gensim.parsing.preprocessing import STOPWORDS\nfrom nltk.stem import WordNetLemmatizer, SnowballStemmer\n\nimport nltk\n# nltk.download('wordnet')\nnltk.download('words')\nwords = set(nltk.corpus.words.words())\nstemmer = SnowballStemmer(\"english\")\n\nimport re\nimport operator\nnp.random.seed(42)\n\nfrom langdetect import detect\nimport emoji\nimport copy\nfrom googletrans import Translator\nfrom tqdm import tqdm, tqdm_pandas\n\n# import warnings\n# warnings.filterwarnings(\"ignore\")\n\nfrom string import punctuation\n\n\n\ndef preprocess_word(word):\n \"\"\" \n Word preprocessing \n \n This function will preprocess particular word \n \n Parameters: \n word: string\n \n Returns: \n string: will return initial string input but preprocessed ,\n so from input string will delete all punctuation and repeated symbols.\n \"\"\"\n \n \n # Remove punctuation\n word = ''.join(c for c in word if c not in punctuation)\n \n # Convert more than 2 letter repetitions to 2 letter\n # funnnnny --> funny\n word = re.sub(r'(.)\\1+', r'\\1\\1', word)\n \n return word\n\ndef is_valid_word(word):\n \"\"\" \n Word checking\n \n This function will check if word starts with alphabet \n \n Parameters: \n word: string\n \n Returns: \n Boolean: Is valid or not , True means that word is valid\n \"\"\"\n \n \n # Check if word begins with an alphabet\n return (re.search(r'^[a-zA-Z][a-z0-9A-Z\\._]*$', word) is not None)\n\ndef handle_emojis(document):\n \"\"\" \n Emoji classifier\n \n This function will replace emojis with EMO_POS or EMO_NEG , depending on its meaning \n \n Parameters: \n document: string\n \n Returns: \n string: initial string input replaced emojis by their meaning, \n for example :) will replaced with EMO_POS but ): will replaced with EMO_NEG\n \"\"\"\n \n \n # Smile -- :), : ), :-), (:, ( :, (-:, :')\n document = re.sub(r'(:\\s?\\)|:-\\)|\\(\\s?:|\\(-:|:\\'\\))', ' positive emoji ', document)\n \n # Laugh -- :D, : D, :-D, xD, x-D, XD, X-D\n document = re.sub(r'(:\\s?D|:-D|x-?D|X-?D)', ' positive emoji ', document)\n \n # Love -- <3, :*\n document = re.sub(r'(<3|:\\*)', ' positive emoji ', document)\n \n # Wink -- ;-), ;), ;-D, ;D, (;, (-;\n document = re.sub(r'(;-?\\)|;-?D|\\(-?;)', ' positive emoji ', document)\n \n # Sad -- :-(, : (, :(, ):, )-:\n document = re.sub(r'(:\\s?\\(|:-\\(|\\)\\s?:|\\)-:)', ' negative emoji ', document)\n \n # Cry -- :,(, :'(, :\"(\n document = re.sub(r'(:,\\(|:\\'\\(|:\"\\()', ' negative emoji ', document)\n \n return document\n\ndef preprocess_document(document, use_stemmer = False):\n \"\"\" \n Text preprocessing\n \n This function will preprocess the input text \n \n Parameters: \n document: string (we can put the entire string row , for instance in our case I will pass conversation)\n use_stemmer: Boolean (If True I will use stemmer as well as all other processes)\n \n Returns: \n string: processed input string\n \"\"\"\n \n \n def lemmatize_stemming(text):\n return stemmer.stem(WordNetLemmatizer().lemmatize(text, pos='v'))\n\n processed_document = []\n \n # Convert to lower case\n document = document.lower()\n \n # Replaces URLs with the word URL\n document = re.sub(r'((www\\.[\\S]+)|(https?://[\\S]+))', ' good sololearn url ', document)\n \n # Replace @handle with the word USER_MENTION\n document = re.sub(r'@[\\S]+', 'USER_MENTION', document)\n \n # Replaces #hashtag with hashtag\n document = re.sub(r'#(\\S+)', r' \\1 ', document)\n \n # Replace 2+ dots with space\n document = re.sub(r'\\.{2,}', ' ', document)\n \n # Strip space, \" and ' from document\n document = document.strip(' \"\\'')\n \n # Replace emojis with either EMO_POS or EMO_NEG\n document = handle_emojis(document)\n \n # Replace multiple spaces with a single space\n document = re.sub(r'\\s+', ' ', document)\n words = document.split()\n\n for word in words:\n word = preprocess_word(word)\n if is_valid_word(word):\n if use_stemmer:\n word = lemmatize_stemming(word)\n if word not in gensim.parsing.preprocessing.STOPWORDS and (len(word) > 3 or word == 'not'):\n processed_document.append(word)\n \n processed_internal_state = ' '.join(processed_document)\n \n processed_internal_state = re.sub(r'\\b\\w{1,3}\\b', '', processed_internal_state)\n \n processed_internal_state = ' '.join(processed_internal_state.split())\n\n return processed_internal_state\n\ndef preprocess(preprocessed_document):\n \"\"\" \n tokenize and combine already preprocessed document\n \n This function will tokenize document and will combine document such a way ,\n that we can containing the number of times a word appears in the training set \n using gensim.corpora.Dictionary\n \n Parameters: \n preprocessed_document: string (particular document obtained from preprocess_document function)\n \n Returns: \n list: tokenized documents in approprite form\n \"\"\"\n \n \n result=[]\n \n for token in gensim.utils.simple_preprocess(preprocessed_document) :\n result.append(token)\n \n return result\n\ndef exclude_emojis(line):\n # line = str(line)\n if isinstance(line, int):\n return line\n elif isinstance(line, float):\n return line\n return ''.join(c for c in line if c not in emoji.UNICODE_EMOJI)\n\ndef replace_url(str):\n return re.sub(r'((www\\.[\\S]+)|(https?://[\\S]+))', ' url ', str)\n\ndef replace_empty_rows(df):\n df = df.copy(deep=True)\n df[col].replace('', np.nan, inplace=True)\n return df\n\ndef replace_programming_language(line):\n programming_languages = ['javascript', 'js', 'php', 'javafx', 'java', 'c#', 'python', 'scala', 'html', 'css']\n for key in programming_languages:\n line = line.lower().replace(key,'programming language')\n return line\n\ndef detect_language(df, col):\n df = df.copy(deep = True)\n for index, row in tqdm(df.iterrows(), total=df.shape[0]):\n try:\n language = detect(row[col])\n except:\n language = 'nl'\n\n df.loc[index, 'Language'] = language\n return df\n\ndef to_english(df, col):\n df = df.copy(deep = True)\n for index, row in tqdm(df.iterrows(), total=df.shape[0]):\n translator = Translator()\n try:\n translated = translator.translate(row[col], dest='en').text\n except:\n translated = row[col]\n \n df.loc[index, col] = translated\n return df\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"576118029","text":"import os\nimport sys\nimport torch\nimport torch.utils.data as data\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\ndef get_faces(txt_path, padding=0.4, save_dir = './data/widerface/faces'):\n \n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n # get image paths and face labels\n imgs_path = []\n words = []\n f = open(txt_path, 'r')\n lines = f.readlines()\n isFirst = True\n labels = []\n for line in lines:\n line = line.rstrip()\n if line.startswith('#'):\n if isFirst is True:\n isFirst = False\n else:\n labels_copy = labels\n words.append(labels_copy)\n labels = []\n path = line[2:]\n path = txt_path.replace('label.txt', 'images/') + path\n imgs_path.append(path)\n else:\n line = line.split(' ')\n label = [float(x) for x in line]\n labels.append(label)\n \n words.append(labels)\n \n count = 0\n for index in range(len(imgs_path)):\n print('Processing image %i out of %i' %(index, len(imgs_path)))\n img = Image.open(imgs_path[index]).convert(\"RGB\")\n name = os.path.basename(imgs_path[index])\n labels = words[index]\n if len(labels) == 0:\n pass\n for idx, label in enumerate(labels):\n if sum(label[0:4]) == 0:\n count += 1\n continue\n \n # bbox\n x1 = label[0]\n y1 = label[1]\n x2 = label[0] + label[2]\n y2 = label[1] + label[3]\n h = label[3]\n w = label[2]\n pad = padding / 2 # for each side\n \n if label[2] <= 0 or label[3] <= 0:\n count += 1\n continue\n \n x1 = max(0, x1-w*pad)\n y1 = max(0, y1-h*pad)\n x2 = min(x2+w*pad, img.size[0])\n y2 = min(y2+h*pad, img.size[1])\n \n face_img = img.crop((x1, y1, x2, y2))\n face_img.save(os.path.join(save_dir, name[:-4] + '_%i.jpg'%idx))\n \n print('All faces saved successfully. Faces skipped: %i'%count)\nif __name__ == '__main__':\n txt_path = './data/widerface/train/label.txt'\n get_faces(txt_path)\n ","sub_path":"get_wider_faces.py","file_name":"get_wider_faces.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"270015527","text":"import requests\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nteamIdList=[]\nplayerIdList=[]\n\nclass Ui_assignationWindow(object):\n def click(self):\n teamId=str(self.teamComboBox.currentIndex())\n playerId=str(self.playerComboBox.currentIndex())\n send=requests.post(\"http://localhost:8080/assignation/add\", json={\n \"player\": playerId,\n \"team\": teamId\n })\n print(send.status_code)\n def setupUi(self, assignationWindow):\n assignationWindow.setObjectName(\"assignationWindow\")\n assignationWindow.resize(640, 114)\n self.centralwidget = QtWidgets.QWidget(assignationWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\n self.verticalLayout.setObjectName(\"verticalLayout\")\n self.formLayout = QtWidgets.QFormLayout()\n self.formLayout.setObjectName(\"formLayout\")\n self.playerLabel = QtWidgets.QLabel(self.centralwidget)\n self.playerLabel.setObjectName(\"playerLabel\")\n self.formLayout.setWidget(0, QtWidgets.QFormLayout.LabelRole, self.playerLabel)\n self.teamLabel = QtWidgets.QLabel(self.centralwidget)\n self.teamLabel.setObjectName(\"teamLabel\")\n self.formLayout.setWidget(1, QtWidgets.QFormLayout.LabelRole, self.teamLabel)\n self.playerComboBox = QtWidgets.QComboBox(self.centralwidget)\n self.playerComboBox.setObjectName(\"playerComboBox\")\n self.formLayout.setWidget(0, QtWidgets.QFormLayout.FieldRole, self.playerComboBox)\n self.teamComboBox = QtWidgets.QComboBox(self.centralwidget)\n self.teamComboBox.setObjectName(\"teamComboBox\")\n self.formLayout.setWidget(1, QtWidgets.QFormLayout.FieldRole, self.teamComboBox)\n self.applyButton = QtWidgets.QPushButton(self.centralwidget)\n self.applyButton.setObjectName(\"applyButton\")\n self.applyButton.clicked.connect(self.click)\n self.formLayout.setWidget(2, QtWidgets.QFormLayout.FieldRole, self.applyButton)\n self.verticalLayout.addLayout(self.formLayout)\n assignationWindow.setCentralWidget(self.centralwidget)\n\n self.retranslateUi(assignationWindow)\n QtCore.QMetaObject.connectSlotsByName(assignationWindow)\n\n self.teamComboBox.clear()\n self.teamComboBox.addItem('')\n response = requests.get(\"http://localhost:8080/team\")\n if response.status_code == 200:\n json = response.json()\n print(json)\n for i in json:\n text = i['city'] + ' ' + i['name']\n self.teamComboBox.addItem(text)\n teamIdList.append(i['teamId'])\n print(teamIdList)\n\n self.playerComboBox.clear()\n self.playerComboBox.addItem('')\n response = requests.get(\"http://localhost:8080/players\")\n if response.status_code == 200:\n json = response.json()\n print(json)\n for i in json:\n text = i['firstName'] + ' ' + i['surname']\n playerIdList.append(i['playerId'])\n self.playerComboBox.addItem(text)\n print(playerIdList)\n\n def retranslateUi(self, assignationWindow):\n _translate = QtCore.QCoreApplication.translate\n assignationWindow.setWindowTitle(_translate(\"assignationWindow\", \"Assign\"))\n self.playerLabel.setText(_translate(\"assignationWindow\", \"Player\"))\n self.teamLabel.setText(_translate(\"assignationWindow\", \"Team\"))\n self.applyButton.setText(_translate(\"assignationWindow\", \"Apply\"))\n\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n assignationWindow = QtWidgets.QMainWindow()\n ui = Ui_assignationWindow()\n ui.setupUi(assignationWindow)\n assignationWindow.show()\n sys.exit(app.exec_())\n","sub_path":"admingui/assignationWindow.py","file_name":"assignationWindow.py","file_ext":"py","file_size_in_byte":3827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"47690383","text":"import numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\nfrom sklearn.utils import shuffle\nfrom util import init_weight, all_parity_pairs_with_sequence_labels\n\n\nclass SimpleRNN(object):\n\tdef __init__(self, M, activation=tf.nn.relu):\n\t\tself.M = M # hidden layer size\n\t\tself.f = activation\n\n\tdef fit(self, X, Y, epochs=100, learning_rate=1e-3, momentum=0.99, debug=False, print_period=10):\n\t\t# for convenience\n\t\tlr = learning_rate\n\t\tmu = momentum\n\n\t\tN, T, D = X.shape\n\t\tK = len(set(Y.flatten()))\n\t\tM = self.M\n\n\t\t# initialize weights\n\t\tWx = init_weight(D, M).astype(np.float32)\n\t\tWh = init_weight(M, M).astype(np.float32)\n\t\tbh = np.zeros(M, dtype=np.float32)\n\t\th0 = np.zeros(M, dtype=np.float32)\n\t\tWo = init_weight(M, K).astype(np.float32)\n\t\tbo = np.zeros(K, dtype=np.float32)\n\n\t\t# make them tensorflow vars\n\t\tself.Wx = tf.Variable(Wx)\n\t\tself.Wh = tf.Variable(Wh)\n\t\tself.bh = tf.Variable(bh)\n\t\tself.h0 = tf.Variable(h0)\n\t\tself.Wo = tf.Variable(Wo)\n\t\tself.bo = tf.Variable(bo)\n\n\t\ttfX = tf.placeholder(tf.float32, shape=(T, D), name='X')\n\t\ttfY = tf.placeholder(tf.int32, shape=(T, ), name='Y')\n\n\t\tXWx = tf.matmul(tfX, self.Wx)\n\n\t\tdef recurrence(h_t1, xwx_t):\n\t\t\t# tf.matmul() only works with 2-D objects\n\t\t\t# we want to return a 1-D object of size M\n\t\t\t# so that the final result is (T, M)\n\t\t\t# not (T, 1, M)\n\t\t\th_t = self.f(xwx_t + tf.matmul(tf.reshape(h_t1, [1, M]), self.Wh) + self.bh)\n\t\t\treturn tf.reshape(h_t, [M, ])\n\n\t\th = tf.scan(\n\t\t\tfn=recurrence,\n\t\t\telems=XWx,\n\t\t\tinitializer=self.h0\n\t\t)\n\n\t\tlogits = tf.matmul(h, self.Wo) + self.bo\n\n\t\tcost = tf.reduce_mean(\n\t\t\ttf.nn.sparse_softmax_cross_entropy_with_logits(\n\t\t\t\tlogits=logits,\n\t\t\t\tlabels=tfY\n\t\t\t)\n\t\t)\n\n\t\tpredict_op = tf.argmax(logits, axis=1)\n\n\t\ttrain_op = tf.train.AdamOptimizer(lr).minimize(cost)\n\n\t\tinit = tf.global_variables_initializer()\n\n\t\tcosts = []\n\t\twith tf.Session() as sess:\n\t\t\tsess.run(init)\n\n\t\t\tfor i in range(epochs):\n\t\t\t\tX, Y = shuffle(X, Y)\n\t\t\t\tif debug:\n\t\t\t\t\tbatch_cost = 0\n\t\t\t\t\tn_correct = 0\n\n\t\t\t\tfor j in range(N):\n\t\t\t\t\tif debug:\n\t\t\t\t\t\tops = [train_op, cost, predict_op]\n\t\t\t\t\t\t_, c, p = sess.run(ops, feed_dict={tfX: X[j], tfY: Y[j]})\n\t\t\t\t\t\tbatch_cost += c\n\t\t\t\t\t\tn_correct += (p[-1] == Y[j, -1])\n\t\t\t\t\telse:\n\t\t\t\t\t\tsess.run(train_op, feed_dict={tfX: X[j], tfY: Y[j]})\n\n\t\t\t\tif debug:\n\t\t\t\t\tcosts.append(batch_cost)\n\t\t\t\t\tscore = float(n_correct) / N\n\t\t\t\t\tprint('epoch: %d, cost: %.6f, score: %.6f%%' % (i, batch_cost, score*100))\n\t\t\t\t\tif n_correct == N:\n\t\t\t\t\t\tbreak\n\n\t\tif debug:\n\t\t\tplt.plot(costs)\n\t\t\tplt.title('Cross-Entropy Cost')\n\t\t\tplt.show()\n\n\ndef parity(nbit=12, learning_rate=1e-3, epochs=50):\n\tX, Y = all_parity_pairs_with_sequence_labels(nbit)\n\tX = X.astype(np.float32)\n\n\trnn = SimpleRNN(4, activation=tf.nn.relu)\n\trnn.fit(X, Y, epochs=epochs, learning_rate=learning_rate, debug=True)\n\n\nif __name__ == '__main__':\n\tparity()\n\n","sub_path":"rnn/srn_parity_tf.py","file_name":"srn_parity_tf.py","file_ext":"py","file_size_in_byte":2825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"588750189","text":"import os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"3\"\nos.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport numpy as np\nfrom sklearn.metrics import classification_report, confusion_matrix, accuracy_score\nimport random\nimport pickle\nimport tensorflow as tf\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import GridSearchCV\nimport pandas as pd\nimport os\nfrom sklearn.decomposition import PCA\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nimport keras\nimport sys\n\ny_true = [2, 0, 2, 2, 0, 1]\ny_pred = [0, 0, 2, 2, 0, 2]\nprint(confusion_matrix(y_true, y_pred))\n\n# Training Settings\nbatch_size = 100\nlatent_dim = 800\nchange = 10\nunits = 800 # num unit in the MLP hidden layer\nnum_filter_ae_cls = [32, 32, 64, 64, 128, 128] # conv_layers and No. of its channels for AE + CLS\nnum_filter_cls = [] # conv_layers and No. of its channel for only cls\nnum_dense = 0 # number of dense layer in classifier excluding the last layer\nkernel_size = (1, 3)\nactivation = tf.nn.relu\npadding = 'same'\nstrides = 1\npool_size = (1, 2)\nnum_class = 5\nreg_l2 = tf.contrib.layers.l1_regularizer(scale=0.1)\ninitializer = tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32)\n#initializer = tf.truncated_normal_initializer()\n\n# Import the data\n#filename = '../Mode-codes-Revised/paper2_data_for_DL_train_val_test.pickle'\nfilename = '/home/sxz/data/geolife_Data/paper2_data_for_DL_kfold_dataset_RL.pickle'\nwith open(filename, 'rb') as f:\n kfold_dataset, X_unlabeled = pickle.load(f)\n # print(len(kfold_dataset[1][1]))\n # print(np.array(kfold_dataset[1][4]).shape)\n # print(X_unlabeled)\n # print(np.array(X_unlabeled).shape)\n # print(len(kfold_dataset))\n # print(len(X_unlabeled))\n#the length of Kfold_dataset is 5(the data already labelled)\n#every part in kfold_dataset contains 441 segments, which is formed as a \n#structure (441 × 1 × 248 × 4) (441,) (110 × 1 × 248 × 4) (110 × 5) (110,)\n#totoal is 5×441 × 1 × 248 × 4\n\n#the lenth of X_unlabeled is size 4310×\n#structure is (4310 × 1 × 248 ×4 )\n# #\n\n\n# Encoder Network\n\n\ndef encoder_network(latent_dim, num_filter_ae_cls, input_labeled):\n #input_combined是做无监督,AE这一部分的input，input_labeled是做cls这一部分的\n # encoded_combined = input_combined\n encoded_labeled = input_labeled\n layers_shape = []\n #这里改了以后len(num_filter_ae_cls)只有一组需要计算的\n for i in range(len(num_filter_ae_cls)):\n #分奇偶层，奇数情况下做maxpooling\n scope_name = 'encoder_set_' + str(i + 1)\n #第一部分是编码input_combined部分的数据\n # with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE, initializer=initializer):\n # encoded_combined = tf.layers.conv2d(inputs=encoded_combined, activation=tf.nn.relu, filters=num_filter_ae_cls[i],\n # name='conv_1', kernel_size=kernel_size, strides=strides,\n # padding=padding)\n #第二部分的网络是编码input_labeled部分的数据\n with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE, initializer=initializer):\n encoded_labeled = tf.layers.conv2d(inputs=encoded_labeled, activation=tf.nn.relu, filters=num_filter_ae_cls[i],\n name='conv_1', kernel_size=kernel_size, strides=strides, padding=padding)\n #奇数情况下做maxpooling\n if i % 2 != 0:\n # encoded_combined = tf.layers.max_pooling2d(encoded_combined, pool_size=pool_size,\n # strides=pool_size, name='pool')\n encoded_labeled = tf.layers.max_pooling2d(encoded_labeled, pool_size=pool_size,\n strides=pool_size, name='pool')\n # print(encoded_combined)\n # print(\"-----------------\")\n # print(\"-----------------\")\n # print(\"-----------------\")\n # print(\"-----------------\")\n # print(encoded_labeled)\n # print(\"-----------------\")\n # print(\"-----------------\")\n # print(\"-----------------\")\n # print(encoded_combined.get_shape().as_list())\n #(encoderd_combined.get_shape().as_list()=[None,1,248,32])\n # layers_shape.append(encoded_combined.get_shape().as_list())\n # print(layers_shape)\n #[[None, 1, 248, 32], [None, 1, 124, 32], [None, 1, 124, 64], [None, 1, 62, 64]\n #[None, 1, 62, 128], [None, 1, 31, 128]]\n # print(i)\n # print(layers_shape)\n # print(encoderd_combined.get_shape().as_list())\n # layers_shape.append(encoded_combined.get_shape().as_list())\n # latent_combined = encoded_combined\n #latent_combined为(\"pool_4/MaxPool:0\", shape=(?,1,31,128))\n #latent_labeled为(\"pool_5/MaxPool:0\",shape(?,1,31,128))\n # print(\"latent_combined is as below:\")\n # print(latent_combined)\n latent_labeled = encoded_labeled\n print(\"latent_labeled is as below:\")\n print(latent_labeled)\n print(\"-----------------------\")\n print(\"------------------------\")\n print(layers_shape)\n return latent_labeled, layers_shape\n\n# # Decoder Network\n\n\ndef decoder_network(latent, input_size, kernel_size, padding, activation):\n decoded_combined = latent\n #num_filter_ae_cls ae_classifier的通道(filter数量即通道数量)\n num_filter_ = num_filter_ae_cls[::-1]\n print(num_filter_ae_cls)\n #[32,32,64,64,128,128]\n print(num_filter_ae_cls[::-1])\n #[::-1] 倒序 [128, 128, 64, 64, 32, 32]\n\n if len(num_filter_) % 2 == 0:\n num_filter_ = sorted(set(num_filter_), reverse=True)\n for i in range(len(num_filter_)):\n decoded_combined = tf.keras.layers.UpSampling2D(name='UpSample', size=pool_size)(decoded_combined)\n scope_name = 'decoder_set_' + str(2*i)\n with tf.variable_scope(scope_name, initializer=initializer):\n decoded_combined = tf.layers.conv2d_transpose(inputs=decoded_combined, activation=activation,\n filters=num_filter_[i], name='deconv_1',\n kernel_size=kernel_size,\n strides=strides, padding=padding)\n scope_name = 'decoder_set_' + str(2*i + 1)\n with tf.variable_scope(scope_name, initializer=initializer):\n filter_size, activation = (input_size[-1], tf.nn.sigmoid) if i == len(num_filter_) - 1 else (int(num_filter_[i] / 2), tf.nn.relu)\n if i == len(num_filter_): # change it len(num_filter_)-1 if spatial size is not dividable by 2\n kernel_size = (1, input_size[1] - (decoded_combined.get_shape().as_list()[2] - 1) * strides)\n padding = 'valid'\n decoded_combined = tf.layers.conv2d_transpose(inputs=decoded_combined, activation=activation,\n filters=filter_size, name='deconv_1',\n kernel_size=kernel_size,\n strides=strides, padding=padding)\n else:\n num_filter_ = sorted(set(num_filter_), reverse=True)\n for i in range(len(num_filter_)):\n scope_name = 'decoder_set_' + str(2 * i)\n with tf.variable_scope(scope_name, initializer=initializer):\n decoded_combined = tf.layers.conv2d_transpose(inputs=decoded_combined, activation=activation,\n filters=num_filter_[i], name='deconv_1',\n kernel_size=kernel_size,\n strides=strides, padding=padding)\n scope_name = 'decoder_set_' + str(2 * i + 1)\n with tf.variable_scope(scope_name, initializer=initializer):\n filter_size, activation = (input_size[-1], tf.nn.sigmoid) if i == len(num_filter_) - 1 else (int(num_filter_[i] / 2), tf.nn.relu)\n if i == len(num_filter_): # change it len(num_filter_)-1 if spatial size is not dividable by 2\n kernel_size = (1, input_size[1] - (decoded_combined.get_shape().as_list()[2] - 1) * strides)\n padding = 'valid'\n decoded_combined = tf.layers.conv2d_transpose(inputs=decoded_combined, activation=activation,\n filters=filter_size, name='deconv_1',\n kernel_size=kernel_size,\n strides=strides, padding=padding)\n if i != len(num_filter_) - 1:\n decoded_combined = tf.keras.layers.UpSampling2D(name='UpSample', size=pool_size)(decoded_combined)\n\n return decoded_combined\n\n\ndef classifier_mlp(latent_labeled, num_class, num_filter_cls, num_dense):\n #clsfier_mlp 为 latent_labeled的网络\n conv_layer = latent_labeled\n for i in range(len(num_filter_cls)):\n scope_name = 'cls_conv_set_' + str(i + 1)\n with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE, initializer=initializer):\n conv_layer = tf.layers.conv2d(inputs=conv_layer, activation=tf.nn.relu, filters=num_filter_cls[i],\n kernel_size=kernel_size, strides=strides, padding=padding,\n kernel_initializer=initializer)\n if len(num_filter_cls) % 2 == 0:\n if i % 2 != 0:\n conv_layer = tf.layers.max_pooling2d(conv_layer, pool_size=pool_size,strides=pool_size, name='pool')\n else:\n if i % 2 == 0:\n conv_layer = tf.layers.max_pooling2d(conv_layer, pool_size=pool_size,strides=pool_size, name='pool')\n print(\"conv_layer\")\n # print(conv_layer)\n #Tensor(\"pool_5/MaxPool:0\", shape=(?, 1, 31, 128))\n #flatten 在保留axis(axis=0)的同时平移输入张量\n \n dense = tf.layers.flatten(conv_layer)\n\n #print(dense)\n #Tensor(\"flatten/Reshape:0\", shape=(?, 3968))\n\n units = int(dense.get_shape().as_list()[-1] / 4)\n # print(units)\n # 992 *dense的shape除4\n for i in range(num_dense):\n scope_name = 'cls_dense_set_' + str(i + 1)\n with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE, initializer=initializer):\n dense = tf.layers.dense(dense, units, activation=tf.nn.relu, kernel_initializer=initializer)\n units /= 2\n print(i)\n #dense这里是0，不知道有啥用，应该是用来挑mlp参数时候用的，最后得到是0\n # sys.exit(0)\n dense_last = dense\n # print(\"dense before dropout\")\n # print(dense)\n #Tensor(\"flatten/Reshape:0\", shape=(?, 3968), dtype=float32)\n dense = tf.layers.dropout(dense, 0.5)\n # print(\"dense after dropout\")\n # print(dense)\n #Tensor(\"dropout/Identity:0\", shape=(?, 3968), dtype=float32)\n\n scope_name = 'cls_last_dense_'\n with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE, initializer=initializer):\n classifier_output = tf.layers.dense(dense, num_class, name='FC_4', kernel_initializer=initializer)\n # print(classifier_output)\n # Tensor(\"cls_last_dense_/FC_4/BiasAdd:0\", shape=(?, 5), dtype=float32)\n #output就是分类出来的5个class\n # sys.exit(0)\n return classifier_output, dense_last\n\ndef unsupervised(input_labeled, num_class , latent_dim, num_filter_ae_cls , num_dense , input_size):\n latent , layers_shape = encoder_network(latent_dim = latent_dim, num_filter_ae_cls = num_filter_ae_cls,\n input_labeled = input_labeled)\n decoded_output = decoder_network(latent = latent, input_size = input_size, kernel_size= kernel_size, activation=activation, padding=padding)\n\n # classifier_output, dense = classifier_mlp(latent = latent, num_class , num_filter_cls = num_filter_cls, num_dense = num_dense)\n loss_AE_label =tf.reduce_mean(tf.square(input_labeled - decoded_output))\n train_op_ae_label = tf.train.AdamOptimizer().minimize(loss_AE_label)\n return loss_AE_label, latent, train_op_ae_label\n \n\n# def PCA_clustering():\n\n\n\n\n\n\ndef semi_supervised(input_labeled, input_combined, true_label, alpha, beta, num_class, latent_dim, num_filter_ae_cls, num_filter_cls, num_dense, input_size):\n #先进行encoder网络进行编码\n latent_combined, latent_labeled, layers_shape = encoder_network(latent_dim=latent_dim, num_filter_ae_cls=num_filter_ae_cls,\n input_combined=input_combined, input_labeled=input_labeled)\n #得到通过神经网络的Latent_combined和latent_labeled以及append出来的layers_shape\n decoded_output = decoder_network(latent_combined = latent_combined, input_size=input_size, kernel_size=kernel_size, activation=activation, padding=padding)\n #得到decodeNet的输出\n classifier_output, dense = classifier_mlp(latent_labeled, num_class, num_filter_cls=num_filter_cls, num_dense=num_dense)\n #classifier_output = classifier_cnn(latent_labeled, num_filter=num_filter)\n #通过mlp感知层对latent_labeled层进行一次分类\n loss_ae = tf.reduce_mean(tf.square(input_combined - decoded_output), name='loss_ae') * 100\n #ae部分的loss_function\n loss_cls = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=true_label, logits=classifier_output),\n name='loss_cls')\n #classifier部分的loss_function\n total_loss = alpha*loss_ae + beta*loss_cls\n #通过调整\\alpha和\\beta的参数来调整loss function的计算方法\n #total_loss = beta * loss_ae + alpha * loss_cls\n loss_reg = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES, 'EasyNet'))\n #tf.get_collection(key , scope=None)\n # 用来获取一个名称是‘key’的集合中的所有元素，返回的是一个列表，列表的顺序是按照变量放入集合中的先后;\n # scope参数可选，表示的是名称空间（名称域），如果指定，就返回名称域中所有放入‘key’的变量的列表，不指\n # 定则返回所有变量。\n train_op_ae = tf.train.AdamOptimizer().minimize(loss_ae)\n #ae optimize 训练的operator\n train_op_cls = tf.train.AdamOptimizer().minimize(loss_cls)\n #classifier optimize 训练的oprtator\n train_op = tf.train.AdamOptimizer().minimize(total_loss)\n # train_op = train_op = tf.layers.optimize_loss(total_loss, optimizer='Adam')\n correct_prediction = tf.equal(tf.argmax(true_label, 1), tf.argmax(classifier_output, 1))\n #计算准确数\n accuracy_cls = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n# #reduce_mean(\n# input_tensor,\n# axis=None,\n# keep_dims=False,\n# name=None,\n# reduction_indices=None\n# )\n# tf.reduce_mean 计算张量的各个维度上的元素的平均值。\n return loss_ae, loss_cls, accuracy_cls, train_op_ae, train_op_cls, classifier_output, dense, train_op, total_loss\n\n\ndef get_combined_index(train_x_comb):\n x_combined_index = np.arange(len(train_x_comb))\n print(len(x_combined_index))\n #646\n # sys.exit(0)\n np.random.shuffle(x_combined_index)\n #将combined_index做shuffle\n # print(x_combined_index)\n # sys.exit(0)\n return x_combined_index\n\n\ndef get_labeled_index(train_x_comb, train_x):\n labeled_index = []\n for i in range(len(train_x_comb) // len(train_x)):\n l = np.arange(len(train_x))\n np.random.shuffle(l)\n labeled_index.append(l)\n labeled_index.append(np.arange(len(train_x_comb) % len(train_x)))\n return np.concatenate(labeled_index)\n\n\ndef ensemble_train_set(Train_X, Train_Y):\n index = np.random.choice(len(Train_X), size=len(Train_X), replace=True, p=None)\n return Train_X[index], Train_Y[index]\n\n\ndef loss_acc_evaluation(Test_X, Test_Y, loss_AE_label, input_labeled, k, sess):\n metrics = []\n i = 0\n# print(Test_X)\n batch_size_val = 10\n print(\"lenth of Test_X\")\n# print(len(Test_X))\n# print(len(Test_X) // batch_size_val)\n print(batch_size_val)\n# global i\n# global Test_X_batch\n# global Test_Y_\n for i in range(len(Test_X) // batch_size_val):\n Test_X_batch = Test_X[i * batch_size_val:(i + 1) * batch_size_val]\n Test_Y_batch = Test_Y[i * batch_size_val:(i + 1) * batch_size_val]\n loss_AE_label_ = sess.run([loss_AE_label],\n feed_dict={input_labeled: Test_X_batch})\n #验证集的loss和accuracy\n metrics.append([loss_AE_label_])\n print(metrics)\n# global i\n Test_X_batch = Test_X[(i + 1) * batch_size_val:]\n Test_Y_batch = Test_Y[(i + 1) * batch_size_val:]\n if len(Test_X_batch) >= 1:\n loss_AE_label_ = sess.run([loss_AE_label],\n feed_dict={input_labeled: Test_X_batch})\n #验证集的loss和accuracy\n metrics.append([loss_AE_label_])\n# print(metrics)\n # sys.exit(0)\n mean_ = np.mean(np.array(metrics), axis=0)\n print(\"___________________________________\")\n print(mean_)\n #print('Epoch Num {}, Loss_cls_Val {}, Accuracy_Val {}'.format(k, mean_[0], mean_[1]))\n return mean_[0]\n #把三次的loss和accuracy做一个平均传回去。\n\ndef encode_AE_data(Test_X, latent, input_labeled, sess):\n encode_result = []\n for i in range(len(Test_X) // batch_size):\n Test_X_batch = Test_X[i * batch_size:(i + 1) * batch_size]\n print(np.array(Test_X_batch).shape)\n encode_result.append(sess.run(tf.nn.softmax(latent), feed_dict={input_labeled: Test_X_batch}))\n print(np.array(encode_result).shape)\n Test_X_batch = Test_X[(i + 1) * batch_size:]\n encode_result.append(sess.run(tf.nn.softmax(latent), feed_dict={input_labeled: Test_X_batch}))\n encode_result = np.vstack(tuple(encode_result))\n return encode_result\n\n\n\ndef prediction_prob(Test_X, classifier_output, input_labeled, sess):\n prediction = []\n for i in range(len(Test_X) // batch_size):\n Test_X_batch = Test_X[i * batch_size:(i + 1) * batch_size]\n prediction.append(sess.run(tf.nn.softmax(classifier_output), feed_dict={input_labeled: Test_X_batch}))\n Test_X_batch = Test_X[(i + 1) * batch_size:]\n prediction.append(sess.run(tf.nn.softmax(classifier_output), feed_dict={input_labeled: Test_X_batch}))\n prediction = np.vstack(tuple(prediction))\n return prediction\n\n\n#lenth of Test_X\n#22\n# Traceback (most recent call last):\n# File \"2-Conv-Semi-AE+Cls.py\", line 446, in \n# label_proportions=[0.15, 0.35], num_filter=[32, 32, 64, 64])\n# File \"2-Conv-Semi-AE+Cls.py\", line 427, in training_all_folds\n# test_accuracy, f1_macro, f1_weight = training(kfold_dataset[i], X_unlabeled=X_unlabeled, seed=7, prop=prop, num_filter_ae_cls_all=num_filter)\n# File \"2-Conv-Semi-AE+Cls.py\", line 355, in training\n# loss_val, acc_val = loss_acc_evaluation(Val_X, Val_Y, loss_cls, accuracy_cls, input_labeled, true_label, k, sess)\n# File \"2-Conv-Semi-AE+Cls.py\", line 237, in loss_acc_evaluation\n# return mean_[0], mean_[1]\n# IndexError: invalid index to scalar variable.\n\n\ndef train_val_split(Train_X, Train_Y_ori):\n val_index = []\n for i in range(num_class):\n # print(np.where(Train_Y_ori==i))\n # print(np.where(Train_Y_ori==i)[0])\n #This match the data to the label\n label_index = np.where(Train_Y_ori == i)[0]\n print(len(label_index))\n \n #round()方法返回浮点数x的四舍五入值。\n\n # print(\"___________\")\n # print(\"label_index\")\n # print(label_index)\n # print(label_index)\n # print(label_index[:round(0.1*len(label_index))])\n #取前1%\n val_index.append(label_index[:round(0.1*len(label_index))])\n# print(val_index)\n val_index = np.hstack(tuple([label for label in val_index]))\n print(val_index)\n Val_X = Train_X[val_index]\n Val_Y_ori = Train_Y_ori[val_index]\n print(np.array(Val_Y_ori).shape)\n Val_Y = keras.utils.to_categorical(Val_Y_ori, num_classes=num_class)\n #把验证集的one-hot矩阵拼出来\n print(np.array(Val_Y).shape)\n train_index_ = np.delete(np.arange(0, len(Train_Y_ori)), val_index)\n #在训练集中去掉验证集\n# print(train_index_)\n print(np.array(train_index_).shape)\n Train_X = Train_X[train_index_]\n print(np.array(Train_X).shape)\n Train_Y_ori = Train_Y_ori[train_index_]\n Train_Y = keras.utils.to_categorical(Train_Y_ori, num_classes=num_class)\n return Train_X, Train_Y, Train_Y_ori, Val_X, Val_Y, Val_Y_ori\n\n\ndef training(one_fold, X_unlabeled, seed, prop, num_filter_ae_cls_all, epochs_ae_cls=20):\n #each time transfer a dataset_fold to here with All unlabeled data\n Train_X = one_fold[0]\n Train_Y_ori = one_fold[1]\n # ori means its classification\n random.seed(seed)\n np.random.seed(seed)\n random_sample = np.random.choice(len(Train_X), size=round(0.5*len(Train_X)), replace=False, p=None)\n print('random_sample')\n# print(random_sample)\n# print(Train_X)\n Train_X1 = Train_X[random_sample]\n\n #This random_sample generate a (220,) matrix which will random make a \n #(220,1,248,4)matrix from (441,1,248,4) if we use the statement A = A[random_sample]\n\n # print(np.array(Train_X1).shape)\n # print(np.array(random_sample).shape)\n\n Train_Y_ori = Train_Y_ori[random_sample]\n #now it's only 220x\n #将验证集从训练集中抽出来\n Train_X, Train_Y, Train_Y_ori, Val_X, Val_Y, Val_Y_ori = train_val_split(Train_X, Train_Y_ori)\n #将验证集从训练集中单独抽出来\n Test_X = one_fold[2]\n # print(len(Test_X))\n # 110\n Test_Y = one_fold[3]\n # print(len(Test_Y))\n # 110\n Test_Y_ori = one_fold[4]\n # print(len(Test_Y_ori))\n # print(Test_Y_ori)\n # sys.exit(0)\n random_sample = np.random.choice(len(X_unlabeled), size=round(prop * len(X_unlabeled)), replace=False, p=None)\n # print(len(X_unlabeled))\n # 4310\n X_unlabeled = X_unlabeled[random_sample]\n # print(len(X_unlabeled))\n # 646\n # sys.exit(0)\n #随机选择指定量的无标签数据\n Train_X_Comb = X_unlabeled\n #别忘了写计网的前端\n input_size = list(np.shape(Test_X)[1:])\n #input_size是第一个维度之后的维度\n #np.shape() 和np.array().shape的功能差不多\n # Various sets of number of filters for ensemble. If choose one set, no ensemble is implemented.\n num_filter_ae_cls_all = [[32, 32], [32, 32, 64], [32, 32, 64, 64], [32, 32, 64, 64, 128],\n [32, 32, 64, 64, 128, 128], [32, 32, 64, 64, 128, 128], [32, 32, 64, 64, 128, 128]]\n num_filter_ae_cls_all = [[32, 32, 64, 64, 128, 128]]\n unsupervised_encoded = []\n\n # This for loop is only for implementing ensemble\n # 以下loop实现了ensemble(你懂得)\n for z in range(len(num_filter_ae_cls_all)):\n # Change the following seed to None only for Ensemble.\n tf.reset_default_graph() # Used for ensemble\n with tf.Session() as sess:\n input_labeled = tf.placeholder(dtype=tf.float32, shape=[None] + input_size, name='input_labeled')\n\n num_filter_ae_cls = num_filter_ae_cls_all[z]\n #此处配置semi_supervised内容，可以新增unsupervised内容来进行无监督启发式训练。\n # loss_ae, loss_cls, accuracy_cls, train_op_ae, train_op_cls, classifier_output, dense, train_op, total_loss = semi_supervised(\n # input_labeled=input_labeled, input_combined=input_combined, true_label=true_label, alpha=alpha,\n # beta=beta, num_class=num_class, latent_dim=latent_dim, num_filter_ae_cls=num_filter_ae_cls,\n # num_filter_cls=num_filter_cls, num_dense=num_dense, input_size=input_size)\n # 配置AE模型\n loss_AE_label, latent , train_op_ae_label = unsupervised(\n input_labeled = input_labeled, num_class = num_class, latent_dim = latent_dim, num_filter_ae_cls = num_filter_ae_cls,\n num_dense = num_dense, input_size = input_size\n )\n \n sess.run(tf.global_variables_initializer())\n #初始化\n saver = tf.train.Saver(max_to_keep=20)\n #模型保存\n # Train_X, Train_Y = ensemble_train_set(orig_Train_X, orig_Train_Y)\n val_accuracy = {-2: 0, -1: 0}\n val_loss = {-2: 10, -1: 10}\n # 对val_loss进行初始化\n num_batches = len(Train_X_Comb) // batch_size\n # alfa_val1 = [0.0, 0.0, 1.0, 1.0, 1.0]\n # beta_val1 = [1.0, 1.0, 0.1, 0.1, 0.1]\n alfa_val = 1 ## 0\n beta_val = 1\n change_to_ae = 1 # the value defines that algorithm is ready to change to joint ae-cls\n change_times = 0 # No. of times change from cls to ae-cls, which is 2 for this training strategy\n third_step = 0\n for k in range(epochs_ae_cls):\n # alfa_val = alfa_val1[k]\n # beta_val = beta_val1[k]\n\n #beta_val = min(((1 - 0.1) / (-epochs_ae_cls)) * k + 1, 0.1) ##\n #alfa_val = max(((1.5 - 1) / (epochs_ae_cls)) * k + 1, 1.5)\n\n x_combined_index = get_combined_index(train_x_comb=Train_X_Comb)\n # print(x_combined_index)\n # 646\n # print(len(x_combined_index))\n x_labeled_index = get_labeled_index(train_x_comb=Train_X_Comb, train_x=Train_X)\n # print(x_labeled_index)\n # 646\n # print(len(x_labeled_index))\n for i in range(num_batches):\n # Train_X_comb=646 batch_size=100 num_batches = (Train_X_comb // batch_size = 6)\n unlab_index_range = x_combined_index[i * batch_size: (i + 1) * batch_size]\n # print(unlab_index_range)\n # print(len(unlab_index_range))\\\n # print(np.array(unlab_index_range).shape)\n # (100,)\n # 100\n # x_combined_index就是unlab_index_range\n lab_index_range = x_labeled_index[i * batch_size: (i + 1) * batch_size]\n # label的index range 格式为(100,)\n # print(np.array(Train_X_Comb).shape)\n # (646,1, 248 ,4)\n # print(Train_X_Comb)\n # print(len(Train_X_Comb))\n X_ae = Train_X_Comb[unlab_index_range]\n # (100,1, 248 , 4)\n #抽100个unlabeled data的Index出来\n # print(X_ae)\n # print(len(X_ae))\n # sys.exit(0)\n X_cls = Train_X[lab_index_range]\n # 抽100个labeled data 数据(input X)的index出来\n Y_cls = Train_Y[lab_index_range]\n # 100个labeled data的label\n loss_ae_, _ = sess.run([loss_AE_label, train_op_ae_label],\n feed_dict={input_labeled: X_cls,})\n # print('Epoch Num {}, Batches Num {}, Loss_AE {}, Loss_cls {}, Accuracy_train {}'.format\n # (k, i, np.round(loss_ae_, 3), np.round(loss_cls_, 3), np.round(accuracy_cls_, 3)))\n # 训练每个batch\n # print(i)\n # 5\n # sys.exit(0)\n unlab_index_range = x_combined_index[(i + 1) * batch_size:]\n lab_index_range = x_labeled_index[(i + 1) * batch_size:]\n X_ae = Train_X_Comb[unlab_index_range]\n X_cls = Train_X[lab_index_range]\n Y_cls = Train_Y[lab_index_range]\n loss_ae_, _ = sess.run([loss_AE_label, train_op_ae_label],\n feed_dict={input_labeled: X_cls,})\n # print('Epoch Num {}, Batches Num {}, Loss_AE {}, Loss_cls {}, Accuracy_train {}'.format\n # (k, i, np.round(loss_ae_, 3), np.round(loss_cls_, 3), np.round(accuracy_cls_, 3)))\n # sys.exit(0)\n\n print('====================================================')\n # def loss_acc_evaluation(Test_X, Test_Y, loss_AE_label, input_labeled, k, sess):\n loss_val = loss_acc_evaluation(Val_X, Val_Y, loss_AE_label, input_labeled, k, sess)\n #使用验证集来验证准确性 取val-batch里几次的平均值作为返回的loss和accuracy\n #loss_val = 1.4179071\n #acc_val = 0.7\n print(val_loss)\n #{ -2:10, -1:10}\n val_loss.update({k: loss_val})\n print(val_loss)\n print({k: loss_val})\n #{ -2:10 , -1:10, 0: 1.2811708}\n #把刚刚算得的accuracy按照 {k:{value}}的形式加到数组上去(update上去)\n print('====================================================')\n saver.save(sess, \"/home/sxz/data/geolife_Data/Conv-Semi-TF-PS/\" + '2/' + str(z) + '/' + str(prop), global_step=k)\n #保存模型\n # save_path = \"/Conv-Semi/\" + str(prop) + '/' + str(k) + \".ckpt\"\n # checkpoint = os.path.join(os.getcwd(), save_path)\n # saver.save(sess, checkpoint)\n # if alfa_val == 1:\n # beta_val += 0.05\n # 找到Max_accuracy\n print(\"Ensemble {}: Val_loss ae+cls Over Epochs {}: \".format(z, val_loss))\n unsupervised_encoded.append(encode_AE_data(Test_X, latent, input_labeled, sess))\n\n # ave_class_posterior = sum(class_posterior) / len(class_posterior)\n # y_pred = np.argmax(ave_class_posterior, axis=1)\n # test_accuracy = accuracy_score(Test_Y_ori, y_pred)\n # #precision = precision_score(Test_Y_ori, y_pred, average='weighted')\n # #recall = recall_score(Test_Y_ori, y_pred, average='weighted')\n # f1_macro = f1_score(Test_Y_ori, y_pred, average='macro')\n # f1_weight = f1_score(Test_Y_ori, y_pred, average='weighted')\n # print('Semi-AE+Cls Test Accuracy of the Ensemble: ', test_accuracy)\n# # print('Confusion Matrix: ', confusion_matrix(Test_Y_ori, y_pred))\n# print(unsupervised_encoded[0])\n# print(np.array(unsupervised_encoded)[0].shape)\n PCA_codearray = unsupervised_encoded[0].reshape(len(Test_X),3968)\n# print(PCA_codearray)\n pca_ = PCA(n_components=2)\n pca_encodeAE = pca_.fit_transform(PCA_codearray)\n # pca_encodeAE.tofile(\"encodeAE.bin\")\n# print(pca_encodeAE)\n# print(pca_encodeAE.dtype)\n # Test_Y_ori.tofile(\"label.bin\")\n# print(Test_Y_ori)\n# print(Test_Y_ori.dtype)\n # sys.exit(0)\n# print(pca_encodeAE.shape)\n x = [i[0] for i in pca_encodeAE]\n y = [i[1] for i in pca_encodeAE]\n# print(x)\n# print(y) \n plt.figure(figsize=[12,12])\n # plt.plot(x, y,'v')\n # plt.show()\n # plt.savefig('test2.png')\n km5 = KMeans(n_clusters=5, init='random',max_iter=300,n_init=10,random_state=0)\n encode_means = km5.fit_predict(pca_encodeAE)\n# print(np.array(encode_means).shape)\n\n return pca_encodeAE, Test_Y_ori\n\ndef training_all_folds(label_proportions, num_filter):\n accuracy = 0\n for i in range(70):\n test_accuracy_fold = [[] for _ in range(len(label_proportions))]\n mean_std_acc = [[] for _ in range(len(label_proportions))]\n test_metrics_fold = [[] for _ in range(len(label_proportions))]\n mean_std_metrics = [[] for _ in range(len(label_proportions))]\n pca_encodeAE_all = []\n label_all = []\n for index, prop in enumerate(label_proportions):\n for i in range(len(kfold_dataset)):\n pca_encodeAE, label = training(kfold_dataset[i], X_unlabeled=X_unlabeled, seed=7, prop=prop, num_filter_ae_cls_all=num_filter)\n if(i == 0):\n pca_encodeAE_all = pca_encodeAE\n label_all = label\n else:\n pca_encodeAE_all = np.vstack((pca_encodeAE_all,pca_encodeAE))\n label_all = np.hstack((label_all,label))\n# print(pca_encodeAE_all)\n# print(np.array(pca_encodeAE_all).shape)\n# print(label_all)\n# print(np.array(label_all).shape)\n pca_encodeAE_all.tofile(\"encodeAE1.bin\")\n label_all.tofile(\"label1.bin\")\n encode_ = np.fromfile(\"encodeAE1.bin\",dtype=np.float32)\n encode_ = encode_.reshape(551,2)\n encode_\n label_ = np.fromfile(\"label1.bin\",dtype = np.int64)\n label_\n km5 = KMeans(n_clusters=5, init='random',max_iter=300,n_init=10,random_state=0)\n encode_means = km5.fit_predict(encode_)\n mini = np.zeros((5), dtype=np.float)\n mini_coodinate = np.zeros((5,2), dtype=np.float)\n mini = [100,100,100,100,100]\n count = 0\n for j in range(5):\n for i in range(len(encode_[encode_means==j])):\n if(mini[j] >(np.sqrt(np.sum(np.square(encode_[encode_means==j][i] - [km5.cluster_centers_[j,0],km5.cluster_centers_[j,1]] ))))):\n mini[j] = (np.sqrt(np.sum(np.square(encode_[encode_means==j][i] - [km5.cluster_centers_[j,0],km5.cluster_centers_[j,1]] ))))\n mini_coodinate[j] = encode_[encode_means==j][i]\n cluster_fake = np.zeros(5,)\n for j in range(5):\n for i in range(len(encode_)):\n if(mini_coodinate[j][0] == encode_[i][0]):\n print(i)\n print(label_[i])\n cluster_fake[j] = label_[i]\n\n label_fake = np.zeros(551,)\n for i in range(5):\n label_fake[encode_means==i] =cluster_fake[i]\n count = 0\n for i in range(len(label_)):\n if(label_fake[i] == label_[i]):\n count = count+1\n print(count)\n print('accuracy for unsupervised model is : {}'.format(count/551))\n accuracy = accuracy + count/551\n\n print('70 times average of accuracy is: {}'.format(accuracy/70)) \n sys.exit(0)\n# end\n\n accuracy_all = np.array(test_accuracy_fold[index])\n mean = np.mean(accuracy_all)\n std = np.std(accuracy_all)\n mean_std_acc[index] = [mean, std]\n metrics_all = np.array(test_metrics_fold[index])\n mean_metrics = np.mean(metrics_all, axis=0)\n std_metrics = np.std(metrics_all, axis=0)\n mean_std_metrics[index] = [mean_metrics, std_metrics]\n for index, prop in enumerate(label_proportions):\n print('All Test Accuracy For Semi-AE+Cls with Prop {} are: {}'.format(prop, test_accuracy_fold[index]))\n print('Semi-AE+Cls test accuracy for prop {}: Mean {}, std {}'.format(prop, mean_std_acc[index][0], mean_std_acc[index][1]))\n print('Semi-AE+Cls test metrics for prop {}: Mean {}, std {}'.format(prop, mean_std_metrics[index][0], mean_std_metrics[index][1]))\n print('\\n')\n return test_accuracy_fold, test_metrics_fold, mean_std_acc, mean_std_metrics\n\nunsupervised_encoded = training_all_folds(\n label_proportions=[0.15], num_filter=[32, 32, 64, 64])\n\n\n","sub_path":"2-temp.py","file_name":"2-temp.py","file_ext":"py","file_size_in_byte":36002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"118983952","text":"from Tkinter import *\r\n# pip install pillow\r\nfrom PIL import Image, ImageTk\r\n#from ttk import Frame\r\nimport time\r\n\r\nclass A:\r\n def __init__(self,master):\r\n #Frame.__init__(self)\r\n master.configure(bg='black')\r\n master.title('Monitor Screen')\r\n #master.pack(fill=BOTH,expand=True)\r\n\r\n # Parent widet for the labels\r\n self.frame = LabelFrame(master)\r\n self.frame.grid(row=0,column=0,sticky=W+E+N+S)\r\n\r\n master.columnconfigure(0,weight=1)\r\n master.rowconfigure(0,weight=1)\r\n\r\n self.frame.columnconfigure(0,weight=1)\r\n self.frame.rowconfigure(1,weight=1)\r\n self.frame.columnconfigure(1,weight=1)\r\n self.frame.rowconfigure(1,weight=1)\r\n self.frame.columnconfigure(2,weight=1)\r\n self.frame.rowconfigure(1,weight=1)\r\n self.frame.columnconfigure(3,weight=1)\r\n self.frame.rowconfigure(1,weight=1)\r\n self.frame.columnconfigure(0,weight=1)\r\n self.frame.rowconfigure(2,weight=1)\r\n self.frame.columnconfigure(1,weight=1)\r\n self.frame.rowconfigure(2,weight=1)\r\n self.frame.columnconfigure(2,weight=1)\r\n self.frame.rowconfigure(2,weight=1)\r\n self.frame.columnconfigure(3,weight=1)\r\n self.frame.rowconfigure(2,weight=1)\r\n \r\n \r\n self.label1=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='1')\r\n self.label2=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='2')\r\n self.label3=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='3')\r\n self.label4=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='4')\r\n self.label5=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='5')\r\n self.label6=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='6')\r\n self.label7=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='7')\r\n self.label8=Label(self.frame,fg=\"white\",font=(\"Times\", 50, \"bold\"), text='8')\r\n\r\n\r\n self.label0=Label(self.frame, text = 'Parking State', fg =\"white\",bg='black',font=(\"Times\", 40, \"bold\") )\r\n self.label0.grid(row=0,columnspan=4,sticky='nswe')\r\n\r\n self.label40=Label(self.frame,fg=\"white\",bg='black',text='Available Slot',font=(\"Times\", 20, \"bold\"))\r\n self.label40.grid(row=3,columnspan=4,sticky='nswe')\r\n \r\n\r\n def excutive(self, master,labelChange, nameString):\r\n if labelChange == 'label1':\r\n self.label1.configure(bg=nameString)\r\n self.label1.grid(row=1, column=0,sticky='nswe',padx=20,pady=20)\r\n #self.label1.update_idletasks()\r\n elif labelChange == 'label2':\r\n self.label2.configure(bg=nameString)\r\n self.label2.grid(row=1, column=1,sticky='nswe',padx=20,pady=20)\r\n #self.label2.update_idletasks()\r\n elif labelChange == 'label3':\r\n self.label3.configure(bg=nameString)\r\n self.label3.grid(row=1, column=2,sticky='nswe',padx=20,pady=20)\r\n #self.label3.update_idletasks()\r\n elif labelChange == 'label4':\r\n self.label4.configure(bg=nameString)\r\n self.label4.grid(row=1, column=3,sticky='nswe',padx=20,pady=20)\r\n #self.label4.update_idletasks()\r\n elif labelChange == 'label5':\r\n self.label5.configure(bg=nameString)\r\n self.label5.grid(row=2, column=0,sticky='nswe',padx=20,pady=20)\r\n #self.label5.update_idletasks()\r\n elif labelChange == 'label6':\r\n self.label6.configure(bg=nameString)\r\n self.label6.grid(row=2, column=1,sticky='nswe',padx=20,pady=20)\r\n #self.label6.update_idletasks()\r\n elif labelChange == 'label7':\r\n self.label7.configure(bg=nameString)\r\n self.label7.grid(row=2, column=2,sticky='nswe',padx=20,pady=20)\r\n #self.label7.update_idletasks()\r\n else:\r\n self.label8.configure(bg=nameString)\r\n self.label8.grid(row=2, column=3,sticky='nswe',padx=20,pady=20)\r\n #self.label8.update_idletasks()\r\n master.update_idletasks()\r\n\r\n def excutiveCount(self,master,count):\r\n self.label40.configure(text = 'Available Slot: ' + str(count),font=(\"Times\", 20, \"bold\") )\r\n self.label40.grid(row=3,columnspan=4,sticky='nswe')\r\n master.update()\r\n \r\n\r\nroot = Tk()\r\ncon = A(root)\r\n\r\ncount = 1\r\n\r\nwhile True:\r\n if count%2 == 0:\r\n con.excutive(root,'label1','red')\r\n con.excutive(root,'label2','dark green')\r\n con.excutive(root,'label3','red')\r\n con.excutive(root,'label4','dark green')\r\n con.excutive(root,'label5','dark green')\r\n con.excutive(root,'label6','red')\r\n con.excutive(root,'label7','dark green')\r\n con.excutive(root,'label8','red')\r\n else:\r\n con.excutive(root,'label1','dark green')\r\n con.excutive(root,'label2','red')\r\n con.excutive(root,'label3','dark green')\r\n con.excutive(root,'label4','red')\r\n con.excutive(root,'label5','red')\r\n con.excutive(root,'label6','dark green')\r\n con.excutive(root,'label7','red')\r\n con.excutive(root,'label8','dark green')\r\n if count == 9:\r\n count = 1\r\n con.excutiveCount(root,count)\r\n count += 1\r\n time.sleep(1)\r\n \r\nroot.mainloop() \r\n\r\n\r\n","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":5332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"273868724","text":"# Assignment 2\r\n# Jaren Hendricks\r\n# 07 March 2014\r\n\r\n# Programme to calculate whether a year is a leap year or not. \r\n\r\nyear = eval(input(\"Enter a year:\\n\"))\r\n\r\nif (year%400== 0) or (year%4==0) and not year%100==0:\r\n print(year,\"is a leap year.\")\r\nelse:\r\n print(year, \"is not a leap year.\")","sub_path":"examples/data/Assignment_2/hndjar002/question1.py","file_name":"question1.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"610513034","text":"import sys\nimport os\nimport fileinput\nimport cv2 as cv\nimport numpy as np\n\nSUFFIX = 'char'\nTMP_DIR = 'tmp'\n\ndef create_tmp_directory():\n if not os.path.exists(TMP_DIR):\n os.makedirs(TMP_DIR)\n\ndef generate_out_filename(file_path, i):\n filename, file_extension = os.path.splitext(file_path)\n filename = filename.split(os.sep)[-1]\n out_filename = './{}/{}.{}-{}{}'.format(TMP_DIR, filename, SUFFIX, i, file_extension)\n return out_filename\n\ndef group_by_proximity(arr, proximity = 6):\n if not arr:\n return []\n left = arr[0]\n right = left\n intervals = [[right]]\n for i in range(1, len(arr)):\n if (arr[i] - right <= proximity):\n right = arr[i]\n intervals[-1].append(right)\n else:\n left = arr[i]\n right = left\n intervals.append([right])\n return intervals\n\ndef show(im):\n cv.imshow('image',im)\n cv.waitKey(0)\n cv.destroyAllWindows()\n\ndef get_color_extracted(im):\n white = cv.inRange(im, np.array([185, 185, 185]), np.array([255, 255, 255]))\n red = cv.inRange(im, np.array([35, 50, 205]), np.array([85, 100, 255]))\n blue = cv.inRange(im, np.array([165, 125, 25]), np.array([240, 175, 75]))\n orange = cv.inRange(im, np.array([29, 121, 225]), np.array([39, 131, 235]))\n return cv.add(cv.add(white, orange), cv.add(red, blue))\n\ndef get_red_blue_extracted(im):\n red = cv.inRange(im, np.array([30, 45, 195]), np.array([90, 105, 255]))\n blue = cv.inRange(im, np.array([165, 125, 25]), np.array([240, 175, 75]))\n orange = cv.inRange(im, np.array([20, 110, 200]), np.array([50, 140, 255]))\n return cv.add(red, cv.add(blue, orange))\n\ndef get_white_extracted(im):\n return cv.inRange(im, np.array([185, 185, 185]), np.array([255, 255, 255]))\n\ndef bound_characters(image_path, im, intervals):\n im_copy = im.copy()\n for interval in intervals:\n # im_copy[0:-1,interval[0]-2:interval[1]+2] = [255, 0, 255]\n middle = (interval[0] + interval[1])//2\n im_copy[0:-1, middle:middle+1] = [0, 223, 255]\n out_filename = generate_out_filename(image_path, 0)\n cv.imwrite(out_filename, im_copy)\n return [out_filename]\n\ndef crop_characters(image_path, im, intervals):\n char_image_paths = []\n i = 0\n for interval in intervals:\n out_filename = generate_out_filename(image_path, i)\n char = im[:, max(interval[0]-2, 0):min(interval[1]+2, im.shape[1] - 1)]\n cv.imwrite(out_filename, char)\n i += 1\n char_image_paths.append(out_filename)\n return char_image_paths\n\ndef action(image_path):\n if not image_path:\n return []\n im = cv.imread(image_path)\n color_extracted = get_color_extracted(im)\n\n active_columns = [column for column in range(im.shape[1]) if np.count_nonzero(color_extracted[:,column]) > 0]\n groups = group_by_proximity(active_columns, proximity=1)\n intervals = [(group[0], group[-1]) for group in groups if group[-1] - group[0] < 20 ]\n\n color_extracted_filename = generate_out_filename(image_path, 1)\n cv.imwrite(color_extracted_filename, color_extracted)\n # return bound_characters(image_path, im, intervals) + [color_extracted_filename]\n return crop_characters(image_path, color_extracted, intervals)\n\ninputs = []\nfor line in fileinput.input():\n inputs.append(line.strip())\n\nl = map(action, inputs)\nprint('\\n'.join([item for sublist in l for item in sublist]))\n","sub_path":"characters.py","file_name":"characters.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"59885082","text":"from selenium import webdriver\nimport time\nimport csv\nimport re\nfrom copy import deepcopy\n\nclass AmazonScraper:\n def __init__(self):\n self.driver = webdriver.Firefox()\n self.books = {}\n\n self.field_names = ['title', 'author', 'book_format', 'old_price', 'curr_price', 'pages', 'publisher', 'publication_date', 'Language', 'ISBN-10', 'ISBN-13', 'Product Dimensions', 'Shipping Weight', 'Amazon Best Sellers Rank', 'ASIN', 'link']\n self.defaults = {k:'N/A' for k in self.field_names}\n\n self.write_header_to_csv()\n\n self.already_done = {}\n with open('amazon_books.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n books = list(csv_reader)\n header = books[0]\n for row in books[1:]:\n link = row[-1]\n self.already_done[link] = {h: v for h, v in zip(header, row)}\n print(len(self.already_done))\n\n def extract_first_page_books(self):\n curr_page_books = {}\n for book in self.driver.find_elements_by_xpath(\"//div[@id='mainResults']/ul/li\"):\n header = book.find_element_by_class_name(\"s-color-twister-title-link\")\n title = header.get_attribute(\"title\")\n link = header.get_attribute(\"href\").split('ref=')[0]\n author = [t.text for t in book.find_elements_by_class_name(\"a-spacing-none\")][2].lstrip(\"by \")\n\n # Not extract and add duplicate books belonging to different categories\n if link in self.books or link in self.already_done:\n print('Already added')\n continue\n\n curr_page_books[link] = {\n \"link\": link,\n \"title\": title,\n \"author\": author\n }\n return curr_page_books\n \n def extract_next_page_books(self):\n curr_page_books = {}\n for book in self.driver.find_elements_by_class_name(\"s-result-item\"):\n header = book.find_elements_by_class_name(\"sg-row\")[1]\n title = header.find_element_by_class_name(\"a-color-base\").text\n link = header.find_element_by_class_name(\"a-link-normal\").get_attribute(\"href\").split('ref=')[0]\n author = header.find_element_by_class_name(\"a-color-secondary\").text.split(\"|\")[0].lstrip(\"by \")\n\n # Not extract and add duplicate books belonging to different categories\n if link in self.books or link in self.already_done:\n print('Already added')\n continue\n\n curr_page_books[link] = {\n \"link\": link,\n \"title\": title,\n \"author\": author\n }\n return curr_page_books\n \n def get_books_data(self, till_page=3):\n categroy_ids = {\n 'Arts' : '1',\n 'Biographies' : '2',\n 'Sports' : '26',\n 'History' : '9',\n 'Mystery' : '18',\n 'Literary' : '17',\n 'Fiction' : '25'\n }\n\n for topic in categroy_ids:\n categroy_id = categroy_ids[topic]\n print('Extracting books from Topic ', topic)\n page_number = 1\n print('First Page URL')\n print('https://www.amazon.com/s?rh=n%3A283155%2Cn%3A!1000%2Cn%3A' + categroy_id +'&page=' + str(page_number) + '&qid=1554049001')\n print()\n while page_number < till_page:\n try:\n page_url = 'https://www.amazon.com/s?rh=n%3A283155%2Cn%3A!1000%2Cn%3A'+ categroy_id + '&page=' + str(page_number) + '&qid='\n self.driver.get(page_url)\n self.driver.implicitly_wait(5)\n print('Extracting books from Page ', page_number)\n if page_number == 1: curr_books = self.extract_first_page_books()\n else: curr_books = self.extract_next_page_books()\n print(len(curr_books), 'books extracted successfully till now from this page.')\n self.get_books_additional_data(curr_books)\n page_number += 1\n except:\n page_number += 1\n continue\n \n def get_books_additional_data(self, curr_books):\n for book in curr_books:\n try:\n self.books[book] = deepcopy(curr_books[book])\n\n print('Extracting from book ', self.books[book]['title'], '(', self.books[book]['link'], ')')\n self.driver.get(book)\n self.driver.implicitly_wait(5)\n details = self.driver.find_element_by_xpath(\"//table[@id='productDetailsTable']\")\n\n for detail in details.text.split('\\n'):\n if ':' in detail:\n k, v = detail.split(':', 1)\n if k in [\"Paperback\", \"Hardcover\"]:\n self.books[book][\"pages\"] = v.strip('pages').strip()\n continue\n elif k == \"Publisher\":\n res = re.search('(.*)(\\(.*\\)).*', v)\n if res:\n self.books[book][\"publisher\"] = res.group(1).strip()\n self.books[book][\"publication_date\"] = res.group(2).strip('()').strip()\n continue\n elif k == \"Average Customer Review\": continue\n else: self.books[book][k.strip()] = v.strip()\n\n # Find the most famous book format and price\n try:\n books = self.driver.find_element_by_xpath(\"//div[@id='tmmSwatches']/ul\")\n selected_book = books.find_element_by_class_name(\"a-button-selected\").text\n except:\n try:\n books = self.driver.find_element_by_xpath(\"//div[@id='mediaTabsHeadings']/ul\")\n selected_book = books.find_element_by_class_name(\"a-active\").text\n except: continue\n self.books[book]['book_format'], self.books[book]['curr_price'] = selected_book.split('\\n', 1)\n\n # Find old price\n try: book_group = self.driver.find_element_by_xpath(\"//div[@id='buyBoxInner']\")\n except:\n try: book_group = self.driver.find_element_by_xpath(\"//div[@id='mediaNoAccordion']\")\n except: continue\n\n try:\n old_price_element = book_group.find_element_by_class_name(\"a-text-strike\")\n if old_price_element: self.books[book]['old_price'] = old_price_element.text\n except:\n self.books[book]['old_price'] = self.books[book]['curr_price']\n continue\n\n # Write to CSV with pre-filled for missing fields\n kv = self.defaults.copy()\n kv.update(self.books[book])\n with open('amazon_books.csv', mode='a') as csv_file:\n self.writer = csv.DictWriter(csv_file, fieldnames=self.field_names, extrasaction='ignore')\n self.writer.writerow(kv)\n except: continue\n\n\n def write_header_to_csv(self):\n with open('amazon_books.csv', mode='w') as csv_file:\n self.writer = csv.DictWriter(csv_file, fieldnames=self.field_names)\n self.writer.writeheader()\n\n def write_to_csv(self):\n with open('amazon_books.csv', mode='w') as csv_file:\n field_names = ['title', 'author', 'link']\n writer = csv.DictWriter(csv_file, fieldnames=field_names)\n writer.writeheader()\n for b in self.books:\n writer.writerow(self.books[b])\n\n def __del__(self):\n self.driver.quit()\n\nAS = AmazonScraper()\nAS.get_books_data(100)\n# AS.write_to_csv()\n# AS.get_books_additional_data()\n","sub_path":"Stage2/Amazon/amazon_scraper.py","file_name":"amazon_scraper.py","file_ext":"py","file_size_in_byte":7892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"14003134","text":"from random import choice, randint\n\n\nclass Markov():\n\n def __init__(self, fname):\n self.chain = self.process_file(fname)\n self.starts = None\n\n def process_file(self, fname):\n \"\"\" This should be dynamic; changing with your corpus as necessary\"\"\"\n\n chain = {}\n\n with open(fname, 'r') as f:\n\n # iterate over lines\n for line in f:\n line = line.strip()\n line = line.split(' ')\n prefix = ('', '')\n\n # iterate over words\n for word in line:\n if word != '':\n\n # Add prefix to chain\n if prefix not in chain.keys():\n chain[prefix] = {'suffixes': [], 'end': False}\n\n # Add next word to suffix list\n if word not in chain[prefix]['suffixes']:\n chain[prefix]['suffixes'].append(word)\n\n # Update prefix i.e: (quick, brown) -> (brown, fox)\n prefix = (prefix[1], word)\n\n # Add final prefix to chain\n if prefix not in chain:\n chain[prefix] = {'suffixes': [], 'end': False}\n\n # Add an end flag to ending prefix\n chain[prefix]['end'] = True\n\n return chain\n\n def generate(self):\n chain = self.chain\n prefix = choice(list(chain.keys()))\n text = prefix[0]\n\n while True: # Change to limit length\n text += ' ' + prefix[1]\n\n if len(chain[prefix]['suffixes']) == 0:\n return text\n\n prefix = (prefix[1], choice(chain[prefix]['suffixes']))\n\n def chain_to_txt(self):\n \"\"\" Save the chain to a text file to reduce overhead\"\"\"\n pass\n\n\nif __name__ == '__main__':\n mark = Markov('discord.txt')\n text = mark.generate()\n print(text)\n","sub_path":"markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"618223097","text":"import pickle\nimport os\n\nimport nltk\n\n\n\nclass POSTagger(object):\n '''\n classdocs\n '''\n\n _instance = None\n\n def __new__(cls, *args, **kwargs):\n if not cls._instance:\n cls._instance = super(POSTagger, cls).__new__(cls, *args, **kwargs)\n return cls._instance\n\n\n\n def __init__(self):\n #self.tagger = pickle.load(open(\"../chunkers/treeban_brill_aubt.pickle\"))\n chunkers_path = \"\"\n\n try:\n chunkers_path = os.environ['PYTHON_NLP_CHUNKERS_HOME']\n\n except KeyError:\n chunkers_path = \"/Users/ralph_brooks/PycharmProjects/snadjango/nlp/chunkers\"\n\n\n\n\n self.tagger = pickle.load(open(chunkers_path + \"/treebank_brill_aubt.pickle\"))\n\n\n def tag_text_with_POS(self, str):\n\n str_tags = self.tagger.tag(nltk.word_tokenize(str))\n return str_tags\n\n","sub_path":"nlp/tagging/POSTagger.py","file_name":"POSTagger.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"497752418","text":"import pandas as pd\nimport numpy as np\nimport os\nimport cv2\nimport csv\nimport random\nimport math\nfrom shapely.geometry import Polygon\nfrom itertools import compress\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\nfrom sklearn.model_selection import train_test_split\n\nimport tensorflow as tf\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.layers import Conv2D, Input, UpSampling2D, BatchNormalization, Concatenate, Input, Dense, Flatten, \\\n Activation\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.utils import plot_model\n\n\n# loading trained detection model\n\n# class feature_merging_branch(tf.keras.layers.Layer):\n# def __init__(self,name='f_m_b',**kwargs):\n# super().__init__(name)\n# def build(self,input_shape1):\n# #print(input_shape1)\n# # input_shape1=input_list[0]\n# # input_shape2=input_list[1]\n# self.upsample=UpSampling2D(2,interpolation='bilinear',data_format='channels_last')\n# self.concat=Concatenate(axis=3)\n# self.conv1=Conv2D(input_shape1[-1]//2,1,padding='same',kernel_initializer=tf.keras.initializers.GlorotNormal(seed=12))\n# self.conv2=Conv2D(input_shape1[-1]//2,3,padding='same',kernel_initializer=tf.keras.initializers.GlorotNormal(seed=12))\n# self.bn1=BatchNormalization()\n# self.bn2=BatchNormalization()\n# self.act1=Activation('relu')\n# self.act2=Activation('relu')\n# def call(self,input_1,input_2):\n# # input_1=input_list[0]\n# # input_2=input_list[1]\n# up1=self.upsample(input_1)\n# concat1=self.concat([input_2,up1])\n# conv1=self.conv1(concat1)\n# bn1=self.bn1(conv1)\n# act1=self.act1(bn1)\n# conv2=self.conv2(act1)\n# bn2=self.bn2(conv2)\n# act2=self.act2(bn2)\n# #onv2=self.bn(conv2)\n# return act2\n\n\nclass feature_merging_branch(tf.keras.layers.Layer):\n def __init__(self, name='f_m_b', kernel=128, **kwargs):\n super().__init__(name)\n self.l_name = name\n self.kernel = kernel\n\n def build(self, input_shape1):\n print(input_shape1, 'input shape1')\n self.conv1 = Conv2D(self.kernel, 1, padding='same', kernel_regularizer=tf.keras.regularizers.l2(1e-5))\n self.conv2 = Conv2D(self.kernel, 3, padding='same', kernel_regularizer=tf.keras.regularizers.l2(1e-5))\n self.bn1 = BatchNormalization()\n self.bn2 = BatchNormalization()\n\n def call(self, input_1):\n conv1 = self.conv1(input_1)\n bn1 = self.bn1(conv1)\n act1 = Activation('relu')(bn1)\n conv2 = self.conv2(act1)\n bn2 = self.bn2(conv2)\n act2 = Activation('relu')(bn2)\n # onv2=self.bn(conv2)\n return act2\n\n def get_config(self):\n config = super().get_config().copy()\n config.update({\n 'name': self.l_name,\n 'kernel': self.kernel,\n })\n return config\n\n # class feature_merging_branch(tf.keras.layers.Layer):\n\n\n# def __init__(self,name='f_m_b',**kwargs):\n# super().__init__(name)\n# def build(self,input_shape1):\n# #print(input_shape1)\n# # input_shape1=input_list[0]\n# # input_shape2=input_list[1]\n# self.upsample=UpSampling2D(2,interpolation='bilinear',data_format='channels_last')\n# self.concat=Concatenate(axis=3)\n# self.conv1=Conv2D(input_shape1[-1]//2,1,padding='same',activation='relu',kernel_initializer=tf.keras.initializers.GlorotNormal(seed=12))\n# self.conv2=Conv2D(input_shape1[-1]//2,3,padding='same',activation='relu',kernel_initializer=tf.keras.initializers.GlorotNormal(seed=12))\n# self.bn=BatchNormalization()\n# def call(self,input_1,input_2):\n# # input_1=input_list[0]\n# # input_2=input_list[1]\n# up1=self.upsample(input_1)\n# concat1=self.concat([input_2,up1])\n# conv1=self.conv1(concat1)\n# conv2=self.conv2(conv1)\n# conv2=self.bn(conv2)\n# return conv2\n\n# computing dice loss\ndef dice_loss(y_true_class, y_pred_class, training_mask):\n # we are taking intersecting pixels ignoring pixels in training masks\n intersection = tf.reduce_sum(y_true_class * y_pred_class * training_mask)\n # calculating sum of total pixels where pixel is text\n union = tf.reduce_sum(y_true_class * training_mask) + tf.reduce_sum(y_pred_class * training_mask)\n eps = 10 ** -7\n # calculating dice loss\n dice_loss_ = 1 - (2 * (intersection / (union + eps)))\n return dice_loss_\n\n\nclass detection_loss(tf.keras.losses.Loss):\n def __init__(self, reduction=tf.keras.losses.Reduction.AUTO, name=None):\n super().__init__(reduction=reduction, name=name)\n\n def call(self, y_true, y_pred):\n y_true_score = y_true[:, :, :, 0]\n y_pred_score = y_pred[:, :, :, 0]\n y_true_geo = y_true[:, :, :, 1:6]\n y_pred_geo = y_pred[:, :, :, 1:6]\n training_mask = y_true[:, :, :, 6]\n loss_for_score = dice_loss(y_true_score, y_pred_score, training_mask)\n loss_for_score *= 0.01\n # print(tf.math.equal(tf.split(value=y_true_geo, num_or_size_splits=5, axis=3)[0],45))\n d_t_gt, d_r_gt, d_b_gt, d_l_gt, angle_gt = tf.split(value=y_true_geo, num_or_size_splits=5, axis=3)\n d_t_pd, d_r_pd, d_b_pd, d_l_pd, angle_pd = tf.split(value=y_pred_geo, num_or_size_splits=5, axis=3)\n # calculating area regarding each pixels with the help of hight and width\n area_pred = (d_t_pd + d_b_pd) * (d_r_pd + d_l_pd)\n # calculating area of original text area\n area_real = (d_t_gt + d_b_gt) * (d_r_gt + d_l_gt)\n # calculating intersected rectangle width\n width_intersected_rectangle = tf.minimum(d_r_gt, d_r_pd) + tf.minimum(d_l_gt, d_l_pd)\n # calculating intersected rectangle height\n height_intersected_rectangle = tf.minimum(d_t_gt, d_t_pd) + tf.minimum(d_b_gt, d_b_pd)\n # intersected area by multiplying intersected height snd intersected width\n intersected_area = width_intersected_rectangle * height_intersected_rectangle\n # total area which belongs to both actual text and the predicted text\n union_area = area_pred + area_real - intersected_area\n # calculating iou\n loss_iou = -tf.math.log((intersected_area + 1) / (union_area + 1))\n # calculating angle loss\n angle_loss = 1 - tf.cos(angle_pd - angle_gt)\n final_loss = (loss_iou) + (20 * angle_loss)\n # print(final_loss.shape)\n # final_loss=tf.squeeze(final_loss,axis=3)\n return 100 * (tf.reduce_mean(final_loss * y_true_score * training_mask) + (loss_for_score))\n\n\n# detectors = tf.keras.models.load_model('detector_model.h5',\n# custom_objects={'feature_merging_branch': feature_merging_branch,\n# 'detection_loss': detection_loss})\n\n\ndef ctc_loss(y_true, y_pred):\n # https://stackoverflow.com/questions/64321779/how-to-use-tf-ctc-loss-with-variable-length-features-and-labels\n label_length = tf.math.count_nonzero(y_true, axis=-1, keepdims=True)\n return tf.keras.backend.ctc_batch_cost(y_true, y_pred, np.ones((32, 1), 'int32') * 64, label_length)\n\n\n# recognizer = tf.keras.models.load_model('best_recognizer.h5', custom_objects={'ctc_loss': ctc_loss})\n\n# for decoding predicted output from recognizer we are loading our saved tokenizer during training\nimport pickle\nwith open('tokenizer.pickle', 'rb') as handle:\n tokenizer = pickle.load(handle)\n\n\n#\n# https://github.com/Pay20Y/FOTS_TF\n# https://github.com/yu20103983/FOTS\n# https://github.com/Masao-Taketani/FOTS_OCR\ndef restore_rectangle_rbox(origin, geometry):\n ''' Resotre rectangle tbox'''\n # print(geometry.shape)\n d = geometry[:, :4]\n # print(d.shape)\n angle = geometry[:, 4]\n # for angle > 0\n # print(angle)\n # print((angle>=0).shape)\n # print(origin)\n origin_0 = origin[angle >= 0]\n # print(origin_0.shape)\n d_0 = d[angle >= 0]\n # print(d_0.shape)\n # print(d_0)\n angle_0 = angle[angle >= 0]\n # print(angle_0.shape)\n # print(-d_0[:, 0] - d_0[:, 2])\n if origin_0.shape[0] > 0:\n p = np.array([np.zeros(d_0.shape[0]), -d_0[:, 0] - d_0[:, 2],\n d_0[:, 1] + d_0[:, 3], -d_0[:, 0] - d_0[:, 2],\n d_0[:, 1] + d_0[:, 3], np.zeros(d_0.shape[0]),\n np.zeros(d_0.shape[0]), np.zeros(d_0.shape[0]),\n d_0[:, 3], -d_0[:, 2]])\n # print(p.shape)\n p = p.transpose((1, 0)).reshape((-1, 5, 2)) # N*5*2\n # print(p.shape)\n\n rotate_matrix_x = np.array([np.cos(angle_0), np.sin(angle_0)]).transpose((1, 0))\n rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1)) # N*5*2\n\n rotate_matrix_y = np.array([-np.sin(angle_0), np.cos(angle_0)]).transpose((1, 0))\n rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))\n\n p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis] # N*5*1\n # print(p_rotate_x)\n p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis] # N*5*1\n\n p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2) # N*5*2\n\n p3_in_origin = origin_0 - p_rotate[:, 4, :]\n new_p0 = p_rotate[:, 0, :] + p3_in_origin # N*2\n new_p1 = p_rotate[:, 1, :] + p3_in_origin\n new_p2 = p_rotate[:, 2, :] + p3_in_origin\n new_p3 = p_rotate[:, 3, :] + p3_in_origin\n\n new_p_0 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],\n new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1) # N*4*2\n # print(origin_0)\n # print(new_p_0)\n else:\n new_p_0 = np.zeros((0, 4, 2))\n # for angle < 0\n origin_1 = origin[angle < 0]\n d_1 = d[angle < 0]\n angle_1 = angle[angle < 0]\n if origin_1.shape[0] > 0:\n p = np.array([-d_1[:, 1] - d_1[:, 3], -d_1[:, 0] - d_1[:, 2],\n np.zeros(d_1.shape[0]), -d_1[:, 0] - d_1[:, 2],\n np.zeros(d_1.shape[0]), np.zeros(d_1.shape[0]),\n -d_1[:, 1] - d_1[:, 3], np.zeros(d_1.shape[0]),\n -d_1[:, 1], -d_1[:, 2]])\n p = p.transpose((1, 0)).reshape((-1, 5, 2)) # N*5*2\n\n rotate_matrix_x = np.array([np.cos(-angle_1), -np.sin(-angle_1)]).transpose((1, 0))\n rotate_matrix_x = np.repeat(rotate_matrix_x, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1)) # N*5*2\n\n rotate_matrix_y = np.array([np.sin(-angle_1), np.cos(-angle_1)]).transpose((1, 0))\n rotate_matrix_y = np.repeat(rotate_matrix_y, 5, axis=1).reshape(-1, 2, 5).transpose((0, 2, 1))\n\n p_rotate_x = np.sum(rotate_matrix_x * p, axis=2)[:, :, np.newaxis] # N*5*1\n p_rotate_y = np.sum(rotate_matrix_y * p, axis=2)[:, :, np.newaxis] # N*5*1\n\n p_rotate = np.concatenate([p_rotate_x, p_rotate_y], axis=2) # N*5*2\n\n p3_in_origin = origin_1 - p_rotate[:, 4, :]\n new_p0 = p_rotate[:, 0, :] + p3_in_origin # N*2\n new_p1 = p_rotate[:, 1, :] + p3_in_origin\n new_p2 = p_rotate[:, 2, :] + p3_in_origin\n new_p3 = p_rotate[:, 3, :] + p3_in_origin\n\n new_p_1 = np.concatenate([new_p0[:, np.newaxis, :], new_p1[:, np.newaxis, :],\n new_p2[:, np.newaxis, :], new_p3[:, np.newaxis, :]], axis=1) # N*4*2\n else:\n new_p_1 = np.zeros((0, 4, 2))\n return np.concatenate([new_p_0, new_p_1])\n\n\ndef restore_rectangle(origin, geometry):\n return restore_rectangle_rbox(origin, geometry)\n\n\ndef getRotateRect(box):\n rect = cv2.minAreaRect(box)\n\n angle = rect[2] # angle = [-90, 0)\n if angle < -45:\n rect = (rect[0], (rect[1][0], rect[1][1]), rect[2])\n angle += 90\n size = (rect[1][1], rect[1][0])\n else:\n rect = (rect[0], (rect[1][0], rect[1][1]), rect[2])\n size = rect[1]\n\n box_ = cv2.boxPoints(rect)\n return np.concatenate([rect[0], size]), angle, box_\n\n\ndef sort_rectangle(poly):\n # sort the four coordinates of the polygon, points in poly should be sorted clockwise\n # First find the lowest point\n p_lowest = np.argmax(poly[:, 1])\n if np.count_nonzero(poly[:, 1] == poly[p_lowest, 1]) == 2:\n # 底边平行于X轴, 那么p0为左上角 - if the bottom line is parallel to x-axis, then p0 must be the upper-left corner\n p0_index = np.argmin(np.sum(poly, axis=1))\n p1_index = (p0_index + 1) % 4\n p2_index = (p0_index + 2) % 4\n p3_index = (p0_index + 3) % 4\n return poly[[p0_index, p1_index, p2_index, p3_index]], 0.\n else:\n # 找到最低点右边的点 - find the point that sits right to the lowest point\n p_lowest_right = (p_lowest - 1) % 4\n p_lowest_left = (p_lowest + 1) % 4\n angle = np.arctan(\n -(poly[p_lowest][1] - poly[p_lowest_right][1]) / (poly[p_lowest][0] - poly[p_lowest_right][0]))\n # assert angle > 0\n if angle <= 0:\n print(angle, poly[p_lowest], poly[p_lowest_right])\n if angle / np.pi * 180 > 45:\n # 这个点为p2 - this point is p2\n p2_index = p_lowest\n p1_index = (p2_index - 1) % 4\n p0_index = (p2_index - 2) % 4\n p3_index = (p2_index + 1) % 4\n return poly[[p0_index, p1_index, p2_index, p3_index]], -(np.pi / 2 - angle)\n else:\n # 这个点��p3 - this point is p3\n p3_index = p_lowest\n p0_index = (p3_index + 1) % 4\n p1_index = (p3_index + 2) % 4\n p2_index = (p3_index + 3) % 4\n return poly[[p0_index, p1_index, p2_index, p3_index]], angle\n\n\ndef generate_roiRotatePara(box, angle, expand_w=60):\n '''Generate all ROI Parameterts'''\n # print(box)\n p0_rect, p1_rect, p2_rect, p3_rect = box\n cxy = (p0_rect + p2_rect) / 2.\n size = np.array([np.linalg.norm(p0_rect - p1_rect), np.linalg.norm(p0_rect - p3_rect)])\n rrect = np.concatenate([cxy, size])\n\n box = np.array(box)\n\n points = np.array(box, dtype=np.int32)\n xmin = np.min(points[:, 0])\n xmax = np.max(points[:, 0])\n ymin = np.min(points[:, 1])\n ymax = np.max(points[:, 1])\n bbox = np.array([xmin, ymin, xmax, ymax])\n if np.any(bbox < -expand_w):\n return None\n\n rrect[:2] -= bbox[:2]\n rrect[:2] -= rrect[2:] / 2\n rrect[2:] += rrect[:2]\n\n bbox[2:] -= bbox[:2]\n\n rrect[::2] = np.clip(rrect[::2], 0, bbox[2])\n rrect[1::2] = np.clip(rrect[1::2], 0, bbox[3])\n rrect[2:] -= rrect[:2]\n\n return bbox.astype(np.int32), rrect.astype(np.int32), - angle\n\n\ndef restore_roiRotatePara(box):\n rectange, rotate_angle = sort_rectangle(box)\n return generate_roiRotatePara(rectange, rotate_angle)\n\n\n### this block of code is for selecting those bounding boxes which have intersected more\n\n# These Are Function that will be used while converting geo_maps to score_maps\n# Codes Taken from\n# https://github.com/Pay20Y/FOTS_TF\n# https://github.com/yu20103983/FOTS\n# https://github.com/Masao-Taketani/FOTS_OCR\n\nimport time\n\n\ndef sort_poly(p):\n min_axis = np.argmin(np.sum(p, axis=1))\n p = p[[min_axis, (min_axis + 1) % 4, (min_axis + 2) % 4, (min_axis + 3) % 4]]\n if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):\n return p\n else:\n return p[[0, 3, 2, 1]]\n\n\ndef intersection(g, p):\n g = Polygon(g[:8].reshape((4, 2)))\n p = Polygon(p[:8].reshape((4, 2)))\n if not g.is_valid or not p.is_valid:\n return 0\n inter = Polygon(g).intersection(Polygon(p)).area\n union = g.area + p.area - inter\n if union == 0:\n return 0\n else:\n return inter / union\n\n\ndef weighted_merge(g, p):\n g[:8] = (g[8] * g[:8] + p[8] * p[:8]) / (g[8] + p[8])\n g[8] = (g[8] + p[8])\n return g\n\n\ndef standard_nms(S, thres):\n order = np.argsort(S[:, 8])[::-1]\n keep = []\n while order.size > 0:\n i = order[0]\n keep.append(i)\n ovr = np.array([intersection(S[i], S[t]) for t in order[1:]])\n\n inds = np.where(ovr <= thres)[0]\n order = order[inds + 1]\n\n return S[keep]\n\n\ndef nms_locality(polys, thres=0.3):\n '''\n :param polys: a N*9 numpy array. first 8 coordinates, then prob\n :return: boxes after nms\n '''\n S = []\n p = None\n for g in polys:\n if p is not None and intersection(g, p) > thres:\n p = weighted_merge(g, p)\n else:\n if p is not None:\n S.append(p)\n p = g\n if p is not None:\n S.append(p)\n\n if len(S) == 0:\n return np.array([])\n return standard_nms(np.array(S), thres)\n\n\n# pipeline\n\n# This is final Inference function used for complete FOTS pipeline\ndetectors = tf.lite.Interpreter(model_path=\"detection.tflite\")\ndetectors.allocate_tensors()\n\ninput_details_detector = detectors.get_input_details()\noutput_details_detector = detectors.get_output_details()\n\n\n\nrecognizer = tf.lite.Interpreter(model_path=\"recognizer.tflite\")\nrecognizer.allocate_tensors()\n\ninput_details_recognizer = recognizer.get_input_details()\noutput_details_recognizer = recognizer.get_output_details()\n\n\n# This is final Inference function used for complete FOTS pipeline\ndef inferencePipeline_lite(img):\n '''This function is main complete pipeline of our Model'''\n start_time = time.time()\n\n # 1.Text Detection\n img = cv2.resize(img, dsize=(512, 512), interpolation=cv2.INTER_AREA)\n detectors.set_tensor(input_details_detector[0]['index'], np.expand_dims(img, axis=0).astype(np.float32))\n detectors.invoke()\n ii = detectors.get_tensor(output_details_detector[0]['index'])\n score_map = ii[0][:, :, 0]\n\n geo_map = ii[0][:, :, 1:]\n\n # print(score_map.shape)\n # print(geo_map.shape)\n # print(geo_map)\n for ind in [0, 1, 2, 3, 4]:\n geo_map[:, :, ind] *= score_map\n\n # print(geo_map.shape)\n # print(geo_map)\n\n # 2.ROI Rotate\n score_map_thresh = 0.5\n box_thresh = 0.3\n nms_thres = 0.1\n if len(score_map.shape) == 4:\n score_map = score_map[0, :, :, 0]\n geo_map = geo_map[0, :, :, :]\n # filter the score map\n xy_text = np.argwhere(score_map > score_map_thresh)\n\n # print(xy_text)\n # sort the text boxes via the y axis\n xy_text = xy_text[np.argsort(xy_text[:, 0])]\n # print(xy_text)\n\n # print(xy_text[:, ::-1])\n # restore\n text_box_restored = restore_rectangle(xy_text[:, ::-1], geo_map[xy_text[:, 0], xy_text[:, 1], :]) # N*4*2\n # print('text_box_restored',text_box_restored.shape)\n boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)\n boxes[:, :8] = text_box_restored.reshape((-1, 8))\n boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]\n # print(boxes)\n boxes = nms_locality(boxes.astype(np.float64), nms_thres)\n # boxes = cv2.dnn.NMSBoxes(boxes, score_map, 0.3, (0.1))\n print(boxes.shape)\n # print(boxes)\n\n res = []\n result = []\n if len(boxes) > 0:\n for box in boxes:\n box_ = box[:8].reshape((4, 2))\n if np.linalg.norm(box_[0] - box_[1]) < 8 or np.linalg.norm(box_[3] - box_[0]) < 8:\n continue\n result.append(box_)\n res.append(np.array(result, np.float32))\n # print(res)\n box_index = []\n brotateParas = []\n filter_bsharedFeatures = []\n for i in range(len(res)):\n rotateParas = []\n rboxes = res[i]\n txt = []\n for j, rbox in enumerate(rboxes):\n # print(rbox)\n para = restore_roiRotatePara(rbox)\n # break\n if para and min(para[1][2:]) > 8:\n rotateParas.append(para)\n box_index.append((i, j))\n pts = []\n\n # print(rotateParas)\n\n # 3. Text Recognition (From boxes given by Text Detection+ROI Rotate)\n\n if len(rotateParas) > 0:\n for num in range(len(rotateParas)):\n text = \"\"\n out = rotateParas[num][0]\n crop = rotateParas[num][1]\n points = np.array([[out[0], out[1]], [out[0] + out[2], out[1]], [out[0] + out[2], out[1] + out[3]],\n [out[0], out[1] + out[3]]])\n angle = rotateParas[num][2]\n # print(out)\n # img1=tf.image.crop_to_bounding_box(img,out[1]-(int(out[1]*(5/100))),out[0]-(int(out[0]*(5/100))),out[3]+(int(out[3]*(55/100))),out[2]+(int(out[2]*(55/100))))\n # print(out)\n img1 = tf.image.crop_to_bounding_box(img, out[1], out[0], out[3], out[2])\n # print(img1.shape)\n # plt.imshow(img1)\n # plt.show()\n img2 = tf.keras.preprocessing.image.random_rotation(img1, angle)\n\n # print(crop)\n # print(crop[0])\n img2 = tf.image.crop_to_bounding_box(img2, crop[1], crop[0], crop[3], crop[2]).numpy()\n # plt.imshow(img2)\n # plt.show()\n img2 = cv2.resize(img2, (64, 15))\n img2 = cv2.detailEnhance(img2)\n recognizer.set_tensor(input_details_recognizer[0]['index'], np.expand_dims(img2, axis=0).astype(np.float32))\n recognizer.invoke()\n ii = recognizer.get_tensor(output_details_recognizer[0]['index'])\n arr = tf.keras.backend.ctc_decode(ii, np.ones((1), 'int8') * 64, )\n for val in arr[0][0].numpy()[0]:\n if val == -1:\n break\n else:\n text += tokenizer.index_word[val]\n txt.append(text)\n pts.append(points)\n # break\n\n # 4. Labeling detected and Recognized Text in Image\n\n for i in range(len(txt)):\n # print(pts[i])\n # pts[i][0][0]=pts[i][0][0]-(pts[i][0][0]*(10/100))\n # pts[i][0][1]=pts[i][0][1]-(pts[i][0][1]*(5/100))\n\n # pts[i][1][0]=pts[i][1][0]+(pts[i][1][0]*(10/100))\n # pts[i][1][1]=pts[i][1][1]-(pts[i][1][1]*(5/100))\n\n # pts[i][2][0]=pts[i][2][0]+(pts[i][2][0]*(10/100))\n # pts[i][2][1]=pts[i][2][1]+(pts[i][2][1]*(5/100))\n\n # pts[i][3][0]=pts[i][3][0]-(pts[i][3][0]*(10/100))\n # pts[i][3][1]=pts[i][3][1]+(pts[i][3][1]*(5/100))\n\n cv2.polylines(img, [pts[i]], isClosed=True, color=(255, 0, 0), thickness=2)\n cv2.putText(img, txt[i], (pts[i][0][0], pts[i][0][1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n print(txt[i])\n end_time = time.time()\n print(\"Time Taken By Pipeline=\" + str(end_time - start_time) + \" seconds\")\n return img\n\n","sub_path":"ml_models.py","file_name":"ml_models.py","file_ext":"py","file_size_in_byte":22694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"272884634","text":"from array_methods import ArrayMethods as np1\nfrom calibrate_camera import Calibrator\n\nimport cv2\nimport os\nimport pickle\n\n\nclass Camera:\n def __init__(self):\n self.ret, self.mtx, self.dist, self.rvecs, self.tvecs = self.read_camera_config()\n obj, img = self.read_world_config()\n self.y_const = obj[0][1]\n ret, self.rvec, self.tvec = cv2.solvePnP(obj, img, self.mtx, self.dist)\n self.rmtx, self.jac = cv2.Rodrigues(self.rvec)\n\n @staticmethod\n def read_camera_config():\n with open(os.getcwd() + '/configs/camera.ini', 'rb') as file:\n data = pickle.load(file)\n\n return data\n\n @staticmethod\n def read_world_config():\n img_corn = np1.array([[0., 0.], [0., 150.], [150., 0.], [150., 150.]])\n\n with open(os.getcwd() + '/configs/coordinates_setup.ini', 'r') as file:\n coordinates = np1.array(\n [list(map(lambda x: float(x), row.strip().split(' '))) for row in file if row[0] != '#'])\n\n return coordinates, img_corn\n\n def calc_real_coordinates(self, x, z):\n left_matrix = np1.dot(np1.dot(np1.inv(self.rmtx), np1.inv(self.mtx)),\n np1.array([float(x), 1., float(z)]).transpose())\n right_matrix = np1.dot(np1.inv(self.rmtx), self.tvec)\n s = (self.y_const + right_matrix[1][0]) / left_matrix[1]\n coords = np1.dot(np1.inv(self.rmtx),\n (s * np1.dot(np1.inv(self.mtx),\n np1.array([float(x), 1., float(z)]).transpose()).reshape(3, 1) - self.tvec))\n\n return coords\n\n def refresh(self):\n self.read_world_config()\n\n @staticmethod\n def calibrate():\n Calibrator().calibrate_camera()\n","sub_path":"swagger_server/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"47856901","text":"a = (5, 33, 77)\nb = (44, 823, 11)\nc = (10, 50, 90)\nprob1 = ([d + e + f for d, e, f in zip(a, b, c)])\nprint(f\"1. {prob1}\")\n\n# conti = \"south america\"\nconti = input(\"2.\\nEnter the name of a continent: \")\nconti = conti.title() # South America\ninfile = open('UN.txt', 'r')\nfor line in infile:\n data = line.split(',')\n if data[1] == conti:\n print(data[0])\n\nclass Purchase:\n def __init__(self, desc, price, quant):\n self.__desc = desc\n self.__price = price\n self.__quant = quant\n \n def print_art(self):\n print(\"{0:12s} ${1:,.2f} {2:5}\".format(self.__desc, self.__price, self.__quant))\n # print(f\"{self.__desc}\\t${self.__price}\\t{self.__quant}\")\n\nclass Cart:\n def __init__(self):\n self.cart = []\n self.tot_cost = 0\n \n def buy(self):\n desc = input(\"3.\\nEnter description of article:\")\n price = int(input(\"Enter price of article:\"))\n quant = int(input(\"Enter quantity of article:\"))\n # desc = \"shirt\"\n # price = 35 \n # quant = 3\n p = Purchase(desc, price, quant)\n self.cart.append(p)\n self.tot_cost += price\n\n def print_cart(self):\n print(\"\\n{0:12} {1:>>>> slice(start, end, step)\n# # >>>>> [:n] starts at beginning, ends JUST BEFORE index n\n# # >>>>> [m:] starts at m and goes to the end\n\n# print(x[0:2]) # ['first', 'second']\n# print(x[1:2]) # ['second']\n# print(x[1]) # second\n\n# print(x[-1]) # fourth\n# print(x[0:-1]) # ['first', 'second', 'third']\n# print(x[1:-1]) # ['second', 'third']\n# print(x[2:-1]) # ['third']\n# print(x[3:-1]) # []\n\n# print(x[-2:-1]) # ['third']\n# print(x[-4:-1]) # ['first', 'second', 'third']\n# print(x[-4:0]) # []\n# print(x[-4:-5]) # []\n\n\n\n# print(x[:2]) # ['first', 'second']\n# print(x[:-1]) # ['first', 'second', 'third']\n# print(x[:-2]) # ['first', 'second']\n\n# print(x[2:]) # ['third', 'fourth']\n# print(x[3:]) # ['fourth']\n\n\n# print(x[:]) # ['first', 'second', 'third', 'fourth']\n\ny = [0, 1, 2, 3]\n# print(y[-4]) # 0\n# print(y[-5]) # IndexError: list index out of range\n\n# # SLICING with lists\n# # >>>>> slice(start, end, step)\n# # >>>>> [:n] starts at beginning, ends JUST BEFORE index n\n# # >>>>> [m:] starts at m and goes to the end\n\n# print(y[0:2]) # [0, 1]\n# print(y[1:2]) # [1]\n# print(y[1]) # 1\n\n# print(y[-1]) # 4\n# print(y[0:-1]) # [0, 1, 2]\n# print(y[1:-1]) # [1, 2]\n# print(y[2:-1]) # [2]\n# print(y[3:-1]) # []\n\n# print(y[-2:-1]) # [2]\n# print(y[-4:-1]) # [0, 1, 2]\n# print(y[-4:0]) # []\n# print(y[-4:-5]) # []\n\n\n# print(y[:2]) # [0, 1]\n# print(y[:-1]) # [0, 1, 2]\n# print(y[:-2]) # [0, 1]\n\n# print(y[2:]) # [2, 3]\n# print(y[3:]) # [3]\n\n\n# print(y[:]) # [0, 1, 2, 3]\n\n# z = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n# z = list(range(0, 10))\n\n# print(z[1:9:3]) # [1, 4, 7]\n# print(z[0:8:11]) # 0\n# print(z[1::2]) # [1, 3, 5, 7, 9]\n# print(z[::2]) # [0, 2, 4, 6, 8]\n# print(z[:1:3]) # [0]\n# print(z[::3]) # [0, 3, 6, 9]\n# print(z[::]) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n\n# list of common collections\nlist = [0, 2, \"apple\", {\"key_name\": \"first_value\"}, {0, 1, 2}, (\"zero\", \"one\")]\nfor index, item in enumerate(list): # must have index first with enumerate\n print(f' index {index} contains {item} of type {type(item)}')\n \n\n# index 0 contains 0 of type \n# index 1 contains 2 of type \n# index 2 contains apple of type \n# index 3 contains {'key_name': 'first_value'} of type \n# index 4 contains {0, 1, 2} of type \n# index 5 contains ('zero', 'one') of type \n\n# list of dictionaries, enumerate to get index\nlist_dict = [{\"first\": \"one\", \"second\": \"two\", \"third\": \"three\"}]\nfor item in list_dict:\n for index, (k,v) in enumerate(item.items()):\n print(f' at index {index:<5} key is \\t {k:<15} value is \\t {v:<10}')\n\n# at index 0 key is first value is one \n# at index 1 key is second value is two \n# at index 2 key is third value is three \n","sub_path":"list_slice.py","file_name":"list_slice.py","file_ext":"py","file_size_in_byte":3123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"423443874","text":"import os\nimport torch\nimport numpy as np\nimport pandas as pd\nimport math\nimport torch.nn as nn\nimport ssim\nimport PIL.Image as pil_image\nimport torchvision.transforms as transforms\nfrom transforms import *\n#======================================================================================\n# create directories\n#======================================================================================\ndef create_dir(path):\n dir = os.path.join(path)\n if not os.path.exists(dir):\n os.mkdir(dir)\n # else :\n # os.remove(dir)\n\n#======================================================================================\n# For avging metrics values\n#======================================================================================\nclass MetricsCalculator(object):\n def __init__(self):\n self.reset()\n\n def reset(self):\n self.val = 0\n self.avg = 0\n self.sum = 0\n self.count = 0\n\n def update(self, val, n=1):\n self.val = val\n self.sum += val * n\n self.count += n\n self.avg = self.sum / self.count\n#======================================================================================\n# Calculates psnr batch wise\n#======================================================================================\ndef psnr(original, compressed): \n running_psnr = 0\n for original_idx, compressed_idx in zip(original, compressed):\n # original_idx = 255*original_idx\n # compressed_idx = 255*compressed_idx\n # original_idx = torch.clamp(original_idx, min=0, max=255)\n # compressed_idx = torch.clamp(compressed_idx, min=0, max=255)\n \n original_idx = denormalize(original_idx.detach())*255\n compressed_idx = denormalize(compressed_idx.detach())*255\n mse = torch.mean((original_idx - compressed_idx) ** 2) \n #print(mse)\n if(mse == 0): # MSE zero-> No noise is present-> PSNR has no importance. \n return 100\n max_pixel_val = 255.0\n running_psnr += 20 * torch.log10(max_pixel_val / torch.sqrt(mse))\n psnr = running_psnr / (original.shape)[0]\n return psnr\n\n#======================================================================================\n# Copy all output data to CSV files. Can be used for plotting later on..\n#======================================================================================\ndef save_history(loss_data, psnr_data, ssim_data, path_dir): \n create_dir(path_dir)\n pd.DataFrame.from_dict(data=loss_data, orient='columns').to_csv(os.path.join(path_dir,'loss.csv'), header=['epoch', 'lr', 'train loss','val loss'])\n pd.DataFrame.from_dict(data=psnr_data, orient='columns').to_csv(os.path.join(path_dir,'psnr.csv'), header=['epoch','train psnr','val psnr'])\n pd.DataFrame.from_dict(data=ssim_data, orient='columns').to_csv(os.path.join(path_dir,'ssim.csv'), header=['epoch','train ssim','val ssim'])\n\n#======================================================================================\n# Weighted loss function - SSIM, PSNR, MSE\n#======================================================================================\ndef weighted_loss(original,compressed):\n '''\n Original and compressed are torch tensors\n 0.4 = MSE\n 0.5 = PSNR\n 0.1 = SSIM\n '''\n mseLoss = nn.MSELoss()\n mse = mseLoss(original, compressed)\n \n psnr_score = psnr(original, compressed) \n #PSNR is maximized so 100-PSNR is a loss function (Or -PSNR)\n psnr_loss = 100 - psnr_score \n\n ssim_score = ssim.ssim(original,compressed)\n ssim_loss = 1 - ssim_score.item()\n\n weighted_loss = (0.4 * mse) + (0.5 * (psnr_loss/100)) + (0.1 * ssim_loss)\n return weighted_loss\n\n#======================================================================================\n# Fetch gaussian variance which is linearly decresing till 6K iterations from 1-->0\n#======================================================================================\ndef fetch_gauss_variance(iteration):\n m = -1/5999\n c = 6000/5999\n variance = m*(iteration+1) + c\n return variance\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"440445730","text":"import os\ntag = 'v6'\nn_chunk = 100\nmc_source_id_start_global = 1\nsize_batch = 5000 \nnumber_of_batches = 10\n\nimport argparse\nparser = argparse.ArgumentParser(description=__doc__)\nparser.add_argument('-q','--queue',default='local')\nargs = parser.parse_args()\n\n# The `csub` command lives in:\n# /home/s1/kadrlica/bin/csub\n# I try to set it here, but you can always do this in shell\n# export PATH=/home/s1/kadrlica/bin:$PATH\n\nos.environ['PATH'] = '/home/s1/kadrlica/bin:'+os.environ['PATH']\n\nlogdir = log\nif not os.path.exists(logdir): os.makedirs(logdir)\n\nfor index_batch in range(0, number_of_batches):\n mc_source_id_start = mc_source_id_start_global + (size_batch * index_batch)\n command = 'python simulate_population.py --tag %s --start %i --size %i --chunk %i'%(tag, mc_source_id_start, size_batch, n_chunk)\n\n logfile = os.path.join(logdir,'%s.log'%mc_source_id_start)\n submit = 'csub -q %s -o %s '%(args.queue,logfile)+ command\n os.system(command)\n\n #print command\n #os.system(command)\n","sub_path":"ugali/scratch/simulation/farm_simulate_population.py","file_name":"farm_simulate_population.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"265574921","text":"# coding=utf-8\n\"\"\"\n\nPROBLEM 019 - Counting Sundays\n\nWritten by: Yuanjie Li\nDate: Oct 26, 2017\n\nYou are given the following information, but you may prefer to do some\nresearch for yourself.\n\n 1 Jan 1900 was a Monday.\n Thirty days has September,\n April, June and November.\n All the rest have thirty-one,\n Saving February alone,\n Which has twenty-eight, rain or shine.\n And on leap years, twenty-nine.\n\n A leap year occurs on any year evenly divisible by 4, but not on a century\n unless it is divisible by 400.\n\nHow many Sundays fell on the first of the month during the twentieth century\n(1 Jan 1901 to 31 Dec 2000)?\n\n\"\"\"\n\ndef main():\n year = 1901\n count = 0 # number of sundays found\n day = 2 #: 0-6 value for the first of the year\n months = {1:31, 2:28, 3:31, 4:30, 5:31, 6:30, 7:31, 8:31, 9:30,\\\n 10:31, 11:30, 12:31}\n\n for i in xrange(year, 2001):\n\n leap = False\n if year % 100 == 0 and year % 400 == 0:\n leap = True\n elif year % 4 == 0:\n leap = True\n\n for month in months:\n if day == 0:\n count += 1\n\n # Update for next month\n if leap and month == 2:\n day += ((months[month] + 1) % 7)\n else:\n day += (months[month] % 7)\n day = day % 7\n\n print(count)\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"p019/counting_sundays.py","file_name":"counting_sundays.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"58256548","text":"from abc import ABC, abstractmethod\nfrom typing import List, Tuple\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.cluster import KMeans\n\n\nclass Model(ABC):\n\n def __init__(\n self,\n vendors: pd.DataFrame,\n # columns: id, latitiude, longitude, vendor_rating\n random_seed: int = 123\n ) -> None:\n self.vendors = vendors\n self.random_seed = random_seed\n self.is_fitted = False\n\n @abstractmethod\n def fit(\n self,\n train_orders: pd.DataFrame,\n # columns: point, vendor_id\n points: pd.DataFrame\n # columns: id, x, y\n ) -> None:\n pass\n\n @abstractmethod\n def get_ranking(\n self,\n location: Tuple[float, float]\n ) -> List[int]:\n pass\n\n def predict(\n self,\n location,\n n_recomendations\n ) -> List[int]:\n\n if not self.is_fitted:\n raise Exception('First fit the model!')\n\n ranking = self.get_ranking(location)\n return ranking[0:n_recomendations]\n\n\nclass RandomModel(Model):\n # Returns random recommendations\n\n def fit(\n self,\n train_orders: pd.DataFrame,\n # columns: point, vendor_id\n points: pd.DataFrame\n # columns: id, x, y\n ) -> None:\n self.is_fitted = True\n\n def get_ranking(\n self,\n location: Tuple[float, float]\n ) -> List[int]:\n\n self.vendors.sample(\n frac=1,\n random_state=self.random_seed\n )\n\n return list(self.vendors['id'])\n\n\nclass DistanceModel(Model):\n # Recommends the nearest vendors\n\n def fit(\n self,\n train_orders: pd.DataFrame,\n # columns: point, vendor_id\n points: pd.DataFrame\n # columns: id, x, y\n ) -> None:\n self.is_fitted = True\n\n def get_ranking(\n self,\n location: Tuple[float, float]\n ) -> List[int]:\n\n x = location[0]\n y = location[1]\n\n self.vendors['distance'] = self.vendors.apply(\n lambda row: np.sqrt(\n (row['longitude'] - x)**2 + (row['latitude'] - y)**2\n ),\n axis=1\n )\n\n self.vendors = self.vendors.sort_values('distance')\n\n return list(self.vendors['id'])\n\n\nclass ClusterModel(Model):\n # Recommends vendors from the nearest cluster with the highest ratings\n\n def __init__(\n self,\n n_clusters: int,\n vendors: pd.DataFrame,\n # columns: id, latitiude, longitude, vendor_rating\n random_seed: int = 123,\n ) -> None:\n super().__init__(\n vendors,\n random_seed\n )\n self.n_clusters = n_clusters\n\n def fit(\n self,\n train_orders: pd.DataFrame,\n # columns: point, vendor_id\n points: pd.DataFrame\n # columns: id, x, y\n ) -> None:\n self.is_fitted = True\n\n kmeans = KMeans(self.n_clusters)\n self.vendors['cluster'] = kmeans.fit_predict(\n self.vendors[['latitude', 'longitude']]\n )\n\n # having knowledge from data exploration we will fix\n # cluster of one specific vendor if n_clusters == 3\n if self.n_clusters == 3:\n row_to_fix = self.vendors[\n self.vendors['id'] == 845\n ].index[0]\n to_cluster = self.vendors[\n self.vendors['id'] == 78\n ].iloc[0]['cluster']\n self.vendors.at[row_to_fix, 'cluster'] = to_cluster\n\n def get_ranking(\n self,\n location: Tuple[float, float]\n ) -> List[int]:\n\n x = location[0]\n y = location[1]\n\n # compute distances to vendors\n self.vendors['distance'] = self.vendors.apply(\n lambda row: np.sqrt(\n (row['longitude'] - x)**2 + (row['latitude'] - y)**2\n ),\n axis=1\n )\n self.vendors = self.vendors.sort_values('distance')\n\n # get cluster of the nearest vendor\n nearest_cluster = self.vendors.iloc[0]['cluster']\n\n # get vendors from that cluster and sort by rating\n sub_vendors = self.vendors[self.vendors['cluster'] == nearest_cluster]\n sub_vendors = self.vendors.sort_values(\n 'vendor_rating',\n ascending=False\n )\n\n return list(sub_vendors['id'])\n\n\nclass GravityModel(Model):\n # Recommends vendors using gravity model with vendors' ratings as mass\n\n def fit(\n self,\n train_orders: pd.DataFrame,\n # columns: point, vendor_id\n points: pd.DataFrame\n # columns: id, x, y\n ) -> None:\n self.is_fitted = True\n\n def get_ranking(\n self,\n location: Tuple[float, float]\n ) -> List[int]:\n\n x = location[0]\n y = location[1]\n\n # compute distances to vendors\n self.vendors['distance'] = self.vendors.apply(\n lambda row: np.sqrt(\n (row['longitude'] - x)**2 + (row['latitude'] - y)**2\n ),\n axis=1\n )\n\n # fill mising ratings with mean\n self.vendors['vendor_rating'].fillna(\n (self.vendors['vendor_rating'].mean()),\n inplace=True\n )\n\n # compute gravity rank\n self.vendors['gravity'] = \\\n self.vendors['vendor_rating'] / self.vendors['distance']\n\n # sort by gravity rank\n self.vendors = self.vendors.sort_values(\n 'gravity',\n ascending=False\n )\n\n return list(self.vendors['id'])\n","sub_path":"assignment-2/location-based/src/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"528714451","text":"import os\nfrom os.path import join, abspath, dirname, split, exists\n\n\ndef process():\n \"\"\"Read in zope.conf configuration file.\n\n This is a hack but there doesn't seem to be a better way.\n \"\"\"\n _prefix = os.environ.get('INSTANCE_HOME')\n if not _prefix:\n try:\n __file__\n except NameError:\n # Test was called directly, so no __file__ global exists.\n _prefix = abspath(os.curdir)\n else:\n # Test was called by another test.\n _prefix = abspath(dirname(__file__))\n _prefix = join(_prefix, '..', '..', '..')\n\n _config = join(_prefix, 'etc', 'zope.conf')\n\n if exists(_config):\n from Zope import configure\n configure(_config)\n","sub_path":"Products.Five/tags/1.0/tests/zopeconf.py","file_name":"zopeconf.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"610786668","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 16 22:14:04 2016\n\n@author: limeng\n\"\"\"\nimport numpy as np\nimport gensim.models.hdpmodel as hdp\nfrom gensim import models\n\nhdpmodel = hdp.HdpModel(corpus, id2word=dictionary, alpha=100, gamma=0.01)\n\n# 将生成的hdp转换成为lda进行使用\nhdp_ldamodel = models.LdaModel(id2word=hdpmodel.id2word,\n num_topics=len(hdpmodel.lda_alpha), \n alpha=hdpmodel.lda_alpha, \n eta=hdpmodel.m_eta) \nhdp_ldamodel.expElogbeta = np.array(hdpmodel.lda_beta, dtype=np.float32)\n\n# 输出主题列表\nhdpmodel.print_topics(topics=20, topn=5)\n\n# 输出文本文件的主题分布\nhdp_ldamodel.get_document_topics(corpus[0])\nhdp_ldamodel.show_topics(num_topics=20, num_words=5)\nhdp_ldamodel.show_topic(0)\n","sub_path":"step3_hdp.py","file_name":"step3_hdp.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"491003989","text":"\"\"\"\nArcGIS Python API Demo File\n\nA short Jupyter Notebook showing how to query data from ArcGIS servers.\n\nArcGIS API for Python Reference: https://developers.arcgis.com/python/api-reference/index.html\n\nAuthor: Josh Birlingmair\n\"\"\"\n\nimport os\nfrom arcgis.features import FeatureLayerCollection\n\n# The API allows you to list all of the feature layers for a location\nftr_lyrs = FeatureLayerCollection(\n 'https://pdihosting.azurecloudgov.us/arcgis/rest/services/AgCROS/COFOARD4/FeatureServer')\nprint(ftr_lyrs.layers)\n\n# Find the table called \"MetaUnits_Unit\"\ni = 0\n\nfor ftr_lyr in ftr_lyrs.layers:\n if ftr_lyr.properties.name == 'MetaUnits_Unit':\n break\n\n i += 1\n\n# Display the number of records\nftr_lyr = ftr_lyrs.layers[i]\nnum_records = ftr_lyr.query(where='1=1', return_count_only=True)\nprint(num_records)\n\n# Display the first record's Unit ID\nrecords = ftr_lyr.query(where='1=1')\nrecords.features[0].attributes['unit_id']\n\n# Display the 5 records only (as a Spatial DataFrame). `return_all_records` must be set to False\n# to set the record count\nrecords = ftr_lyr.query(where='1=1', return_geometry=False,\n return_all_records=False, result_record_count=5)\nprint(records.sdf)\n\n# Find the measurement or management layer with the most records, query it, and display it\nindex = max_records_index = max_num_records = 0\n\nfor ftr_lyr in ftr_lyrs.layers:\n if ftr_lyr.properties.name.startswith('Meas') or ftr_lyr.properties.name.startswith('Mgt'):\n num_records = ftr_lyr.query(where='1=1', return_count_only=True)\n\n if num_records > max_num_records:\n max_num_records = num_records\n max_records_index = index\n\n index += 1\n\nftr_lyr = ftr_lyrs.layers[max_records_index]\nrecords = ftr_lyr.query(where='1=1', return_geometry=False)\nprint(records.sdf)\n\n# Query a subset of the records of the layer `MeasGHGFlux_Unit` (the layer with the most records)\n# and save it to file\nrecords_df = ftr_lyr.query(where='1=1', return_geometry=False,\n return_all_records=False, result_record_count=2000, as_df=True)\ncsv_path = os.path.join(r'C:\\Users\\Public\\Downloads',\n f'{ftr_lyr.properties.name}_subset.csv')\nrecords_df.to_csv(csv_path, index=False)\n\n# Calculate basic statistics of a subset (2,000 records) of the table using the DataFrame\nmean = records_df['co2_gc_ha_d'].mean()\nprint('Mean:', mean)\n\nmedian = records_df['co2_gc_ha_d'].median()\nprint('Median:', median)\n\n# Export the full set of records as a CSV\nrecords_df = ftr_lyr.query(where='1=1', return_geometry=False, as_df=True)\ncsv_path = os.path.join(r'C:\\Users\\Public\\Downloads',\n f'{ftr_lyr.properties.name}.csv')\nrecords_df.to_csv(csv_path, index=False)\n\n# Calculate basic statistics of the entire table using the DataFrame\nmean = records_df['co2_gc_ha_d'].mean()\nprint('Mean:', mean)\n\nmedian = records_df['co2_gc_ha_d'].median()\nprint('Median:', median)\n","sub_path":"v2/Python/arcgis_python_api_demo.py","file_name":"arcgis_python_api_demo.py","file_ext":"py","file_size_in_byte":2949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"86914700","text":"import os\nimport pandas as pd\n\n\ndef read_coco_classes(filename):\n with open(filename) as f:\n lines = f.readlines()\n\n coco_dict_class = {}\n coco_dict_class_inverse = {}\n for l in lines:\n line_data = [x.strip() for x in l.split(',')]\n coco_dict_class[int(line_data[0])] = line_data[1]\n coco_dict_class_inverse[line_data[1]] = int(line_data[0])\n\n return coco_dict_class, coco_dict_class_inverse\n\n\ndef coco_ann_to_pd(coco, image_path):\n all_cats = coco.loadCats(coco.getCatIds())\n\n all_names = [cat['name'] for cat in all_cats]\n all_cocoids = [cat['id'] for cat in all_cats]\n all_super = [cat['supercategory'] for cat in all_cats]\n\n all_cocoids_to_names = {i: n for (n, i) in zip(all_names, all_cocoids)}\n all_cocoids_to_super = {i: n for (n, i) in zip(all_super, all_cocoids)}\n\n all_images_ids = list(coco.imgs.keys())\n\n img_ids, img_pathes = [], []\n img_heights, img_widths = [], []\n\n box_ids, box_classes, box_supers, areas = [], [], [], []\n box_xs, box_ys, box_ws, box_hs = [], [], [], []\n\n for img_id in all_images_ids:\n ann_ids = coco.getAnnIds(imgIds=img_id)\n ann = coco.loadAnns(ann_ids)\n\n path_img = coco.loadImgs(img_id)[0]['file_name']\n path_img = os.path.join(image_path, path_img)\n\n for (i, a) in enumerate(ann):\n img_ids += [img_id]\n img_pathes += [path_img]\n img_heights += [coco.loadImgs(img_id)[0]['height']]\n img_widths += [coco.loadImgs(img_id)[0]['width']]\n\n box_ids += [a['category_id']]\n box_classes += [all_cocoids_to_names[a['category_id']]]\n box_supers += [all_cocoids_to_super[a['category_id']]]\n\n box_xs += [a['bbox'][0]]\n box_ys += [a['bbox'][1]]\n box_ws += [a['bbox'][2]]\n box_hs += [a['bbox'][3]]\n\n areas += [a['bbox'][2] * a['bbox'][3]]\n\n data = {'image_id': img_ids,\n 'image_path': img_pathes,\n 'image_width': img_widths,\n 'image_height': img_heights,\n 'box_id': box_ids,\n 'box_class': box_classes,\n 'box_super': box_supers,\n 'box_x': box_xs,\n 'box_y': box_ys,\n 'box_w': box_ws,\n 'box_h': box_hs,\n 'area': areas,\n }\n\n df = pd.DataFrame.from_dict(data)\n\n return df\n\n\ndef coco_pred_to_pd(coco_res, image_path, coco_style=True):\n areas, iscrowds, class_names, category_id, scores, category_ids, ids = [], [], [], [], [], [], []\n\n if coco_style:\n coco_classes_names = os.path.join(os.getcwd(), os.path.dirname(__file__), \"coco_classes.txt\")\n coco_class_dict, _ = read_coco_classes(coco_classes_names)\n\n img_pathes = []\n\n img_ids = []\n box_xs, box_ys, box_ws, box_hs = [], [], [], []\n\n for k in coco_res.anns:\n ann_current = coco_res.anns[k]\n\n if coco_style:\n img_name = str(ann_current['image_id']).rjust(12, '0') + '.jpg'\n class_name = coco_class_dict[ann_current['category_id']]\n else:\n img_name = ann_current['image_name']\n class_name = ann_current['class_name']\n\n box_xs += [ann_current['bbox'][0]]\n box_ys += [ann_current['bbox'][1]]\n box_ws += [ann_current['bbox'][2]]\n box_hs += [ann_current['bbox'][3]]\n\n img_ids += [ann_current['image_id']]\n\n img_pathes += [os.path.join(image_path, img_name)]\n\n areas += [ann_current['area']]\n iscrowds += [ann_current['iscrowd']]\n class_names += [class_name]\n category_ids += [ann_current['category_id']]\n ids += [ann_current['id']]\n scores += [ann_current['score']]\n\n data = {'image_id': img_ids,\n 'image_path': img_pathes,\n 'box_class': class_names,\n 'box_x': box_xs,\n 'box_y': box_ys,\n 'box_w': box_ws,\n 'box_h': box_hs,\n 'area': areas,\n 'iscrowd': iscrowds,\n 'category_id': category_ids,\n 'id': ids,\n 'score': scores\n }\n\n df = pd.DataFrame.from_dict(data)\n\n return df\n\n\ndef get_box_text_pred(df_pred_image):\n bboxes_pred = df_pred_image[['box_x', 'box_y', 'box_w', 'box_h']].values\n classes_names_pred = df_pred_image['box_class'].values\n score_pred = df_pred_image['score'].values\n bbox_text_pred = [classes_names_pred[i] + '[{:3.2f}]'.format(score_pred[i]) for i in range(len(score_pred))]\n\n return bboxes_pred, bbox_text_pred\n\n\ndef extract_info(df, df_pred_1, df_pred_2, condition_gt, condition_pred):\n\n # gt conditions\n conditions_image = [True] * df.shape[0]\n if len(condition_gt['classes']) > 0:\n conditions_image = conditions_image & df['box_class'].isin(condition_gt['classes'])\n if condition_gt['area_max'] != -1:\n conditions_image &= (df.area <= condition_gt['area_max'])\n if condition_gt['area_min'] != -1:\n conditions_image &= (df.area >= condition_gt['area_min'])\n\n df_selected = df.loc[conditions_image].reset_index(drop=True)\n df_selected_grouped = df_selected.groupby('image_id')\n\n # pred conditions (1)\n if len(df_pred_1) > 0:\n pred_conditions_1 = [True] * df_pred_1.shape[0]\n if len(condition_pred['classes']) > 0:\n pred_conditions_1 &= df_pred_1['box_class'].isin(condition_pred['classes'])\n if condition_pred['score_threshold'] != -1:\n pred_conditions_1 &= (df_pred_1['score'] > condition_pred['score_threshold'])\n\n df_pred_1_selected = df_pred_1.loc[pred_conditions_1].reset_index(drop=True)\n df_pred_1_selected_grouped = df_pred_1_selected.groupby('image_id')\n\n # pred conditions (2)\n if len(df_pred_2) > 0:\n pred_conditions_2 = [True] * df_pred_2.shape[0]\n if len(condition_pred['classes']) > 0:\n pred_conditions_2 &= df_pred_2['box_class'].isin(condition_pred['classes'])\n if condition_pred['score_threshold'] != -1:\n pred_conditions_2 &= (df_pred_2['score'] > condition_pred['score_threshold'])\n\n df_pred_2_selected = df_pred_2.loc[pred_conditions_2].reset_index(drop=True)\n df_pred_2_selected_grouped = df_pred_2_selected.groupby('image_id')\n\n # collect all info\n box_info = []\n for id_image, df_image in df_selected_grouped:\n\n # image path\n path_image = df_image['image_path'].iloc[0] # pick the first\n\n # ground truth bboxes and text\n bboxes = df_image[['box_x', 'box_y', 'box_w', 'box_h']].values\n bbox_text = df_image['box_class'].values\n\n if len(df_pred_1) > 0 and id_image in df_pred_1_selected_grouped.groups.keys():\n df_pred_image = df_pred_1_selected_grouped.get_group(id_image)\n bboxes_pred, bbox_text_pred = get_box_text_pred(df_pred_image)\n else:\n bboxes_pred, bbox_text_pred = [], []\n\n if len(df_pred_2) > 0 and id_image in df_pred_2_selected_grouped.groups.keys():\n df_pred_image = df_pred_2_selected_grouped.get_group(id_image)\n bboxes_pred_2, bbox_text_pred_2 = get_box_text_pred(df_pred_image)\n else:\n bboxes_pred_2, bbox_text_pred_2 = [], []\n\n # collect all info\n box_info.append({'bboxes': bboxes,\n 'bbox_text': bbox_text,\n 'bboxes_pred': bboxes_pred,\n 'bbox_text_pred': bbox_text_pred,\n 'bboxes_pred_2': bboxes_pred_2,\n 'bbox_text_pred_2': bbox_text_pred_2,\n 'path_image': path_image\n })\n\n return box_info\n","sub_path":"src/datasets/coco_to_pd.py","file_name":"coco_to_pd.py","file_ext":"py","file_size_in_byte":7640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"652557034","text":"\nimport os\nfrom ops.argparser import argparser\nfrom ops.os_operation import mkdir\n\n\nif __name__ == \"__main__\":\n params = argparser()\n # print(params)\n if params['mode'] == 0:\n #evaluate using haruspex\n input_path = params['F']#mrc file dir\n study_file_path=params['M']\n type = params['type']\n input_path = os.path.abspath(input_path)\n if type == 0:\n indicate = 'SIMU6'\n elif type == 1:\n indicate = 'SIMU10'\n elif type == 2:\n indicate = 'SIMU'\n elif type == 3:\n indicate = 'REAL'\n else:\n print(\"we only have 4 type predictions: simulated(0,1,2) and real map(3)\")\n exit()\n choose = params['choose']\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = choose\n from Evaluate.Evaluate_Haruspex import Evaluate_Haruspex\n Evaluate_Haruspex(input_path,study_file_path,indicate,type)\n elif params['mode']==1:\n #use the predict.txt generated by our labels to evaluate,\n #since our stride=2, we use 3*3*3 to sum up their predictions to get their predicted label for each point.\n haru_path=params['F']\n study_path=params['M']#predict file path\n type = params['type']\n haru_path = os.path.abspath(haru_path)\n if type == 0:\n indicate = 'SIMU6'\n elif type == 1:\n indicate = 'SIMU10'\n elif type == 2:\n indicate = 'SIMU'\n elif type == 3:\n indicate = 'REAL'\n else:\n print(\"we only have 4 type predictions: simulated(0,1,2) and real map(3)\")\n exit()\n from Evaluate.Evaluate_Voxel import Evaluate_Voxel\n Evaluate_Voxel(haru_path,study_path,type,indicate)\n elif params['mode']==2:\n all_map_path=os.path.abspath(params['F'])\n import time\n import datetime\n\n today = datetime.date.today()\n formatted_today = today.strftime('%y%m%d')\n now = time.strftime(\"%H:%M:%S\")\n save_path=os.path.join(os.getcwd(),\"Gen_Result\")\n mkdir(save_path)\n save_path=os.path.join(save_path,formatted_today+now)\n mkdir(save_path)\n from Evaluate.Gen_Predictions import Gen_Predictions\n Gen_Predictions(all_map_path,save_path)\n\n\n\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"458208406","text":"from django.conf import settings\n# Avoid shadowing the login() and logout() views below.\nfrom django.contrib.auth import (\n REDIRECT_FIELD_NAME, get_user_model, login as auth_login,\n logout as auth_logout, update_session_auth_hash,\n)\n\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.shortcuts import resolve_url\n\nfrom django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.views.decorators.cache import cache_page\nfrom django.http.response import HttpResponse\n\nfrom django.views.decorators.debug import sensitive_post_parameters\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.utils.http import is_safe_url, urlsafe_base64_decode\n\nfrom django.utils.decorators import method_decorator\n\nfrom django.http import HttpResponseRedirect\n\nfrom django.views.generic.edit import FormView\nfrom .models import *\nfrom .forms import AuthenticationForm\nfrom project import settings\n\nfrom django.urls import NoReverseMatch, reverse\n\nSESSION_KEY = '_auth_user_id'\nBACKEND_SESSION_KEY = '_auth_user_backend'\nHASH_SESSION_KEY = '_auth_user_hash'\n\n\n# REDIRECT_FIELD_NAME = 'next'\n\n\n# @cache_page(60 * 15)\n@login_required(login_url='/cabinet/login/')\ndef user_account_page(request, acc_id):\n return render(request, 'personal_cabinet/index.html', context={'acc_id': acc_id})\n\n\n@login_required(login_url='/cabinet/login/')\ndef index(request):\n return render(request, 'personal_cabinet/index.html', context={\n 'DEBUG': settings.DEBUG,\n 'user': request.user,\n 'accounts': Account.objects.filter(euser=request.user)\n })\n\n\nclass SuccessURLAllowedHostsMixin(object):\n success_url_allowed_hosts = set()\n\n def get_success_url_allowed_hosts(self):\n allowed_hosts = {self.request.get_host()}\n allowed_hosts.update(self.success_url_allowed_hosts)\n return allowed_hosts\n\n\nclass LoginView(SuccessURLAllowedHostsMixin, FormView):\n \"\"\"\n Displays the login form and handles the login action.\n \"\"\"\n form_class = AuthenticationForm\n authentication_form = None\n redirect_field_name = REDIRECT_FIELD_NAME\n # template_name = 'personal_cabinet/login.html'\n template_name = 'personal_cabinet/login-2.html'\n\n redirect_authenticated_user = True\n extra_context = None\n\n @method_decorator(sensitive_post_parameters())\n @method_decorator(csrf_protect)\n @method_decorator(never_cache)\n def dispatch(self, request, *args, **kwargs):\n if self.redirect_authenticated_user and self.request.user.is_authenticated:\n redirect_to = self.get_success_url()\n if redirect_to == self.request.path:\n raise ValueError(\n \"Redirection loop for authenticated user detected. Check that \"\n \"your LOGIN_REDIRECT_URL doesn't point to a login page.\"\n )\n return HttpResponseRedirect(redirect_to)\n return super(LoginView, self).dispatch(request, *args, **kwargs)\n\n def get_success_url(self):\n \"\"\"Ensure the user-originating redirection URL is safe.\"\"\"\n redirect_to = self.request.POST.get(\n self.redirect_field_name,\n self.request.GET.get(self.redirect_field_name, '')\n )\n url_is_safe = is_safe_url(\n url=redirect_to,\n allowed_hosts=self.get_success_url_allowed_hosts(),\n require_https=self.request.is_secure(),\n )\n if not url_is_safe:\n return resolve_url(settings.LOGIN_REDIRECT_URL)\n return redirect_to\n\n def get_form_class(self):\n return self.authentication_form or self.form_class\n\n def get_form_kwargs(self):\n kwargs = super(LoginView, self).get_form_kwargs()\n kwargs['request'] = self.request\n return kwargs\n\n def form_valid(self, form):\n \"\"\"Security check complete. Log the user in.\"\"\"\n auth_login(self.request, form.get_user())\n return HttpResponseRedirect(self.get_success_url())\n\n def get_context_data(self, **kwargs):\n context = super(LoginView, self).get_context_data(**kwargs)\n current_site = get_current_site(self.request)\n context.update({\n self.redirect_field_name: self.get_success_url(),\n 'site': current_site,\n 'site_name': current_site.name,\n 'DEBUG': settings.DEBUG,\n 'site_header': 'Авторизация в личном кабенете'\n })\n if self.extra_context is not None:\n context.update(self.extra_context)\n return context\n\n\n@never_cache\ndef logout(req):\n auth_logout(req)\n return HttpResponseRedirect('/')\n\n# @login_required()\n# def load_users(req):\n# if req.user.is_authenticated:\n# return render(req, 'personal_cabinet/ok.html')\n# else:\n# return render(req, 'personal_cabinet/error.html')\n","sub_path":"cabinet/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"520144232","text":"#!/usr/bin/python\nimport time\nimport pychrome\n\ndef request_intercepted(interceptionId, request, **kwargs):\n headers = request.get('headers', {})\n headers['Test-key'] = 'test-value'\n\n tab.Network.continueInterceptedRequest(\n interceptionId=interceptionId,\n headers=headers,\n method='POST',\n postData=\"hello post data: %s\" % time.time()\n )\n\nbrowser = pychrome.Browser()\ntab = browser.new_tab()\n\ntab.Network.requestIntercepted = request_intercepted\n\ntab.start()\ntry:\n tab.Network.setRequestInterception(patterns=[{'urlPattern': '*', 'resourceType': 'Document'}])\nexcept pychrome.exceptions.CallMethodException:\n tab.Network.setRequestInterceptionEnabled(enabled=True)\n\ntab.Page.navigate(url=\"http://httpbin.org/post\")\n\ntab.wait(3)\n\nresult = tab.Runtime.evaluate(expression=\"document.documentElement.outerText\")\nhtml_content = result.get('result', {}).get('value', \"\")\nprint(html_content)\ntab.stop()\n","sub_path":"pytest/test_pychrome4.py","file_name":"test_pychrome4.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"125203184","text":"#!/usr/bin/env python\nimport csv\nimport sys\nimport re\nimport argparse\nimport time\nimport random\nimport urllib3\nimport requests\nimport logging\nfrom bs4 import BeautifulSoup\nfrom itertools import cycle\nfrom multiprocessing import Pool\nfrom fake_useragent import UserAgent\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\nFILE_SEPARATOR = \"\"\nif sys.platform == 'linux':\n FILE_SEPARATOR = \"/\"\nelif sys.platform == 'darwin':\n FILE_SEPARATOR = \"/\"\nelse:\n FILE_SEPARATOR = \"\\\\\"\n\nSRC_URL = \"https://www.zillow.com/{0}/real-estate-agent-reviews/?page=\"\nPAGE_URL = \"https://www.zillow.com/{0}/real-estate-agent-reviews/\"\nOUTPUT_FOLDER = \"raw\"\nRESULT_FILE = \"results\" + FILE_SEPARATOR + \"output.csv\"\nLOG_FILE = \"logs\" + FILE_SEPARATOR + \"out.log\"\nPROXY_FILE = \"resources\" + FILE_SEPARATOR + \"proxy.txt\"\nPROXY_ENABLED = False\nBREAK_TIME = 0\nTHREAD_COUNT = 10\n########################################\n\n\ndef setup_custom_logger(name):\n formatter = logging.Formatter(fmt='%(asctime)s %(levelname)-8s %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S')\n handler = logging.FileHandler(LOG_FILE, mode='w')\n handler.setFormatter(formatter)\n screen_handler = logging.StreamHandler(stream=sys.stdout)\n screen_handler.setFormatter(formatter)\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n logger.addHandler(handler)\n logger.addHandler(screen_handler)\n return logger\n\n\nlogger = setup_custom_logger('zillow')\n########################################\n\"\"\" get proxy from file \"\"\"\n\n\ndef get_proxies():\n proxies = set()\n with open(PROXY_FILE, 'r', encoding='utf-8') as input_file:\n for line in input_file:\n temp = line.rstrip().split(':')\n if temp and len(temp) > 0:\n # if it is normal proxy, no username and password\n if len(temp) == 2:\n proxies.add(line.rstrip())\n # if it is proxy with password, in format ip:port:username:password\n elif len(temp) == 4:\n ip = temp[0]\n port = temp[1]\n username = temp[2]\n password = temp[3]\n proxy = username + \":\" + password + \"@\" + ip + \":\" + port\n proxies.add(proxy)\n return proxies\n\n\n########################################\n\"\"\" append data to file\"\"\"\n\n\ndef append_to_file(datarow):\n with open(RESULT_FILE, 'a', encoding='utf-8') as output:\n writer = csv.writer(output, delimiter=\",\", lineterminator=\"\\n\")\n writer.writerow(datarow)\n\n\n\n########################################\n\"\"\" loop through the proxy and get the data --between 3046 bytes and 1000000 bytes \"\"\"\n\n\ndef request_data(url):\n response = \"\"\n proxies1 = get_proxies()\n proxy_pool = cycle(proxies1)\n time_out = BREAK_TIME\n while True:\n try:\n ua = UserAgent()\n user_agent = ua.random()\n headers = {\n 'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n 'accept-encoding': 'gzip, deflate, sdch, br',\n 'accept-language': 'en-GB,en;q=0.8,en-US;q=0.6,ml;q=0.4',\n 'cache-control': 'max-age=0',\n 'upgrade-insecure-requests': '1',\n 'user-agent': str(user_agent)\n }\n if PROXY_ENABLED:\n proxy = next(proxy_pool)\n logger.info(\"Using proxy: \" + proxy)\n response = requests.get(url, headers=headers, proxies={\n \"http\": proxy, \"https\": proxy}, verify=False)\n else:\n response = requests.get(url, headers=headers, verify=False)\n logger.info(\"Response code: \" + str(response.status_code))\n logger.info(\"Response content size: \" + str(len(response.content)))\n except:\n logger.info(\"Skipipng proxy. Connection error\")\n continue\n if response and len(response.content) < 10000: # capcha check\n continue\n else:\n break\n return response\n########################################\n\"\"\" check how many paginations this url has and returns all the urls \"\"\"\n\n\ndef get_all_page_urls(url):\n urls = []\n total_pages = 0\n response = request_data(url)\n # if it is the wrong url\n if len(response.content) < 180000 and len(response.content) > 10000:\n return urls\n # parsing the text\n soup = BeautifulSoup(response.text, 'html.parser')\n links = soup.select('a[class=\"js-pagination\"]')\n if links and len(links) > 0:\n logger.info(\"There are total \"+str(links[-1].attrs[\"data-idx\"])+\"pages\")\n total_pages = int(links[-1].attrs[\"data-idx\"])\n if total_pages >0:\n for i in range(1,total_pages):\n urls.append(SRC_URL+str(i))\n return urls\n\n\n########################################\n\"\"\" crawl the zillow website to get data\"\"\"\n\n\ndef crawler(zipcode):\n i = 0\n while True:\n i += 1\n url = SRC_URL.format(zipcode) + str(i)\n logger.info(url)\n sys.stdout.flush()\n response = request_data(url)\n # if it is the wrong url\n if len(response.content) < 180000 and len(response.content) > 10000:\n break\n sys.stdout.flush()\n # parsing the text\n soup = BeautifulSoup(response.text, 'html.parser')\n blocks = soup.select('div[data-test-id=\"ldb-boards-results\"]')\n rows = blocks[0].select('div[data-test-id*=\"ldb-board\"]')\n for row in rows:\n name = \"\"\n phone = \"\"\n rating = \"\"\n reviews = \"\"\n office = \"\"\n # agent = row.select('div[class=\"ldb-board-inner\"]')[0]\n agent = row.select(\n 'div[class=\"ldb-col-a\"]')[0].select('div[class=\"ldb-board-inner\"]')[0]\n name = agent.select('p[class*=\"ldb-contact-name\"]')[0].get_text()\n phone = agent.select('p[class*=\"ldb-phone-number\"]')[0].get_text()\n try:\n rating = agent.select('span[class*=\"zsg-rating\"]')[0]['title']\n except:\n pass\n try:\n reviews = agent.select(\n 'a[class*=\"zsg-link zsg-finelogger.info\"]')[0].get_text()\n except:\n pass\n try:\n office = row.select('div[class=\"ldb-col-b\"]')[0].select('div[class=\"ldb-board-inner\"]')[\n 0].select('p[class=\"ldb-business-name\"]')[0].get_text()\n office = re.sub(r'[\\ \\n]{2,}', '', office)\n office = re.sub(r'\\n', '', office)\n except:\n pass\n datarow = []\n datarow.append(zipcode)\n datarow.append(str(i))\n datarow.append(name)\n datarow.append(phone)\n datarow.append(rating)\n datarow.append(reviews)\n datarow.append(office)\n append_to_file(datarow)\n time.sleep(random.randint(1, 2))\n\n\n###########################################\n\"\"\" main function \"\"\"\nif __name__ == \"__main__\":\n argparser = argparse.ArgumentParser(\n formatter_class=argparse.RawTextHelpFormatter)\n argparser.add_argument('--zipcode', nargs='?',\n help='get only 1 single zip code, usage: --zipcode ')\n argparser.add_argument('--zipcode_file', nargs='?',\n help='get list of zip codes from a local file, usage: --zipcode_file ')\n argparser.add_argument('--output_file', nargs='?',\n help='write output to a local file, by default it is output.csv, usage: --output_file ')\n argparser.add_argument('--proxy_file', nargs='?',\n help='the list of proxies: --proxy_file ')\n args = argparser.parse_args()\n zipcode = args.zipcode\n zipcode_file = args.zipcode_file\n output_file = args.output_file\n proxy_file = args.proxy_file\n logger.info(\"zipcode:\" + str(zipcode))\n logger.info(\"zipcode_file: \" + str(zipcode_file))\n logger.info(\"output_file: \" + str(output_file))\n logger.info(\"proxy_file: \" + str(proxy_file))\n sys.stdout.flush()\n if not output_file is None:\n RESULT_FILE = output_file\n if not proxy_file is None:\n PROXY_ENABLED = True\n PROXY_FILE = proxy_file\n # if not zipcode is None:\n # crawler(zipcode)\n url = PAGE_URL.format(zipcode) \n get_all_page_urls(url)\n # if not zipcode_file is None:\n # zipcodes_list = []\n # with open(zipcode_file, 'r') as input_file:\n # for line in input_file:\n # zipcode = line.rstrip()\n # if zipcode.isdigit():\n # zipcodes_list.add(zipcode)\n # p = Pool(THREAD_COUNT) \n # p.map(crawler, zipcodes_list)\n # p.terminate()\n # p.join()\n","sub_path":"zillow/zillow_request/crawler_zillow_multiprocessing.py","file_name":"crawler_zillow_multiprocessing.py","file_ext":"py","file_size_in_byte":8919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"206539735","text":"# -*- coding:UTF-8 -*-\n\nfrom django.core.mail import send_mail\nfrom django.conf import settings\nfrom django.template import loader, RequestContext\nfrom celery import Celery\nimport time\n\n#初始化， 在任务处理者\nimport os\nimport django\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"dailyfresh.settings\")\ndjango.setup()\n\n\nfrom goods.models import GoodsType,IndexGoodsBanner,IndexPromotionBanner,IndexTypeGoodsBanner\nfrom django_redis import get_redis_connection\n\n\napp=Celery('celert_tasks.tasks', broker='redis://127.0.0.1:6379/8')\n\n@app.task\ndef send_register_active_email(to_email, username, token):\n\t#发邮件\n\tsubject='daily fresh'\n\tmessage=''\n\tsender=settings.EMAIL_FROM\n\treceiver=[to_email]\n\thtml_message='

天天生鲜项目, %s, 欢迎你

请点击下列链接激活账号
http://127.0.0.1:8000/user/active/%s'%(username, token, token)\n\tsend_mail(subject, message, sender, receiver, html_message=html_message ) #阻塞执行\n\n\n@app.task\ndef generate_static_index_html():\n\n\ttypes = GoodsType.objects.all()\n\n\tgoods_banners = IndexGoodsBanner.objects.all().order_by('index')\n\n\tpromotion_banners = IndexPromotionBanner.objects.all().order_by('index')\n\n\n\tfor type in types: # GoodsType\n\n\t\timage_banners = IndexTypeGoodsBanner.objects.filter(type=type, display_type=1).order_by('index')\n\n\t\ttitle_banners = IndexTypeGoodsBanner.objects.filter(type=type, display_type=0).order_by('index')\n\n\n\t\ttype.image_banners = image_banners\n\t\ttype.title_banners = title_banners\n\n\n\n\tcontext = {'types': types,\n\t\t'goods_banners': goods_banners,\n\t\t'promotion_banners': promotion_banners}\n\n\n\ttemp = loader.get_template('static_index.html')\n\n\tstatic_index_html = temp.render(context)\n\n\n\tsave_path = os.path.join(settings.BASE_DIR, 'static/index.html')\n\twith open(save_path, 'w') as f:\n\t\tf.write(static_index_html)","sub_path":"celery_tasks/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"353648694","text":"def multiples_of_three_and_five ():\n\n #numbers = range(1, 100000000)\n #sum_of_multiples = 0\n\n #for index in numbers:\n #if((index % 3 == 0) and (index % 5 == 0)):\n #sum_of_multiples += index\n\n #print(sum_of_multiples)\n\n total = 0\n end = 100000000\n\n for index_for_three in range(3, end, 3):\n total += index_for_three\n\n for index_for_five in range(5, end, 5):\n if index_for_five % 3 != 0:\n total += index_for_five\n\n print(total)","sub_path":"venv/module_1/multiples_of_three_and_five.py","file_name":"multiples_of_three_and_five.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"75208857","text":"import json\nimport pandas as pd\nimport sys\nimport spacy\nfrom spacy.tokens import Doc\n\n''' For turning CONLL-2003 data to a CSVs with a multitude of columms (for training models)'''\n\n\ncolumns = ['pos', 'dep', 'lemma', 'norm', 'lower', 'shape', 'is_alpha', 'is_ascii', 'is_digit', 'is_lower',\n 'is_upper', 'is_title', 'is_punct', 'like_num', 'is_oov', 'is_stop', 'cluster', 'like_url', 'is_currency']\n\n\ndef get_features(token, attributes):\n '''extract attributes from spaCy Token object\n \n Parameters\n ----------\n token: Token\n spaCy token object\n attributes: list\n Attributes to extract from Token\n \n Returns\n -------\n features: list\n List of requested attributes from Token object\n \n '''\n \n features = []\n for att in attributes:\n try:\n features.append(getattr(token, att + '_'))\n except:\n features.append(getattr(token, att))\n\n return features\n\n\ndef to_df(filename, delim, sep):\n '''turn conll formatted file to pandas DataFrame\n \n Parameters\n ----------\n filename: str\n ConLL filename to be converted\n delim: str\n delimiter for each line\n sep: str\n line separator in file\n \n Returns\n -------\n df: DataFrame\n Dataframe where each row contains features for one token.\n \n '''\n\n sent_id = 0\n sent = []\n tags = []\n rows = []\n\n # get the english model and override the tokenizer\n nlp = spacy.load('en_core_web_lg')\n nlp.tokenizer = lambda X: Doc(nlp.vocab, words=X)\n\n with open(filename, 'r', encoding='utf-8') as f:\n # for each line in conll file, create a row for the dataframe\n for line in f:\n line = line.rstrip('\\n' + sep)\n if line:\n row = line.split(delim)\n sent.append(row[0])\n tags.append(row[-1])\n else:\n doc = nlp(sent)\n for i, (token, tag) in enumerate(zip(doc, tags)):\n rows.append(get_features(token, columns) +\n [token.text, tag, sent_id, i])\n sent = []\n tags = []\n sent_id += 1\n\n df = pd.DataFrame.from_records(\n rows, columns=columns + ['token', 'tag', 'sentence_id', 'token_id'])\n\n return df\n\n\nif __name__ == '__main__':\n '''\n sys.argv[1:] : filename csvname delimiter separator\n\n Turns conll format to csv\n '''\n\n filename = sys.argv[1]\n output = sys.argv[2]\n delim = sys.argv[3]\n sep = sys.argv[4]\n\n df = to_df(filename, delim, sep)\n\n print(df.head(20))\n\n df.to_csv(output, index=False)\n","sub_path":"src/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"399407699","text":"#!/usr/bin/env python \n# -*- coding: utf-8 -*- \n# @Time : 2018/8/30 11:09 \n# @Author : virus \n# @File : 1-3.py \n# @Desp : python\n\n# 连续求和\n\nn = int(input(\"请输入一个正整数\"))\n\nsum = 0\ni = 1\nwhile n >= i:\n sum = sum + i\n i += 1\n\nprint(\"1 到 %d 之和为：%d\" % (n,sum))\n\n\nsum1 = 0\ni1 = 1\n\nfor i in range(1, n+1):\n sum1 = sum1 + i1\n i1 += 1\n\nprint(\"1 到 %d 之和为：%d\" % (n,sum1))","sub_path":"language/practice/1-3.py","file_name":"1-3.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"391777128","text":"soma=0\ncont=0\nfor c in range(1,7):\n n=int(input('Digite o {} numero:'.format(c)))\n cont=cont+1\n if n%2==0:\n soma=soma+n\nprint('Você informou {} numeros e a soma dos pares é {}.'.format(cont, soma))\n\n\n\n","sub_path":"Exercicios-Python/CursoEmVideo/ex050.py","file_name":"ex050.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"510770648","text":"\"\"\"jc - JSON CLI output utility `hash` command output parser\n\nUsage (cli):\n\n $ hash | jc --hash\n\nUsage (module):\n\n import jc.parsers.hash\n result = jc.parsers.hash.parse(hash_command_output)\n\nCompatibility:\n\n 'linux', 'darwin', 'cygwin', 'aix', 'freebsd'\n\nExamples:\n\n $ hash | jc --hash -p\n [\n {\n \"hits\": 2,\n \"command\": \"/bin/cat\"\n },\n {\n \"hits\": 1,\n \"command\": \"/bin/ls\"\n }\n ]\n\"\"\"\nimport jc.utils\nimport jc.parsers.universal\n\n\nclass info():\n version = '1.0'\n description = '`hash` command parser'\n author = 'Kelly Brazil'\n author_email = 'kellyjonbrazil@gmail.com'\n\n # compatible options: linux, darwin, cygwin, win32, aix, freebsd\n compatible = ['linux', 'darwin', 'cygwin', 'aix', 'freebsd']\n\n\n__version__ = info.version\n\n\ndef process(proc_data):\n \"\"\"\n Final processing to conform to the schema.\n\n Parameters:\n\n proc_data: (List of Dictionaries) raw structured data to process\n\n Returns:\n\n List of Dictionaries. Structured data with the following schema:\n\n [\n {\n \"command\": string,\n \"hits\": integer\n }\n ]\n \"\"\"\n for entry in proc_data:\n # change to int\n int_list = ['hits']\n for key in int_list:\n if key in entry:\n try:\n key_int = int(entry[key])\n entry[key] = key_int\n except (ValueError):\n entry[key] = None\n\n return proc_data\n\n\ndef parse(data, raw=False, quiet=False):\n \"\"\"\n Main text parsing function\n\n Parameters:\n\n data: (string) text data to parse\n raw: (boolean) output preprocessed JSON if True\n quiet: (boolean) suppress warning messages if True\n\n Returns:\n\n List of Dictionaries. Raw or processed structured data.\n \"\"\"\n if not quiet:\n jc.utils.compatibility(__name__, info.compatible)\n\n cleandata = data.splitlines()\n raw_output = []\n\n if jc.utils.has_data(data):\n\n cleandata[0] = cleandata[0].lower()\n raw_output = jc.parsers.universal.simple_table_parse(cleandata)\n\n if raw:\n return raw_output\n else:\n return process(raw_output)\n","sub_path":"jc/parsers/hash.py","file_name":"hash.py","file_ext":"py","file_size_in_byte":2274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"10225072","text":"# Name: David Turnbough\r\n# Date: Thursday, December 19, 2019\r\n# Geeks For Geeks: Fascinating Number\r\n# https://practice.geeksforgeeks.org/problems/fascinating-number/0\r\n\r\ntestCases = int(input())\r\n\r\nwhile(testCases > 0):\r\n testCases -= 1\r\n\r\n inputValue = int(input())\r\n\r\n if(len(str(inputValue)) >= 3):\r\n\r\n doubleInputValue = inputValue * 2\r\n tripleInputValue = inputValue * 3\r\n\r\n inputValue = str(inputValue) + str(doubleInputValue) + str(tripleInputValue)\r\n\r\n valueFound = False\r\n \r\n for i in range(1, 10):\r\n valueFound = False\r\n index = 0\r\n\r\n while(index < len(inputValue) and valueFound == False):\r\n\r\n if(str(i) == inputValue[index]):\r\n valueFound = True\r\n\r\n index += 1\r\n\r\n if(valueFound == False):\r\n break\r\n \r\n if(valueFound == False):\r\n outputValue = 'Not Fascinating'\r\n else:\r\n outputValue = 'Fascinating'\r\n \r\n else:\r\n outputValue = 'Number should be atleast three digits'\r\n print(outputValue)\r\n","sub_path":"Geeks For Geeks/Fascinating Number.py","file_name":"Fascinating Number.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"523818168","text":"#!/usr/bin/env python\r\nimport rospy\r\nimport cv2\r\nimport numpy as np\r\nfrom cv_bridge import CvBridge, CvBridgeError\r\nfrom geometry_msgs.msg import Twist\r\nfrom sensor_msgs.msg import Image\r\nfrom rgb_hsv import BGR_HSV\r\n\r\n\r\nclass LineFollower(object):\r\n def __init__(self, rgb_to_track, colour_error_perc = 10.0,colour_cal=False, camera_topic=\"/raspicam_node/image_raw\", cmd_vel_topic=\"/cmd_vel\"):\r\n\r\n self._colour_cal = colour_cal\r\n self._colour_error_perc = colour_error_perc\r\n self.rgb_hsv = BGR_HSV()\r\n self.hsv, hsv_numpy_percentage = self.rgb_hsv.rgb_hsv(rgb=rgb_to_track)\r\n (self.major, minor, _) = cv2.__version__.split(\".\")\r\n\r\n self.process_this_frame = True\r\n\r\n self.bridge_object = CvBridge()\r\n self.image_sub = rospy.Subscriber(camera_topic, Image, self.camera_callback)\r\n self.cmd_vel_pub = rospy.Publisher(cmd_vel_topic, Twist, queue_size=1)\r\n\r\n def camera_callback(self, data):\r\n\r\n if self.process_this_frame:\r\n self.process_this_frame = False\r\n try:\r\n cv_image = self.bridge_object.imgmsg_to_cv2(data, desired_encoding=\"bgr8\")\r\n except CvBridgeError as e:\r\n print(e)\r\n\r\n small_frame = cv2.resize(cv_image, (0, 0), fx=0.2, fy=0.2)\r\n\r\n height, width, channels = small_frame.shape\r\n\r\n crop_img = small_frame\r\n\r\n hsv = cv2.cvtColor(crop_img, cv2.COLOR_BGR2HSV)\r\n\r\n min_hsv = self.hsv * (1.0-(self._colour_error_perc / 100.0))\r\n max_hsv = self.hsv * (1.0 + (self._colour_error_perc / 100.0))\r\n lower_yellow = np.array(min_hsv)\r\n upper_yellow = np.array(max_hsv)\r\n\r\n mask = cv2.inRange(hsv, lower_yellow, upper_yellow)\r\n\r\n res = cv2.bitwise_and(crop_img, crop_img, mask=mask)\r\n\r\n if self.major == '3':\r\n # If its 3\r\n (_, contours, _) = cv2.findContours(mask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)\r\n\r\n else:\r\n # If its 2 or 4\r\n (contours, _) = cv2.findContours(mask, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_TC89_L1)\r\n centres = []\r\n for i in range(len(contours)):\r\n moments = cv2.moments(contours[i])\r\n try:\r\n centres.append((int(moments['m10'] / moments['m00']), int(moments['m01'] / moments['m00'])))\r\n cv2.circle(res, centres[-1], 10, (0, 255, 0), -1)\r\n except ZeroDivisionError:\r\n pass\r\n\r\n rospy.loginfo(str(centres))\r\n most_right_centroid_index = 0\r\n index = 0\r\n max_x_value = 0\r\n\r\n centroids_detected = []\r\n\r\n for candidate in centres:\r\n cx = candidate[0]\r\n if cx >= max_x_value:\r\n max_x_value = cx\r\n most_right_centroid_index = index\r\n index += 1\r\n\r\n try:\r\n cx = centres[most_right_centroid_index][0]\r\n cy = centres[most_right_centroid_index][1]\r\n except:\r\n cy, cx = height / 2, width / 2\r\n\r\n centroids_detected.append([cx,cy])\r\n cv2.circle(res, (int(cx), int(cy)), 5, (0, 0, 255), -1)\r\n\r\n if self._colour_cal:\r\n cv2.imshow(\"Original\", small_frame)\r\n else:\r\n cv2.imshow(\"HSV\", hsv)\r\n cv2.imshow(\"MASK\", mask)\r\n cv2.imshow(\"RES\", res)\r\n \r\n if len(centroids_detected) > 0:\r\n \r\n cx_final = width\r\n cy_final = height\r\n \r\n for centroid in centroids_detected:\r\n print(centroid)\r\n if centroid[1]< cy_final:\r\n cx_final = centroid[0]\r\n cy_final = centroid[1]\r\n print(\"Selected CENTROID AS FINAL\")\r\n else:\r\n cx_final = None\r\n cy_final = None\r\n \r\n self.move_robot(height, width, cx_final, cy_final)\r\n\r\n cv2.waitKey(1)\r\n else:\r\n self.process_this_frame = True\r\n \r\n \r\n \r\n def move_robot(self, image_dim_y, image_dim_x, cx, cy, linear_vel_base = 0.1, angular_vel_base = 0.1):\r\n \r\n cmd_vel = Twist()\r\n cmd_vel.linear.x = 0.0\r\n cmd_vel.angular.z = 0.0\r\n \r\n FACTOR_LINEAR = 0.001\r\n FACTOR_ANGULAR = 0.1\r\n \r\n \r\n if cx is not None and cy is not None:\r\n origin = [image_dim_x / 2.0, image_dim_y / 2.0]\r\n centroid = [cx, cy]\r\n delta = [centroid[0] - origin[0], centroid[1]]\r\n \r\n \r\n \r\n cmd_vel.angular.z = angular_vel_base * delta[0] * FACTOR_ANGULAR * -1\r\n cmd_vel.linear.x = linear_vel_base - delta[1] * FACTOR_LINEAR\r\n \r\n else:\r\n cmd_vel.angular.z = angular_vel_base * 2\r\n cmd_vel.linear.x = linear_vel_base * 0.5\r\n \r\n self.cmd_vel_pub.publish(cmd_vel)\r\n\r\n def loop(self):\r\n rospy.spin()\r\n\r\nif __name__ == '__main__':\r\n rospy.init_node('line_follower_start', anonymous=True)\r\n rgb_to_track = [255,255,255]\r\n robot_mover = LineFollower(rgb_to_track=rgb_to_track, colour_error_perc= 20.0, colour_cal=False)\r\n robot_mover.loop()\r\n","sub_path":"line_follower_sim.py","file_name":"line_follower_sim.py","file_ext":"py","file_size_in_byte":5487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"621829702","text":"import random\nimport sys\nimport time\ndef mengetik(s):\n for c in s + '\\n':\n sys.stdout.write(c)\n sys.stdout.flush()\n time.sleep(random.random() * 0.2)\nmengetik('EXIT IN MY TOOLS')\nmengetik('Follow Me On The Social Media')\nmengetik('My Github : https://github.com/RabbitCL4Y')\nmengetik('My Instagram : https://www.instagram.com/muhammadrafli_337')\nmengetik('Contact Me : RabbitCL4Y123@gmail.com')\nmengetik('Every successful person must have a failure. Do not be afraid to fail because failureis a part of success')\n\n","sub_path":"woi.py","file_name":"woi.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"260163082","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/argos/Workspace/mamba-framework/mamba-server/mamba_server/__init__.py\n# Compiled at: 2020-05-09 09:17:44\n# Size of source mod 2**32: 469 bytes\n\"\"\"\nMamba - a framework for controlling ground equipment\n\"\"\"\nimport sys\n__all__ = [\n '__version__', 'version_info']\nimport pkgutil\n__version__ = pkgutil.get_data(__package__, 'VERSION').decode('ascii').strip()\nversion_info = tuple(((int(v) if v.isdigit() else v) for v in __version__.split('.')))\ndel pkgutil\nif sys.version_info < (3, 5):\n print('Mamba %s requires Python 3.5' % __version__)\n sys.exit(1)","sub_path":"pycfiles/Mamba_Server-0.0.9-py2.py3-none-any/__init__.cpython-37.py","file_name":"__init__.cpython-37.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"618547850","text":"\"\"\"General.py.\"\"\"\nimport os\nimport errno\nimport pandas as pd\nimport logging\nimport requests\nimport shutil\nfrom datetime import datetime, timedelta, date\nfrom dateutil import tz\nimport pendulum\n\nimport subprocess\nimport csv\nimport json\n\nfrom airflow.models import Variable\nfrom airflow.hooks.base_hook import BaseHook\n\ndef get_last_run(dag):\n last_dag_run = dag.get_last_dagrun()\n if last_dag_run is None:\n return pendulum.now()\n else:\n return last_dag_run.execution_date\n\n\ndef seven_days_ago():\n \"\"\"Return the date seven days ago.\"\"\"\n return datetime.combine(datetime.today() - timedelta(7),\n datetime.min.time())\n\n\ndef today():\n \"\"\"Return today's date.\"\"\"\n return datetime.combine(datetime.today(), datetime.min.time())\n\n\ndef get_year(the_date=datetime.now()):\n \"\"\"Get current year, or year for date passed.\"\"\"\n return the_date.strftime(\"%Y\")\n\n\ndef get_today_date(the_date=datetime.now()):\n \"\"\"Return today's date (no time) as string.\"\"\"\n return the_date.strftime(\"%Y-%m-%d\")\n\ndef get_date_1wk_ago():\n \"\"\" Return last week's date as string.\"\"\"\n return (date.today() - timedelta(days=6)).strftime(\"%d\")\n\ndef get_date_3mo_ago():\n \"\"\"Return 3 months ago date as string.\"\"\"\n return (date.today() - timedelta(3*365/12)).isoformat()\n\n\ndef get_date_6mo_ago():\n \"\"\"Return 6 months ago date as string.\"\"\"\n return (date.today() - timedelta(6*365/12)).isoformat()\n\n\ndef get_FY_year(the_date=datetime.now()):\n \"\"\"Return Fiscal Year based on today's date.\"\"\"\n if the_date.month > 6:\n return 'FY' + str(the_date.year - 2000) + '-' + str(the_date.year -\n 1999)\n else:\n return 'FY' + str(the_date.year - 2001) + '-' + str(the_date.year -\n 2000)\n\n\ndef get_prev_FY_year(the_date=datetime.now()):\n \"\"\"!!! Only use for traffic_counts_jobs.py.\"\"\"\n return 'FY' + str(the_date.year - 2001) + '-' + str(the_date.year - 2000)\n\n\ndef get_FY_short(the_date=datetime.now()):\n \"\"\"Return 2-digit current fiscal year as integar\"\"\"\n if the_date.month > 6:\n return the_date.year - 2000\n else:\n return the_date.year - 2001\n\n\ndef utc_to_pst(timestamp_str, in_fmt, out_fmt):\n \"\"\"Convert UTC timestamp to Local time (PST).\"\"\"\n timestamp = datetime.strptime(timestamp_str, in_fmt)\n utc_tz = tz.gettz('UTC')\n pst_tz = tz.gettz('US/Pacific')\n timestamp = timestamp.replace(tzinfo=utc_tz)\n pst_timestamp = timestamp.astimezone(pst_tz)\n return pst_timestamp.strftime(out_fmt)\n\n\ndef buildConfig(env):\n \"\"\"Take the current environment, generate build configuration.\"\"\"\n config = {\n 'env': (env or 'local').upper(),\n 'default_s3_conn_id': 's3data',\n 'prod_data_dir': \"/data/prod\",\n 'temp_data_dir': \"/data/temp\",\n }\n return config\n\n\nconfig = buildConfig(os.environ.get('SD_ENV'))\n\n# https://crontab.guru/\nschedule = {\n 'fd_incidents' : \"0 8 * * *\", # daily at 8am UTC / 1am PST\n 'claims_stat': \"@monthly\",\n 'pd_ripa': None,\n 'pd_cfs': \"0 0 * * *\", # daily at 12am UTC / 5pm PST\n 'pd_col': \"0 0 * * *\", # daily at 12am UTC / 5pm PST\n 'pd_hc': None,\n 'ttcs': '0 10 * * *', # daily at 10 am UTC / 3am PST\n 'indicator_bacteria_tests': \"0 8 * * *\", # daily at 8am UTC / 1am PST\n 'parking_meters': '0 19 * * *', # daily at 7pm UTC, Noon PST\n 'traffic_counts': \"@weekly\",\n 'read': \"0 8 * * *\", # daily at 8am UTC / 1am PST\n 'dsd_approvals': \"0 16 * * 1\", # Weekly on Monday at 4p UTC / 8a PST\n 'streets':\"0 0,1,2,3,4,14,15,16,17,18,19,20,21,22,23 * * 1-6\", # every hour, 6am to 7pm, Mon-Fri PST\n 'get_it_done': \"0 7 * * *\", # daily at 7am UTC / 11pm PST\n 'special_events': \"0 8 * * *\", # daily at 8am UTC / 1am PST\n 'waze': \"*/5 * * * *\", # every 5 minutes\n 'inventory': \"@monthly\", # Run 1x a month at 00:00 of the 1st day of mo\n 'gis_daily': '0 6 * * *', # daily at 6am UTC / 10pm PST\n 'gis_weekly': '0 10 * * 2', # weekly on Tuesday at 10am UTC / 2am PST\n 'budget': \"0 17 * 5-7 5\", # weekly Fridays at 5p UTC / 10am PST\n 'campaign_fin': \"0 11 * * *\", # daily at 11am UTC / 4am PST\n 'public_art': \"0 11 * * *\", # daily at 11am UTC / 4am PST\n 'sire': \"0 8 * * 1-5\", # 8am UTC / 12am PST every Mon-Fri\n 'onbase': \"*/5 0,1,2,3,4,15,16,17,18,19,20,21,22,23 * * 1-6\", # every 5 mins, 7am to 7pm, Mon-Fri PST\n 'documentum_daily' : \"0 8 * * 1-5\", # 8am UTC / 12am PST every Mon-Fri\n 'documentum_hr_30' : \"30 0,1,2,3,4,15,16,17,18,19,20,21,22,23 * * 1-6\", # 30 mins past the hour, 7am to 7pm, Mon-Fri PST\n 'documentum_hr_15': \"15 0,1,2,3,4,15,16,17,18,19,20,21,22,23 * * 1-6\", # 15 mins past the hour, 7am to 7pm, Mon-Fri PST\n 'tsw_integration': '0 6 * * *', # daily at 6am UTC / 10pm PST\n 'cip': \"0 8 * * *\", # daily at 8am UTC / 1am PST\n\t'cityiq': '@daily',\n 'onbase_test': '*/15 * * * *',\n 'gis_tree_canopy': None,\n 'parking_meter_locs': '0 19 * * *', # daily at 7pm UTC\n 'sidewalks': '@monthly',\n 'amcs': \"0 12 * * *\", # Daily at 4p UTC / 5a PST\n 'ga_portal': '@monthly',\n 'pv_prod':'@hourly',\n 'fleet':\"0 12 * * *\", # Daily at 4p UTC / 5a PST\n}\n\ndefault_date = datetime(2019, 10, 8)\n\nstart_date = {\n 'fd_incidents' : default_date,\n 'pd_cfs': default_date,\n 'pd_col': default_date,\n 'pd_hc': default_date,\n 'pd_ripa': datetime(2020, 3, 5),\n 'claims_stat': default_date,\n 'ttcs': default_date,\n 'indicator_bacteria_tests': default_date,\n 'parking_meters': default_date,\n 'traffic_counts': default_date,\n 'read': default_date,\n 'dsd_approvals': default_date,\n 'dsd_code_enforcement': default_date,\n 'streets_sdif': default_date,\n 'streets_imcat': default_date,\n 'streets': default_date,\n 'get_it_done': default_date,\n 'gid_potholes': default_date,\n 'gid_ava': default_date,\n 'special_events': default_date,\n 'waze': default_date,\n 'inventory': default_date,\n 'buffer_post_promo': default_date,\n 'sonar': default_date,\n 'gis_daily': default_date,\n 'gis_weekly': default_date,\n 'budget': default_date,\n 'campaign_fin': default_date,\n 'public_art': default_date,\n 'sire': default_date,\n 'onbase': default_date,\n 'documentum_daily' : datetime(2019, 10, 29),\n 'documentum_hr_30' : datetime(2019, 10, 29),\n 'documentum_hr_15': datetime(2019, 10, 29),\n 'tsw_integration': default_date,\n 'cip': default_date,\n 'cityiq': default_date,\n 'onbase_test': default_date,\n 'gis_tree_canopy': default_date,\n 'parking_meter_locs': datetime(2019, 12, 25),\n 'amcs': datetime(2020, 4, 14),\n 'pv_prod': datetime(2020, 2, 26),\n 'sidewalks': default_date,\n 'ga_portal': datetime(2020, 5, 19),\n 'fleet': datetime(2020, 8, 26)\n}\n\nargs = {\n 'owner': 'airflow',\n 'depends_on_past': False,\n 'email': \"data@sandiego.gov\",\n 'email_on_failure': True,\n 'email_on_retry': False,\n 'retries': 2,\n 'retry_delay': timedelta(minutes=5),\n 'retry_exponential_backoff': True,\n 'max_retry_delay': timedelta(minutes=120)\n #'on_failure_callback': notify,\n #'on_retry_callback': notify,\n #'on_success_callback': notify\n # TODO - on failure callback can be here,\n # TODO - look into sla\n # 'queue': 'bash_queue',\n # 'pool': 'backfill',\n # 'priority_weight': 10,\n # 'end_date': datetime(2016, 1, 1),\n}\n\ndef create_path_if_not_exists(path):\n \"\"\"Create path if it does not exist.\"\"\"\n try:\n os.makedirs(path)\n logging.info('Created path at ' + path)\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise\n return path\n\n\ndef pos_write_csv(df, fname, **kwargs):\n \"\"\"Write csv file, creating paths as needed, with default confs.\"\"\"\n default = {\n 'index': False,\n 'encoding': 'utf-8',\n 'doublequote': True,\n 'date_format': \"%Y-%m-%d\",\n 'quoting': csv.QUOTE_ALL\n }\n csv_args = default.copy()\n csv_args.update(kwargs)\n try:\n os.makedirs(os.path.dirname(fname))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise\n\n df.to_csv(fname, **csv_args)\n\ndef sf_write_csv(df, fname, **kwargs):\n \"\"\" Write compressed csv for snowflake \"\"\"\n fname_full = f\"{config['prod_data_dir']}/{fname}_snowflake.csv.gz\"\n default = {\n 'index':False,\n 'header':False,\n 'quoting':csv.QUOTE_MINIMAL,\n 'compression':'gzip',\n 'doublequote':True,\n 'na_rep':\"NULL\",\n 'date_format':\"%Y-%m-%d %H:%M:%S\",\n 'escapechar':'\\\\'\n }\n csv_args = default.copy()\n csv_args.update(kwargs)\n df.to_csv(fname_full, **csv_args)\n\ndef file_to_string(rel_file_path, caller=None):\n \"\"\"Read a file into a string variable. Caller is __file___.\"\"\"\n if caller:\n rel_file_path = expand_rel_path(caller, rel_file_path)\n\n with open(rel_file_path, 'r') as ftoread:\n fstring = ftoread.read()\n return fstring\n\n\ndef expand_rel_path(caller, rel_path):\n \"\"\"Expand a relative path.\"\"\"\n return os.path.join(os.path.dirname(os.path.realpath(caller)), rel_path)\n\n\ndef merge_dicts(orig, update):\n new_dict = orig.copy()\n new_dict.update(update)\n return new_dict\n","sub_path":"poseidon/trident/util/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":9335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"584389716","text":"import tensorflow as tf\nimport numpy as np\nfrom template.misc import S, GLOBAL\n# from model.util import layer_from_name\n\n\nclass RayGrad(tf.train.AdamOptimizer):\n\n def __init__(self, learning_rate=0.1, epsilon=None, use_locking=False):\n super(RayGrad, self).__init__(learning_rate, epsilon=epsilon, use_locking=use_locking)\n self.learning_rate = learning_rate\n self.epsilon = epsilon\n self.memory_size = S(\"optimizer.memory_size\")\n self.loss_collect_last = S(\"optimizer.collect_last\")\n\n def minimize(self, loss, global_step=None, var_list=None, aggregation_method=None, colocate_gradients_with_ops=False, name=None, grad_loss=None):\n\n # compute (meaned) gradients for a batch\n grads_and_vars = self.compute_gradients(loss, var_list=var_list, aggregation_method=aggregation_method, colocate_gradients_with_ops=colocate_gradients_with_ops, grad_loss=grad_loss)\n\n # check if any trainable variables provided\n for g,v in grads_and_vars:\n if g is None:\n print(\"Gradient of '\"+v.name+\"' is 'None'. Ignoring\")\n grads_and_vars = [(g,v) for g,v in grads_and_vars if g is not None]\n\n # default adam does:\n # return self.apply_gradients(grads_and_vars, global_step=global_step, name=name)\n\n # get all trainable variables\n variables = [v for g,v in grads_and_vars]\n\n # create a copy of all trainable variables with `0` as initial values\n with tf.name_scope(\"optimizer\"):\n gradient_sum = [tf.get_variable(v.name.replace(\":0\",\"_sum\"), initializer=tf.zeros_like(v.initialized_value()),trainable=False) for v in variables]\n\n def capacity_gradient(grad_sum,grad,name,var):\n if \"hiddenWeight\" in name and \"weight_gradient\" in GLOBAL:\n return GLOBAL[\"weight_gradient\"](grad_sum,grad,var)\n return grad_sum + grad\n\n with tf.control_dependencies([GLOBAL[\"memory_step\"]]):\n\n # collect the batch gradient into accumulated vars\n gradient_sum_update = [\n gs.assign(\n tf.where(GLOBAL[\"memory_step\"]>0,\n capacity_gradient(gs,g,v.name,v),\n g)\n )\n for gs,(g,v) in zip(gradient_sum,grads_and_vars)\n ]\n\n with tf.control_dependencies(gradient_sum_update):\n train_step = tf.cond(GLOBAL[\"memory_step\"] >= S(\"optimizer.memory_size\")-1,\n true_fn=lambda: self.apply_gradients([(gs/S(\"optimizer.memory_size\"), v) for gs,(g,v) in zip(gradient_sum,grads_and_vars)], global_step),\n false_fn=lambda: tf.no_op())\n\n return train_step\n\n\n\n# optimizer = tf.train.AdamOptimizer\noptimizer = RayGrad\n# optimizer = NoOptimizer\n","sub_path":"model/small_conv/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"519391167","text":"# https://leetcode.com/problems/word-search/submissions/\n\nclass Solution:\n def DFS(self,row, col, curr, board, word):\n if 0 <= row < len(board) and 0 <= col < len(board[0]) and board[row][col] == word[curr]: \n temp = board[row][col]\n board[row][col] = '#'\n\n if curr == len(word)-1:\n return True\n\n result = self.DFS(row-1, col, curr+1, board, word) or self.DFS(row+1, col, curr+1, board, word) or self.DFS(row, col-1, curr+1, board, word) or self.DFS(row, col+1, curr+1, board, word)\n\n board[row][col] = temp\n\n return result\n else:\n return False\n \n \n def exist(self, board: List[List[str]], word: str) -> bool: \n\n for row in range(len(board)):\n for col in range(len(board[0])):\n\n check = self.DFS(row, col, 0, board, word)\n \n if check:\n return True\n \n return False\n ","sub_path":"word-search/word-search.py","file_name":"word-search.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"605588140","text":"from random import choice\nimport re\n\nimport helpers\n\n# list with symbols that end a sentence\nEOS = ['.', '?', '!', ':', '\"']\nEOL = [',', ';']\nEOSL = EOS + EOL\n\nclass MarkovGenerator(object):\n def __init__(self, messageFilename, reasonsFilename, topicFilename):\n self.messageDict = self._generateDictionary(messageFilename)\n self.messageStartList = self._generateStartList(self.messageDict)\n self.reasonsDict = self._generateDictionary(reasonsFilename)\n self.reasonsStartList = self._generateStartList(self.reasonsDict)\n self.topicDict = self._generateDictionary(topicFilename)\n self.topicStartList = self._generateStartList(self.topicDict)\n\n def _generateDictionary(self, filename):\n # build morkov dictionary from words in given file\n words = helpers.splitFileToList(filename)\n dictionary = {}\n for i, word in enumerate(words):\n try:\n first, second, third = words[i], words[i+1], words[i+2]\n except IndexError:\n return dictionary\n key = (first, second)\n if key not in dictionary:\n dictionary[key] = []\n dictionary[key].append(third)\n\n def _generateStartList(self, dictionary):\n # generate possible line starts (if the first letter is uppercase)\n return [key for key in dictionary.keys() if key[0][0].isupper()]\n\n def _generateSentence(self, dictionary, startList, endList):\n # generates message sentence\n key = choice(startList)\n sentenceList = []\n\n first, second = key\n sentenceList.append(first)\n sentenceList.append(second)\n while True:\n try:\n third = choice(dictionary[key])\n except KeyError:\n return ' '.join(sentenceList)\n sentenceList.append(third)\n if third[-1] in endList:\n return ' '.join(sentenceList)\n key = (second, third)\n first, second = key\n\n def generateMessages(self):\n return [i for i in re.split('[\",;\\-]', self._generateSentence(self.messageDict, self.messageStartList, EOS)) if i]\n\n def generateMessage(self):\n return self._generateSentence(self.messageDict, self.messageStartList, EOSL)\n\n def generateReason(self):\n return self._generateSentence(self.reasonsDict, self.reasonsStartList, EOSL)\n\n def generateTopic(self):\n return self._generateSentence(self.topicDict, self.topicStartList, EOSL)\n","sub_path":"markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":2514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"18040183","text":"\"\"\"\nGeneric collector code to run config file\n\"\"\"\n\nfrom inputs.serial_connector import SerialSensor\nfrom connectors import (dweet, file_system, io_adafruit, influx)\nfrom settings import SERVICES\nfrom time import sleep\n\n\ndef run():\n sensor_reader = SerialSensor()\n\n reading = sensor_reader.read()\n\n for name, setting in SERVICES.iteritems():\n if name == 'DWEET_NAME':\n conn = dweet.DweetConnector(setting)\n elif name == 'ADAFRUITIO_KEY':\n conn = io_adafruit.IOAdafruitConnector(setting)\n elif name == 'FILE_SYSTEM_PATH':\n conn = file_system.FileSystemConnector(setting)\n elif name == 'INFLUXDB':\n conn = influx.InfluxDBConnector(setting)\n\n conn.send(reading)\n\nif __name__ == \"__main__\":\n run()\n","sub_path":"src/zeep/cron_collector.py","file_name":"cron_collector.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"438413693","text":"from galileo_sdk.compat import mock\nfrom galileo_sdk.business.objects.lz import (\n ELzStatus,\n Lz,\n UpdateLzRequest,\n)\nfrom galileo_sdk.business import LzService\n\nBACKEND = \"http://BACKEND\"\nMID = \"machine_id\"\nAMOUNT = 10\n\n# Arrange\nsettings_repo = mock.Mock()\nsettings_repo.get_settings().backend = BACKEND\nauth_provider = mock.Mock()\nauth_provider.get_access_token.return_value = \"ACCESS_TOKEN\"\nmachines_repo = mock.Mock()\nmachines_service = LzService(machines_repo)\n\n\ndef test_get_machine_by_id():\n x = 1\n machines_repo.get_lz_by_id.return_value = Lz(\n lz_id=str(x),\n gpu_count=str(x),\n cpu_count=str(x),\n arch=str(x),\n memory=str(x),\n name=str(x),\n operating_system=str(x),\n status=ELzStatus.online,\n userid=str(x),\n container_technology=\"container_tech\",\n job_runner=\"job runner\",\n memory_amount=str(x),\n )\n\n # Call\n r = machines_service.get_lz_by_id(MID)\n\n # Assert\n assert r.userid == str(x)\n\n\ndef test_list_machines():\n machines_repo.list_lz.return_value = [\n Lz(\n lz_id=str(x),\n gpu_count=str(x),\n cpu_count=str(x),\n arch=str(x),\n memory=str(x),\n name=str(x),\n operating_system=str(x),\n status=ELzStatus.online,\n userid=str(x),\n container_technology=\"container_tech\",\n job_runner=\"job runner\",\n memory_amount=str(x),\n )\n for x in range(5)\n ]\n\n # Call\n r = machines_service.list_lz()\n\n # Assert\n for i in range(5):\n assert r[i].userid == str(i)\n\n\ndef test_update():\n x = 1\n machines_repo.update.return_value = Lz(\n lz_id=str(x),\n gpu_count=str(x),\n cpu_count=str(x),\n arch=str(x),\n memory=str(x),\n name=str(x),\n operating_system=str(x),\n status=ELzStatus.online,\n userid=str(x),\n container_technology=\"container_tech\",\n job_runner=\"job runner\",\n memory_amount=str(x),\n )\n\n # Call\n r = machines_service.update(UpdateLzRequest(lz_id=MID))\n\n # Assert\n assert r.userid == str(x)\n","sub_path":"tests/unit/services/test_machines_service.py","file_name":"test_machines_service.py","file_ext":"py","file_size_in_byte":2179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"22986365","text":"import collections\n\n\n# https://blog.csdn.net/fuxuemingzhu/article/details/80526298\nclass Solution:\n def leastBricks(self, wall):\n \"\"\"\n :type wall: List[List[int]]\n :rtype: int : return the number of crossed bricks\n \"\"\"\n left_counter = collections.Counter()\n count = 0\n for row in wall:\n left = 0\n for i in range(len(row) - 1):\n left += row[i]\n left_counter.update([left])\n count = max(count, left_counter[left])\n return len(wall) - count\n","sub_path":"Solutions/554. Brick Wall/554.py","file_name":"554.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"624475887","text":"import cv2\nimport time\nimport argparse\nimport os\nimport torch\nimport csv\nimport numpy\nimport posenet\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model', type=int, default=101)\nparser.add_argument('--scale_factor', type=float, default=1.0)\nparser.add_argument('--notxt', action='store_true')\nparser.add_argument('--video_dir', type=str, default='./videos')\nparser.add_argument('--output_dir', type=str, default='./output_csv')\nargs = parser.parse_args()\n\n\ndef main():\n model = posenet.load_model(args.model)\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') ###\n model = model.to(device) ###\n output_stride = model.output_stride\n\n if args.output_dir:\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n \n # filenames return a array of the names of the files inside the directory\n # please make sure the file is video and only one person is in the video\n filenames = [f.path for f in os.scandir(args.video_dir) if f.is_file()] \n\n for f in filenames:\n csv_path = os.path.join(args.output_dir, os.path.relpath(f, args.video_dir))\n csv_path = csv_path[0:csv_path.rfind('.')]\n csv_path += \".csv\"\n #csv_content = [[\"nose.x\", \"nose.y\", \"leftEye.x\", \"leftEye.y\", \"rightEye.x\", \"rightEye.y\", \"leftEar.x\", \"leftEar.y\", \"rightEar.x\", \"rightEar.y\", \"leftShoulder.x\", \"leftShoulder.y\", \"rightShoulder.x\", \"rightShoulder.y\", \"leftElbow.x\", \"leftElbow.y\", \"rightElbow.x\", \"rightElbow.y\", \"leftWrist.x\", \"leftWrist.y\", \"rightWrist.x\", \"rightWrist.y\", \"leftHip.x\", \"leftHip.y\", \"rightHip.x\", \"rightHip.y\", \"leftKnee.x\", \"leftKnee.y\", \"rightKnee.x\", \"rightKnee.y\", \"leftAnkle.x\", \"leftAnkle.y\", \"rightAnkle.x\", \"rightAnkle.y\"]]\n csv_content = [[\"nose.x\", \"leftEye.x\", \"rightEye.x\", \"leftEar.x\", \"rightEar.x\", \"leftShoulder.x\", \"rightShoulder.x\", \"leftElbow.x\", \"rightElbow.x\", \"leftWrist.x\", \"rightWrist.x\", \"leftHip.x\", \"rightHip.x\", \"leftKnee.x\", \"rightKnee.x\", \"leftAnkle.x\", \"rightAnkle.x\", \"nose.y\", \"leftEye.y\", \"rightEye.y\", \"leftEar.y\", \"rightEar.y\", \"leftShoulder.y\", \"rightShoulder.y\", \"leftElbow.y\", \"rightElbow.y\", \"leftWrist.y\", \"rightWrist.y\", \"leftHip.y\", \"rightHip.y\", \"leftKnee.y\", \"rightKnee.y\", \"leftAnkle.y\", \"rightAnkle.y\"]]\n csv_content_coordinates = []\n csv_content_coordinates_x = []\n csv_content_coordinates_y = []\n\n cap = cv2.VideoCapture(f)\n frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)\n height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n #print('Total number of frames in this video is #%d' % frame_count)\n no_of_frame = 0\n\n right_most_x = 0\n left_most_x = width\n uppest_y = height\n lowest_y = 0\n\n while no_of_frame < frame_count:\n no_of_frame += 1 # haven't implement pick frame, read frame one by one\n input_image, display_image, output_scale = posenet.read_cap(cap, scale_factor=args.scale_factor, output_stride=output_stride)\n if input_image is None:\n break\n\n with torch.no_grad():\n input_image = torch.Tensor(input_image).to(device) ###\n\n heatmaps_result, offsets_result, displacement_fwd_result, displacement_bwd_result = model(input_image)\n\n pose_scores, keypoint_scores, keypoint_coords = posenet.decode_multiple_poses(\n heatmaps_result.squeeze(0),\n offsets_result.squeeze(0),\n displacement_fwd_result.squeeze(0),\n displacement_bwd_result.squeeze(0),\n output_stride=output_stride,\n max_pose_detections=10,\n min_pose_score=0.15)\n\n keypoint_coords *= output_scale\n\n if not args.notxt:\n #print(\"Frame No.%s\" % no_of_frame)\n #print(\"Results for video: %s\" % f)\n for pi in range(len(pose_scores)): # there should be one pi only -> one person in the video\n if pose_scores[pi] == 0.: # drop frames that movements cannot be recognized\n break\n #print('Pose #%d, score = %f' % (pi, pose_scores[pi]))\n #row = []\n row_x = []\n row_y = []\n #for ki, (s, c) in enumerate(zip(keypoint_scores[pi, :], keypoint_coords[pi, :, :])):\n # print('Keypoint %s, score = %f, coord = %s' % (posenet.PART_NAMES[ki], s, c))\n\n for i in range(17):\n x_coordinate = float(keypoint_coords[pi][i][0])\n y_coordinate = float(keypoint_coords[pi][i][1])\n if(x_coordinate < left_most_x):\n left_most_x = x_coordinate\n if(x_coordinate > right_most_x):\n right_most_x = x_coordinate\n if(y_coordinate > lowest_y):\n lowest_y = y_coordinate\n if(y_coordinate < uppest_y):\n uppest_y = y_coordinate\n\n row_x.append(float(keypoint_coords[pi][i][0]))\n row_y.append(float(keypoint_coords[pi][i][1]))\n #row.append(keypoint_coords[pi][i][0])\n\n csv_content_coordinates_x.append(row_x)\n csv_content_coordinates_y.append(row_y)\n #csv_content.append(row)\n \n csv_content_coordinates_x = numpy.array(csv_content_coordinates_x)\n csv_content_coordinates_y = numpy.array(csv_content_coordinates_y)\n csv_content_coordinates_x = csv_content_coordinates_x - left_most_x\n csv_content_coordinates_y = csv_content_coordinates_y - uppest_y \n body_width = right_most_x - left_most_x\n body_height = lowest_y - uppest_y\n csv_content_coordinates_x = csv_content_coordinates_x/body_width\n csv_content_coordinates_y = csv_content_coordinates_y/body_height\n csv_content_coordinates = numpy.append(csv_content_coordinates_x, csv_content_coordinates_y , axis=1)\n print(\"shape of the %s content is: \" % f)\n print(csv_content_coordinates.shape)\n #csv_content.append(csv_content_coordinates.tolist())\n\n with open(csv_path, 'w', newline='') as csvfile:\n writer = csv.writer(csvfile)\n writer.writerows(csv_content)\n writer.writerows(csv_content_coordinates)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"batchconvert.py","file_name":"batchconvert.py","file_ext":"py","file_size_in_byte":6591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"194197200","text":"#import setGPU\nimport os\nimport numpy as np\nimport h5py\nimport glob\nimport itertools\nimport sys\nfrom sklearn.utils import shuffle\nimport setGPU\n\ninputFile = sys.argv[1]\npenaltyValue = float(sys.argv[2])\n\nf = h5py.File(inputFile, 'r')\nprint(f.keys())\n\nX1_train = np.array(f.get(\"J1_train\"), dtype=np.float32)\nX1_train = np.concatenate((X1_train, np.array(f.get(\"J2_train\"), dtype=np.float32)), axis=2)\nY_train = np.array(f.get(\"EMD_train\"), dtype=np.float32)\n\nX1_val = np.array(f.get(\"J1_val\"), dtype=np.float32)\nX1_val = np.concatenate((X1_val, np.array(f.get(\"J2_val\"), dtype=np.float32)), axis=2)\nY_val = np.array(f.get(\"EMD_val\"), dtype=np.float32)\n\nX1_val = X1_val[:200000,:,:]\nY_val = Y_val[:200000]\n\nX1_train = np.reshape(X1_train, (X1_train.shape[0], X1_train.shape[1], X1_train.shape[2], 1))\nX1_val = np.reshape(X1_val, (X1_val.shape[0], X1_val.shape[1], X1_val.shape[2], 1))\n\nprint(X1_train.shape, Y_train.shape)\nprint(X1_val.shape, Y_val.shape)\n\n# keras imports\nfrom tensorflow.keras import models, layers, utils\nfrom tensorflow.keras import regularizers\nimport tensorflow as tf\nfrom tensorflow.keras import metrics\nfrom tensorflow.keras import callbacks \nfrom tensorflow.keras import optimizers\n\nimage_shape = (X1_train.shape[-3],X1_train.shape[-2],X1_train.shape[-1])\n\ninput1 = layers.Input(shape=(image_shape))\n#\nx = layers.BatchNormalization()(input1)\nx = layers.Conv2D(32, kernel_size=(3,3), data_format=\"channels_last\", strides=(1, 1), padding=\"valid\", \n input_shape=image_shape, kernel_initializer='lecun_uniform')(x)\nx = layers.BatchNormalization()(x)\nx = layers.Activation(\"relu\")(x)\nx = layers.AveragePooling2D(pool_size=(2,1))(x)\nx = layers.Conv2D(16, kernel_size=(3,3), data_format=\"channels_last\", strides=(1, 1), padding=\"valid\",\n kernel_initializer='lecun_uniform')(x)\nx = layers.BatchNormalization()(x)\nx = layers.Activation(\"relu\")(x)\nx = layers.AveragePooling2D(pool_size=(2,1))(x)\nx = layers.Conv2D(8, kernel_size=(3,2), data_format=\"channels_last\", strides=(1, 1), padding=\"valid\",\n kernel_initializer='lecun_uniform')(x)\nx = layers.BatchNormalization()(x)\nx = layers.Activation(\"relu\")(x)\nx = layers.AveragePooling2D(pool_size=(2,1))(x)\nx = layers.Flatten()(x)\n#\nx = layers.Dense(80, activation=\"relu\", kernel_initializer='lecun_uniform', name='dense_relu1')(x)\nx = layers.Dropout(0.2)(x)\n#\nx = layers.Dense(40, activation=\"relu\", kernel_initializer='lecun_uniform', name='dense_relu2')(x)#\nx = layers.Dropout(0.2)(x)\n#\nx = layers.Dense(20, activation=\"relu\", kernel_initializer='lecun_uniform', name='dense_relu3')(x)\nx = layers.Dropout(0.2)(x)\n#\noutput = layers.Dense(1, activation='linear', kernel_initializer='lecun_uniform')(x) \nmodel = models.Model(inputs=input1, outputs=output)\n\nmodel.compile(optimizer=optimizers.Adam(), loss='mae')\nmodel.summary()\n\nmy_callbacks = [\n callbacks.EarlyStopping(patience=10, verbose=1),\n #callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=2, verbose=1),\n callbacks.TerminateOnNaN()]\n\n# train \nhistory = model.fit(X1_train, Y_train, epochs=500, batch_size=128, verbose = 2,\n validation_data=(X1_val, Y_val), callbacks = my_callbacks)\n\nnameModel = 'EMD_Conv2D_MAE'\n#nameModel = 'EMD_Dense_MAPE'\n#nameModel = 'EMD_Dense_MAE_AsymmetryLarge_%s' %sys.argv[1]\n\n# store history\nf = h5py.File(\"models/%s_history.h5\" %nameModel, \"w\")\nf.create_dataset(\"training_loss\", data=np.array(history.history['loss']),compression='gzip')\nf.create_dataset(\"validation_loss\", data=np.array(history.history['val_loss']),compression='gzip')\nf.close()\n\n# store model\nmodel_json = model.to_json()\nwith open(\"models/%s.json\" %nameModel, \"w\") as json_file:\n json_file.write(model_json)\nmodel.save_weights(\"models/%s.h5\" %nameModel)\n","sub_path":"EMD_Conv2D_Train.py","file_name":"EMD_Conv2D_Train.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"505714853","text":"#!/usr/bin/env python\nimport smbus\nimport time\nimport datetime\nimport curses\nimport MySQLdb\n\n#ADAFRUIT:\nimport Adafruit_DHT\nAdasensor = Adafruit_DHT.DHT11\npin = 26\nlastTemp = 0;\n\n# 2014-08-26 PCF8591-x.py\n\n# Connect Pi 3V3 - VCC, Ground - Ground, SDA - SDA, SCL - SCL.\n\n# ./PCF8591-x.py\n\n\nbus = smbus.SMBus(1)\n\nstdscr = curses.initscr()\ncurses.noecho()\ncurses.cbreak()\n\naout = 0\n\nstdscr.addstr(5, 20, \"BioBox Sensor Reading:\")\nstdscr.addstr(6, 20, \"____________________\")\n\nstdscr.addstr(10, 0, \"Temp\")\nstdscr.addstr(12, 0, \"Light\")\nstdscr.addstr(14, 0, \"Moist\")\n# stdscr.addstr(16, 0, \"Resistor\")\n\nstdscr.nodelay(1)\n\n\ntry:\n\n while True:\n\n #for a in range(0,3):\n # aout = aout + 1\n # bus.write_byte_data(0x48,0x40 | ((a+1) & 0x03), aout)\n # v = bus.read_byte(0x48)\n # hashes = v / 4\n # spaces = 64 - hashes\n # stdscr.addstr(10+a*2, 12, str(v) + ' ')\n # stdscr.addstr(10+a*2, 16, '#' * hashes + ' ' * spaces )\n \n #temp (AIN1)\n aout = aout + 1\n bus.write_byte_data(0x48,0x40 | ((2) & 0x03), aout)\n tempRead = bus.read_byte(0x48)\n hashes = int(tempRead) / 4\n spaces = 64 - hashes\n stdscr.addstr(10, 12, str(int(tempRead)) + ' ')\n stdscr.addstr(10, 16, '#' * hashes + ' ' * spaces )\n #light (AIN2)\n aout = aout + 1\n #print('light: '+aout)\n bus.write_byte_data(0x48,0x40 | ((3) & 0x03), aout)\n lightRead = bus.read_byte(0x48)\n hashes = lightRead / 4\n spaces = 64 - hashes\n stdscr.addstr(12, 12, str(lightRead) + ' ')\n stdscr.addstr(12, 16, '#' * hashes + ' ' * spaces )\n #moisture (AIN3)\n aout = aout + 1\n bus.write_byte_data(0x48,0x40 | ((4) & 0x03), aout)\n moistRead = bus.read_byte(0x48)\n hashes = moistRead / 4\n spaces = 64 - hashes\n stdscr.addstr(14, 12, str(moistRead) + ' ')\n stdscr.addstr(14, 16, '#' * hashes + ' ' * spaces )\n\n \n datetime = (time.strftime(\"%Y-%m-%d \") + time.strftime(\"%H:%M:%S\"))\n\n #Server Connection to MySQL:\n conn = MySQLdb.connect(host= \"localhost\",\n user=\"root\",\n passwd=\"raspberry\",\n db=\"sensor_database\")\n x = conn.cursor()\n try:\n if humidity is not None and tempRead is not None:\n lastTemp = tempRead\n x.execute(\"\"\"INSERT INTO plant_log VALUES (%s,%s,%s,%s)\"\"\",(datetime,int(tempRead),lightRead,moistRead))\n conn.commit()\n else:\n x.execute(\"\"\"INSERT INTO plant_log VALUES (%s,%s,%s,%s)\"\"\",(datetime,int(lastTemp),lightRead,moistRead))\n conn.commit()\n except:\n conn.rollback()\n conn.close() \n #End servier connection\n\n \n stdscr.refresh()\n time.sleep(.5)\n\n c = stdscr.getch()\n\n if c != curses.ERR:\n break\n\nexcept:\n pass\n\ncurses.nocbreak()\ncurses.echo()\ncurses.endwin()\n","sub_path":"i2cReadAllCurses.py","file_name":"i2cReadAllCurses.py","file_ext":"py","file_size_in_byte":2897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"306992226","text":"# -*- coding:utf-8 -*-\nimport baostock as bs\nimport sys\nimport os\nimport pandas as pd\n\nfrom stock.base_stock_info import BaseStockInfo\n\n\nclass BuildLabel(object):\n\n def __init__(self, input_path, output_path):\n\n self.input_paths = []\n self.output_path = output_path\n self.output_labels = None\n\n if os.path.isdir(input_path):\n sub_file_name_lists = os.listdir(input_path)\n for file_name in sub_file_name_lists:\n self.input_paths.append(os.path.join(input_path, file_name))\n else:\n self.input_paths.append(input_path)\n\n def build_label(self, default_pos=1, default_neg=0):\n # 如果7天之后，股价上涨5%以及以上，标记为正样本\n reward_cycle = 7\n\n output_label = []\n for input_path in self.input_paths:\n origin_data = pd.read_csv(input_path)\n for index in range(origin_data.index.size - reward_cycle):\n label = default_neg\n if origin_data.loc[index + reward_cycle][\"close\"] > origin_data.loc[index][\"close\"]:\n label = default_pos\n output_label.append([origin_data.loc[index][\"date\"], origin_data.loc[index][\"code\"], label])\n self.output_labels = pd.DataFrame(data=output_label, columns=[\"date\", \"code\", \"label\"])\n\n def save_label(self):\n self.output_labels.to_csv(self.output_path, index=False)\n\n\nif __name__ == \"__main__\":\n\n input_path = \"../resource/stock_info\"\n output_path = \"../resource/model_data/label/label.dat\"\n bl = BuildLabel(input_path, output_path)\n bl.build_label()\n bl.save_label()","sub_path":"model/build_label.py","file_name":"build_label.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"571579819","text":"#!/usr/bin/python\nimport sys\n\n\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n def __init__(self, val):\n this.val = val\n this.left, this.right = None, None\n\"\"\"\nclass TreeNode:\n def __init__(self, val):\n self.val = val\n self.left, self.right = None, None\n\nclass Solution:\n \"\"\"\n @param: root: a TreeNode, the root of the binary tree\n @return:\n \"\"\"\n\n def flatten(self, root):\n # write your code here\n if not root:\n return root\n stack = [root]\n while stack:\n node = stack.pop()\n if node.right:\n stack.append(node.right)\n if node.left:\n stack.append(node.left)\n node.left = None\n if not stack:\n node.right = None\n else:\n node.right = stack[-1]\n\n\ndef main():\n aa = Solution()\n return 0\n\nif __name__ == \"__main__\":\n sys.exit(main())","sub_path":"LintCode/flattenBinaryTreeToLinkedList.py","file_name":"flattenBinaryTreeToLinkedList.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"514939717","text":"import unittest\nimport asyncio\nimport onetoken as ot\nfrom onetoken import Account, log\nfrom .util import load_api_key_secret, input_api_key_secret\nimport time\nimport pprint\nfrom otlib import OTSOrder\n\n\nclass TestExchanges(unittest.TestCase):\n CONFIG_FILE_PATH = '~/.onetoken/config.yml'\n api_key = None\n api_secret = None\n acc = None\n withdraw = {\n 'currency': 'iost',\n 'amount': 1.1,\n 'address': ''\n }\n\n @classmethod\n def setUpClass(cls):\n cls.api_key, cls.api_secret, cls.account = load_api_key_secret(cls.CONFIG_FILE_PATH)\n if cls.api_key is None or cls.api_secret is None:\n cls.api_key, cls.api_secret, cls.account = input_api_key_secret()\n cls.acc = Account(cls.account, api_key=cls.api_key, api_secret=cls.api_secret)\n cls.loop = asyncio.get_event_loop()\n cls.exchange, cls.name = cls.account.split('/', 1)\n cls.order1 = {\n 'con': cls.exchange + '/eth.usdt',\n 'bs': 's',\n 'price': 100000,\n 'amount': 0.05\n }\n cls.order2 = {\n 'con': cls.exchange + '/iost.usdt',\n 'bs': 's',\n 'price': 10000,\n 'amount': 0.0001\n }\n print('initializing account {}'.format(cls.account))\n time.sleep(3)\n\n @classmethod\n def tearDownClass(cls):\n cls.acc.close()\n\n # @unittest.skip('get info')\n def test_get_info(self):\n info, err = self.loop.run_until_complete(self.acc.get_info())\n print('>>>account info:')\n pprint.pprint(info.data)\n print('>>>err should be None:')\n print(str(err))\n self.assertIsInstance(info, ot.Info)\n self.assertIsNone(err)\n\n # AccountInfo至少包含法币(usdt)和btc\n real_currency = {'usdt', 'cny', 'jpy', 'usd', 'krw'}\n has_real_currency = False\n has_btc = False\n position = info.data['position']\n for pos in position:\n c = pos['contract']\n if c == 'btc':\n has_btc = True\n print('>>>btc is in position:')\n print(c)\n if c in real_currency:\n has_real_currency = True\n print('>>>legal currency or usdt is in position:')\n print(c)\n self.assertTrue(has_real_currency, 'real currency or usdt is not included')\n self.assertTrue(has_btc, 'btc is not included')\n\n # balance = cash + market_value (w.o. futures)\n self.assertEqual(info.data['balance'], info.data['cash'] + info.data['market_value'],\n 'balance != cash + market_value')\n\n for pos in position:\n self.assertEqual(pos['total_amount'], pos['available'] + pos['frozen'],\n 'position total_amount != available + frozen')\n if pos['contract'] == 'usdt':\n # usdt_price = self.loop.run_until_complete(otlib.autil.get_price('index/usdt.usd'))\n self.assertEqual(pos['market_value'], 0.0, 'usdt market_value != 0.0')\n # self.assertAlmostEqual(pos['value_cny'], pos['total_amount'] * qb.Currency.USDCNY * usdt_price,\n # delta=1e-8, msg='usdt value_cny is not correct')\n elif pos['type'] == 'future': # 期货\n self.assertEqual(pos['available'], pos['available_long'] - pos['available_short'],\n 'future available != available_long - available_short')\n\n # @unittest.skip('get pending list')\n def test_get_pending_list(self):\n pending_list, err = self.loop.run_until_complete(self.acc.get_pending_list())\n print(f'>>>pending list')\n pprint.pprint(pending_list)\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(pending_list, list)\n\n @unittest.skip('get order list')\n def test_get_order_list(self):\n order_list, err = self.loop.run_until_complete(self.acc.get_order_list('btc.usdt', OTSOrder.END))\n print('>>> order list')\n print('>>> status end')\n pprint.pprint(order_list)\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order_list, list)\n for o in order_list:\n self.assertIn(o['status'], OTSOrder.END_STATUSES, f'{o} status does not match')\n\n order_list, err = self.loop.run_until_complete(self.acc.get_order_list('btc.usdt', OTSOrder.ACTIVATING))\n print('>>> order list')\n print('>>> status active')\n pprint.pprint(order_list)\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order_list, list)\n for o in order_list:\n self.assertIn(o['status'], OTSOrder.ACTIVATING_STATUS, f'{o} status does not match')\n\n active = OTSOrder.ACTIVATING_STATUS[:-1]\n closed = OTSOrder.END_STATUSES[:-1]\n for status in active + closed:\n order_list, err = self.loop.run_until_complete(self.acc.get_order_list('btc.usdt', status))\n print('>>> order list')\n print('>>> status ' + status)\n pprint.pprint(order_list)\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order_list, list)\n\n for o in order_list:\n self.assertEqual(o['status'], status, f'{o} status does not match')\n\n @unittest.skip('order')\n def test_order(self):\n self.place_order()\n self.cancel_order()\n self.get_order()\n\n def place_order(self):\n print(f'>>>start test place order')\n order1, err = self.loop.run_until_complete(self.acc.place_order(**self.order1))\n print(f'new order: {order1}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order1, dict)\n self.exchange_oid = order1.get('exchange_oid', '')\n self.client_oid = order1.get('client_oid', '')\n self.assertRegex(self.exchange_oid, self.exchange + r'/[a-z]+\\.[a-z]+-[\\d]+')\n self.assertRegex(self.client_oid, self.exchange + r'/[a-z]+\\.[a-z]+-[\\d]+-[\\d]+-[\\w]+')\n order2, err = self.loop.run_until_complete(self.acc.place_order(**self.order2))\n print(f'new order should be None: {order2}')\n print(f'err should be HTTPError: {err}')\n self.assertIsNone(order2)\n self.assertIsNotNone(err)\n order_list, err = self.loop.run_until_complete(self.acc.get_order_list())\n self.assertIsNone(err)\n self.assertIsNotNone(order_list)\n\n exg_oid_in_pending_list = False\n for o in order_list:\n if o['exchange_oid'] == self.exchange_oid:\n exg_oid_in_pending_list = True\n self.assertTrue(exg_oid_in_pending_list, 'exchange_oid not in pending list')\n\n print(f'>>>end test place order')\n time.sleep(1)\n\n def get_order(self):\n print(f'>>>start test get order')\n order, err = self.loop.run_until_complete(self.acc.get_order_use_exchange_oid(self.exchange_oid))\n print(f'order: {order}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order, list)\n self.assertEqual(len(order), 1)\n order = order[0]\n self.assertEqual(order['exchange_oid'], self.exchange_oid)\n self.assertEqual(order['client_oid'], self.client_oid)\n order, err = self.loop.run_until_complete(self.acc.get_order_use_client_oid(self.client_oid))\n print(f'order: {order}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order, list)\n self.assertEqual(len(order), 1)\n order = order[0]\n self.assertEqual(order['exchange_oid'], self.exchange_oid)\n self.assertEqual(order['client_oid'], self.client_oid)\n print(f'>>>end test get order')\n time.sleep(1)\n\n def cancel_order(self):\n print(f'>>>start test cancel order')\n order, err = self.loop.run_until_complete(self.acc.cancel_use_exchange_oid(self.exchange_oid))\n print(f'order: {order}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(order, dict)\n self.assertEqual(order['exchange_oid'], self.exchange_oid)\n\n order_list, err = self.loop.run_until_complete(self.acc.get_order_list())\n self.assertIsNone(err)\n self.assertIsNotNone(order_list)\n\n exg_oid_in_pending_list = False\n for o in order_list:\n if o['exchange_oid'] == self.exchange_oid:\n exg_oid_in_pending_list = True\n self.assertFalse(exg_oid_in_pending_list, 'exchange_oid still in pending list')\n\n print(f'>>>end test cancel order')\n time.sleep(1)\n\n @unittest.skip('cancel all')\n def test_cancel_all(self):\n print(f'>>>start test cancel all')\n res, err = self.loop.run_until_complete(self.acc.cancel_all())\n print(f'response: {res}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(res, list)\n time.sleep(1)\n\n @unittest.skip('withdraw')\n def test_withdraw(self):\n self.post_withdraw()\n self.cancel_withdraw()\n self.get_withdraw()\n\n @unittest.skip('not supported yet')\n def post_withdraw(self):\n print(f'>>>start test post withdraw')\n res, err = self.loop.run_until_complete(self.acc.post_withdraw(**self.withdraw))\n print(f'response: {res}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(res, dict)\n self.assertRegex(res['exchange_wid'], self.exchange + '/' + self.withdraw['currency'] + r'-[\\d]+')\n # self.assertRegex(res['client_wid'], self.exchange + '/' + self.withdraw['currency'] + r'-[\\d]+-[\\d]+-[\\w]+') # not implemented yet\n self.exchange_wid = res['exchange_wid']\n print(f'>>>end test post withdraw')\n time.sleep(1)\n\n @unittest.skip('not supported yet')\n def get_withdraw(self):\n print(f'>>>start test get withdraw')\n res, err = self.loop.run_until_complete(self.acc.get_withdraw_use_exchange_wid(self.exchange_wid))\n print(f'response: {res}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(res, dict)\n self.assertEqual(res['exchange_wid'], self.exchange_wid)\n print('>>>end test get withdraw')\n time.sleep(1)\n\n @unittest.skip('not supported yet')\n def get_withdraw_list(self):\n time.sleep(1)\n\n @unittest.skip('not supported yet')\n def cancel_withdraw(self):\n print('>>>start test cancel withdraw')\n res, err = self.loop.run_until_complete(self.acc.cancel_withdraw_use_exchange_wid(self.exchange_wid))\n print(f'response: {res}')\n print(f'err should be None: {err}')\n self.assertIsNone(err)\n self.assertIsInstance(res, dict)\n self.assertEqual(res['exchange_wid'], self.exchange_wid)\n print('>>>end test cancel withdraw')\n time.sleep(1)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"test_exchange/test_all.py","file_name":"test_all.py","file_ext":"py","file_size_in_byte":11217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"386296475","text":"# -*- coding: utf-8 -*-\nimport json\nimport os\nimport shutil\nimport zipfile\n\nfrom django.conf import settings # For mocking.\nfrom django.core.files.storage import default_storage as storage\n\nimport jwt\nimport mock\nfrom nose.tools import eq_, ok_, raises\nfrom requests import Timeout\n\nimport amo.tests\nfrom lib.crypto import packaged\nfrom lib.crypto.receipt import crack, sign, SigningError\nfrom versions.models import Version\n\n\ndef mock_sign(version_id, reviewer=False):\n \"\"\"\n This is a mock for using in tests, where we really don't want to be\n actually signing the addons. This just copies the file over and returns\n the path. It doesn't have much error checking.\n \"\"\"\n version = Version.objects.get(pk=version_id)\n file_obj = version.all_files[0]\n path = (file_obj.signed_reviewer_file_path if reviewer else\n file_obj.signed_file_path)\n try:\n os.makedirs(os.path.dirname(path))\n except OSError:\n pass\n shutil.copyfile(file_obj.file_path, path)\n return path\n\n\n@mock.patch('lib.crypto.receipt.requests.post')\n@mock.patch.object(settings, 'SIGNING_SERVER', 'http://localhost')\nclass TestReceipt(amo.tests.TestCase):\n def get_response(self, code):\n return mock.Mock(status_code=code,\n content=json.dumps({'receipt': ''}))\n\n def test_called(self, mock_post):\n mock_post.return_value = self.get_response(200)\n sign('my-receipt')\n eq_(mock_post.call_args[1]['data'], 'my-receipt')\n\n def test_some_unicode(self, mock_post):\n mock_post.return_value = self.get_response(200)\n sign({'name': u'Вагиф Сәмәдоғлу'})\n\n def test_good(self, req):\n req.return_value = self.get_response(200)\n sign('x')\n\n @raises(SigningError)\n def test_timeout(self, req):\n req.side_effect = Timeout\n req.return_value = self.get_response(200)\n sign('x')\n\n @raises(SigningError)\n def test_error(self, req):\n req.return_value = self.get_response(403)\n sign('x')\n\n @raises(SigningError)\n def test_other(self, req):\n req.return_value = self.get_response(206)\n sign('x')\n\n\nclass TestCrack(amo.tests.TestCase):\n def test_crack(self):\n eq_(crack(jwt.encode('foo', 'x')), [u'foo'])\n\n def test_crack_mulitple(self):\n eq_(crack('~'.join([jwt.encode('foo', 'x'), jwt.encode('bar', 'y')])),\n [u'foo', u'bar'])\n\n\n@mock.patch('lib.crypto.packaged.os.unlink', new=mock.Mock)\nclass TestPackaged(amo.tests.TestCase):\n def setUp(self):\n super(TestPackaged, self).setUp()\n\n # Change addon file name\n self.addon = amo.tests.addon_factory()\n self.addon.update(guid='xxxxx')\n self.version = self.addon.current_version\n self.file1 = self.version.all_files[0]\n self.file1.update(filename='addon-a.xpi')\n\n self.file2 = amo.tests.file_factory(version=self.version)\n self.file2.update(filename='addon-b.xpi')\n\n # Add actual file to addons\n if not os.path.exists(os.path.dirname(self.file1.file_path)):\n os.makedirs(os.path.dirname(self.file1.file_path))\n\n for f in (self.file1, self.file2):\n fp = zipfile.ZipFile(f.file_path, 'w')\n fp.writestr('install.rdf', '')\n fp.close()\n\n def tearDown(self):\n for f in (self.file1, self.file2):\n for path in (\n f.file_path, f.signed_file_path,\n f.signed_reviewer_file_path):\n if os.path.exists(path):\n os.unlink(path)\n\n @raises(packaged.SigningError)\n def test_no_file(self):\n [f.delete() for f in self.addon.current_version.all_files]\n packaged.sign(self.version.pk)\n\n @raises(packaged.SigningError)\n def test_non_xpi(self):\n self.file1.update(filename='foo.txt')\n packaged._sign_file(\n self.version.pk, self.addon,\n self.file1, False, False)\n\n @mock.patch('lib.crypto.packaged.sign_addon')\n def test_already_exists(self, sign_addon):\n with (storage.open(self.file1.signed_file_path, 'w') and\n storage.open(self.file2.signed_file_path, 'w')):\n assert packaged.sign(self.version.pk)\n assert not sign_addon.called\n\n @mock.patch('lib.crypto.packaged.sign_addon')\n def test_resign_already_exists(self, sign_addon):\n storage.open(self.file1.signed_file_path, 'w')\n storage.open(self.file2.signed_file_path, 'w')\n packaged.sign(self.version.pk, resign=True)\n assert sign_addon.called\n\n @mock.patch('lib.crypto.packaged.sign_addon')\n def test_sign_consumer(self, sign_addon):\n file_list = packaged.sign(self.version.pk)\n assert sign_addon.called\n ids = json.loads(sign_addon.call_args[0][2])\n eq_(ids['id'], self.addon.guid)\n eq_(ids['version'], self.version.pk)\n\n file_list = dict(file_list)\n eq_(file_list[self.file1.pk], self.file1.signed_file_path)\n eq_(file_list[self.file2.pk], self.file2.signed_file_path)\n\n @mock.patch('lib.crypto.packaged.sign_addon')\n def test_sign_reviewer(self, sign_addon):\n file_list = packaged.sign(self.version.pk, reviewer=True)\n assert sign_addon.called\n ids = json.loads(sign_addon.call_args[0][2])\n eq_(ids['id'], 'reviewer-{guid}-{version_id}'.format(\n guid=self.addon.guid, version_id=self.version.pk))\n eq_(ids['version'], self.version.pk)\n\n file_list = dict(file_list)\n eq_(file_list[self.file1.pk], self.file1.signed_reviewer_file_path)\n eq_(file_list[self.file2.pk], self.file2.signed_reviewer_file_path)\n\n @raises(ValueError)\n def test_server_active(self):\n with self.settings(SIGNING_SERVER_ACTIVE=True):\n packaged.sign(self.version.pk)\n\n @raises(ValueError)\n def test_reviewer_server_active(self):\n with self.settings(SIGNING_REVIEWER_SERVER_ACTIVE=True):\n packaged.sign(self.version.pk, reviewer=True)\n\n @mock.patch('lib.crypto.packaged._no_sign')\n def test_server_inactive(self, _no_sign):\n with self.settings(SIGNING_SERVER_ACTIVE=False):\n packaged.sign(self.version.pk)\n assert _no_sign.called\n\n @mock.patch('lib.crypto.packaged._no_sign')\n def test_reviewer_server_inactive(self, _no_sign):\n with self.settings(SIGNING_REVIEWER_SERVER_ACTIVE=False):\n packaged.sign(self.version.pk, reviewer=True)\n assert _no_sign.called\n\n def test_server_endpoint(self):\n with self.settings(SIGNING_SERVER_ACTIVE=True,\n SIGNING_SERVER='http://sign.me',\n SIGNING_REVIEWER_SERVER='http://review.me'):\n endpoint = packaged._get_endpoint()\n ok_(endpoint.startswith('http://sign.me'),\n 'Unexpected endpoint returned.')\n\n def test_server_reviewer_endpoint(self):\n with self.settings(SIGNING_REVIEWER_SERVER_ACTIVE=True,\n SIGNING_SERVER='http://sign.me',\n SIGNING_REVIEWER_SERVER='http://review.me'):\n endpoint = packaged._get_endpoint(reviewer=True)\n ok_(endpoint.startswith('http://review.me'),\n 'Unexpected endpoint returned.')\n\n @mock.patch.object(packaged, '_get_endpoint', lambda _: '/fake/url/')\n @mock.patch('requests.post')\n def test_inject_ids(self, post):\n \"\"\"\n Checks correct signing of a package using fake data\n as returned by Trunion\n \"\"\"\n post().status_code = 200\n post().content = '{\"zigbert.rsa\": \"\"}'\n packaged.sign(self.version.pk)\n zf = zipfile.ZipFile(self.file1.signed_file_path, mode='r')\n ids_data = zf.read('META-INF/ids.json')\n eq_(sorted(json.loads(ids_data).keys()), ['id', 'version'])\n","sub_path":"lib/crypto/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":7888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"368888016","text":"def writeResult(lst):\n with open(\"051.tsv\",'a') as fw:\n result = \"\"\n for i in lst:\n result += (i+\"\\t\")\n fw.write(result[:-1]+\"\\n\")\n\n\nHeader = [\"SampleID\", \"Site1\", \"Site2\", \"Site3\"]\nSample1 = [\"Sample1\", \"GG\", \"AT\", \"CC\"]\nSample2 = [\"Sample2\", \"GT\", \"AA\", \"CC\"]\nSample3 = [\"Sample3\", \"TT\", \"TT\", \"CT\"]\nSample4 = [\"Sample4\", \"GG\", \"AT\", \"TT\"]\n\nwriteResult(Header)\nwriteResult(Sample1)\nwriteResult(Sample2)\nwriteResult(Sample3)\nwriteResult(Sample4)\n\n","sub_path":"051.py","file_name":"051.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"325723829","text":"class Solution:\n def find(self, n, data):\n f = dict()\n for i in range(n):\n\n f[data[i]] = f.get(data[i], 0) + 1\n\n sorted_key_list = sorted(f, key=lambda x: f[x], reverse=True)\n sorted_dict = map(lambda x: {x: f[x]}, sorted_key_list)\n\n f = list(sorted_dict)\n re = []\n for item in f:\n k = list(item.keys())[0]\n re.append([k, item[k]])\n for i in range(len(re)):\n ptr = i+1\n arr = []\n arr.append(re[i][0])\n while ptr int:\n result = math.inf\n left, right, curr_sum = 0, 0, 0\n\n for right in range(len(arr)):\n curr_sum += arr[right]\n while curr_sum >= s:\n result = min(right - left + 1, result)\n curr_sum -= arr[left]\n left += 1\n\n return 0 if result == math.inf else result\n\n\nres = smallest_subarray_with_given_sum(8, [3, 4, 1, 1, 6])\nprint(f'my: {res} | actual: 3')\n","sub_path":"Sliding_Windows/Smallest Subarray with a given sum.py","file_name":"Smallest Subarray with a given sum.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"225035561","text":"'''\nReturns total price paid for individual rentals\n'''\nimport argparse\nimport json\nimport datetime\nimport math\nimport logging\n\n\ndef none():\n \"\"\"\n returns a 50 setting only critical messages to be shown.\n This disables all logging messages\n \"\"\"\n return 50\ndef errs():\n \"\"\"returns 40 setting only errors to be shown\"\"\"\n return 40\ndef errs_warnings():\n \"\"\"returns 30 setting errors and warnings to be shown\"\"\"\n return 30\ndef errs_warnings_debug():\n \"\"\"returns 10 displaying everyething from debug up.\"\"\"\n return 10\n\ndebug_options = {\"0\": none,\n \"1\": errs,\n \"2\": errs_warnings,\n \"3\": errs_warnings_debug}\n\ndef debug_setting(level):\n \"\"\"sets the debugger level\"\"\"\n return debug_options.get(level)()\n\ndef parse_cmd_arguments():\n \"\"\"parses the arguments passed in from the comand line\"\"\"\n parser = argparse.ArgumentParser(description='Process some integers.')\n parser.add_argument('-i', '--input', help='input JSON file', required=True)\n parser.add_argument('-o', '--output', help='ouput JSON file', required=True)\n parser.add_argument('-d', '--debug', help='debuger setting', default=\"0\")\n\n return parser.parse_args()\n\n\ndef load_rentals_file(filename):\n \"\"\"opens the rental files with the input data\"\"\"\n try:\n with open(filename) as file:\n try:\n data = json.load(file)\n logging.debug(\"Input file opened\")\n except json.decoder.JSONDecodeError:\n logging.error(\"Input file not json\")\n except FileNotFoundError:\n logging.error(\"Input file not found\")\n return data\n\ndef calculate_additional_fields(data):\n \"\"\"calculates additional feilds of data\"\"\"\n for key in data.keys():\n value = data[key]\n try:\n rental_start = datetime.datetime.strptime(value['rental_start'], '%m/%d/%y')\n rental_end = datetime.datetime.strptime(value['rental_end'], '%m/%d/%y')\n value['total_days'] = (rental_end - rental_start).days\n value['total_price'] = value['total_days'] * value['price_per_day']\n value['sqrt_total_price'] = math.sqrt(value['total_price'])\n value['unit_cost'] = value['total_price'] / value['units_rented']\n except ValueError:\n logging.error(\"Rental %s end date before start date. Tried to take the squair root of\"\\\n \" a negetive number.\", key)\n except ZeroDivisionError:\n logging.error(\"Rental %s number of units rented is 0. Tried to divide by 0.\", key)\n except KeyError:\n logging.warning(\"Missing data in rental %s\", key)\n\n logging.debug(\"All aditional data calculated.\")\n return data\n\ndef save_to_json(filename, data):\n \"\"\"saves the new data to an output file\"\"\"\n with open(filename, 'w') as file:\n json.dump(data, file)\n logging.debug(\"Output file created.\")\n\nif __name__ == \"__main__\":\n args = parse_cmd_arguments()\n\n LOG_FORMAT = \"%(asctime)s %(filename)s:%(lineno)-3d %(levelname)s %(message)s\"\n log_file = datetime.datetime.now().strftime(\"%Y-%m-%d\")+'.log'\n\n formatter = logging.Formatter(LOG_FORMAT)\n\n file_handler = logging.FileHandler(log_file)\n file_handler.setLevel(logging.WARNING)\n file_handler.setFormatter(formatter)\n\n console_handler = logging.StreamHandler()\n console_handler.setLevel(logging.DEBUG)\n console_handler.setFormatter(formatter)\n\n\n logger = logging.getLogger()\n logger.setLevel(debug_setting(args.debug))\n logger.addHandler(file_handler)\n logger.addHandler(console_handler)\n\n\n DATA = load_rentals_file(args.input)\n DATA = calculate_additional_fields(DATA)\n save_to_json(args.output, DATA)\n","sub_path":"students/David_Baylor/lesson02/charges_calc.py","file_name":"charges_calc.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"423317626","text":"class DirectoryTree:\n# Defining class attributes with values []\n folder = []\n files = []\n\n # Creating init function\n def __init__(self, prev, folder_name):\n self.prev = prev\n self.folder_name = folder_name\n\n# Instance of a class (not attribute)\nsquare_one = DirectoryTree(prev=None, folder_name='square_one')\ncur = square_one\npath = 'square_one'\n\n# Create file(s)\ndef touch(name):\n global cur\n if name in cur.files:\n print(' Duplicate file name found. Please enter new name. ')\n else:\n cur.files.append(name)\n print(' Created a new folder. ')\n\n\n# Make directory (create folder)\ndef mkdir(name):\n global cur\n if name in cur.folder:\n print(' Duplicate folder name! Please try again.')\n else:\n folder = DirectoryTree(prev=cur, folder_name=name)\n cur.folder.append(folder)\n print(' Folder name created. ')\n\n# Change directory\ndef cd(name):\n global cur, path\n if name == '..':\n if cur.prev is not None:\n path = path[:(len(path)-len(cur.folder_name))-1]\n cur = cur.prev\n # if name == '../..':\n # if cur.prev is not None and cur.prev.prev is not None:\n # path = path[:(len(path)-len(cur.folder_name))-2]\n # cur = cur.prev.prev\n else:\n for i in cur.folder:\n if i.folder_name == name: \n cur = i\n path = path + '/' + name\n return\n print(' No folder found. ')\n\n# List directories\ndef ls():\n global cur\n for i in cur.files:\n print(i)\n for j in cur.folder:\n print(j.folder_name)\n\nif __name__ == '__main__':\n while True:\n print(path, end='>')\n command = input().split(' ')\n if command[0] == 'touch':\n try:\n touch(command[1])\n except:\n print('Format error: create File Name. ')\n elif command[0] == 'mkdir':\n try:\n mkdir(command[1])\n except:\n print(' Format error: mkdir Folder Name. ')\n elif command[0] == 'cd':\n try:\n cd(command[1])\n except:\n print('Format error: cd DirectoryName or cd .. or cd ../..')\n elif command[0] == 'ls':\n ls()\n else:\n print('{} is not the correct command.')\n","sub_path":"file_system.py","file_name":"file_system.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"457852035","text":"import glob\nimport os\nimport pandas as pd\nimport argparse\nimport numpy as np\nimport cv2\nimport time\nimport json\n\nfrom fire import Fire\nfrom tqdm import tqdm\nimport random\n\nfrom track_and_detect import Track_And_Detect, data_process\n\n'''\nFor posetrack dataset, the output keypoints is as follow \n\"keypoints\": {\n\t0: \"nose\",\n\t1: \"head_bottom\",\n\t2: \"head_top\",\n\t3: \"left_shoulder\",\n\t4: \"right_shoulder\",\n\t5: \"left_elbow\",\n\t6: \"right_elbow\",\n\t7: \"left_wrist\",\n\t8: \"right_wrist\",\n\t9: \"left_hip\",\n\t10: \"right_hip\",\n\t11: \"left_knee\",\n\t12: \"right_knee\",\n\t13: \"left_ankle\",\n\t14: \"right_ankle\"\n}\nFor competition\n\"keypoints\": {\n 0: \"right_ankle\",\n 1: \"right_knee\",\n 2: \"right_hip\",\n 3: \"left_hip\",\n 4: \"left_knee\",\n 5: \"left_ankle\",\n 6: \"right_wrist\",\n 7: \"right_elbow\",\n 8: \"right_shoulder\",\n 9: \"left_shoulder\",\n 10: \"left_elbow\",\n 11: \"left_wrist\",\n 12: \"neck\",\n 13: \"nose\",\n 14: \"head_top\",\n}\n'''\nmatch_list=[13,12,14,9,8,10,7,11,6,3,2,4,1,5,0]\n\ndef parseArgs():\n\tparser = argparse.ArgumentParser(description=\"Evaluation of Pose Estimation and Tracking (PoseTrack)\")\n\tparser.add_argument(\"-t\", \"--detection_thresh\",dest = 'det_thresh',required=False, default=0.4, type= float)\n\tparser.add_argument(\"-p\", \"--pos_thresh\",dest = 'pose_thresh',required=False, default=0, type= float)\n\tparser.add_argument(\"-v\", \"--vis_flag\",dest = 'vis_flag',required=False, default=False, type= bool)\n\treturn parser.parse_args()\n\nclass DateEncoder(json.JSONEncoder ):\n\tdef default(self, obj):\n\t\t#print(obj,type(obj))\n\t\tif isinstance(obj,np.float32):\n\t\t\treturn float(obj)\n\t\tif isinstance(obj, np.ndarray):\n\t\t\treturn obj.tolist()\n\t\treturn json.JSONEncoder.default(self, obj)\n\n\ndef data_preprocess(json_path, json_name):\n\tjson_file = os.path.join(json_path,json_name)\n\twith open(json_file,'r') as f:\n\t\timages = json.load(f)['images']\n\tfilenames_new = []\n\tframe_names = []\n\tfor image in images:\n\t\tfilename = image['file_name']\n\t\tframe_names.append(filename)\n\t\tfilename = os.path.join('/export/home/zby/SiamFC-PyTorch/data/posetrack',filename)\n\t\tfilenames_new.append(filename)\n\tframes = [cv2.imread(filename) for filename in filenames_new]\n\treturn frames, frame_names\n\ndef track_test(args, gpu_id =[0,0,0,0], json_path='/export/home/zby/SiamFC/data/posetrack/tf_detection_result_test.json'):\n\tpose_vis_thresh = args.pose_thresh\n\tdetection_score_thresh = args.det_thresh\n\tvis_flag = args.vis_flag\n\t#save_dir = '/export/home/zby/SiamFC/data/posetrack/test_with_detection_result_{}'.format(detection_score_thresh)\n\tsave_dir = '/export/home/zby/SiamFC/data/posetrack/detection_iou_embedding_det0.3_oks0.8_testset'\n\tprint('----------------------------------------------------------------------------------')\n\tprint('Detection_score_thresh: {} Vis_flag: {}'.format(detection_score_thresh, vis_flag))\n\tprint('Detection results is set as {}'.format(json_path))\n\tprint('Results will be saved to {}'.format(save_dir))\n\tif not os.path.exists(save_dir):\n\t\tos.mkdir(save_dir)\n\t\n\twith open(json_path,'r') as f:\n\t\tbbox_dict = json.load(f)\n\ttracker = Track_And_Detect(gpu_id=gpu_id,flag=[False, False, True, True])\n\tgt_json_path = '/export/home/zby/SiamFC/data/posetrack/posetrack_val_2017.json'\n\twith open(gt_json_path,'r') as f:\n\t\tgt_dict = json.load(f)\n\tvideo_keys = gt_dict.keys()\n\tpredict_dict = dict()\n\t#random.shuffle(json_files)\n\tpbar = tqdm(range(len(video_keys)))\n\tfor video_name in video_keys:\n\t\t#video_gt = gt_dict[video_name]\n\t\tvideo_json = {'annolist':[]}\n\t\tframe_dict = bbox_dict[video_name]\n\t\tsave_path = os.path.join(save_dir, video_name+'.json')\n\t\tidx =0\n\t\tfor frame_name in sorted(frame_dict.keys()):\n\t\t\tstart = time.time()\n\t\t\tframe_path = os.path.join('/export/home/zby/SiamFC/data/posetrack',frame_name)\n\t\t\tframe = cv2.imread(frame_path)\n\t\t\tbbox_list = frame_dict[frame_name]\n\t\t\tdet_list = []\n\t\t\tfor bbox in bbox_list:\n\t\t\t\tif bbox[4] >= detection_score_thresh:\n\t\t\t\t\tdet_list.append(bbox)\n\t\t\tif idx == 0:\n\t\t\t\tim_H,im_W,im_C = frame.shape\n\t\t\t\tif vis_flag:\n\t\t\t\t\tfourcc = cv2.cv2.VideoWriter_fourcc('m', 'p', '4', 'v')\n\t\t\t\t\tvideoWriter = cv2.VideoWriter('/export/home/zby/SiamFC/data/result/detection_iou_embedding_det0.3_oks0.8_testset_{}.mp4'.format(video_name),fourcc,10,(im_W,im_H))\n\t\t\t\tfinal_list = tracker.init_tracker(frame,det_list)\n\t\t\t\t# for det in det_list:\n\t\t\t\t\t# xmin, ymin, xmax, ymax, score = det\n\t\t\t\t\t# cv2.rectangle(frame, (int(xmin),int(ymin)), (int(xmax),int(ymax)), (0,0,255), 2) \n\t\t\t\t# for det in final_list:\n\t\t\t\t\t# xmin, ymin, xmax, ymax, score, track_id = det\n\t\t\t\t\t# cv2.rectangle(frame, (int(xmin),int(ymin)), (int(xmax),int(ymax)), (0,255,0), 2) \t\n\t\t\t\t# print(len(det_list),len(final_list))\n\t\t\t\t# if (len(det_list)!=len(final_list)):\n\t\t\t\t\t# cv2.imwrite('/export/home/zby/SiamFC/data/result/'+video_name+'_pred.jpg',frame)\n\t\t\telse:\t\n\t\t\t\t#track_list = tracker.multi_track(frame)\n\t\t\t\t#final_list = tracker.match_detection_tracking_iou_embedding(det_list, track_list, frame)\n\t\t\t\tfinal_list = tracker.match_detection_iou_embedding(det_list, frame)\n\t\t\timage_dict = dict()\n\n\t\t\tannorect = []\n\t\t\tfor det in final_list:\n\t\t\t\tpoint_list = []\n\t\t\t\tpose_position, pose_value, pose_heatmap = tracker.pose_detect(frame, det)\n\t\t\t\t#pose_position, pose_value = pose_position.tolist(), pose_value.tolist()\n\t\t\t\tfor i, pose in enumerate(pose_position):\n\t\t\t\t\tscore_i = pose_value[i]\n\t\t\t\t\tpose_id = match_list[i]\n\t\t\t\t\tif score_i >= pose_vis_thresh:\n\t\t\t\t\t\tpoint_list.append({'id':[pose_id],'x':[pose[0]],'y':[pose[1]],'score':[score_i]})\n\t\t\t\t\t\tif vis_flag:\n\t\t\t\t\t\t\tcv2.circle(frame,(int(pose[0]),int(pose[1])),10,(0,0,255),-1)\n\t\t\t\t\t\t\tcv2.putText(frame, str(pose_id), (int(pose[0]+5),int(pose[1]+5)), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)\n\t\t\t\t#print(det)\n\t\t\t\tpoint_dict = {'point':point_list}\n\t\t\t\txmin,ymin,xmax,ymax,score,track_id = det\n\t\t\t\tannorect.append({'x1':[xmin],'x2':[xmax],'y1':[ymin],'y2':[ymax],'score':[score],'track_id':[track_id],'annopoints':[point_dict]})\n\t\t\t\tif vis_flag:\n\t\t\t\t\tcv2.rectangle(frame, (int(xmin),int(ymin)), (int(xmax),int(ymax)), (0,255,0), 2) \n\t\t\t\t\tcv2.putText(frame, 'id:'+str(track_id), (int(xmin),int(ymin)), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)\n\t\t\timage_dict['image'] = [{'name':frame_name}]\n\t\t\timage_dict['annorect'] = annorect\n\t\t\tvideo_json['annolist'].append(image_dict)\n\t\t\tidx += 1\n\t\t\tpbar.set_description('Processing video {}: process {} takes {:.3f} seconds'.format(video_name, frame_name, time.time()-start))\n\t\t\tif vis_flag:\n\t\t\t\tvideoWriter.write(frame)\n\t\tpbar.update(1)\n\t\twith open(save_path,'w') as f:\n\t\t\tjson.dump(video_json, f, cls=DateEncoder)\n\t\t\t#print('Tracking the {}th frame has taken {} seconds'.format(idx+1,end_time-start_time))\n\tpbar.close()\n\n\nif __name__ == \"__main__\":\n\targs = parseArgs()\n\ttrack_test(args=args)\n","sub_path":"inference/test_with_detection.py","file_name":"test_with_detection.py","file_ext":"py","file_size_in_byte":6691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"344201197","text":"\n\nfrom xai.brain.wordbase.nouns._school import _SCHOOL\n\n#calss header\nclass _SCHOOLS(_SCHOOL, ):\n\tdef __init__(self,): \n\t\t_SCHOOL.__init__(self)\n\t\tself.name = \"SCHOOLS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"school\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_schools.py","file_name":"_schools.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"4954925","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Feb 26, 2017\n@author: Weiping Song\n\"\"\"\nimport os\nimport tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport logging\n\nfrom tensorflow.contrib.rnn import GRUCell\nfrom tensorflow.contrib.rnn import DropoutWrapper\nfrom tensorflow.contrib.rnn import MultiRNNCell\n\nfrom collections import namedtuple\n\nGRU4Rec_HParams = namedtuple('GRU4Rec_HParams',\n 'reset_after_session, layers, rnn_size, loss, final_act, hidden_act,'\n 'dropout_p_hidden, batch_size, optimizer, learning_rate, decay, decay_steps, sigma,'\n 'grad_cap, init_as_normal, n_sample, sample_alpha, smoothing, n_epochs')\n\nSession_HParams = namedtuple('Session_HParams',\n 'n_items, session_key, item_key, time_key, train_random_order, time_sort')\n\n\nclass GRU4Rec:\n\n def __init__(self, args, session_args, sess, mode):\n self.logger = logging.getLogger('main.model')\n self.sess = sess\n self.is_training = mode\n\n self.layers = args.layers\n self.rnn_size = args.rnn_size\n self.n_epochs = args.n_epochs\n self.batch_size = args.batch_size\n self.dropout_p_hidden = args.dropout_p_hidden\n self.learning_rate = args.learning_rate\n self.decay = args.decay\n self.decay_steps = args.decay_steps\n self.sigma = args.sigma\n self.init_as_normal = args.init_as_normal\n self.reset_after_session = args.reset_after_session\n self.grad_cap = args.grad_cap\n self.n_sample = args.n_sample\n self.sample_alpha = args.sample_alpha\n self.smoothing = args.smoothing\n self.optimizer = args.optimizer\n\n self.session_key = session_args.session_key\n self.item_key = session_args.item_key\n self.time_key = session_args.time_key\n self.train_random_order = session_args.train_random_order\n self.time_sort = session_args.time_sort\n self.n_items = session_args.n_items\n\n if args.hidden_act == 'tanh':\n self.hidden_act = self.tanh\n elif args.hidden_act == 'relu':\n self.hidden_act = self.relu\n else:\n raise NotImplementedError\n\n if args.final_act == 'linear':\n self.final_activation = self.linear\n elif args.final_act == 'relu':\n self.final_activatin = self.relu\n else:\n self.final_activation = self.tanh\n\n if args.loss == 'cross-entropy':\n if args.final_act == 'tanh':\n self.final_activation = self.softmaxth\n else:\n self.final_activation = self.softmax\n self.loss_function = self.cross_entropy\n elif args.loss == 'bpr':\n self.loss_function = self.bpr\n elif args.loss == 'top1':\n self.loss_function = self.top1\n elif args.loss.startswith('bpr-max-'):\n self.loss_function = self.bpr_max\n self.bpreg = float(args.loss[8:])\n elif args.loss == 'top1-max':\n self.loss_function = self.top1_max\n elif args.loss == 'xe_logit':\n self.loss_function = self.cross_entropy_logits\n else:\n raise NotImplementedError\n\n self.build_model()\n\n # use self.predict_state to hold hidden states during prediction.\n self.predict_state = [np.zeros([self.batch_size, self.rnn_size], dtype=np.float32) for _ in range(self.layers)]\n\n ########################ACTIVATION FUNCTIONS#########################\n def linear(self, X):\n return X\n\n def tanh(self, X):\n return tf.nn.tanh(X)\n\n def relu(self, X):\n return tf.nn.relu(X)\n\n def sigmoid(self, X):\n return tf.nn.sigmoid(X)\n\n def softmax(self, X):\n return tf.nn.softmax(X)\n\n def softmaxth(self, X):\n return tf.nn.softmax(tf.tanh(X))\n\n def softmax_neg(self, X):\n hack_matrix = np.ones((self.batch_size, self.batch_size + self.n_sample), dtype=np.float32)\n np.fill_diagonal(hack_matrix, 0)\n self.hack_matrix = tf.Variable(hack_matrix, trainable=False)\n\n if hasattr(self, 'hack_matrix'):\n X = X * self.hack_matrix\n e_x = tf.exp(X - tf.reduce_max(X, axis=1, keep_dims=True)) * self.hack_matrix\n else:\n e_x = tf.matrix_set_diag(tf.exp(X - tf.reduce_max(X, axis=1, keep_dims=True)),\n tf.zeros(shape=(self.batch_size)))\n\n return e_x / tf.reduce_sum(e_x, axis=1, keep_dims=True)\n\n ############################LOSS FUNCTIONS######################\n def cross_entropy(self, yhat):\n if self.smoothing:\n n_out = self.batch_size + self.n_sample\n term1 = (1.0 - (n_out / (n_out - 1)) * self.smoothing) * (-tf.log(tf.diag_part(yhat) + 1e-24))\n term2 = (self.smoothing / (n_out - 1)) * tf.reduce_sum(-tf.log(yhat + 1e-24), axis=1)\n return tf.reduce_mean(term1 + term2)\n else:\n return tf.reduce_mean(-tf.log(tf.diag_part(yhat) + 1e-24))\n\n def cross_entropy_logits(self, yhat):\n if self.smoothing:\n n_out = self.batch_size + self.n_sample\n term1 = (1.0 - (n_out / (n_out - 1)) * self.smoothing) * tf.diag_part(yhat)\n term2 = (self.smoothing / (n_out - 1)) * tf.reduce_sum(yhat, axis=1)\n return tf.reduce_mean(term1 + term2)\n else:\n return tf.reduce_mean(tf.diag_part(yhat))\n\n def bpr(self, yhat):\n yhatT = tf.transpose(yhat)\n return tf.reduce_mean(-tf.log(tf.nn.sigmoid(tf.diag_part(yhat) - yhatT)))\n\n def bpr_max(self, yhat):\n yhatT = tf.transpose(yhat)\n softmax_scores = tf.transpose(self.softmax_neg(yhat))\n term1 = -tf.log(tf.reduce_sum(tf.nn.sigmoid(tf.diag_part(yhat) - yhatT) * softmax_scores, axis=0) + 1e-24)\n term2 = self.bpreg * tf.reduce_sum((yhatT**2) * softmax_scores, axis=0)\n return tf.reduce_mean(term1 + term2)\n\n def top1(self, yhat):\n yhatT = tf.transpose(yhat)\n term1 = tf.reduce_mean(tf.nn.sigmoid(-tf.diag_part(yhat) + yhatT) + tf.nn.sigmoid(yhatT**2), axis=0)\n term2 = tf.nn.sigmoid(tf.diag_part(yhat)**2) / self.batch_size\n return tf.reduce_mean(term1 - term2)\n\n def top1_max(self, yhat):\n yhatT = tf.transpose(yhat)\n softmax_scores = tf.transpose(self.softmax_neg(yhat))\n y = softmax_scores * (tf.nn.sigmoid(-tf.diag_part(yhat) + yhatT) + tf.nn.sigmoid(yhatT**2))\n return tf.reduce_mean(tf.reduce_sum(y, axis=0))\n\n def build_model(self):\n\n self.X = tf.placeholder(tf.int32, [self.batch_size], name='input')\n self.Y = tf.placeholder(tf.int32, [self.batch_size + self.n_sample], name='output')\n self.dropout = tf.placeholder(tf.float32, name='dropout')\n self.state = [tf.placeholder(tf.float32, [self.batch_size, self.rnn_size], name='rnn_state') for _ in range(self.layers)]\n self.global_step = tf.Variable(0, name='global_step', trainable=False)\n\n with tf.variable_scope('gru_layer'):\n sigma = self.sigma if self.sigma != 0 else np.sqrt(6.0 / (self.n_items + self.rnn_size))\n if self.init_as_normal:\n initializer = tf.random_normal_initializer(mean=0, stddev=sigma)\n else:\n initializer = tf.random_uniform_initializer(minval=-sigma, maxval=sigma)\n embedding = tf.get_variable('embedding', [self.n_items, self.rnn_size], initializer=initializer)\n softmax_W = tf.get_variable('softmax_w', [self.n_items, self.rnn_size], initializer=initializer)\n softmax_b = tf.get_variable('softmax_b', [self.n_items], initializer=tf.constant_initializer(0.0))\n\n cell = GRUCell(self.rnn_size, activation=self.hidden_act,\n kernel_initializer=initializer, bias_initializer=tf.constant_initializer(0.0))\n drop_cell = DropoutWrapper(cell, output_keep_prob=self.dropout)\n stacked_cell = MultiRNNCell([drop_cell] * self.layers)\n\n inputs = tf.nn.embedding_lookup(embedding, self.X)\n output, state = stacked_cell(inputs, tuple(self.state))\n self.final_state = state\n\n '''\n Use other examples of the minibatch as negative samples.\n '''\n sampled_W = tf.nn.embedding_lookup(softmax_W, self.Y)\n sampled_b = tf.nn.embedding_lookup(softmax_b, self.Y)\n logits = tf.matmul(output, sampled_W, transpose_b=True) + sampled_b\n self.yhat = self.final_activation(logits)\n self.cost = self.loss_function(self.yhat)\n\n valid_logits = tf.matmul(output, softmax_W, transpose_b=True) + softmax_b\n self.valid_yhat = self.final_activation(valid_logits)\n\n self.lr = tf.maximum(1e-5, tf.train.exponential_decay(self.learning_rate, self.global_step, self.decay_steps, self.decay, staircase=True))\n\n '''\n Try different optimizers. \n '''\n optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.9, beta2=0.9, epsilon=1e-6)\n if self.optimizer == 'rmsprop':\n optimizer = tf.train.RMSPropOptimizer(self.learning_rate)\n elif self.optimizer == 'momentum':\n optimizer = tf.train.MomentumOptimizer(self.learning_rate, 0.25)\n elif self.optimizer == 'adagrad':\n optimizer = tf.train.AdagradOptimizer(self.learning_rate)\n\n tvars = tf.trainable_variables()\n gvs = optimizer.compute_gradients(self.cost, tvars)\n if self.grad_cap > 0:\n capped_gvs = [(tf.clip_by_norm(grad, self.grad_cap), var) for grad, var in gvs]\n else:\n capped_gvs = gvs\n self.train_op = optimizer.apply_gradients(capped_gvs, global_step=self.global_step)\n\n def init(self, data):\n data.sort_values([self.session_key, self.time_key], inplace=True)\n offset_sessions = np.zeros(data[self.session_key].nunique() + 1, dtype=np.int32)\n offset_sessions[1:] = data.groupby(self.session_key).size().cumsum()\n return offset_sessions\n\n def generate_neg_samples(self, pop, length):\n if self.sample_alpha:\n sample = np.searchsorted(pop, np.random.rand(self.n_sample * length))\n else:\n sample = np.random.choice(self.n_items, size=self.n_sample * length)\n if length > 1:\n sample = sample.reshape((length, self.n_sample))\n return sample\n\n def fit(self, data, valid, sample_store=10000000):\n train_data = data\n\n import time\n\n itemids = data[self.item_key].unique()\n self.n_items = len(itemids)\n self.itemidmap = pd.Series(data=np.arange(self.n_items), index=itemids)\n data = pd.merge(data, pd.DataFrame({self.item_key: itemids, 'ItemIdx': self.itemidmap[itemids].values}), on=self.item_key, how='inner')\n offset_sessions = self.init(data)\n base_order = np.argsort(data.groupby(self.session_key)[self.time_key].min().values) if self.time_sort else np.arange(len(offset_sessions) - 1)\n\n if self.n_sample:\n pop = data.groupby('ItemId').size()\n pop = pop[self.itemidmap.index.values].values**self.sample_alpha\n pop = pop.cumsum() / pop.sum()\n pop[-1] = 1\n if sample_store:\n generate_length = sample_store // self.n_sample\n if generate_length <= 1:\n sample_store = 0\n self.logger.debug('No example store was used')\n else:\n neg_samples = self.generate_neg_samples(pop, generate_length)\n sample_pointer = 0\n self.logger.debug('Created sample store with {} batches of samples'.format(generate_length))\n else:\n self.logger.debug('No example store was used')\n\n for epoch in range(self.n_epochs):\n t1 = time.time()\n\n epoch_cost = []\n state = [np.zeros([self.batch_size, self.rnn_size], dtype=np.float32) for _ in range(self.layers)]\n\n session_idx_arr = np.random.permutation(len(offset_sessions) - 1) if self.train_random_order else base_order\n iters = np.arange(self.batch_size)\n maxiter = iters.max()\n start = offset_sessions[session_idx_arr[iters]]\n end = offset_sessions[session_idx_arr[iters] + 1]\n finished = False\n while not finished:\n minlen = (end - start).min()\n out_idx = data.ItemIdx.values[start]\n for i in range(minlen - 1):\n in_idx = out_idx\n out_idx = data.ItemIdx.values[start + i + 1]\n\n if self.n_sample:\n if sample_store:\n if sample_pointer == generate_length:\n neg_samples = self.generate_neg_samples(pop, generate_length)\n sample_pointer = 0\n sample = neg_samples[sample_pointer]\n sample_pointer += 1\n else:\n sample = self.generate_neg_samples(pop, 1)\n y = np.hstack([out_idx, sample])\n else:\n y = out_idx\n\n # prepare inputs, targeted outputs and hidden states\n fetches = [self.cost, self.final_state, self.global_step, self.lr, self.train_op]\n feed_dict = {self.X: in_idx, self.Y: y, self.dropout: self.dropout_p_hidden}\n for j in range(self.layers):\n feed_dict[self.state[j]] = state[j]\n\n cost, state, step, lr, _ = self.sess.run(fetches, feed_dict)\n epoch_cost.append(cost)\n if np.isnan(cost):\n self.logger.error(str(epoch) + ':Nan error!')\n return\n #if step == 1 or step % self.decay_steps == 0:\n # avgc = np.mean(epoch_cost)\n # t2 = time.time()\n # self.logger.info('epoch:%2s, step:%5d, lr:%.6f, loss:%.6f, elpased(s):%6.3f',epoch, step, lr, avgc, t2-t1)\n # #print('Elpased: %.4fs' % (t2 - t1))\n\n start = start + minlen - 1\n mask = np.arange(len(iters))[(end - start) <= 1]\n for idx in mask:\n maxiter += 1\n if maxiter >= len(offset_sessions) - 1:\n finished = True\n break\n iters[idx] = maxiter\n start[idx] = offset_sessions[session_idx_arr[maxiter]]\n end[idx] = offset_sessions[session_idx_arr[maxiter] + 1]\n if len(mask) and self.reset_after_session:\n for i in range(self.layers):\n state[i][mask] = 0\n\n avgc = np.mean(epoch_cost)\n\n if np.isnan(avgc):\n self.logger.error('Epoch {}: Nan error!'.format(epoch, avgc))\n return\n #saver.save(self.sess, '{}/gru-model'.format(checkpoint_dir), global_step=epoch)\n \n if epoch % 2 == 0:\n res = self.evaluate_sessions_batch(train_data, valid, batch_size=self.batch_size)\n self.logger.info('epoch:%2d, loss:%.6f, recall@20:%.10f, mrr@20:%.10f',epoch, avgc, res[0], res[1])\n\n\n\n def evaluate_sessions_batch(self, train_data, test_data, batch_size, cut_off=20, session_key='SessionId', item_key='ItemId', time_key='Time'):\n '''\n Evaluates the GRU4Rec network wrt. recommendation accuracy measured by recall@N and MRR@N.\n \n Parameters\n --------\n train_data : It contains the transactions of the train set. In evaluation phrase, this is used to build item-to-id map.\n test_data : It contains the transactions of the test set. It has one column for session IDs, one for item IDs and one for the timestamp of the events (unix timestamps).\n cut-off : int\n Cut-off value (i.e. the length of the recommendation list; N for recall@N and MRR@N). Defauld value is 20.\n batch_size : int\n Number of events bundled into a batch during evaluation. Speeds up evaluation. If it is set high, the memory consumption increases. Default value is 100.\n session_key : string\n Header of the session ID column in the input file (default: 'SessionId')\n item_key : string\n Header of the item ID column in the input file (default: 'ItemId')\n time_key : string\n Header of the timestamp column in the input file (default: 'Time')\n \n Returns\n --------\n out : tuple\n (Recall@N, MRR@N)\n \n '''\n self.predict = False\n # Build itemidmap from train data.\n itemids = train_data[item_key].unique()\n itemidmap = pd.Series(data=np.arange(len(itemids)), index=itemids)\n \n test_data.sort_values([session_key, time_key], inplace=True)\n offset_sessions = np.zeros(test_data[session_key].nunique() + 1, dtype=np.int32)\n offset_sessions[1:] = test_data.groupby(session_key).size().cumsum()\n evalutation_point_count = 0\n mrr, recall = 0.0, 0.0\n if len(offset_sessions) - 1 < batch_size:\n batch_size = len(offset_sessions) - 1\n iters = np.arange(batch_size).astype(np.int32)\n maxiter = iters.max()\n start = offset_sessions[iters]\n end = offset_sessions[iters + 1]\n in_idx = np.zeros(batch_size, dtype=np.int32)\n np.random.seed(42)\n \n while True:\n valid_mask = iters >= 0\n if valid_mask.sum() == 0:\n break\n start_valid = start[valid_mask]\n minlen = (end[valid_mask] - start_valid).min()\n in_idx[valid_mask] = test_data[item_key].values[start_valid]\n for i in range(minlen - 1):\n out_idx = test_data[item_key].values[start_valid + i + 1]\n preds = self.predict_next_batch(iters, in_idx, itemidmap, batch=batch_size)\n preds.fillna(0, inplace=True)\n in_idx[valid_mask] = out_idx\n ranks = (preds.values.T[valid_mask].T > np.diag(preds.ix[in_idx].values)[valid_mask]).sum(axis=0) + 1\n rank_ok = ranks < cut_off\n recall += rank_ok.sum()\n mrr += (1.0 / ranks[rank_ok]).sum()\n evalutation_point_count += len(ranks)\n start = start + minlen - 1\n mask = np.arange(len(iters))[(valid_mask) & (end - start <= 1)]\n for idx in mask:\n maxiter += 1\n if maxiter >= len(offset_sessions) - 1:\n iters[idx] = -1\n else:\n iters[idx] = maxiter\n start[idx] = offset_sessions[maxiter]\n end[idx] = offset_sessions[maxiter + 1]\n \n return recall / evalutation_point_count, mrr / evalutation_point_count\n\n\n def predict_next_batch(self, session_ids, input_item_ids, itemidmap, batch):\n '''\n Gives predicton scores for a selected set of items. Can be used in batch mode to predict for multiple independent events (i.e. events of different sessions) at once and thus speed up evaluation.\n\n If the session ID at a given coordinate of the session_ids parameter remains the same during subsequent calls of the function, the corresponding hidden state of the network will be kept intact (i.e. that's how one can predict an item to a session).\n If it changes, the hidden state of the network is reset to zeros.\n\n Parameters\n --------\n session_ids : 1D array\n Contains the session IDs of the events of the batch. Its length must equal to the prediction batch size (batch param).\n input_item_ids : 1D array\n Contains the item IDs of the events of the batch. Every item ID must be must be in the training data of the network. Its length must equal to the prediction batch size (batch param).\n batch : int\n Prediction batch size.\n\n Returns\n --------\n out : pandas.DataFrame\n Prediction scores for selected items for every event of the batch.\n Columns: events of the batch; rows: items. Rows are indexed by the item IDs.\n\n '''\n if batch != self.batch_size:\n raise Exception('Predict batch size({}) must match train batch size({})'.format(batch, self.batch_size))\n if not self.predict:\n self.current_session = np.ones(batch) * -1\n self.predict = True\n\n session_change = np.arange(batch)[session_ids != self.current_session]\n if len(session_change) > 0: # change internal states with session changes\n for i in range(self.layers):\n self.predict_state[i][session_change] = 0.0\n self.current_session = session_ids.copy()\n\n in_idxs = itemidmap[input_item_ids]\n fetches = [self.valid_yhat, self.final_state]\n feed_dict = {self.X: in_idxs, self.dropout: 1.0}\n for i in range(self.layers):\n feed_dict[self.state[i]] = self.predict_state[i]\n preds, self.predict_state = self.sess.run(fetches, feed_dict)\n preds = np.asarray(preds).T\n return pd.DataFrame(data=preds, index=itemidmap.index)\n\n","sub_path":"tf-gru4rec/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":21463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"434675436","text":"\nfrom flask import Flask, Blueprint, render_template, session, redirect, request, url_for\nfrom flask_login import login_user, logout_user\nfrom requests_oauthlib import OAuth2Session\nfrom requests.auth import HTTPBasicAuth\nfrom flask.json import jsonify\n\nfrom aliceBlue.models import User\n\napplication = Flask(__name__)\napplication.config.from_object('config.DevelopementConfig')\n\naliceBlue_blueprint = Blueprint('aliceBlue_home', __name__, template_folder='templates', static_folder='static')\n\nredirect_url = \"\"\n\n@aliceBlue_blueprint.route('/')\ndef home():\n return render_template(\"home.html\")\n\n@aliceBlue_blueprint.route('/login', methods=[\"GET\", \"POST\"])\ndef login():\n aliceBlue = OAuth2Session(application.config['CLIENT_ID'])\n authorization_url, state = aliceBlue.authorization_url(application.config['AUTHORIZATION_BASE_URL'])\n session['oauth_state'] = state\n user = User(application.config['CLIENT_ID'])\n authorization_url = authorization_url + \"&redirect_uri=\" + application.config['REDIRECT_URI']\n login_user(user)\n \n return redirect(authorization_url)\n\n@aliceBlue_blueprint.route(\"/callback\", methods=[\"GET\"])\ndef callback():\n aliceBlue = OAuth2Session(application.config['CLIENT_ID'], state=session['oauth_state'])\n \n all_args = request.args.to_dict()\n code = all_args['code']\n headers = {'Accept':'application/json'}\n auth = HTTPBasicAuth(application.config['CLIENT_ID'], application.config['CLIENT_SECRET'])\n \n body = 'grant_type=authorization_code&code=' + code + '&redirect_uri=' + application.config['REDIRECT_URI']\n token = aliceBlue.fetch_token(application.config['TOKEN_URL'], code=code, auth=auth, body=body, headers=headers)\n session['oauth_token'] = token\n \n return redirect(url_for('trade_home.trade'))\n\n@aliceBlue_blueprint.route(\"/profile\", methods=[\"GET\"])\ndef profile():\n aliceBlue = OAuth2Session(application.config['CLIENT_ID'], token=session['oauth_token'])\n return jsonify(aliceBlue.get('https://ant.aliceblueonline.com/api/v2/profile').json())\n\n@aliceBlue_blueprint.route(\"/logout\")\ndef logout():\n logout_user()\n return render_template(\"home.html\")\n","sub_path":"pyTest_1/aliceBlue/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"317537103","text":"import unittest\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse\nfrom sklearn.metrics import mean_squared_error\nimport math\nimport smurff\nimport itertools\nimport collections\n\nverbose = 0\n\n# Taken from BMF_PP/postprocess_posterior_samples\ndef calc_posteriorMeanPrec(predict_session, axis):\n # collect U/V for all samples\n Us = [ s.latents[axis] for s in predict_session.samples() ]\n\n # stack them and compute mean\n Ustacked = np.stack(Us)\n mu = np.mean(Ustacked, axis = 0)\n\n # Compute Lambdaariance, first unstack in different way\n Uunstacked = np.squeeze(np.split(Ustacked, Ustacked.shape[2], axis = 2))\n Uprec = [ np.linalg.inv(np.cov(u, rowvar = False)) for u in Uunstacked ]\n\n # restack, shape: (K, K, N)\n Uprecstacked = np.stack(Uprec, axis = 2)\n\n return mu, Uprecstacked\n\nclass TestPP(unittest.TestCase):\n def test_bmf_pp(self):\n Y = scipy.sparse.rand(30, 20, 0.2)\n Y, Ytest = smurff.make_train_test(Y, 0.5)\n session = smurff.BPMFSession(Y, Ytest=Ytest, num_latent=4, verbose=verbose, burnin=5, nsamples=20, save_freq=1)\n session.run()\n predict_session = session.makePredictSession()\n\n sess_rmse = float(predict_session.statsYTest()[\"rmse_avg\"])\n Ypred, Yvar = predict_session.predictionsYTest()\n calc_rmse = math.sqrt(mean_squared_error(Ytest.tocoo().data, Ypred.tocoo().data))\n\n self.assertAlmostEqual(sess_rmse, calc_rmse, 2)\n\n for m in range(predict_session.nmodes):\n calc_mu, calc_Lambda = calc_posteriorMeanPrec(predict_session, m)\n sess_mu, sess_Lambda = predict_session.postMuLambda(m)\n\n np.testing.assert_almost_equal(calc_mu, sess_mu)\n np.testing.assert_almost_equal(calc_Lambda, sess_Lambda)\n\n # print(\"calculated mu: \", calc_mu[0:2,0])\n # print(\" session mu: \", sess_mu[0:2,0])\n\n # print(\"calculated Lambda \", calc_Lambda.shape, \": \", calc_Lambda[0:2,0:2,1] )\n # print(\" session Lambda \", sess_Lambda.shape, \": \", sess_Lambda[0:2,0:2,1] )\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"python/smurff/test/test_pp.py","file_name":"test_pp.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"174270416","text":"import pymongo\n\nif __name__ == '__main__':\n client = pymongo.MongoClient()\n mdb = client.arxiv\n papers = mdb.papers\n\n\n res = papers.create_index(\n [\n ('title', 'text'),\n ('authors.name', 'text'),\n ('summary', 'text'),\n ('tags.term', 'text')\n ],\n weights={\n 'title': 10,\n 'authors.name': 5,\n 'summary': 5,\n 'tags.term': 5,\n }\n )","sub_path":"create_index.py","file_name":"create_index.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"641419616","text":"import os\r\nimport numpy as np\r\n\r\nfrom grid_env import GridEnv\r\nfrom agent import Agent\r\n\r\nNUM_EPISODE = 1000\r\n\r\n\r\nif __name__ == \"__main__\":\r\n env = GridEnv()\r\n agent = Agent(env)\r\n\r\n for n_episode in range(NUM_EPISODE):\r\n state = env.reset()\r\n\r\n while True:\r\n action = agent.get_action(state)\r\n next_state, reward, done = env.step(action)\r\n next_action = agent.get_action(next_state)\r\n\r\n agent.update_table(state, action, reward, next_state, next_action)\r\n state = next_state\r\n\r\n if done:\r\n break\r\n\r\n debug_str = \"\"\r\n for h in range(env.height):\r\n for w in range(env.width):\r\n debug_str += '****************'\r\n debug_str += \"*\\n\"\r\n for w in range(env.width):\r\n debug_str += '# up:' + str('%.2f ' % (agent.q_table[h, w, 0])).rjust(11)\r\n debug_str += \"#\\n\" \r\n for w in range(env.width):\r\n debug_str += '# right:' + str('%.2f ' % (agent.q_table[h, w, 1])).rjust(8)\r\n debug_str += \"#\\n\"\r\n for w in range(env.width):\r\n debug_str += '# down:' + str('%.2f ' % (agent.q_table[h, w, 2])).rjust(9)\r\n debug_str += \"#\\n\"\r\n for w in range(env.width):\r\n debug_str += '# left:' + str('%.2f ' % (agent.q_table[h, w, 3])).rjust(9)\r\n debug_str += \"#\\n\"\r\n for c in range(env.width):\r\n debug_str += '****************'\r\n debug_str += \"*\\n\"\r\n debug_str += \"num_episode=%d\" % n_episode\r\n\r\n os.system(\"clear\")\r\n print(debug_str)\r\n\r\n # save table\r\n np.save(\"q_table.npy\", agent.q_table)","sub_path":"SARSA/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"320561156","text":"#!/usr/bin/env python3\n#\n# Copyright 2021 PSB\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\"\"\"This scripts loads and cleans the EcoliCore2 SBML as given in its publication (Hädicke & Klamt, 2017) and saves it again with cobrapy.\n\nThis is done in order to gain a version which is not altered by cobrapy while loading it.\nThe \"cleaning\" contains steps such as setting 1000 flux bounds to inf.\n\nReferences:\nHädicke, O., & Klamt, S. (2017). EColiCore2: a reference network model of the central metabolism\nof Escherichia coli and relationships to its genome-scale parent model.\nScientific reports, 7, 39647.\n\"\"\"\nimport cobra\n\nprint(\"=>Loading and saving of EcoliCore2 while cleaning up some wrong reaction and metabolite ID parts\")\n\nprint(\"Loading original EcoliCore2 SBML as given in its publication...\")\nmodel = cobra.io.read_sbml_model(\"publication_runs/ecoli_models/original_sbml_models/ecolicore2compressed.xml\")\n\nprint(\"Set -1000/1000 bounds to -inf/inf...\")\nfor reaction in model.reactions:\n if reaction.upper_bound >= 1000:\n reaction.upper_bound = float(\"inf\")\n if reaction.lower_bound <= -1000:\n reaction.lower_bound = -float(\"inf\")\n\nprint(\"Cleaning reaction ID parts...\")\nfor reaction in model.reactions:\n if \"_DASH_\" in reaction.id:\n reaction.id = reaction.id.replace(\"_DASH_\", \"__\")\n if \"_LPAREN_\" in reaction.id:\n reaction.id = reaction.id.replace(\"_LPAREN_\", \"_\")\n if \"_RPAREN_\" in reaction.id:\n reaction.id = reaction.id.replace(\"_RPAREN_\", \"\")\n\n\nprint(\"Delete boundary-condition-free exchange metabolites ending with _ex...\")\nboundary_free_metabolite_ids = [\n x.id.replace(\"EX_\", \"\") for x in model.reactions\n if x.id.startswith(\"EX_\") and x.id.endswith(\"_ex\")\n]\nboundary_free_metabolites = []\nfor x in boundary_free_metabolite_ids:\n try:\n metabolite = model.metabolites.get_by_id(x)\n boundary_free_metabolites.append(metabolite)\n except KeyError:\n continue\nfor metabolite in boundary_free_metabolites:\n model.remove_metabolites([metabolite])\n\nprint(\"Rename wrong metabolite name parts...\")\nfor metabolite in model.metabolites:\n if \"_DASH_\" in metabolite.id:\n metabolite.id = metabolite.id.replace(\"_DASH_\", \"__\")\n if \"_LPAREN_\" in metabolite.id:\n metabolite.id = metabolite.id.replace(\"_LPAREN_\", \"_\")\n if \"_RPAREN_\" in metabolite.id:\n metabolite.id = metabolite.id.replace(\"_RPAREN_\", \"\")\n\nprint(\"Delete redundant glucose _ex metabolite...\")\nmodel.remove_metabolites([model.metabolites.get_by_id(\"glc__D_ex\")])\n\nprint(\"Delete unused boundary-free metabolite exchange reactions...\")\nex_reaction_ids = [x.id for x in model.reactions if x.id.startswith(\"EX_\")]\nfor ex_reaction_id in ex_reaction_ids:\n if model.reactions.get_by_id(ex_reaction_id).metabolites == {}:\n model.remove_reactions([ex_reaction_id])\n\nprint(\"Add periplasmic metabolite for all metabolties in EX_ reactions ending with _c...\")\nex_c_reaction_ids = [x.id for x in model.reactions if x.id.startswith(\"EX_\") and x.id.endswith(\"_c\")]\nfor ex_c_reaction_id in ex_c_reaction_ids:\n reaction = model.reactions.get_by_id(ex_c_reaction_id)\n metabolite = list(reaction.metabolites.keys())[0]\n new_p_metabolite = cobra.Metabolite(id=metabolite.id.replace(\"_c\", \"_p\"), compartment=\"p\")\n reaction.add_metabolites({\n new_p_metabolite: 1\n })\n new_ex_reaction = cobra.Reaction(id=\"EX_\"+new_p_metabolite.id,\n lower_bound=reaction.lower_bound,\n upper_bound=reaction.upper_bound)\n new_ex_reaction.add_metabolites ({\n new_p_metabolite: -1\n })\n model.add_reactions([new_ex_reaction])\n reaction.id = \"Transport_c_to_p_\" + metabolite.id.replace(\"_c\", \"\")\n\nprint(\"Get all reaction IDs ending with Ex and Up...\")\nex_reaction_ids = [x.id for x in model.reactions if x.id.endswith(\"Ex\")]\nup_reaction_ids = [x.id for x in model.reactions if x.id.endswith(\"Up\")]\nall_exchange_ids = ex_reaction_ids + up_reaction_ids\n\nclass ExchangedMetabolite:\n def __init__(self, metabolite_id, ex_reaction_id, up_reaction_id):\n self.metabolite_id = metabolite_id\n self.ex_reaction_id = ex_reaction_id\n self.up_reaction_id = up_reaction_id\n\nexchanged_metabolites = []\nfor exchange_id in all_exchange_ids:\n ex_reaction_id = \"\"\n up_reaction_id = \"\"\n\n if exchange_id.endswith(\"Up\"):\n up_reaction_id = exchange_id\n elif exchange_id.endswith(\"Ex\"):\n ex_reaction_id = exchange_id\n else:\n print(exchange_id)\n print(\"Error 1!\")\n input()\n\n reaction = model.reactions.get_by_id(exchange_id)\n reaction_id_start = reaction.id[:2].lower()\n exchanged_metabolite = None\n for metabolite in reaction.metabolites:\n if metabolite.id.startswith(reaction_id_start):\n if exchanged_metabolite != None:\n print(\"Error 1B!\")\n input()\n exchanged_metabolite = metabolite\n\n current_ids = [x.metabolite_id for x in exchanged_metabolites]\n if exchanged_metabolite.id in current_ids:\n element_index = current_ids.index(exchanged_metabolite.id)\n if ex_reaction_id != \"\":\n exchanged_metabolites[element_index].ex_reaction_id = ex_reaction_id\n elif up_reaction_id != \"\":\n exchanged_metabolites[element_index].up_reaction_id = up_reaction_id\n else:\n print(\"Error 2!\")\n input()\n else:\n if ex_reaction_id != \"\":\n up_reaction_id = \"\"\n elif up_reaction_id != \"\":\n ex_reaction_id = \"\"\n else:\n print(\"Error 3!\")\n input()\n exchanged_metabolites.append(ExchangedMetabolite(\n metabolite_id=exchanged_metabolite.id,\n ex_reaction_id=ex_reaction_id,\n up_reaction_id=up_reaction_id\n ))\n\nprint(\"Create new EX_ metabolites with, if not given, new periplasmic intermediates...\")\nfor exchanged_metabolite in exchanged_metabolites:\n has_ex = exchanged_metabolite.ex_reaction_id != \"\"\n has_up = exchanged_metabolite.up_reaction_id != \"\"\n\n if exchanged_metabolite.metabolite_id.endswith(\"_p\"):\n if has_ex and has_up:\n ex_reaction = model.reactions.get_by_id(exchanged_metabolite.ex_reaction_id)\n up_reaction = model.reactions.get_by_id(exchanged_metabolite.up_reaction_id)\n\n new_ex_reaction = cobra.Reaction(id=\"EX_\"+exchanged_metabolite.metabolite_id,\n lower_bound=-up_reaction.upper_bound,\n upper_bound=ex_reaction.upper_bound)\n new_ex_reaction.add_metabolites({\n model.metabolites.get_by_id(exchanged_metabolite.metabolite_id): -1\n })\n model.add_reactions([new_ex_reaction])\n model.remove_reactions([\n ex_reaction,\n up_reaction\n ])\n elif has_ex:\n ex_reaction = model.reactions.get_by_id(exchanged_metabolite.ex_reaction_id)\n if len(list(ex_reaction.metabolites.keys())) > 1:\n print(\"Error A1!\")\n input()\n ex_reaction.id = \"EX_\" + exchanged_metabolite.metabolite_id\n elif has_up:\n up_reaction = model.reactions.get_by_id(exchanged_metabolite.up_reaction_id)\n if len(list(up_reaction.metabolites.keys())) > 1:\n print(\"Error A2!\")\n input()\n up_reaction.id = \"EX_\" + exchanged_metabolite.metabolite_id\n\n old_lower_bound = up_reaction.lower_bound\n old_upper_bound = up_reaction.upper_bound\n if exchanged_metabolite.metabolite_id.startswith(\"glc__\"):\n print(\"A\")\n up_reaction.lower_bound = -old_upper_bound\n up_reaction.upper_bound = -old_lower_bound\n\n up_reaction.add_metabolites({\n model.metabolites.get_by_id(exchanged_metabolite.metabolite_id): -2\n })\n else:\n print(\"Error Zeta!\")\n input()\n elif exchanged_metabolite.metabolite_id.endswith(\"_c\"):\n new_p_metabolite_id = exchanged_metabolite.metabolite_id.replace(\"_c\", \"_p\")\n new_p_metabolite = cobra.Metabolite(id=new_p_metabolite_id, compartment=\"p\")\n model.add_metabolites(new_p_metabolite)\n\n new_p_ex_reaction = cobra.Reaction(id=\"EX_\" + new_p_metabolite_id)\n new_p_ex_reaction.add_metabolites({\n new_p_metabolite: -1\n })\n\n if has_ex and has_up:\n ex_reaction = model.reactions.get_by_id(exchanged_metabolite.ex_reaction_id)\n up_reaction = model.reactions.get_by_id(exchanged_metabolite.up_reaction_id)\n\n new_p_ex_reaction.lower_bound = -up_reaction.upper_bound\n new_p_ex_reaction.upper_bound = ex_reaction.upper_bound\n\n ex_reaction.add_metabolites({\n new_p_metabolite: 1\n })\n up_reaction.add_metabolites({\n new_p_metabolite: -1\n })\n ex_reaction.id = \"Transport_c_to_p_\" + new_p_metabolite.id.replace(\"_p\", \"\")\n up_reaction.id = \"Transport_p_to_c_\" + new_p_metabolite.id.replace(\"_p\", \"\")\n elif has_ex:\n ex_reaction = model.reactions.get_by_id(exchanged_metabolite.ex_reaction_id)\n\n new_p_ex_reaction.lower_bound = 0\n new_p_ex_reaction.upper_bound = ex_reaction.upper_bound\n\n ex_reaction.add_metabolites({\n new_p_metabolite: 1\n })\n ex_reaction.id = \"Transport_c_to_p_\" + new_p_metabolite.id.replace(\"_p\", \"\")\n elif has_up:\n up_reaction = model.reactions.get_by_id(exchanged_metabolite.up_reaction_id)\n\n new_p_ex_reaction.lower_bound = -up_reaction.upper_bound\n\n up_reaction.add_metabolites({\n new_p_metabolite: -1\n })\n up_reaction.id = \"Transport_p_to_c_\" + new_p_metabolite.id.replace(\"_p\", \"\")\n else:\n print(\"Error Beta!\")\n input()\n\n model.add_reactions([new_p_ex_reaction])\n else:\n print(\"Error Alpha!\")\n input()\n\nprint(\"Delete biomass metabolite and associated reaction...\")\nmodel.remove_metabolites([model.metabolites.get_by_id(\"Biomass\")])\nmodel.remove_reactions([model.reactions.get_by_id(\"EX_Biomass\")])\n\nprint(\"Deactivate all C sources except of D-glucose...\")\nmodel.reactions.EX_succ_p.lower_bound = 0\nmodel.reactions.EX_glyc_p.lower_bound = 0\nmodel.reactions.EX_ac_p.lower_bound = 0\nmodel.reactions.EX_glc__D_p.lower_bound = -10\n\nprint(\"Test cleaned model with FBA...\")\nwith model:\n print(\"Single model FBA solution:\")\n fba_solution = model.optimize()\n print(model.summary())\n for reaction in model.reactions:\n if not reaction.id.startswith(\"EX_\"):\n continue\n if fba_solution.fluxes[reaction.id] != 0:\n print(f\"{reaction.id}: {fba_solution.fluxes[reaction.id] }\")\n print(\"~~~\")\n\nprint(\"Print exchange metabolites...\")\nin_string = \"\"\nout_string = \"\"\nfor reaction in model.reactions:\n if reaction.id.startswith(\"EX_\"):\n string_part = f'\"{reaction.id.replace(\"EX_\", \"\")}\",\\n'\n if reaction.lower_bound < 0:\n in_string += string_part\n if reaction.upper_bound > 0:\n out_string += string_part\nprint(\"In metabolites:\")\nprint(in_string)\nprint(\"Out metabolites:\")\nprint(out_string)\n\nprint(\"Saving SBML of cleaned EcoliCore2compressed model...\")\ncobra.io.write_sbml_model(model, \"./publication_runs/ecoli_models/original_sbml_models_in_cleaned_form/ecc2comp_loaded_and_saved_by_cobrapy_cleaned.xml\")\n\nprint(\"Done!\")\nprint(\"\")\n\nfor reaction in model.reactions:\n if (reaction.lower_bound < 0) and (reaction.id.startswith(\"EX_\")):\n print(reaction.id)\n","sub_path":"publication_runs/ecoli_models/ecolicore2comp_load_and_save_with_cobrapy_and_clean.py","file_name":"ecolicore2comp_load_and_save_with_cobrapy_and_clean.py","file_ext":"py","file_size_in_byte":12188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"23708423","text":"import tensorflow as tf\nimport numpy as np\nfrom .master_agent import ActorCriticMasterAgent\nfrom ..utilities.discount import discount\nfrom ..utilities.update_target_graph import update_target_graph\n\n\nclass ActorCriticWorkerAgent(ActorCriticMasterAgent):\n \"\"\"Actor Critic Worker Agent Class\n\n The worker agent is responsible for interacting with the environment and\n receiving rewards. The worker will then compute a gradient descent direction\n using its locally stored version of the master network and apply that update\n to the globally shared weights. Note that because these gradient updates are\n performed asynchronously, a gradient descent direction may be \"out-of-date\"\n with respect to the master network's current parameters.\n\n These worker agents correspond to the actor-learners that accumulate updates\n for the master network, which improve training stability.\n \"\"\"\n def __init__(\n self,\n thread_idx,\n session,\n env,\n build_model,\n opt,\n gamma=0.99\n ):\n \"\"\"Initialize parameters of the actor-critic worker agent object.\n\n Parameters:\n thread_idx (int): An integer reflecting the thread index of the\n current worker. This integer is appended to a string with prefix\n \"worker\" to identify the scope of operations belonging to this\n thread.\n session (TensorFlow session): The TensorFlow session that is shared\n across the master and worker agents.\n env (GymWrapper): An OpenAI Gym environment or environment with an\n identical interface. This is the environment with which the\n agent will interact.\n model_builder (AbstractModelBuilder): An object that inherits from\n the abstract model builder object. This class implements\n functions to produce the policy, value, and state parameters of\n the reinforcement learning algorithm, in addition to methods for\n updating the feed dictionaries, advancing state parameters, and\n resetting internal variables.\n opt (TensorFlow Optimizer): A gradient descent optimizer object from\n TensorFlow. This is used to compute the gradient descent updates\n for the master network. By default, this is the Adam optimizer\n with a learning rate of 0.0001. Note that `opt` needs to be\n shared across all threads; that means threads cannot have their\n own instantiation of the optimizer.\n gamma (float, optional): The reward discount factor to use when\n computing future returns. The default discount parameter is\n 0.99.\n \"\"\"\n super(ActorCriticWorkerAgent, self).__init__(\n \"worker_{}\".format(thread_idx), session, env, build_model\n )\n self.thread_idx = thread_idx\n # Assign discount factor for future rewards.\n self.gamma = gamma\n # Synchronization operations.\n self.update_local_ops = update_target_graph(self.scope)\n\n # Create loss functions and gradient descent operations.\n with tf.variable_scope(\"optimizer\"):\n self.actions = tf.placeholder(shape=[None], dtype=tf.int32)\n self.target_values = tf.placeholder(shape=[None], dtype=tf.float32)\n self.advantages = tf.placeholder(shape=[None], dtype=tf.float32)\n actions_onehot = tf.one_hot(\n self.actions,\n self.env.action_size,\n dtype=tf.float32\n )\n resp_actions = tf.reduce_sum(self.policy*actions_onehot, axis=1)\n value_loss = 0.25 * tf.reduce_sum(\n tf.square(self.target_values - tf.squeeze(self.value))\n )\n entropy_loss = 0.01 * tf.reduce_sum(\n self.policy * tf.log(self.policy + 1e-8)\n )\n policy_loss = -tf.reduce_sum(\n tf.log(resp_actions) * self.advantages\n )\n self.loss = value_loss + entropy_loss + policy_loss\n\n # Get gradients from local network using local losses.\n local_vars = tf.get_collection(\n tf.GraphKeys.TRAINABLE_VARIABLES, self.scope\n )\n gradients = tf.gradients(self.loss, local_vars)\n grads, _ = tf.clip_by_global_norm(gradients, 40.0)\n # Apply local gradients to global network.\n master_vars = tf.get_collection(\n tf.GraphKeys.TRAINABLE_VARIABLES, \"master\"\n )\n self.apply_grads = opt.apply_gradients(zip(grads, master_vars))\n\n def train(\n self,\n states,\n actions,\n rewards,\n values,\n bootstrap=0.\n ):\n \"\"\"This function computes the gradient of the asynchronous actor-critic\n advantage loss function with respect to the parameters of the policy and\n value networks. This function provides the state observations, the\n actions taken, the rewards obtained (not discounted), and estimated\n value scores for each state. In addition, if a terminal state has not\n been reached in the environment, then a bootstrap value is added to the\n advantage function and a update is performed using a \"best guess\" of the\n future reward.\n\n Parameters:\n states (numpy array): A batch of screens. Note that we may\n concatenate previous screens to control for temporal effects.\n Therefore, the states array will contain a number of previous\n frames as representing a single observation.\n actions (list): A vector corresponding to the index of the action\n that was performed in a given state.\n rewards (list): The instantaneous reward from taking a given action\n in a given state.\n values (list): An estimate of the value of being in a given state.\n bootstrap (float, optional): An estimate of the expected future\n rewards if an update is performed before a terminal state is\n reached.\n\n Returns:\n Float: The value of the loss function.\n \"\"\"\n rewards_plus = np.asarray(rewards + [bootstrap])\n discounted_rewards = discount(rewards_plus, self.gamma)[:-1]\n value_plus = np.asarray(values + [bootstrap])\n advantages = rewards + self.gamma * value_plus[1:] - value_plus[:-1]\n advantages = discount(advantages, self.gamma)\n # Compute gradients.\n grads, loss = self.sess.run(\n [self.apply_grads, self.loss],\n self.model_builder.update({\n self.states: states,\n self.actions: actions,\n self.target_values: discounted_rewards,\n self.advantages: advantages\n }))\n\n return loss\n\n def synchronize(self):\n \"\"\"Synchonrizes the parameters of the local worker network with the\n weights and biases of the globally shared master network. This simply\n copies the current values of the master network parameters to the\n corresponding objects in the worker network.\n \"\"\"\n self.sess.run(self.update_local_ops)\n","sub_path":"asyncrl/agents/worker_agent.py","file_name":"worker_agent.py","file_ext":"py","file_size_in_byte":7392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"189688478","text":"# The point of this is learning by supervised training. ie: You use the rollouts from the expert\n# policies and you train your network to output the same action when you observe the same thing\n\nimport tensorflow as tf\nimport gym\nimport numpy as np\nimport os\nfrom tqdm import tqdm\nimport pickle\nfrom absl import flags, app\nimport load_policy_eager\n\nflags.DEFINE_string('envname', 'Ant-v2', 'Name of the env')\n\nflags.DEFINE_integer('steps', 4000, 'Number of training steps on the rollout dataset')\nflags.DEFINE_integer('generation_rollouts', 5, 'Number of rollouts you generate to then be annotated by the expert policy')\nflags.DEFINE_integer('iterations', 5, 'Number of iterations of the algorithm')\n\nflags.DEFINE_boolean('render', False, 'Render the env')\nflags.DEFINE_float('lr', 1e-4, 'Learning rate')\nflags.DEFINE_integer('evaluation_rollouts', 10, 'Number of evalution rollouts')\nflags.DEFINE_list('hidden', '100,100', 'network hidden size')\nFLAGS = flags.FLAGS\n\n\ndef build_model(hidden, input_shape, number_outputs):\n m = []\n for i, h in enumerate(hidden):\n if i == 0:\n m.append(tf.keras.layers.Dense(h, activation=tf.nn.relu, input_shape=input_shape))\n else:\n m.append(tf.keras.layers.Dense(h, activation=tf.nn.relu))\n m.append(tf.keras.layers.Dropout(0.3))\n m.append(tf.keras.layers.Dense(number_outputs))\n return tf.keras.Sequential(m)\n\ndef generate_rollouts(env, model, num_rollouts, model_action_to_env_action=lambda x: x, render=False):\n returns = []\n observations = []\n actions = []\n for i in range(num_rollouts):\n print('Generating rollout number {}'.format(i + 1))\n obs = env.reset()\n done = False\n totalr = 0.\n steps = 0\n while not done:\n obs = obs.astype(np.float32)\n action = model(obs[None, :])\n action = model_action_to_env_action(action)\n observations.append(obs)\n actions.append(action)\n obs, r, done, _ = env.step(action)\n if render:\n env.render()\n totalr += r\n steps += 1\n if steps >= env.spec.timestep_limit:\n break\n returns.append(totalr)\n return np.array(observations), np.array(actions), returns\n\n\ndef dagger(env, dataset=None, optim=None, model=None, training_steps=None, generation_rollouts=None, expert_policy_fn=None):\n #Run one iteration of dagger and return a new array of {observations, actions} annotated by the expert policy\n #1) Train the model on the dataset for training_steps step\n for (_, (obs, actions)) in enumerate(tqdm(dataset.take(training_steps), total=training_steps)):\n with tf.GradientTape() as tape:\n predicted_action = model(obs, training=True)\n error = tf.losses.mean_squared_error(predicted_action, actions)\n\n grads = tape.gradient(error, model.trainable_variables)\n optim.apply_gradients(zip(grads, model.trainable_variables),\n global_step=tf.train.get_or_create_global_step())\n #2) Generate rollouts from the model\n observations, _, _ = generate_rollouts(env, model, generation_rollouts, model_action_to_env_action=lambda action: action[np.newaxis, :])\n #3) Annotate the observations by the expert policy\n print(\"Annotation...\")\n annotated_actions = expert_policy_fn(observations)[:, None, :]\n print(\"Annotation done\")\n return observations, annotated_actions\n\n\ndef generate_dataset(observations, actions):\n observations = observations.astype(np.float32)\n actions = actions.astype(np.float32)\n assert observations.shape[0] == actions.shape[0]\n if len(actions.shape) == 3:\n new_actions = np.empty([actions.shape[0], actions.shape[2]])\n new_actions = actions[:,0,:]\n actions = new_actions\n dataset = tf.data.Dataset.from_tensor_slices((observations, actions))\n dataset = dataset.repeat().batch(64).shuffle(observations.shape[0])\n return dataset\n\n\n\ndef main(args):\n del args\n # 1) Load the Gym env and introspect the action and observation space\n env = gym.make(FLAGS.envname)\n hidden = [int(x) for x in FLAGS.hidden]\n print(\"Model: {}\".format(hidden))\n model = build_model(\n hidden, env.observation_space.shape, env.action_space.shape[0])\n model.summary()\n # 2) Load the expert rollouts\n with open(os.path.join('expert_data', FLAGS.envname + '.pkl'), 'rb') as f:\n rollouts = pickle.loads(f.read())\n observations, actions = rollouts['observations'], rollouts['actions']\n # 3) Load the expert policy\n policy_fn = load_policy_eager.load_policy(os.path.join(\"experts\", FLAGS.envname + \".pkl\"))\n # Set up everything\n optim = tf.train.AdamOptimizer(learning_rate=FLAGS.lr)\n # Run dagger iterations time\n for iteration in range(FLAGS.iterations):\n print('Dagger iteration {}'.format(iteration + 1))\n dataset = generate_dataset(observations, actions)\n obs, acts = dagger(env, dataset=dataset, optim=optim, model=model, training_steps=FLAGS.steps, generation_rollouts=FLAGS.generation_rollouts, expert_policy_fn=policy_fn)\n observations = np.vstack((observations,obs))\n actions = np.vstack((actions , acts))\n\n # 4) Save the model\n output_dir = os.path.join(\"dagger_models\", FLAGS.envname, str(hidden))\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n model.save(os.path.join(output_dir, 'model.h5'))\n # 5) Evaluate the model\n _, _, returns = generate_rollouts(env, model, FLAGS.evaluation_rollouts, model_action_to_env_action=lambda action: action[np.newaxis, :], render=FLAGS.render)\n\n print('returns', returns)\n print('mean return', np.mean(returns))\n print('std of return', np.std(returns))\n with open(os.path.join(output_dir, 'stats.pkl'), 'wb') as f:\n pickle.dump(returns, f, pickle.HIGHEST_PROTOCOL)\n\n\nif __name__ == \"__main__\":\n app.run(main)\n","sub_path":"hw1/dagger.py","file_name":"dagger.py","file_ext":"py","file_size_in_byte":5957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"236155276","text":"import os\r\nimport pickle\r\nimport string\r\nimport re\r\n\r\nimport numpy as np\r\nfrom nltk.tokenize import sent_tokenize\r\nfrom collections import Counter, OrderedDict\r\nfrom abc import abstractmethod, ABC\r\n\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\nfrom sklearn.preprocessing import StandardScaler, Normalizer\r\n\r\nimport Data_Utils\r\nimport Extractor.DatasetInfo as DatasetInfo\r\n\r\nimport logging\r\n\r\nlogging.getLogger()\r\n\r\ncompound_keywords = [\"for\", \"and\", \"nor\", \"but\", \"or\", \"yet\", \"so\", \"however\", \"moreover\", \"nevertheless\",\r\n \"nonetheless\", \"therefore\"]\r\ncomplex_keywords = [\"because\", \"since\", \"so\", \"that\", \"although\", \"even\", \"though\", \"whereas\", \"while\", \"where\",\r\n \"wherever\", \"how\", \"however\", \"if\", \"whether\", \"unless\", \"that\", \"which\", \"who\", \"whom\", \"after\",\r\n \"as\", \"before\", \"since\", \"when\", \"whenever\", \"while\", \"until\"]\r\n\r\ndatabase_dir = \"\"\r\n\r\nWRITE_TO_FILE = 0\r\nWRITE_TO_MEMORY = 1\r\n\r\n\r\nclass Extractor:\r\n def __init__(self, data_dir, name, test_dir=None, out_file=None, descriptor=\"auto\", is_dir=False,\r\n dataset_level=\"auto\"):\r\n self.descriptor = descriptor\r\n self.is_dir = is_dir\r\n self.dataset_level = dataset_level\r\n self.name = name\r\n\r\n self._out_file = out_file\r\n\r\n self.feature_size = -1\r\n if 'feature_name' not in self.__dict__:\r\n self.feature_name = \"dummy\"\r\n self.data_dir = data_dir\r\n self.test_dir = test_dir\r\n\r\n self._data = {'features': {}, 'lookup_table': {}}\r\n self._preprocessors = []\r\n self.params = [[], []]\r\n self.name_ext = \"\"\r\n\r\n self.write_type = WRITE_TO_FILE\r\n\r\n def start(self):\r\n logging.info(\"Extractor Started\")\r\n\r\n if len(self.params[1]) > 0:\r\n logging.info(\"Processing parameters\")\r\n defaults = self.params[0].__init__.__defaults__[1:1 + len(self.params[1])]\r\n ext = \"\"\r\n for i, param in enumerate(self.params[1]):\r\n if param not in self.__dict__ or self.__dict__[param] == defaults[i]:\r\n continue\r\n\r\n if ext != \"\":\r\n ext += \"-\"\r\n\r\n ext += param.replace(\"_\", \"-\") + \"=\" + str(self.__dict__[param])\r\n self.name_ext = ext\r\n\r\n logging.info(\"DatasetInfo is loading\")\r\n self.info = DatasetInfo.DatasetInfo(self.out_file, descriptor=self.descriptor, is_dir=self.is_dir)\r\n\r\n if self.dataset_level == \"auto\":\r\n if self.info.descriptor.level is None:\r\n self.info.descriptor.loadLevel(self.data_dir)\r\n else:\r\n self.info.descriptor.setLevel(self.dataset_level)\r\n\r\n self.info.read()\r\n logging.info(\"DatasetInfo loaded\")\r\n\r\n set_info = self.info.data\r\n if not set_info.dexists('info', 'data_dir') or set_info.dget('info', 'data_dir') == '':\r\n set_info.dadd('info', ('data_dir', self.data_dir))\r\n\r\n if self.test_dir is not None and not set_info.dexists('info', 'test_dir'):\r\n logging.info(\"Test set detected\")\r\n set_info.dadd('info', ('test_dir', self.test_dir))\r\n\r\n # Pipeline\r\n logging.info(\"Extractor pipeline -> prepare\")\r\n self.prepare()\r\n logging.info(\"Extractor pipeline -> run\")\r\n self.run()\r\n logging.info(\"Extractor pipeline -> post run\")\r\n self.post_run()\r\n logging.info(\"Finished\")\r\n\r\n if self.write_type == WRITE_TO_MEMORY:\r\n return self._data['features']\r\n return ['filepath']\r\n\r\n @property\r\n def out_file(self):\r\n if self._out_file is None:\r\n file_name = self.name + \"_\" + self.feature_name\r\n\r\n if self.name_ext != \"\":\r\n file_name += \"_\" + self.name_ext\r\n\r\n return file_name\r\n\r\n return self._out_file\r\n\r\n\r\n @property\r\n def lookup_table(self):\r\n return self._data['lookup_table']\r\n\r\n def get_target_and_extension(self, is_test=False):\r\n if is_test:\r\n target_dir = self.test_dir\r\n extension = \".test\"\r\n else:\r\n target_dir = self.data_dir\r\n extension = \".txt\"\r\n return target_dir, extension\r\n\r\n @abstractmethod\r\n def prepare(self):\r\n pass\r\n\r\n def post_run(self):\r\n self.info.save()\r\n self.preprocess()\r\n\r\n def run(self):\r\n logging.info(\"Extractor pipeline -> run -> process\")\r\n self.process()\r\n logging.info(\"Extractor pipeline -> run -> after run process\")\r\n self.after_run_process()\r\n\r\n if self.test_dir is not None:\r\n logging.info(\"Extractor pipeline -> run -> before test process\")\r\n self.before_test_process()\r\n logging.info(\"Extractor pipeline -> run -> process\")\r\n self.process(True)\r\n logging.info(\"Extractor pipeline -> run -> after test process\")\r\n self.after_test_process()\r\n\r\n if self.feature_size != -1:\r\n self.info.set_feature_prop(\"size\", self.feature_size)\r\n\r\n def after_run_process(self):\r\n return None\r\n\r\n def before_test_process(self):\r\n pass\r\n\r\n def after_test_process(self):\r\n pass\r\n\r\n def process(self, is_test=False):\r\n raise NotImplementedError\r\n\r\n def parse_file(self, file_name, info, is_test=False):\r\n pass\r\n\r\n def write_result(self, feature_dict, lookup_table=None, is_test=False):\r\n _, extension = self.get_target_and_extension(is_test)\r\n\r\n if lookup_table is not None:\r\n self._data['lookup_table'] = lookup_table\r\n\r\n if self.write_type == WRITE_TO_MEMORY:\r\n self._data['features'][int(is_test)] = feature_dict\r\n return feature_dict\r\n\r\n buffer = \"\"\r\n for file_name in feature_dict:\r\n feature_string = ','.join(str(v) for v in feature_dict[file_name])\r\n buffer += \"%s,%s\\n\" % (file_name, feature_string)\r\n\r\n file = open('./datasets/' + self.out_file + extension, 'w')\r\n file.write(buffer)\r\n file.close()\r\n\r\n def preprocess(self):\r\n if len(self._preprocessors) == 0:\r\n return True\r\n pass\r\n\r\n\r\nclass Unigram(Extractor):\r\n def __init__(self, data_dir, name, test_dir=None, out_file=None, descriptor=\"auto\"):\r\n self.feature_name = DatasetInfo.UNIGRAM_FEATURE_SET\r\n super().__init__(data_dir, name, test_dir, out_file, descriptor)\r\n\r\n\r\n def process(self, is_test=False):\r\n target_dir, _ = self.get_target_and_extension(is_test)\r\n\r\n files = self.info.descriptor.getFiles(target_dir)\r\n\r\n feature_dict = {}\r\n for file_name in files:\r\n features = self.parse_file(file_name, files[file_name], is_test)\r\n if features is not None:\r\n feature_dict[file_name.split(\".\")[0]] = features\r\n # buffer += (file_name.split(\".\")[0] + ',' + features + \"\\n\") # Read contents of the file\r\n\r\n lookup_table = [chr(x+32) for x in range(95)]\r\n\r\n self.write_result(feature_dict, lookup_table=lookup_table, is_test= is_test)\r\n return files\r\n\r\n def parse_file(self, key, info, is_test=False):\r\n if not ('path' in info and os.path.exists(info.get('path'))):\r\n print(\"Warning path not found. Key = \" + str(key) + \" Data = \", info)\r\n return None\r\n\r\n file = open(info.get(\"path\"), \"r\", errors='ignore')\r\n self.info.add_instance(key.split(\".\")[0], info.get(\"author\", None), is_test)\r\n feature_vector = self.calculate(file.read())\r\n if self.feature_size == -1:\r\n self.feature_size = len(feature_vector)\r\n features = feature_vector\r\n file.close()\r\n return features\r\n\r\n @staticmethod\r\n def calculate(text):\r\n unigram_list = [0] * 95\r\n for i, c in enumerate(text):\r\n index = ord(c) - 32\r\n if index > 94 or index < 0:\r\n continue\r\n unigram_list[index] = unigram_list[index] + 1\r\n return unigram_list\r\n\r\nclass CharacterGram(Extractor):\r\n def __init__(self, data_dir, name, test_dir=None, gram=4, limit=0, out_file=None,\r\n descriptor=\"auto\"):\r\n self.gram = gram\r\n self.limit = limit\r\n self.file_dict = {}\r\n self.test_dict = {}\r\n self.feature_name = DatasetInfo.CHARACTER_GRAM\r\n # ext = \"gram=\"+str(self.gram)+\"-limit=\"+str(self.limit)+\"-occurrence=\"+str(self.occurrence)\r\n super().__init__(data_dir, name, test_dir, out_file, descriptor)\r\n\r\n self.params = [CharacterGram, [\"gram\", \"limit\", \"occurrence\", \"unique_occurrence\"]]\r\n\r\n def after_run_process(self):\r\n self.collect_feature_list(self.file_dict, self._data[\"run_files\"])\r\n self.generate(self.out_file, False)\r\n\r\n def after_test_process(self):\r\n self.generate(self.out_file, True)\r\n\r\n def find_ngrams(self, input_list):\r\n return zip(*[input_list[i:] for i in range(self.gram)])\r\n\r\n def process(self, is_test=False):\r\n if is_test:\r\n target_dir = self.test_dir\r\n current_dict = self.test_dict\r\n data_key = \"test_files\"\r\n else:\r\n target_dir = self.data_dir\r\n current_dict = self.file_dict\r\n data_key = \"run_files\"\r\n\r\n files = self.info.descriptor.getFiles(target_dir)\r\n for file_name in files:\r\n data = files[file_name]\r\n\r\n file = open(data['path'], \"r\", errors='ignore')\r\n features = file.read().lower() # Read contents of the file\r\n file.close() # We don't need that file anymore.\r\n self.info.add_instance(file_name.split(\".\")[0], data.get(\"author\"))\r\n\r\n if data.get('author') is None:\r\n data['author'] = self.info.descriptor.getAuthor(file_name)\r\n\r\n if features is not None:\r\n current_dict[file_name] = Counter(self.find_ngrams(features)) # Read contents of the file\r\n else:\r\n del files[file_name]\r\n continue\r\n\r\n self._data[data_key] = files\r\n return files\r\n\r\n def collect_feature_list(self, file_data: dict, info):\r\n self.grammar = Counter()\r\n for key in file_data:\r\n tokens = file_data[key]\r\n self.grammar += tokens\r\n\r\n if 0 < self.limit < len(self.grammar.keys()):\r\n limited = self.grammar.most_common(self.limit)\r\n new = {}\r\n for element in limited:\r\n new[element[0]] = element[1]\r\n self.grammar = Counter(new)\r\n\r\n def generate(self, out_file, is_test=False):\r\n if is_test:\r\n extension = \".test\"\r\n file_data = self.test_dict\r\n else:\r\n extension = \".txt\"\r\n file_data = self.file_dict\r\n\r\n buffer = \"\"\r\n if not is_test:\r\n self.feature_size = -1\r\n\r\n fresh_grammar = Counter({x: 0 for x in self.grammar})\r\n\r\n feature_dict = {}\r\n lookup_table = []\r\n for key in file_data:\r\n word_dict = dict(fresh_grammar.copy())\r\n tokens = file_data[key]\r\n for token in tokens:\r\n if token in word_dict:\r\n word_dict[token] = tokens[token]\r\n # output_dict[key] = word_dict\r\n if not is_test and self.feature_size == -1:\r\n self.feature_size = len(word_dict.keys())\r\n\r\n feature_dict[key.split('.')[0]] = list(word_dict.values())\r\n lookup_table = list(word_dict.keys())\r\n\r\n self.write_result(feature_dict, lookup_table=lookup_table, is_test=is_test)\r\n\r\n\r\nclass BagOfWords(Extractor):\r\n FeatureCounter: {}\r\n\r\n def __init__(self, data_dir, name, test_dir=None, unique_occurrence=0, out_file=None, descriptor=\"auto\"):\r\n self.is_dir = False\r\n self.feature_name = DatasetInfo.BOW_FEATURE_SET\r\n self.FeatureCounter = {}\r\n self.unique_occurrence = 0\r\n self.occurrence = 0\r\n super().__init__(data_dir, name, test_dir=test_dir, out_file=out_file, descriptor=descriptor)\r\n self.unique_occurrence = unique_occurrence\r\n self.params = [BagOfWords, [\"unique_occurrence\"]]\r\n self.unique_dict = Counter()\r\n self.file_dict = {}\r\n self.test_dict = {}\r\n\r\n def collect_feature_list(self, file_data: dict, info):\r\n self.FeatureCounter = Counter()\r\n self.unique_grammar = Counter()\r\n for key in file_data:\r\n tokens = file_data[key]\r\n unique_tokens = Counter({x: 1 for x in tokens})\r\n\r\n self.FeatureCounter += tokens\r\n self.unique_grammar += unique_tokens\r\n\r\n if self.unique_occurrence > 0:\r\n self.FeatureCounter = {x: self.FeatureCounter[x] for x in self.FeatureCounter\r\n if x in self.unique_grammar and self.unique_grammar[x] >= self.unique_occurrence}\r\n\r\n self.FeatureCounter = dict(sorted(self.FeatureCounter.items()))\r\n\r\n def after_run_process(self):\r\n self.collect_feature_list(self.file_dict, self._data[\"run_files\"])\r\n self.generate(self.out_file, False)\r\n\r\n def after_test_process(self):\r\n self.generate(self.out_file, True)\r\n\r\n def process(self, is_test=False):\r\n if is_test:\r\n target_dir = self.test_dir\r\n current_dict = self.test_dict\r\n data_key = \"test_files\"\r\n else:\r\n target_dir = self.data_dir\r\n current_dict = self.file_dict\r\n data_key = \"run_files\"\r\n\r\n files = self.info.descriptor.getFiles(target_dir)\r\n for file_name in files:\r\n data = files[file_name]\r\n\r\n features = self.parse_file(file_name, data, is_test)\r\n\r\n if features is not None:\r\n current_dict[file_name] = features # Read contents of the file\r\n else:\r\n del files[file_name]\r\n continue\r\n\r\n self._data[data_key] = files\r\n return files\r\n\r\n def parse_file(self, file_name, info, is_test=False):\r\n if not ('path' in info):\r\n return None\r\n\r\n if not ('author' in info):\r\n author = self.info.descriptor.getAuthor(file_name)\r\n if author is None:\r\n print(\"Author is not identified for \" + file_name, info, self.info.descriptor.level)\r\n exit()\r\n info['author'] = author\r\n\r\n file = open(info['path'], \"r\", errors='ignore')\r\n features = self.extract_from_file(file) # Read contents of the file\r\n file.close() # We don't need that file anymore.\r\n self.info.add_instance(file_name.split(\".\")[0], info.get(\"author\"), is_test)\r\n return features\r\n\r\n def extract_from_file(self, file):\r\n contents = file.read().lower()\r\n tokens = tokenize(contents)\r\n counter = Counter()\r\n for token in tokens:\r\n if token in counter:\r\n counter[token] += 1\r\n else:\r\n counter[token] = 1\r\n\r\n return counter\r\n\r\n def generate(self, out_file, is_test=False):\r\n if is_test:\r\n extension = \".test\"\r\n file_data = self.test_dict\r\n else:\r\n extension = \".txt\"\r\n file_data = self.file_dict\r\n\r\n buffer = \"\"\r\n if not is_test:\r\n self.feature_size = -1\r\n\r\n empty_set = Counter({x: 0 for x in self.FeatureCounter})\r\n feature_dict = {}\r\n lookup_table = []\r\n for key in file_data:\r\n word_dict = dict(empty_set.copy())\r\n tokens = file_data[key]\r\n for token in tokens:\r\n if token in word_dict:\r\n word_dict[token] += tokens[token]\r\n # output_dict[key] = word_dict\r\n\r\n if not is_test and self.feature_size == -1:\r\n self.feature_size = len(word_dict.keys())\r\n\r\n feature_dict[key.split('.')[0]] = list(word_dict.values())\r\n lookup_table = list(word_dict.keys())\r\n # feature_string = ','.join(str(word_dict[v]) for v in word_dict)\r\n # buffer += (\"%s,%s\\n\" % (key, feature_string))\r\n\r\n self.write_result(feature_dict, lookup_table=lookup_table, is_test=is_test)\r\n\r\ndef camel(s):\r\n return s != s.lower() and s != s.upper() and \"_\" not in s\r\n\r\ndef get_func_word_freq(words, funct_words):\r\n out = [0] * len(funct_words)\r\n for i, fc in enumerate(funct_words):\r\n out[i] = words.count(fc)\r\n\r\n return out\r\n\r\n\r\ndef tokenize(s):\r\n tokens = re.split(r\"[^0-9A-Za-z\\-'_]+\", s)\r\n return tokens\r\n\r\n\r\ndef get_yules(tokens):\r\n \"\"\"\r\n Returns a tuple with Yule's K and Yule's I.\r\n (cf. Oakes, M.P. 1998. Statistics for Corpus Linguistics.\r\n International Journal of Applied Linguistics, Vol 10 Issue 2)\r\n In production this needs exception handling.\r\n \"\"\"\r\n token_counter = Counter(tokens)\r\n m1 = sum(token_counter.values())\r\n m2 = sum([freq ** 2 for freq in token_counter.values()])\r\n if m1 == m2:\r\n i = (m1 * m1)\r\n else:\r\n i = (m1 * m1) / (m2 - m1)\r\n k = 1 / i * 10000\r\n return k\r\n\r\n\r\nclass Stylomerty(Extractor):\r\n dir: \"\"\r\n\r\n def __init__(self, data_dir, name, test_dir=None, out_file=None, descriptor=\"auto\"):\r\n self.file_dict = {}\r\n self.test_dict = {}\r\n super().__init__(data_dir, name, test_dir=test_dir, out_file=out_file, descriptor=descriptor)\r\n self.feature_name = DatasetInfo.STYLOMETRY_FEATURE_SET\r\n\r\n def process(self, is_test=False):\r\n if is_test:\r\n target_dir = self.test_dir\r\n current_dict = self.test_dict\r\n else:\r\n target_dir = self.data_dir\r\n current_dict = self.file_dict\r\n\r\n files = self.info.descriptor.getFiles(target_dir)\r\n for file_name in files:\r\n data = files[file_name]\r\n\r\n file_name_no_ext = file_name.split(\".\")[0]\r\n file = open(data['path'], \"r\", errors='ignore')\r\n features = file.read().lower() # Read contents of the file\r\n file.close() # We don't need that file anymore.\r\n self.info.add_instance(file_name_no_ext, data.get(\"author\"), is_test)\r\n\r\n if features is not None:\r\n current_dict[file_name_no_ext] = features # Read contents of the file\r\n else:\r\n del files[file_name]\r\n continue\r\n\r\n self.generate(self.out_file, is_test)\r\n return files\r\n\r\n @staticmethod\r\n def stylometry(text):\r\n fv = []\r\n # note: the nltk.word_tokenize includes punctuation\r\n words2 = tokenize(text)\r\n text = text.lower()\r\n words = tokenize(text)\r\n sentences = sent_tokenize(text)\r\n vocab = set(words)\r\n words_per_sentence = len(words) / len(sentences)\r\n\r\n vocab_richness = [get_yules(words), np.average(words_per_sentence)]\r\n\r\n for i in range(10):\r\n vocab_richness.append(len([item for item in vocab if words.count(item) == i + 1]))\r\n fv.append(vocab_richness)\r\n\r\n # lengths (2)\r\n lengths = [len(words), len(text)]\r\n fv.append(lengths)\r\n\r\n fv_shape = [0] * 5\r\n for word in words2:\r\n if all(c.isupper() for c in word):\r\n fv_shape[0] += 1\r\n elif all(c.islower() for c in word):\r\n fv_shape[1] += 1\r\n elif word.istitle():\r\n fv_shape[2] += 1\r\n elif camel(word):\r\n fv_shape[3] += 1\r\n else:\r\n fv_shape[4] += 1\r\n\r\n fv.append(fv_shape)\r\n\r\n word_length = [0] * 20\r\n for word in words:\r\n wlen = len(word)\r\n if wlen < 21:\r\n word_length[wlen - 1] += 1\r\n fv.append(word_length)\r\n\r\n atoz = string.ascii_lowercase\r\n letter_count = [0] * len(atoz)\r\n for i, l in enumerate(atoz):\r\n letter_count[i] = text.count(l)\r\n fv.append(letter_count)\r\n\r\n digits = '0123456789'\r\n digit_count = [0] * len(digits)\r\n for i, d in enumerate(digits):\r\n digit_count[i] = text.count(d)\r\n fv.append(digit_count)\r\n\r\n punctuation = '.?!,;:()\"-\\''\r\n fv_punct = []\r\n for char in punctuation:\r\n fv_punct.append(text.count(char))\r\n fv.append(fv_punct)\r\n\r\n special_characters = \"`‘˜@#$%ˆ&*_+=[]{}\\\\|/<>\"\r\n fv_special = []\r\n for char in special_characters:\r\n fv_special.append(text.count(char))\r\n fv.append(fv_special)\r\n\r\n function_words = tokenize(\r\n \"a about above after again ago all almost along already also although always am among an and another any \"\r\n \"anybody anything anywhere are aren't around as at back else be been before being below beneath beside \"\r\n \"between beyond billion billionth both each but by can can't could couldn't did didn't do does doesn't \"\r\n \"doing done don't down during eight eighteen eighteenth eighth eightieth eighty either eleven eleventh \"\r\n \"enough even ever every everybody everyone everything everywhere except far few fewer fifteen fifteenth \"\r\n \"fifth fiftieth fifty first five for fortieth forty four fourteen fourteenth fourth hundred from get gets \"\r\n \"getting got had hadn't has hasn't have haven't having he he'd he'll hence her here hers herself he's him \"\r\n \"himself his hither how however near hundredth i i'd if i'll i'm in into is i've isn't it its it's itself \"\r\n \"last less many me may might million millionth mine more most much must mustn't my myself near nearby \"\r\n \"nearly neither never next nine nineteen nineteenth ninetieth ninety ninth no nobody none noone nothing \"\r\n \"nor not now nowhere of off often on or once one only other others ought oughtn't our ours ourselves out \"\r\n \"over quite rather round second seven seventeen seventeenth seventh seventieth seventy shall shan't she'd \"\r\n \"she she'll she's should shouldn't since six sixteen sixteenth sixth sixtieth sixty so some somebody \"\r\n \"someone something sometimes somewhere soon still such ten tenth than that that that's the their theirs \"\r\n \"them themselves these then thence there therefore they they'd they'll they're third thirteen thirteenth \"\r\n \"thirtieth thirty this thither those though thousand thousandth three thrice through thus till to towards \"\r\n \"today tomorrow too twelfth twelve twentieth twenty twice two under underneath unless until up us very \"\r\n \"when was wasn't we we'd we'll were we're weren't we've what whence where whereas which while whither who \"\r\n \"whom whose why will with within without won't would wouldn't yes yesterday yet you your you'd you'll \"\r\n \"you're yours yourself yourselves you've\")\r\n\r\n function_frequency = get_func_word_freq(text, function_words)\r\n fv.append(function_frequency)\r\n\r\n return [item for sublist in fv for item in sublist]\r\n\r\n def generate(self, out_file, is_test=False):\r\n if is_test:\r\n extension = \".test\"\r\n file_data = self.test_dict\r\n else:\r\n extension = \".txt\"\r\n file_data = self.file_dict\r\n\r\n buffer = \"\"\r\n if not is_test:\r\n self.feature_size = -1\r\n\r\n feature_dict = {}\r\n for key in file_data:\r\n text = file_data[key]\r\n\r\n feature_vector = self.stylometry(text)\r\n feature_dict[key] = feature_vector\r\n if not is_test and self.feature_size == -1:\r\n self.feature_size = len(feature_vector)\r\n\r\n self.write_result(feature_dict, is_test)\r\n\r\n","sub_path":"Project5/Extractor/Extractors.py","file_name":"Extractors.py","file_ext":"py","file_size_in_byte":23860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"172100399","text":"from ast import literal_eval\nimport json\n\nfrom tqdm import tqdm\nimport wikipediaapi\n\n\nclass Article(object):\n\n def __init__(self, question: str, answers:tuple, source_title: str, source_url: str, target_title: str,\n target_url: str, same_target: str):\n self.question = question\n self.answers = answers\n self.source_title = source_title\n self.source_url = source_url\n self.target_title = target_title\n self.target_url = target_url\n\n def set_target_text(self, text: str):\n self.target_text = text\n\n\ndef get_corpus_urls(filename):\n pass\n\n\ndef get_answers(filename):\n answers = {}\n with open(filename) as fin:\n for line in fin:\n json_object = json.loads(line.strip())\n raw_answers = json_object[\"answers\"]\n raw_answers.sort(key=lambda x: len(x))\n answers[tuple(raw_answers)] = json_object[\"question\"]\n return answers\n\n\ndef get_original_articles(answers, part, target_language=\"de\"):\n wiki_en = wikipediaapi.Wikipedia('en')\n url_map = {}\n translation_map = {}\n not_found_pages = []\n\n with open(f\"{part}_url_map.tsv\", \"w\") as url_file:\n for answer_set in tqdm(answers):\n found = False\n target_page = None\n source_page = None\n old_source_page = None\n for answer_option in answer_set:\n source_page = wiki_en.page(answer_option)\n if source_page.exists():\n found = True\n old_source_page = source_page\n url_map[answer_set] = source_page.fullurl\n langlinks = source_page.langlinks\n same_target = False\n if target_language in langlinks:\n same_target = True\n target_page = langlinks[target_language]\n translation_map[answer_set] = target_page\n else:\n source_page = old_source_page\n if source_page is None:\n source_title = None\n source_url = None\n else:\n source_title = source_page.title\n source_url = source_page.fullurl\n if target_page is None:\n target_title = None\n target_url = None\n else:\n target_title = target_page.title\n target_url = target_page.fullurl\n line = \"\\t\".join(str(x) for x in (answers[answer_set], answer_set, source_title, source_url, target_title,\n target_url, same_target))\n url_file.write(line + \"\\n\")\n url_file.flush()\n if not found:\n not_found_pages.append(answer_set)\n print(f\"{len(answers)} pages processed\")\n print(f\"{len(url_map)} pages found in English Wikipedia: {len(url_map) / len(answers)}\")\n print(f\"{len(translation_map)} pages found in German Wikipedia: {len(translation_map) / len(answers)}\")\n print(f\"Not found pages {not_found_pages}\")\n\n\ndef read_url_map(filename):\n urls = []\n with open(filename) as fin:\n for line in fin:\n parts = line.strip().split(\"\\t\")\n parts[1] = literal_eval(parts[1])\n urls.append(Article(*parts))\n return urls\n\n\ndef get_target_article(infile, outfile):\n urls = read_url_map(infile)\n # wiki_en = wikipediaapi.Wikipedia('en')\n wiki_de = wikipediaapi.Wikipedia('de')\n with open(outfile, \"w\") as fout:\n for url in tqdm(urls):\n current_json = {\"question\": url.question,\n \"answers\": url.answers,\n \"source_title\": url.source_title,\n \"source_url\": url.source_url,\n \"target_title\": url.target_title,\n \"target:url\": url.target_url}\n if url.target_title == \"None\":\n current_json[\"target_text\"] = \"page does not exist\"\n current_json[\"categories\"] = \"page does not exist\"\n else:\n page = wiki_de.page(url.target_title)\n if page.exists():\n current_json[\"target_text\"] = page.text\n current_json[\"categories\"] = [x.title() for x in page.categories]\n else:\n current_json[\"target_text\"] = \"page does not exist\"\n current_json[\"categories\"] = \"page does not exist\"\n # current_json[\"sections\"] = page.sections\n json.dump(current_json, fout, ensure_ascii=False)\n fout.write(\"\\n\")\n fout.flush()\n\n\ndef main():\n # part = \"train_tail\"\n # corpus = f\"/home/ca/Documents/Uni/Masterarbeit/data/XQA_original/en/{part}.txt\"\n #\n # answers = get_answers(corpus)\n # print(len(answers))\n # get_original_articles(answers, part)\n # infile = \"/home/ca/Documents/Uni/Masterarbeit/crosslingual-qa-scripts/corpus_creation/2020_06_09/full_train_url_map_2.tsv\"\n # outfile = \"/home/ca/Documents/Uni/Masterarbeit/crosslingual-qa-scripts/corpus_creation/train_urls.json\"\n # get_target_article(infile, outfile)\n pass\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"corpus_creation/get_wiki_articles.py","file_name":"get_wiki_articles.py","file_ext":"py","file_size_in_byte":5268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"585154680","text":"from django.urls import path\nfrom . import views\n\napp_name = 'dashboard'\n\n\nurlpatterns = [\n\n path('catalogo/', views.ver_catalogo, name='catalogo'),\n path('nosotros/', views.about_us, name='nosotros'),\n path('contacto/', views.contact_us, name='contacto'),\n]\n","sub_path":"autopartes/dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"325680664","text":"\nimport bayes_algorithm\nimport feedparser\n\npostingList, classVec = bayes_algorithm.loadDataSet()\n\n# 单词列表，不含重复单词\nmyVocabList = bayes_algorithm.createVocabList(postingList)\n\n# print(myVocabList)\n\nworthlessIndex = 0\nstupidIndex = 0\nhelpIndex = 0\nisIndex = 0\ndogIndex = 0\n\nfor i in range(len(myVocabList)):\n if myVocabList[i] is \"stupid\":\n stupidIndex = i\n elif myVocabList[i] is \"worthless\":\n worthlessIndex = i\n elif myVocabList[i] is \"help\":\n helpIndex = i\n elif myVocabList[i] is \"is\":\n isIndex = i\n elif myVocabList[i] is \"dog\":\n dogIndex = i\n\n#\ntrainMat = []\nfor postInDoc in postingList:\n trainMat.append(bayes_algorithm.setOfWords2Vec(myVocabList, postInDoc))\n\np0V, p1V, pAb = bayes_algorithm.trainNB0(trainMat, classVec)\n\nbayes_algorithm.testingNB()\n\n\n# --------------------------------------------------------------------------------- #\n\nprint(\"\\n4.7 使用朴素贝叶斯分类器从个人广告中获取区域倾向\\n\")\n\nny = feedparser.parse('https://newyork.craigslist.org/search/stp?format=rss')\nsf = feedparser.parse('https://sfbay.craigslist.org/search/stp?format=rss')\n\nvocabList, pSF, pNY = bayes_algorithm.localWords(ny, sf)\n\nvocabList, pSF, pNY = bayes_algorithm.localWords(ny, sf)\n\nbayes_algorithm.getTopWords(ny, sf)\n\n\n","sub_path":"04_Bayes/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"560668602","text":"import sys\nif __name__==\"__main__\":\n for linea in sys.stdin:\n key=linea.split(\" \")[0]\n fec=linea.split(\" \")[1]\n val=int(linea.split(\" \")[2])\n val=str(val)\n val=val.zfill(3)\n llave=key+val\n sys.stdout.write(\"{}\\t{}\\t{}\\t{}\\n\".format(llave,key,fec,val))","sub_path":"01-hadoop-50/q07-10/mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"4610287","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"HiggsGenTest\")\n\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.MessageLogger.categories.append('HiggsGenEvent')\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 10\n\n\nprocess.options = cms.untracked.PSet(\n wantSummary = cms.untracked.bool(True)\n)\n\n\n#process.load(\"TopAnalysis.Configuration.Summer12.TTH_HToBB_M_125_8TeV_pythia6_Summer12_DR53X_PU_S10_START53_V7A_v1_cff\")\nprocess.load(\"TopAnalysis.Configuration.Summer12.TTH_Inclusive_M_125_8TeV_pythia6_Summer12_DR53X_PU_S10_START53_V7A_v1_cff\")\nprocess.maxEvents.input = 11\n\n\nprocess.load(\"TopAnalysis.HiggsUtils.producers.HiggsDecaySubset_cfi\")\nprocess.decaySubsetHiggs.fillMode = 'kME'\nprocess.decaySubsetHiggs.addRadiation = False\nprocess.load(\"TopAnalysis.HiggsUtils.producers.HiggsGenEvtProducer_cfi\")\n\n\nprocess.p = cms.Path(process.decaySubsetHiggs*process.genEvtHiggs)\n\n\n\n\nprocess.out = cms.OutputModule(\"PoolOutputModule\",\n fileName = cms.untracked.string('test_higgsGenEvtProducer.root'),\n outputCommands = cms.untracked.vstring(\n 'drop *',\n 'keep recoGenParticles_*_*_SIM',\n #'keep recoGenParticles_*_*_HiggsGenTest',\n #'keep HiggsGenEvent_*_*_HiggsGenTest',\n 'keep *_*_*_HiggsGenTest',\n ),\n\n)\n\nprocess.e = cms.EndPath(process.out)\n","sub_path":"HiggsUtils/test/higgsGenEvtProducer_cfg.py","file_name":"higgsGenEvtProducer_cfg.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"495303280","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n###############################################################################\n# Copyright Kitware Inc.\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\nimport os\nimport shutil\nimport pymongo\nimport tempfile\n\nfrom girder.api import access\nfrom girder.api.describe import Description\nfrom girder.api.rest import Resource, loadmodel, RestException\nfrom girder.constants import AccessType\nfrom girder.utility import config\n\nfrom girder.plugins.minerva.constants import PluginSettings\nfrom girder.plugins.minerva.utility.minerva_utility import findDatasetFolder, \\\n updateMinervaMetadata\nfrom girder.plugins.minerva.utility.dataset_utility import \\\n jsonArrayHead, GeoJsonMapper, jsonObjectReader\n\nimport girder_client\n\n\nclass Dataset(Resource):\n def __init__(self):\n self.resourceName = 'minerva_dataset'\n self.route('GET', (), self.listDatasets)\n self.route('GET', ('folder',), self.getDatasetFolder)\n self.route('POST', ('folder',), self.createDatasetFolder)\n self.route('POST', (':id', 'item'), self.promoteItemToDataset)\n self.route('GET', (':id', 'dataset'), self.getDataset)\n self.route('POST', (':id', 'geojson'), self.createGeojson)\n self.route('POST', (':id', 'jsonrow'), self.createJsonRow)\n self.client = None\n\n def _initClient(self):\n if self.client is None:\n girderPort = config.getConfig()['server.socket_port']\n self.client = girder_client.GirderClient(port=girderPort)\n user, token = self.getCurrentUser(returnToken=True)\n self.client.token = token['_id']\n\n def _downloadItemFiles(self, itemId, tmpdir):\n self._initClient()\n self.client.downloadItem(itemId, tmpdir)\n # TODO worry about stale authentication\n\n def _addFileToItem(self, item, filepath):\n itemId = str(item['_id'])\n self._initClient()\n self.client.uploadFileToItem(itemId, filepath)\n # TODO worry about stale authentication\n\n def _findGeoJsonFile(self, item):\n itemGeoJson = item['name'] + PluginSettings.GEOJSON_EXTENSION\n existing = self.model('file').findOne({\n 'itemId': item['_id'],\n 'name': itemGeoJson\n })\n if existing:\n return existing\n else:\n return None\n\n def datasetJob(self, item, job):\n itemid = str(item['_id'])\n tmpdir = tempfile.mkdtemp()\n self._downloadItemFiles(itemid, tmpdir)\n job(item, tmpdir)\n shutil.rmtree(tmpdir)\n self.model('item').setMetadata(item, item['meta'])\n return item['meta']['minerva']\n\n def _convertJsonfileToGeoJson(self, item, tmpdir):\n # use the first filename with json ext found in original_files\n filename = None\n files = item['meta']['minerva']['original_files']\n for f in files:\n if f['name'].endswith('.json'):\n filename = f['name']\n if filename is None:\n raise RestException('Dataset %s has no json files' % item['name'])\n jsonFilepath = os.path.join(tmpdir, item['name'],\n filename)\n geoJsonFilename = item['name'] + PluginSettings.GEOJSON_EXTENSION\n geoJsonFilepath = os.path.join(tmpdir, item['name'], geoJsonFilename)\n\n mapping = item['meta']['minerva']['mapper']\n geoJsonMapper = GeoJsonMapper(objConverter=None,\n mapping=mapping)\n objects = jsonObjectReader(jsonFilepath)\n geoJsonMapper.mapToJsonFile(tmpdir, objects, geoJsonFilepath)\n\n return geoJsonFilepath\n\n def _convertMongoToGeoJson(self, item, params):\n minerva_metadata = item['meta']['minerva']\n # TODO time to break this code out to another method\n\n # TODO maybe here we can store limit and offset?\n # set a placeholder for geojson file, even though\n # there is no actual geojson file, rather we will\n # generate it on the fly\n\n # in this case we don't actually want to store a file\n # but store the metadata we used to create the geojson\n\n if 'geojson' not in minerva_metadata:\n minerva_metadata['geojson'] = {}\n\n # TODO no reason couldn't have query and limit/offset\n\n # add the geojson to the minerva metadata returned\n # but don't save it\n # TODO think on caching implications\n connection = minerva_metadata['mongo_connection']\n dbConnectionUri = connection['db_uri']\n collectionName = connection['collection_name']\n collection = self.mongoCollection(dbConnectionUri, collectionName)\n\n query_count = collection.find().count()\n minerva_metadata['geojson']['query_count'] = query_count\n objects = collection.find()\n mapping = item['meta']['minerva']['mapper']\n geoJsonMapper = GeoJsonMapper(objConverter=None,\n mapping=mapping)\n import cStringIO\n writer = cStringIO.StringIO()\n geoJsonMapper.mapToJson(objects, writer)\n\n item['meta']['minerva'] = minerva_metadata\n item['meta']['minerva']['geojson']['data'] = writer.getvalue()\n self.model('item').setMetadata(item, item['meta'])\n return minerva_metadata\n\n def createGeoJsonFromDataset(self, item, params):\n # TODO there is probably a problem when\n # we look for a name in an item as a duplicate\n # i.e. looking for filex, but the item name is filex (1)\n\n minerva_metadata = item['meta']['minerva']\n if minerva_metadata['original_type'] == 'json':\n converter = self._convertJsonfileToGeoJson\n elif minerva_metadata['original_type'] == 'mongo':\n return self._convertMongoToGeoJson(item, params)\n else:\n raise Exception('Unsupported conversion type %s' %\n minerva_metadata['original_type'])\n\n def converterJob(item, tmpdir):\n geojsonFilepath = converter(item, tmpdir)\n self._addFileToItem(item, geojsonFilepath)\n geojsonFile = self._findGeoJsonFile(item)\n item['meta']['minerva']['source'] = {\n 'layer_source': 'GeoJSON'}\n item['meta']['minerva']['geojson_file'] = {\n 'name': geojsonFile['name'],\n '_id': geojsonFile['_id']\n }\n\n return self.datasetJob(item, converterJob)\n\n def createJsonRowFromJsonArray(self, item):\n\n def createJsonRowJob(item, tmpdir):\n jsonFilename = item['name'] + '.json'\n jsonFilepath = os.path.join(tmpdir, item['name'], jsonFilename)\n # take the only entry of the array\n jsonRow = jsonArrayHead(jsonFilepath, limit=1)[0]\n item['meta']['minerva']['json_row'] = jsonRow\n\n return self.datasetJob(item, createJsonRowJob)\n\n def mongoCollection(self, connectionUri, collectionName):\n # TODO not sure if this is a good idea to do this db stuff here\n # maybe this suggests a new model?\n from girder.models import getDbConnection\n dbConn = getDbConnection(connectionUri)\n db = dbConn.get_default_database()\n from girder.external.mongodb_proxy import MongoProxy\n collection = MongoProxy(db[collectionName])\n return collection\n\n # TODO rename to createDataset once the existing createDataset\n # endpoint method is removed.\n def constructDataset(self, name, minerva_metadata, desc=''):\n user = self.getCurrentUser()\n folder = findDatasetFolder(user, user, create=True)\n if folder is None:\n raise Exception('User has no Minerva Dataset folder.')\n dataset = self.model('item').createItem(name, user, folder, desc)\n updateMinervaMetadata(dataset, minerva_metadata)\n return dataset\n\n # REST Endpoints\n\n @access.public\n @loadmodel(map={'userId': 'user'}, model='user', level=AccessType.READ)\n def listDatasets(self, user, params):\n folder = findDatasetFolder(self.getCurrentUser(), user)\n if folder is None:\n return []\n else:\n limit, offset, sort = \\\n self.getPagingParameters(params,\n defaultSortDir=pymongo.DESCENDING)\n items = [self.model('item').filter(item, self.getCurrentUser()) for\n item in self.model('folder').childItems(folder,\n limit=limit, offset=offset, sort=sort)]\n return items\n listDatasets.description = (\n Description('List minerva datasets owned by a user.')\n .param('userId', 'User is the owner of minerva datasets.',\n required=True)\n .param('limit', \"Result set size limit (default=50).\", required=False,\n dataType='int')\n .param('offset', \"Offset into result set (default=0).\", required=False,\n dataType='int')\n .param('sort', 'Field to sort the result list by ('\n 'default=name)', required=False)\n .param('sortdir', \"1 for ascending, -1 for descending (default=-1)\",\n required=False, dataType='int'))\n\n @access.public\n @loadmodel(map={'userId': 'user'}, model='user', level=AccessType.READ)\n def getDatasetFolder(self, user, params):\n folder = findDatasetFolder(self.getCurrentUser(), user, create=True)\n return {'folder': folder}\n getDatasetFolder.description = (\n Description('Get the minerva dataset folder owned by a user.')\n .param('userId', 'User is the owner of minerva datasets.',\n required=True))\n\n @access.public\n @loadmodel(map={'userId': 'user'}, model='user', level=AccessType.WRITE)\n def createDatasetFolder(self, user, params):\n folder = findDatasetFolder(self.getCurrentUser(), user, create=True)\n return {'folder': folder}\n createDatasetFolder.description = (\n Description('Create the minerva dataset folder owned by a user.')\n .param('userId', 'User is the owner of minerva datasets.',\n required=True))\n\n @access.public\n @loadmodel(model='item', level=AccessType.READ)\n def getDataset(self, item, params):\n meta = item['meta']\n if 'minerva' in meta:\n return meta['minerva']\n else:\n return {}\n getDataset.description = (\n Description('Get Minerva metadata for an Item.')\n .param('id', 'The Item ID', paramType='path')\n .errorResponse('ID was invalid.')\n .errorResponse('Read permission denied on the Item.', 403))\n\n @access.public\n @loadmodel(model='item', level=AccessType.WRITE)\n def promoteItemToDataset(self, item, params):\n \"\"\"\n Take an Item in the user's Minerva Dataset folder, and promote\n it to a Minerva Dataset by adding proper Minerva metadata.\n \"\"\"\n\n user = self.getCurrentUser()\n folder = findDatasetFolder(user, user, create=True)\n if folder is None:\n raise RestException('User has no Minerva Dataset folder.')\n if folder['_id'] != item['folderId']:\n raise RestException(\"Items need to be in user's Minerva Dataset \" +\n \"folder.\")\n # Don't overwrite if minerva metadata already exists.\n if 'meta' in item and 'minerva' in item['meta']:\n return item\n\n minerva_metadata = {\n 'source_type': 'item'\n }\n for file in self.model('item').childFiles(item=item, limit=0):\n # Check the first few k of a file to see if this might be a\n # geojson timeseries. Crudely, we expect this to be a json array\n # which contains objects, each of which has at least a geojson\n # element. This test will fail if there are other elements in the\n # first object that push the geojson element beyond the tested\n # header length. It could give a false positive, too. The correct\n # way would be to download and parse the whole file, but that would\n # be more expensive in memory and time.\n headerLen = 2048\n fileHeader = ''\n for headerData in self.model('file').download(file, headers=False, endByte=headerLen)():\n fileHeader = (fileHeader + headerData)[:headerLen]\n if len(fileHeader) >= headerLen:\n break\n if (fileHeader.lstrip()[:1] == '[' and\n fileHeader.lstrip()[1:].lstrip()[:1] == '{' and\n '\"geojson\"' in fileHeader):\n minerva_metadata['original_type'] = 'geojson-timeseries'\n minerva_metadata['dataset_type'] = 'geojson-timeseries'\n minerva_metadata['original_files'] = [{\n 'name': file['name'], '_id': file['_id']}]\n minerva_metadata['geojson_file'] = {\n 'name': file['name'], '_id': file['_id']}\n minerva_metadata['source'] = {\n 'layer_source': 'GeoJSON'}\n break\n # TODO This switching based on which file is found first is\n # fairly brittle and should only be called after first upload.\n if 'geojson' in file['exts']:\n # we found a geojson, assume this is geojson original\n minerva_metadata['original_type'] = 'geojson'\n minerva_metadata['dataset_type'] = 'geojson'\n minerva_metadata['original_files'] = [{\n 'name': file['name'], '_id': file['_id']}]\n minerva_metadata['geojson_file'] = {\n 'name': file['name'], '_id': file['_id']}\n minerva_metadata['source'] = {\n 'layer_source': 'GeoJSON'}\n break\n elif 'json' in file['exts']:\n minerva_metadata['original_type'] = 'json'\n minerva_metadata['dataset_type'] = 'json'\n minerva_metadata['original_files'] = [{\n 'name': file['name'], '_id': file['_id']}]\n break\n elif 'csv' in file['exts']:\n minerva_metadata['original_type'] = 'csv'\n minerva_metadata['dataset_type'] = 'csv'\n minerva_metadata['original_files'] = [{\n 'name': file['name'], '_id': file['_id']}]\n break\n updateMinervaMetadata(item, minerva_metadata)\n return item\n promoteItemToDataset.description = (\n Description('Create metadata for an Item in a user\\'s Minerva Dataset' +\n ' folder, promoting the Item to a Dataset.')\n .responseClass('Item')\n .param('id', 'The Item ID', paramType='path')\n .errorResponse('ID was invalid.')\n .errorResponse('Write permission denied on the Item.', 403))\n\n @access.public\n @loadmodel(model='item', level=AccessType.WRITE)\n def createJsonRow(self, item, params):\n item_meta = item['meta']\n minerva_meta = item_meta['minerva']\n if not minerva_meta['original_type'] == 'json':\n raise RestException(\n 'Dataset is not json.',\n 'girder.api.v1.minerva_dataset.create-json-row')\n minerva_meta = self.createJsonRowFromJsonArray(item)\n return minerva_meta\n createJsonRow.description = (\n Description('Extract the top row from a json array dataset, adds '\n 'to the minerva metadata.')\n .param('id', 'The Item ID', paramType='path')\n .errorResponse('ID was invalid.')\n .errorResponse('Write permission denied on the Item.', 403))\n\n @access.public\n @loadmodel(model='item', level=AccessType.WRITE)\n def createGeojson(self, item, params):\n # always create the geojson as perhaps the params have changed\n item_meta = item['meta']\n minerva_meta = item_meta['minerva']\n supported_conversions = ['json', 'mongo']\n if minerva_meta['original_type'] in supported_conversions:\n # TODO passing params for limit and offset\n # maybe better to make those explicit and for all original_type\n minerva_meta = self.createGeoJsonFromDataset(item, params)\n elif minerva_meta['original_type'] == 'geojson':\n return minerva_meta\n elif minerva_meta['original_type'] == 'csv':\n raise RestException('CSV to geojson not implemented')\n else:\n raise RestException('create geojson on unknown type')\n return minerva_meta\n createGeojson.description = (\n Description('Create geojson for a dataset, if possible.')\n .param('id', 'The Item ID', paramType='path')\n .param('dateField', 'date field for filtering results, required for ' +\n 'startTime or endTime params', required=False)\n .param('startTime', 'earliest time to include result', required=False)\n .param('endTime', 'latest time to include result', required=False)\n .errorResponse('ID was invalid.')\n .errorResponse('Write permission denied on the Item.', 403))\n","sub_path":"server/rest/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":17752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"392043351","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\nimport time\nimport tornado.web\nimport tornado.gen\nfrom databases.tables import Access_Token,Admin_token\nimport json\n\nclass BaseHandler(tornado.web.RequestHandler):\n @property\n def db(self):\n return self.application.db\n def on_finish(self):\n self.db.close()\n\n def options(self):\n retjson = {'code':200}\n self.write_back(retjson)\n def get_current_user(self):\n # token = self.get_argument(\"token\",None)\n token = self.request.headers['Token'] if 'Token' in self.request.headers.keys() else None\n if token:\n try:\n token = self.db.query(Access_Token).filter(Access_Token.token==token).one()\n return token\n except:\n return False\n else:\n return False\n def get_current_admin(self):\n token = self.request.headers['Admin'] if 'Admin' in self.request.headers.keys() else None\n if token:\n try:\n token = self.db.query(Admin_token).filter(Admin_token.token==token).one()\n return token\n except:\n return False\n else:\n return False\n def change_time(self,init,mod):\n if mod==0:\n return int(time.mktime(time.strptime(init,\"%Y-%m-%d\")))\n elif mod==1:\n return time.strftime(\"%Y-%m-%d\",time.localtime(int(init)))\n\n def write_back(self,content):\n self.set_header('Access-Control-Allow-Origin','*')\n self.set_header('Access-Control-Allow-Methods','GET,POST')\n self.set_header('Access-Control-Allow-Headers','token,admin')\n self.write(json.dumps(content,ensure_ascii=False, indent=2))\n self.finish()","sub_path":"shalongAppApi/mod/Basehandler.py","file_name":"Basehandler.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"330157903","text":"import ephem\nimport datetime\n\n\ndef get_planet(planet):\n bodies = {\n 'mercury': ephem.Mercury,\n 'venus': ephem.Venus,\n 'mars': ephem.Mars,\n 'jupiter': ephem.Jupiter,\n 'saturn': ephem.Saturn,\n 'uranus': ephem.Uranus,\n 'neptune': ephem.Neptune,\n }\n current_planet = bodies.get(planet)\n return current_planet\n\n\ndef get_constellation(planet):\n current_planet = get_planet(planet)\n if not current_planet:\n return None\n\n planet_class = current_planet()\n planet_class.compute(datetime.datetime.now())\n constellation = ephem.constellation(planet_class)[1]\n return constellation\n\n\ndef full_moon_definer(data):\n date = date_parser(data)\n if not date:\n return None\n return ephem.next_full_moon(date)\n\n\ndef date_parser(date):\n try:\n new_date = datetime.datetime.strptime(date, '%Y-%m-%d')\n return new_date\n except ValueError:\n return None\n except TypeError:\n return None\n","sub_path":"bot/utils/planet.py","file_name":"planet.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"317778379","text":"#JES- Jython Environment for Students\r\n#Copyright (C) 2002 Jason Ergle, Claire Bailey, David Raines, Joshua Sklare\r\n#See JESCopyright.txt for full licensing information\r\n\r\n#TODO - better type names\r\nimport java.awt as awt\r\nimport javax.swing as swing\r\nimport time\r\nfrom java.awt.event import ActionListener\r\n\r\nDEBUG_WINDOW_TITLE = 'JES Watcher Window #%s - %H:%M:%S'\r\nDEBUG_WINDOW_SIZE = (400,400)\r\nCLOSE_BUTTON_CAPTION = 'Close'\r\nVAR_L_NAME_COL_CAPTION = 'Local Variables'\r\n#VAR_G_NAME_COL_CAPTION = 'Global Variables'\r\nVAR_G_NAME_COL_CAPTION = 'Command Area Variables'\r\nVAR_TYPE_COL_CAPTION = 'Type'\r\nVAR_VALUE_COL_CAPTION = 'Value'\r\n\r\nINTEGER = 'org.python.core.PyInteger'\r\nLONG = 'org.python.core.PyLong'\r\nFLOAT = 'org.python.core.PyFloat'\r\nCOMPLEX = 'org.python.core.PyComplex'\r\nSTRING = 'org.python.core.PyString'\r\nTUPLE = 'org.python.core.PyTuple'\r\nLIST = 'org.python.core.PyList'\r\nDICTIONARY = 'org.python.core.PyDictionary'\r\nFUNCTION = 'org.python.core.PyFunction'\r\nCLASS = 'org.python.core.PyClass'\r\nMODULE = 'org.python.core.PyModule'\r\nNONE = 'org.python.core.PyNone'\r\n\r\nIMPROVED_TYPE_NAMES = {INTEGER: 'Integer',\r\n NONE: 'None',\r\n LONG: 'Long',\r\n FLOAT: 'Float',\r\n COMPLEX: 'Complex',\r\n STRING: 'String',\r\n TUPLE: 'Tuple',\r\n LIST: 'List',\r\n DICTIONARY: 'Dictionary',\r\n FUNCTION: 'Function',\r\n MODULE: 'Module',\r\n CLASS: 'Class'}\r\n\r\n \r\n \r\nclass JESDebugWindow(swing.JFrame, ActionListener):\r\n################################################################################\r\n# Function name: __init__\r\n# Parameters:\r\n# -varsToDisplay: collection of variables to be displayed.\r\n# -windowNumber: window number (or ID) which can be used to differentiate\r\n# one window from another.\r\n# Return:\r\n# An instance of the JESDebugWindow class.\r\n# Description:\r\n# Creates an instance of the JESDebugWindow class and displays it.\r\n################################################################################\r\n def __init__(self,localVars, globalVars, windowNumber, varsToFilter):\r\n import time\r\n self.varsToFilter = varsToFilter\r\n\r\n now = time.localtime(time.time())\r\n self.title = time.strftime(DEBUG_WINDOW_TITLE, now)\r\n self.title = self.title % windowNumber\r\n self.size = DEBUG_WINDOW_SIZE\r\n self.contentPane.setLayout(swing.BoxLayout(self.contentPane,\r\n swing.BoxLayout.Y_AXIS))\r\n\r\n #Load the variables from varsToDisplay into an array for the JTable\r\n\r\n\r\n #Create panels and button, and place them on the frame\r\n closeButton = swing.JButton(CLOSE_BUTTON_CAPTION, actionListener=self)\r\n\r\n bottomPanel = swing.JPanel()\r\n bottomPanel.add(closeButton)\r\n\r\n (topPane, bottomPane) = self.buildContentPane(localVars, globalVars)\r\n self.contentPane.add(topPane)\r\n self.contentPane.add(bottomPane)\r\n self.contentPane.add(bottomPanel)\r\n\r\n self.setDefaultCloseOperation(1)\r\n self.setVisible(1)\r\n\r\n\r\n def buildContentPane(self, localVars, globalVars):\r\n\r\n \r\n # TODO\r\n # replace varVal.__class__.__name__ with something more meaningful\r\n # and scan for things without \"__class__\" or \"__name__\" fields\r\n # some swing components don't have them\r\n\r\n localVarsDict = self.__buildVarDict__(localVars)\r\n globalVarsDict = self.__buildVarDict__(globalVars)\r\n\r\n localVarsDict = self.filterVars(localVarsDict)\r\n globalVarsDict = self.filterVars(globalVarsDict)\r\n\r\n localVarsDict = self.sortVars(localVarsDict)\r\n globalVarsDict = self.sortVars(globalVarsDict)\r\n\r\n localVarsDict = self.improveTypeNames(localVarsDict)\r\n globalVarsDict = self.improveTypeNames(globalVarsDict)\r\n\r\n\r\n \r\n #Create the TableModel and JTable components\r\n localTableModel = swing.table.DefaultTableModel(localVarsDict, [VAR_L_NAME_COL_CAPTION,\r\n VAR_TYPE_COL_CAPTION,\r\n VAR_VALUE_COL_CAPTION])\r\n\r\n \r\n globalTableModel = swing.table.DefaultTableModel(globalVarsDict, [VAR_G_NAME_COL_CAPTION,\r\n VAR_TYPE_COL_CAPTION,\r\n VAR_VALUE_COL_CAPTION])\r\n \r\n localVarTable = swing.JTable(localTableModel)\r\n localVarTable.getColumnModel().getColumn(0).setPreferredWidth(1);\r\n\r\n globalVarTable = swing.JTable(globalTableModel)\r\n globalVarTable.getColumnModel().getColumn(0).setPreferredWidth(1);\r\n\r\n\r\n topPane = swing.JScrollPane(localVarTable)\r\n bottomPane = swing.JScrollPane(globalVarTable)\r\n return (topPane, bottomPane)\r\n\r\n ###############################################################################\r\n # improveTypeNames\r\n #\r\n # accepts the \"varsDict\" array, and replaces the string in the type name field\r\n # with something more readable. \"org.core.PyInteger\" becomes \"Integer\", for\r\n # example.\r\n #\r\n # params: varsDict- an array, where each element of the array is a 3-tuple of\r\n # format is [ [ , , ] ,\r\n # [,,] , ... ]\r\n # return: the same array, but the strings in the second field have been\r\n # replaced\r\n ###############################################################################\r\n def improveTypeNames(self, varsDict):\r\n\r\n for var in varsDict:\r\n \r\n if IMPROVED_TYPE_NAMES.has_key(var[1]):\r\n var[1] = IMPROVED_TYPE_NAMES[ var[1] ]\r\n\r\n return varsDict\r\n \r\n\r\n ###############################################################################\r\n # filterVars\r\n #\r\n # uses the varsToFilter dictionary to remove from the visible debug window\r\n # any variables that are part of the debug system.\r\n #\r\n # param: varsDict - the array that will be displayed in the debug window\r\n #\r\n # varsToFilter - not a parameter; part of the object;\r\n # if a variable appears in varsToFilter, then it will\r\n # be removed from the list\r\n # return: newVarsDict - the dictionary without the \"hidden\" variables\r\n ###############################################################################\r\n def filterVars(self,varsDict):\r\n\r\n newVarsDict= []\r\n for i in range( len( varsDict) ):\r\n if self.varsToFilter.has_key( varsDict[i][0] ) and \\\r\n varsDict[i][0] != 'showVars':\r\n pass\r\n else:\r\n newVarsDict.append( varsDict[i])\r\n \r\n return newVarsDict\r\n\r\n #############################################################################\r\n # sortVars\r\n #\r\n # sorts the variables list varsDict\r\n # \r\n #\r\n # param - varsDict - the array that will be displayed in the debug window\r\n # return- varsDict - now sorted\r\n #\r\n ############################################################################## \r\n def sortVars(self,varsDict):\r\n varsDict.sort( self.compareFun )\r\n return varsDict\r\n\r\n ##########################################################################\r\n # compareFun\r\n #\r\n # the function passed to varsDict's sort function\r\n # used to tell the sort function how which variables are bigger than which\r\n #\r\n # param - x,y - two variables that will be compared\r\n # return- [-1,0,1]\r\n ###########################################################################\r\n def compareFun(self,x,y):\r\n \r\n xTypeSortNum = self.getTypeSortNum(x[1])\r\n yTypeSortNum = self.getTypeSortNum(y[1])\r\n\r\n if xTypeSortNum < yTypeSortNum:\r\n return -1\r\n \r\n elif xTypeSortNum > yTypeSortNum:\r\n return 1\r\n\r\n if x[0] < y[0]:\r\n return -1\r\n elif x[0] > y[0]:\r\n return 1\r\n\r\n return 0\r\n\r\n #############################################################################\r\n # getTypeSortNum\r\n #\r\n # helps the compareFun order the variables based on their type\r\n #\r\n # param - x - the type of a variable\r\n # return - an integer on the set {1,2,3,4,5}\r\n #############################################################################\r\n def getTypeSortNum(self,x):\r\n if x == INTEGER or \\\r\n x == LONG or \\\r\n x == FLOAT or \\\r\n x == COMPLEX or \\\r\n x == STRING or \\\r\n x == TUPLE or \\\r\n x == LIST or \\\r\n x == DICTIONARY:\r\n \r\n return 1\r\n\r\n if x == FUNCTION:\r\n return 3\r\n if x == CLASS:\r\n return 4\r\n if x == MODULE:\r\n return 5\r\n\r\n return 2\r\n################################################################################\r\n# Function name: actionPerformed\r\n# Parameters:\r\n# -event: event object that represents action that occured\r\n# Description:\r\n# This function closes the debug window when the close button is pressed. \r\n################################################################################\r\n def actionPerformed(self, event):\r\n actionCommand = event.getActionCommand()\r\n\r\n if actionCommand == CLOSE_BUTTON_CAPTION:\r\n self.setVisible(0)\r\n\r\n\r\n################################################################################\r\n# Function name: __buildVarDict__\r\n# Parameters:\r\n# -varsToDisplay: the dictionary of variable to display\r\n# Return:\r\n# An array of three value collections (variable name, variable class name,\r\n# and variable value)\r\n# Description:\r\n# Accepts a hash table of items and returns an an array. The array is to\r\n# be shown in the debug window; This will ignore variables that don't\r\n# have __class__ fields.\r\n################################################################################\r\n def __buildVarDict__(self, varsToDisplay ):\r\n varDict = []\r\n for varName, varVal in varsToDisplay.items():\r\n \r\n try:\r\n varDict += [[varName, varVal.__class__.__name__, varVal]]\r\n\r\n except:\r\n # some objects (swing objects, say) don't have the class field\r\n # this is a crude way to avoid that generating errors\r\n pass\r\n \r\n return varDict\r\n\r\n\r\n\r\n","sub_path":"jes/jes-v4.3-linux/Sources/JESDebugWindow.py","file_name":"JESDebugWindow.py","file_ext":"py","file_size_in_byte":10926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"138505796","text":"from sys import argv, stdout\r\nfrom time import time\r\nfrom os.path import exists\r\nfrom traceback2 import format_exc\r\n\r\nfrom crickroll import run_in_cpp\r\nfrom pyrickroll import run_in_py\r\n# from AudioGenerator import init\r\n\r\n\r\n# Help message\r\nrick_help = \"\"\"\r\nProgramming by writing code: rickroll -s [File_Name]\r\nGenerate an audio: rickroll -r [File_Name] -audio [Audio_Name]\r\nSing code: rickroll -sing [Audio_Name] [File_Name]\r\n\r\nOther Options:\r\n--time: Show execution time ofyour code\r\n--help/--h: Help\r\n\"\"\"\r\n\r\n# Set and start a timer\r\nstart = time()\r\n\r\naudio_engine = None\r\n\r\n# def generate_audio(self, src_file_name):\r\n\r\n# with open(src_file_name, mode='r', encoding='utf-8') as src:\r\n# for statement in src.readlines():\r\n# obj = Token(statement)\r\n\r\n# for i in range(len(obj.t_types)):\r\n# audio_engine.generate(obj.t_values[i])\r\n\r\ndef main():\r\n # is_audio = False\r\n is_help = False\r\n show_time = False\r\n is_cpp = False\r\n src_file_name = ''\r\n\r\n\r\n if len(argv) <= 1:\r\n exit(rick_help)\r\n\r\n for i in range(len(argv)):\r\n current_arg = argv[i].lower()\r\n\r\n # Run code. -r [file_name]\r\n if current_arg == '-r':\r\n src_file_name = argv[i + 1]\r\n\r\n # Generate audio. -audio [Output audio file name]\r\n # if current_arg == '-audio':\r\n # global audio_engine\r\n # is_audio = True\r\n\r\n # audio_engine = init(argv[i + 1])\r\n\r\n if current_arg == '--cpp' or current_arg == '--c++':\r\n is_cpp = True\r\n\r\n # Help message\r\n if current_arg == '--help' or current_arg == '--h':\r\n is_help = True\r\n\r\n # Show execution time\r\n if current_arg == '--time':\r\n show_time = True\r\n\r\n # Run the RickRoll program\r\n if src_file_name:\r\n if exists(src_file_name):\r\n if is_cpp: run_in_cpp(src_file_name)\r\n else:\r\n try: exec(run_in_py(src_file_name), locals(), globals())\r\n except:\r\n error = format_exc().split('File \"\",')[-1]\r\n stdout.write(f'Exception in{error}\\n' + '-------'*10)\r\n stdout.write('\"'+\"There ain't no mistaking, is true love we are making~\"+'\"')\r\n else: exit(f\"File [{src_file_name}] doesn't exist...\")\r\n else: stdout.write('Warning: [Not executing any script...]')\r\n\r\n\r\n # if is_audio:\r\n # self.generate_audio(src_file_name)\r\n # audio_engine.export()\r\n\r\n if is_help:\r\n stdout.write(rick_help)\r\n\r\n if show_time:\r\n stdout.write(f'\\nExecution Time: [{time() - start}] sec.')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","sub_path":"src-py/RickRoll.py","file_name":"RickRoll.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"629581816","text":"from .util import parametrizedDecorator\nimport logging\nfrom enum import Enum\n\nclass TestResultOutcome():\n class State(Enum):\n PASS = \"Pass\"\n FAIL = \"Fail\"\n ERROR = \"ERROR\"\n WARNING = \"Warning\"\n\n def __init__(self, state: State, message= None, error= None):\n self.state = state\n self.message = message\n self.error = error\n\nclass TestResultFailure(TestResultOutcome):\n def __init__(self, message= None):\n super().__init__(TestResultOutcome.State.FAIL, message)\n\nclass TestResultWarning(TestResultOutcome):\n def __init__(self, message= None):\n super().__init__(TestResultOutcome.State.WARNING, message)\n\n@parametrizedDecorator\ndef testResultFormat(func, description, units=None, displayed=True):\n result = TestResultFormat(description=description, criteria=func, units=units, displayed=displayed)\n return result\n\nclass TestResultFormat(object):\n def __init__(self, description, criteria= lambda x : True if x != None else False , units=None, displayed=True):\n self.description = description\n self.units = units\n self.displayed = displayed\n if not callable(criteria):\n raise ValueError(\"criteria must be callable: a function or lambda\")\n\n # Decorator\n def convertedOutcome(function):\n def wrapped(x):\n try:\n outcome = function(x)\n except Exception as e:\n return TestResultOutcome(TestResultOutcome.State.ERROR, str(e), error=e)\n\n if isinstance(outcome, bool):\n status = TestResultOutcome.State.PASS if outcome else TestResultOutcome.State.FAIL\n return TestResultOutcome(status)\n elif isinstance(outcome, TestResultOutcome) or issubclass(outcome, TestResultOutcome):\n return outcome\n else:\n raise ValueError(\"Criteria function must return a valid outcome\")\n return wrapped\n self.criteria = convertedOutcome(criteria)\n\n def outcomeForValue(self, value) -> TestResultOutcome:\n return self.criteria(value)","sub_path":"AutoTest/TestResultFormat.py","file_name":"TestResultFormat.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"495140535","text":"#! /usr/bin/env python\n# by caozj\n# Sep 7, 2018\n# 12:46:00 PM\n\n\nimport os\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-t\", \"--taxids\", dest=\"taxids\", type=int, nargs=2)\nparser.add_argument(\"-n\", \"--use-name\", dest=\"use_name\", default=False, action=\"store_true\")\ncmd_args = parser.parse_args()\n\ndf = pd.read_csv(os.path.join(\n \"..\", \"Ensembl\", \"orthology\", \"%d_%d.csv\" % tuple(cmd_args.taxids)\n), header=None)\nif cmd_args.use_name:\n df = df.iloc[:, [1, 3]]\nelse:\n df = df.iloc[:, [0, 2]]\ndf.columns = [0, 1]\ngenes = {\n cmd_args.taxids[0]: np.unique(df[0]),\n cmd_args.taxids[1]: np.unique(df[1])\n}\ncombined_genes = np.concatenate(list(genes.values()))\ngene_lut = {combined_genes[i]: i for i in range(len(combined_genes))}\n\ngraph = sp.lil_matrix((len(combined_genes), len(combined_genes)), dtype=np.int8)\nfor i, row in df.iterrows():\n graph[gene_lut[row[0]], gene_lut[row[1]]] = 1\n graph[gene_lut[row[1]], gene_lut[row[0]]] = 1\ncomponents = sp.csgraph.connected_components(graph, directed=False)[1]\n\ntarget_path = os.path.join(\"..\", \"Ensembl\", \"orthology\", \"%d_%d\" % tuple(cmd_args.taxids))\nif not os.path.exists(target_path):\n os.makedirs(target_path)\nfor taxid in cmd_args.taxids:\n with open(os.path.join(target_path, \"%d.csv\" % taxid), \"w\") as f:\n for gene in genes[taxid]:\n f.write(\"%s,OG:%06d\\n\" % (gene, components[gene_lut[gene]]))\n\nprint(\"Done!\")\n","sub_path":"Datasets/ortholog/scripts/generate_og_from_ensembl_orthology.py","file_name":"generate_og_from_ensembl_orthology.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"466313959","text":"def is_truncable_prime(n):\n\tif not is_prime[n]:\n\t\treturn False\n\ttemp1 = str(n)\n\ttemp2 = str(n)\n\twhile len(temp1) > 0:\n\t\tif not is_prime[int(temp1)]:\n\t\t\treturn False\n\t\ttemp1 = temp1[:-1]\n\twhile len(temp2) > 0:\n\t\tif not is_prime[int(temp2)]:\n\t\t\treturn False\n\t\ttemp2 = temp2[1:]\n\treturn True\n\nis_prime = {}\nis_prime[1] = False\nfor i in range(2, 1000001):\n\tis_prime[i] = True\n\nfor i in range(2, 1000001):\n\tif is_prime[i]:\n\t\tfor j in range(2, 1000001):\n\t\t\tindex = i * j\n\t\t\tif index > 1000000:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tis_prime[index] = False\nsum = 0\nfor i in range(10, 1000000):\n\tif is_truncable_prime(i):\n\t\tsum += i\n\t\tprint(i)\nprint(sum)\n\n#print(is_truncable_prime(29))\n#print(is_prime[29])\n","sub_path":"problem37.py","file_name":"problem37.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"60914033","text":"import numpy as np\n\n# RECOMPUTETRAJECTORIES Summary of this function goes here\n# Detailed explanation goes here\ndef recomputeTrajectories(trajectories):\n segmentLength = 20\n \n for i in range(len(trajectories)):\n segmentStart = trajectories[i]['segmentStart']\n segmentEnd = trajectories[i]['segmentEnd']\n \n numSegments = (segmentEnd + 1 - segmentStart) / segmentLength\n\n alldata = []\n for k in range(len(trajectories[i]['tracklets'])):\n alldata.extend(trajectories[i]['tracklets'][k]['data'])\n alldata = np.array(alldata)\n alldata = alldata[alldata[:,0].argsort(),]\n alldata = alldata[alldata[:,1].argsort(),]\n \n tmpdata, uniqueRows = np.unique(alldata[:,0], return_index=True)\n uniqueRows = uniqueRows.astype(np.int32)\n \n alldata = alldata[uniqueRows]\n dataFrames = alldata[:,0]\n dataFrames = np.array(dataFrames)\n \n frames = np.linspace(segmentStart,segmentEnd,segmentEnd-segmentStart+1)\n \n tmp_frames = []\n tmp_frames.append(np.min(dataFrames))\n \n segnum = (frames[-1] - frames[0] - segmentLength/2)/segmentLength\n segint = np.linspace(frames[0]+ segmentLength/2,frames[-1],int(segnum))\n segint = list(segint)\n tmp_frames.extend(segint)\n tmp_frames.append(np.max(dataFrames))\n print(segmentStart)\n print(segmentEnd)\n print(frames[0])\n print(frames[-1])\n print(tmp_frames)\n# interestingFrames = round([min(dataFrames), frames(1) + segmentLength/2:segmentLength:frames(end), max(dataFrames)]);\n \n\n","sub_path":"L2trajectories/recomputeTrajectories.py","file_name":"recomputeTrajectories.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"99651921","text":"import bs4\r\nimport json\r\nimport time\r\nimport torch\r\nimport datetime\r\nimport requests\r\nimport numpy as np\r\nimport transformers\r\nimport pandas as pd\r\nimport seaborn as sns\r\nimport streamlit as st\r\nfrom matplotlib import rc\r\nfrom textwrap import wrap\r\nfrom pylab import rcParams\r\nfrom torch import nn, optim\r\nfrom bs4 import BeautifulSoup\r\nimport matplotlib.pyplot as plt\r\nfrom GoogleNews import GoogleNews\r\nfrom collections import defaultdict\r\nfrom requests_oauthlib import OAuth1\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom IPython.display import set_matplotlib_formats\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import confusion_matrix, classification_report\r\nfrom transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup\r\n\r\nfrom bokeh.io import output_file, show\r\nfrom bokeh.models import ColumnDataSource\r\nfrom bokeh.palettes import Spectral6\r\nfrom bokeh.plotting import figure\r\n\r\n\r\nRANDOM_SEED = 42\r\nnp.random.seed(RANDOM_SEED)\r\ntorch.manual_seed(RANDOM_SEED)\r\nrcParams['figure.figsize'] = 12, 8\r\ngooglenews = GoogleNews(lang='en')\r\nurl_rest = \"https://api.twitter.com/1.1/search/tweets.json\"\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\nauth_params = {\r\n 'app_key':'zVuMpGebsDOlKvn2TxZbieF6A',\r\n 'app_secret':'ilxtHZJMKpE2txq3erWfORNzCaa6DQxJULEQVlt6sqvPVl9Dm5',\r\n 'oauth_token':'1059637136511520768-IsGRaBmmUQMAXgW97vBmqysqFM80db',\r\n 'oauth_token_secret':'jgbnA1KOpNId91tsgZpvRkr1aVbtGfSDTKONH29O1OQrN'\r\n }\r\nauth = OAuth1 (\r\n auth_params['app_key'],\r\n auth_params['app_secret'],\r\n auth_params['oauth_token'],\r\n auth_params['oauth_token_secret']\r\n)\r\n\r\nclass SentimentClassifier(nn.Module):\r\n def __init__(self, n_classes):\r\n super(SentimentClassifier, self).__init__()\r\n self.bert = BertModel.from_pretrained(PRE_TRAINED_MODEL_NAME)\r\n self.drop = nn.Dropout(p=0.3)\r\n self.out = nn.Linear(self.bert.config.hidden_size, n_classes)\r\n def forward(self, input_ids, attention_mask):\r\n _, pooled_output = self.bert(\r\n input_ids=input_ids,\r\n attention_mask=attention_mask)\r\n output = self.drop(pooled_output)\r\n return self.out(output)\r\nclass tqdm:\r\n def __init__(self, iterable, title=None):\r\n if title:\r\n st.write(title)\r\n self.prog_bar = st.progress(0)\r\n self.iterable = iterable\r\n self.length = len(iterable)\r\n self.i = 0\r\n\r\n def __iter__(self):\r\n for obj in self.iterable:\r\n yield obj\r\n self.i += 1\r\n current_prog = self.i / self.length\r\n self.prog_bar.progress(current_prog)\r\n\r\n\r\nst.title('Public sentiments')\r\nst.sidebar.title('User Inputs')\r\nagree = st.sidebar.checkbox('frequency')\r\nif agree:\r\n option = st.sidebar.selectbox('How would you like to be contacted?',('1h','1d','7d','1y'))\r\n googlenews.setperiod(option)\r\nelse:\r\n st.sidebar.markdown('Select the time range for the search')\r\n dt1 = st.sidebar.date_input('from date',datetime.date.today())\r\n dt2 = st.sidebar.date_input('till date',datetime.date.today())\r\n if dt1>dt2:\r\n st.sidebar.error('SELECT A VALID \"FROM\" DATE')\r\n else:\r\n googlenews.setTimeRange(dt1,dt2)\r\n\r\n\r\n\r\nsearchInput = st.sidebar.text_input('search query')\r\nval = len(searchInput)\r\nif val>0:\r\n with st.spinner('Getting data...'):\r\n googlenews.search(searchInput)\r\n news_content = []\r\n\r\n ## ''' Google News start '''\r\n for i in range(1,1+1):\r\n googlenews.getpage(i)\r\n for i in googlenews.result():\r\n news_content.append(i['desc'])\r\n googlenews.clear()\r\n\r\n ## ''' Twitter handle '''\r\n q = '%40'+'#'+searchInput+' -filter:retweets -filter:replies'\r\n # count : no of tweets to be retrieved per one call and parameters according to twitter API\r\n params = {'q': q, 'count': 1000, 'lang': 'en', 'result_type': 'recent'}\r\n results = requests.get(url_rest, params=params, auth=auth)\r\n tweets = results.json()\r\n messages = [BeautifulSoup(tweet['text'], 'html.parser').get_text() for tweet in tweets['statuses']]\r\n # End\r\n st.success('Done!')\r\n\r\n finalList = []\r\n finalList.extend(news_content)\r\n finalList.extend(messages)\r\n\r\n\r\n\r\n PRE_TRAINED_MODEL_NAME = 'bert-base-cased'\r\n class_names = ['negative', 'neutral', 'positive']\r\n tokenizer = BertTokenizer.from_pretrained(PRE_TRAINED_MODEL_NAME)\r\n MAX_LEN = 160\r\n\r\n model = SentimentClassifier(len(class_names))\r\n model.load_state_dict(torch.load('model/best_model_state.bin',map_location='cpu')) ## remove the map_location when running in GPU\r\n model = model.to(device)\r\n\r\n\r\n\r\n # for i in tqdm(range(200), title='tqdm style progress bar'):\r\n # time.sleep(0.05)\r\n\r\n\r\n posCnt= 0\r\n netCnt= 0\r\n negCnt= 0\r\n for i in tqdm(finalList):\r\n encoded_review = tokenizer.encode_plus(i,\r\n max_length=MAX_LEN,\r\n add_special_tokens=True,\r\n return_token_type_ids=False,\r\n pad_to_max_length=True,\r\n return_attention_mask=True,\r\n return_tensors='pt',\r\n )\r\n\r\n input_ids = encoded_review['input_ids'].to(device)\r\n attention_mask = encoded_review['attention_mask'].to(device)\r\n\r\n output = model(input_ids, attention_mask)\r\n _, prediction = torch.max(output, dim=1)\r\n\r\n if class_names[prediction] == 'negative':\r\n negCnt = negCnt+1\r\n elif class_names[prediction]== 'positive':\r\n posCnt = posCnt +1\r\n elif class_names[prediction]== 'neutral':\r\n netCnt = netCnt+1\r\n\r\n posCnt= round(posCnt/len(finalList)*100,2)\r\n netCnt= round(netCnt/len(finalList)*100,2)\r\n negCnt= round(negCnt/len(finalList)*100,2)\r\n\r\n cnames = ['positive', 'negative', 'neutral']\r\n cnt = [posCnt,negCnt,netCnt]\r\n\r\n chart_data = pd.DataFrame(columns=[\"label\", \"count\"])\r\n chart_data['label'] = cnames\r\n chart_data['count'] = cnt\r\n st.bar_chart(chart_data)\r\n\r\n\r\nelse:\r\n st.error('Eneter a valid Search Query!...')\r\n","sub_path":"streamlit.py","file_name":"streamlit.py","file_ext":"py","file_size_in_byte":6434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"466444089","text":"import sqlalchemy\nimport pandas as pd\n# from sqlalchemy.orm import sessionmaker\nimport requests\nimport json\nfrom datetime import datetime\nimport datetime\nimport sqlite3\n\n\n### Validation function\ndef check_if_data_is_valid(df: pd.DataFrame) -> bool:\n # Check if dataframe is empty\n if df.empty:\n print(\"No genres downloaded. Finishing execution.\")\n return False\n \n # Primary Key Check\n if pd.Series(df['genres']).is_unique:\n pass\n else:\n raise Exception(\"Primary Key check is violated.\")\n \n # Check for nulls\n if df.isnull().values.any():\n raise Exception(\"Null values found.\")\n\n return True\n\n\ndef run_spotify_etl():\n\n # Should be lowercase as now these variables are not global\n DATABASE_LOCATION = \"sqlite:///my_played_tracks.sqlite\" # we call it whatever we like\n USER_ID = \"miko_zit\"\n TOKEN = \"BQA-MXyHKEWKH_l9ZWOvDPUHwRMitwtyLYhZT4ZTnBksRah97E_RnMt4xWWforC46-i3W_16p4FHpcGdwwxyxcBBmdk9DgpK_oqz2CjoW-pJTINvC9Fsq_6635UtwsB2HtpPynbAnUzCGg\"\n\n\n headers = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/json\",\n \"Authorization\": \"Bearer {token}\".format(token=TOKEN)\n }\n\n today = datetime.datetime.now()\n yesterday = today - datetime.timedelta(days=1)\n yesterday_unix_timestamp = int(yesterday.timestamp())*1000 # in unix miliseconds\n\n print('today', today)\n print('timedelta', datetime.timedelta(days=1))\n print('yesterday', yesterday)\n print('timestamp', yesterday.timestamp())\n print('yesterday_unix_timestamp', yesterday_unix_timestamp)\n\n \n # ### Recently played version\n # r = requests.get(\"https://api.spotify.com/v1/me/player/recently-played/after={time}\".format(time=yesterday_unix_timestamp), \n # headers=headers)\n # r.raise_for_status()\n # data = r.json()\n # print(data)\n\n\n ### Available genre seeds\n r = requests.get(\"https://api.spotify.com/v1/recommendations/available-genre-seeds\", headers=headers)\n data = r.json()\n print(data)\n\n\n ### Loading into df\n genres_dict = {\n 'genres': data['genres']\n }\n\n genres_df = pd.DataFrame(genres_dict)\n print(genres_df)\n\n\n # Validate\n if check_if_data_is_valid(genres_df):\n print(\"Data valid, proceed to Load stage\")\n \n\n # Load\n engine = sqlalchemy.create_engine(DATABASE_LOCATION)\n conn = sqlite3.connect('my_played_tracks.sqlite')\n cursor = conn.cursor()\n\n sql_query = \"\"\"\n CREATE TABLE IF NOT EXISTS my_played_tracks(\n genres VARCHAR(200),\n CONSTRAINT primary_key_constraint PRIMARY KEY (genres)\n )\n \"\"\"\n\n cursor.execute(sql_query)\n print(\"Opened database successfully\")\n\n try:\n genres_df.to_sql(\"my_played_tracks\", engine, index=False, if_exists='append')\n except:\n print(\"Data already exists in the database\")\n\n conn.close()\n print(\"Closed database successfully\")","sub_path":"dags/main_using_airflow.py","file_name":"main_using_airflow.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"164869274","text":"import struct, sys\n\nclass StructType(tuple):\n\tdef __getitem__(self, value):\n\t\treturn [self] * value\n\tdef __call__(self, value, endian='<'):\n\t\tif isinstance(value, str):\n\t\t\treturn struct.unpack(endian + tuple.__getitem__(self, 0), value[:tuple.__getitem__(self, 1)])[0]\n\t\telse:\n\t\t\treturn struct.pack(endian + tuple.__getitem__(self, 0), value)\n\nclass StructException(Exception):\n\tpass\n\nclass Struct(object):\n\t__slots__ = ('__attrs__', '__baked__', '__defs__', '__next__', '__sizes__', '__values__')\n\tint8 = StructType(('b', 1))\n\tuint8 = StructType(('B', 1))\n\t\n\tint16 = StructType(('h', 2))\n\tuint16 = StructType(('H', 2))\n\t\n\tint32 = StructType(('l', 4))\n\tuint32 = StructType(('L', 4))\n\t\n\tint64 = StructType(('q', 8))\n\tuint64 = StructType(('Q', 8))\n\t\n\tfloat = StructType(('f', 4))\n\n\tdef string(cls, len, offset=0, encoding=None, stripNulls=False, value=''):\n\t\treturn StructType(('string', (len, offset, encoding, stripNulls, value)))\n\tstring = classmethod(string)\n\t\n\tLE = '<'\n\tBE = '>'\n\t__endian__ = '<'\n\t\n\tdef __init__(self, func=None, unpack=None, **kwargs):\n\t\tself.__defs__ = []\n\t\tself.__sizes__ = []\n\t\tself.__attrs__ = []\n\t\tself.__values__ = {}\n\t\tself.__next__ = True\n\t\tself.__baked__ = False\n\t\t\n\t\tif func == None:\n\t\t\tself.__format__()\n\t\telse:\n\t\t\tsys.settrace(self.__trace__)\n\t\t\tfunc()\n\t\t\tfor name in func.__code__.co_varnames:\n\t\t\t\tvalue = self.__frame__.f_locals[name]\n\t\t\t\tself.__setattr__(name, value)\n\t\t\n\t\tself.__baked__ = True\n\t\t\n\t\tif unpack != None:\n\t\t\tif isinstance(unpack, tuple):\n\t\t\t\tself.unpack(*unpack)\n\t\t\telse:\n\t\t\t\tself.unpack(unpack)\n\t\t\n\t\tif len(kwargs):\n\t\t\tfor name in kwargs:\n\t\t\t\tself.__values__[name] = kwargs[name]\n\t\n\tdef __trace__(self, frame, event, arg):\n\t\tself.__frame__ = frame\n\t\tsys.settrace(None)\n\t\n\tdef __setattr__(self, name, value):\n\t\tif name in self.__slots__:\n\t\t\treturn object.__setattr__(self, name, value)\n\t\t\n\t\tif self.__baked__ == False:\n\t\t\tif not isinstance(value, list):\n\t\t\t\tvalue = [value]\n\t\t\t\tattrname = name\n\t\t\telse:\n\t\t\t\tattrname = '*' + name\n\t\t\t\n\t\t\tself.__values__[name] = None\n\t\t\t\n\t\t\tfor sub in value:\n\t\t\t\tif isinstance(sub, Struct):\n\t\t\t\t\tsub = sub.__class__\n\t\t\t\ttry:\n\t\t\t\t\tif issubclass(sub, Struct):\n\t\t\t\t\t\tsub = ('struct', sub)\n\t\t\t\texcept TypeError:\n\t\t\t\t\tpass\n\t\t\t\ttype_, size = tuple(sub)\n\t\t\t\tif type_ == 'string':\n\t\t\t\t\tself.__defs__.append(Struct.string)\n\t\t\t\t\tself.__sizes__.append(size)\n\t\t\t\t\tself.__attrs__.append(attrname)\n\t\t\t\t\tself.__next__ = True\n\t\t\t\t\t\n\t\t\t\t\tif attrname[0] != '*':\n\t\t\t\t\t\tself.__values__[name] = size[3]\n\t\t\t\t\telif self.__values__[name] == None:\n\t\t\t\t\t\tself.__values__[name] = [size[3] for val in value]\n\t\t\t\telif type_ == 'struct':\n\t\t\t\t\tself.__defs__.append(Struct)\n\t\t\t\t\tself.__sizes__.append(size)\n\t\t\t\t\tself.__attrs__.append(attrname)\n\t\t\t\t\tself.__next__ = True\n\t\t\t\t\t\n\t\t\t\t\tif attrname[0] != '*':\n\t\t\t\t\t\tself.__values__[name] = size()\n\t\t\t\t\telif self.__values__[name] == None:\n\t\t\t\t\t\tself.__values__[name] = [size() for val in value]\n\t\t\t\telse:\n\t\t\t\t\tif self.__next__:\n\t\t\t\t\t\tself.__defs__.append('')\n\t\t\t\t\t\tself.__sizes__.append(0)\n\t\t\t\t\t\tself.__attrs__.append([])\n\t\t\t\t\t\tself.__next__ = False\n\t\t\t\t\t\n\t\t\t\t\tself.__defs__[-1] += type_\n\t\t\t\t\tself.__sizes__[-1] += size\n\t\t\t\t\tself.__attrs__[-1].append(attrname)\n\t\t\t\t\t\n\t\t\t\t\tif attrname[0] != '*':\n\t\t\t\t\t\tself.__values__[name] = 0\n\t\t\t\t\telif self.__values__[name] == None:\n\t\t\t\t\t\tself.__values__[name] = [0 for val in value]\n\t\telse:\n\t\t\ttry:\n\t\t\t\tself.__values__[name] = value\n\t\t\texcept KeyError:\n\t\t\t\traise AttributeError(name)\n\t\n\tdef __getattr__(self, name):\n\t\tif self.__baked__ == False:\n\t\t\treturn name\n\t\telse:\n\t\t\ttry:\n\t\t\t\treturn self.__values__[name]\n\t\t\texcept KeyError:\n\t\t\t\traise AttributeError(name)\n\t\n\tdef __len__(self):\n\t\tret = 0\n\t\tarraypos, arrayname = None, None\n\t\t\n\t\tfor i in range(len(self.__defs__)):\n\t\t\tsdef, size, attrs = self.__defs__[i], self.__sizes__[i], self.__attrs__[i]\n\t\t\t\n\t\t\tif sdef == Struct.string:\n\t\t\t\tsize, offset, encoding, stripNulls, value = size\n\t\t\t\tif isinstance(size, str):\n\t\t\t\t\tsize = self.__values__[size] + offset\n\t\t\telif sdef == Struct:\n\t\t\t\tif attrs[0] == '*':\n\t\t\t\t\tif arrayname != attrs:\n\t\t\t\t\t\tarrayname = attrs\n\t\t\t\t\t\tarraypos = 0\n\t\t\t\t\tsize = len(self.__values__[attrs[1:]][arraypos])\n\t\t\t\tsize = len(self.__values__[attrs])\n\t\t\t\n\t\t\tret += size\n\t\t\n\t\treturn ret\n\t\n\tdef unpack(self, data, pos=0):\n\t\tfor name in self.__values__:\n\t\t\tif not isinstance(self.__values__[name], Struct):\n\t\t\t\tself.__values__[name] = None\n\t\t\telif self.__values__[name].__class__ == list and len(self.__values__[name]) != 0:\n\t\t\t\tif not isinstance(self.__values__[name][0], Struct):\n\t\t\t\t\tself.__values__[name] = None\n\t\t\n\t\tarraypos, arrayname = None, None\n\t\t\n\t\tfor i in range(len(self.__defs__)):\n\t\t\tsdef, size, attrs = self.__defs__[i], self.__sizes__[i], self.__attrs__[i]\n\t\t\t\n\t\t\tif sdef == Struct.string:\n\t\t\t\tsize, offset, encoding, stripNulls, value = size\n\t\t\t\tif isinstance(size, str):\n\t\t\t\t\tsize = self.__values__[size] + offset\n\t\t\t\t\n\t\t\t\ttemp = data[pos:pos+size]\n\t\t\t\tif len(temp) != size:\n\t\t\t\t\traise StructException('Expected %i byte string, got %i' % (size, len(temp)))\n\t\t\t\t\n\t\t\t\tif encoding != None:\n\t\t\t\t\ttemp = temp.decode(encoding)\n\t\t\t\t\n\t\t\t\tif stripNulls:\n\t\t\t\t\ttemp = temp.rstrip('\\0')\n\t\t\t\t\n\t\t\t\tif attrs[0] == '*':\n\t\t\t\t\tname = attrs[1:]\n\t\t\t\t\tif self.__values__[name] == None:\n\t\t\t\t\t\tself.__values__[name] = []\n\t\t\t\t\tself.__values__[name].append(temp)\n\t\t\t\telse:\n\t\t\t\t\tself.__values__[attrs] = temp\n\t\t\t\tpos += size\n\t\t\telif sdef == Struct:\n\t\t\t\tif attrs[0] == '*':\n\t\t\t\t\tif arrayname != attrs:\n\t\t\t\t\t\tarrayname = attrs\n\t\t\t\t\t\tarraypos = 0\n\t\t\t\t\tname = attrs[1:]\n\t\t\t\t\tself.__values__[attrs][arraypos].unpack(data, pos)\n\t\t\t\t\tpos += len(self.__values__[attrs][arraypos])\n\t\t\t\t\tarraypos += 1\n\t\t\t\telse:\n\t\t\t\t\tself.__values__[attrs].unpack(data, pos)\n\t\t\t\t\tpos += len(self.__values__[attrs])\n\t\t\telse:\n\t\t\t\tvalues = struct.unpack(self.__endian__+sdef, data[pos:pos+size])\n\t\t\t\tpos += size\n\t\t\t\tj = 0\n\t\t\t\tfor name in attrs:\n\t\t\t\t\tif name[0] == '*':\n\t\t\t\t\t\tname = name[1:]\n\t\t\t\t\t\tif self.__values__[name] == None:\n\t\t\t\t\t\t\tself.__values__[name] = []\n\t\t\t\t\t\tself.__values__[name].append(values[j])\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.__values__[name] = values[j]\n\t\t\t\t\tj += 1\n\t\t\n\t\treturn self\n\t\n\tdef pack(self):\n\t\tarraypos, arrayname = None, None\n\t\t\n\t\tret = b''\n\t\tfor i in range(len(self.__defs__)):\n\t\t\tsdef, size, attrs = self.__defs__[i], self.__sizes__[i], self.__attrs__[i]\n\t\t\t\n\t\t\tif sdef == Struct.string:\n\t\t\t\tsize, offset, encoding, stripNulls, value = size\n\t\t\t\tif isinstance(size, str):\n\t\t\t\t\tsize = self.__values__[size]+offset\n\t\t\t\t\n\t\t\t\tif attrs[0] == '*':\n\t\t\t\t\tif arrayname != attrs:\n\t\t\t\t\t\tarraypos = 0\n\t\t\t\t\t\tarrayname = attrs\n\t\t\t\t\ttemp = self.__values__[attrs[1:]][arraypos]\n\t\t\t\t\tarraypos += 1\n\t\t\t\telse:\n\t\t\t\t\ttemp = self.__values__[attrs]\n\t\t\t\t\n\t\t\t\tif encoding != None:\n\t\t\t\t\ttemp = temp.encode(encoding)\n\t\t\t\t\n\t\t\t\ttemp = temp[:size]\n\t\t\t\tret += temp + (b'\\0' * (size - len(temp)))\n\t\t\telif sdef == Struct:\n\t\t\t\tif attrs[0] == '*':\n\t\t\t\t\tif arrayname != attrs:\n\t\t\t\t\t\tarraypos = 0\n\t\t\t\t\t\tarrayname = attrs\n\t\t\t\t\tret += self.__values__[attrs[1:]][arraypos].pack()\n\t\t\t\t\tarraypos += 1\n\t\t\t\telse:\n\t\t\t\t\tret += self.__values__[attrs].pack()\n\t\t\telse:\n\t\t\t\tvalues = []\n\t\t\t\tfor name in attrs:\n\t\t\t\t\tif name[0] == '*':\n\t\t\t\t\t\tif arrayname != name:\n\t\t\t\t\t\t\tarraypos = 0\n\t\t\t\t\t\t\tarrayname = name\n\t\t\t\t\t\tvalues.append(self.__values__[name[1:]][arraypos])\n\t\t\t\t\t\tarraypos += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tvalues.append(self.__values__[name])\n\t\t\t\t\n\t\t\t\tret += struct.pack(self.__endian__+sdef, *values)\n\t\treturn ret\n\t\n\tdef __getitem__(self, value):\n\t\treturn [('struct', self.__class__)] * value\n","sub_path":"tools/ps3py/Struct.py","file_name":"Struct.py","file_ext":"py","file_size_in_byte":7411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"48456239","text":"def yakusu(n):\n defyaku = []\n for i in range(1, int(n ** 0.5) + 1):\n if n % i == 0:\n defyaku.append(i)\n if i != n // i:\n defyaku.append(n // i)\n return defyaku\n\nN = int(input())\nyaku = yakusu(N)\nans = 0\nfor i in yaku:\n if i == 1: continue\n v, n = divmod(N, i - 1)\n if v == n:\n ans += (i - 1)\n\nprint(ans)","sub_path":"Python_codes/p03050/s420708738.py","file_name":"s420708738.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"194202160","text":"#A file containing all of wak's functions for tony spark\n#Might be broken into a bunch of files if the bot gets bloated\n\nimport requests\nimport random\nimport re\nimport discord\nimport asyncio\nfrom .util import JSONStore #relative import means this wak_funcs.py can only be used as part of the tony_modules package now\nimport os\nfrom pathlib import Path\nimport io\nimport json\n\nROOTPATH = os.environ['TONYROOT']\nSTORAGE_FILE = os.path.join(ROOTPATH, 'storage', 'wak_storage.json')\nCONFIG = json.load(open(os.path.join(ROOTPATH, 'storage', 'config.json')))\n\nclass WakStore(JSONStore):\n def __init__(self):\n super().__init__(STORAGE_FILE)\n if self['playables'] is None: #init playables so I don't have to keep checking if they're None\n self['playables'] = []\n \n\ndef setup(bot):\n\n storage = WakStore()\n\n @bot.command(name = \"eval\")\n async def execute(ctx, *, cmd): #if cmd arg is keyword only it lets discordpy know to pass in args as one string\n import random #import locally because I don't want eval to have global namespace\n import math\n import re\n try:\n return_val = str(eval(cmd, locals())) #only executes expressions, not statements.\n #ALSO: FOR SOME UNGODLY REASON eval WILL NOT CONSERVE locals IF YOU HAVE NESTED SCOPE\n #ie, eval(\"[math.sin(x) for x in range(10)]\", None, locals()) WILL FAIL BECAUSE WHEN YOU DO LIST COMPREHENSION YOU CREATE A NESTED SCOPE, AND FOR SOME REASON THAT NESTED SCOPE IS EMPTY. IT DOESN'T CONTAIN locals\n #BUT NOTICE THAT THIS WILL ONLY BREAK FOR LOCAL SCOPE. SO ALL YOUR VARIABLES HAVE TO BE DEFINED IN GLOBAL SCOPE\n #THAT'S WHY IM PASSING IN locals() WHERE IM SUPPOSED TO BE PASSING IN globals()!!!!!!\n #ALSO BE AWARE THAT WHEN YOU USE eval OUTSIDE OF A FUNCTION AND DON'T SPECIFY ANY SCOPE PARAMS IT'LL USE THE CORRECT LOCAL AND GLOBAL SCOPES BY DEFAULT, AND LOCAL SCOPE WILL BE EQUAL TO GLOBAL SCOPE SO IT'LL ALL WORK OUT\n #THIS MEANS THAT I ALSO COULD HAVE IMPORTED THE MODULES OUTSIDE OF THE FUNCTION AND PASSED NO PARAMS INTO eval, HOWEVER THEN ALL MY GLOBALS WOULD BE AVAILABLE IN THE CALL TO eval WHICH WOULD BE A BIT OF A MESS\n except BaseException as e:\n return_val = \"{}: {}\".format(e.__class__.__name__, e)\n if len(return_val) > 2000:\n await ctx.send(\"Sorry, the return value's too long to send\")\n else:\n await ctx.send(return_val)\n\n @bot.command()\n async def img(ctx, *args):\n print('asdf')\n query = '+'.join(args) + '&source=lnms&tbm=isch'\n url = 'https://www.google.ca/search?q=' + query\n data = requests.get(url).content.decode(errors = 'ignore')\n imgs = re.findall(r'src=\"(https://encrypted-tbn0\\.gstatic\\.com/images.+?)\"', data)\n if len(imgs) == 0:\n await ctx.send('No images found')\n else:\n embed = discord.Embed()\n embed.set_image(url = random.choice(imgs))\n await ctx.send(embed = embed)\n\n @bot.command()\n async def play(ctx, *, cmd):\n if len(cmd) <= 128:\n storage['playables'].append(cmd)\n storage.update()\n await bot.change_presence(activity = discord.Game(name = cmd))\n await ctx.send('added playable')\n else:\n await ctx.send(\"Playable wasn't added because it was > 128 chars long\")\n\n @bot.command()\n async def restart(ctx, *args):\n if ctx.channel.id == 513536262507069443:\n await ctx.send(\"Rebooting...\") #I would bot.close() but then it tries to cancel the ctx.send or something and throws an error which in turn stops the restart corutine so I ain't gonna bother\n os.system(\". ~/Python/tony_modules/pull_and_reboot.sh\")\n exit()\n else:\n await ctx.send(\"Sorry, you can only restart in the bot-testing channel of the memechat server\")\n\n @bot.command()\n async def history(ctx, *args):\n await ctx.send(\"Reading all messages in this channel (might take a while)...\")\n all_msgs = []\n async for msg in ctx.history(limit = None): #reverse = True doesn't reverse message order properly so I have reverse the order myself\n new_bytes = msg.author.display_name.encode() + b': ' + msg.content.encode() #also the reason I'm building a list is because I don't think there's any way to use an async generator as a regular generator (ie, do something like (b'\\n'.join(all_msgs) )\n all_msgs.insert(0, new_bytes) #USE insert() TO PREPEND TO LISTS EFFICIENTLY\n pseudo_file = io.BytesIO(b'\\n'.join(all_msgs)) #WOULD BE NICE TO JUST DO pseuo_file.write() INSTEAD OF all_msgs.insert BUT MESSAGES HAVE TO BE REVERSED SO THIS IS PROBABLY THE BEST I CAN DO\n message = \"Found {} messages\".format(len(all_msgs)) #len apparently constant time for lists\n await ctx.send(message, file = discord.File(pseudo_file, filename = \"dump.txt\"))\n\n async def play_random_playable():\n playables = storage['playables']\n if len(playables) > 0:\n new_game = discord.Game(name = random.choice(playables))\n await bot.change_presence(activity = new_game)\n else:\n await bot.change_presence(activity = None)\n\n @bot.command()\n async def unplay(ctx, *, cmd):\n playables = storage['playables']\n if cmd in playables:\n playables.remove(cmd)\n storage.update()\n if ctx.guild is not None and ctx.guild.me.game.name not in playables: #ctx.guild is None if in DMs\n await play_random_playable()\n await ctx.send(\"removed playable\")\n else:\n await ctx.send(\"Couldn't find playable: \" + cmd)\n\n async def background():\n print('wak background process started')\n while bot.ws is None: #wait until ws connection is made (there is a short period of time after bot.run is called during which the event loop has started but a discord websocket hasn't been established)\n await asyncio.sleep(1)\n while True:\n await play_random_playable()\n await asyncio.sleep(int((random.random()+0.2)*30*60)) #add 0.2 so minimal time isn't 0\n bot.loop.create_task(background())\n\n @bot.event(bot.on_message)\n async def tenor_react(mess):\n roll = random.randint(1, CONFIG['TENOR_CHANCE'])\n if mess.author.id != bot.user.id and roll == 1:\n endpoint = \"https://api.tenor.com/v1/search?q={search}&key={api_key}&limit=5\" #limit search to 5 gifs\n api_key = \"CNAW21Y2RSUB\"\n msg = mess.content\n msg = re.sub('[.;,!]', '', msg) #remove punctuation from msg (EVEN IF MSG IS 100% PUNCTUATION EVERYTHING WORKS, this is because ''.split(' ') will become [''] which will then search tenor for nothing, which just gets back trending gifs or something so it's fine)\n words = msg.split(' ')\n num_search_terms = len(words)\n if num_search_terms > 3: #max 3 search terms\n num_search_terms = 3\n words.sort(key = len, reverse = True) #sort words from longest to shortest\n for num_words in range(num_search_terms, 0, -1):\n search_words = words[0: num_words]\n search_term = ' '.join(search_words)\n res = requests.get(endpoint.format(search = search_term, api_key = api_key)).json()\n results = res['results']\n if len(results) > 0:\n gif = random.choice(results)\n await mess.channel.send(gif['url'])\n break\n print(\"no results for '{}'\".format(search_term))\n","sub_path":"tony_modules/wak_funcs.py","file_name":"wak_funcs.py","file_ext":"py","file_size_in_byte":7703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"631998635","text":"#!/usr/bin/env python\nfrom scipy.interpolate import interp1d\nfrom scipy.signal import savgol_filter\nfrom sense_hat import SenseHat\nfrom time import sleep, strftime, gmtime\nimport time\nimport numpy as np\nimport sys\nimport datetime\n\nsense = SenseHat()\n\nSLEEP_TIME = 1 # wait time between measurements\nUPDATE_INTERVAL = 5557 # evaluate the buffer every UPDATE_INTERVAL seconds\nBUFFER_SIZE = 6000 # maximum number of measurements stored in mem\n\n\ndef run():\n \"\"\"\n Load sensor data every second. Every minute, evaluate the buffer detecting\n changes\n \"\"\"\n count = 0\n\n # containers for sensor data\n x = []\n y = []\n z = []\n time_stamps = []\n\n while True:\n try:\n raw = sense.get_compass_raw()\n x.append(raw.get('x', None))\n y.append(raw.get('y', None))\n z.append(raw.get('z', None))\n time_stamps.append(time.time())\n count += SLEEP_TIME\n print(count)\n sleep(SLEEP_TIME) \n if count % UPDATE_INTERVAL == 0:\n sensor_buffer = np.asarray(\n [time_stamps[-BUFFER_SIZE:], x[-BUFFER_SIZE:],\n y[-BUFFER_SIZE:], z[-BUFFER_SIZE:]])\n evaluate(sensor_buffer)\n except KeyboardInterrupt:\n cur_time = strftime(\"%d-%m-%Y %H:%M:%S\", gmtime())\n total = np.asarray([time_stamps, x, y, z, temperature, humidity,\n pressure])\n np.savetxt(\"metingen/meting-{0}.csv\".format(cur_time),\n total, delimiter=\",\")\n sys.exit()\n\n\ndef evaluate(sensor_buffer):\n \"\"\"\n Parse the measurements, detect changes by checking sign switches in first\n and second order gradient.\n :param sensor_buffer:\n :return:\n \"\"\"\n # unpack the sensor readings\n timestamps, mag_x, mag_y, mag_z = sensor_buffer\n\n # reset led matrix\n sense.clear()\n x = np.arange(len(timestamps))\n xx = np.linspace(x.min(), x.max(), len(timestamps))\n # interpolate + smooth on mag_x\n itp = interp1d(x, mag_x, kind='linear')\n window_size, poly_order = 5, 3 #101, 3\n #import pdb; pdb.set_trace()\n filtered_x = savgol_filter(itp(xx), window_size, poly_order)\n\n # interpolate + smooth on mag_y\n itp = interp1d(x, mag_y, kind='linear')\n window_size, poly_order = 5, 3 # 101, 3\n filtered_y = savgol_filter(itp(xx), window_size, poly_order)\n result = np.add(filtered_x, filtered_y)\n print (result)\n\n #evaluating result array to detect norh/south\n counter = 0\n signispos = None\n for i in result:\n signispos = i > 0\n if counter == 0:\n counter += 1\n continue\n if i > 0 != signispos:\n prev = result[counter - 1] > 0\n timeconvert = datetime.datetime.fromtimestamp(timestamps[counter]).strftime('%Y-%m-%d %H:%M:%S')\n if prev and not signispos:\n #entering southern half of earth \n print(\"entered southern half of earth at {0}\".format(timeconvert))\n else:\n #entering northern half of earth\n print(\"entered northern half of earth at {0}\".format(timeconvert))\n \n counter += 1\n\n #evaluating result array to detect sun/darkness\n maxindex = result.argmax(axis=0)\n timeconvert = datetime.datetime.fromtimestamp(timestamps[maxindex]).strftime('%Y-%m-%d %H:%M:%S')\n print(\"peak sunlight was at {0}\".format(timeconvert))\n \n \nif __name__ == '__main__':\n run()\n","sub_path":"phase2/phase2.py","file_name":"phase2.py","file_ext":"py","file_size_in_byte":3537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"26013670","text":"def main():\n 'Main function'\n module = GcpModule(argument_spec=dict(state=dict(default='present', choices=['present', 'absent'], type='str'), name=dict(required=True, type='str'), extra_statements=dict(type='list', elements='str'), instance=dict(required=True, type='dict')))\n if (not module.params['scopes']):\n module.params['scopes'] = ['https://www.googleapis.com/auth/spanner.admin']\n state = module.params['state']\n fetch = fetch_resource(module, self_link(module))\n changed = False\n if fetch:\n if (state == 'present'):\n if is_different(module, fetch):\n update(module, self_link(module))\n fetch = fetch_resource(module, self_link(module))\n changed = True\n else:\n delete(module, self_link(module))\n fetch = {\n \n }\n changed = True\n elif (state == 'present'):\n fetch = create(module, collection(module))\n changed = True\n else:\n fetch = {\n \n }\n fetch.update({\n 'changed': changed,\n })\n module.exit_json(**fetch)","sub_path":"Data Set/bug-fixing-5/b20bfc104863e7b2d90c1a639ae686dbd290eb84-
-bug.py","file_name":"b20bfc104863e7b2d90c1a639ae686dbd290eb84-
-bug.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"530235456","text":"import os\nimport pandas as pd\nfrom pprint import pprint\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom parser.parser_main import ParserMain\nfrom db.db_creator import (Base,\n User,\n Gender,\n Astrology,\n Profession,\n association_table_user_gender,\n association_table_user_astrology,\n association_table_user_profession)\nfrom utillities.check_all import (check_storage, \n produce_storage, \n check_file_presence)\nfrom config import (Db, \n Folders, \n dictionary_astrology)\n\n\nclass DataBaseMain:\n \"\"\"\n class which is dedicated to make the basic tests insertion and operations for it\n \"\"\"\n def __init__(self) -> None:\n self.session = None\n self.parser_main = ParserMain()\n self.folder_path = os.path.join(Folders.folder_main, Folders.folder_storage)\n self.file_path = os.path.join(self.folder_path, Db.sqlite_name)\n self.engine = self.produce_engine_file()\n \n def check_route(self) -> bool:\n \"\"\"\n Method which is dedicated to develop check of the route of sqlite\n Input: None; all what we have created\n Output: we developed path to the database and produced everything\n \"\"\"\n if not check_storage(self.folder_path):\n produce_storage(self.folder_path)\n return False\n if not check_file_presence(self.file_path):\n return False\n return True\n\n def produce_engine_file(self) -> object:\n \"\"\"\n Method which is dedicated to create engine from the file\n Input: None\n Output: we created engine files\n \"\"\"\n self.check_route()\n return create_engine(f\"sqlite:///{self.file_path}\", echo=Db.echo)\n \n def check_database(self) -> bool:\n \"\"\"\n Method which is dedicated to develop checking the database\n Input: \n Output: boolean value which signify that database is developed\n \"\"\"\n check_route = self.check_route()\n if not check_route:\n print('Base is completely new')\n self.develop_database()\n return bool(self.session)\n\n def develop_database(self):\n \"\"\"\n Method which is dedicated to produce the database it that cases\n Input: None\n Output: we created values from the db_creator file and produced the database\n \"\"\"\n try:\n Base.metadata.create_all(self.engine)\n except Exception as e:\n print(f\"We faced problems with Base: {e}\")\n print('------------------------------------------')\n\n def return_session(self) -> object:\n \"\"\"\n Method which is dedicated to develop session\n Input: None\n Output: We created session of the values\n \"\"\"\n try:\n Session = sessionmaker(bind=self.engine)\n return Session()\n except Exception as e:\n print(e)\n print('-----------------------------')\n\n def close_session(self) -> None:\n \"\"\"\n Method which is dedicated to close session of the sql alchemy\n Input: None values\n Output: we close this session\n \"\"\"\n if self.session:\n self.session.close()\n\n def make_mass_insertion(self, value_list:list) -> None:\n \"\"\"\n Method for inserting objects at one iterations\n Input: value_list = list of the selected objects\n Output: we commited to the database\n \"\"\"\n if value_list:\n self.session.add_all(value_list)\n self.session.commit()\n\n def make_basic_insertion(self, value_list:list) -> None:\n \"\"\"\n Method which is dedicated to insert basic without add_all command\n Input: value_list = list of the commands\n Output: we inserted values of it\n \"\"\"\n if value_list:\n for f in value_list:\n self.session.execute(f)\n self.session.commit()\n\n def produce_insertion_model_gender(self, list_gender:list, list_gender_id:list) -> None:\n \"\"\"\n Method which is dedicated to make insertion of the selected values\n Input: list_gender = set of the values to insert genders\n list_gender_id = list of lists of the values which is innerconnected to it\n Output: we developed insertion values which developed \n \"\"\"\n if not self.check_database():\n self.session = self.return_session()\n gender_used = [f[0] for f in self.session.query(Gender.id).all()]\n list_gender = [[id, name] for id, name in list_gender if id not in gender_used]\n gender_specified = [f[0] for f in self.session.query(User.id).filter(\n association_table_user_gender.c.id_user== User.id).all()]\n list_gender_id = [[id_user, id_gender] for id_user, id_gender \n in list_gender_id if id_user not in gender_specified]\n objects = [Gender(id=id, name=name) for id, name in list_gender]\n self.make_mass_insertion(objects)\n objects = [association_table_user_gender.insert().values(id_user=id_user, id_gender=id_gender)\n for id_user, id_gender in list_gender_id]\n self.make_basic_insertion(objects)\n self.close_session()\n print(f'Finished inserting for {list_gender_id[0][0]}-{list_gender_id[-1][0]}')\n print('==================================================')\n\n def produce_insertion(self, *args:set) -> None:\n \"\"\"\n Method which is dedicated to insert all values to the database\n Input: args = set with used values of the created database:\n list_astrology = list with the astrology table\n list_profession = list with the profession table\n list_users = list with the users table\n list_id_astrology = list of the connecting user/astrology\n list_id_professions = list of the connecting id/professions\n Output: we inserted values to the database if it is necessary\n \"\"\"\n list_astrology, list_profession, list_users, \\\n list_id_astrology, list_id_professions = args\n if not self.check_database():\n self.session = self.return_session()\n \n list_id_astrologies = [[list_users[i-1][1], list_ids] for i, list_ids in list_id_astrology]\n list_id_profession = [[list_users[i-1][1], list_ids] for i, list_ids in list_id_professions]\n \n value_links_prev = [f[0] for f in self.session.query(User.link).all()]\n list_users = [[i, link, name, link_image, description, date_begin, date_end]\n for i, link, name, link_image, description, date_begin, date_end \n in list_users if link not in value_links_prev]\n \n list_id_astrologies = [[list_user, list_ids] for list_user, list_ids in list_id_astrologies\n if list_user in [f[1] for f in list_users]]\n \n list_id_profession = [[list_user, list_ids] for list_user, list_ids in list_id_professions\n if list_user in [f[1] for f in list_users]]\n \n objects = [User(name=name, link=link, description=description, \n date_birth=date_birth, date_death=date_death, link_image=link_image)\n for _, link, name, link_image, description, date_birth, date_death in list_users]\n self.make_mass_insertion(objects)\n \n list_astrology = [[i, name, date_begin, date_end] for i, name, date_begin, date_end \n in list_astrology if name \n not in [f[0] for f in self.session.query(Astrology.name).all()]] \n objects = [Astrology(id=i, name=name, date_begin=date_begin, date_end=date_end)\n for i, name, date_begin, date_end in list_astrology]\n self.make_mass_insertion(objects)\n\n list_profession_prev = [f[0] for f in self.session.query(Profession.name).all()]\n list_profession = [[i, name] for i, name in list_profession if name not in \n list_profession_prev]\n objects = [Profession(name=name) for _, name in list_profession]\n self.make_mass_insertion(objects)\n\n ids_users = [f[0] for f in self.session.query(User.id).filter(\n User.link.in_([f[0] for f in list_id_astrologies])).all()]\n list_id_astrologies = [[id_user, id_astrology] for \n id_user, (_, id_astrology) in zip(ids_users, list_id_astrologies)]\n objects = [association_table_user_astrology.insert().values(id_user=id_user, id_astrology=int(id_astrology))\n for id_user, id_astrology in list_id_astrologies]\n self.make_basic_insertion(objects)\n \n ids_users = [f[0] for f in self.session.query(User.id).filter(\n User.link.in_(f[0] for f in list_id_profession)).all()]\n list_id_professions = []\n for (_, id_professions), ids_user in zip(list_id_profession, ids_users):\n for id_profession in id_professions:\n list_id_professions.append([ids_user, id_profession])\n objects = [association_table_user_profession.insert().values(id_user=ids_user, id_profession=id_profession)\n for ids_user, id_profession in list_id_professions]\n self.make_basic_insertion(objects)\n self.close_session()\n \n def get_values_user(self, value_id:int) -> set:\n \"\"\"\n Method which is dedicated usage of the values\n Input: value_id = id of the user \n Output: values for the development \n \"\"\"\n if not self.check_database():\n self.session = self.return_session()\n table_user = self.session.query(User.id, User.name, User.link, \n User.link_image, User.date_birth, User.date_death, \n User.description).filter(User.id==value_id).first()\n \n return table_user\n\n def produce_basic_values_insertion(self, value_refind:bool=False) -> None:\n \"\"\"\n Method which is dedicated to transform basic values of the insertion\n Input: value_refind = boolean value which signify that \n we need to refill the csv file from the Imdb\n Output: we prepared all possible values and removed them from the \n \"\"\"\n if value_refind:\n self.parser_main.produce_dataframe()\n if not os.path.exists(self.parser_main.dataframe_storage):\n self.parser_main.produce_dataframe()\n df_value = pd.read_csv(self.parser_main.dataframe_storage)\n df_value = self.parser_main.produce_dataframe_filtration(df_value)\n df_value = pd.read_csv(self.parser_main.dataframe_storage)\n list_astrology = [[index + 1, f.get('name'), \n f.get('begin'), f.get('end')]\n for index, f in enumerate(dictionary_astrology)] \n list_profession = pd.read_csv(self.parser_main.dataframe_professions).values.tolist()\n list_users = df_value[['id', 'url', 'name', 'image', \n 'description', 'birthdate', 'deathdate']].values.tolist()\n list_id_astrology = [[value_id, value_astrology] for value_id, value_astrology \n in zip(df_value['id'].values, \n df_value['astrology_index'].values)]\n list_id_astrology = [[value_id, value_astrology] for value_id, value_astrology \n in list_id_astrology if value_astrology != 0]\n list_id_professions = [[value_id, [int(i) for i in value_id_profession.split('|')]] \n for value_id, value_id_profession in zip(df_value['id'].values, \n df_value['jobs_indexes'].values) \n if isinstance(value_id_profession, str)]\n self.produce_insertion(list_astrology, list_profession, \n list_users, list_id_astrology, list_id_professions)\n self.close_session()","sub_path":"db/db_main.py","file_name":"db_main.py","file_ext":"py","file_size_in_byte":12483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"274435726","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 20 11:18:15 2019\n\n@author: Thomas Verduyn\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 8 16:43:02 2019\n\n@author: Thomas Verduyn\n\"\"\"\nimport sys\nsys.path.append('../../../../../')\nimport numpy as np\nfrom A22DSE.Models.CostModel.Current.ProdCost import *\nfrom A22DSE.Models.CostModel.Current.RoskamFuncList import \\\nWampr, MHRManProg, MHRToolProg, MHRtoolr, MHRmanr, cmat, eas, veas\nfrom A22DSE.Parameters.Par_Class_Diff_Configs import Conv, ISA_model\nfrom A22DSE.Parameters.Par_Class_All import Aircraft\n\n\n #MTOW in lbs\n #Vc in m/s\n #Cer in 1990 USD\ndef crdtemil(Aircraft, ISA_module, Cer):\n #INPUT:Maximum take-off weight, cruise velocity, engine costs\n #OUTPUT:RDTE Costs\n #Description: Research, Development, Test and Evaluation costs\n \n# AnFP = Aircraft.ParAnFP\n par = Aircraft.ParCostLst\n Convers = Aircraft.ConversTool # for ease of re-engineering code\n Struct = Aircraft.ParStruc\n FP = Aircraft.ParAnFP\n \n# CEF=par.get('CEF19') #Inflation between 2019-1985\n rer=par.rer*par.CEF8919 #Engineering dollar rate \n rmr=par.rmr*par.CEF8919 #Manufacturing dollar rate\n rtr=par.rtr*par.CEF8919 #Tooling dollar rate\n CEF7019 = par.CEF7019\n Nrdte=par.Nrdte *10/6 #Number of test ac, is between 6-20\n Fdiff=par.Fdiff #Difficulty level of design 1-1.5-2\n Fcad=par.Fcad #Cad model factor\n# CEF=parCEF19')/parCEF89') #Cost expansion factor\n Nst=par.Nst #Static test ac nr\n Fobs=par.Fobs #Factor for observable characteristics\n Fpror=par.Fpror #Profit factor\n Ffinr=par.Ffinr #Finance costs\n Ftsf=par.Ftsf #Test, simulation, facility costs\n Fmat=par.Fmat #Correction factor for type of material\n cavionics= par.Cavionics #costs of ac?\n cer = Cer #Costs per engine at 2019\n ne = Struct.N_engines #number of engines\n Nrr= par.Nrr #RDTE production rate\n Vmax=veas(((FP.Mdd-0.03)*np.sqrt(288.15*1.4*287)),ISA_module,Aircraft) #keas\n kg2lbs = 1/Convers.lbs2kg\n# ms2kts = 1/parkts2ms') \n MTOW = kg2lbs * Struct.MTOW # change from kg to lbs\n \n cear=(cer*ne+cavionics)*(Nrdte-Nst) #Costs of engine and avionics\n cmanr=MHRmanr(MTOW,Vmax,Nrdte,Fdiff)*rmr #Labour costs\n cmatr=cmat(MTOW,Vmax,Nrdte,Fmat, CEF7019) #Material costs\n ctoolr=MHRtoolr(MTOW,Vmax,Nrdte,Nrr,Fdiff)*rtr #Tooling costs\n cqcr=0.13*cmanr #Quality control costs\n \n #Engineering manhours\n mhraedr=0.0396*Wampr(MTOW)**0.791*Vmax**1.526*Nrdte**0.183*Fdiff*Fcad\n #Aerframe engineering and design costs\n caedr=mhraedr*rer \n #Development, support and testing costs\n cdstr=0.008325*Wampr(MTOW)**0.873*Vmax**1.89*Nrdte**0.346*Fdiff\\\n *par.CEF7019\n #Flight test airplane costs\n cftar=cear+cmanr+cmatr+ctoolr+cqcr\n# print (np.array([cear,cmanr,cmatr,ctoolr,cqcr,cftar])*10**(-6))\n #Flight test operation costs\n cftor=0.001244*Wampr(MTOW)**1.160*Vmax**1.371*(Nrdte-Nst)**1.281*Fdiff\\\n *Fobs*par.CEF7019\n #Research, development, test and evaluation costs\n crdte=(caedr+cdstr+cftar+cftor)/(1-Ftsf-Fpror-Ffinr)\n return crdte\n# return np.array([caedr, cdstr, cftar, cftor, crdte])\n#print (crdtemil(Conv,ISA_model,Conv.ParCostLst.Cengine))\n\n#print (crdte(150000,210,3*10**7)*10**(-6))\n#print (crdte(150000,210,3*10**7)*100/crdte(150000,210,3*10**7)[-1])\n\n","sub_path":"A22DSE/Models/CostModel/Current/RNDmilitary.py","file_name":"RNDmilitary.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"361338827","text":"import os\nfrom read_counties import ReadCounties\nfrom read_states import ReadStates\nfrom read_US import ReadUS\n\ndataType = input('Please type in the dataset you want to see (Counties, States, or US):')\n\n\npath = os.getcwd()\npath_data = path + '/data'\n\npath_counties = path_data + '/us-counties.csv'\npath_states = path_data + '/us-states.csv'\npath_US = path_data + '/us.csv'\n\ndef print_list(listname):\n for row in listname:\n print(row,end = '\\n')\n\nif dataType == 'Counties':\n data = ReadCounties(path_counties)\n print_list(data.read_all(path_counties))\nelif dataType == 'States':\n data = ReadStates(path_states)\n print_list(data.read_all(path_states))\nelif dataType == 'US':\n data = ReadUS(path_US)\n print_list(data.read_all(path_US))\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"243968464","text":"# -*- coding: utf-8 -*- #文件也为UTF-8\nimport os\nimport os.path\nfrom PIL import Image, ImageDraw, ImageFont\nrootdir='.'\nfor parent,dirnames,filenames in os.walk(rootdir):\n for dirname in dirnames: #输出文件夹信息\n print (\"dirname is\" + dirname)\n for filename in filenames:\n if \"png\" in filename:\n if \"num\" in filename:\n continue\n for i in range(0,10):\n img = Image.open(filename)\n draw = ImageDraw.Draw(img)\n if \"red\" in filename:\n draw.text((3,-1), str(i), fill = 'green')\n else:\n draw.text((3,-1), str(i), fill = 'red')\n newfilename=filename[:-4]\n img.save(newfilename + \"num\" + str(i) + '.png')\n i = i+1\n \n","sub_path":"main/html/pointico/10pix/生成数字.py","file_name":"生成数字.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"2913025","text":"import os\nimport sys\nimport mock\nimport unittest\nimport inaugurator\nimport rackattack.common.inaugurate\nfrom rackattack.common import globallock\n\n\nclass Test(unittest.TestCase):\n def setUp(self):\n inaugurator.server.rabbitmqwrapper.RabbitMQWrapper = mock.Mock()\n inaugurator.server.server.Server = mock.Mock()\n self.tested = rackattack.common.inaugurate.Inaugurate(None)\n self.checkIn = mock.Mock()\n self.done = mock.Mock()\n self.progress = mock.Mock()\n self.failed = mock.Mock()\n\n def test_register(self):\n with globallock.lock():\n self.tested.register('awesome-server', self.checkIn, self.done, self.progress, self.failed)\n self.tested._server.listenOnID.assert_called_once_with('awesome-server')\n self.assertEquals(self.checkIn.call_count, 0)\n self.assertEquals(self.done.call_count, 0)\n self.assertEquals(self.progress.call_count, 0)\n self.tested._checkIn('awesome-server')\n self.tested._checkIn('non-awesome-server')\n self.checkIn.assert_called_once_with()\n self.tested._done('awesome-server')\n self.tested._done('non-awesome-server')\n self.done.assert_called_once_with()\n self.tested._progress('awesome-server', 'some progress')\n self.tested._progress('non-awesome-server', 'some other progress')\n self.progress.assert_called_once_with('some progress')\n with globallock.lock():\n self.assertRaises(AssertionError, self.tested.register, 'awesome-server', None, None, None,\n None)\n self.done.assert_called_once_with()\n self.progress.assert_called_once_with('some progress')\n self.tested._server.listenOnID.assert_called_once_with('awesome-server')\n\n def test_unregister(self):\n with globallock.lock():\n self.tested.register('awesome-server', self.checkIn, self.done, self.progress, self.failed)\n self.assertEquals(self.checkIn.call_count, 0)\n self.tested._checkIn('awesome-server')\n self.checkIn.assert_called_once_with()\n with globallock.lock():\n self.tested.unregister('awesome-server')\n self.tested._server.stopListeningOnID.assert_called_once_with('awesome-server')\n self.checkIn.assert_called_once_with()\n self.tested._checkIn('awesome-server')\n self.checkIn.assert_called_once_with()\n with globallock.lock():\n self.assertRaises(AssertionError, self.tested.unregister, 'awesome-server')\n self.tested._server.stopListeningOnID.assert_called_once_with('awesome-server')\n\n def test_provideLabel(self):\n self.tested.provideLabel('awesome-server', 'awesome-label')\n self.tested._server.provideLabel.assert_called_once_with(id='awesome-server',\n label='awesome-label')\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"rackattack/common/tests/test_inaugurate.py","file_name":"test_inaugurate.py","file_ext":"py","file_size_in_byte":2946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"486994794","text":"import json\r\nfrom urllib.request import urlopen\r\nwith urlopen(\"https://api.covid19india.org/data.json\") as response:\r\n source=response.read()\r\n# print(source)\r\n\r\ndata=json.loads(source)\r\n# print(json.dumps(data, indent=2))\r\n# print(len(data['raw_data']))\r\nfilename=\"State Level Latest.csv\"\r\nf=open(filename,\"w\")\r\nheaders=\" State ID ,Last Updated Time,Total Confirmed Cases,Total Deceased Cases,Total Recovered Cases,Total Active Cases,Delta Confirmed Cases,Delta Deceased Cases,Delta Recovered Cases\\n\"\r\nf.write(headers)\r\n\r\nfor item in data['statewise']:\r\n state_id=item['statecode']\r\n if state_id !='TT':\r\n lastupdatedtime=item['lastupdatedtime']\r\n total_confirmed=item['confirmed']\r\n total_deceased=item['deaths']\r\n total_recovered=item['recovered']\r\n active=item['active']\r\n delta_cofirmed=item['deltaconfirmed']\r\n delta_deceased=item['deltadeaths']\r\n delta_recovered=item['deltarecovered']\r\n\r\n f.write(state_id.replace(\",\",\"\")+\",\"+lastupdatedtime.replace(\",\",\"\") +\",\"+ total_confirmed.replace(\",\",\"\") +\",\"+ total_deceased.replace(\",\",\"\")+\",\"+total_recovered.replace(\",\",\"\")+\",\"+ active.replace(\",\",\"\") +\",\"+ delta_cofirmed.replace(\",\",\"\")+\",\"+ delta_deceased.replace(\",\",\"\")+\",\"+delta_recovered.replace(\",\",\"\")+\"\\n\")\r\n # print(patientnumber, statecode, statepatientnumber, nationality,\r\n # detectedcity, dateannounced, age, gender, currentstatus)\r\nf.close()\r\n","sub_path":"statelevel.py","file_name":"statelevel.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"471283058","text":"#!/usr/bin/env python\n\n\nfrom collections import Iterable\n\n# TODO: For such simple task - it's TOOO MUCH of the code here.\n# Always strive for least amount of code.\n\ndef cycle(sequence):\n \"\"\"Function checks the incoming value\n Args:\n sequence (any): The sequence to generate item, from start to last value.\n\n Return:\n item: The next 'item' from sequence. When the last value is reached,\n the cycle is repeated from start.\n\n Examples:\n Assign a function to a variable with a sequence. Pass the variable to \"next\".\n >>> cycle = cycle('ad')\n >>> next(cycle)\n 'a'\n >>> next(cycle)\n 'd'\n >>> next(cycle)\n 'a'\n\n\n \"\"\"\n # check 'sequence' is Iterable and not empty\n if sequence and isinstance(sequence, Iterable):\n return _normal_cycle(sequence)\n # check 'sequence' is not empty but not Iterable\n if sequence and not isinstance(sequence, Iterable):\n return _integers(sequence)\n # check 'sequence' is Iterable and empty\n if not sequence and isinstance(sequence, Iterable):\n return _empty_cycle()\n\n\ndef _normal_cycle(sequence):\n \"\"\"Function make an iterator returning elements from the iterable and saving a copy of each.\n Args:\n sequence (any): The sequence to generate item, from start to last value.\n\n Yields:\n item: The next 'item' from sequence. When the last value is reached,\n the cycle is repeated from start.\n\n Examples:\n Assign a function to a variable with a sequence. Pass the variable to \"next\".\n >>> cycle = cycle('ad')\n >>> next(cycle)\n 'a'\n >>> next(cycle)\n 'd'\n >>> next(cycle)\n 'a'\n \"\"\"\n\n temp_iterable = []\n for element in sequence:\n yield element\n temp_iterable.append(element)\n while temp_iterable:\n for element in temp_iterable:\n yield element\n\n\ndef _empty_cycle():\n \"\"\"Function make infinity `None` generator\"\"\"\n while True:\n yield None\n\n\ndef _integers(integer):\n \"\"\"An infinite number generator, from 0 to the maximum limit put in the function\n\n Args:\n integer (int): The upper limit of the range to generate, from 0 to `integers`.\n\n Yields:\n int: The next number in the range of 0 to integers + 1. When the maximum value is reached,\n the cycle is repeated from 0.\n Raises:\n TypeError: Raises an exception.\n\n Examples:\n Assign a function to a variable with a number. Pass the variable to \"next\".\n >>> cycle = _integers(1)\n >>> next(cycle)\n 0\n >>> next(cycle)\n 1\n >>> next(cycle)\n 0\n \"\"\"\n\n if integer > 0:\n integer += 1\n while True:\n for x in range(integer):\n yield x\n\n if integer < 0:\n integer -= 1\n while True:\n for x in range(0, integer, -1):\n yield x\n\n if integer == 0:\n while True:\n yield 0\n","sub_path":"day5/tmp/cycle.py","file_name":"cycle.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"502056312","text":"from django.http import HttpResponseRedirect,HttpResponseForbidden\nfrom django.shortcuts import render_to_response,get_object_or_404\nfrom django.contrib.auth.decorators import login_required \n# from cab.forms import AddSnippetForm\nfrom cab.models import Snippet\nfrom django.forms import ModelForm\nfrom django.core.context_processors import csrf\n# from django.views.decorators.csrf import csrf_exempt\nfrom django.template import RequestContext\n\nclass SnippetForm(ModelForm):\n\tclass Meta:\n\t\tmodel = Snippet\n\t\texclude = ['author']\n\n# @csrf_exempt\ndef add_snippet(request):\n\tif request.method == 'POST':\n\t\tform = SnippetForm(data=request.POST)\n\t\tif form.is_valid():\n\t\t\tnew_snippet = form.save(commit=False)\n\t\t\tnew_snippet.author = request.user\n\t\t\tnew_snippet.save()\n\t\t\treturn HttpResponseRedirect(new_snippet.get_absolute_url())\n\telse:\n\t\tform = SnippetForm()\n\treturn render_to_response('cab/snippet_form.html',\n\t\t\t\t\t\t\t\t{'form':form,'add':True},context_instance=RequestContext(request,processors=[csrf]))\n\nadd_snippet = login_required(add_snippet)\n\n\ndef edit_snippet(request,snippet_id):\n\tsnippet = get_object_or_404(Snippet,pk=snippet_id)\n\tif request.user.id != snippet.author.id:\n\t\treturn HttpResponseForbidden()\n\tif request.method == 'POST':\n\t\tform = SnippetForm(instance=snippet,data=request.POST)\n\t\tif form.is_valid():\n\t\t\tsnippet = form.save()\n\t\t\treturn HttpResponseRedirect(snippet.get_absolute_url())\n\telse:\n\t\tform = SnippetForm(instance=snippet)\n\treturn render_to_response('cab/snippet_form.html',\n\t\t\t\t\t\t\t\t{'form':form,'add':False},context_instance=RequestContext(request,processors=[csrf]))\nedit_snippet = login_required(edit_snippet)\n\n\n\n\n\n\n","sub_path":"cab/views/snippets.py","file_name":"snippets.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"493586093","text":"# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nimport calendar\nfrom datetime import date, datetime\nfrom dateutil.relativedelta import relativedelta\nfrom datetime import timedelta \n\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError, ValidationError\nfrom odoo.tools import float_compare, float_is_zero\n\n\nclass AccountAssetCategory(models.Model):\n\t_name = 'account.asset.category'\n\t_description = 'Asset category'\n\n\tactive = fields.Boolean(default=True)\n\tname = fields.Char(required=True, index=True, string=\"Categoria de Activo\")\n\taccount_analytic_id = fields.Many2one('account.analytic.account', string='Cuenta Analitica')\n\tanalytic_tag_ids = fields.Many2many('account.analytic.tag', string='Etiqueta Analitca')\n\taccount_asset_id = fields.Many2one('account.account', string='Cuenta de Activo', required=True, domain=[('internal_type','=','other'), ('deprecated', '=', False)], help=\"Account used to record the purchase of the asset at its original price.\")\n\taccount_depreciation_id = fields.Many2one('account.account', string='Cuenta de Depreciacion', required=True, domain=[('internal_type','=','other'), ('deprecated', '=', False)], help=\"Account used in the depreciation entries, to decrease the asset value.\")\n\taccount_depreciation_expense_id = fields.Many2one('account.account', string='Cuenta de Gasto Depreciacion', required=True, domain=[('internal_type','=','other'), ('deprecated', '=', False)], help=\"Account used in the periodical entries, to record a part of the asset as expense.\")\n\taccount_retire_id = fields.Many2one('account.account', string='Cuenta de Retiro', domain=[('internal_type','=','other'), ('deprecated', '=', False)])\n\tjournal_id = fields.Many2one('account.journal', string='Diario', required=True)\n\tcompany_id = fields.Many2one('res.company', string=u'Compañia', required=True, default=lambda self: self.env['res.company']._company_default_get('account.asset.category'))\n\tmethod = fields.Selection([('linear', 'Linear'), ('degressive', 'Degressive')], string='Metodo de Calculo', required=True, default='linear')\n\tmethod_number = fields.Integer(string='Numero de Entradas', default=5, help=\"The number of depreciations needed to depreciate your asset\")\n\tmethod_period = fields.Integer(string='Una Entrada Cada', default=1, help=\"State here the time between 2 depreciations, in months\", required=True)\n\tmethod_progress_factor = fields.Float('Degressive Factor', default=0.3)\n\tmethod_time = fields.Selection([('number', 'Numero de Entradas'), ('end', 'Ultimo Dia')], string='Tiempo Basado En', required=True, default='number')\n\tmethod_end = fields.Date('Ending date')\n\tprorata = fields.Boolean(string='Prorata Temporis', help='Indicates that the first depreciation entry for this asset have to be done from the purchase date instead of the first of January')\n\topen_asset = fields.Boolean(string='Auto-Confirmar Activos', help=\"Check this if you want to automatically confirm the assets of this category when created by invoices.\")\n\tgroup_entries = fields.Boolean(string='Agrupar Entradas de Diario', help=\"Check this if you want to group the generated entries by categories.\")\n\ttype = fields.Selection([('sale', 'Sale: Revenue Recognition'), ('purchase', 'Purchase: Asset')], required=True, index=True, default='purchase')\n\tdate_first_depreciation = fields.Selection([\n\t\t('last_day_period', 'Primer Dia del Mes Siguiente'),\n\t\t('manual', 'Manual')],\n\t\tstring=u'Inicio de Depreciacion', default='manual', required=True)\n\tdepreciation_rate = fields.Float(string=u'Tasa de Depreciación')\n\n\t@api.onchange('account_asset_id')\n\tdef onchange_account_asset(self):\n\t\tif self.type == \"purchase\":\n\t\t\tself.account_depreciation_id = self.account_asset_id\n\t\telif self.type == \"sale\":\n\t\t\tself.account_depreciation_expense_id = self.account_asset_id\n\n\t@api.onchange('depreciation_rate')\n\tdef onchange_depreciation_rate(self):\n\t\tif self.depreciation_rate and self.depreciation_rate != 0:\n\t\t\tself.method_number = (100/(self.depreciation_rate))*12\n\n\t@api.onchange('type')\n\tdef onchange_type(self):\n\t\tif self.type == 'sale':\n\t\t\tself.prorata = True\n\t\t\tself.method_period = 1\n\t\telse:\n\t\t\tself.method_period = 12\n\n\t@api.onchange('method_time')\n\tdef _onchange_method_time(self):\n\t\tif self.method_time != 'number':\n\t\t\tself.prorata = False\n\n\nclass AccountAssetAsset(models.Model):\n\t_name = 'account.asset.asset'\n\t_description = 'Asset/Revenue Recognition'\n\t_inherit = ['mail.thread']\n\n\tentry_count = fields.Integer(compute='_entry_count', string='# Asset Entries')\n\tname = fields.Char(string='Activo', required=True, readonly=True, states={'draft': [('readonly', False)]})\n\tcode = fields.Char(string='Codigo', size=32, readonly=True, states={'draft': [('readonly', False)]})\n\tvalue = fields.Float(string='Valor de Compra', required=True, readonly=True, digits=0, states={'draft': [('readonly', False)]})\n\tcurrency_id = fields.Many2one('res.currency', string='Moneda', required=True, readonly=True, states={'draft': [('readonly', False)]},\n\t\tdefault=lambda self: self.env.user.company_id.currency_id.id)\n\tcompany_id = fields.Many2one('res.company', string=u'Compañía', required=True, readonly=True, states={'draft': [('readonly', False)]},\n\t\tdefault=lambda self: self.env['res.company']._company_default_get('account.asset.asset'))\n\tnote = fields.Text()\n\tcategory_id = fields.Many2one('account.asset.category', string='Categoria de Activo', required=True, change_default=True, readonly=True, states={'draft': [('readonly', False)]})\n\tparent_id = fields.Many2one('account.asset.asset', string='Padre del activo', change_default=True, readonly=True, states={'draft': [('readonly', False)]})\n\tdate = fields.Date(string='Fecha de Compra', required=True, default=fields.Date.context_today)\n\tstate = fields.Selection([('draft', 'Borrador'), ('open', 'Ejecutando'), ('unsubscribe', 'De Baja'), ('close', 'Cerrado')], 'Estado', required=True, copy=False, default='draft')\n\tactive = fields.Boolean(default=True)\n\tpartner_id = fields.Many2one('res.partner', string='Proveedor', readonly=True, states={'draft': [('readonly', False)]})\n\tmethod = fields.Selection([('linear', 'Linear'), ('degressive', 'Degressive')], string='Metodo de Calcula', required=True, readonly=True, states={'draft': [('readonly', False)]}, default='linear')\n\tmethod_number = fields.Integer(string='Numero de Depreciaciones', readonly=True, states={'draft': [('readonly', False)]}, default=5, help=\"The number of depreciations needed to depreciate your asset\")\n\tmethod_period = fields.Integer(string='Numero de Meses en Cada Periodo', required=True, readonly=True, default=12, states={'draft': [('readonly', False)]})\n\tmethod_end = fields.Date(string='Ending Date', readonly=True, states={'draft': [('readonly', False)]})\n\tmethod_progress_factor = fields.Float(string='Degressive Factor', readonly=True, default=0.3, states={'draft': [('readonly', False)]})\n\tvalue_residual = fields.Float(compute='_amount_residual', method=True, digits=0, string='Valor Residual')\n\tmethod_time = fields.Selection([('number', 'Number of Entries'), ('end', 'Ending Date')], string='Time Method', required=True, readonly=True, default='number', states={'draft': [('readonly', False)]},\n\t\thelp=\"Choose the method to use to compute the dates and number of entries.\\n\"\n\t\t\t \" * Number of Entries: Fix the number of entries and the time between 2 depreciations.\\n\"\n\t\t\t \" * Ending Date: Choose the time between 2 depreciations and the date the depreciations won't go beyond.\")\n\tprorata = fields.Boolean(string='Prorata Temporis', readonly=True, states={'draft': [('readonly', False)]},\n\t\thelp='Indicates that the first depreciation entry for this asset have to be done from the asset date (purchase date) instead of the first January / Start date of fiscal year',default=False)\n\tdepreciation_line_ids = fields.One2many('account.asset.depreciation.line', 'asset_id', string='Depreciation Lines', readonly=True, states={'draft': [('readonly', False)], 'open': [('readonly', False)]})\n\tsalvage_value = fields.Float(string=u'Valor de Recuperación', digits=0, readonly=True, states={'draft': [('readonly', False)]},\n\t\thelp=\"It is the amount you plan to have that you cannot depreciate.\")\n\tinvoice_id = fields.Many2one('account.move', string='Factura', states={'draft': [('readonly', False)]}, copy=False)\n\ttype = fields.Selection(related=\"category_id.type\", string='Type', required=True)\n\ttipo = fields.Selection([('adquisicion', 'Adquisiciones'), ('mejoras', 'Mejoras'),('otros','Otros Ajustes')], default='adquisicion')\n\taccount_analytic_id = fields.Many2one('account.analytic.account', string=u'Cuenta Analítica')\n\tanalytic_tag_ids = fields.Many2many('account.analytic.tag', string=u'Etiqueta Analítica')\n\tvalor_retiro = fields.Float(string='Valor de Retiro',digits=(64,2))\n\tdepreciacion_retiro = fields.Float(string='Depreciacion del Retiro',digits=(64,2))\n\treferencia = fields.Char(string='Referencia', readonly=True, states={'draft': [('readonly', False)]},size=32)\n\tautorizacion_depreciacion = fields.Char(string=u'Autorización para la Depreciación',size=100)\n\tnro_comprobante = fields.Char(string='Nro Comprobante')\n\tf_baja = fields.Date(string='Fecha de Baja')\n\tean13 = fields.Char(string=u'Código EAN13')\n\n\tdate_first_depreciation = fields.Selection([\n\t\t('last_day_period', 'Primer dia del mes siguiente'),\n\t\t('manual', 'Manual')],\n\t\tstring=u'Inicio de Depreciación', default='manual',\n\t\treadonly=True, states={'draft': [('readonly', False)]}, required=True)\n\tfirst_depreciation_manual_date = fields.Date(\n\t\tstring=u'Fecha Inicio de Depreciación',\n\t\thelp='Note that this date does not alter the computation of the first journal entry in case of prorata temporis assets. It simply changes its accounting date'\n\t)\n\n\t#PARAMETERS IT\n\tlocation = fields.Char(string=u'Ubicación')\n\tbrand = fields.Char(string=u'Marca')\n\tmodel = fields.Char(string=u'Modelo')\n\tplaque = fields.Char(string=u'Serie y/o Placa')\n\tcontract_date = fields.Date(string='Fecha de Contrato')\n\tcontract_number = fields.Char(string=u'Nro. Contrato Arrendamiento Financiero',size=50)\n\tdate_start_contract = fields.Date(string=u'Fecha de Inicio del Contrato Arrendamiento')\n\tfees_number = fields.Integer(string=u'Nro. Cuotas Pactadas')\n\tamount_total_contract = fields.Float(string=u'Monto Total Contrato De Arrendamiento',digits=(12,2))\n\tonly_format_74 = fields.Boolean(string='Mostrar solo en Formato 7.4',default=False)\n\tyears_depreciations = fields.Integer(string=u'Años de Depreciación')\n\tdepreciation_rate = fields.Float(string=u'Tasa de Depreciación')\n\tdepreciation_authorization = fields.Char(string=u'Autorización de Depreciación',size=50)\n\n\t@api.onchange('method_number','method_period')\n\tdef change_method_number(self):\n\t\tyears_dep = 0\n\t\tif self.method_number and self.method_period:\n\t\t\tyears_dep = (self.method_number*self.method_period)/12\n\t\t\tself.years_depreciations = years_dep\n\n\t@api.onchange('years_depreciations')\n\tdef change_years_depreciations(self):\n\t\tdep_rate = 0\n\t\tif self.years_depreciations:\n\t\t\tdep_rate = 100/(self.years_depreciations)\n\t\t\tself.depreciation_rate = dep_rate\n\n\tdef unlink(self):\n\t\tfor asset in self:\n\t\t\tif asset.state in ['open', 'close']:\n\t\t\t\traise UserError(_('You cannot delete a document that is in %s state.') % (asset.state,))\n\t\t\tfor depreciation_line in asset.depreciation_line_ids:\n\t\t\t\tif depreciation_line.move_id:\n\t\t\t\t\traise UserError(_('You cannot delete a document that contains posted entries.'))\n\t\treturn super(AccountAssetAsset, self).unlink()\n\n\t@api.model\n\tdef _cron_generate_entries(self):\n\t\tfirst_day_of_month = date.today().replace(day=1)\n\t\tlast_day_of_month = date.today().replace(day=calendar.monthrange(date.today().year,date.today().month)[1])\n\t\tself.compute_generated_entries(first_day_of_month,last_day_of_month)\n\n\tdef set_move_check(self):\n\t\tsql_check = \"\"\"select move_check from account_asset_depreciation_line where asset_id = %s and move_id is not null\"\"\" % (str(self.id))\n\t\tself.env.cr.execute(sql_check)\n\t\tres = self.env.cr.dictfetchall()\n\t\tif len(res) > 0:\n\t\t\traise UserError(\"Debe eliminar primero los asientos generados\")\n\n\t\tsql_update = \"\"\"update account_asset_depreciation_line set move_check = false where asset_id = %s\"\"\" % (str(self.id))\n\t\tself.env.cr.execute(sql_update)\n\t\treturn self.env['popup.it'].get_message('Se desbloquearon todas las lineas de Depreciacion.')\n\n\tdef change_to_unsubscribe(self):\n\t\traise UserError(\"Aun no esta disponible esta funcion.\")\n\n\t@api.model\n\tdef compute_generated_entries(self, date_start, date_end, asset_type=None):\n\t\t# Entries generated : one by grouped category and one by asset from ungrouped category\n\t\tcreated_move_ids = []\n\t\ttype_domain = []\n\t\tif asset_type:\n\t\t\ttype_domain = [('type', '=', asset_type)]\n\n\t\tungrouped_assets = self.env['account.asset.asset'].search(type_domain + [('state', '=', 'open'), ('category_id.group_entries', '=', False)])\n\t\tcreated_move_ids += ungrouped_assets._compute_entries(date_start, date_end, group_entries=False)\n\n\t\tfor grouped_category in self.env['account.asset.category'].search(type_domain + [('group_entries', '=', True)]):\n\t\t\tassets = self.env['account.asset.asset'].search([('state', '=', 'open'), ('category_id', '=', grouped_category.id)])\n\t\t\tcreated_move_ids += assets._compute_entries(date_start, date_end, group_entries=True)\n\t\treturn created_move_ids\n\n\tdef _compute_board_amount(self, sequence, residual_amount, amount_to_depr, undone_dotation_number, posted_depreciation_line_ids, total_days, depreciation_date):\n\t\tamount = 0\n\t\tif sequence == undone_dotation_number:\n\t\t\tamount = residual_amount\n\t\telse:\n\t\t\tif self.method == 'linear':\n\t\t\t\tamount = amount_to_depr / (undone_dotation_number - len(posted_depreciation_line_ids))\n\t\t\t\tif self.prorata:\n\t\t\t\t\tamount = amount_to_depr / self.method_number\n\t\t\t\t\tif sequence == 1:\n\t\t\t\t\t\tdate = self.date\n\t\t\t\t\t\tif self.method_period % 12 != 0:\n\t\t\t\t\t\t\tmonth_days = calendar.monthrange(date.year, date.month)[1]\n\t\t\t\t\t\t\tdays = month_days - date.day + 1\n\t\t\t\t\t\t\tamount = (amount_to_depr / self.method_number) / month_days * days\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tdays = (self.company_id.compute_fiscalyear_dates(date)['date_to'] - date).days + 1\n\t\t\t\t\t\t\tamount = (amount_to_depr / self.method_number) / total_days * days\n\t\t\telif self.method == 'degressive':\n\t\t\t\tamount = residual_amount * self.method_progress_factor\n\t\t\t\tif self.prorata:\n\t\t\t\t\tif sequence == 1:\n\t\t\t\t\t\tdate = self.date\n\t\t\t\t\t\tif self.method_period % 12 != 0:\n\t\t\t\t\t\t\tmonth_days = calendar.monthrange(date.year, date.month)[1]\n\t\t\t\t\t\t\tdays = month_days - date.day + 1\n\t\t\t\t\t\t\tamount = (residual_amount * self.method_progress_factor) / month_days * days\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tdays = (self.company_id.compute_fiscalyear_dates(date)['date_to'] - date).days + 1\n\t\t\t\t\t\t\tamount = (residual_amount * self.method_progress_factor) / total_days * days\n\t\treturn amount\n\n\tdef _compute_board_undone_dotation_nb(self, depreciation_date, total_days):\n\t\tundone_dotation_number = self.method_number\n\t\tif self.method_time == 'end':\n\t\t\tend_date = self.method_end\n\t\t\tundone_dotation_number = 0\n\t\t\twhile depreciation_date <= end_date:\n\t\t\t\tdepreciation_date = date(depreciation_date.year, depreciation_date.month, depreciation_date.day) + relativedelta(months=+self.method_period)\n\t\t\t\tundone_dotation_number += 1\n\t\tif self.prorata:\n\t\t\tundone_dotation_number += 1\n\t\treturn undone_dotation_number\n\n\t\n\tdef compute_depreciation_board(self):\n\t\tself.ensure_one()\n\n\t\tposted_depreciation_line_ids = self.depreciation_line_ids.filtered(lambda x: x.move_check).sorted(key=lambda l: l.depreciation_date)\n\t\tunposted_depreciation_line_ids = self.depreciation_line_ids.filtered(lambda x: not x.move_check)\n\n\t\t# Remove old unposted depreciation lines. We cannot use unlink() with One2many field\n\t\tcommands = [(2, line_id.id, False) for line_id in unposted_depreciation_line_ids]\n\n\t\tif self.value_residual != 0.0:\n\t\t\tamount_to_depr = residual_amount = self.value_residual\n\n\t\t\t# if we already have some previous validated entries, starting date is last entry + method period\n\t\t\tif posted_depreciation_line_ids and posted_depreciation_line_ids[-1].depreciation_date:\n\t\t\t\tlast_depreciation_date = fields.Date.from_string(posted_depreciation_line_ids[-1].depreciation_date)\n\t\t\t\tdepreciation_date = last_depreciation_date + relativedelta(months=+self.method_period)\n\t\t\telse:\n\t\t\t\t# depreciation_date computed from the purchase date\n\t\t\t\tdepreciation_date = self.date\n\t\t\t\tif self.date_first_depreciation == 'last_day_period':\n\t\t\t\t\t# depreciation_date = the last day of the month\n\t\t\t\t\tdepreciation_date = depreciation_date + relativedelta(day=1) + relativedelta(months=1)\n\t\t\t\t\t# ... or fiscalyear depending the number of period\n\t\t\t\t\tif self.method_period == 12:\n\t\t\t\t\t\tdepreciation_date = depreciation_date + relativedelta(month=self.company_id.fiscalyear_last_month)\n\t\t\t\t\t\tdepreciation_date = depreciation_date + relativedelta(day=self.company_id.fiscalyear_last_day)\n\t\t\t\t\t\tif depreciation_date < self.date:\n\t\t\t\t\t\t\tdepreciation_date = depreciation_date + relativedelta(years=1)\n\t\t\t\telif self.first_depreciation_manual_date and self.first_depreciation_manual_date != self.date:\n\t\t\t\t\t# depreciation_date set manually from the 'first_depreciation_manual_date' field\n\t\t\t\t\tdepreciation_date = self.first_depreciation_manual_date\n\n\t\t\ttotal_days = (depreciation_date.year % 4) and 365 or 366\n\t\t\tmonth_day = depreciation_date.day\n\t\t\tundone_dotation_number = self._compute_board_undone_dotation_nb(depreciation_date, total_days)\n\n\t\t\tfor x in range(len(posted_depreciation_line_ids), undone_dotation_number):\n\t\t\t\tsequence = x + 1\n\t\t\t\tamount = self._compute_board_amount(sequence, residual_amount, amount_to_depr, undone_dotation_number, posted_depreciation_line_ids, total_days, depreciation_date)\n\t\t\t\tamount = self.currency_id.round(amount)\n\t\t\t\tif float_is_zero(amount, precision_rounding=self.currency_id.rounding):\n\t\t\t\t\tcontinue\n\t\t\t\tresidual_amount -= amount\n\t\t\t\tvals = {\n\t\t\t\t\t'amount': amount,\n\t\t\t\t\t'asset_id': self.id,\n\t\t\t\t\t'sequence': sequence,\n\t\t\t\t\t'name': (self.code or '') + '/' + str(sequence),\n\t\t\t\t\t'remaining_value': residual_amount,\n\t\t\t\t\t'depreciated_value': self.value - (self.salvage_value + residual_amount),\n\t\t\t\t\t'depreciation_date': depreciation_date,\n\t\t\t\t}\n\t\t\t\tcommands.append((0, False, vals))\n\n\t\t\t\tdepreciation_date = depreciation_date + relativedelta(months=+self.method_period)\n\n\t\t\t\tif month_day > 28 and self.date_first_depreciation == 'manual':\n\t\t\t\t\tmax_day_in_month = calendar.monthrange(depreciation_date.year, depreciation_date.month)[1]\n\t\t\t\t\tdepreciation_date = depreciation_date.replace(day=min(max_day_in_month, month_day))\n\n\t\t\t\t# datetime doesn't take into account that the number of days is not the same for each month\n\t\t\t\tif not self.prorata and self.method_period % 12 != 0 and self.date_first_depreciation == 'last_day_period':\n\t\t\t\t\tmax_day_in_month = calendar.monthrange(depreciation_date.year, depreciation_date.month)[1]\n\t\t\t\t\tdepreciation_date = depreciation_date.replace(day=1)\n\n\t\tself.write({'depreciation_line_ids': commands})\n\n\t\treturn True\n\n\t\n\tdef validate(self):\n\t\tself.write({'state': 'open'})\n\t\tfields = [\n\t\t\t'method',\n\t\t\t'method_number',\n\t\t\t'method_period',\n\t\t\t'method_end',\n\t\t\t'method_progress_factor',\n\t\t\t'method_time',\n\t\t\t'salvage_value',\n\t\t\t'invoice_id',\n\t\t]\n\t\tref_tracked_fields = self.env['account.asset.asset'].fields_get(fields)\n\t\tfor asset in self:\n\t\t\ttracked_fields = ref_tracked_fields.copy()\n\t\t\tif asset.method == 'linear':\n\t\t\t\tdel(tracked_fields['method_progress_factor'])\n\t\t\tif asset.method_time != 'end':\n\t\t\t\tdel(tracked_fields['method_end'])\n\t\t\telse:\n\t\t\t\tdel(tracked_fields['method_number'])\n\t\t\tdummy, tracking_value_ids = asset._message_track(tracked_fields, dict.fromkeys(fields))\n\t\t\tasset.message_post(subject=_('Asset created'), tracking_value_ids=tracking_value_ids)\n\n\tdef _return_disposal_view(self, move_ids):\n\t\tname = _('Disposal Move')\n\t\tview_mode = 'form'\n\t\tif len(move_ids) > 1:\n\t\t\tname = _('Disposal Moves')\n\t\t\tview_mode = 'tree,form'\n\t\treturn {\n\t\t\t'name': name,\n\t\t\t'view_type': 'form',\n\t\t\t'view_mode': view_mode,\n\t\t\t'res_model': 'account.move',\n\t\t\t'type': 'ir.actions.act_window',\n\t\t\t'target': 'current',\n\t\t\t'res_id': move_ids[0],\n\t\t}\n\n\tdef _get_disposal_moves(self):\n\t\tmove_ids = []\n\t\tfor asset in self:\n\t\t\tunposted_depreciation_line_ids = asset.depreciation_line_ids.filtered(lambda x: not x.move_check)\n\t\t\tif unposted_depreciation_line_ids:\n\t\t\t\told_values = {\n\t\t\t\t\t'method_end': asset.method_end,\n\t\t\t\t\t'method_number': asset.method_number,\n\t\t\t\t}\n\n\t\t\t\t# Remove all unposted depr. lines\n\t\t\t\tcommands = [(2, line_id.id, False) for line_id in unposted_depreciation_line_ids]\n\n\t\t\t\t# Create a new depr. line with the residual amount and post it\n\t\t\t\tsequence = len(asset.depreciation_line_ids) - len(unposted_depreciation_line_ids) + 1\n\t\t\t\ttoday = fields.Datetime.today()\n\t\t\t\tvals = {\n\t\t\t\t\t'amount': asset.value_residual,\n\t\t\t\t\t'asset_id': asset.id,\n\t\t\t\t\t'sequence': sequence,\n\t\t\t\t\t'name': (asset.code or '') + '/' + str(sequence),\n\t\t\t\t\t'remaining_value': 0,\n\t\t\t\t\t'depreciated_value': asset.value - asset.salvage_value, # the asset is completely depreciated\n\t\t\t\t\t'depreciation_date': today,\n\t\t\t\t}\n\t\t\t\tcommands.append((0, False, vals))\n\t\t\t\tasset.write({'depreciation_line_ids': commands, 'method_end': today, 'method_number': sequence})\n\t\t\t\ttracked_fields = self.env['account.asset.asset'].fields_get(['method_number', 'method_end'])\n\t\t\t\tchanges, tracking_value_ids = asset._message_track(tracked_fields, old_values)\n\t\t\t\tif changes:\n\t\t\t\t\tasset.message_post(subject=_('Asset sold or disposed. Accounting entry awaiting for validation.'), tracking_value_ids=tracking_value_ids)\n\t\t\t\tmove_ids += asset.depreciation_line_ids[-1].create_move(post_move=False)\n\n\t\treturn move_ids\n\n\t\n\tdef set_to_close(self):\n\t\tmove_ids = self._get_disposal_moves()\n\t\tif move_ids:\n\t\t\treturn self._return_disposal_view(move_ids)\n\t\t# Fallback, as if we just clicked on the smartbutton\n\t\treturn self.open_entries()\n\n\tdef set_to_draft(self):\n\t\tself.write({'state': 'draft'})\n\n\t@api.depends('value', 'salvage_value', 'depreciation_line_ids.move_check', 'depreciation_line_ids.amount')\n\tdef _amount_residual(self):\n\t\tfor rec in self:\n\t\t\ttotal_amount = 0.0\n\t\t\tfor line in rec.depreciation_line_ids:\n\t\t\t\tif line.move_check:\n\t\t\t\t\ttotal_amount += line.amount\n\t\t\trec.value_residual = rec.value - total_amount - rec.salvage_value\n\n\t@api.onchange('company_id')\n\tdef onchange_company_id(self):\n\t\tself.currency_id = self.company_id.currency_id.id\n\n\t\n\t@api.onchange('date','date_first_depreciation')\n\tdef onchange_date_first_depreciation(self):\n\t\tif self.date_first_depreciation == 'manual':\n\t\t\tself.first_depreciation_manual_date = self.date\n\n\t\tif self.date_first_depreciation == 'last_day_period':\n\t\t\tdate_first = self.date\n\t\t\tself.first_depreciation_manual_date = date_first.replace(day=1) + relativedelta(months=1)\n\t\t\t\n\n\t@api.depends('depreciation_line_ids.move_id')\n\tdef _entry_count(self):\n\t\tfor asset in self:\n\t\t\tres = self.env['account.asset.depreciation.line'].search_count([('asset_id', '=', asset.id), ('move_id', '!=', False)])\n\t\t\tasset.entry_count = res or 0\n\n\n\t@api.constrains('prorata', 'method_time')\n\tdef _check_prorata(self):\n\t\tif self.prorata and self.method_time != 'number':\n\t\t\traise ValidationError(_('Prorata temporis can be applied only for the \"number of depreciations\" time method.'))\n\n\t@api.onchange('category_id')\n\tdef onchange_category_id(self):\n\t\tvals = self.onchange_category_id_values(self.category_id.id)\n\t\t# We cannot use 'write' on an object that doesn't exist yet\n\t\tif vals:\n\t\t\tfor k, v in vals['value'].items():\n\t\t\t\tsetattr(self, k, v)\n\n\tdef onchange_category_id_values(self, category_id):\n\t\tif category_id:\n\t\t\tcategory = self.env['account.asset.category'].browse(category_id)\n\t\t\treturn {\n\t\t\t\t'value': {\n\t\t\t\t\t'method': category.method,\n\t\t\t\t\t'method_number': category.method_number,\n\t\t\t\t\t'method_time': category.method_time,\n\t\t\t\t\t'method_period': category.method_period,\n\t\t\t\t\t'method_progress_factor': category.method_progress_factor,\n\t\t\t\t\t'method_end': category.method_end,\n\t\t\t\t\t'prorata': category.prorata,\n\t\t\t\t\t'date_first_depreciation': category.date_first_depreciation,\n\t\t\t\t\t'account_analytic_id': category.account_analytic_id.id,\n\t\t\t\t\t'analytic_tag_ids': [(6, 0, category.analytic_tag_ids.ids)],\n\t\t\t\t}\n\t\t\t}\n\n\t@api.onchange('method_time')\n\tdef onchange_method_time(self):\n\t\tif self.method_time != 'number':\n\t\t\tself.prorata = False\n\n\t\n\tdef copy_data(self, default=None):\n\t\tif default is None:\n\t\t\tdefault = {}\n\t\tdefault['name'] = self.name + _(' (copy)')\n\t\treturn super(AccountAssetAsset, self).copy_data(default)\n\n\t\n\tdef _compute_entries(self, date_start, date_end, group_entries=False):\n\t\tdepreciation_ids = self.env['account.asset.depreciation.line'].search([\n\t\t\t('asset_id', 'in', self.ids), ('depreciation_date', '>=', date_start), ('depreciation_date', '<=', date_end),\n\t\t\t('move_check', '=', False)])\n\t\tif group_entries:\n\t\t\tdepreciation_ids = self.env['account.asset.depreciation.line'].search([\n\t\t\t('asset_id', 'in', self.ids), ('depreciation_date', '>=', date_start), ('depreciation_date', '<=', date_end),\n\t\t\t('move_check', '=', False)])\n\t\t\treturn depreciation_ids.create_grouped_move(date_end)\n\t\treturn depreciation_ids.create_move(date_end)\n\n\t@api.model\n\tdef create(self, vals):\n\t\tasset = super(AccountAssetAsset, self.with_context(mail_create_nolog=True)).create(vals)\n\t\tasset.sudo().compute_depreciation_board()\n\t\treturn asset\n\t\n\tdef write(self, vals):\n\t\tres = super(AccountAssetAsset, self).write(vals)\n\t\tif 'depreciation_line_ids' not in vals and 'state' not in vals:\n\t\t\tfor rec in self:\n\t\t\t\trec.compute_depreciation_board()\n\t\treturn res\n\n\tdef open_entries(self):\n\t\tmove_ids = []\n\t\tfor asset in self:\n\t\t\tfor depreciation_line in asset.depreciation_line_ids:\n\t\t\t\tif depreciation_line.move_id:\n\t\t\t\t\tmove_ids.append(depreciation_line.move_id.id)\n\t\treturn {\n\t\t\t'name': _('Journal Entries'),\n\t\t\t'view_type': 'form',\n\t\t\t'view_mode': 'tree,form',\n\t\t\t'res_model': 'account.move',\n\t\t\t'view_id': False,\n\t\t\t'type': 'ir.actions.act_window',\n\t\t\t'domain': [('id', 'in', move_ids)],\n\t\t}\n\n\nclass AccountAssetDepreciationLine(models.Model):\n\t_name = 'account.asset.depreciation.line'\n\t_description = 'Asset depreciation line'\n\n\tname = fields.Char(string='Depreciation Name', required=True, index=True)\n\tsequence = fields.Integer(required=True)\n\tasset_id = fields.Many2one('account.asset.asset', string='Asset', required=True, ondelete='cascade')\n\tparent_state = fields.Selection(related='asset_id.state', string='State of Asset')\n\tamount = fields.Float(string=u'Depreciación', digits=0, required=True)\n\tremaining_value = fields.Float(string='Residual', digits=0, required=True)\n\tdepreciated_value = fields.Float(string=u'Depreciación Acumulada', required=True)\n\tdepreciation_date = fields.Date(string=u'Fecha de Depreciación', index=True)\n\tmove_id = fields.Many2one('account.move', string='Depreciation Entry')\n\tmove_check = fields.Boolean(compute='_get_move_check', string='Linked', track_visibility='always', store=True)\n\tmove_posted_check = fields.Boolean(compute='_get_move_posted_check', string='Posted', track_visibility='always', store=True)\n\n\t\n\t@api.depends('move_id')\n\tdef _get_move_check(self):\n\t\tfor line in self:\n\t\t\tline.move_check = bool(line.move_id)\n\n\t\n\t@api.depends('move_id.state')\n\tdef _get_move_posted_check(self):\n\t\tfor line in self:\n\t\t\tline.move_posted_check = True if line.move_id and line.move_id.state == 'posted' else False\n\n\t\n\tdef create_move(self, date_end, post_move=True):\n\t\tcreated_moves = self.env['account.move']\n\t\tfor line in self:\n\t\t\tif line.move_id:\n\t\t\t\traise UserError(_(u'Esta depreciación ya está vinculada a un asiento de diario. Por favor publícalo o bórralo.'))\n\t\t\tmove_vals = self._prepare_move(line,date_end)\n\t\t\tmove = self.env['account.move'].create(move_vals)\n\t\t\tline.write({'move_id': move.id, 'move_check': True})\n\t\t\tcreated_moves |= move\n\n\t\tif post_move and created_moves:\n\t\t\tcreated_moves.filtered(lambda m: any(m.asset_depreciation_ids.mapped('asset_id.category_id.open_asset'))).post()\n\t\treturn [x.id for x in created_moves]\n\n\tdef _prepare_move(self, line, date_end):\n\t\tcategory_id = line.asset_id.category_id\n\t\taccount_analytic_id = line.asset_id.account_analytic_id\n\t\tanalytic_tag_ids = line.asset_id.analytic_tag_ids\n\t\tdepreciation_date = self.env.context.get('depreciation_date') or line.depreciation_date or fields.Date.context_today(self)\n\t\tcompany_currency = line.asset_id.company_id.currency_id\n\t\tcurrent_currency = line.asset_id.currency_id\n\t\tprec = company_currency.decimal_places\n\t\tamount = current_currency._convert(\n\t\t\tline.amount, company_currency, line.asset_id.company_id, depreciation_date)\n\t\tasset_name = line.asset_id.name + ' (%s/%s)' % (line.sequence, len(line.asset_id.depreciation_line_ids))\n\t\tmove_line_1 = {\n\t\t\t'name': asset_name,\n\t\t\t'account_id': category_id.account_depreciation_id.id,\n\t\t\t'debit': 0.0 if float_compare(amount, 0.0, precision_digits=prec) > 0 else -amount,\n\t\t\t'credit': amount if float_compare(amount, 0.0, precision_digits=prec) > 0 else 0.0,\n\t\t\t'partner_id': line.asset_id.partner_id.id,\n\t\t\t'analytic_account_id': account_analytic_id.id if category_id.type == 'sale' else False,\n\t\t\t'analytic_tag_ids': [(6, 0, category_id.analytic_tag_ids.ids)] if category_id.type == 'sale' else False,\n\t\t\t'currency_id': company_currency != current_currency and current_currency.id or False,\n\t\t\t'amount_currency': company_currency != current_currency and - 1.0 * line.amount or 0.0,\n\t\t}\n\t\tmove_line_2 = {\n\t\t\t'name': asset_name,\n\t\t\t'account_id': category_id.account_depreciation_expense_id.id,\n\t\t\t'credit': 0.0 if float_compare(amount, 0.0, precision_digits=prec) > 0 else -amount,\n\t\t\t'debit': amount if float_compare(amount, 0.0, precision_digits=prec) > 0 else 0.0,\n\t\t\t'partner_id': line.asset_id.partner_id.id,\n\t\t\t'analytic_account_id': account_analytic_id.id if category_id.type == 'purchase' else False,\n\t\t\t'analytic_tag_ids': [(6, 0, category_id.analytic_tag_ids.ids)] if category_id.type == 'purchase' else False,\n\t\t\t'currency_id': company_currency != current_currency and current_currency.id or False,\n\t\t\t'amount_currency': company_currency != current_currency and line.amount or 0.0,\n\t\t}\n\t\tmove_vals = {\n\t\t\t'ref': line.asset_id.code,\n\t\t\t'date': date_end,\n\t\t\t'journal_id': category_id.journal_id.id,\n\t\t\t'line_ids': [(0, 0, move_line_1), (0, 0, move_line_2)],\n\t\t}\n\t\treturn move_vals\n\n\tdef _prepare_move_grouped(self,date_end):\n\t\tasset_id = self[0].asset_id\n\t\tcategory_id = asset_id.category_id # we can suppose that all lines have the same category\n\t\taccount_analytic_id = asset_id.account_analytic_id\n\t\tanalytic_tag_ids = asset_id.analytic_tag_ids\n\t\t#depreciation_date = self.env.context.get('depreciation_date') or fields.Date.context_today(self)\n\t\tamount = 0.0\n\t\tfor line in self:\n\t\t\t# Sum amount of all depreciation lines\n\t\t\tcompany_currency = line.asset_id.company_id.currency_id\n\t\t\tcurrent_currency = line.asset_id.currency_id\n\t\t\tcompany = line.asset_id.company_id\n\t\t\tamount += current_currency._convert(line.amount, company_currency, company, fields.Date.today())\n\n\t\tname = category_id.name + _(' (grouped)')\n\t\tmove_line_1 = {\n\t\t\t'name': name,\n\t\t\t'account_id': category_id.account_depreciation_id.id,\n\t\t\t'debit': 0.0,\n\t\t\t'credit': amount,\n\t\t\t'journal_id': category_id.journal_id.id,\n\t\t\t'analytic_account_id': account_analytic_id.id if category_id.type == 'sale' else False,\n\t\t\t'analytic_tag_ids': [(6, 0, category_id.analytic_tag_ids.ids)] if category_id.type == 'sale' else False,\n\t\t}\n\t\tmove_line_2 = {\n\t\t\t'name': name,\n\t\t\t'account_id': category_id.account_depreciation_expense_id.id,\n\t\t\t'credit': 0.0,\n\t\t\t'debit': amount,\n\t\t\t'journal_id': category_id.journal_id.id,\n\t\t\t'analytic_account_id': account_analytic_id.id if category_id.type == 'purchase' else False,\n\t\t\t'analytic_tag_ids': [(6, 0, category_id.analytic_tag_ids.ids)] if category_id.type == 'purchase' else False,\n\t\t}\n\t\tmove_vals = {\n\t\t\t'ref': category_id.name,\n\t\t\t'date': date_end,\n\t\t\t'journal_id': category_id.journal_id.id,\n\t\t\t'line_ids': [(0, 0, move_line_1), (0, 0, move_line_2)],\n\t\t}\n\n\t\treturn move_vals\n\n\t\n\tdef create_grouped_move(self, date_end,post_move=True):\n\t\tif not self.exists():\n\t\t\treturn []\n\n\t\tcreated_moves = self.env['account.move']\n\t\tmove = self.env['account.move'].create(self._prepare_move_grouped(date_end))\n\t\tself.write({'move_id': move.id, 'move_check': True})\n\t\tcreated_moves |= move\n\n\t\tif post_move and created_moves:\n\t\t\tself.post_lines_and_close_asset()\n\t\t\tcreated_moves.post()\n\t\treturn [x.id for x in created_moves]\n\n\t\n\tdef post_lines_and_close_asset(self):\n\t\t# we re-evaluate the assets to determine whether we can close them\n\t\tfor line in self:\n\t\t\tline.log_message_when_posted()\n\t\t\tasset = line.asset_id\n\t\t\tif asset.currency_id.is_zero(asset.value_residual):\n\t\t\t\tasset.message_post(body=_(\"Document closed.\"))\n\t\t\t\tasset.write({'state': 'close'})\n\n\t\n\tdef log_message_when_posted(self):\n\t\tdef _format_message(message_description, tracked_values):\n\t\t\tmessage = ''\n\t\t\tif message_description:\n\t\t\t\tmessage = '%s' % message_description\n\t\t\tfor name, values in tracked_values.items():\n\t\t\t\tmessage += '
    • %s: ' % name\n\t\t\t\tmessage += '%s
' % values\n\t\t\treturn message\n\n\t\tfor line in self:\n\t\t\tif line.move_id and line.move_id.state == 'draft':\n\t\t\t\tpartner_name = line.asset_id.partner_id.name\n\t\t\t\tcurrency_name = line.asset_id.currency_id.name\n\t\t\t\tmsg_values = {_('Currency'): currency_name, _('Amount'): line.amount}\n\t\t\t\tif partner_name:\n\t\t\t\t\tmsg_values[_('Partner')] = partner_name\n\t\t\t\tmsg = _format_message(_('Depreciation line posted.'), msg_values)\n\t\t\t\tline.asset_id.message_post(body=msg)\n\n\t\n\tdef unlink(self):\n\t\tfor record in self:\n\t\t\tif record.move_check:\n\t\t\t\tif record.asset_id.category_id.type == 'purchase':\n\t\t\t\t\tmsg = _(\"You cannot delete posted depreciation lines.\")\n\t\t\t\telse:\n\t\t\t\t\tmsg = _(\"You cannot delete posted installment lines.\")\n\t\t\t\traise UserError(msg)\n\t\treturn super(AccountAssetDepreciationLine, self).unlink()\n","sub_path":"om_account_asset/models/account_asset.py","file_name":"account_asset.py","file_ext":"py","file_size_in_byte":33819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"552964573","text":"#https://www.kaggle.com/joshmiller656/classifying-movies-from-raw-image-using-convnets inspiration from this \r\n#https://github.com/benckx/dnn-movie-posters/blob/master/ interesting repo\r\n\r\nimport keras\r\nfrom keras.callbacks import TensorBoard\r\nfrom time import time\r\nimport vgg16\r\nimport resnet\r\nimport fcnet\r\nimport numpy as np # pip install numpy\r\nimport pandas as pd # pip install pandas\r\nimport random\r\nimport os\r\nfrom subprocess import check_output\r\n\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\n\r\n#Adjust the path to the posters here:\r\npath = 'Data/Arrays/'\r\nimport glob #pip install glob\r\nimport scipy.misc #pip install ..\r\nimport imageio #pip install imageio\r\nfrom PIL import Image #pip install Pillow\r\n\r\n#Boolean to choose if we want to use YOLO:\r\nUSE_YOLO = False\r\n\r\nprint(\"Reading data\")\r\n\r\ndef get_id(filename):\r\n index_s = max(filename.rfind(\"\\\\\")+1, filename.rfind(\"/\")+1)\r\n index_f = filename.rfind(\".npy\")\r\n return int(filename[index_s:index_f])\r\n\r\n# Populate image dicts\r\nimg_dict = {get_id(fn):fn for fn in glob.glob(path+\"*.npy\")}\r\n\r\n# Load yolo data\r\nif(USE_YOLO):\r\n yolo_df = pd.read_csv(\"Data/yolo.csv\", index_col=0, encoding=\"utf-8-sig\")\r\n yolo_df = yolo_df.fillna(0)\r\n\r\n#Reads the movie genres\r\ndf = pd.read_csv(\"Data/cleaned.csv\",index_col=\"imdbId\")\r\ndf.Genre = [x.split(\"|\") for x in df.Genre]\r\n\r\n# Remove posters that do not occur in the csv and remove movies that have no poster\r\nfor id_key in list(img_dict):\r\n if id_key not in df.index:\r\n del img_dict[id_key] \r\n if USE_YOLO and id_key not in yolo_df.index:\r\n del img_dict[id_key]\r\n\r\ndf = df.loc[list(img_dict)]\r\nif USE_YOLO:\r\n yolo_df = yolo_df.loc[list(img_dict)]\r\n\r\n# Process genres\r\ngenres = sorted(set(y for x in df.Genre for y in x))\r\nclasses = pd.DataFrame(data={g:[g in r for r in df.Genre] for g in genres}, index=df.index)\r\n\r\n\r\nprint(\"Processing data\")\r\n#Constant to keep track of our image size\r\nIMG_SIZE = (128, 128)\r\nTRAIN_SIZE = round(len(img_dict)*0.2)\r\n\r\n# Split and process the dataset\r\nindices = random.sample(list(img_dict),TRAIN_SIZE)\r\nx_img = []\r\nx_img_test = []\r\ny = []\r\ny_test = []\r\nx_yolo = []\r\nx_yolo_test = []\r\nfor id_key in list(img_dict):\r\n if id_key in indices:\r\n x_img.append(np.load(img_dict[id_key]))\r\n y.append(classes.loc[id_key])\r\n if USE_YOLO:\r\n x_yolo.append([yolo_df.loc[id_key]])\r\n else:\r\n x_img_test.append(np.load(img_dict[id_key]))\r\n y_test.append(classes.loc[id_key])\r\n if USE_YOLO:\r\n x_yolo_test.append([yolo_df.loc[id_key]])\r\n\r\nx_img = np.asarray(x_img)\r\nx_img_test = np.asarray(x_img_test)\r\ny = np.asarray(y)\r\ny_test = np.asarray(y_test)\r\nx_yolo = np.asarray(x_yolo)\r\nx_yolo_test = np.asarray(x_yolo_test)\r\n\r\nprint(\"Running Model\")\r\n\r\n# mode 0, 1, 2, 3\r\n# translates to: vgg16, resnet50, vgg16-obj, resnet50-ob\r\ndef runmode(mode = 0, epochs = 5, batch_size = 50):\r\n modestr = \"\"\r\n \r\n if (mode < 2):\r\n if (mode == 0):\r\n modestr = \"vgg16-70t-20e\"\r\n img_type = \"vgg16\"\r\n model = vgg16.vggmodel(len(genres), IMG_SIZE)\r\n else:\r\n modestr = \"resnet50-70t-20e\"\r\n img_type = \"resnet50\"\r\n model = resnet.resnet50(len(genres), IMG_SIZE)\r\n \r\n print(\"Fitting \" + modestr + \":\")\r\n\r\n tensorboard = TensorBoard(log_dir=\"logs\\{}\".format(time()) + modestr) #initialise Tensorboard\r\n model.fit(x_img, y, batch_size=batchsize, epochs=epochs, validation_data=(x_img_test, y_test),callbacks=[tensorboard])\r\n score = model.evaluate(x_img_test, y_test)\r\n else: \r\n if (mode == 2):\r\n modestr = \"vgg16-objdet-70t20e\"\r\n img_type = \"vgg16\"\r\n img_model = vgg16.vggmodel(len(genres), IMG_SIZE, False)\r\n else:\r\n modestr = \"resnet50-objdet-70t20e\"\r\n img_type = \"resnet50\"\r\n img_model = resnet.resnet50(len(genres), IMG_SIZE, False)\r\n\r\n model = fcnet.fcnmodel(len(genres), len(x_yolo[0][0]), img_model, img_type)\r\n print(\"Fitting \" + modestr + \":\")\r\n tensorboard = TensorBoard(log_dir=\"logs\\{}\".format(time()) + modestr) #initialise Tensorboard\r\n model.fit([x_yolo,x_img], y, batch_size=batchsize, epochs=epochs, validation_data=([x_yolo_test, x_img_test], y_test),callbacks=[tensorboard])\r\n score = model.evaluate([x_yolo_test, x_img_test], y_test)\r\n \r\n # print metrics\r\n print(\"Model metrics for \" + modestr + \":\")\r\n for i in range(len(model.metrics_names)):\r\n print(model.metrics_names[i]+':', score[i])\r\n\r\n # save model\r\n model.save_weights(modestr + \".h5\")\r\n print(\"Saved model \" + modestr + \"to disk!\")\r\n\r\ndef runmodeall(epochs = 5, batchsize = 50):\r\n runmode(0, epochs, batchsize)\r\n #runmode(1, epochs, batchsize)\r\n #runmode(2, epochs, batchsize)\r\n #runmode(3, epochs, batchsize)\r\n\r\n\r\nrunmodeall(20, 200)\r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"278679810","text":"import re\nfrom src.Weapon import Weapon\nfrom src.Permutation import Permutation\n\n\n#1|A\n#1-6|A,B\n#1-6|A,B[0-4|C,D]\n#1-6|A,B[0-4|C,[0-4|E,F][0-4|D]]\n#1-6|[0-3|A,B][0-3|C,D][0-3|E,F]\n\nclass WeaponGrouping:\n def __init__(self, stringIn, weaponDictionary):\n # Aanmaken lijsten\n self.weapons = list()\n self.weaponGroupings = list()\n # Schonen string\n geschoondeString = re.sub(' *, *', ',', stringIn)\n # left from | is the possible occurrences\n possibleOccurencesString = re.match(\"[0-9\\-]+[\\|*]\", geschoondeString).group(0)\n possibleOccurences = possibleOccurencesString[:-1].split(\"-\")\n if possibleOccurencesString[-1:] == \"|\":\n self.minOccurences = int(possibleOccurences[0])\n self.wapensInSlot = 1\n if (len(possibleOccurences)>1):\n self.maxOccurrences = int(possibleOccurences[1])\n else:\n self.maxOccurrences = self.minOccurences\n else:\n self.minOccurences = 0\n self.maxOccurrences = 100\n self.wapensInSlot = int(possibleOccurences[0])\n\n if self.minOccurences > self.maxOccurrences:\n print(\"############\")\n print(\" De volgende wapengroeperingsregel gaat tot problemen leiden: \", stringIn)\n print(\" Minimunaantal groter dan maximumaantal.\")\n\n groupContents = geschoondeString[len(possibleOccurencesString):]\n weaponName = \"\"\n haakjesDiepte = 0\n for i in range(len(groupContents)):\n if groupContents[i] == \",\" and haakjesDiepte == 0:\n weapon = weaponDictionary[weaponName]\n self.weapons.append(weapon)\n weaponName = \"\"\n elif groupContents[i] == \"[\":\n if haakjesDiepte == 0:\n groepString = \"\"\n if weaponName != \"\":\n weapon = weaponDictionary[weaponName]\n self.weapons.append(weapon)\n weaponName = \"\"\n else:\n groepString = groepString + groupContents[i]\n haakjesDiepte = haakjesDiepte + 1\n elif groupContents[i] == \"]\":\n haakjesDiepte = haakjesDiepte - 1\n if haakjesDiepte == 0:\n nieuweWeaponGroup = WeaponGrouping(groepString, weaponDictionary)\n self.weaponGroupings.append(nieuweWeaponGroup)\n else:\n groepString = groepString + groupContents[i]\n elif haakjesDiepte == 0:\n weaponName = weaponName + groupContents[i]\n else:\n groepString = groepString + groupContents[i]\n if weaponName != \"\":\n try:\n weapon = weaponDictionary[weaponName]\n except KeyError as e:\n print(\"############\")\n print(\" De volgende wapengroeperingsregel kon niet verwerkt worden: \", stringIn)\n print(\" Wapen:\", weaponName, \" kon niet gevonden worden in de dictionary\")\n print(\" De foutmelding is:\", e.args)\n self.weapons.append(weapon)\n\n def combineerMetPermutatiesZelfdeLevel(self, weaponsSlotsToUseMin, weaponsSlotsToUseMax, counter, permutatieList):\n permutationsReturn = []\n for permutatieDL in permutatieList:\n permutatiesZelfdeLevel = self.permutationsOfGroupsInSameLevel(weaponsSlotsToUseMin - permutatieDL.slotsGebruikt(),\n weaponsSlotsToUseMax - permutatieDL.slotsGebruikt(),\n counter)\n\n for permutatieZL in permutatiesZelfdeLevel:\n permutatie = permutatieDL.copy()\n permutatie.merge(permutatieZL)\n permutationsReturn.append(permutatie)\n permutationsReturn.append(permutatieDL)\n return permutationsReturn\n\n def permutationsOfGroupsInSameLevel(self, weaponsSlotsToUseMin, weaponsSlotsToUseMax, counter):\n # Checkt op min en max en besteedt zoeken naar permutaties met maximale en minimale vulling voor dit slot uit\n\n # Ontsnappingsclausules\n if counter == len(self.weaponGroupings): return ([]) # er is maar 1 mogelijkheid\n if weaponsSlotsToUseMax == 0: return ([]) # er is maar 1 mogelijkheid\n if weaponsSlotsToUseMax < weaponsSlotsToUseMin:\n return ([]) # niet valide, dus we geven geen permutaties terug\n\n permutationsReturn = []\n # we proberen eerst te duiken voor deze slot en kijken of andere slots op dit level aan de condities kunnen voldoen\n if weaponsSlotsToUseMin == 0 or counter < len(self.weaponGroupings) + len(self.weapons) - 1:\n gevondenPermutaties = self.permutationsOfGroupsInSameLevel(weaponsSlotsToUseMin, weaponsSlotsToUseMax, counter + 1)\n permutationsReturn.extend(gevondenPermutaties)\n # wat als we zorgen dat we voldoen aan minOccurences door nu die minoccurences te pakken of in ieder geval voor de max proberen te gaan\n if weaponsSlotsToUseMin > 0:\n gevondenPermutatiesDieperLevel = self.weaponGroupings[counter].permutatiesNewLevel(0, weaponsSlotsToUseMin)\n for permutatie in gevondenPermutatiesDieperLevel: permutatie.gedoken = True\n permutationsReturn.extend(gevondenPermutatiesDieperLevel)\n\n # max out on this weapon\n if weaponsSlotsToUseMin < weaponsSlotsToUseMax: # anders gelijk aan min\n gevondenPermutatiesDieperLevel = self.weaponGroupings[counter].permutatiesNewLevel(weaponsSlotsToUseMin, weaponsSlotsToUseMax)\n #verwijderen gedoken permutaties\n gevondenPermutatiesDieperLevel = [p for p in gevondenPermutatiesDieperLevel if p.gedoken == False]\n permutationsReturn.extend(self.combineerMetPermutatiesZelfdeLevel(weaponsSlotsToUseMin,\n weaponsSlotsToUseMax,\n counter + 1, gevondenPermutatiesDieperLevel))\n return permutationsReturn\n\n def permutatiesNewLevel(self, weaponsSlotsToUseMinIn, weaponsSlotsToUseMaxIn):\n # retourneert een lijst met permutaties (lijst met lijsten)\n # we zoeken eerst permutaties van onderliggende weapon groepen\n # vervolgens maxen en minnen we uit met losse weapons in deze groep,\n # In dat geval is telkens 1 wapen optimaal, dus dat hoeft niet recursief.\n # Elke wapen wordt gecombineerd in een max en een min variant.\n\n # Preposities:\n # Elke groep min en max is kleiner of gelijk aan die van de omvattende groep\n # Groeps max binnen omvattende groep zijn opklimmend\n\n # Creeren nieuwe min en max op basis van input en locale min en max\n weaponsSlotsToUseMin = max(weaponsSlotsToUseMinIn, self.minOccurences)\n weaponsSlotsToUseMax = min(weaponsSlotsToUseMaxIn, self.maxOccurrences)\n\n # Checks of dit level aan de eisen kan voldoen qua min en max\n if weaponsSlotsToUseMax < weaponsSlotsToUseMin: return [] # niet valide, dus we geven geen permutaties terug\n if weaponsSlotsToUseMax == 0: return ([Permutation()]) # er is maar 1 mogelijkheid\n\n permutationsInMyGoups = self.permutationsOfGroupsInSameLevel(weaponsSlotsToUseMin, weaponsSlotsToUseMax, 0)\n permutationsReturn = []\n for permutationInMyGroup in permutationsInMyGoups:\n if len(permutationInMyGroup) > 0:\n minLosseWeapons = max(0, weaponsSlotsToUseMin - permutationInMyGroup.slotsGebruikt())\n if minLosseWeapons == 0: # Er hoeven geen losse wapens toegevoegd te worden.\n permutationsReturn.append(permutationInMyGroup)\n if permutationInMyGroup.gedoken == False:\n permutationsReturn.extend(permutationInMyGroup.combineToNewPermutationsWithWeapons(minLosseWeapons, self.weapons))\n maxLosseWeapons = weaponsSlotsToUseMax - permutationInMyGroup.slotsGebruikt()\n if maxLosseWeapons > 0:\n permutationsReturn.extend(permutationInMyGroup.combineToNewPermutationsWithWeapons(maxLosseWeapons, self.weapons))\n if weaponsSlotsToUseMax >0:\n for weapon in self.weapons:\n permutationsReturn.append(Permutation.createWithOneElement(weapon, weaponsSlotsToUseMax))\n if weaponsSlotsToUseMin > 0 and weaponsSlotsToUseMin < weaponsSlotsToUseMax:\n permutation = Permutation.createWithOneElement(weapon, weaponsSlotsToUseMin)\n permutation.gedoken = True\n permutationsReturn.append(permutation)\n return permutationsReturn\n\n def permutations(self):\n perms = self.permutatiesNewLevel(0, 1000)\n for perm in perms:\n perm.order()\n perms.sort(key=lambda x: x.rank(), reverse=False)\n return perms\n\n\n\n\n\n\n\n","sub_path":"src/WeaponGrouping.py","file_name":"WeaponGrouping.py","file_ext":"py","file_size_in_byte":9088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"78626607","text":"# Copyright (C) 2020 Zurich Instruments\n#\n# This software may be modified and distributed under the terms\n# of the MIT license. See the LICENSE file for details.\n\nimport pytest\nfrom hypothesis import given, assume, strategies as st\nfrom hypothesis.stateful import rule, precondition, RuleBasedStateMachine\nimport numpy as np\n\nfrom .context import SequenceProgram, SequenceType, TriggerMode, Alignment, DeviceTypes\n\n\nclass SequenceProgramMachine(RuleBasedStateMachine):\n def __init__(self):\n super().__init__()\n self.sequenceProgram = SequenceProgram()\n\n @rule(t=st.integers(1, 3))\n def change_device_type(self, t):\n types = {\n 1: DeviceTypes.HDAWG,\n 2: DeviceTypes.UHFQA,\n 3: DeviceTypes.UHFLI,\n }\n self.sequenceProgram.set_params(target=types[t])\n params = self.sequenceProgram.list_params()\n target = params[\"sequence_parameters\"][\"target\"]\n assert target == types[t]\n\n @rule(t=st.integers(0, 9))\n def change_sequence_by_enum(self, t):\n types = {\n 0: SequenceType.NONE,\n 1: SequenceType.SIMPLE,\n 2: SequenceType.T1,\n 3: SequenceType.T2,\n 4: SequenceType.READOUT,\n 5: SequenceType.CUSTOM,\n 6: SequenceType.CW_SPEC,\n 7: SequenceType.PULSED_SPEC,\n 8: SequenceType.TRIGGER,\n 9: SequenceType.RABI,\n 10: SequenceType.PULSETRAIN,\n }\n self.sequenceProgram.set_params(sequence_type=types[t])\n assert self.sequenceProgram.sequence_type == types[t]\n\n @rule(t=st.integers(-1, 10))\n def change_sequence_by_string(self, t):\n types = {\n -1: \"None\",\n 0: None,\n 1: \"Simple\",\n 2: \"T1\",\n 3: \"T2*\",\n 4: \"Readout\",\n 5: \"Custom\",\n 6: \"CW Spectroscopy\",\n 7: \"Pulsed Spectroscopy\",\n 8: \"Trigger\",\n 9: \"Rabi\",\n 10: \"Pulse Train\",\n }\n self.sequenceProgram.set_params(sequence_type=types[t])\n if t == -1:\n assert self.sequenceProgram.sequence_type.value is None\n else:\n assert self.sequenceProgram.sequence_type.value == types[t]\n\n @rule(t=st.integers(0, 5))\n def change_trigger_by_enum(self, t):\n types = {\n 0: TriggerMode.NONE,\n 1: TriggerMode.SEND_TRIGGER,\n 2: TriggerMode.EXTERNAL_TRIGGER,\n 3: TriggerMode.RECEIVE_TRIGGER,\n 4: TriggerMode.SEND_AND_RECEIVE_TRIGGER,\n 5: TriggerMode.ZSYNC_TRIGGER,\n }\n self.sequenceProgram.set_params(trigger_mode=types[t])\n params = self.sequenceProgram.list_params()\n sequence_type = params[\"sequence_type\"]\n trigger_mode = params[\"sequence_parameters\"][\"trigger_mode\"]\n if t not in [1, 4] and sequence_type == SequenceType.TRIGGER:\n assert trigger_mode == types[1]\n else:\n assert trigger_mode == types[t]\n\n @rule(t=st.integers(-1, 5))\n def change_trigger_by_string(self, t):\n types = {\n -1: \"None\",\n 0: None,\n 1: \"Send Trigger\",\n 2: \"External Trigger\",\n 3: \"Receive Trigger\",\n 4: \"Send and Receive Trigger\",\n 5: \"ZSync Trigger\",\n }\n self.sequenceProgram.set_params(trigger_mode=types[t])\n params = self.sequenceProgram.list_params()\n sequence_type = params[\"sequence_type\"]\n trigger_mode = params[\"sequence_parameters\"][\"trigger_mode\"]\n if t not in [1, 4] and sequence_type == SequenceType.TRIGGER:\n assert trigger_mode.value == types[1]\n elif t == -1:\n assert trigger_mode.value is None\n else:\n assert trigger_mode.value == types[t]\n\n @rule(t=st.integers(0, 1))\n def change_alignment_by_enum(self, t):\n types = {\n 0: Alignment.START_WITH_TRIGGER,\n 1: Alignment.END_WITH_TRIGGER,\n }\n self.sequenceProgram.set_params(alignment=types[t])\n params = self.sequenceProgram.list_params()\n params = params[\"sequence_parameters\"]\n assert params[\"alignment\"] == types[t]\n\n @rule(t=st.integers(0, 1))\n def change_alignment_by_string(self, t):\n types = {\n 0: \"Start with Trigger\",\n 1: \"End with Trigger\",\n }\n self.sequenceProgram.set_params(alignment=types[t])\n params = self.sequenceProgram.list_params()\n params = params[\"sequence_parameters\"]\n assert params[\"alignment\"].value == types[t]\n\n @rule(l=st.integers(1, 1000), amp=st.floats(0, 1.0))\n def change_amps(self, l, amp):\n test_array = np.random.uniform(0, amp, l)\n self.sequenceProgram.set_params(pulse_amplitudes=test_array)\n params = self.sequenceProgram.list_params()\n if self.sequenceProgram.sequence_type == SequenceType.RABI:\n assert np.array_equal(\n params[\"sequence_parameters\"][\"pulse_amplitudes\"], test_array\n )\n else:\n assert \"pulse_amplitudes\" not in params[\"sequence_parameters\"].keys()\n\n @rule(i=st.integers(-10, 1000))\n def change_trigger_samples(self, i):\n granularity = 16\n minimum_length = 32\n if i % granularity != 0 or i < minimum_length:\n with pytest.raises(ValueError):\n self.sequenceProgram.set_params(trigger_samples=i)\n else:\n self.sequenceProgram.set_params(trigger_samples=i)\n params = self.sequenceProgram.list_params()\n assert params[\"sequence_parameters\"][\"trigger_samples\"] == i\n\n @rule(i=st.integers(-10, 100))\n def change_latency_adjustment(self, i):\n if i < 0:\n with pytest.raises(ValueError):\n self.sequenceProgram.set_params(latency_adjustment=i)\n else:\n self.sequenceProgram.set_params(latency_adjustment=i)\n params = self.sequenceProgram.list_params()\n target = params[\"sequence_parameters\"][\"target\"]\n trigger_mode = params[\"sequence_parameters\"][\"trigger_mode\"]\n latency_adjustment = params[\"sequence_parameters\"][\"latency_adjustment\"]\n latency_cycles = params[\"sequence_parameters\"][\"latency_cycles\"]\n assert latency_adjustment == i\n if target in [DeviceTypes.HDAWG]:\n if trigger_mode in [TriggerMode.ZSYNC_TRIGGER]:\n assert latency_cycles == 0 + latency_adjustment\n else:\n assert latency_cycles == 27 + latency_adjustment\n elif target in [DeviceTypes.UHFLI, DeviceTypes.UHFQA]:\n assert latency_cycles == 0 + latency_adjustment\n\n @rule(i=st.booleans())\n def change_reset_phase(self, i):\n self.sequenceProgram.set_params(reset_phase=i)\n params = self.sequenceProgram.list_params()\n sequence_type = params[\"sequence_type\"]\n reset_phase = params[\"sequence_parameters\"][\"reset_phase\"]\n if sequence_type == SequenceType.PULSED_SPEC:\n assert reset_phase is True\n else:\n assert reset_phase is i\n\n # @rule(l=st.integers(1, 1000), t=st.floats(100e-9, 10e-6))\n # def change_delays(self, l, t):\n # test_array = np.linspace(0, t, l)\n # self.sequenceProgram.set_params(delay_times=test_array)\n # params = self.sequenceProgram.list_params()\n # if self.sequenceProgram.sequence_type in [\"T1\", \"T2*\"]:\n # assert np.array_equal(\n # params[\"sequence_parameters\"][\"delay_times\"], test_array\n # )\n # else:\n # assert \"delay_times\" not in params[\"sequence_parameters\"].keys()\n\n @rule()\n def get_sequence(self):\n sequence = self.sequenceProgram.get_seqc()\n sequence_type = self.sequenceProgram.sequence_type\n assert str(sequence_type.value) in sequence\n\n\nTestPrograms = SequenceProgramMachine.TestCase\n","sub_path":"tests/test_sequenceProgram.py","file_name":"test_sequenceProgram.py","file_ext":"py","file_size_in_byte":7992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"112811614","text":"mult = ['S', 'D', 'T']\nscoring = {}\nfor i in range(0, 21):\n scoring[i] = i\nscoring[21] = 25\nm = 21\nd = {'S': 1, 'D': 2, 'T': 3}\nc1 = 'D'\ncount = 0\nfor a in range(0, m+1):\n for a1 in mult:\n if a == 21 and a1 == 'T':\n continue\n for b in range(a, m+1):\n for b1 in mult:\n if b == 21 and b1 == 'T':\n continue\n if a == b and d[a1] < d[b1]:\n break\n for c in range(1, m+1):\n score = d[a1]*scoring[a] + d[b1]*scoring[b] + d[c1]*scoring[c]\n if score < 100:\n# print(a1+str(a), b1+str(b), c1+str(c))\n count += 1\n if b == 0:\n break\n if a == 0:\n break\nprint(count)","sub_path":"p109.py","file_name":"p109.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"31167766","text":"\nimport pandas as pd\n\n# 导入自定义工具\nimport Utils.utils as utils\nimport Utils.plot_utils as plot_utils\n\npath = './datasets/house-price/'\n\n# 读取数据集\nbj_house = utils.load_data(path, file_name='bj_house.csv')\n# sh_house = utils.load_data(path, file_name='shanghai_7w.xlsx', is_csv=False)\n\n# 查看数据集信息\nna_data = utils.nan_data_rate(bj_house, 10)\nnum_lst, class_lst = utils.describle_data(bj_house)\n\n# 绘制缺失率直方图\nutils.show_bar(na_data.index, na_data['Missing_Ratio'], \"Rate of Missing Value\")\n\n# ----- 填补缺失值 ----- #\n# 房屋的厨房数，卫生间数。客厅数等都与住房面积高度相关,利用住房面积,对数据集分组用于填补缺失\nsqu_bins = [-1, 20, 50, 90, 120, 160, 200, 2000]\nbj_house['squ_level'] = pd.cut(bj_house['square'], bins=squ_bins,\n labels=['20平以下', '20-50', '50-90', '90-120', '120-160', '160-200', '200平以上'])\n\n# livingRoom 缺失值填补\nbj_house.loc[bj_house['livingRoom'] == '#NAME?', 'livingRoom'] = None\nbj_house['livingRoom'] = bj_house.groupby('squ_level')['livingRoom'].transform(lambda x: x.fillna(x.median()))\nbj_house['livingRoom'] = bj_house['livingRoom'].map(lambda x: int(x))\n# drawingRoom 缺失值填补\nbj_house['drawingRoom'] = bj_house.groupby('squ_level')['drawingRoom'].transform(lambda x: x.fillna(x.median()))\nbj_house['drawingRoom'] = bj_house['drawingRoom'].map(lambda x: int(x))\n# kitchen 缺失值填补\nbj_house['kitchen'] = bj_house.groupby('squ_level')['kitchen'].transform(lambda x: x.fillna(x.median()))\nbj_house['kitchen'] = bj_house['kitchen'].map(lambda x: int(x))\n# bathRoom 缺失值填补\nbj_house['bathRoom'] = bj_house.groupby('squ_level')['bathRoom'].transform(lambda x: x.fillna(x.median()))\nbj_house['bathRoom'] = bj_house['bathRoom'].map(lambda x: int(x))\n# buildingType 缺失值填补\nbj_house['buildingType'] = bj_house['buildingType'].fillna('无类型')\n# renovationCondition 缺失值填补\nbj_house['renovationCondition'] = bj_house['renovationCondition'].fillna('其他')\n\n# ----- 填补缺失值 ----- #\n# 异常值处理\n# bj_house.loc[bj_house['constructionTime'] == '未知', 'constructionTime'] = None\n\n# ----- 特征工程 ----- #\n# 变量扩充 新增 房间总数 和 起居室占比\nbj_house['num_room'] = bj_house['livingRoom'].map(lambda x: int(x)) + bj_house['drawingRoom'].map(lambda x: int(x)) + \\\n bj_house['kitchen'].map(lambda x: int(x)) + bj_house['bathRoom'].map(lambda x: int(x))\nbj_house['living_rate'] = bj_house['livingRoom'].map(lambda x: int(x)) / bj_house['num_room']\n\n# 变量替换\n# buildingType 变量替换\nbj_house.loc[bj_house['buildingType'] == 1, 'buildingType'] = '塔楼'\nbj_house.loc[bj_house['buildingType'] == 2, 'buildingType'] = '平房'\nbj_house.loc[bj_house['buildingType'] == 3, 'buildingType'] = '板塔结合'\nbj_house.loc[bj_house['buildingType'] == 4, 'buildingType'] = '板楼'\n\n# renovationCondition 变量替换\nbj_house.loc[bj_house['renovationCondition'] == 1,'renovationCondition'] = '其他'\nbj_house.loc[bj_house['renovationCondition'] == 2,'renovationCondition'] = '毛坯'\nbj_house.loc[bj_house['renovationCondition'] == 3,'renovationCondition'] = '简装'\nbj_house.loc[bj_house['renovationCondition'] == 4,'renovationCondition'] = '精装'\n\n# district 变量替换\nbj_house.loc[bj_house['district'] == 1, 'district'] = '东城'\nbj_house.loc[bj_house['district'] == 2, 'district'] = '丰台'\nbj_house.loc[bj_house['district'] == 3, 'district'] = '亦庄'\nbj_house.loc[bj_house['district'] == 4, 'district'] = '大兴'\nbj_house.loc[bj_house['district'] == 5, 'district'] = '房山'\nbj_house.loc[bj_house['district'] == 6, 'district'] = '昌平'\nbj_house.loc[bj_house['district'] == 7, 'district'] = '朝阳'\nbj_house.loc[bj_house['district'] == 8, 'district'] = '海淀'\nbj_house.loc[bj_house['district'] == 9, 'district'] = '石景山'\nbj_house.loc[bj_house['district'] == 10, 'district'] = '西城'\nbj_house.loc[bj_house['district'] == 11, 'district'] = '通州'\nbj_house.loc[bj_house['district'] == 12, 'district'] = '门头沟'\nbj_house.loc[bj_house['district'] == 13, 'district'] = '顺义'\n\n# buildingStructure 变量替换\nbj_house.loc[bj_house['buildingStructure'] == 1, 'buildingStructure'] = '未知结构'\nbj_house.loc[bj_house['buildingStructure'] == 2, 'buildingStructure'] = '混和结构'\nbj_house.loc[bj_house['buildingStructure'] == 3, 'buildingStructure'] = '砖木结构'\nbj_house.loc[bj_house['buildingStructure'] == 4, 'buildingStructure'] = '砖混结构'\nbj_house.loc[bj_house['buildingStructure'] == 5, 'buildingStructure'] = '钢结构'\nbj_house.loc[bj_house['buildingStructure'] == 6, 'buildingStructure'] = '钢混结构'\n\n# 绘制缺失率直方图\nutils.show_bar(na_data.index, na_data['Missing_Ratio'], \"Rate of Missing Value\")\n\n# 只研究2000年-2016年的房价信息\ntime_bins = ['1999', '2004', '2008', '2012', '2017']\nbj_house_period = pd.cut(bj_house['constructionTime'], bins=time_bins,\n labels=['2000-2004', '2005-2008', '2009-2012', '2013-2016'])\nbj_house_period.value_counts()\n\n\"\"\"\n# 统计不同行政区不同年份房价中位数\ntime_price = utils.get_time_median(bj_house, 'district', 'price')\n# 绘制不同行政区域不同年份房价中位数热力图\nplot_utils.timeline_map(time_price).render('beijing_price_map')\n\"\"\"\n\n# 绘制各年份有无地铁平均房价分布图\nplot_utils.create_box(bj_house, 'period', 'price', 'The Boxplot of House Price from 2000 to 2016', 'subway',\n order_=['2000-2004', '2005-2008', '2009-2012', '2013-2016'], scatter_=False)\n# 绘制建筑结构和房屋每平米价格分布图\nplot_utils.create_box(bj_house, 'buildingStructure', 'price',\n 'The Boxplot of House Price in deffierent period and building structure', 'period',\n order_=[1, 2, 3, 4, 5, 6], scatter_=False)\n# 绘制建筑类型和房屋每平米价格分布图\nplot_utils.create_box(bj_house, 'buildingType', 'price',\n 'The Boxplot of House Price in deffierent period and building type', 'period',\n order_=[1, 2, 3, 4, 5, 6], scatter_=False)\n\n\nplot_utils.distribute_plot(df=bj_house, columns=['price', 'square', 'communityAverage', 'ladderRatio'])\n\nplot_utils.kde_polt(background='./datasets/beijing_map.png', df=bj_house, lng='Lng', lat='Lat',\n title='the distribution of Beijing house')\n\n# 判断成交总价是否等于每平米价格x建筑面积 不等于\nsum(bj_house['totalPrice']-bj_house['square']*bj_house['price'])\n# 但有极强的共线性，因此用这个指标进行预测是无意义的\nbj_house['avg_price'] = bj_house['totalPrice']/bj_house['square']\nplot_utils.create_sca_join(bj_house,'avg_price','price')\n\nbj_house['avg_community'] = utils.max_min_scale(bj_house['communityAverage'])\nplot_utils.create_sca_join(bj_house,'avg_community','sta_price')","sub_path":"reference/pre-work.py","file_name":"pre-work.py","file_ext":"py","file_size_in_byte":6928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"15082011","text":"#Rock Paper Scissors\nimport random\ncomputer_choice = random.randint(1,4)\nprint('Choose Rock, Paper, or Scissors')\nhuman_choice = input()\ndef choice_to_int(num):\n if num == 'Rock':\n human_choice = 1\n elif num == 'Paper':\n human_choice = 2\n elif num == 'Scissors':\n human_choice = 3\n else:\n return\nchoice_to_int(human_choice)\ndef find_winner(choice1, choice2):\n if choice1 == 1 and choice2 == 2:\n print('Paper beats rock, computer wins')\n elif choice1 == 2 and choice2 == 3:\n print('Scissors beats paper, computer wins')\n elif choice1 == 3 and choice2 == 1:\n print('Rock beats scissors, computer wins')\n elif choice1 == 2 and choice2 == 1:\n print('Paper beats rock, player wins')\n elif choice1 == 3 and choice2 == 2:\n print('Scissors beats paper, player wins')\n elif choice1 == 1 and choice2 == 3:\n print('Rock beats scissors, player wins')\n else:\n print('Tie')\nif computer_choice == 1:\n print('Computer chooses Rock')\nelif computer_choice == 2:\n print('Computer Chooses Paper')\nelse:\n print('Computer Chooses scissors')\nfind_winner(computer_choice, human_choice)","sub_path":"Day4.py","file_name":"Day4.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"468267700","text":"import sublime, sublime_plugin\nimport os\nfrom subprocess import Popen, call, PIPE\n\nclass MyJenkinsSubmit(sublime_plugin.TextCommand):\n def run(self, edit):\n if not self.view.file_name(): return\n full_name = os.path.realpath(self.view.file_name())\n folder_name, _ = os.path.split(full_name)\n\n shell = Popen([os.environ[\"SHELL\"], \"-c\", \"jenkins-submit\"], cwd = folder_name, stdout=PIPE)\n output = shell.communicate()[0].decode()[:-1]\n if output:\n output = output[:1].upper() + output[1:]\n sublime.status_message(output)\n call([\"growlnotify\", \"-n\", \"Jenkins Submit\", \"-m\", output])\n","sub_path":"Library/Application Support/Sublime Text 3/Packages/MyDev/MyJenkinsSubmit.py","file_name":"MyJenkinsSubmit.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"134001981","text":"import tkinter as tk\n\n\nclass Application(tk.Frame):\n def __init__(self, master=None):\n super().__init__(master)\n self.master = master\n self.grid()\n self.selection = tk.StringVar()\n self.selection_label = None\n self.selection_entry = None\n self.selection_entry_number = tk.IntVar()\n self.crossover = tk.StringVar()\n self.crossover_prob = tk.DoubleVar()\n self.mutation = tk.StringVar()\n self.mutation_prob = tk.DoubleVar()\n self.beginning_of_interval = tk.DoubleVar()\n self.end_of_interval = tk.DoubleVar()\n self.population_size = tk.IntVar()\n self.number_of_epochs = tk.IntVar()\n self.elite_strategy_amount = tk.IntVar()\n self.maximization = tk.IntVar()\n self.start_button = tk.Button()\n self.create_menu()\n\n def create_menu(self):\n tk.Label(self.master, text=\"Evolutionary Algorithms\", font=('arial', 12, 'bold'), bg=\"#ccc\").grid(row=0, columnspan=2, sticky='WE', pady=10)\n self.create_menu_left()\n self.create_menu_right()\n self.start_button = tk.Button(self.master, text='Start', width=20, font=('arial', 10, 'bold'), bg='#808080', fg='white')\n self.start_button.grid(row=2, columnspan=2, pady=10)\n\n def create_menu_left(self):\n\n frame = tk.Frame(self.master, relief='raised')\n frame.config(bg='#e3e3e3')\n frame.grid(row=1, column=0, padx=(20, 10), pady=10, sticky='N')\n\n self.create_label_with_entry(frame, 'Beginning of interval:', self.beginning_of_interval)\n self.create_label_with_entry(frame, 'End of internal:', self.end_of_interval)\n self.create_label_with_entry(frame, 'Population size:', self.population_size)\n self.create_label_with_entry(frame, 'Number of epochs:', self.number_of_epochs)\n self.create_label_with_entry(frame, 'Elite strategy amount:', self.elite_strategy_amount)\n tk.Checkbutton(frame, text='Maximization', variable=self.maximization, bg='#e3e3e3').grid(padx=20, pady=(10, 0), sticky='W')\n\n def create_label_with_entry(self, frame, title, data, label_pady=(10, 0), entry_pady=(0, 0)):\n label = tk.Label(frame, text=title, bg='#e3e3e3')\n label.grid(padx=20, pady=label_pady, sticky='W')\n entry = tk.Entry(frame, textvariable=data, width=25, bd=3)\n entry.grid(padx=24, pady=entry_pady, sticky='WE')\n return entry\n\n def create_menu_right(self):\n frame = tk.Frame(self.master, relief='raised')\n frame.config(bg='#e3e3e3')\n frame.grid(row=1, column=1, padx=(10, 20), pady=10, sticky='N')\n\n self.create_selections_menu(frame)\n self.create_crossovers_menu(frame)\n self.create_mutations_menu(frame)\n\n def create_selections_menu(self, frame):\n selections_menu_label = tk.Label(frame, text=\"Choose selection option:\", bg='#e3e3e3')\n selections_menu_label.grid(padx=50, pady=(10, 0))\n selections = ['Tournament', 'The Best Ones', 'Roulette Wheel']\n self.selection.set(selections[0])\n self.selection.trace('w', self.show)\n menu = tk.OptionMenu(frame, self.selection, *selections)\n menu.config(indicator=0, font=('candara', 12), borderwidth=0, bg='white', width=20)\n menu.grid()\n self.selection_label = tk.Label(frame, text='Number of groups:', bg='#e3e3e3', anchor='w')\n self.selection_label.grid(padx=22, sticky='W')\n self.selection_entry = tk.Entry(frame, textvariable=self.selection_entry_number, bd=3)\n self.selection_entry.grid(padx=24, sticky='WE', pady=(0, 5))\n\n def create_crossovers_menu(self, frame):\n crossovers_menu_label = tk.Label(frame, text=\"Choose crossover option:\", bg='#e3e3e3')\n crossovers_menu_label.grid(pady=(10, 0))\n crossovers = ['Arithmetic', 'Heuristic']\n self.crossover.set(crossovers[0])\n menu = tk.OptionMenu(frame, self.crossover, *crossovers)\n menu.config(indicator=0, font=('candara', 12), borderwidth=0, bg='white', width=20)\n menu.grid()\n tk.Label(frame, text='Crossover probability:', bg='#e3e3e3', anchor='w').grid(padx=22, sticky='W')\n tk.Entry(frame, textvariable=self.crossover_prob, bd=3).grid(padx=24, sticky='WE', pady=(0, 5))\n\n def create_mutations_menu(self, frame):\n mutations_menu_label = tk.Label(frame, text=\"Choose mutation option:\", bg='#e3e3e3')\n mutations_menu_label.grid(pady=(10, 0))\n mutations = ['Uniform']\n self.mutation.set(mutations[0])\n menu = tk.OptionMenu(frame, self.mutation, *mutations)\n menu.config(indicator=0, font=('candara', 12), borderwidth=0, bg='white', width=20)\n menu.grid()\n tk.Label(frame, text='Mutation probability:', bg='#e3e3e3', anchor='w').grid(padx=22, sticky='W')\n tk.Entry(frame, textvariable=self.mutation_prob, bd=3).grid(padx=24, sticky='WE', pady=(0, 5))\n\n def show(self, *args):\n selections = {'Tournament': 0, 'The Best Ones': 1, 'Roulette Wheel': 2}\n selected_option = selections[self.selection.get()]\n if selected_option == 0:\n self.selection_label.config(text='Number of groups:')\n self.selection_label.grid()\n self.selection_entry.grid()\n elif selected_option == 1:\n self.selection_label.config(text='Number of the best ones:')\n self.selection_label.grid()\n self.selection_entry.grid()\n else:\n self.selection_label.grid_remove()\n self.selection_entry.grid_remove()\n\n def get_all_values(self):\n values = {\"selection\": self.selection.get(),\n \"crossover\": self.crossover.get(),\n \"mutation\": self.mutation.get(),\n \"cross_prob\": self.crossover_prob.get(),\n \"mutation_prob\": self.mutation_prob.get(),\n \"beginning\": self.beginning_of_interval.get(),\n \"end\": self.end_of_interval.get(),\n \"population\": self.population_size.get(),\n \"epochs\": self.number_of_epochs.get(),\n \"elite\": self.elite_strategy_amount.get(),\n \"max\": self.maximization.get()}\n\n if self.selection.get() == 'Tournament' or self.selection.get() == 'The Best Ones':\n values['k'] = self.selection_entry_number.get()\n else:\n values['k'] = -1\n\n return values\n\n def create_timer_window(self, time, arg, value):\n timer_window = tk.Toplevel(self.master)\n tk.Label(timer_window, text=f\"Solution found in = {time} seconds. \\n Value f({arg}) = {value}.\",\n font=('arial', 12, 'bold'), bg=\"#ccc\").grid(row=0, columnspan=2,\n sticky='WE', pady=10)\n","sub_path":"oe/com/gui/Application.py","file_name":"Application.py","file_ext":"py","file_size_in_byte":6795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"565565377","text":"import sqlite3\nfrom datetime import datetime, date, timezone\n\nclass queries:\n\n def __init__():\n return\n\n def connect():\n global con \n try:\n con = sqlite3.connect(\"book.db\", detect_types=sqlite3.PARSE_DECLTYPES)\n except:\n print(\"Could no connect\")\n\n def disconnect():\n con.close()\n\n def addBook(book):\n cur = con.cursor()\n info = [book.getTitle(), book.getLength(), book.getAvailabilty()]\n cur.execute(\"INSERT into Book (title, length, availability) values (?,?,?)\",info)\n con.commit()\n\n def makeBookId(book):\n cur = con.cursor()\n cur.execute('SELECT bookId FROM Book WHERE title =:tit AND length =:len', {\"tit\": book.getTitle(), \"len\": book.getLength()})\n data = cur.fetchall()\n for d in data:\n book.setBookId(d[0])\n\n def checkBook(book):\n cur = con.cursor()\n cur.execute('Select count(*) FROM Book WHERE bookId =:num', {\"num\": book.getBookId()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def updateBookTitle(bookId, title):\n cur = con.cursor()\n cur.execute(\"UPDATE Book SET title = :val WHERE bookId = :id\", {\"val\": title, \"id\": bookId})\n con.commit()\n \n def updateBookLength(bookId, length):\n cur = con.cursor()\n cur.execute(\"UPDATE Book SET length = :val WHERE bookId = :id\", {\"val\": length, \"id\": bookId})\n con.commit()\n\n def updateMember(memberId, phoneNumber):\n cur = con.cursor()\n cur.execute(\"UPDATE Member SET phoneNumber = :val WHERE memberId = :id\", {\"val\": phoneNumber, \"id\": memberId})\n con.commit()\n\n def addAuthor(author):\n cur = con.cursor()\n info = [author.getFirstName(), author.getLastName()]\n cur.execute(\"INSERT into Author (firstName, lastName) values (?,?)\",(info))\n con.commit()\n\n def makeAuthorId(author):\n cur = con.cursor()\n cur.execute('SELECT authorId FROM Author WHERE firstName =:first AND lastName =:last', {\"first\": author.getFirstName(), \"last\": author.getLastName()})\n data = cur.fetchall()\n for d in data:\n author.setAuthorId(d[0])\n\n def checkAuthor(author):\n cur = con.cursor()\n cur.execute('Select count(*) FROM Author WHERE firstName =:first AND lastName =:last', {\"first\": author.getFirstName(), \"last\": author.getLastName()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def addPublisher(publisher):\n cur = con.cursor()\n info = [publisher.getName(), publisher.getAddress()]\n cur.execute(\"INSERT into Publisher (name, address) values (?,?)\",(info))\n con.commit()\n\n def makePublisherId(publisher):\n cur = con.cursor()\n cur.execute('SELECT publisherId FROM Publisher WHERE name =:pname AND address =:addr', {\"pname\": publisher.getName(), \"addr\": publisher.getAddress()})\n data = cur.fetchall()\n for d in data:\n publisher.setPublisherId(d[0])\n\n def checkPublisher(publisher):\n cur = con.cursor()\n cur.execute('Select count(*) FROM Publisher WHERE name =:pname AND address =:addr', {\"pname\": publisher.getName(), \"addr\": publisher.getAddress()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def addMember(member):\n cur = con.cursor()\n info = [member.getMemberFirstName(), member.getMemberLastName(), member.getBirthday(), member.getPhoneNumber()]\n cur.execute(\"INSERT into Member (firstName, lastName, birthday, phoneNumber) values (?,?,?,?)\",(info))\n con.commit()\n\n def makeMemberId(publisher):\n cur = con.cursor()\n cur.execute('SELECT memberId FROM Member WHERE firstName =:fname AND lastName =:lname', \\\n {\"fname\": member.getMemberFirstName(), \"lname\": member.getMemberLastName()})\n data = cur.fetchall()\n for d in data:\n member.setMemberId(d[0])\n\n def checkMember(member):\n cur = con.cursor()\n cur.execute('Select count(*) FROM Member WHERE memberId =:num', {\"num\": member.getMemberId()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def addWrittenBy(book, author):\n cur = con.cursor()\n info = [book.getBookId(), author.getAuthorId()]\n cur.execute('INSERT into WrittenBy (bookId, authorId) values (?,?)', info)\n con.commit()\n\n def alreadyWritten(book, author):\n cur = con.cursor()\n cur.execute('SELECT count(*) FROM WrittenBy WHERE bookId=:first AND authorId =:second',\\\n {\"first\": book.getBookId(), \"second\": author.getAuthorId()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n print(\"in else in queries\")\n return False\n\n def addPublishedBy(book, publisher, datePublished):\n print(\"in add publishedby\")\n cur = con.cursor()\n info = [book.getBookId(), publisher.getPublisherId(), datePublished]\n print(book.getBookId())\n print(publisher.getPublisherId())\n print(datePublished)\n cur.execute('INSERT into PublishedBy (bookId, publisherId, datePublished) values (?,?,?)', info)\n con.commit()\n\n def alreadyPublished(book, publisher):\n cur = con.cursor()\n cur.execute('SELECT count(*) FROM PublishedBy WHERE bookId=:first AND publisherId =:second',\\\n {\"first\": book.getBookId(), \"second\": publisher.getPublisherId()})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def addBorrowedBy(bookId, memberId):\n cur = con.cursor()\n mydate = date.today()\n info = [bookId, memberId, mydate]\n cur.execute('INSERT into BorrowedBy (bookId, memberId, issueDate) values (?,?,?)', info)\n con.commit()\n\n def removeBorrowedBy(bookId):\n cur = con.cursor()\n info = [bookId]\n cur.execute('DELETE from BorrowedBy WHERE bookId=:first', info)\n con.commit()\n\n def alreadyBorrowed(bookId):\n cur = con.cursor()\n cur.execute('SELECT count(*) FROM BorrowedBy WHERE bookId=:first',\\\n {\"first\": bookId})\n check = cur.fetchall()\n for c in check:\n valid = c[0]\n if valid == 1:\n return True\n else:\n return False\n\n def deleteBook(bookId):\n cur = con.cursor()\n print(bookId)\n cur.execute(\"DELETE FROM Book WHERE bookId = ?\",bookId)\n con.commit()\n\n def deleteMember(memberId):\n cur = con.cursor()\n print(memberId)\n cur.execute(\"DELETE FROM Member WHERE memberId = ?\",memberId)\n con.commit()\n\n def checkoutBook(bookId):\n cur = con.cursor()\n cur.execute('UPDATE Book SET availability = 0 WHERE bookId=:first', {\"first\": bookId})\n con.commit()\n\n def checkinBook(bookId):\n cur = con.cursor()\n cur.execute('UPDATE Book SET availability = 1 WHERE bookId=:first', {\"first\": bookId})\n con.commit()\n ","sub_path":"library/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":7557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"344358950","text":"import re\n\n\ndef load_dataset(return_metadata=False):\n \"\"\"\n Load dataset from Persona Chat paper: https://arxiv.org/abs/1801.07243\n :return: list of contexts, list of responses, list of personae\n \"\"\"\n with open(\"Datasets/pc/personachat/train_both_revised.txt\") as f:\n persona_a = []\n personae_a = []\n persona_b = []\n personae_b = []\n dialogue = []\n dialogues = []\n reading_persona = True\n lines = f.readlines()\n for line in lines:\n if \"your persona:\" in line:\n if reading_persona is False:\n personae_a.append(persona_a)\n personae_b.append(persona_b)\n dialogues.append(dialogue)\n persona_a = []\n persona_b = []\n dialogue = []\n reading_persona = True\n persona_a.append(re.sub(r\"\\A[0-9]+ (your persona: |partner's persona: )\", \"\", line))\n elif \"partner's persona:\" in line:\n persona_b.append(re.sub(r\"\\A[0-9]+ (your persona: |partner's persona: )\", \"\", line))\n else:\n for utt in line.split(\"\\t\")[:2]:\n utt = re.sub(r\"\\A[0-9]+ \", \"\", utt) # remove line numbering\n dialogue.append(utt)\n reading_persona = False\n\n contexts = []\n responses = []\n metadata = []\n for pa, pb, dialog in zip(personae_a, personae_b, dialogues):\n for i in range(1, len(dialog)):\n history = dialog[:i-1]\n persona = []\n # Select which persona belongs to the responder\n if i % 2 == 0:\n for p in pa:\n persona.append(p)\n else:\n for p in pb:\n persona.append(p)\n\n meta = persona + history\n metadata.append(meta)\n\n contexts.append(dialog[i-1])\n responses.append(dialog[i])\n \n if return_metadata: \n return contexts, responses, metadata\n else:\n return contexts, responses\n","sub_path":"Datasets/persona_chat.py","file_name":"persona_chat.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"308752196","text":"\"\"\"\r\n Rule-based method for method mention extraction \r\n @Author : Hospice Houngbo\r\n\"\"\"\r\n\r\n\r\nimport codecs\r\nimport numpy as np\r\nimport nltk\r\nimport pycrfsuite\r\nfrom bs4 import BeautifulSoup as bs\r\nfrom bs4.element import Tag\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.model_selection import cross_validate\r\n# \r\nimport re\r\nimport random\r\n\r\nwith open(\"methodsentwithkw2annotate.txt\", \"r\") as fpm:\r\n docs = fpm.readlines()\r\n\r\n# \r\ndata = []\r\nfor doc in docs:\r\n \r\n tokens = [t.replace(\"<\",\"\").replace(\">\",\"\") for t in doc.split()]\r\n tagged = nltk.pos_tag(tokens)\r\n data.append([t for t in tagged])\r\ni=0\r\nk=0\r\nt = 0\r\nfor dt in data:\r\n k+=1\r\n st = \"\"\r\n for d in dt:\r\n if d[0] in [\"method\", \"approach\", \"model\", \"algorithm\", \"analysis\"]:\r\n #print(d[0])\r\n st+=d[0]+\" \"\r\n tks = []\r\n tgs = []\r\n else:\r\n st+=d[1]+\" \"\r\n pattern = re.compile(r\"(( NN| JJ| NNP| JJS| JJR)+( method | analysis | algorithm | approach | model ))\")\r\n try:\r\n #print(re.search(pattern, st))\r\n res = re.search(pattern, st)\r\n if res is not None:\r\n t+=1\r\n except:\r\n None\r\n try:\r\n regex = r\"(( NN| JJ| NNP| JJS| JJR)+( method | analysis | algorithm | approach | model ))\"\r\n rel = re.findall(regex, st)\r\n #print(rel[0][0])\r\n result = rel[0][0]\r\n # if result!=\"\":\r\n i+=1\r\n except:\r\n None\r\nprint (\"Accuracy: \", i/k)\r\nprint(\"Accuracy : \", t/k)\r\n","sub_path":"run_nemm_rule_based.py","file_name":"run_nemm_rule_based.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"276009249","text":"# 백준[11000]\n\nfrom queue import PriorityQueue\nimport sys\ns=sys.stdin.readline\ndef main():\n n = int(s())\n num = []\n pque = PriorityQueue()\n for i in range(n):\n num.append(list(map(int,s().split())))\n num = sorted(num,key = lambda x: (x[0],x[1]))\n\n for i in range(n):\n if pque.qsize() != 0 and pque.queue[0][1]<= num[i][0]:\n pque.get()\n pque.put((num[i][1],num[i][1]))\n print(pque.qsize())\n\nif __name__ == '__main__':\n main()\n","sub_path":"baekjoon11000.py","file_name":"baekjoon11000.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"325102418","text":"#!/usr/bin/env python\n\n# encoding: utf-8\n\n'''\n\n@author: Roy Law\n\n@license: (C) Copyright 2018.\n\n@contact: https://github.com/RoyLaw\n\n@file: dydq_vip_checker.py\n\n@time: 2018/1/31 上午1:21\n\n@desc:\n\n'''\n\nimport requests\n\ncookie = '834e_phone=13813914000; PHPSESSID=f194c2e59fb60ef174aef17377ea6b46'\nheader = {\n 'Origin': 'http://viplnmp.tyhvip.com',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n 'Accept-Language': 'zh-CN,en-US;q=0.8',\n 'User-Agent': 'Mozilla/5.0 (Linux; Android 8.0; ALP-AL00 Build/HUAWEIALP-AL00; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/57.0.2987.132 MQQBrowser/6.2 TBS/043906 Mobile Safari/537.36 Appcan/3.1',\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'Referer': 'http://viplnmp.tyhvip.com/app/index.php?i=1&c=entry&eid=9&op=card',\n 'Cookie': cookie\n}\n\n\ndef get_token():\n resp = requests.get('http://viplnmp.tyhvip.com/app/index.php?i=1&c=entry&eid=9&op=card', headers=header).text\n token_index = resp.index('token')\n token = resp[token_index + 14:][0:4]\n return token\n\n\ndef check_card(card_num):\n payload = 'card=' + card_num + '&submit=%E6%BF%80%E6%B4%BB%E4%BC%9A%E5%91%98%E5%8D%A1&token=' + get_token()\n resp = requests.post('http://viplnmp.tyhvip.com/app/index.php?i=1&c=entry&eid=9&op=card', headers=header,\n data=payload).text\n return not resp.find('兑换码无效')\n\n\ndef main():\n dict_file = open('.\\\\tools\\\\dict.txt', encoding='utf-8')\n valid_count = 0\n for line in dict_file:\n line = line.strip('\\n')\n line = line.strip('\\ufeff')\n print(line)\n if check_card(line):\n print('\\n***有效卡号***\\n')\n valid_count += 1\n else:\n print('无效卡号')\n print('\\n命中有效卡号' + str(valid_count) + '个')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"dydq_vip_checker.py","file_name":"dydq_vip_checker.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"332258546","text":"import datetime\nimport json\nfrom lxml import etree\nfrom nose.tools import (\n eq_, \n assert_raises,\n assert_raises_regexp,\n set_trace,\n)\nimport os\nfrom StringIO import StringIO\n\nfrom core.config import (\n Configuration, \n temp_config,\n)\n\nfrom core.model import (\n get_one_or_create,\n Contributor,\n DataSource,\n DeliveryMechanism,\n Edition,\n ExternalIntegration,\n Identifier,\n LicensePool,\n Patron,\n Representation,\n Subject,\n)\n\nfrom core.metadata_layer import (\n CirculationData,\n ContributorData,\n IdentifierData,\n Metadata,\n SubjectData,\n)\n\nfrom api.oneclick import (\n OneClickAPI,\n OneClickCirculationMonitor, \n MockOneClickAPI,\n)\n\nfrom api.circulation import (\n LoanInfo,\n HoldInfo,\n FulfillmentInfo,\n)\n\nfrom api.circulation_exceptions import *\n\nfrom . import (\n DatabaseTest,\n)\n\n\n\nclass OneClickAPITest(DatabaseTest):\n\n def setup(self):\n super(OneClickAPITest, self).setup()\n\n self.base_path = os.path.split(__file__)[0]\n self.collection = MockOneClickAPI.mock_collection(self._db)\n self.api = MockOneClickAPI(\n self._db, self.collection, base_path=self.base_path\n )\n\n self.default_patron = self._patron(external_identifier=\"oneclick_testuser\")\n self.default_patron.authorization_identifier=\"13057226\"\n\n\n\nclass TestOneClickAPI(OneClickAPITest):\n\n def test_get_patron_internal_id(self):\n datastr, datadict = self.api.get_data(\"response_patron_internal_id_not_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n oneclick_patron_id = self.api.get_patron_internal_id(patron_cardno='9305722621')\n eq_(None, oneclick_patron_id)\n\n datastr, datadict = self.api.get_data(\"response_patron_internal_id_error.json\")\n self.api.queue_response(status_code=500, content=datastr)\n assert_raises_regexp(\n InvalidInputException, \"patron_id:\", \n self.api.get_patron_internal_id, patron_cardno='130572262x'\n )\n\n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n oneclick_patron_id = self.api.get_patron_internal_id(patron_cardno='1305722621')\n eq_(939981, oneclick_patron_id)\n\n\n def test_get_patron_information(self):\n datastr, datadict = self.api.get_data(\"response_patron_info_not_found.json\")\n self.api.queue_response(status_code=404, content=datastr)\n assert_raises_regexp(\n NotFoundOnRemote, \"patron_info:\", \n self.api.get_patron_information, patron_id='939987'\n )\n\n datastr, datadict = self.api.get_data(\"response_patron_info_error.json\")\n self.api.queue_response(status_code=400, content=datastr)\n assert_raises_regexp(\n InvalidInputException, \"patron_info:\", \n self.api.get_patron_information, patron_id='939981fdsfdsf'\n )\n\n datastr, datadict = self.api.get_data(\"response_patron_info_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n patron = self.api.get_patron_information(patron_id='939981')\n eq_(u'1305722621', patron['libraryCardNumber'])\n eq_(u'Mic', patron['firstName'])\n eq_(u'Mouse', patron['lastName'])\n eq_(u'mickeymouse1', patron['userName'])\n eq_(u'mickey1@mouse.com', patron['email'])\n\n\n def test_circulate_item(self):\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781441260468'\n )\n datastr, datadict = self.api.get_data(\"response_checkout_success.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n patron = self.default_patron\n # TODO: decide if want to add oneclick_id as Credential to PatronData db object\n patron.oneclick_id = 939981\n\n # borrow functionality checks\n response_dictionary = self.api.circulate_item(patron.oneclick_id, edition.primary_identifier.identifier)\n assert('error_code' not in response_dictionary)\n eq_(\"9781441260468\", response_dictionary['isbn'])\n eq_(\"SUCCESS\", response_dictionary['output'])\n eq_(False, response_dictionary['canRenew'])\n #eq_(9828517, response_dictionary['transactionId'])\n eq_(939981, response_dictionary['patronId'])\n eq_(1931, response_dictionary['libraryId'])\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"checkouts\" in request_url\n eq_(\"post\", request_kwargs.get(\"method\"))\n\n datastr, datadict = self.api.get_data(\"response_checkout_unavailable.json\")\n self.api.queue_response(status_code=409, content=datastr)\n assert_raises_regexp(\n NoAvailableCopies, \"Title is not available for checkout\", \n self.api.circulate_item, patron.oneclick_id, edition.primary_identifier.identifier\n )\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"checkouts\" in request_url\n eq_(\"post\", request_kwargs.get(\"method\"))\n\n # book return functionality checks\n self.api.queue_response(status_code=200, content=\"\")\n\n response_dictionary = self.api.circulate_item(patron.oneclick_id, edition.primary_identifier.identifier, \n return_item=True)\n eq_({}, response_dictionary)\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"checkouts\" in request_url\n eq_(\"delete\", request_kwargs.get(\"method\"))\n\n datastr, datadict = self.api.get_data(\"response_return_unavailable.json\")\n self.api.queue_response(status_code=409, content=datastr)\n assert_raises_regexp(\n NotCheckedOut, \"checkin:\", \n self.api.circulate_item, patron.oneclick_id, edition.primary_identifier.identifier, \n return_item=True\n )\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"checkouts\" in request_url\n eq_(\"delete\", request_kwargs.get(\"method\"))\n\n # hold functionality checks\n datastr, datadict = self.api.get_data(\"response_patron_hold_success.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n response = self.api.circulate_item(patron.oneclick_id, edition.primary_identifier.identifier,\n hold=True)\n eq_(9828560, response)\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"holds\" in request_url\n eq_(\"post\", request_kwargs.get(\"method\"))\n\n datastr, datadict = self.api.get_data(\"response_patron_hold_fail_409_reached_limit.json\")\n self.api.queue_response(status_code=409, content=datastr)\n\n response = self.api.circulate_item(patron.oneclick_id, edition.primary_identifier.identifier,\n hold=True)\n eq_(\"You have reached your checkout limit and therefore are unable to place additional holds.\",\n response)\n request_url, request_args, request_kwargs = self.api.requests[-1]\n assert \"holds\" in request_url\n eq_(\"post\", request_kwargs.get(\"method\"))\n\n def test_checkin(self):\n # Returning a book is, for now, more of a \"notify OneClick that we've \n # returned through Adobe\" formality than critical functionality.\n # There's no information returned from the server on success, so we use a \n # boolean success flag.\n\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781441260468'\n )\n work = self._work(presentation_edition=edition)\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n # queue checkin success\n self.api.queue_response(status_code=200, content='{\"message\": \"success\"}')\n\n success = self.api.checkin(patron, None, pool)\n eq_(True, success)\n\n # queue patron id\n self.api.queue_response(status_code=200, content=datastr)\n # queue unexpected non-empty response from the server\n self.api.queue_response(status_code=200, content=json.dumps({\"error_code\": \"error\"}))\n\n assert_raises(CirculationException, self.api.checkin,\n patron, None, pool)\n\n\n def test_checkout(self):\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781441260468'\n )\n work = self._work(presentation_edition=edition)\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n # queue checkout success\n datastr, datadict = self.api.get_data(\"response_checkout_success.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n loan_info = self.api.checkout(patron, None, pool, None)\n eq_(Identifier.RB_DIGITAL_ID, loan_info.identifier_type)\n eq_(pool.identifier.identifier, loan_info.identifier)\n today = datetime.datetime.now()\n assert (loan_info.start_date - today).total_seconds() < 20\n assert (loan_info.end_date - today).days < 60\n eq_(None, loan_info.fulfillment_info)\n\n\n def test_create_patron(self):\n patron = self.default_patron\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_create_fail_already_exists.json\")\n self.api.queue_response(status_code=409, content=datastr)\n assert_raises_regexp(\n RemotePatronCreationFailedException, 'create_patron: http=409, response={\"message\":\"A patron account with the specified username, email address, or card number already exists for this library.\"}', \n self.api.create_patron, patron\n )\n\n datastr, datadict = self.api.get_data(\"response_patron_create_success.json\")\n self.api.queue_response(status_code=201, content=datastr)\n patron_oneclick_id = self.api.create_patron(patron)\n\n eq_(940000, patron_oneclick_id)\n\n\n def test_fulfill(self):\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n identifier = self._identifier(\n identifier_type=Identifier.RB_DIGITAL_ID, \n foreign_id='9781426893483')\n\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781426893483'\n )\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n # queue checkouts list\n datastr, datadict = self.api.get_data(\"response_patron_checkouts_200_list.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n epub_manifest = json.dumps({ \"url\": 'http://api.oneclickdigital.us/v1/media/133504/parts/133504/download-url?f=EB00014158.epub&ff=EPUB&acsRId=urn%3Auuid%3A76fca044-0b31-47f7-8ac5-ee0befbda698&tId=9828560&expDt=1479531600',\n \"type\": Representation.EPUB_MEDIA_TYPE })\n self.api.queue_response(status_code=200, content=epub_manifest)\n\n found_fulfillment = self.api.fulfill(patron, None, pool, None)\n\n eq_(Identifier.RB_DIGITAL_ID, found_fulfillment.identifier_type)\n eq_(u'9781426893483', found_fulfillment.identifier.identifier)\n eq_(u'http://api.oneclickdigital.us/v1/media/133504/parts/133504/download-url?f=EB00014158.epub&ff=EPUB&acsRId=urn%3Auuid%3A76fca044-0b31-47f7-8ac5-ee0befbda698&tId=9828560&expDt=1479531600', found_fulfillment.content_link)\n eq_(u'application/epub+zip', found_fulfillment.content_type)\n eq_(None, found_fulfillment.content)\n\n # Here's another pool that the patron doesn't have checked out.\n edition2, pool2 = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '123456789'\n )\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n # queue checkouts list\n datastr, datadict = self.api.get_data(\"response_patron_checkouts_200_list.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n epub_manifest = json.dumps({ \"url\": 'http://api.oneclickdigital.us/v1/media/133504/parts/133504/download-url?f=EB00014158.epub&ff=EPUB&acsRId=urn%3Auuid%3A76fca044-0b31-47f7-8ac5-ee0befbda698&tId=9828560&expDt=1479531600',\n \"type\": Representation.EPUB_MEDIA_TYPE })\n self.api.queue_response(status_code=200, content=epub_manifest)\n\n # The patron can't fulfill the book if it's not one of their checkouts.\n assert_raises(NoActiveLoan, self.api.fulfill,\n patron, None, pool2, None)\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n # queue checkouts list\n datastr, datadict = self.api.get_data(\"response_patron_checkouts_200_emptylist.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n # The patron also can't fulfill the book if they have no checkouts.\n assert_raises(NoActiveLoan, self.api.fulfill,\n patron, None, pool, None)\n\n\n def test_patron_activity(self):\n # Get patron's current checkouts and holds.\n # Make sure LoanInfo objects were created and filled \n # with FulfillmentInfo objects. Make sure HoldInfo objects \n # were created.\n\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n identifier = self._identifier(\n identifier_type=Identifier.RB_DIGITAL_ID, \n foreign_id='9781456103859')\n\n identifier = self._identifier(\n identifier_type=Identifier.RB_DIGITAL_ID, \n foreign_id='9781426893483')\n\n # queue patron id \n patron_datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=patron_datastr)\n\n # queue checkouts list\n datastr, datadict = self.api.get_data(\"response_patron_checkouts_200_list.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n # queue a manifest for each checkout\n audio_manifest = json.dumps({ \"url\": 'http://api.oneclickdigital.us/v1/media/9781456103859/parts/1646772/download-url?s3=78226&f=78226_007_P004',\n \"type\": Representation.MP3_MEDIA_TYPE })\n epub_manifest = json.dumps({ \"url\": 'http://api.oneclickdigital.us/v1/media/133504/parts/133504/download-url?f=EB00014158.epub&ff=EPUB&acsRId=urn%3Auuid%3A76fca044-0b31-47f7-8ac5-ee0befbda698&tId=9828560&expDt=1479531600',\n \"type\": Representation.EPUB_MEDIA_TYPE })\n self.api.queue_response(status_code=200, content=audio_manifest)\n self.api.queue_response(status_code=200, content=epub_manifest)\n\n # queue holds list\n datastr, datadict = self.api.get_data(\"response_patron_holds_200_list.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n patron_activity = self.api.patron_activity(patron, None)\n\n eq_(Identifier.RB_DIGITAL_ID, patron_activity[0].identifier_type)\n eq_(u'9781456103859', patron_activity[0].identifier)\n eq_(None, patron_activity[0].start_date)\n eq_(datetime.date(2016, 11, 19), patron_activity[0].end_date)\n eq_(u'http://api.oneclickdigital.us/v1/media/9781456103859/parts/1646772/download-url?s3=78226&f=78226_007_P004', patron_activity[0].fulfillment_info.content_link)\n eq_(u'audio/mpeg', patron_activity[0].fulfillment_info.content_type)\n \n eq_(Identifier.RB_DIGITAL_ID, patron_activity[1].identifier_type)\n eq_(u'9781426893483', patron_activity[1].identifier)\n eq_(None, patron_activity[1].start_date)\n eq_(datetime.date(2016, 11, 19), patron_activity[1].end_date)\n eq_(u'http://api.oneclickdigital.us/v1/media/133504/parts/133504/download-url?f=EB00014158.epub&ff=EPUB&acsRId=urn%3Auuid%3A76fca044-0b31-47f7-8ac5-ee0befbda698&tId=9828560&expDt=1479531600', patron_activity[1].fulfillment_info.content_link)\n eq_(u'application/epub+zip', patron_activity[1].fulfillment_info.content_type)\n \n eq_(Identifier.RB_DIGITAL_ID, patron_activity[2].identifier_type)\n eq_('9781426893483', patron_activity[2].identifier)\n eq_(None, patron_activity[2].start_date)\n eq_(datetime.date(2050, 12, 31), patron_activity[2].end_date)\n eq_(None, patron_activity[2].hold_position)\n\n\n\n def test_place_hold(self):\n # Test reserving a book.\n\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781441260468'\n )\n\n # queue patron id \n patron_datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n\n self.api.queue_response(status_code=200, content=patron_datastr)\n datastr, datadict = self.api.get_data(\"response_patron_hold_fail_409_already_exists.json\")\n self.api.queue_response(status_code=409, content=datastr)\n assert_raises_regexp(\n CannotHold, \".*Hold or Checkout already exists.\", \n self.api.place_hold, patron, None, pool, None\n )\n\n self.api.queue_response(status_code=200, content=patron_datastr)\n datastr, datadict = self.api.get_data(\"response_patron_hold_fail_409_reached_limit.json\")\n self.api.queue_response(status_code=409, content=datastr)\n assert_raises_regexp(\n CannotHold, \".*You have reached your checkout limit and therefore are unable to place additional holds.\", \n self.api.place_hold, patron, None, pool, None\n )\n\n self.api.queue_response(status_code=200, content=patron_datastr)\n datastr, datadict = self.api.get_data(\"response_patron_hold_success.json\")\n self.api.queue_response(status_code=200, content=datastr)\n\n hold_info = self.api.place_hold(patron, None, pool, None)\n\n eq_(Identifier.RB_DIGITAL_ID, hold_info.identifier_type)\n eq_(pool.identifier.identifier, hold_info.identifier)\n today = datetime.datetime.now()\n assert (hold_info.start_date - today).total_seconds() < 20\n\n\n def test_release_hold(self):\n # Test releasing a book resevation early.\n\n patron = self.default_patron\n patron.oneclick_id = 939981\n\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, \n identifier_id = '9781441260468'\n )\n\n # queue patron id \n datastr, datadict = self.api.get_data(\"response_patron_internal_id_found.json\")\n self.api.queue_response(status_code=200, content=datastr)\n # queue release success\n self.api.queue_response(status_code=200, content='{\"message\": \"success\"}')\n\n success = self.api.release_hold(patron, None, pool)\n eq_(True, success)\n\n # queue patron id\n self.api.queue_response(status_code=200, content=datastr)\n # queue unexpected non-empty response from the server\n self.api.queue_response(status_code=200, content=json.dumps({\"error_code\": \"error\"}))\n\n assert_raises(CirculationException, self.api.release_hold,\n patron, None, pool)\n\n\n def test_update_licensepool_for_identifier(self):\n \"\"\"Test the OneClick implementation of the update_availability method\n defined by the CirculationAPI interface.\n \"\"\"\n\n # Update a LicensePool that doesn't exist yet, and it gets created.\n identifier = self._identifier(identifier_type=Identifier.RB_DIGITAL_ID)\n isbn = identifier.identifier.encode(\"ascii\")\n\n # The BibliographicCoverageProvider gets called for a new license pool.\n self.api.queue_response(200, content=json.dumps({}))\n\n pool, is_new, circulation_changed = self.api.update_licensepool_for_identifier(\n isbn, True, 'ebook'\n )\n eq_(True, is_new)\n eq_(True, circulation_changed)\n eq_(1, pool.licenses_owned)\n eq_(1, pool.licenses_available)\n [lpdm] = pool.delivery_mechanisms\n eq_(Representation.EPUB_MEDIA_TYPE, lpdm.delivery_mechanism.content_type)\n eq_(DeliveryMechanism.ADOBE_DRM, lpdm.delivery_mechanism.drm_scheme)\n\n # Create a LicensePool that needs updating.\n edition, pool = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, collection=self.collection\n )\n\n # We have never checked the circulation information for this\n # LicensePool. Put some random junk in the pool to verify\n # that it gets changed.\n pool.licenses_owned = 5\n pool.licenses_available = 3\n pool.patrons_in_hold_queue = 3\n eq_(None, pool.last_checked)\n\n isbn = pool.identifier.identifier.encode(\"ascii\")\n\n pool, is_new, circulation_changed = self.api.update_licensepool_for_identifier(\n isbn, False, 'eaudio'\n )\n eq_(False, is_new)\n eq_(True, circulation_changed)\n\n # The availability information has been updated, as has the\n # date the availability information was last checked.\n #\n # We still own a license, but it's no longer available for\n # checkout.\n eq_(1, pool.licenses_owned)\n eq_(0, pool.licenses_available)\n eq_(3, pool.patrons_in_hold_queue)\n assert pool.last_checked is not None\n\n # A delivery mechanism was also added to the pool.\n [lpdm] = pool.delivery_mechanisms\n eq_(Representation.MP3_MEDIA_TYPE, lpdm.delivery_mechanism.content_type)\n eq_(None, lpdm.delivery_mechanism.drm_scheme)\n\n self.api.update_licensepool_for_identifier(isbn, True, 'ebook')\n eq_(1, pool.licenses_owned)\n eq_(1, pool.licenses_available)\n eq_(3, pool.patrons_in_hold_queue)\n\n\nclass TestCirculationMonitor(OneClickAPITest):\n\n def test_process_availability(self):\n monitor = OneClickCirculationMonitor(\n self._db, self.collection, api_class=MockOneClickAPI, \n api_class_kwargs=dict(base_path=self.base_path)\n )\n eq_(ExternalIntegration.RB_DIGITAL, monitor.protocol)\n\n # Create a LicensePool that needs updating.\n edition_ebook, pool_ebook = self._edition(\n identifier_type=Identifier.RB_DIGITAL_ID,\n data_source_name=DataSource.RB_DIGITAL,\n with_license_pool=True, collection=self.collection\n )\n pool_ebook.licenses_owned = 3\n pool_ebook.licenses_available = 2\n pool_ebook.patrons_in_hold_queue = 1\n eq_(None, pool_ebook.last_checked)\n\n # Prepare availability information.\n datastr, datadict = monitor.api.get_data(\"response_availability_single_ebook.json\")\n\n # Modify the data so that it appears to be talking about the\n # book we just created.\n new_identifier = pool_ebook.identifier.identifier.encode(\"ascii\")\n datastr = datastr.replace(\"9781781107041\", new_identifier)\n monitor.api.queue_response(status_code=200, content=datastr)\n\n item_count = monitor.process_availability()\n eq_(1, item_count)\n pool_ebook.licenses_available = 0\n","sub_path":"tests/test_oneclick.py","file_name":"test_oneclick.py","file_ext":"py","file_size_in_byte":24773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"406464576","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 ('cwdm', '0006_auto_20150207_2348'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Transit',\n fields=[\n ('id', models.AutoField(auto_created=True, serialize=False, verbose_name='ID', primary_key=True)),\n ('a_wl', models.IntegerField()),\n ('a_direciton', models.CharField(max_length=10)),\n ('a_port', models.CharField(max_length=10)),\n ('z_wl', models.IntegerField()),\n ('z_direciton', models.CharField(max_length=10)),\n ('z_port', models.CharField(max_length=10)),\n ('a_element', models.ForeignKey(to='cwdm.ElementInLine', related_name='a')),\n ('line', models.ForeignKey(to='cwdm.Line')),\n ('z_element', models.ForeignKey(to='cwdm.ElementInLine', related_name='z')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"intranet/cwdm/migrations/0007_transit.py","file_name":"0007_transit.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"372958604","text":"#!/usr/bin/env python\n\"\"\"Score the quality of blends.\"\"\"\n\nimport sys\nimport csv\nimport math\n\nfrom lingtools.util.r import convert_r_bool\n\nfrom make_probs import WORD, TYPE, PRON, PROB, START\nfrom add_prons import OUTPUT, WORD1, WORD2, PRON1, PRON2, BLEND1, BLEND2\n\nPROB1 = 'prob1'\nPROB2 = 'prob2'\nAMEAN = 'amean'\nGMEAN = 'gmean'\nHMEAN = 'hmean'\nWORD1_FULL = 'full1'\nWORD2_FULL = 'full2'\nWORD1_RATIO = 'ratio1'\nWORD2_RATIO = 'ratio2'\nWORD1_CONTENT = 'content1'\nWORD2_CONTENT = 'content2'\nWORD1_LENGTH = 'length1'\nWORD2_LENGTH = 'length2'\nCOMPLETE = 'complete'\nOVERLAP = 'overlap'\n\nFIELDS = [\n OUTPUT,\n WORD1, WORD2,\n PRON1, PRON2,\n BLEND1, BLEND2,\n PROB1, PROB2,\n AMEAN, GMEAN, HMEAN,\n WORD1_RATIO, WORD2_RATIO,\n WORD1_CONTENT, WORD2_CONTENT,\n WORD1_LENGTH, WORD2_LENGTH,\n WORD1_FULL, WORD2_FULL,\n OVERLAP,\n COMPLETE,\n]\n\n\ndef eval_blends():\n \"\"\"Evalute blends.\"\"\"\n try:\n prob_path, input_path, output_path = sys.argv[1:4]\n except ValueError:\n print >> sys.stderr, 'Usage: eval_blends.py probs blends outfile'\n sys.exit(1)\n\n # Read in the probabilities\n start_probs = {}\n end_probs = {}\n with open(prob_path, 'U') as prob_file:\n reader = csv.DictReader(prob_file)\n for row in reader:\n prob_dict = start_probs if row[TYPE] == START else end_probs\n prob_dict[(row[WORD], row[PRON])] = float(row[PROB])\n\n with open(input_path, 'U') as input_file, open(output_path, 'wb') as output_file:\n reader = csv.DictReader(input_file)\n writer = csv.DictWriter(output_file, FIELDS)\n writer.writeheader()\n for row in reader:\n word1 = row[WORD1]\n word2 = row[WORD2]\n pron1 = row[PRON1]\n pron2 = row[PRON2]\n blend1 = row[BLEND1]\n blend2 = row[BLEND2]\n # Catch any unknown words\n try:\n row[PROB1] = prob1 = start_probs[(word1, blend1)]\n except KeyError:\n print >> sys.stderr, \"Skipping unknown start word\", (word1, blend1)\n continue\n try:\n row[PROB2] = prob2 = end_probs[(word2, blend2)]\n except KeyError:\n print >> sys.stderr, \"Skipping unknown end word\", (word2, blend2)\n continue\n row[AMEAN] = (prob1 + prob2) / 2.0\n row[GMEAN] = math.pow((prob1 * prob2), 0.5)\n row[HMEAN] = 2.0 / (1.0 / prob1 + 1.0 / prob2)\n full1 = pron1 == blend1\n row[WORD1_FULL] = convert_r_bool(full1)\n full2 = pron2 == blend2\n row[WORD2_FULL] = convert_r_bool(full2)\n row[COMPLETE] = convert_r_bool(full1 and full2)\n row[WORD1_LENGTH] = pron1_nphones = len(pron1.split(' '))\n row[WORD2_LENGTH] = pron2_nphones = len(pron2.split(' '))\n row[WORD1_CONTENT] = blend1_nphones = len(blend1.split(' '))\n row[WORD2_CONTENT] = blend2_nphones = len(blend2.split(' '))\n row[WORD1_RATIO] = blend1_nphones / float(pron1_nphones)\n row[WORD2_RATIO] = blend2_nphones / float(pron2_nphones)\n # Reformat overlap from Y/N to TRUE/FALSE\n row[OVERLAP] = convert_r_bool(row[OVERLAP].lower() == 'y')\n writer.writerow(row)\n\n\nif __name__ == \"__main__\":\n eval_blends()\n","sub_path":"blends/eval_blends.py","file_name":"eval_blends.py","file_ext":"py","file_size_in_byte":3345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"620064993","text":"# -*- coding:utf-8 -*-\nimport json\nimport time\nimport datetime\nfrom star_builder.types import Type, validators, TypeEncoder\n\n\nclass Book(Type):\n # 使用正则限制名称格式\n name = validators.String(pattern=r\"<<.*?>>\")\n # 枚举类型\n publisher = validators.String(enum=[\"新华出版社\", \"人民教育出版社\", \"人民邮电出版社\"])\n # 作者最大长度不能超20\n author = validators.String(max_length=20)\n # 创建日期类型%Y-%m-%d 目前只支持这一种，可能自定义类型\n publish_date = validators.Date(default=datetime.datetime.now)\n # 不能小于100页\n page_num = validators.Integer(minimum=100)\n # float类型支持\n price = validators.Number()\n # 整除支持, 要2本2本买\n sale_per_count = validators.Integer(multiple_of=2)\n # 支持%Y-%m-%d %H:%M:%S\n created_at = validators.DateTime()\n # 是否在售\n on_sale = validators.Boolean()\n # 当allow_null=True时，若其值为None, 如果指定了default，会使用default\n description = validators.String(allow_null=True, default=\"这是一段描述\")\n\n# 原始json对象\nbook = {\n \"page_num\": 628,\n \"price\": 139.00,\n \"sale_per_count\": 2,\n \"created_at\": time.strftime(\"%Y-%m-%d %H:%M:%S\"),\n \"on_sale\": True,\n \"description\": None\n }\n# 创建Book对象， 当指定Book(book, force_format=True)时，会强制校验数据合法性\nb = Book(book)\n# 赋值时会校验数据合法性\nb.name = \"13444\" # 这条数据会报错\n\n# 下面的数据不会报错\nb.name = \"<<流畅的python>>\"\nb.publisher = \"人民邮电出版社\"\nb.author = \"Luciano Ramalho\"\nb.publish_date = \"2017-5-15\"\n\n# 直接dumps,需要指定cls=TypeEncoder, 同时会校验数据合法性\nprint(json.dumps(b, cls=TypeEncoder, indent=2))\n# to_json返回json字典，同时会校验数据合法性，当to_json(force_format=False)时，数据合法性不会校验\nprint(b.to_json())\n\n\n# model可以从对象中获取\nclass A:\n def __init__(self):\n self.name = \"<<流畅的python>>\"\n self.publisher = \"人民邮电出版社\"\n self.author = \"Luciano Ramalho\"\na = A()\nt = Book(a)\nprint(json.dumps(t, cls=TypeEncoder))\n\n\nclass Name(Type):\n first_name = validators.String()\n last_name = validators.String()\n # 可以引用自身\n full_name = validators.Ref(\"Name\", default=\"全名\")\n\n # 当Name作为其它对象的属性时，可以允许是Null\n @classmethod\n def allow_null(cls):\n return True\n\n\nclass Tape(Type):\n hours = validators.Integer()\n name = validators.String()\n\n\nclass Pad(Type):\n wight = validators.Number()\n brand = validators.String()\n\n\nclass People(Type):\n name = Name\n # exclusive_maximum指定包含边界，即len(id) <= 32\n id = validators.Integer(maximum=32, exclusive_maximum=True)\n # 当items是People时，Array中的对象全是People\n lovers = validators.Array(items=validators.Ref(\"People\"))\n # 最多两项好爱好，第一项只能是唱片，第二项只能是Pad，不接受大于items长度的项\n hobbies = validators.Array(items=[Tape, Pad], additional_items=False)\n\n\npeople = {\"name\": {\"first_name\": \"Tom\",\n \"last_name\": \"Cat\",\n \"full_name\": None},\n \"id\": 13,\n \"lovers\": [{\"name\": {\n \"first_name\": \"Bill\",\n \"last_name\": \"Gate\",\n \"full_name\": {\"first_name\": \"bill\", \"last_name\": \"gate\"}\n },\n \"id\": 1, \"lovers\": [], \"hobbies\": [{\"hours\": 2, \"name\": \"my heart will go on \"}]}],\n \"hobbies\": [{\"hours\": 3, \"name\": \"audio\"}, {\"wight\": 3, \"brand\": \"apple\"}]}\n\np = People(people, force_format=True)\nprint(p.lovers[0].name.full_name)\n\n\nclass Ipad(Pad):\n # 覆盖父类属性\n brand = validators.String(enum=[\"apple\"])\n # 创建一个union对象，price可以是Number类型或String类型\n price = validators.Union([validators.Number(), validators.String()])\n\n\nclass Box(Type):\n ipads = validators.Array(items=Ipad)\n\n\nbox = {\"ipads\": [{\"wight\": 10.1,\n \"brand\": \"apple\",\n \"price\": 10.0}, {\"wight\": 10.1,\n \"brand\": \"apple\",\n \"price\": \"拾圆整\"}]}\n\nb = Box(box, force_format=True)\nprint(b.ipads[1].price)\n\n\n","sub_path":"test/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":4260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"209874458","text":"import re\n\nurl_regex = r\"http[s+]?:[s+]?//(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+\"\n\n\ndef byte_2_str(text):\n if isinstance(text, bytes):\n return text.decode('utf-8')\n else:\n return text\n\n\ndef get_urls(xtext):\n try:\n search_result = re.findall(url_regex, byte_2_str(xtext))\n except:\n import traceback\n traceback.print_exc()\n print(\"Fehler in get_urls():\", xtext)\n print(type(xtext))\n input(\"...\")\n return []\n return search_result\n","sub_path":"heimat/nlp/detect/urls_in_string.py","file_name":"urls_in_string.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"242603964","text":"\n\nfrom xai.brain.wordbase.nouns._equinox import _EQUINOX\n\n#calss header\nclass _EQUINOXES(_EQUINOX, ):\n\tdef __init__(self,): \n\t\t_EQUINOX.__init__(self)\n\t\tself.name = \"EQUINOXES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"equinox\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_equinoxes.py","file_name":"_equinoxes.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"442072429","text":"\"\"\"PyDID\"\"\"\n\nfrom .common import DIDError\nfrom .did import DID, InvalidDIDError\nfrom .did_url import DIDUrl, InvalidDIDUrlError\nfrom .doc import DIDDocumentError\nfrom .doc.doc import DIDDocument, DIDDocumentValidationError\nfrom .doc.builder import DIDDocumentBuilder\nfrom .doc.verification_method import (\n VerificationMethod,\n VerificationSuite,\n VerificationMethodValidationError,\n)\nfrom .doc.service import Service, ServiceValidationError\nfrom .doc.didcomm_service import DIDCommService\n\n__all__ = [\n \"DID\",\n \"DIDCommService\",\n \"DIDDocument\",\n \"DIDDocumentBuilder\",\n \"DIDDocumentError\",\n \"DIDDocumentValidationError\",\n \"DIDError\",\n \"DIDUrl\",\n \"InvalidDIDError\",\n \"InvalidDIDUrlError\",\n \"Service\",\n \"ServiceValidationError\",\n \"VerificationMethod\",\n \"VerificationMethodValidationError\",\n \"VerificationSuite\",\n]\n","sub_path":"pydid/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"17862294","text":"n, m = [int(x) for x in input().split()]\r\ntext = input()\r\n\r\ncount = 0\r\nmatrix = []\r\n\r\nfor row in range(n):\r\n matrix.append([])\r\n for col in range(m):\r\n matrix[row].append(count)\r\n count += 1\r\n\r\nprint(*matrix, sep='\\n')","sub_path":"PyCharm_projects_2020/Advanced/multidimentional/matrix_init_1.py","file_name":"matrix_init_1.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"2633033","text":"import matplotlib.pyplot as plt\n\ndef draw(train_loss, valid_loss):\n n_epoch = len(train_loss)\n fig, ax1 = plt.subplots(1, 1)\n num_data = [i for i in range(n_epoch)]\n ax1.plot(num_data, train_loss, 'orange', label='train_loss')\n ax1.plot(num_data, valid_loss, 'turquoise',label='valid_loss')\n ax1.set_xlabel('number of epochs')\n ax1.set_ylabel('loss on training and vlidation set')\n ax1.grid(True)\n ax1.legend()","sub_path":"DANN/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"537472809","text":"import sys\nimport click\nimport logging\nfrom aws_profile_manager import Common, Add\n\n@click.command()\n@click.option('--aws-profile-name', required=False, help=\"New AWS profile name\", type=str)\n@click.option('--aws-access-key-id', required=False, help=\"AWS Access Key ID\", type=str)\n@click.option('--aws-secret-access-key', required=False, help=\"AWS Secret Access Key\", type=str)\ndef cli(aws_profile_name, aws_access_key_id, aws_secret_access_key):\n \"\"\" Add new AWS profile to your ~/.aws/credentials \"\"\"\n common = Common()\n add = Add()\n users_home = common.get_users_home()\n all_profiles = common.get_all_profiles(users_home)\n if not aws_profile_name:\n aws_profile_name = add.ask_profile_name()\n if not aws_access_key_id:\n while True:\n aws_access_key_id = add.ask_aws_access_key_id()\n if not common.aws_access_key_id_is_valid(aws_access_key_id):\n logging.error(\"Invalid AWS_ACCESS_KEY_ID format. Please, try again\")\n else:\n break\n if not aws_secret_access_key:\n while True:\n aws_secret_access_key = add.ask_aws_secret_access_key()\n if not common.aws_secret_access_key_is_valid(aws_secret_access_key):\n logging.error(\"Invalid AWS_SECRET_ACCESS_KEY format. Please, try again\")\n else:\n break\n all_profiles[aws_profile_name] = { \n 'aws_access_key_id': aws_access_key_id, \n 'aws_secret_access_key': aws_secret_access_key\n }\n common.rewrite_credentials_file(all_profiles, users_home)\n","sub_path":"aws_profile_manager/commands/profile-add.py","file_name":"profile-add.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"135589448","text":"#!/usr/bin/env python\r\n\"\"\" \r\nAER1415 Computer Optimization - Assignment 3\r\n\r\nAuthor: Atilla Saadat\r\nSubmitted: April 28, 2021\r\nEmail: asaadat@utias-sfl.net\r\n\r\nDescripton: Question 5 - Original PSO Algorithm\r\n\r\n\"\"\"\r\n\r\nfrom numpy import *\r\n\r\nclass Particle:\r\n\tdef __init__(self, costFunc, x0, bounds, params):\r\n\t\tself.costFunc = costFunc\r\n\t\tself.pos = array(x0[:])\r\n\t\tself.vel = array([random.uniform(*i) for i in bounds])\r\n\t\tself.posBest = None\r\n\t\tself.valBest = None\r\n\t\tself.val = None\r\n\t\tself.params = params\r\n\t\tself.bounds = bounds\r\n\t\tself.penaltyParam = 1\r\n\r\n\tdef calc(self,iterNum):\r\n\t\t#setup exponential penalty function\r\n\t\tif self.params.get('penalty',False):\r\n\t\t\tself.penaltyParam = self.params['penalty']**iterNum\r\n\t\telif self.params.get('penaltyStatic',False):\r\n\t\t\tself.penaltyParam = self.params['penaltyStatic']\r\n\t\t#Call cost function with current particle position\r\n\t\tself.val = self.costFunc(self.pos,iterNum=iterNum,penaltyParam=self.penaltyParam,params=self.params)\r\n\t\t#if new val is less than stored minimum particle val, update value and position\r\n\t\tif self.valBest is None or self.val < self.valBest:\r\n\t\t\tself.posBest = self.pos\r\n\t\t\tself.valBest = self.val\r\n\r\n\tdef update_position(self,posGlobBest):\r\n\t\tr1, r2 = random.uniform(size=2)\r\n\t\tvel_cognitive = self.params['c1']*r1*(self.posBest-self.pos.copy())\r\n\t\tvel_social = self.params['c2']*r2*(posGlobBest-self.pos.copy())\r\n\t\t#calcualte new particle velocity\r\n\t\tself.vel = self.params['w']*self.vel + vel_cognitive + vel_social\r\n\t\t\r\n\t\t#Hyperbolic bound approach\r\n\t\tif self.params['boundMethod'] == 'hyperbolic':\r\n\t\t\tfor idx,xNew in enumerate(self.pos):\r\n\t\t\t\tif self.vel[idx] > 0:\r\n\t\t\t\t\tself.vel[idx] = self.vel[idx] / (1. + abs(self.vel[idx]/(self.bounds[idx][1]-self.pos[idx])))\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.vel[idx] = self.vel[idx] / (1. + abs(self.vel[idx]/(self.pos[idx]-self.bounds[idx][0])))\r\n\r\n\t\t#set new particle position from velcotiy addition\r\n\t\tself.pos = add(self.pos,self.vel)\r\n\r\n\t\t#Reflect bound approach\r\n\t\tif self.params['boundMethod'] == 'reflect':\r\n\t\t\tfor idx,xNew in enumerate(self.pos):\r\n\t\t\t\tif xNew < self.bounds[idx][0]:\r\n\t\t\t\t\tself.pos[idx] = self.bounds[idx][0] + (self.bounds[idx][0] - self.pos[idx])\r\n\t\t\t\telif xNew > self.bounds[idx][1]:\r\n\t\t\t\t\tself.pos[idx] = self.bounds[idx][1] - (self.bounds[idx][1] - self.pos[idx])\r\n\t\t#Nearest bound approach\r\n\t\tif self.params['boundMethod'] == 'nearest':\r\n\t\t\tfor idx,xNew in enumerate(self.pos):\r\n\t\t\t\tif xNew < self.bounds[idx][0]:\r\n\t\t\t\t\tself.pos[idx] = self.bounds[idx][0]\r\n\t\t\t\t\t#self.vel[idx] = 0\r\n\t\t\t\telif xNew > self.bounds[idx][1]:\r\n\t\t\t\t\tself.pos[idx] = self.bounds[idx][1]\r\n\t\t\t\t\t#self.vel[idx] = 0\r\n\t\t\r\n\r\nclass PSO:\r\n\tdef __init__(self,costFunc,x0,bounds,numParticles,maxRepeat,maxIter,params):\r\n\t\tself.costBestVal = None\r\n\t\tself.x0 = x0\r\n\t\tself.posGlobBest = None\r\n\t\tself.iterGlobBest = None\r\n\t\tself.costFunc = costFunc\r\n\t\tself.numParticles = numParticles\r\n\t\tself.maxIter = maxIter\r\n\t\tself.maxRepeat = maxRepeat\r\n\t\tself.bounds = bounds\r\n\t\tself.params = params\r\n\r\n\tdef optimize(self,verbose=False):\r\n\t\tallResultsDict = {}\r\n\t\tprint(self.params)\r\n\t\t#repeat M times to get mean values for parameter set\r\n\t\tfor repeatIter in range(self.maxRepeat):\r\n\t\t\tself.swarm = [Particle(self.costFunc,self.x0,self.bounds,self.params) for i in range(self.numParticles)]\r\n\t\t\tself.currentDiff = None\r\n\t\t\tself.costBestVal = None\r\n\t\t\tself.posGlobBest = None\r\n\t\t\titerResults = []\r\n\t\t\t#for N number of iterations per independent run\r\n\t\t\tfor idx in range(self.maxIter):\r\n\t\t\t\tfor particle in self.swarm:\r\n\t\t\t\t\t#for every particle, calculate new particle val and position at current iteration step\r\n\t\t\t\t\tparticle.calc(idx)\r\n\t\t\t\t\t#update gloval cost function value and position based on all the new particle positions and values\r\n\t\t\t\t\tif self.costBestVal is None or particle.val < self.costBestVal:\r\n\t\t\t\t\t\t#calcualte current iterations gloval best value differential for convergence\r\n\t\t\t\t\t\tself.currentDiff = abs(subtract(self.costBestVal,particle.val)) if idx != 0 else None\r\n\t\t\t\t\t\tself.costBestVal = particle.val\r\n\t\t\t\t\t\tself.posGlobBest = particle.pos\r\n\t\t\t\titerResults.append(append(self.posGlobBest,[self.costBestVal,self.currentDiff]))\r\n\t\t\t\t#store index at which differntial convergence first happens\r\n\t\t\t\ttry:\r\n\t\t\t\t\tif idx != 0 and self.currentDiff is not None and abs(self.currentDiff) <= self.params['rel_tol'] and self.iterGlobBest is None:\r\n\t\t\t\t\t\tself.iterGlobBest = idx\r\n\t\t\t\texcept:\r\n\t\t\t\t\tembed()\r\n\t\t\t\t#update all particles with new global best value\r\n\t\t\t\tfor particle in self.swarm:\r\n\t\t\t\t\tparticle.update_position(self.posGlobBest)\r\n\t\t\t\tif verbose:\r\n\t\t\t\t\tprint('Iter: {}/{} - CostFunc: {}, val: {}, df: {}'.format(idx,self.maxIter,self.posGlobBest,self.costBestVal,self.currentDiff))\r\n\t\t\tprint('{} / {} - CostFunc: {}, val: {}'.format(repeatIter,self.maxRepeat,self.posGlobBest,self.costBestVal))\r\n\t\t\tallResultsDict[repeatIter] = array(iterResults)\r\n\t\t#calculate mean and std values for later plotting\r\n\t\trepeatResults = array([v.T[-2].T for v in allResultsDict.values()]).T.astype(float)\r\n\t\tbestRun, bestIter = divmod(repeatResults.T.argmin(),repeatResults.T.shape[1])\r\n\t\tmeanVals = mean(repeatResults,axis=1)\r\n\t\tmeanPos = array([mean(i,axis=1) for i in array([v.T[:-2].T for v in allResultsDict.values()]).T]) \r\n\t\tmeanPosVal = vstack([meanPos,meanVals]).astype(float)\r\n\t\t#results = {'minVal': float(repeatResults.T[bestRun][bestIter]), 'x*': (allResultsDict[bestRun][bestIter][:-2]).astype(float), 'iter': bestIter, 'relTolPass': True, 'meanPosVal': meanPosVal,'meanRepeatValues': meanVals, 'stdRepeatValues': std(repeatResults,axis=1)}\r\n\t\tresults = {'minMeanVal': meanVals[-1], 'x*': (allResultsDict[bestRun][bestIter][:-2]).astype(float), 'iter': bestIter, 'relTolPass': True, 'meanPosVal': meanPosVal,'meanRepeatValues': meanVals, 'stdRepeatValues': std(repeatResults,axis=1)}\r\n\t\tif self.iterGlobBest is None:\r\n\t\t\tresults['iter'] = idx\r\n\t\t\tresults['relTolPass'] = False\r\n\t\t#if getAllResults:\r\n\t\t#\tresults['allResultsDict'] = allResultsDict\r\n\t\treturn results\r\n","sub_path":"Assignment 3/PSO.py","file_name":"PSO.py","file_ext":"py","file_size_in_byte":5979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"494507643","text":"import aiwolfpy\nimport aiwolfpy.contentbuilder as cb\n\nimport numpy as np\nimport re\nimport os\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport time\nimport random\n\nimport matplotlib.pyplot as plt\nimport collections\nfrom collections import *\nX_T = namedtuple(\"X_T\",(\"state\",\"label\"))\n\nfrom predict_model import PredictRole\nfrom brain import Brain\nfrom divine_model import DivineModel\n\n\nimport sys\nfrom pathlib import Path\nsys.path.append(str(Path(__file__).parent.parent))\n\n\n\nclass Agent():\n def __init__(self,pred_n_input, pred_n_hidden, pred_n_output,dqn_n_input, dqn_n_hidden, dqn_n_output, agent_num,role_num,t_role_cnt,train_predict_mode=False,train_dqn_mode=False,train_divine_mode=False,explore=False):\n self.agent_num = agent_num\n self.role_num = role_num\n self.train_predict_mode = train_predict_mode\n self.train_dqn_mode = train_dqn_mode\n self.train_divine_mode = train_divine_mode\n self.explore = explore\n self.kanning=True\n self.pred_model = PredictRole(pred_n_input,pred_n_hidden,agent_num,role_num)\n self.brain = Brain(n_input=dqn_n_input,n_hidden=dqn_n_hidden,n_output=dqn_n_output)\n self.divine_model = DivineModel(n_input=dqn_n_input,n_hidden=dqn_n_hidden,n_output=agent_num*2)\n self.last_pred_result = None\n \n self.epsilon = 0.1\n\n if self.agent_num <= 6:\n self.role_to_num = {\"VILLAGER\":0,\"SEER\":1,\"WEREWOLF\":2,\"POSSESSED\":3} \n elif self.agent_num == 7:\n self.role_to_num = {\"VILLAGER\":0,\"SEER\":1,\"WEREWOLF\":2}\n elif self.agent_num <= 9:\n self.role_to_num = {\"VILLAGER\":0,\"SEER\":1,\"WEREWOLF\":2,\"MEDIUM\":5}\n else:\n self.role_to_num = {\"VILLAGER\":0,\"SEER\":1,\"WEREWOLF\":2,\"POSSESSED\":3,\"BODYGUARD\":4,\"MEDIUM\":5} \n \n def update_pred_model(self):\n self.pred_model.train(agent_num=self.agent_num,role_num=self.role_num)\n\n def eval_pred_model(self,state,label):\n # print(state)\n # state = torch.tensor(state).float()\n # label = torch.tensor(label).float()\n pred = self.pred_model.eval(state=state,label=label,agent_num=self.agent_num,role_num=self.role_num)\n return pred\n\n def memorize_pred_label(self,state,label):\n state = torch.tensor(state).float()\n label = torch.tensor(label).float()\n self.pred_model.memory.push(state,label)\n\n def randomSelect(self,votable_mask):\n while(True):\n target = np.random.randint(0,len(votable_mask))\n # if votable_mask[target]==True:\n # return target\n return target\n\n def createDqnState(self,state):\n # print(self.kanning)\n if self.kanning==True:\n self.answer = []\n with open(\"../AIWolf-server/role.txt\",\"r\") as f:\n for agent in f.readlines():\n agent = agent.strip(\"\\n\")\n self.answer.append([i==self.role_to_num[agent] for i in range(self.role_num)])\n self.answer = torch.FloatTensor(self.answer).view(1,-1)\n pred_result = self.answer\n else:\n pred_result = self.get_predict_output(state)\n # return torch.FloatTensor(pred_result)\n # return torch.FloatTensor(state)\n return torch.cat((torch.FloatTensor(state),torch.FloatTensor(pred_result)),dim=1).float()\n\n def selectAgent(self,state,votable_mask,agent_num,role_num,num_to_role):\n #返り値は１始まり\n # return self.brain.selectAgent(state,episode=0).argmax(0).item() + 1\n\n # if self.kanning == True:\n # for target,role in enumerate(self.answer):\n # if role == target_role and votable_mask[target]:\n # return target + 1\n # return self.randomSelect(votable_mask),None\n\n # if self.explore == True and self.train_dqn_mode == True:\n if self.train_dqn_mode == True:\n if np.random.random() < self.epsilon:\n # print(\"random\",pred_result.detach().numpy())\n return self.randomSelect(votable_mask)\n\n self.brain.target_q_model.eval()\n with torch.no_grad():\n state = torch.tensor(state).float()\n # action_type = torch.tensor(action_type).float()\n\n # pred_result = self.brain.get_output(state)\n # print(type(pred_result))\n \n \n state = self.createDqnState(state=state)\n vote_list = sorted(enumerate(self.brain.get_output(state).squeeze().detach().numpy()),key=lambda x:x[1],reverse=True)\n for target,_ in vote_list:\n if votable_mask[target] == True:\n # print(\"target\",pred_result.detach().numpy())\n return target\n # print(\"random\",pred_result.detach().numpy())\n return randomSelect(votable_mask)\n\n\n # est_role_list = self.pred_model.model(state).reshape(-1).detach().numpy().reshape(agent_num,role_num)\n\n # est_role_list = [(est_role_list[agent,role],agent) for agent,role in enumerate(np.argmax(est_role_list,axis=1)) if num_to_role[role] == target_role]\n # est_role_list = sorted(est_role_list,reverse=True)\n\n # for _,target in est_role_list:\n # if votable_mask[target]==True:\n # return target + 1\n\n # return self.randomSelect(votable_mask)\n\n def update_q_function(self):\n self.brain.replay()\n \n def update_divine_function(self):\n self.divine_model.replay()\n\n # def get_action(self,state,episode):\n # state =torch.tensor(state).float()\n # action = self.brain.decide_action(state,episode)\n\n def memorize_state(self,state,action,next_state,reward):\n state = torch.FloatTensor(state)\n state = self.createDqnState(state=state)\n action = torch.tensor(action).long()\n if next_state is not None:\n next_state = torch.FloatTensor(next_state)\n next_state = self.createDqnState(state=next_state)\n reward = torch.tensor(reward).float()\n self.brain.memory.push(state,action,next_state,reward)\n\n def memorize_divine_state(self,state,action,next_state,reward):\n state = torch.FloatTensor(state)\n state = self.createDqnState(state=state)\n action = torch.tensor(action).long()\n if next_state is not None:\n next_state = torch.FloatTensor(next_state)\n next_state = self.createDqnState(state=next_state)\n reward = torch.tensor(reward).float()\n self.divine_model.memory.push(state,action,next_state,reward)\n\n\n # def updateWinRatio(self,win):\n # win = 1 if win else 0\n # self.brain.memory.pushWinRatio(win)\n\n def get_predict_output(self,state):\n self.last_pred_result = self.pred_model.get_output(state).detach().numpy()\n return self.last_pred_result\n\n\n def randomDivineSelect(self,divinable_mask):\n while(True):\n target = np.random.randint(0,len(divinable_mask))\n # if divinable_mask[target]==True:\n # role = np.random.randint(0,2)\n # return target*2 + role\n role = np.random.randint(0,2)\n return target*2 + role\n\n def selectDivineAgent(self,state,divinable_mask):\n if state is None:\n return self.randomDivineSelect(divinable_mask)\n self.divine_model.target_q_model.eval()\n with torch.no_grad():\n\n # if self.explore == True and self.train_divine_mode == True:\n if self.train_divine_mode == True:\n if np.random.random() < self.epsilon:\n return self.randomDivineSelect(divinable_mask)\n\n state = torch.tensor(state).float()\n \n # pred_result = self.pred_model.get_output(state)\n # print(type(pred_result))\n \n # if self.train_divine_mode == True:\n # if np.random.random() < self.epsilon:\n # # print(\"random\",pred_result.detach().numpy())\n # return self.randomDivineSelect(divinable_mask)\n \n state = self.createDqnState(state=state)\n divine_list = sorted(enumerate(self.divine_model.get_output(state).squeeze().detach().numpy()),key=lambda x:x[1],reverse=True)\n # print(divine_list)\n # print(divinable_mask)\n for target,_ in divine_list:\n if divinable_mask[target//2] == True:\n # print(target)\n # print()\n return target\n return randomDivineSelect(divinable_mask)\n\n\n def update_target_q_function(self):\n self.brain.update_target_q_model()\n self.divine_model.update_target_q_model()\n","sub_path":"forroop/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":8900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"622375814","text":"import xlwings as xl\nimport pandas as pd\nimport numpy as np\n\nclass Fileset:\n\n def __init__(self, file, path=\"Data\"):\n\n self.path=path\n self.file=file\n self.fullpath=f\"\"\"{path}/{self.file}\"\"\"\n\n def file_open(self):\n \n wb=xl.Book(self.fullpath)\n return wb\n\nclass TimeSeriesFileset(Fileset):\n\n ## Frequency \n ## Period\n @staticmethod\n def column_creator(x):\n\n ## x should be list object\n if len(x) < 1:\n raise Exception('length of list x should be more than 1')\n else: \n return f\"B8\"\n\n def clear_sheet(self, sheets):\n\n for sheet in sheets:\n sheets[sheet.name].range('B8:XFD9').value = None\n sheets[sheet.name].range('A15').options(transpose=True).value = None\n \n def date_setting(self, sheets, st_date, ed_date, date_type):\n\n for sheet in sheets:\n sheets[sheet.name].range(\"B4\").value = date_type\n sheets[sheet.name].range(\"B5\").value = st_date\n sheets[sheet.name].range(\"B6\").value = ed_date\n\n def column_setting(self, sheets, secs):\n\n ## XDF8 Hard Coding\n\n rng_cols = TimeSeriesFileset.column_creator(secs)\n\n for sheet in sheets:\n sheets[sheet.name].range(\"B8:XFD8\").value = None\n sheets[sheet.name].range(rng_cols).value = secs\n\n def full_update_sheet(self, st_date, ed_date, secs, date_type = 'd'):\n\n ## 종목, Date 모두 업데이트\n\n wb = self.file_open()\n sheets = wb.sheets\n\n self.clear_sheet(sheets)\n self.date_setting(sheets, st_date, ed_date, date_type)\n self.column_setting(sheets, secs)\n\n mac = wb.macro(\"Refresh_Button\")\n mac()\n \n wb.save(self.fullpath)\n\n def column_update_sheet(self, secs):\n\n ## 종목 업데이트만 필요할 떄\n\n wb = self.file_open()\n sheets = wb.sheets\n\n self.clear_sheet(sheets)\n self.column_setting(sheets, secs)\n\n mac = wb.macro(\"Refresh_Button\")\n mac()\n \n wb.save(self.fullpath) \n\n def frequency_update(self, st_date, ed_date, date_type='D'):\n\n ## Date Change만 필요할 때\n\n wb = self.file_open()\n sheets = wb.sheets\n\n self.clear_sheet(sheets)\n self.date_setting(sheets, st_date, ed_date, date_type)\n\n mac = wb.macro(\"Refresh_Button\")\n mac()\n \n wb.save(self.fullpath)\n\nclass FinancialDataSet(Fileset):\n\n ## C9: XFD10 = None\n ## A14: A14\n\n def clear_sheet(self, sheets, options=0):\n\n if options == 0:\n\n for sheet in sheets:\n sheets[sheet.name].range('C9:XFD40000').value = None\n sheets[sheet.name].range('A14:B40000').value = None\n \n elif options == 1:\n\n for sheet in sheets:\n sheets[sheet.name].range('C9:XFD40000').value = None\n\n elif options == 2:\n\n for sheet in sheets:\n sheets[sheet.name].range('A14:B40000').value = None\n\n def column_setting(self, sheets, account, period):\n\n len_period = len(period)\n rp_account = account.repeat(len_period)\n \n for sheet in sheets:\n sheets[sheet.name].range(\"C9\").value = rp_account\n sheets[sheet.name].range(\"C10\").value = period\n\n def company_code(self, sheets, comp_code):\n\n for sheet in sheets:\n sheets[sheet.name].range(f\"A14:A16\").options(transpose=True).value = comp_code\n\n def column_update(self, account, period, options=1):\n\n wb = self.file_open()\n sheets = wb.sheets\n\n self.clear_sheet(sheets, options=options)\n self.column_setting(sheets, account, period)\n\n mac = wb.macro(\"Refresh_Button\")\n mac()\n \n wb.save(self.fullpath)\n\n def company_update(self, account, period, options=2):\n\n wb = self.file_open()\n sheets = wb.sheets\n\n self.clear_sheet(sheets, options=options)\n self.company_code(account, period)\n\n mac = wb.macro(\"Refresh_Button\")\n mac()\n \n wb.save(self.fullpath)","sub_path":"FileOpener/Fileset/fileset.py","file_name":"fileset.py","file_ext":"py","file_size_in_byte":4135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"90802989","text":"from random import randint\nimport time\nimport codecs\n\ndef get_posts():\n # data = open(\"Client/r_dataisbeautiful_posts.csv\")\n data_input_stream = codecs.open(\"ClientYou/GBvideos.csv\", \"r\", encoding='utf-8')\n\n file_lines = data_input_stream.readlines()\n\n # The first line of the file is the headings\n # id, title, score, author, author_flair_text, removed_by, total_awards_received, awarders, created_utc, full_link, num_comments,over_18\n headings = file_lines.pop(0)\n\n size_of_data = len(file_lines)\n \n # Get the amount of posts that was streamed in\n for i in range(size_of_data):\n # Defines how many posts to send\n # num_of_posts = randint(2,10)\n num_of_posts = 50\n\n message = \"\"\n\n # Get the amount of posts that was streamed in\n for j in range(num_of_posts):\n # Take random posts from the dataset\n line = file_lines[randint(0, size_of_data)]\n\n # If the line is empty then just keep going\n if len(line) == 0:\n continue\n\n # Construct the message string\n message += line + \" @ \"\n \n # message += \"_\" + str(i)\n\n yield message\n\n time.sleep(20)\n \n data.close()\n","sub_path":"ClientYou/data_reader.py","file_name":"data_reader.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"112137154","text":"# If you're using python 2?\n# from __future__ import division\nimport numpy as np\n\n# Definitions:\n# pg: error rate per gate (20ns)\n# pr: error rate per rest step (4250ns)\n# sg: standard deviation in pg\n# sr: standard deviation in pra\n\n\n# Function that I'm fitting to\ndef pfunc(n_vec, pg, pr):\n\n # Boundary conditions in case you want to fit this via scipy.\n if pg < 0:\n pg == 0\n if pg > 1:\n pg == 1\n\n # P=0.5-(1-2pg)^n(0.5-pr)\n return [100*(0.5-(1-2*pg)**n*(0.5-pr)) for n in n_vec]\n\n\ndef sfunc(n_vec, pg, pr, sg, sr, covar_gr=0):\n \"\"\"\n noise function version two, including covariances.\n\n\n \"\"\"\n\n # Get variances\n var_rest = sr**2\n var_gate = sg**2\n\n # init return data list\n sm_vec = []\n for Ncl in n_vec:\n # Calculate error probability\n # P=0.5-(1-2pg)^n(0.5-pr)\n P = (0.5-(1-2*pg)**Ncl*(0.5-pr))\n\n # Calculate standard deviation of probability of error in\n # one round of error correction.\n # s_{P}^2=(dP/dpg)^2s_{pg}^2+(dP/dpr)^2s_{pr}^2\n sp2 = ((1-2*pg)**(2*Ncl)*var_rest +\n Ncl**2*(1-2*pr)**2*(1-2*pg)**(2*(Ncl-1))*var_gate +\n 2*Ncl*(1-2*pr)*(1-2*pg)**(2*Ncl - 1)*covar_gr)\n\n # Combine variance from distribution of P and variance\n # from the binomial distribution that we take in a fairly\n # crude manner.\n N = 8000 # number of shots that go into each error fraction\n var_sm = P*(1-P)/N + (N-1)*sp2/N\n sm_vec.append(np.sqrt(var_sm)*100)\n\n # Return to user\n return sm_vec\n\n\ndef make_difference_plot(x_vec, j1, j2):\n\n # Generates the difference plot\n\n # Generate individual curves using preset data defined below\n y_vec1 = pfunc(x_vec, pg_vec[j1], pr_fixed)\n y_vec2 = pfunc(x_vec, pg_vec[j2], pr_fixed)\n\n # Take difference, return\n return [y1-y2 for y1, y2 in zip(y_vec1, y_vec2)]\n\n\ndef make_std_plot(x_vec, j1, j2):\n\n # Generates the std plot\n\n\n\n # Generate individual curves using preset data defined below\n y_vec1 = sfunc(x_vec, pg_vec[j1], pr_fixed, sg_fixed, sr_fixed)\n y_vec2 = sfunc(x_vec, pg_vec[j2], pr_fixed, sg_fixed, sr_fixed)\n\n # Take difference, return\n return [np.sqrt(y1**2+y2**2) for y1, y2 in zip(y_vec1, y_vec2)]\n\n\n# Fit coefficients obtained from fitting pfunc/sfunc to the experimental data,\n# via scipy.optimize.curve_fit\n# Placed here by hand so you don't have to import the data etc. Feel free to\n# do otherwise.\n\n# Please note, the below is stored as a fraction (i.e. 1=100%), otherwise the\n# math doesn't work.\n\n# We have good reasons to average the rest error probability (pr_fixed)\n# and standard deviation (sr_fixed) data, leaving only one free parameter\n# between different data sets (I feel this is better).\n\n# However, the standard deviation in gate error rates (sg_fixed) could\n# hypothetically vary as you detune the gates, so this isn't so cut and dry.\n# It can also be set to zero without much effect; we only see something at\n# around 1e-4.\n\npg_vec = [0.00098213056186337668, 0.0015671241839269017,\n 0.0025267070493921332, 0.0034871658898885118,\n 0.005803222326512896, 0.0070708654552344092,\n 0.011270378736976753, 0.016452382020151746]\nsr_fixed = 0.0101053841726\nsg_fixed = 1.1444357016e-05\npr_fixed = 0.1176079925\n","sub_path":"pycqed/scripts/Experiments/Restless/paper_figs/theory_RB_fits.py","file_name":"theory_RB_fits.py","file_ext":"py","file_size_in_byte":3305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"460699623","text":"from discord_webhook import DiscordWebhook\nfrom config import *\nimport sys, requests, json\nfrom datetime import date, timedelta\n\ndef get_info(stocks, api_key):\n infos = []\n \n for stock in stocks:\n yesterday = (date.today() - timedelta(days=1)).strftime('%Y-%m-%d')\n r = requests.get(f'https://api.polygon.io/v1/open-close/{stock}/{yesterday}?apiKey={api_key}')\n infos.append(json.loads(r.text))\n\n return infos\n\ndef discord(message):\n webhook = DiscordWebhook(\n url=webhook_uri, \n content=message)\n webhook.execute()\n\ndef notify(message):\n discord(message)\n\nif __name__ == \"__main__\":\n api_key, stocks = sys.argv[1], sys.argv[2]\n stocks_list = stocks.split(',')\n infos = get_info(stocks_list, api_key)\n\n msg = 'Today\\'s Date: {}\\nYesterday\\'s stock prices:\\n\\n'.format(date.today().strftime('%Y-%m-%d'))\n for i in range(len(infos)):\n gain = ((infos[i]['close'] / infos[i]['open']) - 1.0) * 100\n msg += '{} ({})\\nOpen: ${:.2f}\\nClose: ${:.2f}\\nGain: {:.2f}%\\n\\n'.format(\n stocks_list[i],\n infos[i]['from'],\n infos[i]['open'], \n infos[i]['close'],\n gain\n )\n\n notify(msg.strip())\n ","sub_path":"src/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"631935619","text":"\"\"\"\nbatch-process.py\n\nIterates over items in S3 bucket and invokes a lambda function for it.\n\nUsage:\n batch-process.py [--batch_size=] [--filter=] [--no-pause]\n\nArguments:\n S3 bucket name\n Lambda to be invoked for each item.\n\nOptions:\n --filter= Prefix filter applied to items. Only items that pass this\n filter will be processed [default: ].\n --batch_size= Number of items to collect before invoking lambda [default: 1].\n --no-pause Disables interactive mode (when script pauses after every items).\nExamples:\n python bucket-batch-process.py fssi2019-s3-ingest upload/* fssi2019-lambda-media-rekognition-proc\n\"\"\"\n\nimport json\nimport boto3\nimport sys, os, traceback\nfrom decimal import Decimal\nfrom fssi_common import *\nfrom docopt import docopt\nimport re\nimport time\n\ndef processItem(item):\n print('processing item {}'.format(item))\n\ndef iterateBucketItems(bucket, prefix):\n \"\"\"\n Generator that iterates over all objects in a given s3 bucket\n\n See http://boto3.readthedocs.io/en/latest/reference/services/s3.html#S3.Client.list_objects_v2\n for return data format\n :param bucket: name of s3 bucket\n :return: dict of metadata for an object\n \"\"\"\n\n client = boto3.client('s3')\n paginator = client.get_paginator('list_objects_v2')\n if prefix != '':\n pageIterator = paginator.paginate(**{'Bucket':bucket, 'Prefix':prefix})\n else:\n pageIterator = paginator.paginate(Bucket=bucket)\n\n for page in pageIterator:\n if page['KeyCount'] > 0:\n for item in page['Contents']:\n yield item\n\ndef iterateBucket(bucketName, lambdaName, prefixFilter, batchSize, noPause):\n batch = []\n try:\n nIter = 0\n nItems = 0\n nItemsProcessed = 0\n runTime = []\n lambdaClient = boto3.client('lambda')\n for item in iterateBucketItems(bucketName, prefixFilter):\n nItems += 1\n mimeType = guessMimeTypeFromExt(item['Key'])\n if mimeType and 'image' in mimeType:\n batch.append({'bucket': bucketName, 'objectKey': item['Key']})\n if len(batch) >= batchSize:\n print('gathered {} items. will invoke lambda now'.format(len(batch)))\n # print(batch)\n start = time.time()\n # res = {'ResponseMetadata' : {'HTTPStatusCode': 200}}\n # print('invoke lambda')\n payload = json.dumps({'items': batch})\n print('payload size', len(payload))\n res = lambdaClient.invoke(FunctionName=lambdaName, Payload=payload)\n runTime.append(time.time() - start)\n statusCode = res['ResponseMetadata']['HTTPStatusCode']\n print('lambda returned code {}'.format(statusCode))\n nItemsProcessed += len(batch)\n nIter += 1\n batch = []\n if not noPause:\n reply = input('continue? [Y/n/r -- run with no pause]')\n if reply in ['n', 'N']:\n break\n elif reply in ['R', 'r']:\n noPause = True\n\n print('items iterated {}, processed {}. avg processing time {:.2f}ms, '\n 'total processing time {:.4f}sec'.format(nItems, nItemsProcessed,\n sum(runTime)/len(runTime)*1000., sum(runTime)))\n except:\n type, err, tb = sys.exc_info()\n print('caught exception:', err)\n traceback.print_exc(file=sys.stdout)\n\nif __name__ == '__main__':\n options = docopt(__doc__, version='0.0.1')\n # print(options)\n bucketName = options['']\n lambdaName = options['']\n prefixFilter = options['--filter']\n batchSize = options['--batch_size']\n\n print('running batch process for bucket {} (filter {}). will invoke lambda '\n '{} for every {} record(s)'.format(bucketName, prefixFilter, lambdaName, batchSize))\n\n iterateBucket(bucketName, lambdaName, prefixFilter, int(batchSize), options['--no-pause'])\n\n\ndef iterate_bucket_items(bucket):\n client = boto3.client('s3')\n paginator = client.get_paginator('list_objects_v2')\n page_iterator = paginator.paginate(Bucket=bucket)\n for page in page_iterator:\n if page['KeyCount'] > 0:\n for item in page['Contents']:\n yield item\n","sub_path":"lambda/batch-proc/batch-process.py","file_name":"batch-process.py","file_ext":"py","file_size_in_byte":4540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"617437899","text":"DRIVE_FOLDER = \"\\\\driveAccess\"\nMAPS_FOLDER = \"\\\\country_maps\"\nRESULT_FOLDER = \"\\\\results\"\nENGINE_FOLDER = \"\\\\..\\\\core\"\nGENERATOR_FOLDER = \"\\\\generation\"\nOPTIMIZER_FOLDER = \"\\\\optimization\"\nGENERATOR_EXE = \"\\\\generator\"\nOPTIMIZER_EXE = \"\\\\optimizer\"\nTMP_FILE = \"\\\\data.tmp\"\nTMP2_FILE = \"\\\\data2.tmp\"\n\nTRAV_INF_CAPACITY = 100000\nMAX_COUNTRIES = 5\n\nDASHBOAD_URL = \"https://datastudio.google.com/u/2/reporting/719e36c2-23e3-4697-8a94-30e2cb147167/page/ZNsDC\"\n","sub_path":"launcher/defines.py","file_name":"defines.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"205004791","text":"import os\nimport pandas as pd\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\n\n\n\ncolumns = ['Filename', 'ClassId']\n#Combine all train annotations into one table \ndef load_annotations(data_dir):\n\n \n annotations = pd.DataFrame(columns = columns)\n\n\n annotations_path = os.path.join(data_dir, 'data/Train')\n\n #iterate over all classes folders to get the class annotations\n for dir_ in os.listdir(annotations_path):\n if dir_.startswith('.'):\n continue\n\n path = os.path.join(annotations_path, dir_, 'GT-' + dir_+ '.csv')\n annot_dir = pd.read_csv(path, sep=';', usecols=columns)\n annot_dir['Filename'] = annot_dir['Filename'].apply(lambda path : annotations_path+'/'+dir_+'/'+path)\n annotations = pd.concat([annotations, annot_dir], axis = 0)\n\n return shuffle(annotations).reset_index()\n\n\n#Select Filename (entire paths) and ClassId\ndef test_ann(path_csv):\n filename = 'GT-final_test.csv'\n ann = pd.read_csv(os.path.join(path_csv,filename), sep =';', usecols=columns)\n ann['Filename'] = ann['Filename'].apply(lambda img : path_csv+'/'+img)\n return ann\n \ndef main():\n #create the whole data annotations file\n # We will save the test csv in the same \n train_annotations = load_annotations('.')\n test_annotations = test_ann('./data/Test')\n print(test_annotations.head())\n print(train_annotations.head())\n # write csv\n train_annotations.to_csv('./data/Train.csv')\n test_annotations.to_csv('./data/Test.csv')\n print('Annotations generated ! ')\n\nif __name__=='__main__':\n main()","sub_path":"annotations_gen.py","file_name":"annotations_gen.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"390386953","text":"\r\nimport sqlite3\r\nfrom myconst import DBPATH\r\nfrom datetime import datetime, date\r\nfrom myutils import str2date, incday, date2str\r\nfrom myutils import xirr\r\n\r\nDBGTAG = 'policy'\r\n\r\ndef dbg(msg, tag=''):\r\n if tag == DBGTAG:\r\n print(msg)\r\ndef log(msg):\r\n print(msg)\r\n\r\ndef getvalues(fundid):\r\n conn = sqlite3.connect(DBPATH)\r\n cursor = conn.execute('select * from funds where fundid = \"%s\"' % fundid)\r\n rows = cursor.fetchall()\r\n fundvalues = {}\r\n for row in rows:\r\n fundvalues[str2date(row[1])] = float(row[2])\r\n \r\n return fundvalues\r\n \r\ndef getValueOfdays(fundvalues, beginDateStr, endDateStr, day):\r\n beginDate = str2date(beginDateStr)\r\n endDate = str2date(endDateStr)\r\n currDate = beginDate\r\n ret = {}\r\n \r\n while (currDate < endDate and currDate < datetime.now()):\r\n if (currDate.strftime(\"%a\") == day or currDate.day == day or day == 0):\r\n if (not currDate in fundvalues.keys()):\r\n dbg(date2str(currDate))\r\n currDate = incday(currDate)\r\n while(not currDate in fundvalues.keys()):\r\n dbg(date2str(currDate))\r\n currDate = incday(currDate)\r\n ret[currDate] = fundvalues[currDate]\r\n \r\n currDate = incday(currDate)\r\n \r\n return ret\r\n\r\n \r\ndef getReturnOfInvest(fundid, rate, beginDateStr, endDateStr, day, investEveryTime=1):\r\n fv = getvalues(fundid)\r\n days = getValueOfdays(fv, beginDateStr, endDateStr, day)\r\n ret = {}\r\n allinvest = 0\r\n allshare = 0\r\n \r\n alldays = list(days.keys())\r\n for day in alldays[:-1]:\r\n allinvest += investEveryTime\r\n share = investEveryTime * (1-rate) / days[day]\r\n allshare += share\r\n allreturn = allshare * days[day]\r\n ret[day] = [investEveryTime, allinvest, share, allshare, allreturn, fv[day]]\r\n ret[alldays[-1]] = [0, allinvest, 0, allshare, allshare*days[alldays[-1]], fv[alldays[-1]]]\r\n \r\n return ret\r\n \r\ndef addPolicy(fv, detailInfo, more, rate, threshold=-0.01):\r\n days = list(detailInfo.keys())\r\n added = {}\r\n \r\n for i in range(1, len(days) - 2):\r\n #latestinfo = detailInfo[days[i]]\r\n #preinfo = detailInfo[days[i-1]]\r\n if (fv[days[i]] - fv[days[i-1]])/fv[days[i-1]] < threshold:#1%\r\n #nextinfo = detailInfo[days[i+2]]\r\n moreshare = more * (1-rate)/fv[days[i+2]]\r\n added[days[i+2]] = [more, moreshare]\r\n #detailInfo[days[i + 2]] = [nextinfo[0]+more, nextinfo[1]+more, nextinfo[2]+moreshare, nextinfo[3]+moreshare, detailInfo[days[i + 2]][-1]]\r\n #detailInfo[days[-1]] = [0, detailInfo[days[-1]][1]+more, detailInfo[days[-1]][2]+moreshare, detailInfo[days[-1]][3]+moreshare, (detailInfo[days[-1]][3]+moreshare)*]\r\n \r\n log('Today: [%s], Mkt: %f; Preday: [%s], Mkt: %f. [%s] Add: %f, Share: %f' \\\r\n % (date2str(days[i]), fv[days[i]], date2str(days[i-1]), fv[days[i-1]], \\\r\n date2str(days[i+2]), more, moreshare))\r\n elif (fv[days[i]] - fv[days[i-1]])/fv[days[i-1]] > -threshold:\r\n moreshare = more * (1-rate)/fv[days[i+2]]\r\n added[days[i+2]] = [-more, -moreshare]\r\n \r\n return added\r\n\r\ndef getSummary(detailInfo, fv, added={}):\r\n days = list(detailInfo.keys())\r\n values = []\r\n totaladdedShare = 0\r\n totaladdMoney = 0\r\n for day in days[:-1]:\r\n if day in added.keys():\r\n values.append(-detailInfo[day][0]-added[day][0])\r\n totaladdedShare += added[day][1]\r\n totaladdMoney += added[day][0]\r\n else:\r\n values.append(-detailInfo[day][0])\r\n \r\n values.append(detailInfo[days[-1]][-2] + fv[days[-1]]*totaladdedShare)\r\n xirrvalue = xirr(values, days) * 100\r\n \r\n return detailInfo[days[-1]][1]+totaladdMoney, round(values[-1], 4), round(xirrvalue, 2)\r\n \r\nfv = getvalues('160716')\r\n#days = getValueOfDays(fv, '2016-8-19', '2017-2-24', 'Tue')\r\n'''\r\nfor i in range(1, 28):\r\n days = getReturnOfInvest('160716', 0.0012, '2015-7-12', '2017-7-12', i)\r\n print(i, getSummary(days))\r\n'''\r\nfixday = 0#'Tue'\r\n\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days, fv))\r\nprint('---')\r\nadded = addPolicy(fv, days, 1, 0.0012)\r\nprint(getSummary(days, fv, added))\r\nprint('---')\r\n'''\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2013-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2014-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2015-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2016-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2012-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days))\r\n\r\ndays = getReturnOfInvest('160716', 0.0012, '2013-7-12', '2014-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2013-7-12', '2015-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2013-7-12', '2016-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2013-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days))\r\n\r\ndays = getReturnOfInvest('160716', 0.0012, '2014-7-12', '2015-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2014-7-12', '2016-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2014-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days))\r\n\r\ndays = getReturnOfInvest('160716', 0.0012, '2015-7-12', '2016-7-12', fixday) \r\nprint(getSummary(days))\r\ndays = getReturnOfInvest('160716', 0.0012, '2015-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days))\r\n\r\ndays = getReturnOfInvest('160716', 0.0012, '2016-7-12', '2017-7-12', fixday) \r\nprint(getSummary(days))\r\n'''\r\n'''\r\nfor day in days.keys():\r\n print(day.strftime(\"%a %Y-%m-%d\"), days[day])\r\n'''\r\n\r\n'''\r\n\r\ndef getEveryBuyDateOfWeekly(beginDateStr, endDateStr, weekday):\r\n beginDate = str2date(beginDateStr)\r\n endDate = str2date(endDateStr)\r\n currDate = beginDate\r\n ret = []\r\n while (currDate < endDate):\r\n if (currDate.strftime(\"%a\") == weekday):\r\n ret.append(currDate)\r\n \r\n currDate = incday(currDate)\r\n \r\n return ret\r\n''' \r\n \r\n'''\r\ndef genFixedIncome(investEachTime, timesPerYear, incomePerYear):#compound per year\r\n sumInvest = 0\r\n sumInvestNewYear = 0\r\n sumIncome = 0\r\n incomeEachTime = 1.0 * incomePerYear / timesPerYear\r\n \r\n i = 0\r\n j = 0\r\n while i < len(investEachTime):\r\n sumInvest += investEachTime[i]\r\n if j < timesPerYear:\r\n j += 1\r\n else:\r\n j = 1\r\n print(sumInvest, j)\r\n i += 1\r\n'''\r\n'''\r\nimport sys\r\nprint(sys.argv[1])\r\nprint(len(sys.argv) != 2)\r\n'''","sub_path":"fund/fund_invest.py","file_name":"fund_invest.py","file_ext":"py","file_size_in_byte":7022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"365988978","text":"config = { \n 'model_name': 'sim_siam', \n 'log_path': 'data/log',\n 'batch_sizes': (16, 24, 12),\n 'use_inputs':['images'],\n 'stream_type': ['visual'], #\n 'cache_image_vectors': True,\n 'datasets': 'imagenet',\n 'image_path': 'data/imagenet', #data/cifar10 #data/cifar100\n 'val_type': 'all', # 'all' | 'ch_only'\n 'max_epochs': 100,\n 'num_workers': 0, \n 'image_dim': 512, # hardcoded for ResNet18\n 'n_dim': 300, \n 'layers': 3,\n 'dropout': 0.5,\n 'learning_rate': 1e-4,\n 'weight_decay': 1e-5,\n 'loss_name': 'sim_siam_loss',\n 'optimizer': 'adam',\n 'metrics': [],\n 'log_cmd': True,\n 'ckpt_path': 'data/ckpt',\n 'ckpt_name': None,\n 'shuffle': (False, False, False)\n}\n\n\ndebug_options = {\n # 'image_path': './data/images/samples',\n}\n\nlog_keys = [\n 'model_name',\n # 'feature_pooling_method', # useless\n]\n","sub_path":"code/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"629062570","text":"import json\nimport time\nEXPORTFILE = \"NYC.json\"\n\n\n\nfirst_lr = 0\n\nwith open(EXPORTFILE, 'r') as data:\n data = data.read()\n '''\n data = data.replace(\"si\", \"station_id\")\n data = data.replace(\"bi\", \"bcycle_indego\")\n data = data.replace(\"nba\", \"num_bikes_available\")\n data = data.replace(\"nda\", \"num_docks_available\")\n data = data.replace(\"inst\", \"is_installed\")\n data = data.replace(\"rntng\", \"is_renting\")\n data = data.replace(\"rtrng\", \"is_returning\")\n data = data.replace(\"lr\", \"last_reported\")\n '''\n data = data.split(\"\\n\")[:-1]\n lengthOfDate = len(data)\n dataLenPercentage = int(lengthOfDate * .01)\n unit = data[-2]\n unit = json.loads(unit)\n last_lr = unit[\"last_updated\"]\n unit = data[0]\n unit = json.loads(unit)\n first_lr = unit[\"last_updated\"]\n for i in range(100):\n start = time.time()\n writeOut = \"\"\n print(i)\n dataX = data[dataLenPercentage * i : dataLenPercentage * (i+1)]\n for unit in dataX:\n unit = json.loads(unit)\n for station in unit[\"data\"][\"stations\"]:\n writeOut += str(station[\"lr\"]) + \",\" + str(station[\"si\"]) + \",\" + \\\n str(station[\"nba\"]) + \",\" + str(station[\"nda\"]) + \",\" + \\\n str(station[\"inst\"]) + \",\" + str(station[\"rntng\"]) + \",\" + str(station[\"rtrng\"]) + \"\\n\"\n\n\n with open(\"NYC_data_\" + str(first_lr) + \"_to_\" + str(last_lr) + \".csv\", \"a+\") as file:\n file.write(writeOut)\n file.close()\n print(time.time() - start)","sub_path":"BicycleDataJSONtoCSV/processNYC.py","file_name":"processNYC.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"633812643","text":"# coding=utf-8\n# 代码文件：chapter11/ch11.3.6.py\n\n\nclass Animal(object):\n \"\"\"定义动物类\"\"\"\n\n def __init__(self, age, sex=1, weight=0.0):\n self.age = age # 定义年龄实例变量\n self.sex = sex # 定义性别实例变量\n self.weight = weight # 定义体重实例变量\n\n def eat(self):\n self.weight += 0.05\n print('eat...')\n\n def run(self):\n self.weight -= 0.01\n print('run...')\n\n\na1 = Animal(2, 0, 10.0)\nprint('a1体重：{0:0.2f}'.format(a1.weight))\na1.eat()\nprint('a1体重：{0:0.2f}'.format(a1.weight))\na1.run()\nprint('a1体重：{0:0.2f}'.format(a1.weight))\n","sub_path":"zhijieketang/chapter11/ch11.3.6.py","file_name":"ch11.3.6.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"141755044","text":"import pyvirtualcam\nimport numpy as np\nimport cv2\nfrom PIL import Image\nimport os\n\nvideo = cv2.VideoCapture(0)\n\ninputShape = video.read()[1].shape\ncamHeight = inputShape[0]\ncamWidth = inputShape[1]\n\n\ndef getEmotions(frame):\n #ML results\n return \"sad\"\n\ndef writeEmotions(result):\n \n script_dir = os.path.dirname(__file__)\n rel_path = f\"assets/{result}.png\"\n output = os.path.join(script_dir, rel_path)\n return output\n\ndef alpha_to_color(image, color=(255, 255, 255)):\n x = np.array(image)\n r, g, b, a = np.rollaxis(x, axis=-1)\n r[a == 0] = color[0]\n g[a == 0] = color[1]\n b[a == 0] = color[2] \n x = np.dstack([r, g, b, a])\n return Image.fromarray(x, 'RGBA')\n\n\ndef main(toggle):\n with pyvirtualcam.Camera(height=camHeight, width=camWidth, fps=30, backend = 'obs') as cam:\n print(f'Using virtual camera: {cam.device}')\n while toggle == True:\n ret, frame = video.read()\n if not ret:\n raise RuntimeError('Error fetching frame')\n \n RGBframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\n emojiFrame = Image.new('RGB', (camWidth, camHeight), color = 'black')\n \n emojiOverlay = alpha_to_color(Image.open(writeEmotions(getEmotions(RGBframe))))\n width, height= emojiOverlay.size\n \n offset = ((camWidth - int(width )) // 2, (camHeight - int(height)) // 2)\n emojiFrame.paste(emojiOverlay, offset)\n emojiFrame = np.array(emojiFrame)\n \n cam.send(emojiFrame)\n cam.sleep_until_next_frame()\n\nif __name__ == \"__main__\":\n main(True)\n\n","sub_path":"Virtual-Camera/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"301289991","text":"from yapsy.PluginManager import PluginManager\n\ndef main(): \n # Load the plugins from the plugin directory.\n manager = PluginManager(plugin_info_ext='cfg')\n manager.setPluginPlaces([\"../plugins\"])\n manager.collectPlugins()\n\n # Loop round the plugins and print their names.\n for plugin in manager.getAllPlugins():\n plugin.plugin_object.print_name()\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"hive/core/plugin_manager.py","file_name":"plugin_manager.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"648472020","text":"def count_words(filename):\n try:\n with open(filename) as f_obj:\n contents=f_obj.read()\n except FileNotFoundError:\n print(\"Sorry,the file \"+filename+\" doesn't exist.\")\n else:\n words=contents.split()\n num_words=len(words)\n print(\"The file \"+filename+\" has about \"+str(num_words)+\" words.\")\nfilenames=['alice.txt','siddhartha.txt','roshan.txt']\nfor filename in filenames:\n count_words(filename)","sub_path":"basic_knowledge/10_3_7.py","file_name":"10_3_7.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"359058927","text":"class Patient:\n def __init__(self):\n self.ip = \"\"\n self.nom = \"\"\n self.prenom = \"\"\n self.date_naissance = \"\"\n self.sexe = \"\"\n self.adresse = \"\"\n\n def __str__(self):\n return \"Patient(ip: %s, nom: %s, prenom: %s, date naissance: %s, sexe: %s, adresse: %s)\" % \\\n (self.ip, self.nom, self.prenom, self.date_naissance, self.sexe, self.adresse)","sub_path":"api/models/patient.py","file_name":"patient.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"550421124","text":"import numpy as np\nimport pickle\nimport pandas as pd\n#from flasgger import Swagger\nimport streamlit as st \n\nfrom PIL import Image\n\n#app=Flask(__name__)\n#Swagger(app)\n\n\npickle_in = open(\"classTapApp.pkl\",\"rb\")\nclassifierTap=pickle.load(pickle_in)\n\n\n#@app.route('/')\ndef welcome():\n return \"Welcome All\"\n\n#@app.route('/predict',methods=[\"Get\"])\ndef predictionParkinson(age,Male,TapPerform):\n \n \n predictionTap=classifierTap.predict([[TapPerform, age,Male]])\n \n if predictionTap==0:\n pred=0\n else:\n pred=1 \n return pred\n\n\n\ndef main():\n st.title(\"AM Parkinson\")\n #html_temp = \"\"\"\n #
\n #

Streamlit Bank Authenticator ML App

\n #
\n #\"\"\"\n #st.markdown(html_temp,unsafe_allow_html=True)\n Im=Image.open(\"PicApp.jpg\")\n st.image(Im,width=300)\n \n age = st.text_input(\"Age\",\"Type Here\")\n Male = st.text_input(\"Gender: 1 if Male, 0 if Female\",\"Type Here\")\n TapPerform = st.text_input(\"Tapping Performance\",\"Type Here\")\n result=\"\"\n if st.button(\"Evaluate my performance\"):\n result=predictionParkinson(int(age),int(Male),int(TapPerform))\n if result==0:\n st.success('You do not present Parkinson related symptoms')\n else: \n st.success('Based on your general performance, you should stay alert and visit your doctor')\n\nif __name__=='__main__':\n main()","sub_path":"ParkinsonApp/AppV3.py","file_name":"AppV3.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"378235664","text":"import logging\n\nfrom guillotina import configure\nfrom guillotina.api.service import Service\nfrom guillotina.catalog.utils import reindex_in_future\nfrom guillotina.component import query_utility\nfrom guillotina.interfaces import ICatalogUtility\nfrom guillotina.interfaces import IResource\n\n\nlogger = logging.getLogger('guillotina')\n\n\nasync def _search(context, request, query):\n search = query_utility(ICatalogUtility)\n if search is None:\n return {\n 'items_count': 0,\n 'member': []\n }\n\n return await search.query(context, query)\n\n\n@configure.service(\n context=IResource, method='GET', permission='guillotina.SearchContent', name='@search',\n summary='Make search request',\n parameters=[],\n responses={\n \"200\": {\n \"description\": \"Search results\",\n \"type\": \"object\",\n \"schema\": {\n \"$ref\": \"#/definitions/SearchResults\"\n }\n }\n })\nasync def search_get(context, request):\n q = request.url.query.copy()\n return await _search(context, request, q)\n\n\n@configure.service(\n context=IResource, method='POST',\n permission='guillotina.RawSearchContent', name='@search',\n summary='Make a complex search query',\n parameters=[{\n \"name\": \"body\",\n \"in\": \"body\",\n \"schema\": {\n \"properties\": {}\n }\n }],\n responses={\n \"200\": {\n \"description\": \"Search results\",\n \"type\": \"object\",\n \"schema\": {\n \"$ref\": \"#/definitions/SearchResults\"\n }\n }\n })\nasync def search_post(context, request):\n q = await request.json()\n return await _search(context, request, q)\n\n\n@configure.service(\n context=IResource, method='POST',\n permission='guillotina.ReindexContent', name='@catalog-reindex',\n summary='Reindex entire container content',\n responses={\n \"200\": {\n \"description\": \"Successfully reindexed content\"\n }\n })\nclass CatalogReindex(Service):\n\n def __init__(self, context, request, security=False):\n super(CatalogReindex, self).__init__(context, request)\n self._security_reindex = security\n\n async def __call__(self):\n search = query_utility(ICatalogUtility)\n if search is not None:\n await search.reindex_all_content(self.context, self._security_reindex)\n return {}\n\n\n@configure.service(\n context=IResource, method='POST',\n permission='guillotina.ReindexContent', name='@async-catalog-reindex',\n summary='Asynchronously reindex entire container content',\n responses={\n \"200\": {\n \"description\": \"Successfully initiated reindexing\"\n }\n })\nclass AsyncCatalogReindex(Service):\n\n def __init__(self, context, request, security=False):\n super(AsyncCatalogReindex, self).__init__(context, request)\n self._security_reindex = security\n\n async def __call__(self):\n reindex_in_future(self.context, False)\n return {}\n\n\n@configure.service(\n context=IResource, method='POST',\n permission='guillotina.ManageCatalog', name='@catalog',\n summary='Initialize catalog',\n responses={\n \"200\": {\n \"description\": \"Successfully initialized catalog\"\n }\n })\nasync def catalog_post(context, request):\n search = query_utility(ICatalogUtility)\n await search.initialize_catalog(context)\n return {}\n\n\n@configure.service(\n context=IResource, method='DELETE',\n permission='guillotina.ManageCatalog', name='@catalog',\n summary='Delete search catalog',\n responses={\n \"200\": {\n \"description\": \"Successfully deleted catalog\"\n }\n })\nasync def catalog_delete(context, request):\n search = query_utility(ICatalogUtility)\n await search.remove_catalog(context)\n return {}\n","sub_path":"guillotina/api/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":3832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"609466877","text":"from Car import Car\n\ncar = Car()\nprint(\"Press a Key: Forward(W) Left(A) Right(D) Reverse(X) Stop(S) Quit(q)\")\n\nwhile(True):\n value = raw_input() # If you use Python 2\n #value = input() # If you use Python 3\n #print(value)\n\n if(value=='q' or value=='Q'):\n car.stopCar()\n car.__exit__()\n exit(0)\n elif(value == 'w' or value == 'w') :\n car.forward()\n elif (value == 'a' or value == 'A'):\n car.turn_left()\n elif (value == 'd' or value == 'D'):\n car.turn_right()\n elif (value == 'x' or value == 'X'):\n car.reverse()\n elif (value == 's' or value == 'S'):\n car.stopCar()\n\n\ncar.__exit__()","sub_path":"pi-car/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"118364253","text":"# encoding='utf-8'\n\n'''\n author:zhangyu\n date:2019.8.22\n descriptrion:重复字符串进行分组(最传统的做法)\n'''\n\n\ndef group_anagrams_list(strs):\n str_set = set()\n new_list = []\n for s in strs:\n str_set.add(get_sort_str(s))\n for ele in str_set:\n list = []\n for i in range(len(strs)):\n sort_s = get_sort_str(strs[i])\n if ele.__eq__(sort_s):\n list.append(strs[i])\n new_list.append(list)\n return new_list\n\n\ndef get_sort_str(s):\n s = sorted(list(s))\n return ''.join(s)\n\n\nif __name__ == '__main__':\n strs = [\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]\n list = group_anagrams_list(strs)\n print(list)\n","sub_path":"src/python/group_anagrams_49_1.py","file_name":"group_anagrams_49_1.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"535765691","text":"# Talking to the music player and sanitizing data.\nimport datetime\n\nimport requests\nfrom requests.exceptions import Timeout\nfrom unihandecode import Unihandecoder\n\nfrom settings import *\n\n\ndef asciify(script, args):\n if args.lang_code is None:\n return Unihandecoder().decode(script)\n return Unihandecoder(lang=args.lang_code.casefold()).decode(script)\n\n\ndef check_connection():\n try:\n requests.get(server_url, timeout=0.2)\n except Timeout:\n print(\"Connection timed out. Make sure Foobar is running and the beefsam plugin is installed.\")\n raise()\n\n\ndef request_playlist_info():\n response = requests.get(server_url + '/api/playlists')\n # return playlist ID and number of tracks\n return response.json()['playlists'][0]['id'], response.json()['playlists'][0]['itemCount']\n\n\ndef request_playlist_content(playlist_id, item_count, args):\n t_list = []\n total_time = 0\n payload = {'playlists': 'true', 'playlistItems': 'true',\n 'plref': playlist_id, 'plrange': '0:' + str(item_count),\n 'plcolumns': args.label+', %length_seconds%'}\n response = requests.get(server_url+'/api/query', params=payload)\n\n for i in range(item_count):\n ascii_track_name = asciify(response.json()['playlistItems']['items'][i]['columns'][0], args)\n print(ascii_track_name)\n t_list.append(ascii_track_name)\n total_time += int(response.json()['playlistItems']['items'][i]['columns'][1])\n print(f'Total playlist duration: {datetime.timedelta(seconds=total_time)}')\n if total_time >= 4800:\n print('Warning: duration exceeds 80 minutes!')\n if item_count > 254:\n print('Warning: cannot record more than 254 tracks!')\n # return a list of tracks to label and total time\n return t_list, total_time\n\n\ndef request_track_time():\n response = requests.get(server_url + '/api/player')\n duration = response.json()['player']['activeItem']['duration']\n position = response.json()['player']['activeItem']['position']\n # return remaining time in track (seconds)\n return duration - position\n\n\ndef set_mode_play(playlist_id):\n requests.post(server_url + '/api/player', params={'isMuted': 'false', 'playbackMode': '0'}) # unmute, no shuffle\n requests.post(server_url + '/api/player/play/' + playlist_id+'/0') # start from the top\n\n\ndef set_player(command):\n requests.post(server_url + '/api/player/' + command) # play, pause, stop\n","sub_path":"webapi.py","file_name":"webapi.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"546463755","text":"from django.conf.urls import patterns, include, url\n\nfrom django.contrib import admin\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\n\n\n\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^$', 'uploader.views.main', name='main'),\n url(r'^login/$', 'uploader.views.login', name='login'),\n url(r'^verify/$', 'uploader.views.verify', name='verify'),\n url(r'^list_videos/$', 'uploader.views.list_videos', name='list_videos'),\n url(r'^addvideo/$', 'uploader.views.addvideo', name='addvideo'),\n url(r'^cache/$', 'uploader.views.cache', name='cache'),\n url(r'^(?P[\\w|\\W]+)/checking/(?P[\\w|\\W]+)$', 'uploader.views.listoriginal', name='listoriginal'),\n url(r'^cdndatabase/$', 'uploader.views.cdndatabase', name='cdndatabase'),\n url(r'^logout/$', 'uploader.views.logout', name='logout'),\n url(r'^streamer/$', 'uploader.views.streamer', name='streamer'),\n )+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","sub_path":"sample/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"119541547","text":"import os\nimport cv2\nimport math\nimport random\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nimport albumentations\nfrom albumentations.pytorch.transforms import ToTensorV2\n\nimport torch\nimport timm\nimport torch\nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom torch.utils.data import Dataset,DataLoader\n\nimport gc\nimport matplotlib.pyplot as plt\nimport cudf\nimport cuml\nimport cupy\nfrom cuml.feature_extraction.text import TfidfVectorizer\nfrom cuml import PCA\nfrom cuml.neighbors import NearestNeighbors\nimport pdb\n\n\nclass CFG:\n seed = 54\n classes = 11014 \n scale = 30 \n margin = 0.5\n model_name = 'tf_efficientnet_b4'\n fc_dim = 512\n img_size = 512\n batch_size = 2\n num_workers = 4\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n model_path = '../input/utils-shopee/arcface_512x512_tf_efficientnet_b4_LR.pt'\n\ndef read_dataset():\n\n df = pd.read_csv('./shopee-product-matching/train.csv')\n df_cu = cudf.DataFrame(df)\n image_paths = './shopee-product-matching/train_images/' + df['image']\n\n return df, df_cu, image_paths\n\ndef seed_torch(seed=42):\n random.seed(seed)\n os.environ['PYTHONHASHSEED'] = str(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.backends.cudnn.deterministic = True\n \nseed_torch(CFG.seed)\n\ndef f1_score(y_true, y_pred):\n y_true = y_true.apply(lambda x: set(x.split()))\n y_pred = y_pred.apply(lambda x: set(x.split()))\n intersection = np.array([len(x[0] & x[1]) for x in zip(y_true, y_pred)])\n len_y_pred = y_pred.apply(lambda x: len(x)).values\n len_y_true = y_true.apply(lambda x: len(x)).values\n f1 = 2 * intersection / (len_y_pred + len_y_true)\n return f1\n\ndef precision(y_true, y_pred):\n y_true = y_true.apply(lambda x: set(x.split()))\n y_pred = y_pred.apply(lambda x: set(x.split()))\n intersection = np.array([len(x[0] & x[1]) for x in zip(y_true, y_pred)])\n len_y_pred = y_pred.apply(lambda x: len(x)).values\n len_y_true = y_true.apply(lambda x: len(x)).values\n precision = intersection/len_y_pred\n return precision\n\ndef recall(y_true, y_pred):\n y_true = y_true.apply(lambda x: set(x.split()))\n y_pred = y_pred.apply(lambda x: set(x.split()))\n intersection = np.array([len(x[0] & x[1]) for x in zip(y_true, y_pred)])\n len_y_pred = y_pred.apply(lambda x: len(x)).values\n len_y_true = y_true.apply(lambda x: len(x)).values\n recall = intersection/len_y_true\n return recall\n\ndef score(y_true, y_pred):\n y_true = y_true.apply(lambda x: set(x.split()))\n y_pred = y_pred.apply(lambda x: set(x.split()))\n intersection = np.array([len(x[0] & x[1]) for x in zip(y_true, y_pred)])\n len_y_pred = y_pred.apply(lambda x: len(x)).values\n len_y_true = y_true.apply(lambda x: len(x)).values\n precision = intersection/len_y_pred\n recall = intersection/len_y_true\n f1 = 2 * intersection / (len_y_pred + len_y_true)\n return precision,recall,f1\n\ndef combine_predictions(row):\n image_prediction = row['image_predictions'].split()\n text_prediction = row['text_predictions'].split()\n# pdb.set_trace()\n x = np.concatenate([np.array(image_prediction), np.array(text_prediction)]) \n return ' '.join( np.unique(x) )\n\n\nclass ArcMarginProduct(nn.Module):\n def __init__(self, in_features, out_features, scale=30.0, margin=0.50, easy_margin=False, ls_eps=0.0):\n super(ArcMarginProduct, self).__init__()\n self.in_features = in_features\n self.out_features = out_features\n self.scale = scale\n self.margin = margin\n self.ls_eps = ls_eps\n self.weight = nn.Parameter(torch.FloatTensor(out_features, in_features))\n nn.init.xavier_uniform_(self.weight)\n\n self.easy_margin = easy_margin\n self.cos_m = math.cos(margin)\n self.sin_m = math.sin(margin)\n self.th = math.cos(math.pi - margin)\n self.mm = math.sin(math.pi - margin) * margin\n\n def forward(self, input, label):\n cosine = F.linear(F.normalize(input), F.normalize(self.weight))\n sine = torch.sqrt(1.0 - torch.pow(cosine, 2))\n phi = cosine * self.cos_m - sine * self.sin_m\n if self.easy_margin:\n phi = torch.where(cosine > 0, phi, cosine)\n else:\n phi = torch.where(cosine > self.th, phi, cosine - self.mm)\n \n one_hot = torch.zeros(cosine.size(), device='cuda')\n one_hot.scatter_(1, label.view(-1, 1).long(), 1)\n if self.ls_eps > 0:\n one_hot = (1 - self.ls_eps) * one_hot + self.ls_eps / self.out_features\n\n output = (one_hot * phi) + ((1.0 - one_hot) * cosine)\n output *= self.scale\n\n return output, nn.CrossEntropyLoss()(output,label)\n\nclass ShopeeModel(nn.Module):\n\n def __init__(\n self,\n n_classes = CFG.classes,\n model_name = CFG.model_name,\n fc_dim = CFG.fc_dim,\n margin = CFG.margin,\n scale = CFG.scale,\n use_fc = True,\n pretrained = True):\n\n super(ShopeeModel,self).__init__()\n print('Building Model Backbone for {} model'.format(model_name))\n\n self.backbone = timm.create_model(model_name, pretrained=pretrained)\n in_features = self.backbone.classifier.in_features\n self.backbone.classifier = nn.Identity()\n self.backbone.global_pool = nn.Identity()\n self.pooling = nn.AdaptiveAvgPool2d(1)\n self.use_fc = use_fc\n\n if use_fc:\n self.dropout = nn.Dropout(p=0.1)\n self.classifier = nn.Linear(in_features, fc_dim)\n self.bn = nn.BatchNorm1d(fc_dim)\n self._init_params()\n in_features = fc_dim\n\n self.final = ArcMarginProduct(\n in_features,\n n_classes,\n scale = scale,\n margin = margin,\n easy_margin = False,\n ls_eps = 0.0\n )\n \n def _init_params(self):\n nn.init.xavier_normal_(self.classifier.weight)\n nn.init.constant_(self.classifier.bias, 0)\n nn.init.constant_(self.bn.weight, 1)\n nn.init.constant_(self.bn.bias, 0)\n\n def forward(self, image, label):\n features = self.extract_features(image)\n if self.training:\n logits = self.final(features, label)\n return logits\n else:\n return features\n\n def extract_features(self, x):\n batch_size = x.shape[0]\n x = self.backbone(x) #(2, 1792, 16, 16)\n# print(x.shape)\n x = self.pooling(x).view(batch_size, -1) #(2, 1792)\n# print(x.shape)\n# pdb.set_trace()\n\n if self.use_fc and self.training:\n x = self.dropout(x)\n x = self.classifier(x)\n x = self.bn(x)\n return x\n\ndef get_image_neighbors(df, embeddings, KNN=50):\n\n model = NearestNeighbors(n_neighbors = KNN)\n model.fit(embeddings)\n distances, indices = model.kneighbors(embeddings)\n threshold = 4\n predictions = []\n for k in tqdm(range(embeddings.shape[0])):\n idx = np.where(distances[k,] < threshold)[0]\n ids = indices[k,idx]\n# posting_ids = df['posting_id'].iloc[ids].values\n posting_ids = ' '.join(df['posting_id'].iloc[ids].values)\n predictions.append(posting_ids)\n \n# pdb.set_trace()\n del model, distances, indices\n gc.collect()\n return df, predictions\n\ndef get_test_transforms():\n return albumentations.Compose([\n albumentations.Resize(CFG.img_size, CFG.img_size, always_apply=True),\n albumentations.Normalize(),\n ToTensorV2(p=1.0)\n ])\n\nclass ShopeeDataset(Dataset):\n\n def __init__(self, image_paths, transforms=None):\n self.image_paths = image_paths\n self.augmentations = transforms\n\n def __len__(self):\n return self.image_paths.shape[0]\n\n def __getitem__(self, index):\n image_path = self.image_paths[index]\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n if self.augmentations:\n augmented = self.augmentations(image=image)\n image = augmented['image']\n \n return image, torch.tensor(1)\n\ndef get_image_embeddings(image_paths):\n\n model = ShopeeModel(pretrained=True).to(CFG.device)\n# model = ShopeeModel(pretrained=False).to(CFG.device)\n# model.load_state_dict(torch.load(CFG.model_path))\n model.eval()\n\n image_dataset = ShopeeDataset(image_paths=image_paths, transforms=get_test_transforms())\n image_loader = torch.utils.data.DataLoader(\n image_dataset,\n batch_size=CFG.batch_size,\n num_workers=CFG.num_workers\n )\n\n embeds = []\n with torch.no_grad():\n for img,label in tqdm(image_loader): \n img = img.cuda()\n label = label.cuda()\n features = model(img,label)\n image_embeddings = features.detach().cpu().numpy()\n embeds.append(image_embeddings)\n\n del model\n image_embeddings = np.concatenate(embeds)\n print(f'Our image embeddings shape is {image_embeddings.shape}')\n del embeds\n gc.collect()\n return image_embeddings\n\ndef get_text_predictions(df, max_features=25_000):\n \n model = TfidfVectorizer(stop_words='english',\n binary=True,\n max_features=max_features)\n text_embeddings = model.fit_transform(df_cu['title']).toarray()\n\n print('Finding similar titles...')\n CHUNK = 1024 * 4\n CTS = len(df) // CHUNK\n if (len(df)%CHUNK) != 0:\n CTS += 1\n\n preds = []\n for j in range( CTS ):\n a = j * CHUNK\n b = (j+1) * CHUNK\n b = min(b, len(df))\n print('chunk', a, 'to', b)\n\n # COSINE SIMILARITY DISTANCE\n cts = cupy.matmul(text_embeddings, text_embeddings[a:b].T).T\n# pdb.set_trace()\n for k in range(b-a):\n IDX = cupy.where(cts[k,]>0.6)[0] #0.75\n# o = df.iloc[cupy.asnumpy(IDX)].posting_id.values\n o = ' '.join(df.iloc[cupy.asnumpy(IDX)].posting_id.values)\n preds.append(o)\n\n del model,text_embeddings\n gc.collect()\n return preds\n\ndf,df_cu,image_paths = read_dataset()\ntmp = df.groupby(['label_group'])['posting_id'].unique().to_dict()\ndf['matches'] = df['label_group'].map(tmp)\ndf['matches'] = df['matches'].apply(lambda x: ' '.join(x))\ndf.head()\n\ntext_predictions = get_text_predictions(df, max_features=25_000)\ndf['text_predictions'] = text_predictions\n\n# df['text_precision'] = precision(df['matches'], df['text_predictions'])\n# df['text_recall'] = recall(df['matches'], df['text_predictions'])\n# df['text_f1'] = f1_score(df['matches'], df['text_predictions'])\n\ndf['text_precision'],df['text_recall'],df['text_f1'] = score(df['matches'], df['text_predictions'])\ntext_precision = df['text_precision'].mean()\ntext_recall = df['text_recall'].mean()\ntext_f1 = df['text_f1'].mean()\nprint(text_precision,text_recall,text_f1)\n\n# save好了注释掉\nimage_embeddings = get_image_embeddings(image_paths.values)\ntorch.save(image_embeddings, './image_embeddings.pt')\n\nimage_embeddings = torch.load('./image_embeddings.pt')\ndf, image_predictions = get_image_neighbors(df, image_embeddings, KNN=50 if len(df)>3 else 3)\ndf['image_predictions'] = image_predictions\n\n# df['image_f1'] = score(df['matches'], df['image_predictions'])\n# image_f1 = df['image_f1'].mean()\n# print(image_f1)\n\ndf['image_precision'],df['image_recall'],df['image_f1'] = score(df['matches'], df['image_predictions'])\nimage_precision = df['image_precision'].mean()\nimage_recall = df['image_recall'].mean()\nimage_f1 = df['image_f1'].mean()\nprint(image_precision,image_recall,image_f1)\n\ndf['joint_predictions'] = df.apply(combine_predictions, axis=1)\n# df['joint_f1'] = f1_score(df['matches'], df['joint_predictions'])\n# joint_f1 = df['joint_f1'].mean()\n# print(joint_f1)\ndf['joint_precision'],df['joint_recall'],df['joint_f1'] = score(df['matches'], df['joint_predictions'])\njoint_precision = df['joint_precision'].mean()\njoint_recall = df['joint_recall'].mean()\njoint_f1 = df['joint_f1'].mean()\nprint(joint_precision,joint_recall,joint_f1)","sub_path":"ref_code2.py","file_name":"ref_code2.py","file_ext":"py","file_size_in_byte":12086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"211696364","text":"from typing import List\n\n\nclass Solution:\n def rotate(self, matrix: List[List[int]]) -> None:\n \"\"\"\n Do not return anything, modify matrix in-place instead.\n \"\"\"\n if matrix is None or len(matrix) <= 1:\n return\n n = len(matrix)\n\n i = 0\n j = n - 1\n while i < j:\n matrix[i], matrix[j] = matrix[j], matrix[i]\n i += 1\n j -= 1\n\n for i in range(1, n):\n for j in range(0, i):\n matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n","sub_path":"problem0048/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"233531988","text":"from ..packages.AbstractPackages import Package, DataPackage, AnalysisPackage\nfrom ..packages.LightsheetPackages import LightsheetScan, LightsheetBrainVasculatureScan, StrokeVolumeAnalysis, VesselDiameterAnalysis, LengthDensityMap3DAnalysis\nfrom ..main import config\nfrom pathlib import Path\nfrom uuid import uuid4\n\nclass PackageFactory(object):\n\n#---------------------------------------------------------------------------------------------#\n# Functions that are general to all Package types. #\n#---------------------------------------------------------------------------------------------#\n # Takes a libpath Path object and attempts to create the directory.\n # Returns false on failure and true on success.\n def create_directory(self, pathObj):\n\n try:\n Path(pathObj).mkdir(parents=False, exist_ok=False)\n except FileExistsError:\n self.print_error_message(\"FileExistsError. Package directory creation failed.\")\n except FileNotFoundError:\n self.print_error_message(\"FileNotFoundError. Package directory creation failed.\")\n except:\n self.print_error_message(\"UnknownError. Package directory creation failed.\")\n else:\n return True\n\n return False\n\n def create_Package_directory_and_UID(self):\n\n rootDir = config['data-destination']['PACKAGE_DIR']\n uniqueID = self.gen_unique_Package_ID()\n relativePath = rootDir + uniqueID + \"/\"\n\n dirCreationSuccess = self.create_directory(relativePath)\n if dirCreationSuccess:\n return uniqueID, relativePath\n\n return None, None\n\n def gen_unique_Package_ID(self):\n # TODO: Implement proper unique package ID function\n return str(uuid4())\n\n#---------------------------------------------------------------------------------------------#\n# Functions for selecting which PackageCreator function we want to use #\n#---------------------------------------------------------------------------------------------#\n def create_package(self, packageType):\n\n if issubclass(packageType, Package):\n packageCreator = self.get_package_creator(packageType)\n return packageCreator(packageType)\n else:\n return None\n\n def get_package_creator(self, packageType):\n\n if issubclass(packageType, DataPackage):\n return self.get_DataPackage_creator(packageType)\n elif issubclass(packageType, AnalysisPackage):\n return self.get_AnalysisPackage_creator(packageType)\n else:\n return None\n\n def get_DataPackage_creator(self, packageType):\n\n if issubclass(packageType, LightsheetScan):\n return self.get_LightsheetScan_creator(packageType)\n else:\n return None\n\n def get_AnalysisPackage_creator(self, packageType):\n\n if packageType == LengthDensityMap3DAnalysis:\n return self.create_LengthDensityMap3DAnalysis\n elif packageType == StrokeVolumeAnalysis:\n return self.create_StrokeVolumeAnalysis\n elif packageType == VesselDiameterAnalysis:\n return self.create_VesselDiameterAnalysis\n else:\n return None\n\n def get_LightsheetScan_creator(self, packageType):\n if packageType == LightsheetBrainVasculatureScan:\n return self.create_LightsheetBrainVasculatureScan\n\n\n#---------------------------------------------------------------------------------------------#\n# LightsheetBrainVasculatureScan creation is done here. #\n#---------------------------------------------------------------------------------------------#\n\n # This function attempts to create a LightsheetScan object. It will attempt to set and validate\n # all the required parameters for creating a LightsheetScan object. If it fails to set and validate\n # any of the required parameters, it will return None. If it succeeds, it will return a valid\n # LightsheetScan object.\n def create_LightsheetBrainVasculatureScan(self, packageType):\n\n # TODO: Creator methods should ensure all the required attributes are set\n # and valid before passing the attrDict to the class's constructor.\n # Once all the required attributes are set and validated, object initialization\n # is passed off to the class's __init__().\n # Think of this like the Essence pattern for object creation.\n # The attrDict is the essence and the creator method\n # is responsible for initializing the essence. This\n # ensures we never accidentally create objects with\n # an invalid state and gives a clear separation of\n # responsibility (Creator ensures everything needed for object creation\n # is present, while object __init__() handles actual creation).\n #\n # TODO: This function sucks because it depends on details internal to LightsheetBrainVasculatureScan objects.\n # This function should be told which values need to be set by the LightsheetBrainVasculatureScan class to\n # eliminate the dependency.\n\n # Default success is true, then we go through a list of checks that\n # will flip this to false if any of them fail.\n objectCreationSuccess = True\n scan = None\n # Get a dictionary that contains all the attributes our object has. All attributes\n # default to None.\n attrDict = LightsheetBrainVasculatureScan.get_empty_attr_dict()\n\n # Attempt to set and validate all required attributes.\n # TODO: Setting everything manually until this gets hooked up to some front end that can set them automatically\n # These are also missing any kind of sanitization/validation.\n # Also missing any kind of user guide/descriptions.\n attrDict['name'] = str(input(\"Input Scan Name: \"))\n attrDict['uniqueID'], attrDict['relativePath'] = self.create_Package_directory_and_UID()\n attrDict['creationDate'] = str(input(\"Input Creation Date: \"))\n attrDict['stitchedPath'] = str(input(\"Input Stitched Path: \"))\n attrDict['tilesPath'] = str(input(\"Input Tiles Path: \"))\n attrDict['numTiles'] = str(input(\"Input Tile Dims: \"))\n attrDict['tileSizeZ'] = int(input(\"Input tileSizeZ: \"))\n attrDict['tileSizeY'] = int(input(\"Input tileSizeY: \"))\n attrDict['tileSizeX'] = int(input(\"Input tileSizeX: \"))\n attrDict['bitDepth'] = int(input(\"Input bitDepth: \"))\n attrDict['authorName'] = str(input(\"Input authorName: \"))\n attrDict['specimenName'] = str(input(\"Input specimenName: \"))\n attrDict['specimenPrepProtocol'] = str(input(\"Input Protocol Name: \"))\n attrDict['notes'] = str(input(\"Input notes: \"))\n attrDict['umStepSizeZ'] = float(input(\"Input umStepSizeZ: \"))\n attrDict['umPerStep'] = float(input(\"Input umPerStep: \"))\n attrDict['scanStepSpeed'] = float(input(\"Input scanStepSpeed: \"))\n attrDict['sleepDurationAfterMovement'] = float(input(\"Input sleepDurationAfterMovement: \"))\n attrDict['timelapseN'] = int(input(\"Input timelapseN: \"))\n attrDict['timelapseIntervalS'] = int(input(\"Input timelapseIntervalS: \"))\n attrDict['tileScanDimensions'] = str(input(\"Input tileScanDims: \"))\n attrDict['imagingObjectiveMagnification'] = float(input(\"Input Imaging Objective Mag: \"))\n attrDict['umPerPixel'] = float(input(\"Input umPerPixel: \"))\n attrDict['refractiveIndexImmersion'] = float(input(\"Input Refractive Index Immersion: \"))\n attrDict['numericalAperture'] = float(input(\"Input Numerical Aperture Collection: \"))\n attrDict['fluorescenceWavelength'] = int(input(\"Input Fluorescence Wavelength: \"))\n attrDict['umTileOverlapX'] = float(input(\"Input Tile Overlap X µm: \"))\n attrDict['umTileOverlapY'] = float(input(\"Input Tile Overlap Y µm: \"))\n\n stitchImportSuccess, stitchScanFinalPath = self.import_stitched_LightsheetScan(attrDict['stitchedPath'], attrDict['relativePath'])\n tilesImportSuccess, tilesFinalPath = self.import_tiles_LightsheetScan(attrDict['tilesPath'], attrDict['relativePath'])\n if not stitchImportSuccess or not tilesImportSuccess:\n objectCreationSuccess = False\n\n # If objectCreationSuccess has not been set to false, we're safe to attempt object creation.\n if objectCreationSuccess:\n attrDict['stitchedPath'] = stitchScanFinalPath\n attrDict['tilesPath'] = tilesFinalPath\n scan = LightsheetBrainVasculatureScan(attrDict)\n lightsheetScanObjDumpPath = Path(attrDict['relativePath']).joinpath(Path(attrDict['uniqueID'] + '.p'))\n objectCreationSuccess = scan.save_package(lightsheetScanObjDumpPath)\n\n return scan\n\n def import_stitched_LightsheetScan(self, inputStitchedScanPath, objectRootDir):\n\n # Default success is true, then we go through a list of checks that\n # will flip this to false if any of them fail.\n fileImportSuccess = True\n outputStitchedScanPath = None\n\n # Make sure the file to be imported exists and that the location it's being imported to\n # exists as well.\n inputStitchedScanPath = Path(inputStitchedScanPath)\n objectRootDir = Path(objectRootDir)\n\n if not inputStitchedScanPath.exists():\n fileImportSuccess = False\n if not objectRootDir.exists():\n fileImportSuccess = False\n\n # Create the subdirectory where the file is going to be imported to.\n outputStitchedScanPath = Path(objectRootDir.joinpath('Stitched/'))\n dirCreationSuccess = self.create_directory(outputStitchedScanPath)\n if not dirCreationSuccess:\n fileImportSuccess = False\n\n # If everything above worked properly, import the file.\n if fileImportSuccess:\n stitchedScanFileName = inputStitchedScanPath.stem + inputStitchedScanPath.suffix\n outputStitchedScanPath = outputStitchedScanPath.joinpath(stitchedScanFileName)\n inputStitchedScanPath.rename(outputStitchedScanPath)\n\n # Return code specifies whether import failed or succeeded.\n return fileImportSuccess, outputStitchedScanPath\n\n\n def import_tiles_LightsheetScan(self, inputTilesScanPath, objectRootDir):\n\n # Default success is true, then we go through a list of checks that\n # will flip this to false if any of them fail.\n fileImportSuccess = True\n outputTilesPath = None\n\n # Make sure the file to be imported exists and that the location it's being imported to\n # exists as well.\n inputTilesScanPath = Path(inputTilesScanPath)\n objectRootDir = Path(objectRootDir)\n\n if not inputTilesScanPath.exists():\n fileImportSuccess = False\n if not objectRootDir.exists():\n fileImportSuccess = False\n\n # If everything above worked properly, import the file.\n if fileImportSuccess:\n stitchedScanFileName = inputTilesScanPath.stem + inputTilesScanPath.suffix\n outputTilesPath = objectRootDir.joinpath(stitchedScanFileName)\n inputTilesScanPath.rename(outputTilesPath)\n\n # Return code specifies whether import failed or succeeded.\n return fileImportSuccess, outputTilesPath\n\n\n\n\n\n\n#---------------------------------------------------------------------------------------------#\n# LengthDensityMap3DAnalysis creation is handled here. #\n#---------------------------------------------------------------------------------------------#\n def create_LengthDensityMap3DAnalysis(self, packageType):\n print(\"Created LengthDensityMap3DAnalysis\")\n attrDict = LengthDensityMap3DAnalysis.get_empty_attr_dict()\n return LengthDensityMap3DAnalysis(attrDict)\n\n#---------------------------------------------------------------------------------------------#\n# StrokeVolumeAnalysis creation is handled here. #\n#---------------------------------------------------------------------------------------------#\n def create_StrokeVolumeAnalysis(self, packageType):\n print(\"Created StrokeVolumeAnalysis\")\n attrDict = StrokeVolumeAnalysis.get_empty_attr_dict()\n return StrokeVolumeAnalysis(attrDict)\n\n#---------------------------------------------------------------------------------------------#\n# VesselDiameterAnalysis creation is handled here. #\n#---------------------------------------------------------------------------------------------#\n def create_VesselDiameterAnalysis(self, packageType):\n print(\"Created VesselDiameterAnalysis\")\n attrDict = VesselDiameterAnalysis.get_empty_attr_dict()\n return VesselDiameterAnalysis(attrDict)\n\n\n\n","sub_path":"data_management_demo/packages/PackageFactory.py","file_name":"PackageFactory.py","file_ext":"py","file_size_in_byte":12962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"531877944","text":"import re\nimport time\nimport platform\n\nfrom PySide.QtGui import *\nfrom PySide.QtCore import *\n\nfrom Logcat import LogcatReader\n\n\nif platform.system() == 'Linux':\n MONOSPACE_FONT = QFont('Monospace')\nelif platform.system() == 'Windows':\n MONOSPACE_FONT = QFont('Consolas')\nelse:\n raise Exception('Unsupported system: {}'.format(platform.system()))\n\nMONOSPACE_FONT.setStyleHint(QFont.Monospace)\n\nclass LogModel(QAbstractTableModel):\n COLUMNS = ['Log Level', 'Tag', 'PID', 'Log Line']\n # TODO(dany): Use named tokens in the RegEx\n LINE_REGEX = re.compile(r'([VLEIWD])/(.*)\\(\\s*(\\d+)\\): (.*)')\n\n data_changed = Signal()\n\n def __init__(self, start_time):\n super(LogModel, self).__init__()\n self.start_time = start_time\n self.rows = []\n self.pending = []\n self.last_update = time.time()\n self.filename = None\n self.reader = None\n self.search_params = None\n\n def rowCount(self, parent=QModelIndex()):\n return len(self.rows)\n\n def columnCount(self, parent=QModelIndex()):\n return len(self.COLUMNS)\n\n def is_matching_row(self, row):\n string, is_cs, is_regex = self.search_params\n if is_regex:\n # TODO(dany): Maybe it would be faster to cache the regex object\n # and use it instead of having re compile it every time\n return re.match(string, row['Log Line'], re.I if is_cs else 0)\n else:\n if is_cs:\n return string.lower() in row['Log Line'].lower()\n else:\n return string in row['Log Line']\n\n def data(self, index, role=Qt.DisplayRole):\n row = index.row()\n col = index.column()\n if role == Qt.DisplayRole:\n return self.rows[row][self.COLUMNS[col]]\n elif role == Qt.BackgroundColorRole:\n if self.search_params is None:\n return None\n if self.is_matching_row(self.rows[row]):\n return QColor(255, 255, 0)\n else:\n return None\n\n def headerData(self, index, orientation, role):\n if orientation == Qt.Horizontal and role == Qt.DisplayRole:\n return self.COLUMNS[index]\n else:\n return None\n\n def addItem(self, item):\n match = self.LINE_REGEX.match(item)\n if match is not None:\n log_level, tag, pid, line = match.groups()\n # TODO(dany): When the regex has tokens, I can just use groupdict\n self.pending.append({\n 'Tag': tag.strip(),\n 'Log Level': log_level,\n 'PID': pid.strip(),\n 'Log Line': line\n })\n if (time.time() - self.last_update) > 1:\n self.beginInsertRows(QModelIndex(), len(self.rows),\n len(self.rows) + len(self.pending) - 1)\n self.rows.extend(self.pending)\n self.endInsertRows()\n self.data_changed.emit()\n self.pending = []\n self.last_update = time.time()\n\n def set_search_params(self, string, is_cs, is_re):\n if string != '':\n self.search_params = (string, is_cs, is_re)\n else:\n self.search_params = None\n self.dataChanged.emit(self.index(0, 0),\n self.index(self.rowCount(), self.columnCount()))\n\n def start(self):\n self.reader = LogcatReader(self, self.start_time)\n self.reader.start()\n\n def stop(self):\n if self.reader and self.reader.running.is_set():\n self.reader.stop()\n self.reader.join()\n self.reader = None\n\nclass LogView(QTableView):\n def __init__(self, parent=None):\n super(LogView, self).__init__(parent)\n self.setFont(MONOSPACE_FONT)\n self.verticalHeader().hide()\n self.setSelectionBehavior(QAbstractItemView.SelectRows)\n\nclass LogWidget(QWidget):\n def __init__(self, start_time):\n super(LogWidget, self).__init__()\n \n self.start_time = start_time\n\n vbox = QVBoxLayout()\n\n self.model = LogModel(start_time)\n view = LogView()\n view.setModel(self.model)\n self.model.data_changed.connect(view.resizeColumnsToContents)\n\n # TODO(dany): Do I really need all this hbox/vbox gymnastics?\n vbox.addWidget(view)\n\n hbox = QHBoxLayout()\n vbox.addLayout(hbox)\n\n self.setLayout(vbox)\n\n # TODO(dany): There should be a way to concatenate signals without defining\n # a trivial proxy method\n def start(self):\n self.model.start()\n\n def stop(self):\n self.model.stop()\n\n def set_search_params(self, string, is_cs, is_re):\n self.model.set_search_params(string, is_cs, is_re)\n","sub_path":"LogTable.py","file_name":"LogTable.py","file_ext":"py","file_size_in_byte":4711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"224842508","text":"import socketserver, threading, sys, struct, pickle\ndef main():\n\ttry:\n\t\tserver = ChatServer(('10.0.0.147', 9653), RequestHandler)\n\t\tserver.serve_forever()\n\texcept Exception as err:\n\t\tprint ('ERROR: {0}'.format(err))\n\t\tsys.exit(1)\nclass Server(socketserver.ThreadingMixIn, socketserver.TCPServer):pass\nclass RequestHandler(socketserver.StreamRequestHandler):\n\tdef handle(self):\n\t\tSizeStruct = struct.Struct('!I')\n\t\tsize_data = self.rfile.read(SizeStruct.size)\n\t\tsize = SizeStruct.unpack(size_data)\n\t\tsize = size[0]\n\t\tdata = pickle.loads(self.rfile.read(size))\n\t\twith CallLock:\n\t\t\treply = CallDict[data[0]](self, *data[1:])\n\t\tprint (reply)\n\t\treply = pickle.dumps(reply, 3)\n\t\tself.wfile.write(SizeStruct.pack(len(reply)))\n\t\tself.wfile.write(reply)\n","sub_path":"Server/TCPmodServer.py","file_name":"TCPmodServer.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"204786739","text":"# Created by: Miguel Sancho\n\nimport mathutils\nimport base64\nimport numpy\nimport cv2\n\nfrom map import *\nfrom math import pi\nfrom pymorse import Morse\nfrom random import randint\n\n\nclass DataNode:\n\n def __init__(self):\n self.saveDirection = False\n self.normalize = False\n self.saveToDisk = False\n self.poseHasChanged = False\n self.lastDirection = 0\n self.lastNpDir = 0\n self.lastPose = 0\n self.pitchFactor = -0.077\n self.normFactor = 10\n self.map = Map(\"maps/forest.csv\")\n self.lapCounter = 0\n self.dataCounter = 0\n self.zeroDir = 0\n self.otherDir = 0\n\n def startQuadrotor(self, morse):\n quadVel = morse.quadrotor.motion\n quadPose = morse.quadrotor.pose.get()\n\n self.lastDirection = self.map.getDirection(round(quadPose['x']), round(quadPose['y']))['deg']\n vel = { \"v\": 2, \"w\": 0 }\n\n quadVel.publish(vel)\n\n def checkLimit(self, x, y):\n if x > 60:\n return True\n else:\n return False\n\n def checkNormalization(self):\n if self.lapCounter > self.normFactor:\n self.normalize = True\n\n def teleport(self, quadTele, morse):\n self.f.write(str(self.lapCounter) + ',' + str(self.zeroDir) + ',' + str(self.otherDir) + '\\n')\n x = randint(-65, -45)\n x = randint(-65, -45)\n y = randint(-51, 51)\n #x = randint(-56, -53)\n #y = randint(-51, -46)\n\n morse.deactivate('quadrotor.motion')\n morse.activate('quadrotor.teleport')\n\n destination = { \"x\": x, \\\n \"y\": y, \\\n \"z\": 7, \\\n \"yaw\": 0, \\\n \"pitch\": 0, \\\n \"roll\": 0, \\\n }\n quadTele.publish(destination)\n \n morse.deactivate('quadrotor.teleport')\n morse.activate('quadrotor.motion')\n\n self.lapCounter = self.lapCounter + 1\n\n def getOrientation(self, x, y, direction):\n #print(\"Location X,Y :({} , {}) Direction: {}\".format(x, y, direction))\n\n return { \"yaw\": direction['rads'], \"pitch\": self.pitchFactor, \"roll\": 0.0 }\n\n def imageCallback(self, camera):\n width = camera['width'] # 256 default\n height = camera['height'] # 256 default\n buff = base64.b64decode(camera['image']) # RGBA base64 encoded\n\n image = numpy.ndarray(shape=(height, width, 4), buffer=buff, dtype='uint8')\n image = cv2.cvtColor(image, cv2.COLOR_RGBA2BGR)\n # image = cv2.resize(image, (512, 512))\n cv2.imshow(\"Quadrotor view\", image)\n\n return image\n\n def poseChanged(self, quadPose):\n newPose = round(quadPose['yaw'], 5)\n\n if (abs(self.lastPose-newPose) > 0.00000):\n\n self.poseHasChanged = True\n self.lastPose = newPose\n return True\n else:\n self.poseHasChanged = False\n return False\n \n def dirToNp(self, newDir):\n if newDir > 0:\n if newDir > 20:\n toNp = 4\n else:\n toNp = 3\n elif newDir < 0:\n if newDir < -20:\n toNp = 1\n else:\n toNp = 2\n else:\n toNp = self.lastNpDir\n\n self.lastNpDir = toNp\n\n return toNp\n\n def getTurningDir(self, direction, quadPose):\n newDir = (self.lastDirection - direction['deg'])\n if abs(newDir) < 180:\n newDir = -newDir\n toNp = 0\n\n if (self.poseChanged(quadPose)):\n self.lastDirection = direction['deg']\n toNp = self.dirToNp(newDir)\n self.lastNpDir = toNp\n\n return toNp\n\n def save(self, image, direction, pose):\n if self.saveDirection:\n self.checkSave(image, direction['dir'])\n else:\n self.checkSave(image, self.getTurningDir(direction, pose))\n\n\n def checkSave(self, image, direction):\n if self.normalize:\n if self.poseHasChanged:\n self.writeFile(image, direction) \n else:\n self.writeFile(image, direction)\n\n def run(self):\n self.f = open('dataLogger.txt','a')\n \n with Morse() as morse:\n morse.deactivate('quadrotor.teleport')\n quadTele = morse.quadrotor.teleport\n quadDir = morse.quadrotor.orientation\n \n self.startQuadrotor(morse)\n\n while True:\n print(\"Iteration: \" + str(self.dataCounter))\n camera = morse.quadrotor.camera.get()\n quadPose = morse.quadrotor.pose.get()\n x = quadPose['x']\n y = quadPose['y']\n\n direction = self.map.getDirection(round(x), round(y))\n if direction['dir'] > 0:\n self.otherDir += 1\n else:\n self.zeroDir += 1\n orientation = self.getOrientation(x, y, direction)\n quadDir.publish(orientation)\n\n image = self.imageCallback(camera)\n\n if self.saveToDisk:\n self.save(image, direction, quadPose)\n\n if cv2.waitKey(1) & 0xff == ord('q'):\n break\n \n if self.checkLimit(x, y):\n self.teleport(quadTele, morse)\n self.checkNormalization()\n\n self.dataCounter += 1\n\n cv2.destroyAllWindows()\n\ndef main(args):\n\n node = DataNode()\n node.run()\n\n\nif __name__ == '__main__':\n main(sys.argv)\n","sub_path":"MORSE/Miscellaneous Scripts/dataLogger.py","file_name":"dataLogger.py","file_ext":"py","file_size_in_byte":5593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"138816800","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'hh'\n\n\"\"\"\nrandom forest solution for kaggle competition Digit Recognizer\n\"\"\"\nimport os\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import RandomForestClassifier\n\n# 为了练习os.path.join()\ndata_dir = './dataset'\nsubmission_dir = './submissions'\n\n\n# 读取文件\ndef read_data():\n data_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))\n data_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))\n\n train_data = data_train.values[:, 1:]\n train_label = data_train.values[:, 0]\n test_data = data_test.values\n return train_data, train_label, test_data\n\n\n# 降维\ndef dR_PCA(x_train, x_test, COMPONENT_NUM):\n train_data = np.array(x_train)\n test_data = np.array(x_test)\n pca = PCA(COMPONENT_NUM)\n pca.fit(train_data)\n pca_train_data = pca.transform(train_data)\n pca_test_data = pca.transform(test_data)\n return pca_train_data, pca_test_data\n\n\n# random forest\ndef train_model(train_data, train_label):\n clf = RandomForestClassifier(\n n_estimators=20,\n max_depth=10)\n clf.fit(train_data, train_label)\n return clf\n\n\n# 导出结果\ndef save_result(result, file_name):\n submission = pd.DataFrame({\"ImageID\": list(range(1, len(result) + 1)),\n \"Label\": result})\n submission.to_csv(file_name, index=False)\n\n\nif __name__ == '__main__':\n train_data, train_label, test_data = read_data()\n train_data_PCA, test_data_PCA = dR_PCA(train_data, test_data, 0.7)\n clf = train_model(train_data_PCA, train_label)\n test_label = clf.predict(test_data_PCA)\n save_result(test_label, os.path.join(submission_dir, 'rf_solution.csv'))\n","sub_path":"digit-recognizer/rf_solution.py","file_name":"rf_solution.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"217575589","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Feb 5 18:17:53 2019\r\n\r\n@author: c1647707\r\n\"\"\"\r\n\r\n\r\nimport numpy as np \r\nimport matplotlib.pyplot as plt\r\nfrom scipy.optimize import curve_fit\r\n\r\n\r\n#Correct numbers - each corresponding to the number of Orders in each Class and Phylum, allowing for\r\n#us to \"tick off\" each Class/Phylum as Orders are selected\r\nPhylaOrder=(4,8,11,16,18,24,29,33,36,40,44,49,53,56,60,66,73,77,82,86,90)\r\nClassOrder=(4,8,11,16,18,24,29,33,36,40,44,49,53,56,60,66,73,77,82,86,90,\r\n 94,96,103,105,110,115,118,121,127,133,138,143,149,152,158,161,\r\n 169,173,179,183,186,190,194,200,204,208,211,216,218,224,229,\r\n 233,236,240,244,249,256,260,266,273,277,286,290,296,303,305,\r\n 310,315,321,325,329,333,338,343,349,352,358,361,367,369,375,\r\n 379,383,386,390,394,400,406,410)\r\n\r\n\r\nplt.figure()\r\nPhyla_number=21\r\nClass_number=90\r\nOrder_number=410\r\n\r\nfor i in range(1):\r\n \r\n #A is a masked array used to simulate the missing taxa and their arrival through uncovering masked orders as time progresses\r\n A4=np.full(Order_number, False)\r\n A4[:(Order_number-410)]=True\r\n np.random.shuffle(A4)\r\n A3=np.full(Order_number, False)\r\n A3[:(Order_number-410)]=True\r\n np.random.shuffle(A3)\r\n A2=np.full(Order_number, False)\r\n A2[:(Order_number-410)]=True\r\n np.random.shuffle(A2)\r\n A1=np.full(Order_number, False)\r\n A1[:(Order_number-205)]=True\r\n np.random.shuffle(A1)\r\n \r\n \r\n A3=A3+A4\r\n A2=A2+A3+A4\r\n A1=A1+A2+A3+A4\r\n \r\n base=np.arange(Order_number)\r\n repeats=25\r\n length=np.random.randint(0,11,repeats)\r\n maxlength=10\r\n b=np.zeros(maxlength)\r\n R=np.arange(100)\r\n \r\n Order1=np.ma.masked_array(base,A1)\r\n Order2=np.ma.masked_array(base,A2)\r\n Order3=np.ma.masked_array(base,A3)\r\n Order4=np.ma.masked_array(base,A4)\r\n \r\n \r\n #Important values for how many times the loops will repeat,and how the random selection process will\r\n #work. Using a random number of random Orders each step, with between 0 and 30 selected each time.\r\n #length shows the amount selected for each step - will change for each loop\r\n \r\n \r\n #The Xnew arrays will be where we tick off each taxa as it is selected, by turning the zeros to ones\r\n #as the loop runs. Choice is where we will put the randomly selected numbers for the Order selection\r\n Onew=np.tile(np.zeros(Order_number),(repeats,1))\r\n Cnew=np.tile(np.zeros(Class_number),(repeats,1))\r\n Pnew=np.tile(np.zeros(Phyla_number),(repeats,1))\r\n choice=np.zeros((repeats,maxlength))\r\n \r\n \r\n Order1=np.ma.masked_array(base,A1)\r\n #Section A of the loop - random number selection, where a randomly selects a number within the range of\r\n #the Order_number limit a random number of times, and repeats this for how many repeats are set. By then\r\n #adding these to the zero matrix b we can construct the newly selected orders\r\n for i in np.arange(repeats):\r\n a=(np.random.choice(Order1[~A1],length[i]))\r\n a.resize(b.shape)\r\n a=(b+a)\r\n choice[i]=a\r\n a=np.array([a], dtype=int)\r\n Onew[i,a]=1\r\n \r\n #For each newly selected group we need to \"tick off\" and prevent repeats - done with this check where if\r\n #any values are already a 1, the rest of the column will be set to all 0s and thus the Order has been\r\n #\"ticked off\"\r\n for i in np.arange(Order_number):\r\n for j in np.arange(repeats):\r\n if Onew[j,i]==1:\r\n Onew[j+1:,i]=0\r\n \r\n #Section B - To find the Classes these Orders correspond to, we use the ClassOrder array and match the ticked off \r\n #Orders to each Class based off the numbers of Orders in each Class. The initial Class has to be done \r\n #seperately due to the nature of the calculation\r\n for i in np.arange(repeats):\r\n for j in np.arange(Class_number):\r\n if j==0:\r\n if np.sum(Onew[i][ClassOrder[0]:ClassOrder[j]])==0:\r\n Cnew[i][j]=0\r\n else:\r\n Cnew[i][j]=1\r\n else: \r\n if np.sum(Onew[i][ClassOrder[j-1]:ClassOrder[j]])==0:\r\n Cnew[i][j]=0\r\n else:\r\n Cnew[i][j]=1\r\n \r\n #Have to check for repeats again\r\n for i in np.arange(Class_number):\r\n for j in np.arange(repeats):\r\n if Cnew[j,i]==1:\r\n Cnew[j+1:,i]=0\r\n \r\n #Section C - The same process is then repeated for the Phyla, using PhylaOrder. \r\n for i in np.arange(repeats):\r\n for j in np.arange(Phyla_number):\r\n if j==0:\r\n if np.sum(Onew[i][PhylaOrder[0]:PhylaOrder[j]])==0:\r\n Pnew[i][j]=0\r\n else:\r\n Pnew[i][j]=1\r\n else: \r\n if np.sum(Onew[i][PhylaOrder[j-1]:PhylaOrder[j]])==0:\r\n Pnew[i][j]=0\r\n else:\r\n Pnew[i][j]=1\r\n \r\n #Checking for repeats\r\n for i in np.arange(Phyla_number):\r\n for j in np.arange(repeats):\r\n if Pnew[j,i]==1:\r\n Pnew[j+1:,i]=0\r\n \r\n #finally Section D - constructing the Xplot arrays. These are the summed values of\r\n #each row of the Xnew arrays, and are going to be used to plot the final results.\r\n Oplot=np.zeros(repeats)\r\n Cplot=np.zeros(repeats)\r\n Pplot=np.zeros(repeats)\r\n \r\n for i in np.arange(repeats):\r\n Oplot[i]=np.sum(Onew[i,:],dtype=int)\r\n Cplot[i]=np.sum(Cnew[i,:],dtype=int)\r\n Pplot[i]=np.sum(Pnew[i,:],dtype=int)\r\n \r\n #Finding the percentage of each taxa appearing in this period\r\n Opicked1=np.sum(Oplot)\r\n Opercentage1=Opicked1/Order_number\r\n Cpicked1=np.sum(Cplot)\r\n Cpercentage1=Cpicked1/Class_number\r\n Ppicked1=np.sum(Pplot)\r\n Ppercentage1=Ppicked1/Phyla_number\r\n \r\n print(\"\"\"The percentage of taxa appearing in the first 25 timesteps are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage1, Cpercentage1, Opercentage1))\r\n \"_____________________________________________________________________________\"\r\n #Unmasking more of the Order array\r\n \r\n Onew2=np.tile(np.zeros(Order_number),(repeats,1))\r\n Cnew2=np.tile(np.zeros(Class_number),(repeats,1))\r\n Pnew2=np.tile(np.zeros(Phyla_number),(repeats,1))\r\n length=np.random.randint(0,11,repeats)\r\n \r\n for i in np.arange(repeats):\r\n a2=(np.random.choice(Order2[~A2],length[i]))\r\n a2.resize(b.shape)\r\n a2=(b+a2)\r\n choice[i]=a2\r\n a2=np.array([a2], dtype=int)\r\n Onew2[i,a2]=1\r\n \r\n for i in np.arange(Order_number):\r\n for j in np.arange(repeats):\r\n if Onew2[j,i]==1:\r\n Onew2[j+1:,i]=0\r\n if np.sum(Onew[j:,i])!=0:\r\n Onew2[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Class_number):\r\n if j==0:\r\n if np.sum(Onew2[i][ClassOrder[0]:ClassOrder[j]])==0:\r\n Cnew2[i][j]=0\r\n else:\r\n Cnew2[i][j]=1\r\n else: \r\n if np.sum(Onew2[i][ClassOrder[j-1]:ClassOrder[j]])==0:\r\n Cnew2[i][j]=0\r\n else:\r\n Cnew2[i][j]=1\r\n \r\n for i in np.arange(Class_number):\r\n for j in np.arange(repeats):\r\n if Cnew2[j,i]==1:\r\n Cnew2[j+1:,i]=0\r\n if np.sum(Cnew[j:,i])!=0:\r\n Cnew2[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Phyla_number):\r\n if j==0:\r\n if np.sum(Onew2[i][PhylaOrder[0]:PhylaOrder[j]])==0:\r\n Pnew2[i][j]=0\r\n else:\r\n Pnew2[i][j]=1\r\n else: \r\n if np.sum(Onew2[i][PhylaOrder[j-1]:PhylaOrder[j]])==0:\r\n Pnew2[i][j]=0\r\n else:\r\n Pnew2[i][j]=1\r\n \r\n for i in np.arange(Phyla_number):\r\n for j in np.arange(repeats):\r\n if Pnew2[j,i]==1:\r\n Pnew2[j+1:,i]=0\r\n if np.sum(Pnew[j:,i])!=0:\r\n Pnew2[j:,i]=0\r\n \r\n \r\n Oplot2=np.zeros(repeats)\r\n Cplot2=np.zeros(repeats)\r\n Pplot2=np.zeros(repeats)\r\n \r\n for i in np.arange(repeats):\r\n Oplot2[i]=np.sum(Onew2[i,:],dtype=int)\r\n Cplot2[i]=np.sum(Cnew2[i,:],dtype=int)\r\n Pplot2[i]=np.sum(Pnew2[i,:],dtype=int)\r\n \r\n #Finding the percentage of each taxa appearing in this period\r\n Opicked2=np.sum(Oplot2)\r\n Opercentage2=Opicked2/Order_number\r\n Cpicked2=np.sum(Cplot2)\r\n Cpercentage2=Cpicked2/Class_number\r\n Ppicked2=np.sum(Pplot2)\r\n Ppercentage2=Ppicked2/Phyla_number\r\n \r\n print(\"\"\"The percentage of taxa appearing in the second 25 timesteps are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage2, Cpercentage2, Opercentage2))\r\n print(\"\"\"The total percentage of taxa appearing by the 50th timestep are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage2+Ppercentage1, \r\n Cpercentage2+Cpercentage1, Opercentage2+Opercentage1))\r\n \"_____________________________________________________________________________\"\r\n \r\n Onew3=np.tile(np.zeros(Order_number),(repeats,1))\r\n Cnew3=np.tile(np.zeros(Class_number),(repeats,1))\r\n Pnew3=np.tile(np.zeros(Phyla_number),(repeats,1))\r\n length=np.random.randint(0,11,repeats)\r\n \r\n for i in np.arange(repeats):\r\n a3=(np.random.choice(Order3[~A3],length[i]))\r\n a3.resize(b.shape)\r\n a3=(b+a3)\r\n choice[i]=a3\r\n a3=np.array([a3], dtype=int)\r\n Onew3[i,a3]=1\r\n \r\n for i in np.arange(Order_number):\r\n for j in np.arange(repeats):\r\n if Onew3[j,i]==1:\r\n Onew3[j+1:,i]=0\r\n if np.sum(Onew[j:,i])!=0:\r\n Onew3[j:,i]=0\r\n if np.sum(Onew2[j:,i])!=0:\r\n Onew3[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Class_number):\r\n if j==0:\r\n if np.sum(Onew3[i][ClassOrder[0]:ClassOrder[j]])==0:\r\n Cnew3[i][j]=0\r\n else:\r\n Cnew3[i][j]=1\r\n else: \r\n if np.sum(Onew3[i][ClassOrder[j-1]:ClassOrder[j]])==0:\r\n Cnew3[i][j]=0\r\n else:\r\n Cnew3[i][j]=1\r\n \r\n for i in np.arange(Class_number):\r\n for j in np.arange(repeats):\r\n if Cnew3[j,i]==1:\r\n Cnew3[j+1:,i]=0\r\n if np.sum(Cnew[j:,i])!=0:\r\n Cnew3[j:,i]=0\r\n if np.sum(Cnew2[j:,i])!=0:\r\n Cnew3[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Phyla_number):\r\n if j==0:\r\n if np.sum(Onew3[i][PhylaOrder[0]:PhylaOrder[j]])==0:\r\n Pnew3[i][j]=0\r\n else:\r\n Pnew3[i][j]=1\r\n else: \r\n if np.sum(Onew3[i][PhylaOrder[j-1]:PhylaOrder[j]])==0:\r\n Pnew3[i][j]=0\r\n else:\r\n Pnew3[i][j]=1\r\n \r\n for i in np.arange(Phyla_number):\r\n for j in np.arange(repeats):\r\n if Pnew3[j,i]==1:\r\n Pnew3[j+1:,i]=0\r\n if np.sum(Pnew[j:,i])!=0:\r\n Pnew3[j:,i]=0\r\n if np.sum(Pnew2[j:,i])!=0:\r\n Pnew3[j:,i]=0\r\n \r\n Oplot3=np.zeros(repeats)\r\n Cplot3=np.zeros(repeats)\r\n Pplot3=np.zeros(repeats)\r\n \r\n for i in np.arange(repeats):\r\n Oplot3[i]=np.sum(Onew3[i,:],dtype=int)\r\n Cplot3[i]=np.sum(Cnew3[i,:],dtype=int)\r\n Pplot3[i]=np.sum(Pnew3[i,:],dtype=int)\r\n \r\n #Finding the percentage of each taxa appearing in this period\r\n Opicked3=np.sum(Oplot3)\r\n Opercentage3=Opicked3/Order_number\r\n Cpicked3=np.sum(Cplot3)\r\n Cpercentage3=Cpicked3/Class_number\r\n Ppicked3=np.sum(Pplot3)\r\n Ppercentage3=Ppicked3/Phyla_number\r\n \r\n print(\"\"\"The percentage of taxa appearing in the third 25 timesteps are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage3, Cpercentage3, Opercentage3))\r\n print(\"\"\"The total percentage of taxa appearing by the 75th timestep are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage3+Ppercentage2+Ppercentage1, \r\n Cpercentage3+Cpercentage2+Cpercentage1, Opercentage3+Opercentage2+Opercentage1))\r\n \"_____________________________________________________________________________\"\r\n \r\n Onew4=np.tile(np.zeros(Order_number),(repeats,1))\r\n Cnew4=np.tile(np.zeros(Class_number),(repeats,1))\r\n Pnew4=np.tile(np.zeros(Phyla_number),(repeats,1))\r\n length=np.random.randint(0,11,repeats)\r\n \r\n for i in np.arange(repeats):\r\n a4=(np.random.choice(Order4[~A4],length[i]))\r\n a4.resize(b.shape)\r\n a4=(b+a4)\r\n choice[i]=a4\r\n a4=np.array([a4], dtype=int)\r\n Onew4[i,a4]=1\r\n \r\n for i in np.arange(Order_number):\r\n for j in np.arange(repeats):\r\n if Onew4[j,i]==1:\r\n Onew4[j+1:,i]=0\r\n if np.sum(Onew[j:,i])!=0:\r\n Onew4[j:,i]=0\r\n if np.sum(Onew2[j:,i])!=0:\r\n Onew4[j:,i]=0\r\n if np.sum(Onew3[j:,i])!=0:\r\n Onew4[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Class_number):\r\n if j==0:\r\n if np.sum(Onew4[i][ClassOrder[0]:ClassOrder[j]])==0:\r\n Cnew4[i][j]=0\r\n else:\r\n Cnew4[i][j]=1\r\n else: \r\n if np.sum(Onew4[i][ClassOrder[j-1]:ClassOrder[j]])==0:\r\n Cnew4[i][j]=0\r\n else:\r\n Cnew4[i][j]=1\r\n \r\n for i in np.arange(Class_number):\r\n for j in np.arange(repeats):\r\n if Cnew4[j,i]==1:\r\n Cnew4[j+1:,i]=0\r\n if np.sum(Cnew[j:,i])!=0:\r\n Cnew4[j:,i]=0\r\n if np.sum(Cnew2[j:,i])!=0:\r\n Cnew4[j:,i]=0\r\n if np.sum(Cnew3[j:,i])!=0:\r\n Cnew4[j:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n for j in np.arange(Phyla_number):\r\n if j==0:\r\n if np.sum(Onew4[i][PhylaOrder[0]:PhylaOrder[j]])==0:\r\n Pnew4[i][j]=0\r\n else:\r\n Pnew4[i][j]=1\r\n else: \r\n if np.sum(Onew4[i][PhylaOrder[j-1]:PhylaOrder[j]])==0:\r\n Pnew4[i][j]=0\r\n else:\r\n Pnew4[i][j]=1\r\n \r\n for i in np.arange(Phyla_number):\r\n for j in np.arange(repeats):\r\n if Pnew4[j,i]==1:\r\n Pnew4[j+1:,i]=0\r\n if np.sum(Pnew[j:,i])!=0:\r\n Pnew4[j:,i]=0\r\n if np.sum(Pnew2[j:,i])!=0:\r\n Pnew4[j:,i]=0\r\n if np.sum(Pnew3[j:,i])!=0:\r\n Pnew4[j:,i]=0\r\n \r\n \r\n Oplot4=np.zeros(repeats)\r\n Cplot4=np.zeros(repeats)\r\n Pplot4=np.zeros(repeats)\r\n \r\n for i in np.arange(repeats):\r\n Oplot4[i]=np.sum(Onew4[i,:],dtype=int)\r\n Cplot4[i]=np.sum(Cnew4[i,:],dtype=int)\r\n Pplot4[i]=np.sum(Pnew4[i,:],dtype=int)\r\n \r\n #Finding the percentage of each taxa appearing in this period\r\n Opicked4=np.sum(Oplot4)\r\n Opercentage4=Opicked4/Order_number\r\n Cpicked4=np.sum(Cplot4)\r\n Cpercentage4=Cpicked4/Class_number\r\n Ppicked4=np.sum(Pplot4)\r\n Ppercentage4=Ppicked4/Phyla_number\r\n \r\n print(\"\"\"The percentage of taxa appearing in the final 25 timesteps are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage4, Cpercentage4, Opercentage4))\r\n print(\"\"\"The total percentage of taxa appearing by the final timestep are:\r\n Phyla %.5f, Classes %.5f, Orders %.5f\"\"\" %(Ppercentage4+Ppercentage3+Ppercentage2+Ppercentage1, \r\n Cpercentage4+Cpercentage3+Cpercentage2+Cpercentage1, Opercentage4+Opercentage3+Opercentage2+Opercentage1))\r\n \r\n #The Xplot arrays now need to be combined to create the final results, which\r\n #can be plotted together along the full time period of the combined loops\r\n Oplot=np.append(Oplot,Oplot2)\r\n Oplot=np.append(Oplot,Oplot3)\r\n Oplot=np.append(Oplot,Oplot4)\r\n \r\n Cplot=np.append(Cplot,Cplot2)\r\n Cplot=np.append(Cplot,Cplot3)\r\n Cplot=np.append(Cplot,Cplot4)\r\n \r\n Pplot=np.append(Pplot,Pplot2)\r\n Pplot=np.append(Pplot,Pplot3)\r\n Pplot=np.append(Pplot,Pplot4)\r\n \r\n \r\n #Xfinal arrays are combining all the Xnew arrays - the times each array was selected - into one single array.\r\n #Used to find the total numbers picked of each taxa as well as looking at which Phyla, Classes and Orders\r\n #were never selected by the code\r\n Ofinal=Onew+Onew2+Onew3+Onew4\r\n Cfinal=Cnew+Cnew2+Cnew3+Cnew4\r\n Pfinal=Pnew+Pnew2+Pnew3+Pnew4\r\n \r\n #Checking which Phyla, Classes and Orders were picked and which ones were missed - will be more useful when\r\n #changing taxa numbers and extinctions are included.\r\n Opicked=np.sum(Ofinal, axis=0)\r\n Cpicked=np.sum(Cfinal, axis=0)\r\n Ppicked=np.sum(Pfinal, axis=0)\r\n \r\n #Plotting the results - using R, a sum of all the times the loop has repeated, and each of the Xplot arrays\r\n \r\n \r\n plt.subplot(3,1,1)\r\n plt.title(\"Proportional Taxa Distribution compared to Erwin Data\",fontsize='18')\r\n plt.bar(R,Pplot,color=(0.2, 0.4, 0.6, 1))\r\n plt.ylabel(\"Phyla\",fontsize=\"16\")\r\n plt.subplot(3,1,2)\r\n plt.bar(R,Cplot,color=(0.2, 0.4, 0.6, 1))\r\n plt.ylabel(\"Classes\",fontsize=\"16\")\r\n plt.subplot(3,1,3)\r\n plt.bar(R,Oplot,color=(0.2, 0.4, 0.6, 1))\r\n plt.ylabel(\"Orders\",fontsize=\"16\")\r\n plt.xlabel(\"Time\",fontsize='18')\r\n \r\n \r\n #Finally looking at the numbers of each taxa selected, and comparing it as a percentage of the total available\r\n Ofinal=np.sum(Ofinal, axis=1)\r\n Onum=np.sum(Ofinal)\r\n Opercent4=Onum/Order_number\r\n Cfinal=np.sum(Cfinal, axis=1)\r\n Cnum=np.sum(Cfinal)\r\n Cpercent4=Cnum/Class_number\r\n Pfinal=np.sum(Pfinal, axis=1)\r\n Pnum=np.sum(Pfinal)\r\n Ppercent4=Pnum/Phyla_number\r\n\r\n #PLotting a best fit line\r\nx_phyla=np.arange(1,101)\r\ny_phyla=Pplot\r\nz_phyla=np.polyfit(x_phyla,y_phyla,2)\r\nf_phyla=np.poly1d(z_phyla)\r\nx_phyla_new=np.linspace(x_phyla[0],x_phyla[-1],100)\r\ny_phyla_new=f_phyla(x_phyla_new)\r\nplt.subplot(3,1,1)\r\ndef func(x, a, b, c):\r\n return a * np.exp(-b * x) + c\r\npoptp, pcovp = curve_fit(func, x_phyla, y_phyla)\r\nplt.plot(x_phyla, func(x_phyla, *poptp), color=(0.2,0.4,0.6,1), label=\"Varying Random Selection\")\r\n\r\n\r\nx_class=np.arange(1,101)\r\ny_class=Cplot\r\nz_class=np.polyfit(x_class,y_class,6)\r\nf_class=np.poly1d(z_class)\r\nx_class_new=np.linspace(x_class[0],x_class[-1],100)\r\ny_class_new=f_class(x_class_new)\r\nplt.subplot(3,1,2)\r\npoptc, pcovc = curve_fit(func, x_class, y_class)\r\nplt.plot(x_class, func(x_class, *poptc), color=(0.2,0.4,0.6,1))\r\n \r\n\r\nx_order=np.arange(1,101)\r\ny_order=Oplot\r\nz_order=np.polyfit(x_order,y_order,9)\r\nf_order=np.poly1d(z_order)\r\nx_order_new=np.linspace(x_order[0],x_order[-1],100)\r\ny_order_new=f_order(x_order_new)\r\nplt.subplot(3,1,3)\r\n\r\npopto, pcovo = curve_fit(func, x_order, y_order)\r\nplt.plot(x_order, func(x_order, *popto), color=(0.2,0.4,0.6,1))\r\n\r\n\r\nPhylaOrder=(4,8,11,16,18,24,29,33,36,40,44,49,53,56,60,66,73,77,82,86,90)\r\nClassOrder=(4,8,11,16,18,24,29,33,36,40,44,49,53,56,60,66,73,77,82,86,90,\r\n 94,96,103,105,110,115,118,121,127,133,138,143,149,152,158,161,\r\n 169,173,179,183,186,190,194,200,204,208,211,216,218,224,229,\r\n 233,236,240,244,249,256,260,266,273,277,286,290,296,303,305,\r\n 310,315,321,325,329,333,338,343,349,352,358,361,367,369,375,\r\n 379,383,386,390,394,400,406,410)\r\n\r\n\r\n\r\nPhyla_number=21\r\nClass_number=90\r\nOrder_number=410\r\n\r\nbase=np.arange(Order_number)\r\nrepeats=100\r\nlength=5\r\nR=np.arange(repeats)\r\n\r\n\r\nOnew=np.tile(np.zeros(Order_number),(repeats,1))\r\nCnew=np.tile(np.zeros(Class_number),(repeats,1))\r\nPnew=np.tile(np.zeros(Phyla_number),(repeats,1))\r\nchoice=np.zeros((repeats,length))\r\n\r\nfor i in range(1):\r\n for i in np.arange(repeats):\r\n a=(np.random.choice(base,length))\r\n choice[i]=a\r\n Onew[i,a]=1\r\n \r\n for i in np.arange(Order_number):\r\n for j in np.arange(repeats):\r\n if Onew[j,i]==1:\r\n Onew[j+1:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n val=0\r\n for j in np.arange(Class_number):\r\n if np.sum(Onew[i][val:val+4])==0:\r\n Cnew[i][j]=0\r\n else:\r\n Cnew[i][j]=1\r\n val=val+4\r\n \r\n for i in np.arange(Class_number):\r\n for j in np.arange(repeats):\r\n if Cnew[j,i]==1:\r\n Cnew[j+1:,i]=0\r\n \r\n for i in np.arange(repeats):\r\n val=0\r\n for j in np.arange(Phyla_number):\r\n if np.sum(Onew[i][val:val+16])==0:\r\n Pnew[i][j]=0\r\n else:\r\n Pnew[i][j]=1\r\n val=val+16\r\n \r\n for i in np.arange(Phyla_number):\r\n for j in np.arange(repeats):\r\n if Pnew[j,i]==1:\r\n Pnew[j+1:,i]=0\r\n \r\n Oplot=np.zeros(repeats)\r\n Cplot=np.zeros(repeats)\r\n Pplot=np.zeros(repeats)\r\n \r\n for i in np.arange(repeats):\r\n Oplot[i]=np.sum(Onew[i,:])\r\n Cplot[i]=np.sum(Cnew[i,:])\r\n Pplot[i]=np.sum(Pnew[i,:])\r\n \r\n \r\n\r\n \"\"\"plt.subplot(3,1,1)\r\n plt.title(\"Arrival of Phyla, Classes and Order\",fontsize='16')\r\n plt.bar(R,Pplot,color=(0.2, 0.4, 0.6, 0.4))\r\n plt.ylabel(\"Phyla\",fontsize=\"16\")\r\n plt.subplot(3,1,2)\r\n plt.bar(R,Cplot,color=(0.2, 0.4, 0.6, 0.4))\r\n plt.ylabel(\"Classes\",fontsize=\"16\")\r\n plt.subplot(3,1,3)\r\n plt.bar(R,Oplot,color=(0.2, 0.4, 0.6, 0.4))\r\n plt.ylabel(\"Orders\",fontsize=\"16\")\r\n plt.xlabel(\"Time\",fontsize='18')\r\n plt.show\"\"\"\r\n \r\n#PLotting a best fit line\r\nx_phyla2=np.arange(1,101)\r\ny_phyla2=Pplot\r\nz_phyla2=np.polyfit(x_phyla2,y_phyla2,2)\r\nf_phyla2=np.poly1d(z_phyla2)\r\nx_phyla2_new=np.linspace(x_phyla2[0],x_phyla2[-1],100)\r\ny_phyla2_new=f_phyla2(x_phyla2_new)\r\nplt.subplot(3,1,1)\r\n#plt.plot(x_phyla_new, y_phyla_new, color=(0,1,0,0.6))\r\ndef func2(x, a, b, c):\r\n return a * np.exp(-b * x) + c\r\n\r\npopt2p, pcov2p = curve_fit(func, x_phyla2, y_phyla2)\r\nplt.plot(x_phyla, func2(x_phyla2, *popt2p), \"r--\", label=\"Constant Selection\")\r\nplt.legend()\r\n\r\nx_class2=np.arange(1,101)\r\ny_class2=Cplot\r\nz_class2=np.polyfit(x_class2,y_class2,6)\r\nf_class2=np.poly1d(z_class2)\r\nx_class2_new=np.linspace(x_class2[0],x_class2[-1],100)\r\ny_class2_new=f_class2(x_class2_new)\r\nplt.subplot(3,1,2)\r\n#plt.plot(x_class_new, y_class_new, color=(0,1,0,0.6))\r\npopt2c, pcov2c= curve_fit(func2, x_class2, y_class2)\r\n\r\nplt.plot(x_class2, func2(x_class2, *popt2c), \"r--\")\r\n\r\n \r\n\r\nx_order2=np.arange(1,101)\r\ny_order2=Oplot\r\nz_order2=np.polyfit(x_order2,y_order2,9)\r\nf_order2=np.poly1d(z_order2)\r\nx_order2_new=np.linspace(x_order2[0],x_order2[-1],100)\r\ny_order2_new=f_order2(x_order2_new)\r\nplt.subplot(3,1,3)\r\n#plt.plot(x_order_new, y_order_new, color=(0,1,0,0.6))\r\npopt2o, pcov2o = curve_fit(func2, x_order2, y_order2)\r\nplt.plot(x_order2, func2(x_order, *popt2o), \"r--\")\r\n\r\nplt.show\r\n\r\n\r\n\r\n\"\"\"\r\n\r\n#Finding the difference in the lines\r\nNormalPhyla=func2(x_phyla2, *popt2p)\r\nNormalClass=func2(x_class2, *popt2c)\r\nNormalOrder=func2(x_order2, *popt2o)\r\n\r\nVaryingPhyla=func(x_phyla, *poptp)\r\nVaryingClass=func(x_class, *poptc)\r\nVaryingOrder=func(x_order, *popto)\r\n\r\nPdiff=VaryingPhyla-NormalPhyla\r\nCdiff=VaryingClass-NormalClass\r\nOdiff=VaryingOrder-NormalOrder\r\n\r\nzeros=np.zeros_like(R)\r\n\r\nplt.figure()\r\nplt.subplot(3,1,1)\r\nplt.title(\"Difference in taxa levels for the Proportional Distribution\",fontsize='18')\r\nplt.plot(R,Pdiff,color=(0.2, 0.4, 0.6, 1))\r\nplt.plot(R,zeros, \"k--\")\r\nplt.ylabel(\"Phyla\",fontsize=\"16\")\r\nplt.subplot(3,1,2)\r\nplt.plot(R,Cdiff,color=(0.2, 0.4, 0.6, 1))\r\nplt.plot(R,zeros, \"k--\")\r\nplt.ylabel(\"Classes\",fontsize=\"16\")\r\nplt.subplot(3,1,3)\r\nplt.plot(R,Odiff,color=(0.2, 0.4, 0.6, 1))\r\nplt.plot(R,zeros, \"k--\")\r\nplt.ylabel(\"Orders\",fontsize=\"16\")\r\nplt.xlabel(\"Time\",fontsize='18')\r\n\"\"\"","sub_path":"Lithopanspermia Varying - Fake.py","file_name":"Lithopanspermia Varying - Fake.py","file_ext":"py","file_size_in_byte":24268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"485006912","text":"import sys\nsys.path.append(\"..\")\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom django.http.response import HttpResponseRedirect, JsonResponse, HttpResponse\nfrom gameinfo.models import Game\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.decorators import permission_required\nfrom .forms import Developer_GameForm\nfrom django.contrib import messages\nfrom django.urls import reverse\nimport json\nimport datetime\nfrom cart.models import Order\nfrom django.db import connection\nfrom django.db.models import Sum, Count\n\n\n@login_required(login_url='log_in')\n@permission_required(perm='Profile.developers')\ndef developer_games(request):\n # List all the games published by the developer and provide a form for adding a new one\n if request.method == 'GET':\n # Retrieve all the games of the current developer\n dev_games = Game.objects.filter(publisher=request.user.user_profile)\n\n response = {}\n games = []\n response['games'] = games\n for g in dev_games:\n tmp = g.to_json()\n response['games'].append(tmp)\n\n # If the request is via Ajax, return json data representation\n if request.is_ajax():\n return JsonResponse(data=response)\n\n else:\n # Create a new form to be used in the template\n form = Developer_GameForm()\n return render(request, 'developers/developed_games.html', {'form': form, 'user': request.user, 'games': games})\n elif request.method == 'POST':\n # Let's check if the form we got is OK. If there is something wrong, just return it to the view.\n # Otherwise proceed.\n form = Developer_GameForm(request.POST)\n if not form.is_valid():\n messages.error(request=request, message='The form is invalid')\n return redirect(reverse('dev_games'))\n # return HttpResponse(status=405, content=\"Invalid method.\")\n else:\n # Regardless of the user's choice, update the publisher of this game.\n form.instance.publisher = request.user.user_profile\n form.save()\n # Retrieve all the games of the current developer\n dev_games = Game.objects.filter(publisher=request.user.user_profile)\n games = [x.to_json() for x in dev_games]\n\n messages.success(request=request, message='The game has been added successfully!')\n return render(request, 'developers/developed_games.html', {'form': Developer_GameForm, 'user': request.user, 'games':games})\n else:\n return HttpResponse(status=405, content=\"Invalid method.\")\n\n\n@login_required(login_url='log_in')\n@permission_required(perm='Profile.developers')\ndef edit_game(request, game_id):\n if request.method == 'GET':\n\n # Retrieve the data from the db, starting from the game_id and use it to pre-populate the form\n # If the game does not belong to him, we send a 404 instead of a 401 error.\n game = get_object_or_404(Game, id=game_id, publisher=request.user.user_profile)\n form = Developer_GameForm(instance=game)\n return render(request, 'developers/edit_game.html', {'form': form, 'user': request.user})\n\n elif request.method == 'POST':\n # Retrieve the data from the db, starting from the game_id and use it to pre-populate the form\n # If the game does not belong to him, we send a 404 instead of a 401 error.\n game = get_object_or_404(Game, id=game_id, publisher=request.user.user_profile)\n form =Developer_GameForm(request.POST, instance=game)\n\n if not form.is_valid():\n messages.error(request, \"Please check the form errors and try egain.\")\n return render(request, 'developers/edit_game.html', {'form': form, 'user': request.user})\n else:\n if ('action' not in request.POST) or (request.POST['action'].lower() not in ['delete', 'save']):\n messages.error(request=request, message='Missing or invalid action parameter.')\n return HttpResponseRedirect(redirect_to=reverse('dev_games'))\n\n # Ok, perform the requested operation\n if request.POST['action'].lower() == 'save':\n game = form.save()\n messages.success(request=request, message='Game updated successfully.')\n return HttpResponseRedirect(redirect_to=reverse('dev_games'))\n elif request.POST['action'].lower() == 'delete':\n game.delete()\n messages.success(request=request, message='Game removed successfully.')\n return HttpResponseRedirect(redirect_to=reverse('dev_games'))\n else:\n messages.error(request=request, message='Missing or invalid action parameter.')\n return render(request, 'developers/developed_games.html', {'form': form, 'user': request.user})\n\n else:\n return HttpResponse(status=405, content=\"Invalid method.\")\n\n\n@login_required(login_url='log_in')\n@permission_required(perm='Profile.developers')\ndef request_developer_statistics(request):\n\n year = request.GET.get('year')\n\n if year is None:\n year = str(datetime.datetime.now().year)\n\n stats_by_game = get_transactions_by_game(request.user.user_profile, year = year)\n stats_history = get_transaction_history(request.user.user_profile, year=year)\n\n context = {}\n\n context[\"stats_by_game\"] = stats_by_game\n context[\"stats_history\"] = stats_history\n context[\"year\"] = year\n context[\"last_year\"] = int(year) - 1\n context[\"next_year\"] = int(year) + 1\n\n return render(request, \"developers/dev_stats.html\", context)\n\n@login_required(login_url='log_in')\n@permission_required(perm='Profile.developers')\ndef get_transactions_by_game(user, year=None, reverse=True):\n games = Game.objects.filter(publisher=user)\n\n bought_counts = []\n\n # games_data = Order.objects.filter(games_id_in=games,\n # status='yes',\n # )\n game_list = []\n for game in games:\n # game_list.append(game)\n # transactions = game.objects.filter()\n # number_of_transactions = transactions.count()\n # revenue = game.price * number_of_transactions\n\n transactions = Order.objects.filter(_games__id=game.id, paid='yes')\n # print(len())\n number_of_transactions = transactions.count()\n revenue = game.price * number_of_transactions\n # revenue = transactions.aggregate(Sum('total'))['total__sum']\n # Group_by _games\n # data = games_data.values(\"_games\").annotate(Count('id'))\n # for d in data:\n # game_name = Game.objects.get(id=d['_games']).name\n # pie.append({'label': game_name, 'value': d['id__count']})\n\n # transactions = game.transactions\n # transactions = 0\n\n\n # data = transactions.values('_games').annotate(Count('id'))\n\n games_stats = {\"game\": game, \"copies_sold\": number_of_transactions, 'revenue': revenue}\n\n bought_counts.append(games_stats)\n\n return sorted(bought_counts, key=lambda k: k['revenue'], reverse=reverse)\n\n@login_required(login_url='log_in')\n@permission_required(perm='Profile.developers')\ndef get_transaction_history(user, year=None):\n # 1. 提取游戏中包含某个作者的订单\n # 2. 提取该作者的所有游戏\n # 3. 提取包含在某个订单中的所有游戏\n # 4. 创建字典，Key为时间\n buy_history = []\n # transactions = Order.objects.filter(_games__publisher=user)\n games = Game.objects.filter(publisher=user)\n for transaction in Order.objects.all():\n trans_games_names = [trans_game.name for trans_game in transaction._games.all()]\n for game in games:\n if game.name in trans_games_names:\n buy_history.append([transaction.payment_time, game.name, game.price, transaction._player.user.username])\n\n # total_amount = 0\n # games = Game.objects.filter(publisher=user)\n # if len(games) <= 1:\n # games = list(games)\n # for game in games:\n # if year:\n # transactions = Order.objects.filter(_games__id=game.id, paid='yes', payment_time=year)\n # else:\n # transactions = Order.objects.filter(_games__id=game.id, paid='yes')\n # number_of_transactions = transactions.count()\n # revenue = game.price * number_of_transactions\n # total_amount += revenue\n # transactions = Order.objects.filter(_games__in=games,\n # paid='yes',\n # )\n # if year:\n # transactions = transactions.filter(payment_time__year=year)\n #\n # truncate_date = connection.ops.date_trunc_sql('month', 'payment_time')\n # qs = transactions.extra({'month': truncate_date})\n # report = qs.values('month').annotate(copies_sold=Count('pk'), revenue=Sum('total')).order_by('month')\n\n return buy_history\n\n","sub_path":"game_store/developers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"632929256","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.menu, name='menu'),\n path('play', views.board, name='board'),\n path('play/', views.board, name='board'),\n path('double/', views.board2, name='board2'),\n path('showanswer1///', views.showanswer1, name='showanswer1'),\n path('showanswer2////', views.showanswer2, name='showanswer2'),\n path('showexample//', views.showexample, name='showexample'),\n path('reset/', views.reset, name='reset'),\n path('final/', views.final, name='final'),\n path('create_game', views.edit_game, name='edit_game'),\n path('make_game', views.make_game, name='make_game'),\n path('make_category', views.make_category, name='make_category'),\n path('make_final', views.make_final, name='make_final'),\n path('create_game/', views.edit_game, name='edit_game'),\n path('create_game2/', views.edit_game2, name='edit_game2'),\n path('create_gamef/', views.edit_gamef, name='edit_gamef'),\n path('tallypoints', views.tallypoints, name='tallypoints'),\n path('tallydouble', views.tallydouble, name='tallydouble'),\n path('endgame', views.endgame, name='endgame'),\n path('results///', views.results, name='results'),\n path('editteam', views.editteam, name='editteam'),\n path('delete_game/', views.delete_game, name='delete_game'),\n path('change_name', views.change_name, name='change_name'),\n path('move_turn', views.move_turn, name='move_turn'),\n path('shiftup', views.shiftup, name='shiftup'),\n path('shiftdown', views.shiftdown, name='shiftdown'),\n path('shiftright', views.shiftright, name='shiftright'),\n path('hello', views.hello, name='hello')\n]\n","sub_path":"ceccwebsite/jeopardy/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"640097989","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass Model():\n def __init__(self, hidden_unit=3, input_dim=2, output_dim=2):\n self.params = {}\n self.reg_lambda = 0.01\n self.lr = 0.01\n self.params[\"w1\"] = np.random.randn(input_dim,\n hidden_unit) / np.sqrt(input_dim)\n self.params[\"b1\"] = np.random.randn(1, hidden_unit)\n self.params[\"w2\"] = np.random.randn(hidden_unit,\n output_dim) / np.sqrt(hidden_unit)\n self.params[\"b2\"] = np.zeros((1, output_dim))\n\n def forward(self, X, y):\n num_instance = np.shape(X)[0]\n z1 = np.dot(X, self.params[\"w1\"]) + self.params[\"b1\"]\n self.params[\"z1\"] = z1\n a1 = np.tanh(z1)\n self.params[\"a1\"] = a1\n z2 = np.dot(a1, self.params[\"w2\"]) + self.params[\"b2\"]\n self.params[\"z2\"] = z2\n exp_z2 = np.exp(z2)\n probs = exp_z2 / np.sum(exp_z2, axis=1, keepdims=True)\n self.params[\"probs\"] = probs\n logprobs = -np.log(probs[range(num_instance), y])\n loss = np.sum(logprobs)\n loss += self.reg_lambda / 2 * (\n np.sum(self.params[\"w1\"]) + np.sum(self.params[\"w2\"]))\n return loss / num_instance\n\n def predict(self, x):\n z1 = np.dot(x, self.params[\"w1\"]) + self.params[\"b1\"]\n a1 = np.tanh(z1)\n z2 = np.dot(a1, self.params[\"w2\"]) + self.params[\"b2\"]\n exp_z2 = np.exp(z2)\n probs = exp_z2 / np.sum(exp_z2, axis=1, keepdims=True)\n return np.argmax(probs, axis=1)\n\n def backpropagation(self, X, y):\n pred = self.params[\"probs\"]\n num_instance = np.shape(pred)[0]\n pred[range(num_instance), y] -= 1\n grad_out = pred\n grad2 = grad_out.dot(\n self.params[\"w2\"].T) * (1 - np.power(self.params[\"a1\"], 2))\n dw2 = np.dot(self.params[\"a1\"].T, grad_out)\n db2 = np.sum(grad_out, axis=0, keepdims=True)\n dw1 = np.dot(X.T, grad2)\n db1 = np.sum(grad2, axis=0)\n\n dw2 += self.reg_lambda * self.params[\"w2\"]\n dw1 += self.reg_lambda * self.params[\"w1\"]\n\n self.params[\"w1\"] += -self.lr * dw1\n self.params[\"w2\"] += -self.lr * dw2\n self.params[\"b1\"] += -self.lr * db1\n self.params[\"b2\"] += -self.lr * db2\n\n\ndef plot_decision_boundary(X, y, pred_func):\n x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n h = 0.01\n xx, yy = np.meshgrid(\n np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n # Predict the function value for the whole gid\n Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n # Plot the contour and training examples\n plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)","sub_path":"nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"152937863","text":"# -*-coding:utf-8-*-\r\nimport sqlite3\r\n\r\nclass DBlog:\r\n conn = None\r\n cursor = None\r\n\r\n def __init__(self):\r\n self.conn = sqlite3.connect('log.db')\r\n self.cursor = self.conn.cursor()\r\n self.cursor.execute('CREATE TABLE IF NOT EXISTS log (type int(11), total int(11), done int(11))')\r\n\r\n def querySQL(self, sql):\r\n res = self.cursor.execute(sql)\r\n self.conn.commit()\r\n return res","sub_path":"python/video/sqliteTools.py","file_name":"sqliteTools.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"133548181","text":"import time\nimport wallet\nimport eosapi\nimport initeos\n\nproducer = eosapi.Producer()\n\nprint('please make sure you are running the following command before test')\nprint('./pyeos/pyeos --manual-gen-block --debug -i')\n\ndef init():\n if not eosapi.get_account('bugs').permissions:\n with producer:\n r = eosapi.create_account('eosio', 'bugs', initeos.key1, initeos.key2)\n assert r\n\n with producer:\n r = eosapi.set_contract('bugs','../../programs/pyeos/contracts/bugs/bugs.py','../../programs/pyeos/contracts/bugs/bugs.abi', 1)\n assert r\n eosapi.produce_block()\n\ndef t():\n with producer:\n r = eosapi.push_message('bugs','t1','',{'bugs':'active'},rawargs=True)\n assert r\n eosapi.produce_block()\n\n#test deeply recursive generators\ndef t2():\n with producer:\n r = eosapi.push_message('bugs','t2','',{'bugs':'active'},rawargs=True)\n assert r\n eosapi.produce_block()\n\n","sub_path":"programs/goeos/contracts/bugs/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"83907923","text":"'''\r\nFaça um algoritmo que o usuário informe a quantidade de linhas e colunas que a matriz deve ter.\r\nO sistema deve criar a matriz atribuindo zero para cada campo.\r\n'''\r\n\r\n\r\nfrom random import randint\r\nl = [[\"fabiano\", \"joão\", \"maria\"], [\"1\", \"2\", \"3\"]]\r\nx = randint(0, 2)\r\ncont = 0\r\nprint(x)\r\nprint(l)\r\n\r\nprint(l[0][x])\r\nprint(l[1][x])\r\nwhile cont < 3:\r\n resp = input(\"Digite a senha:\")\r\n if resp == l[1][x]:\r\n print(\"Acesso Liberato\")\r\n break\r\n cont = cont + 1\r\n","sub_path":"linhas-colunas-matriz.py","file_name":"linhas-colunas-matriz.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"178750693","text":"import math as m\nimport backend.visualization as vs\nfrom shapely.geometry import Polygon\n\n\nclass Face:\n def __init__(self):\n self.name = None\n self.outer_component = None # One half edge of the outer-cycle\n\n def __repr__(self):\n return f\"Face : (n[{self.name}], outer[{self.outer_component.origin.x}, {self.outer_component.origin.y}])\"\n\n def __eq__(self, rhs):\n return self.name is rhs.name and self.name is rhs.name\n\n\nclass HalfEdge:\n def __init__(self, origin, destination):\n self.origin = origin\n self.destination = destination\n self.incident_face = None\n self.twin = None\n self.next = None\n self.prev = None\n\n def __repr__(self):\n return f\"E(o:[{self.origin.x}, {self.origin.y}], d:[{self.destination.x}, {self.destination.y}])\"\n\n def __eq__(self, rhs):\n return self.origin is rhs.origin and self.destination is rhs.destination\n\n def get_length(self):\n return m.sqrt((self.destination.x - self.origin.x) ** 2 + (self.destination.y - self.origin.y) ** 2)\n\n def get_angle(self):\n dx = self.destination.x - self.origin.x\n dy = self.destination.y - self.origin.y\n l = m.sqrt(dx * dx + dy * dy)\n if dy > 0:\n return m.acos(dx / l)\n else:\n return 2 * m.pi - m.acos(dx / l)\n\n\nclass Edge:\n def __init__(self, half_edge1, half_edge2):\n if half_edge1.destination.x > half_edge2.destination.x:\n self.right_arrow = half_edge1\n self.left_arrow = half_edge2\n else:\n self.right_arrow = half_edge2\n self.left_arrow = half_edge1\n\n # Assumed is that of undirected edges, the destination is the `right-most' endpoint\n self.origin = self.right_arrow.origin\n # Assumed is that of undirected edges, the origin is the `left-most' endpoint\n self.destination = self.right_arrow.destination\n\n def __repr__(self):\n return f\"Edge: [ ({self.origin.x}, {self.origin.y}), ({self.destination.x}, {self.destination.y})]\"\n\n def get_edge_length(self):\n return self.right_arrow.get_length()\n\n def get_y_at_x(self, x):\n # In case the x coordinate lies outside of the range of the line return None\n if x < self.origin.x or x > self.destination.x:\n return None\n\n slope = self.get_slope()\n y_at_x = slope * (x - self.origin.x) + self.origin.y\n return y_at_x\n\n def get_slope(self):\n edge_x_width = self.destination.x - self.origin.x\n return (self.destination.y - self.origin.y) / edge_x_width\n\n def point_lies_above_edge(self, point):\n if point.y > self.get_y_at_x(point.x):\n return True\n else:\n return False\n\n def point_lies_on_edge(self, point):\n if point.x < self.origin.x or point.x > self.destination.x:\n return False\n elif self.get_y_at_x(point.x) == point.y:\n return True\n else:\n return False\n\n\nclass Vertex:\n def __init__(self, x, y, name):\n self.x = x\n self.y = y\n self.name = name\n\n def __repr__(self):\n return f\"Vertex coords: ({self.x}, {self.y})\"\n\n def __eq__(self, rhs):\n return self.x == rhs.x and self.y == rhs.y\n\n def __hash__(self):\n return hash(self.x) + hash(self.y)\n\n\n# Mapping used to quickly find hedge belonging to a certain origin and destination\nclass HedgesMap:\n def __init__(self):\n self.origin_destination_map = {}\n self.destination_origin_map = {}\n\n def insert_hedge(self, origin, destination, hedge):\n self.origin_destination_map.setdefault(origin, {})\n self.origin_destination_map[origin][destination] = hedge\n\n self.destination_origin_map.setdefault(destination, {})\n self.destination_origin_map[destination][origin] = hedge\n\n def get_hedge(self, origin, destination):\n return self.origin_destination_map[origin][destination]\n\n def get_outgoing_hedges(self, origin):\n outgoing_hedges = list(self.origin_destination_map[origin].values())\n return outgoing_hedges\n\n def get_incoming_hedges(self, destination):\n incoming_hedges = list(self.destination_origin_map[destination].values())\n return incoming_hedges\n\n # Returns outgoing half edges in clockwise order\n def get_outgoing_hedges_clockwise(self, origin):\n outgoing_hedges = list(self.origin_destination_map[origin].values())\n outgoing_hedges.sort(key=lambda e: e.get_angle(), reverse=True)\n return outgoing_hedges\n\n # Returns incoming half edges in clockwise order\n def get_incoming_hedges_clockwise(self, destination):\n incoming_hedges = list(self.destination_origin_map[destination].values())\n incoming_hedges.sort(key=lambda e: e.get_angle(), reverse=True)\n return incoming_hedges\n\n # Returns all the incoming and outgoing half edges\n def get_all_hedges_of_vertex(self, vertex):\n hedges = self.get_incoming_hedges_clockwise(vertex) + self.get_outgoing_hedges(vertex)\n return hedges\n\n # Returns all hedges of the mapping\n def get_all_hedges(self):\n all_hedges = []\n for key, hedges_dic in self.origin_destination_map.items():\n all_hedges = all_hedges + (list(hedges_dic.values()))\n return all_hedges\n\n # Deletes half edge from the mapping\n def delete_hedge(self, origin, destination):\n del self.origin_destination_map[origin][destination]\n del self.destination_origin_map[destination][origin]\n\n\nclass Outerface(Face):\n def __init__(self):\n super().__init__()\n self.name = \"BBox\"\n self.upper_left = None\n self.bottom_left = None\n self.upper_right = None\n self.bottom_right = None\n self.segments = None\n self.top_segment = None\n self.bottom_segment = None\n self.inner_component = None\n\n def set_vertices(self, vertices):\n self.upper_left = vertices[0]\n self.bottom_left = vertices[1]\n self.upper_right = vertices[2]\n self.bottom_right = vertices[3]\n\n def set_edges(self, edges):\n self.segments = edges\n self.top_segment = edges[0]\n self.bottom_segment = edges[2]\n self.outer_component = edges[2].right_arrow\n\n\nclass Dcel:\n def __init__(self):\n # (x coordinate, y coordinate) -> vertex\n self.vertices_map = {}\n self.hedges_map = HedgesMap()\n self.faces = []\n self.edges = []\n self.outer_face = Outerface()\n\n def build_dcel(self, points, segments):\n self.__add_points(points)\n self.__add_edges_and_twins(segments)\n self.__add_next_and_previous_pointers()\n self.__add_face_pointers()\n self.__create_outer_face(points)\n\n def show_dcel(self, query=None):\n if query is not None:\n vs.plot_graph(self, query)\n else:\n vs.plot_graph(self)\n\n def get_vertices(self):\n return list(self.vertices_map.values())\n\n def get_edges(self):\n return self.edges\n\n def __add_points(self, points):\n # Creates a hashmap (x coordinate, y coordinate) -> vertex\n label = 'A'\n for point in points:\n self.vertices_map[point] = Vertex(point[0], point[1], label)\n label = chr(ord(label) + 1)\n\n def __add_edges_and_twins(self, segments):\n # Connects vertices and hedges and assign twins\n for segment in segments:\n origin = self.vertices_map[segment[0]]\n destination = self.vertices_map[segment[1]]\n\n hedge = HalfEdge(origin, destination)\n twin_hedge = HalfEdge(destination, origin)\n\n hedge.twin = twin_hedge\n twin_hedge.twin = hedge\n\n self.hedges_map.insert_hedge(hedge.origin, hedge.destination, hedge)\n self.hedges_map.insert_hedge(twin_hedge.origin, twin_hedge.destination, twin_hedge)\n\n self.edges.append(Edge(hedge, twin_hedge))\n\n def __create_outer_face(self, points):\n min_x = points[0][0]\n max_x = points[0][0]\n min_y = points[0][1]\n max_y = points[0][1]\n for point in points:\n if point[0] < min_x: min_x = point[0]\n if point[0] > max_x: max_x = point[0]\n if point[1] < min_y: min_y = point[1]\n if point[1] > max_y: max_y = point[1]\n\n bounding_box_upper_left = Vertex(min_x - 1, max_y + 1, \"ul\")\n bounding_box_lower_left = Vertex(min_x - 1, min_y - 1, \"ll\")\n bounding_box_upper_right = Vertex(max_x + 1, max_y + 1, \"rr\")\n bounding_box_lower_right = Vertex(max_x + 1, min_y - 1, \"lr\")\n\n outer_face_vertices = []\n outer_face_edges = []\n\n outer_face_vertices.append(bounding_box_upper_left)\n outer_face_vertices.append(bounding_box_lower_left)\n outer_face_vertices.append(bounding_box_upper_right)\n outer_face_vertices.append(bounding_box_lower_right)\n\n self.outer_face.set_vertices(outer_face_vertices)\n\n hedge = HalfEdge(bounding_box_upper_left, bounding_box_upper_right)\n twin_hedge = HalfEdge(bounding_box_upper_right, bounding_box_upper_left)\n twin_hedge.incident_face = self.outer_face\n hedge.twin = twin_hedge\n twin_hedge.twin = hedge\n outer_face_edges.append(Edge(hedge, twin_hedge))\n\n hedge = HalfEdge(bounding_box_upper_right, bounding_box_lower_right)\n twin_hedge = HalfEdge(bounding_box_lower_right, bounding_box_upper_right)\n hedge.twin = twin_hedge\n twin_hedge.twin = hedge\n outer_face_edges.append(Edge(hedge, twin_hedge))\n\n hedge = HalfEdge(bounding_box_lower_right, bounding_box_lower_left)\n twin_hedge = HalfEdge(bounding_box_lower_left, bounding_box_lower_right)\n twin_hedge.incident_face = self.outer_face\n hedge.twin = twin_hedge\n twin_hedge.twin = hedge\n outer_face_edges.append(Edge(hedge, twin_hedge))\n\n hedge = HalfEdge(bounding_box_lower_left, bounding_box_upper_left)\n twin_hedge = HalfEdge(bounding_box_upper_left, bounding_box_lower_left)\n hedge.twin = twin_hedge\n twin_hedge.twin = hedge\n outer_face_edges.append(Edge(hedge, twin_hedge))\n\n self.outer_face.set_edges(outer_face_edges)\n\n def __add_next_and_previous_pointers(self):\n # Identify next and previous half edges\n for vertex in list(self.vertices_map.values()):\n outgoing_hedges = self.hedges_map.get_outgoing_hedges_clockwise(vertex)\n # Consider the outgoing half edges in clockwise order\n # Assign to the twin of each outgoing half edge the next outgoing half edge\n for i in range(len(outgoing_hedges)):\n h1 = outgoing_hedges[i]\n h2 = outgoing_hedges[(i + 1) % len(outgoing_hedges)]\n\n h1.twin.next = h2\n h2.prev = h1.twin\n\n def __add_face_pointers(self):\n # Create a face for every cycle of half edges\n number_of_faces = 0\n max_face = None\n max_face_area = 0\n for hedge in self.hedges_map.get_all_hedges():\n if hedge.incident_face is None: # If this half edge has no incident face yet\n vertex_list = [(hedge.origin.x, hedge.origin.y)] # For calculating face size later\n\n number_of_faces += 1\n\n f = Face()\n f.name = \"f\" + str(number_of_faces)\n\n f.outer_component = hedge\n hedge.incident_face = f\n\n h = hedge\n while not h.next == hedge: # Walk through all hedges of the cycle and set incident face\n h.incident_face = f\n h = h.next\n vertex_list.append((h.origin.x, h.origin.y))\n h.incident_face = f\n\n self.faces.append(f)\n\n # Calculate area of face formed by the half-edges\n polygon = Polygon(vertex_list)\n if polygon.area > max_face_area: # Find largest face\n max_face_area = polygon.area\n max_face = f\n\n # The max face is actually the inner cycle of the outer face under assumption that faces\n # do not contain holes or are separated\n self.faces.remove(max_face)\n h = max_face.outer_component\n h.incident_face = self.outer_face\n self.outer_face.inner_component = h\n while not h.next == max_face.outer_component:\n h = h.next\n h.incident_face = self.outer_face\n","sub_path":"backend/dcel.py","file_name":"dcel.py","file_ext":"py","file_size_in_byte":12581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"277119715","text":"from django.shortcuts import render\nfrom sj_cmfz.settings import API_KEY\n# Create your views here.\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom utils.send_mess import Yun_Pian\nfrom utils.captcha import Captcha\n\nimport re\nfrom redis import Redis\n\nredis = Redis(host='123.57.70.140', port=6379)\n\n\n# Create your views here.\n\n\ndef index(request):\n return render(request, \"main.html\")\n\n\ndef login(request):\n return render(request, \"login.html\")\n\n\n@csrf_exempt\ndef login_logic(request):\n mobile = request.GET.get('mobile')\n name = request.GET.get(\"name\")\n code = request.GET.get('code')\n phone_result = re.match(r\"^1[35678]\\d{9}$\", mobile)\n code_result = re.match(r\"\\d{6}$\", code)\n # 校验信息是否合法\n # 验证码是否在有效期内 使用redis\n # redis.get(\"18500230996_2\")\n if phone_result and code_result:\n try:\n saved_code = redis.get(f'{mobile}_2')\n if saved_code.decode() == code:\n return JsonResponse({'status': 1, 'msg': '登陆成功！'})\n except BaseException as error:\n print(error)\n return JsonResponse({'status': 0, 'msg': f'{error}请先发送验证码！'})\n return JsonResponse({'status': 0, 'msg': '登陆失败！'})\n else:\n return JsonResponse({'status': 0, 'msg': '有字段输入不合法！请检查！'})\n\n\n@csrf_exempt\ndef get_captcha(request):\n mobile = request.POST.get('mobile')\n # 获取手机号有没有对应的验证码 查看验证码是否在120S内 如果在 提示验证码已经发送\n # value = redis.get(\"18500230996_1\") 如果返回的值存在 代表120s之内还不能发送\n # 判断手机号是否存在 正则验证是否合法\n # 正则判断输入手机号是否合法\n result = re.match(r\"^1[35678]\\d{9}$\", mobile)\n if result:\n # 将手机号与对应的验证码存入redis 防止无限制发送\n # redis.set(\"18500230996_1\", \"666666\", 120S)\n # 保证验证码的有效期\n # redis.set(\"18500230996_2\", \"666666\", 600s)\n saved_code = redis.get(f'{mobile}_1')\n if saved_code:\n return JsonResponse({'status': 0, 'msg': '已发送验证码，请注意查收！'})\n code = Captcha().create_code()\n print(f'验证码：{code}')\n yun_pian = Yun_Pian(API_KEY)\n yun_pian.send_message(mobile, code)\n\n # 将手机号与对应的验证码存入redis 防止无限制发送\n redis.set(f\"{mobile}_1\", code, 120)\n # 保证验证码的有效期\n redis.set(f\"{mobile}_2\", code, 600)\n\n return JsonResponse({'status': 1, 'msg': '发送成功！'})\n else:\n return JsonResponse({'status': 0, 'msg': '请输入合法的手机号！'})\n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"7078135","text":"from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n #Bercario\n url(r'^cuna/$', 'cuna.views.index'),\n url(r'^cuna/listar/$', 'cuna.views.listar'),\n url(r'^cuna/(?P\\d+)/$', 'cuna.views.detail'),\n url(r'^cuna/add/$', 'cuna.views.add'),\n url(r'^cuna/search/$', 'cuna.views.search'),\n \n url(r'^cuna/sobre/$', 'cuna.views.sobre'),\n url(r'^cuna/faq/$', 'cuna.views.faq'),\n\n url(r'^cuna/create/$', 'cuna.views.create'),\n url(r'^cuna/edit/$', 'cuna.views.edit'),\n url(r'^cuna/filters/$', 'cuna.views.filters'),\n\n #Logins\n url(r'^contas/$', 'contas.views.index'),\n url(r'^contas/login/$', 'contas.views.auth_login'),\n url(r'^contas/logout/$', 'contas.views.auth_logout'),\n\n \n url(r'^admin/', include(admin.site.urls)),\n)","sub_path":"bercario/bercario/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"151984432","text":"\n\nfrom xai.brain.wordbase.nouns._whitewash import _WHITEWASH\n\n#calss header\nclass _WHITEWASHING(_WHITEWASH, ):\n\tdef __init__(self,): \n\t\t_WHITEWASH.__init__(self)\n\t\tself.name = \"WHITEWASHING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"whitewash\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_whitewashing.py","file_name":"_whitewashing.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"627219803","text":"from django.urls import path\nfrom . import views\n\nurlpatterns=[\n path('',views.home,name='home'),\n path('login',views.login,name='login'),\n \n path('prephd',views.prephd,name='prephd'),\n path('resmtd',views.resmtd,name='resmtd'),\n path('rrm',views.rrm,name='rrm'),\n path('collaq',views.collaq,name='collaq'),\n path('revsubmit',views.revsubmit,name='revsubmit'),\n path('plagarism',views.plagarism,name=\"plagarism\"),\n path('thesisSubmit',views.theSubmit,name=\"theSubmit\"),\n path('vivavoca',views.vivavoca,name=\"vivavoca\"),\n path('extend',views.extend,name='extend'),\n path('changeSuper',views.changeSuper,name='changeSuper'),\n \n path('prephdSubmit/',views.prephdSubmit),\n path('resmtdSubmit/',views.resmtdSubmit),\n path('rrmSubmit/',views.rrmSubmit),\n path('collaqSubmit/',views.collaqSubmit),\n path('revsubmitSubmit/',views.revsubmitSubmit),\n path('plagarismSubmit/',views.plagarismSubmit),\n path('thesisSubmitSubmit/',views.theSubmitSubmit),\n path('vivavocaSubmit/',views.vivavocaSubmit),\n path('extendSubmit/',views.extendSubmit),\n path('changeSuperSubmit/',views.changeSuperSubmit)\n\n\n]\n# path('postlogin/',views.postlogin),","sub_path":"rnd/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"370794505","text":"from collections.abc import Iterable, Iterator\nlist1 =range(5)\nfor i in list1:\n print(i)\n#将可迭代对象转换为迭代器对象\nif isinstance(list1,Iterable):\n print(\"该对象是可迭代对象\")\nelif isinstance(list1,Iterator):\n print(\"该对象是迭代器\")\nelse:\n print(\"您的判断不存在\")\n\nlist2 = iter(list1)\ntry:\n print(\"迭代器对象获取数据:\",next(list2))\nexcept StopIteration as e:\n print(\"That is all floks!\")\n\n","sub_path":"basic/function/diedaiqi_use.py","file_name":"diedaiqi_use.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"223893790","text":"from scraper import main\nfrom twitterSentiment import mainAnalysis\nimport pickle\nimport datetime\n#from celery import task\n\n\nFILE = 'mainDict.txt'\n\n\n# Use celery to make it a schedulable task\n\ndef loadData():\n\teventsAndHorses = main()\n\n\tnewDict = {}\n\n\tfor i in eventsAndHorses:\n\n\t\thorseSents = {}\n\t\tfor j in eventsAndHorses[i]:\n\t\t\tsents = mainAnalysis(j)\n\t\t\thorseSents[j] = sents\n\n\t\tprint(horseSents)\n\t\tnewDict[i] = horseSents\n\n\tprint(newDict)\n\n\twith open(FILE, 'wb') as handle:\n\t\tpickle.dump(newDict, handle)\n\n","sub_path":"UI/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"352563888","text":"#\n# [216] Combination Sum III\n#\n# https://leetcode.com/problems/combination-sum-iii/description/\n#\n# algorithms\n# Medium (47.75%)\n# Total Accepted: 92.1K\n# Total Submissions: 192.8K\n# Testcase Example: '3\\n7'\n#\n#\n# Find all possible combinations of k numbers that add up to a number n, given\n# that only numbers from 1 to 9 can be used and each combination should be a\n# unique set of numbers.\n#\n# Note:\n#\n#\n# All numbers will be positive integers.\n# The solution set must not contain duplicate combinations.\n#\n#\n# Example 1:\n#\n#\n# Input: k = 3, n = 7\n# Output: [[1,2,4]]\n#\n#\n# Example 2:\n#\n#\n# Input: k = 3, n = 9\n# Output: [[1,2,6], [1,3,5], [2,3,4]]\n#\n\n\nclass Solution:\n def combinationSum3(self, k, n):\n \"\"\"\n :type k: int\n :type n: int\n :rtype: List[List[int]]\n \"\"\"\n nums = [x for x in range(1, 10)]\n res = []\n self.DFS(nums, [], 0, res, k, n, 0)\n return res\n\n def DFS(self, nums, cur, s, res, k, n, pos):\n if len(cur) == k:\n if s == n:\n res.append(cur)\n return\n for i in range(pos, len(nums)):\n self.DFS(nums, cur+[nums[i]], s+nums[i], res, k, n, i+1)\n","sub_path":"216.combination-sum-iii.python3.py","file_name":"216.combination-sum-iii.python3.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"636841853","text":"import os, pygame\nimport datetime\nimport time\nfrom tanks import Tank\n\npygame.init()\nscreen = pygame.display.set_mode((640, 480))\n\ntank1 = Tank(\"blue\")\ntank2 = Tank(\"green\")\n\ndef loadField(name):\n main_dir = os.path.split(os.path.abspath(__file__))[0]\n path = os.path.join(main_dir, 'Media', name)\n return pygame.image.load(path).convert()\n \ndef main():\n WHITE = (255, 255, 255)\n\n clock = pygame.time.Clock()\n\n background = loadField(\"field640x480.png\")\n\n screen.blit(background, (0, 0))\n pygame.display.update()\n\n tank1Pos = (100,100)\n tank2Pos = (300,300)\n\n tank1Rotation = 0\n tank2Rotation = 0\n\n #\n # the move point is the CENTER of the tank\n #\n tank1.move(tank1Pos,tank1Rotation)\n tank2.move(tank2Pos,tank2Rotation)\n \n screen.blit(tank1.image,tank1.pos,tank1.pos)\n screen.blit(tank2.image,tank2.pos,tank2.pos)\n\n pygame.display.update()\n\n while True:\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n return\n\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_LEFT:\n tank1Rotation += 10\n tank1Pos = (tank1Pos[0]+1,tank1Pos[1]-1)\n tank2Rotation -= 10\n tank2Pos = (tank2Pos[0]-1,tank2Pos[1]+1)\n if event.key == pygame.K_RIGHT:\n tank1Rotation -= 10\n tank1Pos = (tank1Pos[0]-1,tank1Pos[1]+1)\n tank2Rotation += 10\n tank2Pos = (tank2Pos[0]+1,tank2Pos[1]-1)\n\n # this can be replaced with other erasing routines, but this is fast\n screen.blit(background,tank1.pos,tank1.pos)\n screen.blit(background,tank2.pos,tank2.pos)\n\n # no movement (just rotation) can be done with:\n # tank1.move(tank1.pos.center,tank1Rotation)\n #\n tank1.move(tank1Pos,tank1Rotation)\n tank2.move(tank2Pos,tank2Rotation)\n\n tank1.draw(screen)\n tank2.draw(screen)\n\n pygame.display.update()\n\n clock.tick(5)\n\nmain()\npygame.quit()\n","sub_path":"Final Programs/tanktest.py","file_name":"tanktest.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"54830214","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nif __name__ == \"__main__\":\n def function(x):\n return x**2-2 # The main function\n def derivative(x):\n return 2*x # The derivative of the main function\n\ndef newton(function, derivative, x0, tolerance, number_of_max_iterations=100):\n x1 = 0\n\n if abs(x0-x1)<= tolerance and abs((x0-x1)/x0)<= tolerance:\n return x0\n\n print(\"k\\t x0\\t\\t function(x0)\")\n k = 1\n\n while k <= number_of_max_iterations:\n x1 = x0 - (function(x0)/derivative(x0))\n print(\"x%d\\t%e\\t%e\"%(k,x1,function(x1)))\n\n if abs(x0-x1)<= tolerance and abs((x0-x1)/x0)<= tolerance:\n plt.plot(x0, function(x0), 'or')\n return x1\n\n x0 = x1\n k = k + 1\n plt.plot(x0, function(x0), 'or')\n\n # Stops the method if it hits the number of maximum iterations\n if k > number_of_max_iterations:\n print(\"ERROR: Exceeded max number of iterations\")\n\n return x1 # Returns the value\n\nsqrt = newton(function, derivative, 1.7, 0.0000001)\nprint(\"The approximate value of x is: \"+str(sqrt))\n\n# Plotting configuration\nu = np.arange(1.0, 2.0, 0.0001) # Setting up values for x in the plot\nw = u**2 - 2 # Define the main function again\n\nplt.plot(u, w)\nplt.axhline(y=0.0, color='black', linestyle='-')\nplt.title('Newton-Raphson Graphics for' + ' y = x^2 - 2')\nplt.xlabel('X')\nplt.ylabel('Y')\nplt.grid(True)\nplt.legend(['Xn'], loc='upper left')\nplt.show()\n","sub_path":"newton_raphson_method.py","file_name":"newton_raphson_method.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"603213921","text":"# coding=utf-8\n\"\"\"fix clld assoc tables\n\nRevision ID: \nRevises: \nCreate Date: \n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = ''\ndown_revision = ''\n\nimport datetime\n\nfrom alembic import op\nimport sqlalchemy as sa\n\nUNIQUE_NULL = [\n # TODO: make empty page reference description = '' in *reference\n ('contributioncontributor', ['contribution_pk', 'contributor_pk'], []),\n ('contributionreference', ['contribution_pk', 'source_pk', 'description'], []),\n ('editor', ['dataset_pk', 'contributor_pk'], []),\n ('languageidentifier', ['language_pk', 'identifier_pk'], []),\n ('languagesource', ['language_pk', 'source_pk'], []),\n ('sentencereference', ['sentence_pk', 'source_pk', 'description'], []),\n ('unitvalue', ['unit_pk', 'unitparameter_pk', 'unitdomainelement_pk'], ['unitdomainelement_pk']),\n ('value', ['valueset_pk', 'domainelement_pk'], ['domainelement_pk']),\n ('valuesentence', ['value_pk', 'sentence_pk'], []),\n # TODO: revisit contribution in valueset docstring \n ('valueset', ['language_pk', 'parameter_pk', 'contribution_pk'], []),\n ('valuesetreference', ['valueset_pk', 'source_pk', 'description'], []),\n]\n\nUNIQUE = [(tab, cols) for tab, cols, nullable in UNIQUE_NULL]\nNOTNULL = [(tab, [c for c in cols if c not in nullable]) for tab, cols, nullable in UNIQUE_NULL]\n\n\ndef upgrade():\n conn = op.get_bind()\n\n def select_null(tab, cols):\n condition = ' OR '.join('%s IS NULL' % c for c in cols)\n return sa.text('SELECT * FROM %(tab)s WHERE %(condition)s' % locals(), conn)\n\n nulls = [(tab, cols, select_null(tab, cols).execute().fetchall())\n for tab, cols in NOTNULL]\n violating = [(tab, cols) for tab, cols, rows in nulls if rows]\n if violating:\n for tab, cols, rows in violating:\n print('violating %s NOT NULL(%s): %d' % (tab, ', '.join(cols), len(rows)))\n raise RuntimeError\n\n def select_duplicate(tab, cols):\n cols = ', '.join(cols)\n return sa.text('SELECT %(cols)s, count(*) FROM %(tab)s '\n 'GROUP BY %(cols)s HAVING count(*) > 1 ORDER BY %(cols)s' % locals(), conn)\n\n duplicates = [(tab, cols, select_duplicate(tab, cols).execute().fetchall())\n for tab, cols in UNIQUE]\n violating = [(tab, cols, rows) for tab, cols, rows in duplicates if rows]\n if violating:\n for tab, cols, rows in violating:\n print('violating %s UNIQUE(%s): %d' % (tab, ', '.join(cols), len(rows)))\n raise RuntimeError\n\n select_nullable = sa.text('SELECT attname FROM pg_attribute '\n 'WHERE attrelid = (SELECT oid FROM pg_class WHERE relname = :tab) '\n 'AND NOT attnotnull AND attname = ANY(:cols) ORDER BY attnum', conn)\n\n select_const = sa.text('SELECT name, definition FROM ('\n 'SELECT c.conname AS name, pg_get_constraintdef(c.oid) AS definition, '\n 'array(SELECT a.attname::text FROM unnest(c.conkey) AS n '\n 'JOIN pg_attribute AS a ON a.attrelid = c.conrelid AND a.attnum = n '\n 'ORDER BY a.attname) AS names '\n 'FROM pg_constraint AS c WHERE c.contype = :type AND c.conrelid = '\n '(SELECT oid FROM pg_class AS t WHERE t.relname = :tab)) AS s '\n 'WHERE s.names @> :cols AND s.names <@ :cols',\n conn).bindparams(type='u')\n\n for tab, cols in NOTNULL:\n for col, in select_nullable.execute(tab=tab, cols=cols).fetchall():\n op.alter_column(tab, col, nullable=False)\n\n for tab, cols in UNIQUE:\n matching = select_const.execute(tab=tab, cols=cols).fetchall()\n if matching:\n assert len(matching) == 1\n name, definition = matching[0]\n if definition == 'UNIQUE (%s)' % ', '.join(cols):\n continue\n op.drop_constraint(name, tab)\n name = '%s_%s_key' % (tab, '_'.join(cols))\n op.create_unique_constraint(name, tab, cols)\n\n raise NotImplementedError\n\n\ndef downgrade():\n pass\n","sub_path":"migrations/pending/fix_clld_assoc_tables.py","file_name":"fix_clld_assoc_tables.py","file_ext":"py","file_size_in_byte":3920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"113053295","text":"import re\nimport os\n\nwirepaths_file = open(\"wirepaths.txt\", \"r\")\nwirepaths_lines = wirepaths_file.readlines()\ncoordinates_file = open(\"coordinates.txt\", \"w\")\ncoordinates_file.truncate(0)\n\nfor wirepath in wirepaths_lines:\n\n if os.stat(\"coordinates.txt\").st_size==0:\n coordinates_file = open(\"coordinates.txt\", \"w+\")\n else:\n coordinates_file = open(\"coordinates.txt\", \"a+\")\n coordinates_file.write(\"\\n\")\n\n wirepath = wirepath.split(\",\")\n point_x = 0\n point_y = 0\n\n for coordinate in wirepath:\n direction = re.search(r\"[a-zA-Z]\", coordinate)\n direction = str(direction[0])\n distance = re.search(r\"[0-9]+\", coordinate)\n distance = int(distance[0])\n if str(direction) == \"R\":\n i = 0\n while i < distance:\n point_x += 1\n i += 1\n coordinates_file.write(str(point_x) + \" \" + str(point_y)+ \",\")\n if str(direction) == \"L\":\n i = 0\n while i < distance:\n point_x -= 1\n i += 1\n coordinates_file.write(str(point_x) + \" \" + str(point_y)+ \",\")\n if str(direction) == \"U\":\n i = 0\n while i < distance:\n point_y += 1\n i += 1\n coordinates_file.write(str(point_x) + \" \" + str(point_y)+ \",\")\n if str(direction) == \"D\":\n i = 0\n while i < distance:\n point_y -= 1\n i += 1\n coordinates_file.write(str(point_x) + \" \" + str(point_y)+ \",\")\n coordinates_file.close()\n\ncoordinates_file = open(\"coordinates.txt\", \"r\")\ncoordinates_lines = coordinates_file.readlines()\ncoordinates_lines1 = coordinates_lines[0].split(\",\")\ncoordinates_lines2 = coordinates_lines[1].split(\",\")\n\nmanhattan_distances = []\ntotal_steps = []\n\nfor coordinates in coordinates_lines1:\n if coordinates in coordinates_lines2:\n print(\"Found a match: \" + str(coordinates))\n distances = re.findall(r\"[0-9]+\", coordinates)\n manhattan_distance = int(distances[0]) + 1 + int(distances[1] + 1)\n print(\"sum : \" + str(manhattan_distance) + \"\\n\")\n manhattan_distances.append(int(manhattan_distance))\n\n totalsteps = int(coordinates_lines1.index(coordinates)) + int(coordinates_lines2.index(coordinates))\n total_steps.append(int(totalsteps))\n print(\"The total step count : \" + str(totalsteps))\n \nprint(\"Manhattan distance: \")\nmanhattan_distances.sort()\nprint(manhattan_distances)\nprint(str(manhattan_distances[0]))\n\nprint(\"Total steps to closest intersection: \")\ntotal_steps.sort()\nprint(total_steps)\nprint(str(total_steps[0]))\n\n\n\n\n ","sub_path":"day3part1and2.py","file_name":"day3part1and2.py","file_ext":"py","file_size_in_byte":2450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"211516940","text":"from random import random\nfrom math import sqrt\nfrom utils.func import get_k_idx\n\n\nclass GACrossover(object):\n \"\"\"\n Class for providing an interface to easily extend the behavior of crossover\n operation between two individuals for children breeding.\n \"\"\"\n\n def __init__(self, cross_rate=0.8):\n self.cross_rate = cross_rate\n\n def cross(self, father, mother):\n \"\"\"\n Called when we need to cross parents to generate children.\n \"\"\"\n raise NotImplementedError\n\n\nclass TwoPointOrderedCrossover(GACrossover):\n def cross(self, father, mother):\n if random() < self.cross_rate:\n num_genes = len(father.genes)\n # get the interval\n start_at, finish_at = get_k_idx(0, num_genes, k=2, sort=True)\n child1 = father.new_individual()\n child2 = mother.new_individual()\n child1.genes[start_at:finish_at] = mother.genes[start_at:finish_at]\n child2.genes[start_at:finish_at] = father.genes[start_at:finish_at]\n\n temp1 = [g for g in father.genes if g not in child1.genes]\n temp2 = [g for g in mother.genes if g not in child2.genes]\n cnt1, cnt2 = 0, 0\n t1, t2 = len(temp1), len(temp2)\n for i in range(num_genes):\n if child1.genes[i] is None:\n child1.genes[i] = temp1[cnt1]\n cnt1 += 1\n if child2.genes[i] is None:\n child2.genes[i] = temp2[cnt2]\n cnt2 += 1\n if t1 == cnt1 and t2 == cnt2:\n break\n return child1, child2\n else:\n return father, mother\n\n\nname2crossover = {\n 'TwoPointOrderedCrossover': TwoPointOrderedCrossover\n}\n\n\ndef get_crossover(args):\n crossover_type = args.crossover_type\n if crossover_type not in name2crossover:\n raise ValueError('Only support crossover type: %s' ','.join(list(name2crossover.keys())))\n print('Using Crossover: %s' % crossover_type)\n Crossover = name2crossover[crossover_type]\n return Crossover(args.cr)\n","sub_path":"ga/operation/crossover.py","file_name":"crossover.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"388874982","text":"N = int(input())\na = list(map(int,input().split()))\n\ncnt = 0\nfor i,value in enumerate(a):\n b = a[value-1]\n if b == i+1:\n cnt += 1\ncnt //=2\nprint(cnt)","sub_path":"Python_codes/p03993/s073362824.py","file_name":"s073362824.py","file_ext":"py","file_size_in_byte":162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"235593625","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, JsonResponse\nfrom myblog import models\nfrom . import mySer\nfrom django.core import paginator\nimport json\n\n\ndef index(req):\n # content = models.Article.objects.all()[0]\n # return render(req, 'index.html')\n html = open('./templates/index.html', 'r')\n return HttpResponse(html)\n\ndef comment(req):\n if req.method == 'POST':\n # print(json.loads(req.body))\n body = json.loads(req.body)\n models.Comment.objects.create( **body)\n return JsonResponse({\n \"message\": \"ok\"\n }, safe=False)\n\ndef article(req):\n query = models.Article.objects.get(id=req.GET.get('id'))\n query.viewcount += 1\n query.save()\n # query = models.Article.objects.filter(id=req.GET.get('id'))\n ser = mySer.Article(query)\n return JsonResponse(ser.data, safe=False)\n\ndef comments_of_article(request):\n artcle_id = request.GET.get('id')\n ser = mySer.CommentSer(models.Comment.objects.all().filter(toarticle=artcle_id), many=True)\n return JsonResponse(ser.data, safe=False)\n\n\ndef article_list_date_count(request):\n query = models.Article.objects\\\n .raw(\n \"\"\"\n SELECT id, times, count(times) counts \n from (SELECT id, strftime('%Y-%m',time) times from myblog_article) \n GROUP by times order by times DESC ;\n \"\"\")\n ser = mySer.ArticleSerOfdatecount(query, many=True)\n return JsonResponse(ser.data, safe=False)\n\n\ndef articlesofdate(req):\n if(req.method == 'GET'):\n if'month' not in req.GET.dict():\n query = models.Article.objects.filter(time__contains=req.GET.get('year')+'-')\n ser = mySer.ArticleSerOfdatelist(query, many=True)\n return JsonResponse(ser.data, safe=False)\n else:\n query = models.Article.objects.filter(time__contains=req.GET.get('year')+'-'+req.GET.get('month'))\n ser = mySer.ArticleSerOfdatelist(query, many=True)\n return JsonResponse(ser.data, safe=False)\n\n\ndef artciles(req):\n if(req.method == 'GET'):\n if 'page' in req.GET.dict():\n page = paginator.Paginator(models.Article.objects.all(),5)\n page_result = page.get_page(req.GET.get('page'))\n if page.num_pages < int(req.GET.get('page')):\n return HttpResponse('404')\n ser = mySer.Article(page_result.object_list.all(), many=True)\n for i in ser.data:\n i['article_instance'] = len(i['article_instance'])\n return JsonResponse(ser.data, safe=False)\n\ndef categoryofarticles(req):\n if(req.method == 'GET'):\n query = models.Category.objects.raw(\n \"\"\"\n SELECT a.id, category,count(1) counts\n FROM myblog_category a INNER JOIN myblog_article b\n ON a.id = b.category_id GROUP BY a.id;\n \"\"\" \n )\n ser = mySer.CategorySer(query,many=True)\n return JsonResponse(ser.data, safe=False)\n\ndef categorylistofarticles(req):\n if(req.method == 'GET'):\n query = models.Category.objects.all()\\\n .get(category=req.GET.get('category'))\\\n .article_set.all()\n ser = mySer.ArticleSerOfdatelist(query, many=True)\n return JsonResponse(ser.data, safe=False)\n","sub_path":"myblog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"414872076","text":"from typing import cast\nfrom unittest import TestCase, skip\nfrom sympy import symbols\n\nfrom pyecsca.ec.coordinates import AffineCoordinateModel\nfrom pyecsca.ec.curve import EllipticCurve\nfrom pyecsca.ec.error import UnsatisfiedAssumptionError\nfrom pyecsca.ec.formula import AdditionFormula, DoublingFormula, ScalingFormula\nfrom pyecsca.ec.mod import Mod, SymbolicMod\nfrom pyecsca.ec.model import MontgomeryModel, EdwardsModel\nfrom pyecsca.ec.params import get_params\nfrom pyecsca.ec.mult import LTRMultiplier\nfrom pyecsca.ec.point import Point, InfinityPoint\n\n\nclass RegressionTests(TestCase):\n def test_issue_7(self):\n secp128r1 = get_params(\"secg\", \"secp128r1\", \"projective\")\n base = secp128r1.generator\n coords = secp128r1.curve.coordinate_model\n add = cast(AdditionFormula, coords.formulas[\"add-1998-cmo\"])\n dbl = cast(DoublingFormula, coords.formulas[\"dbl-1998-cmo\"])\n scl = cast(ScalingFormula, coords.formulas[\"z\"])\n mult = LTRMultiplier(\n add, dbl, scl, always=False, complete=False, short_circuit=True\n )\n mult.init(secp128r1, base)\n pt = mult.multiply(13613624287328732)\n self.assertIsInstance(pt.coords[\"X\"], Mod)\n self.assertIsInstance(pt.coords[\"Y\"], Mod)\n self.assertIsInstance(pt.coords[\"Z\"], Mod)\n mult.init(secp128r1, pt)\n a = mult.multiply(1)\n self.assertNotIsInstance(a.coords[\"X\"].x, float)\n self.assertNotIsInstance(a.coords[\"Y\"].x, float)\n self.assertNotIsInstance(a.coords[\"Z\"].x, float)\n\n def test_issue_8(self):\n e222 = get_params(\"other\", \"E-222\", \"projective\")\n base = e222.generator\n affine_base = base.to_affine()\n affine_double = e222.curve.affine_double(affine_base)\n affine_triple = e222.curve.affine_add(affine_base, affine_double)\n self.assertIsNotNone(affine_double)\n self.assertIsNotNone(affine_triple)\n\n def test_issue_9(self):\n model = MontgomeryModel()\n coords = model.coordinates[\"xz\"]\n p = 19\n neutral = Point(coords, X=Mod(1, p), Z=Mod(0, p))\n curve = EllipticCurve(\n model, coords, p, neutral, {\"a\": Mod(8, p), \"b\": Mod(1, p)}\n )\n base = Point(coords, X=Mod(12, p), Z=Mod(1, p))\n formula = coords.formulas[\"dbl-1987-m-2\"]\n res = formula(p, base, **curve.parameters)[0]\n self.assertIsNotNone(res)\n affine_base = Point(AffineCoordinateModel(model), x=Mod(12, p), y=Mod(2, p))\n dbase = curve.affine_double(affine_base).to_model(coords, curve)\n ladder = coords.formulas[\"ladd-1987-m-3\"]\n one, other = ladder(p, base, dbase, base, **curve.parameters)\n self.assertIsNotNone(one)\n self.assertIsNotNone(other)\n\n def test_issue_10(self):\n model = EdwardsModel()\n coords = model.coordinates[\"yz\"]\n coords_sqr = model.coordinates[\"yzsquared\"]\n p = 0x1D\n c = Mod(1, p)\n d = Mod(0x1C, p)\n r = d.sqrt()\n neutral = Point(coords, Y=c * r, Z=Mod(1, p))\n curve = EllipticCurve(model, coords, p, neutral, {\"c\": c, \"d\": d, \"r\": r})\n neutral_affine = Point(AffineCoordinateModel(model), x=Mod(0, p), y=c)\n self.assertEqual(neutral, neutral_affine.to_model(coords, curve))\n neutral_sqr = Point(coords_sqr, Y=c ** 2 * r, Z=Mod(1, p))\n self.assertEqual(neutral_sqr, neutral_affine.to_model(coords_sqr, curve))\n\n def test_issue_13(self):\n model = EdwardsModel()\n coords = model.coordinates[\"yz\"]\n c, r, d = symbols(\"c r d\")\n p = 53\n c = SymbolicMod(c, p)\n r = SymbolicMod(r, p)\n d = SymbolicMod(d, p)\n yd, zd, yp, zp, yq, zq = symbols(\"yd zd yp zp yq zq\")\n PmQ = Point(coords, Y=SymbolicMod(yd, p), Z=SymbolicMod(zd, p))\n P = Point(coords, Y=SymbolicMod(yp, p), Z=SymbolicMod(zp, p))\n Q = Point(coords, Y=SymbolicMod(yq, p), Z=SymbolicMod(zq, p))\n formula = coords.formulas[\"dadd-2006-g-2\"]\n formula(p, PmQ, P, Q, c=c, r=r, d=d)\n\n @skip(\"Unresolved issue currently.\")\n def test_issue_14(self):\n model = EdwardsModel()\n coords = model.coordinates[\"projective\"]\n affine = AffineCoordinateModel(model)\n formula = coords.formulas[\"add-2007-bl-4\"]\n p = 19\n c = Mod(2, p)\n d = Mod(10, p)\n curve = EllipticCurve(model, coords, p, InfinityPoint(coords), {\"c\": c, \"d\": d})\n Paff = Point(affine, x=Mod(0xD, p), y=Mod(0x9, p))\n P = Paff.to_model(coords, curve)\n Qaff = Point(affine, x=Mod(0x4, p), y=Mod(0x12, p))\n Q = Qaff.to_model(coords, curve)\n PQaff = curve.affine_add(Paff, Qaff)\n R = formula(p, P, Q, **curve.parameters)[0]\n Raff = R.to_affine()\n self.assertEqual(PQaff, Raff)\n","sub_path":"test/ec/test_regress.py","file_name":"test_regress.py","file_ext":"py","file_size_in_byte":4815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"81304484","text":"# -*- coding:utf-8 -*-\n# editor: zzh\n# date: 2020/11/21\n\nfrom keras_bert import load_trained_model_from_checkpoint, Tokenizer\nfrom re_assisstant import *\nimport keras.backend as K\nfrom sklearn.utils import shuffle\nimport codecs\nfrom sklearn.model_selection import train_test_split\n\nimport numpy as np\nimport pandas as pd\nfrom keras.utils import np_utils\nfrom sklearn import preprocessing\n\nfrom keras.layers import *\nfrom keras.models import Model\nimport keras.backend as K\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ModelCheckpoint\nimport random\n\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n\n# 目的是扩充vocab.txt 文件 ， 实现这个类中的方法\nclass OurTokenizer(Tokenizer):\n def _tokenize(self, text):\n R = []\n for c in text:\n if c in self._token_dict:\n R.append(c)\n elif self._is_space(c):\n R.append('[unused1]') # space类用未经训练的[unused1]表示\n else:\n R.append('[UNK]') # 剩余的字符是[UNK]\n return R # 匹配字典集\n\n\ndef lables2onehot(data, num_class):\n \"\"\"\n 字符串标签类别转为one_hot类型数据\n :param data:\n :param num_class:\n :return:\n \"\"\"\n le = preprocessing.LabelEncoder()\n target = le.fit_transform(data)\n target = np_utils.to_categorical(target, num_classes=num_class)\n return target\n\nclassifications = [\"disaster\",\"not_disaster\"]\n\n# classifications2 = ['低温灾害', '冰雹灾害', '台风灾害', '地质灾害', '城市内涝', '大雾灾害', '大风灾害', '干旱灾害', '暴雨洪涝', '森林火灾', '雪灾灾害', '雷电灾害']\n\nconfig_path = r'D:\\MyProject\\天气灾害分类及信息提取\\chinese_L-12_H-768_A-12\\bert_config.json'\ncheckpoint_path = r'D:\\MyProject\\天气灾害分类及信息提取\\chinese_L-12_H-768_A-12\\bert_model.ckpt'\ndict_path = r'D:\\MyProject\\天气灾害分类及信息提取\\chinese_L-12_H-768_A-12\\vocab.txt'\n\nclass SynBertWeatherRecongizer(object):\n\n def build_bert(self,trainable = True):\n bert_model = load_trained_model_from_checkpoint(self.config_path, self.checkpoint_path, seq_len=None)\n for l in bert_model.layers:\n l.trainable = trainable # 设定为BERT可训练\n x1_in = Input(shape=(None,))\n x2_in = Input(shape=(None,))\n # x1_in = Lambda(self.mask_data,arguments={'mask_rate':0.1})(x1_in)\n x = bert_model([x1_in, x2_in])\n x2 = Lambda(lambda x: x[:, 0])(x)\n x2 = Dropout(0.5)(x2)\n p = Dense(2,activation='softmax')(x2)\n self.bert = Model([x1_in, x2_in], p)\n\n self.bert.compile(\n # loss = 'binary_crossentropy',\n loss='categorical_crossentropy',\n optimizer=Adam(1e-5), # 用足够小的学习率\n metrics=['accuracy']\n )\n self.bert.summary()\n\n self.mid_layer = K.function([self.bert.layers[0].input,self.bert.layers[1].input],[self.bert.layers[3].output])\n\n\n def bert_middle_output(self,news):\n encoded_news = self.get_encode([news])\n mid_out = self.mid_layer(encoded_news)[0][0]\n return mid_out\n\n def build_lstm(self):\n x_in = Input(shape=(None,768,))\n x_in2 = Masking(mask_value=0,input_shape=(None,768,))(x_in)\n encoded_text = Bidirectional(LSTM(units=100, return_sequences=False))(x_in2)\n # encoded_text = LSTM(100)(x_in)\n encoded_text = Dropout(0.5)(encoded_text)\n out_dense = Dense(30,activation='relu')(encoded_text)\n out = Dense(2,activation='softmax')(out_dense)\n self.lstm_model = Model(x_in,out)\n self.lstm_model.compile(optimizer='adam',\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n self.lstm_model.summary()\n\n\n def __init__(self,config_path,checkpoint_path,dict_path,max_len = 500,split_len=200,overlap_len=30):\n self.config_path = config_path\n self.checkpoint_path = checkpoint_path\n self.dict_path = dict_path # 三个BERT文件，需修改路径\n self.token_dict = {}\n self.token_list = []\n self.split_len = split_len\n self.overlap_len = overlap_len\n self.maxlen = max_len\n with codecs.open(dict_path, 'r', 'utf8') as reader:\n for line in reader:\n token = line.strip()\n self.token_dict[token] = len(self.token_dict) # 读取BERT字典\n self.token_list.append(token)\n self.tokenizer = OurTokenizer(self.token_dict)\n self.build_bert()\n # self.build_lstm()\n\n\n def get_encode(self,data):\n all_data = data\n X1 = []\n X2 = []\n for i in all_data:\n x1, x2 = self.tokenizer.encode(first=i, max_len=self.maxlen)\n X1.append(x1)\n X2.append(x2)\n return [X1,X2]\n\n def mask_data(self,datas,mask_rate):\n \"\"\"\n 随机在句子中添加mask\n :param data:\n :return:\n \"\"\"\n # print(datas)\n datas_ = []\n def mask_data(data, mask_rate):\n len = data.index(self.token_dict[\"[SEP]\"]) - 1\n mask_pos = random.sample(range(1, len + 1), int(len * mask_rate))\n lis = [0]*self.maxlen\n for pos in mask_pos:\n data[pos] = self.token_dict[\"[MASK]\"]\n lis[pos] = 1\n return data\n for data in datas:\n data = mask_data(data, mask_rate=mask_rate)\n datas_.append(data)\n return np.array(datas_)\n\n def generater(self,x_train,y_train,batch_size,mask_rate=0.1):\n while True:\n x1_train = x_train[0]\n x2_train = x_train[1]\n len1 = len(x1_train)\n len2 = len(y_train)\n if(len1 != len2):\n raise Exception(\"len1 != len2\")\n i = 0\n while i*batch_size + batch_size < len1:\n x1_in = x1_train[i*batch_size:i*batch_size + batch_size]\n x1_in = self.mask_data(x1_in,mask_rate=mask_rate)\n x2_in = np.array(x2_train[i*batch_size:i*batch_size + batch_size])\n y = y_train[i*batch_size:i*batch_size + batch_size]\n i+=1\n yield({'input_1':x1_in,'input_2':x2_in},{'dense_1':y})\n\n def get_split_text(self,text, split_len=None, overlap_len=None, max_piece_num = 10):\n if split_len == None:\n split_len = self.split_len\n if overlap_len == None:\n overlap_len = self.overlap_len\n split_texts = []\n window_len = split_len - overlap_len\n for w in range(min(len(text) // split_len + 1,max_piece_num)):\n if w == 0:\n text_piece = text[:split_len]\n else:\n text_piece = text[w * window_len:w * window_len + split_len]\n split_texts.append(text_piece)\n return split_texts\n\n def predict_short(self,news):\n news = news.replace('\\r', '').replace('\\n', '').replace('\\t', '').replace(' ', '').replace('\\u3000', '')\n s = self.get_encode([news])\n result = self.bert.predict(s)\n index = result.argmax(axis=1)\n return index[0],result[0][index[0]]\n\n def predict_long(self,news):\n pad = np.zeros(shape=(768,))\n text_splits = self.get_split_text(news)\n texts = []\n for text in text_splits:\n text_feature = self.bert_middle_output(text)\n texts.append(text_feature)\n while len(texts) < 10:\n texts.append(pad)\n texts = np.array(texts)\n result = self.lstm_model.predict([[texts]])\n index = np.argmax(result[0])\n return index,result[0][index]\n\n def predict_str(self,news):\n if len(news) > 500:\n index,result = self.predict_long(news)\n else:\n index,result = self.predict_short(news)\n\n # if(index == 0 and result < 0.5):\n # index = 1\n\n return index,result\n\n\n\nif __name__ == '__main__':\n\n pass\n#训练短文本\n # train_data = pd.read_csv(\"DATA\\\\BALANCED\\\\CSV\\\\train.csv\")\n # train_data = shuffle(train_data)\n # print(train_data[\"label\"].value_counts())\n # titles = train_data[\"title\"]\n # newss = train_data[\"news\"]\n # labels = train_data[\"label\"]\n #\n # # for t,n,l in zip(titles,newss,labels):\n # # if type(t) == float or type(n) == float or type(l) == float:\n # # print(t,n,l)\n # sbwr = SynBertWeatherRecongizer(config_path, checkpoint_path, dict_path, max_len=400)\n # Xs = [str(t)+n for t,n in zip(titles,newss)]\n # Y = lables2onehot(labels,2)\n # train_x, test_x, train_y, test_y = train_test_split(Xs, Y, random_state=2021, test_size=0.05)\n # train_x = sbwr.get_encode(train_x)\n # test_x = sbwr.get_encode(test_x)\n #\n # print(len(train_x[0]))\n # print(len(test_x[0]))\n # print(len(train_y))\n # print(len(test_y))\n #\n # checkpoint = ModelCheckpoint('MODELS\\\\balanced\\\\classifier_{epoch:03d}.h5', save_weights_only=True, period=1)\n #\n # sbwr.bert.fit_generator(validation_data=(test_x,test_y),generator=sbwr.generater(train_x,train_y,batch_size=2,mask_rate=0.1),epochs=5,steps_per_epoch=int(len(train_y)/2),callbacks=[checkpoint])\n #\n # sbwr.bert.save(\"MODELS\\\\balanced\\\\bert_short.h5\")\n\n#\n pass\n#开始处理长文本生成npy\n # sbwr.bert.load_weights(\"MODELS\\\\all_data\\\\classifier_003.h5\")\n # ldatas = pd.read_csv(\"DATA\\\\ALL_DATA\\\\CSV\\\\ltrain.csv\")\n # titles = ldatas[\"title\"]\n # newss = ldatas[\"news\"]\n # labels = ldatas[\"label\"]\n #\n # xs = [str(t)+'\\n'+n for t,n, in zip(titles,newss)]\n # X = []\n # for text in xs:\n # pad = np.zeros(shape=(768,))\n # text_splits = sbwr.get_split_text(text)\n # texts = []\n # for text in text_splits:\n # text_feature = sbwr.bert_middle_output(text)\n # texts.append(text_feature)\n # while len(texts) < 10:\n # texts.append(pad)\n # texts = np.array(texts)\n # X.append(texts)\n # print(len(X))\n # Y = lables2onehot(labels,num_class=2)\n #\n # X = np.array(X)\n # Y = np.array(Y)\n #\n # print(X.shape,Y.shape)\n #\n # np.save(\"train_X\",X)\n # np.save(\"train_Y\",Y)\n\n#\n pass\n#训练lstm\n #(13616, 10, 768) (13616, 2)\n #(33741, 10, 768)(33741, 2)\n # X = np.load(\"DATA\\\\ALL_DATA\\\\NPY\\\\train_X.npy\")\n # Y = np.load(\"DATA\\\\ALL_DATA\\\\NPY\\\\train_Y.npy\")\n #\n # train_x, test_x, train_y, test_y = train_test_split(X,Y,random_state=2020, test_size=0.03)\n #\n # print(train_x.shape)\n # print(train_y.shape)\n # print(test_x.shape)\n # print(test_y.shape)\n #\n # checkpoint = ModelCheckpoint('MODELS\\\\all_data\\\\lstm_{epoch:03d}.h5', save_weights_only=True, period=1)\n #\n # sbwr.lstm_model.fit(validation_data=(test_x,test_y),x = train_x,y = train_y,batch_size=32,epochs=30,callbacks=[checkpoint])\n #\n # sbwr.lstm_model.save(\"MODELS\\\\all_data\\\\lstm_finnal.h5\")\n\n#\n pass\n\n# 文本测试\n# sbwr.bert.load_weights(\"MODELS\\\\all_data\\\\classifier_003.h5\")\n# sbwr.lstm_model.load_weights(\"MODELS\\\\all_data\\\\lstm_029.h5\")\n#\n# test_data = pd.read_csv(\"DATA\\\\ALL_DATA\\\\CSV\\\\test.csv\")\n# titles = test_data[\"title\"]\n# newss = test_data[\"news\"]\n# labels = test_data[\"label\"]\n#\n# r = 0\n# w = 0\n#\n# final_result = {\"disaster\":{\"disaster\":0,\"not_disaster\":0},\"not_disaster\":{\"disaster\":0,\"not_disaster\":0}}\n#\n# for t,n,l in zip(titles,newss,labels):\n# try:\n# # index = sbwr.predict_str(str(t) + n)\n# index = sbwr.predict_short(str(t) + n)\n# result = classifications[index]\n# if result == l:\n# r += 1\n# else:\n# w += 1\n# final_result[l][result] += 1\n# print(t, l, result, result == l)\n# except Exception as e:\n# pass\n#\n#\n# print(r,w,r/(r+w))\n#\n#\n# print(final_result)\n# precise = final_result[\"disaster\"][\"disaster\"]/(final_result[\"disaster\"][\"disaster\"] + final_result[\"not_disaster\"][\"disaster\"])\n# recall = final_result[\"disaster\"][\"disaster\"]/(final_result[\"disaster\"][\"disaster\"] + final_result[\"disaster\"][\"not_disaster\"])\n# f1 = 2*precise*recall/(precise + recall)\n# print(precise,recall,f1)\n # 0.9593432369038312 0.9504260263361735 0.9548638132295719\n # {'disaster': {'disaster': 1227, 'not_disaster': 64}, 'not_disaster': {'disaster': 52, 'not_disaster': 1465}}\n","sub_path":"灾害新闻识别（二分）/syn_bert_classifer.py","file_name":"syn_bert_classifer.py","file_ext":"py","file_size_in_byte":12531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"52022534","text":"import urllib.request\nimport urllib.parse\n\ndef read_text():\n quotes= open(\"/Users/yes/Documents/Learning/Programming/movie_quotes.txt\")\n contents = quotes.read()\n #print(contents)\n profanity_check(contents)\n quotes.close()\n \ndef profanity_check(text_to_check):\n url_quote = urllib.parse.quote(text_to_check)\n response = urllib.request.urlopen(\"http://www.wdylike.appspot.com/?q=\"+url_quote)\n output = response.read()\n print(output)\n response.close()\n if output == b'true':\n print(\"This text is profanity checked.\")\n elif output == b'false':\n print(\"This text contains curse words!\")\n else:\n print(\"Couldn't read the document properly\")\n\nread_text()\n\n#profanity_check(\"i have troubles\")\n","sub_path":"Check_profanity.py","file_name":"Check_profanity.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"80735575","text":"import cv2\nimport numpy as np\nvid=cv2.VideoCapture(0)\nbackground=cv2.imread(\"background.jpg\") #we are taking in the background image\nwhile vid.isOpened():\n ret,frame=vid.read() #we are reading the video from the web cam\n if ret==True: \n hsv_image=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV) #converting the bgr value to hsv \n red=np.uint8([[[0,0,255]]]) \n hsv_red=cv2.cvtColor(red,cv2.COLOR_BGR2HSV) #finding the hsv pixels values for red color\n #print(hsv_red)\n \n #the formula for setting lower bound value is hue-10,100,100\n #the formula for setting the higher bound value is hue+10,255,255 \n #we can increase the range for desired results\n \n lower_bound=np.array([0,100,100]) \n higher_bound=np.array([80,255,255])\n mask=cv2.inRange(hsv_image,lower_bound,higher_bound) #assigning the range for red hsv values.\n \n#we use morphology to remove the unwanted parts from the image, in this case we are removing the edges from the cloth.\n mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN,np.ones((3,3),np.uint8))\n mask=cv2.morphologyEx(mask, cv2.MORPH_DILATE,np.ones((3,3),np.uint8))\n part1=cv2.bitwise_and(background,background,mask=mask) #here we are masking the backgroung images against the red color pixels\n mask2=cv2.bitwise_not(mask) #for taking in the range of pixels which are \"not\" red.\n part2=cv2.bitwise_and(frame,frame,mask=mask2)\n masking=part1+part2\n #cv2.imshow(\"masking\",part2)\n cv2.imshow(\"Invisible\",masking)\n if cv2.waitKey(1)==ord('q'):\n break\nvid.release()\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"472540067","text":"#!/usr/bin/env python3\nimport argparse\nimport json\nimport math\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, ModelCheckpoint\nfrom tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Input\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import SGD, Adam, RMSprop, Adadelta\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom PostQuantizer import ASYMMETRIC\n\nfrom model_definition import image_size, preprocess_imagenet\nimport utilities as util\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n\ndef get_model(num_classes):\n input_tensor = Input(shape=(224, 224, 3)) # this assumes K.image_data_format() == 'channels_last'\n\n # create the base pre-trained model\n base_model = MobileNetV2(input_tensor=input_tensor, weights='imagenet', include_top=False)\n\n # for layer in base_model.layers:\n # layer.trainable = False\n\n x = base_model.output\n x = GlobalAveragePooling2D()(x)\n x = Dense(1024, activation='relu')(\n x) # we add dense layers so that the model can learn more complex functions and classify for better results.\n x = Dense(1024, activation='relu')(x) # dense layer 2\n x = Dense(512, activation='relu')(x) # dense layer 3\n x = Dense(num_classes, activation='softmax')(x) # final layer with softmax activation\n\n updatedModel = Model(base_model.input, x)\n\n return updatedModel\n\n\ndef compile_model(compiledModel):\n compiledModel.compile(loss=keras.losses.categorical_crossentropy,\n optimizer=SGD(learning_rate=0.01,momentum=0.9,nesterov=True),\n metrics=['accuracy'])\n\nclass QuantizeWeights(keras.callbacks.Callback):\n def __init__(self):\n self.quantizer = ASYMMETRIC()\n\n def on_epoch_end(self, epoch, logs=None):\n for layer in self.model.layers:\n t = layer.get_weights()\n for idx, tensor in enumerate(t):\n if len(tensor.shape) in [2,4]:\n new_tensor = self.quantizer.quantize(tensor,bits=args.bits)['dequant_array']\n t[idx] = new_tensor\n layer.set_weights(t)\n\nclass CustomLearningRateScheduler(keras.callbacks.Callback):\n def __init__(self, schedule):\n super(CustomLearningRateScheduler, self).__init__()\n self.schedule = schedule\n\n def on_epoch_begin(self, epoch, logs=None):\n if not hasattr(self.model.optimizer, \"lr\"):\n raise ValueError('Optimizer must have a \"lr\" attribute.')\n lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))\n scheduled_lr = self.schedule(epoch, lr)\n tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)\n # if epoch in LR_SCHEDULE:\n print(\"Epoch: \", epoch, \" Learning rate:\", scheduled_lr)\n\nLR_SCHEDULE = [\n # (epoch to start, learning rate) tuples\n (40, 0.1),\n (70, 0.01),\n (100, 0.001),\n (130, 0.0001),\n]\n\n\ndef lr_schedule(epoch, lr):\n \"\"\"Helper function to retrieve the scheduled learning rate based on epoch.\"\"\"\n if epoch < LR_SCHEDULE[0][0]:\n return lr\n for i in range(1,len(LR_SCHEDULE)):\n if epoch < LR_SCHEDULE[i][0]:\n return LR_SCHEDULE[i-1][1]\n return LR_SCHEDULE[-1][1]\n\ndef modelFitGenerator():\n\n train_datagen = ImageDataGenerator(\n rotation_range=90,\n horizontal_flip=True,\n vertical_flip=True,\n zoom_range=0.4,\n preprocessing_function=preprocess_imagenet\n )\n\n test_datagen = ImageDataGenerator(\n preprocessing_function=preprocess_imagenet\n )\n\n train_generator = train_datagen.flow_from_directory(\n train_data_dir,\n target_size=image_size,\n batch_size=batch_size,\n class_mode='categorical', shuffle=True,\n interpolation='lanczos'\n )\n\n validation_generator = test_datagen.flow_from_directory(\n validation_data_dir,\n target_size=image_size,\n batch_size=batch_size,\n class_mode='categorical', shuffle=True,\n interpolation='lanczos'\n )\n\n num_train_samples = len(train_generator.classes)\n num_valid_samples = len(validation_generator.classes)\n\n num_train_steps = math.floor(num_train_samples / batch_size)\n num_valid_steps = math.floor(num_valid_samples / batch_size)\n\n train_classes = len(set(train_generator.classes))\n test_classes = len(set(validation_generator.classes))\n\n if train_classes != test_classes:\n print('number of classes in train and test do not match, train {}, test {}'.format(train_classes, test_classes))\n exit(1)\n\n # save class names list before training\n\n label_map = train_generator.class_indices\n class_idx_to_label = {v: k for k, v in label_map.items()}\n labels = []\n for i in range(len(class_idx_to_label)):\n label = class_idx_to_label[i]\n labels.append(label)\n\n labels_txt = u\"\\n\".join(labels)\n with open(output_class_names_path, 'w') as classes_f:\n classes_f.write(labels_txt)\n print(\"Saved class names list file to {}\".format(output_class_names_path))\n\n fitModel = get_model(num_classes=train_classes)\n \n fitModel.save('model.h5')\n quantizer = {\n \"class_name\": \"QARegularizer\",\n \"config\": {\n \"num_bits\": 4,\n \"lambda_1\": 0.0,\n \"lambda_2\": float(args.lamb2),\n \"lambda_3\": float(args.lamb3),\n \"lambda_4\": 0.0,\n \"lambda_5\": 0.0,\n \"quantizer_name\": \"asymmetric\"\n }\n }\n layer_list = util.list_tf_keras_model('model.h5')\n for layer_name, layer_attr in layer_list.items():\n if 'kernel_regularizer' in layer_attr:\n layer_attr['kernel_regularizer'] = quantizer\n if 'depthwise_regularizer' in layer_attr:\n layer_attr['depthwise_regularizer'] = quantizer\n \n fitModel = util.attach_regularizers(\n os.path.join(\"model.h5\"), \n layer_list,\n target_keras_h5_file=None, \n verbose=False, \n backend_session_reset=True,)\n\n fitModel.load_weights('keras.h5')\n\n # for layer in fitModel.layers:\n # if 'depthwise' in layer.name:\n # weight_list = layer.get_weights()\n # if len(weight_list) == 1:\n # tensor = weight_list[0]\n # new_tensor = np.clip(tensor,-0.75,0.75)\n # layer.set_weights([new_tensor])\n\n # for layer in fitModel.layers:\n # # weights = layer.get_weights()\n # # for wt in weights:\n # # print(wt.max() - wt.min())\n # if 'depthwise' in layer.name:\n # weight_list = layer.get_weights()\n # if len(weight_list) == 1:\n # tensor = weight_list[0]\n # print(layer.name)\n # print(tensor.max() - tensor.min())\n\n compile_model(fitModel)\n earlyStopping = EarlyStopping(monitor='val_loss', patience=30, verbose=0, mode='min')\n mcp_save = ModelCheckpoint('.mdl_wts.h5', save_best_only=True, monitor='val_loss', mode='min')\n reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, epsilon=1e-4, mode='min')\n fitModel.fit_generator(\n train_generator,\n steps_per_epoch=num_train_steps,\n epochs=nb_epoch,\n validation_data=validation_generator,\n validation_steps=num_valid_steps,\n callbacks=[mcp_save,\n # CustomLearningRateScheduler(lr_schedule),\n QuantizeWeights(),\n reduce_lr_loss,\n # earlyStopping,\n ]\n )\n\n fitModel.save(output_model_path, include_optimizer=False)\n print(\"Saved trained model to {}\".format(output_model_path))\n\n\ndef main():\n modelFitGenerator()\n\n\nif __name__ == '__main__':\n # constants\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--dataset_path',\n type=str,\n default=None,\n required=True,\n help='Path to folders of labeled images. Expects \"train\" and \"eval\" subfolders'\n )\n parser.add_argument(\n '--eval_dataset_path',\n type=str,\n default=None,\n required=True,\n help='Path to folders of labeled eval images'\n )\n parser.add_argument(\n '--output_model_path',\n type=str,\n default='trained_model',\n required=False,\n help='Where to save the trained model.'\n )\n parser.add_argument(\n '--epochs',\n type=int,\n default=1,\n help='Number of training epochs, full passes through the dataset'\n )\n parser.add_argument(\n '--batch_size',\n type=int,\n default=32,\n help='Training batch size. Number of images to process at each gradient descent step.'\n )\n parser.add_argument(\n '--lamb2',\n type=float,\n default=0,\n help='lambda value'\n )\n parser.add_argument(\n '--lamb3',\n type=float,\n default=0,\n help='lambda value'\n )\n parser.add_argument(\n '--bits',\n type=float,\n default=4,\n help='number of bits to quantize'\n )\n\n args = parser.parse_args()\n train_data_dir = args.dataset_path\n validation_data_dir = args.eval_dataset_path\n nb_epoch = args.epochs\n batch_size = args.batch_size\n output_model_path = args.output_model_path\n output_class_names_path = os.path.join(output_model_path, 'class_names.txt')\n\n os.makedirs(output_model_path, exist_ok=True)\n\n with open(os.path.join(output_model_path,'model_schema.json'), 'w') as schema_f:\n schema_f.write(json.dumps({\n \"output_names\": \"dense_3/Softmax\",\n \"input_names\": \"input_1\",\n \"preprocessor\": \"imagenet\",\n \"input_shapes\": \"1,224,224,3\",\n \"task\": \"classifier\",\n \"dataset\": \"custom\"\n }, indent=4))\n\n output_model_path = os.path.join(output_model_path, 'model.h5')\n\n main()\n","sub_path":"mobilenetv2/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"72298411","text":"from flask import Flask, render_template, request, redirect, url_for, make_response\nimport sqlite3\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n return render_template(\"/index.html\")\n\n@app.route('/shortq', methods = ['POST', 'GET'])\ndef shortq():\n return render_template(\"/shortq.html\")\n\n@app.route('/basicq', methods = ['POST','GET'])\ndef basicq():\n return render_template(\"/basicq.html\")\n\n@app.route('/questionnaire', methods = ['POST','GET'])\ndef questionnaire():\n return render_template(\"/questionnaire.html\")\n\n@app.route('/fullq', methods = ['POST','GET'])\ndef fullq():\n return render_template(\"/fullq.html\")\n\n@app.route('/homepageTEST')\ndef homepageTEST():\n return render_template(\"homepageTEST.html\")\n\n@app.route('/temp')\ndef temp():\n return render_template(\"temp.html\")\n\n@app.route('/about')\ndef about():\n return render_template(\"/about.html\")\n\n@app.route('/product_pg', methods = ['POST','GET'])\ndef product_pg():\n if request.method == 'POST':\n\n prodnum=[]\n name=[]\n price=[]\n desc=[]\n img=[]\n\n gender = request.form['gender']\n\n if gender == \"IDK!\": # RANDOM option\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = (\"\"\"SELECT product_no, product_name, product_price, product_desc, product_img FROM tblProducts ORDER BY RANDOM();\"\"\")\n for row in curs.execute(sql):\n product_no,product_name,product_price,product_desc,product_img = row\n prodnum.append(product_no)\n name.append(product_name)\n price.append(product_price)\n desc.append(product_desc)\n img.append(product_img)\n\n conn.close()\n return render_template('product_pg.html', product_no=prodnum, name=name, price=price, desc=desc, img=img, length = len(name))\n\n else:\n # basic refine options\n btags= []\n\n if gender == \"othergen\": # if they select other gender, both male and female products will come up\n pass\n else:\n btags.append(gender)\n\n age = request.form['age']\n btags.append(age)\n\n priceRange = request.form['priceRange']\n if priceRange == \"anyprice\": # if they select no budget, any product with any price will show up\n pass\n else:\n btags.append(priceRange)\n\n specEvent = request.form['specEvent']\n if specEvent == \"evnone\":\n pass # if they pick no event, any product with any event will show up\n else:\n btags.append(specEvent)\n\n # specific categories\n tags= []\n\n clothing = request.form.getlist('clothing[]')\n if len(clothing) != 0:\n #tags.append('clothing')\n for tag in clothing:\n tags.append(tag)\n\n makeup = request.form.getlist('makeup[]')\n if len(makeup) != 0:\n #tags.append('makeup')\n for tag in makeup:\n tags.append(tag)\n\n skincare = request.form.getlist('skincare[]')\n if len(skincare) != 0:\n #tags.append('skincare')\n for tag in skincare:\n tags.append(tag)\n\n jewelry = request.form.getlist('jewelry[]')\n if len(jewelry) != 0:\n #tags.append('jewelry')\n for tag in jewelry:\n tags.append(tag)\n\n reading = request.form.getlist('reading[]')\n if len(reading) != 0:\n #tags.append('reading')\n for tag in reading:\n tags.append(tag)\n\n games = request.form.getlist('games[]')\n if len(games) != 0:\n #tags.append('games')\n for tag in games:\n tags.append(tag)\n\n tech = request.form.getlist('tech[]')\n if len(tech) != 0:\n #tags.append('tech')\n for tag in tech:\n tags.append(tag)\n\n deco = request.form.getlist('deco[]')\n if len(deco) != 0:\n #tags.append('deco')\n for tag in deco:\n tags.append(tag)\n\n quirky = request.form.getlist('quirky[]')\n if len(quirky) != 0:\n #tags.append('quirky')\n for tag in quirky:\n tags.append(tag)\n\n health = request.form.getlist('health[]')\n if len(health) != 0:\n #tags.append('health')\n for tag in health:\n tags.append(tag)\n\n giftcard = request.form.getlist('giftcard[]')\n if len(giftcard) != 0:\n tags.append('giftcard')\n\n prodnum=[]\n name=[]\n price=[]\n desc=[]\n img=[]\n\n btagsnum=[]\n\n # finds tag_no of basic tags\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n for btag in btags:\n val = \"'\"+btag+\"'\"\n sqltag = \"\"\"SELECT tag_no from tblTags where tag_name =\"\"\"\n for row in curs.execute(sqltag+val):\n tag_no = row\n tag_no = str(row[0])\n btagsnum.append(tag_no)\n\n products= []\n\n # finds product_no of main tags\n for tag in tags:\n val = \"'\"+tag+\"'\"\n sqltag = \"\"\"SELECT tag_no from tblTags where tag_name =\"\"\"\n for row in curs.execute(sqltag+val):\n tag_no = str(row[0])\n sqlprod = \"\"\"SELECT tblProducts.product_no FROM tblProducts INNER JOIN tblProductTags ON tblProducts.product_no = tblProductTags.product_no where tag_No =\"\"\"\n val2=\"'\"+tag_no+\"'\"\n # find all the products which have the tag number\n for row in curs.execute(sqlprod+val2):\n product_no= int(row[0])\n if product_no not in products:\n products.append(product_no) # consists of all products with main tags\n\n producttags=[]\n finalproducts=[]\n\n # finds tags of each product\n for product in products:\n sql= \"\"\"SELECT tag_no FROM tblProductTags WHERE product_no=?\"\"\"\n for row in curs.execute(sql, (product,)):\n tag_no = row\n #tag_no = curs.fetchall()\n tag_no = str(row[0])\n producttags.append(tag_no) # consists of tags_no's of the specific product\n result = all(elem in producttags for elem in btagsnum) # check if the product has the basic tags\n if result: # if it does, add it to the final list of products\n finalproducts.append(product)\n\n # finds tags of each product\n for product in products:\n producttags=[]\n sql= \"\"\"SELECT tag_no FROM tblProductTags WHERE product_no=?\"\"\"\n for tag in curs.execute(sql, (product,)):\n tag_no = str(row[0])\n producttags.append(tag_no) # consists of tags_no's of the specific product\n result= set(btagsnum).issubset(producttags)\n #result = all(elem in producttags for elem in btagsnum) # check if the product has the basic tags\n if result == True: # if it does, add it to the final list of products\n finalproducts.append(product)\n\n\n # find data on each product\n for num in finalproducts:\n sqlprod = (\"\"\"SELECT product_no,product_name,product_price,product_desc,product_img FROM tblProducts WHERE product_no=? \"\"\")\n for row in curs.execute(sqlprod, (num,)):\n product_no,product_name,product_price,product_desc,product_img = row # assign names to each row\n prodnum.append(product_no) # append each product detail to a list\n name.append(product_name)\n price.append(product_price)\n desc.append(product_desc)\n img.append(product_img)\n\n conn.close()\n return render_template('results.html', product_no=prodnum, name=name, price=price, desc=desc, img=img, length = len(name))\n\n\n@app.route('/results', methods = ['POST','GET'])\ndef results():\n if request.method == 'POST':\n\n tags= []\n\n prodnum=[]\n name=[]\n price=[]\n desc=[]\n img=[]\n\n gender = request.form['gender']\n\n if gender == \"IDK!\": # RANDOM option\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = (\"\"\"SELECT product_no, product_name, product_price, product_desc, product_img FROM tblProducts ORDER BY RANDOM();\"\"\")\n for row in curs.execute(sql):\n product_no,product_name,product_price,product_desc,product_img = row\n prodnum.append(product_no)\n name.append(product_name)\n price.append(product_price)\n desc.append(product_desc)\n img.append(product_img)\n\n conn.close()\n return render_template('product_pg.html', product_no=prodnum, name=name, price=price, desc=desc, img=img, length = len(name))\n\n else: # Questionnaire Option\n\n tags.append(gender)\n\n age = request.form['age']\n if age != \"None\":\n tags.append(age)\n\n priceRange = request.form['priceRange']\n if priceRange != \"None\":\n tags.append(priceRange)\n\n specEvent = request.form['specEvent']\n if specEvent != \"None\":\n tags.append(specEvent)\n\n # specific categories\n\n clothing = request.form.getlist('clothing[]')\n if len(clothing) != 0:\n tags.append('clothing')\n for tag in clothing:\n tags.append(tag)\n\n makeup = request.form.getlist('makeup[]')\n if len(makeup) != 0:\n tags.append('makeup')\n for tag in makeup:\n tags.append(tag)\n\n skincare = request.form.getlist('skincare[]')\n if len(skincare) != 0:\n tags.append('skincare')\n for tag in skincare:\n tags.append(tag)\n\n jewelry = request.form.getlist('jewelry[]')\n if len(jewelry) != 0:\n tags.append('jewelry')\n for tag in jewelry:\n tags.append(tag)\n\n reading = request.form.getlist('reading[]')\n if len(reading) != 0:\n tags.append('reading')\n for tag in reading:\n tags.append(tag)\n\n games = request.form.getlist('games[]')\n if len(games) != 0:\n tags.append('games')\n for tag in games:\n tags.append(tag)\n\n tech = request.form.getlist('tech[]')\n if len(tech) != 0:\n tags.append('tech')\n for tag in tech:\n tags.append(tag)\n\n deco = request.form.getlist('deco[]')\n if len(deco) != 0:\n tags.append('deco')\n for tag in deco:\n tags.append(tag)\n\n quirky = request.form.getlist('quirky[]')\n if len(quirky) != 0:\n tags.append('quirky')\n for tag in quirky:\n tags.append(tag)\n\n health = request.form.getlist('health[]')\n if len(health) != 0:\n tags.append('health')\n for tag in health:\n tags.append(tag)\n\n giftcard = request.form.getlist('giftcard[]')\n if len(giftcard) != 0:\n tags.append('giftcard')\n\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n for tag in tags:\n # put the tag into quotation marks, than concatenate it with the sql statement\n val = \"'\"+tag+\"'\"\n sqltag = (\"\"\"SELECT tag_no from tblTags where tag_name = \"\"\")\n # find the tag number for the tag\n for row in curs.execute(sqltag+val):\n tag_no = str(row[0])\n #print(row)\n sqlprod = (\"\"\"SELECT tblProducts.product_no,tblProducts.product_name,tblProducts.product_price,tblProducts.product_desc,tblProducts.product_img FROM tblProducts\n Inner JOIN tblProductTags ON tblProducts.product_no = tblProductTags.product_no where tag_No = \"\"\")\n val2=\"'\"+tag_no+\"'\"\n # find all the products which have the tag number\n for row in curs.execute(sqlprod+val2):\n product_no,product_name,product_price,product_desc,product_img = row\n if product_name not in name:\n prodnum.append(product_no)\n name.append(product_name)\n price.append(product_price)\n desc.append(product_desc)\n img.append(product_img)\n\n conn.close()\n return render_template('product_pg.html', product_no=prodnum, name=name, price=price, desc=desc, img=img, length = len(name))\n\n@app.route('/product_listing', methods=['GET'])\ndef product_listing():\n if request.method == 'GET':\n prodnum = request.args.get('product_no') # requests the product_no as a unique url code\n\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = \"\"\"SELECT product_name, product_price, product_desc, product_img, product_url, company_no FROM tblProducts WHERE product_no= (?)\"\"\" # fetch the product data of the product clicked on\n for row in curs.execute(sql,(prodnum,)):\n product_name,product_price,product_desc,product_img,product_url,company_no = row\n\n sql2= \"\"\"SELECT company_name FROM tblCompany WHERE company_no= ?\"\"\"\n curs.execute(sql2, (company_no,))\n company_name = curs.fetchall()\n company_name = company_name[0][0]\n\n conn.close()\n return render_template('product_listing.html', name=product_name, price=product_price, desc=product_desc, img=product_img, url=product_url, company_name=company_name)\n\n@app.route('/login', methods = ['POST', 'GET'])\ndef login():\n error = None\n if request.method == 'POST':\n if request.form['username'] != 'admin' or request.form['password'] != 'admin':\n error = 'Invalid Credentials. Please try again.'\n else:\n return redirect(url_for('menu'))\n\n return render_template('login.html', error=error)\n\n\n@app.route('/dbMenu')\ndef menu():\n return render_template(\"/dbMenu.html\")\n\n@app.route('/addprod', methods = ['POST','GET'])\ndef addProduct():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n company_no= []\n company_name= []\n sql = \"\"\"SELECT * FROM tblCompany\"\"\"\n for row in curs.execute(sql):\n number, name = row\n company_no.append(number)\n company_name.append(name)\n\n tag_no= []\n tag_name= []\n sql = \"\"\"SELECT * FROM tblTags\"\"\"\n for row in curs.execute(sql):\n number, name = row\n tag_no.append(number)\n tag_name.append(name)\n\n\n conn.close()\n return render_template(\"addprod.html\", company_no=company_no, company_name=company_name, length1=len(company_no), tag_no=tag_no, tag_name=tag_name, length2=len(tag_name))\n\n\n@app.route('/addresults', methods = ['POST', 'GET'])\ndef addresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n product_name = request.form['product_name']\n product_desc = request.form['product_desc']\n product_price = request.form['product_price']\n product_url = request.form['product_url']\n product_img = request.form['product_img']\n company_no = request.form['company_no']\n # sql query to find the maximum product number\n sql = \"\"\"SELECT MAX(product_no) FROM tblProducts\"\"\"\n # assign the maximum product_no from the sql to the variable maxNum\n for row in curs.execute(sql):\n maxNum = row[0]\n maxNum = maxNum+1\n\n val = [] # creates empty list\n\n # appending the list with all the values\n val.append(maxNum)\n val.append(product_name)\n val.append(product_price)\n val.append(product_desc)\n val.append(product_url)\n val.append(product_img)\n val.append(int(company_no))\n\n sql = \"\"\"INSERT into tblProducts VALUES(?,?,?,?,?,?,?)\"\"\" # sql query\n curs.execute(sql,val) # execute the sql with the values in val\n # To save the changes in the files\n conn.commit()\n\n msg = \"Successful\"\n return render_template(\"addresults.html\", msg=msg)\n conn.close()\n else:\n msg = \"Unsuccessful\"\n return render_template(\"addresults.html\", msg=msg)\n\n\n@app.route('/amendprod', methods = ['POST','GET'])\ndef amendprod():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n product_no= []\n product_name= []\n sql = (\"SELECT product_no, product_name FROM tblProducts\")\n for row in curs.execute(sql):\n number, name = row\n product_no.append(number)\n product_name.append(name)\n\n conn.close()\n return render_template(\"amendprod.html\", product_no=product_no, product_name=product_name, length=len(product_name))\n\n@app.route('/amendresults', methods = ['POST', 'GET'])\ndef amendresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n product_no = request.form['product']\n inp = request.form['inp']\n newVal = request.form['newVal']\n\n val = []\n val.append(newVal)\n val.append(int(product_no))\n\n if inp == \"name\": # name\n sql = \"\"\"UPDATE tblProducts SET product_name = ? WHERE product_no = ?\"\"\"\n curs.execute(sql,val)\n\n if inp == \"desc\": # desc\n sql = \"\"\"UPDATE tblProducts SET product_desc = ? WHERE product_no = ?\"\"\"\n curs.execute(sql,val)\n\n if inp == \"price\": # price\n sql = \"\"\"UPDATE tblProducts SET product_price = ? WHERE product_no = ?\"\"\"\n curs.execute(sql,val)\n\n if inp == \"url\": # url\n sql = (\"UPDATE tblProducts SET product_url = ? WHERE product_no = ?\")\n curs.execute(sql,val)\n\n if inp == \"img\": # image\n sql = \"\"\"UPDATE tblProducts SET product_img = ? WHERE product_no = ?\"\"\"\n curs.execute(sql,val)\n\n if inp == \"company_no\": # image\n sql = \"\"\"UPDATE tblProducts SET company_no = ? WHERE product_no = ?\"\"\"\n curs.execute(sql,val)\n\n conn.commit()\n conn.close()\n\n msg = \"Successful\"\n return render_template(\"amendresults.html\", msg=msg)\n conn.close()\n else:\n msg = \"Unsuccessful\"\n return render_template(\"amendresults.html\", msg=msg)\n\n@app.route('/addcomp', methods = ['POST','GET'])\ndef addcomp():\n return render_template(\"addcomp.html\")\n\n@app.route('/addcompresults', methods = ['POST', 'GET'])\ndef addcompresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n company_name = request.form['company_name']\n\n val = []\n sql = (\"SELECT MAX(company_no) FROM tblCompany\")\n for row in curs.execute(sql):\n maxNum = row[0]\n maxNum = maxNum+1\n val.append(maxNum)\n val.append(company_name)\n sql = (\"INSERT into tblCompany (company_no, company_name) VALUES (?,?)\")\n curs.execute(sql, val)\n # To save the changes in the files\n\n conn.commit()\n conn.close()\n\n msg = \"Successful\"\n return render_template(\"addcompresults.html\", msg=msg)\n conn.close()\n else:\n msg = \"Unsuccessful\"\n return render_template(\"addcompresults.html\", msg=msg)\n\n@app.route('/delcomp', methods = ['POST','GET'])\ndef delcomp():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n company_no= []\n company_name= []\n sql = \"\"\"SELECT * FROM tblCompany\"\"\"\n for row in curs.execute(sql):\n number, name = row\n company_no.append(number)\n company_name.append(name)\n\n return render_template(\"delcomp.html\", company_no=company_no, company_name=company_name, length=len(company_no))\n conn.close()\n\n@app.route('/delcompresults', methods = ['POST', 'GET'])\ndef delcompresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n num = request.form['company_no']\n\n val=[]\n val.append(int(num))\n # sql query for the deletion of the record\n sql = (\"DELETE FROM tblCompany WHERE company_no = (?)\")\n curs.execute(sql,val)\n conn.commit()\n\n conn.close()\n msg = \"Successful\"\n return render_template(\"delcompresults.html\", msg=msg)\n else:\n msg = \"Unsuccessful\"\n return render_template(\"delcompresults.html\", msg=msg)\n\n conn.close()\n\n@app.route('/delprod', methods = ['POST','GET'])\ndef delprod():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n product_no= []\n product_name= []\n sql = (\"SELECT product_no, product_name FROM tblProducts\")\n for row in curs.execute(sql):\n number, name = row\n product_no.append(number)\n product_name.append(name)\n\n return render_template(\"delprod.html\", product_no=product_no, product_name=product_name, length=len(product_name))\n conn.close()\n\n@app.route('/delresults', methods = ['POST', 'GET'])\ndef delresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n product_no = request.form['product']\n\n #val = []\n # append the list with the product_no\n #val.append(int(product_no))\n # sql query for the deletion of the record\n sql = (\"DELETE FROM tblProducts WHERE product_no = ?\")\n curs.execute(sql, (product_no,))\n conn.commit()\n\n conn.close()\n msg = \"Successful\"\n return render_template(\"delresults.html\", msg=msg)\n else:\n conn.close()\n msg = \"Unsuccessful\"\n return render_template(\"delresults.html\", msg=msg)\n\n@app.route('/addtag', methods = ['POST','GET'])\ndef addtag():\n return render_template('addtag.html')\n\n@app.route('/addtagresults', methods = ['POST','GET'])\ndef addtagresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n sql = \"\"\"SELECT MAX(tag_no) FROM tblTags\"\"\"\n # assign the maximum tag_no from the sql to the variable maxNum\n for row in curs.execute(sql):\n maxNum = row[0]\n maxNum = maxNum+1\n\n val=[]\n name = request.form['tag_name']\n val.append(int(maxNum))\n val.append(name)\n\n sql = (\"INSERT INTO tblTags(tag_no, tag_name) VALUES(?,?)\")\n curs.execute(sql, val)\n conn.commit()\n\n conn.close()\n msg = \"Successful\"\n return render_template(\"addtagresults.html\", msg=msg)\n else:\n conn.close()\n msg = \"Unsuccessful\"\n return render_template(\"addtagresults.html\", msg=msg)\n\n\n@app.route('/addprodtag', methods = ['POST','GET'])\ndef addprodtag():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n product_no= [] # initialise list for product numbers\n product_name= [] # initialise list for product names\n # sql query to select product numbers and names from the Database\n sql = (\"SELECT product_no, product_name FROM tblProducts\")\n for row in curs.execute(sql):\n number, name = row\n product_no.append(number) # adds the product number to the list\n product_name.append(name) # adds the product name to the list\n\n tag_no= [] # initialise list for tag numbers\n tag_name= [] # initialise list for tag names\n # sql query to select all tags from the Database\n sql2 = (\"SELECT * FROM tblTags\")\n for row in curs.execute(sql2):\n number1, name1 = row\n tag_no.append(number) # adds the tag number to the list\n tag_name.append(name) # adds the tag name to the list\n\n conn.close()\n return render_template(\"addprodtag.html\", product_no=product_no, product_name=product_name, length=len(product_name), tag_no=tag_no, tag_name=tag_name, length2=len(tag_name))\n\n\n@app.route('/productTags', methods = ['GET'])\ndef productTags():\n product_no = request.args.get('product_no')\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n tag_no= []\n tag_name= []\n sql1= (\"SELECT tag_no, tag_name FROM tblTags\")\n for row in curs.execute(sql1):\n num1, tag= row\n tag_no.append(num1)\n tag_name.append(tag)\n\n ptag_no=[]\n ptag_name=[]\n sql2 = (\"SELECT t.tag_no, t.tag_name FROM tblTags t LEFT JOIN tblProductTags p ON p.tag_no = t.tag_no WHERE p.product_no = ?\")\n for row in curs.execute(sql2, (product_no,)):\n num2, name = row\n ptag_no.append(num2)\n ptag_name.append(name)\n\n\n template = render_template(\"productTags.xml\", tag_no=tag_no, tag_name=tag_name, tlength=len(tag_name), ptag_no=ptag_no, ptag_name=ptag_name, ptlength=len(ptag_name))\n\n response = make_response(template) # added so that I could add XML support.\n response.headers['Content-Type'] = 'application/xml' # I imported \"make_response\" at the start of the document/\n return response # replaced \"ProductTags.html\" with \"ProductTags.xml\" so ajax can read it.\n conn.close()\n\n@app.route('/prodtagresults', methods = ['POST', 'GET'])\ndef prodtagresults():\n if request.method == 'POST':\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n\n newtags = [] # all input\n newtags = request.form.getlist('newtags')\n product_no = request.form['product']\n\n ptags=[] # list of tags already in product\n tempList=[] # temporary list for sql results\n\n sql1 = (\"SELECT tag_no FROM tblProductTags WHERE product_no=(?)\")\n for row in curs.execute(sql1, (product_no, )):\n num= row\n tempList.append(num)\n\n ptags= [str(i[0]) for i in tempList] # remove \"(,)\" from the sql results and turn them into strings.\n\n for tag in newtags: # checks to add tags\n if tag not in ptags:\n val=[]\n val.append(int(product_no))\n val.append(int(tag))\n sql2 = (\"INSERT INTO tblProductTags(product_no, tag_no) VALUES (?,?)\")\n curs.execute(sql2, val)\n conn.commit()\n\n for tag in ptags: # checks to delete tags\n if tag not in newtags:\n val=[]\n val.append(int(product_no))\n val.append(int(tag))\n sql3 = (\"DELETE FROM tblProductTags WHERE product_no=? AND tag_no=?\")\n curs.execute(sql3, val)\n conn.commit()\n\n msg = \"Successful\"\n conn.close()\n return render_template(\"prodtagresults.html\", msg=msg, newtags=newtags, ptags=ptags)\n\n else:\n msg = \"Unsuccessful\"\n conn.close()\n return render_template(\"prodtagresults.html\", msg=msg)\n\n\n@app.route('/list')\ndef list():\n return render_template(\"/list.html\")\n\n@app.route('/listTags')\ndef listTags():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = \"\"\"SELECT * FROM tblTags ORDER BY tag_no DESC\"\"\"\n results = curs.execute(sql)\n return render_template(\"/listTags.html\", rows=results)\n conn.close()\n\n@app.route('/listProds')\ndef listProds():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = \"\"\"SELECT * FROM tblProducts ORDER BY product_no DESC\"\"\"\n results = curs.execute(sql)\n return render_template(\"/listProds.html\", rows=results)\n conn.close()\n\n@app.route('/listComps')\ndef listComps():\n with sqlite3.connect('GiftFinder.db') as conn:\n curs = conn.cursor()\n sql = \"\"\"SELECT * FROM tblCompany ORDER BY company_no DESC\"\"\"\n results = curs.execute(sql)\n return render_template(\"/listComps.html\", rows=results)\n conn.close()","sub_path":"flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":30533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"56614038","text":"\"\"\"\n[Dictionary]\n\nRuntime: 60 ms, faster than 54.14% of Python3 online submissions for Single Number II.\nMemory Usage: 15.4 MB, less than 6.67% of Python3 online submissions for Single Number II.\n\"\"\"\nclass Solution:\n def singleNumber(self, nums: List[int]) -> int:\n dictionary ={}\n for i in nums:\n if i in dictionary.keys():\n if dictionary[i] == 2:\n del dictionary[i]\n else:\n dictionary[i] = dictionary[i] +1 \n else:\n dictionary[i]=1\n return list(dictionary.keys())[0]\n\n\n\"\"\"\n[bitwise operation method for single numbers]\n\nRuntime: 60 ms, faster than 54.14% of Python3 online submissions for Single Number II.\nMemory Usage: 15.4 MB, less than 6.67% of Python3 online submissions for Single Number II.\n\nhttps://blog.csdn.net/wlwh90/article/details/89712795\n\"\"\"\nclass Solution:\n def singleNumber(self, nums: List[int]) -> int:\n x1 = 0\n x2 = 0\n for num in nums:\n x2 = x2 ^ (x1 & num)\n x1 = x1 ^ num\n mask = ~ (x1 & x2)\n x2 = x2 & mask\n x1 = x1 & mask\n return x1\n","sub_path":"Problems/Bit Manipulation/137_Single_Number_II.py","file_name":"137_Single_Number_II.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"54364426","text":"import io\nimport sys\nimport pstats\nimport shutil\nimport inspect\nimport pathlib\nimport tempfile\nimport cProfile\nimport argparse\nimport subprocess\nimport logging.config\nfrom pathlib import Path\nfrom typing import List, Tuple, Optional, Callable\n\nimport matplotlib\n\nmatplotlib.use('Agg')\ndel matplotlib\n\nimport seaborn\nseaborn.set()\ndel seaborn\n\nfrom koder_utils import make_storage, TypedStorage, AnyXML\n\nfrom . import setup_logging, get_file\nfrom .cluster import load_all, fill_usage, fill_cluster_nets_roles\nfrom .report import Report\n\nfrom .visualize_utils import StopError\nfrom .visualize_cluster import (show_cluster_summary, show_issues_table, show_primary_settings, show_ruleset_info,\n show_io_status, show_mons_info, show_cluster_err_warn, show_whole_cluster_nets,\n show_cluster_err_warn_summary)\nfrom .visualize_pools_pgs import (show_pools_info, show_pg_state, show_pg_size_kde, show_pools_lifetime_load,\n show_pools_curr_load)\nfrom .visualize_hosts import show_hosts_config, host_info, show_hosts_status, show_hosts_pg_info\nfrom .visualize_host_load import show_host_io_load_in_color, show_host_network_load_in_color\nfrom .visualize_osds import (show_osd_state, show_osd_info, show_osd_perf_info, show_osd_pool_pg_distribution,\n show_osd_pool_agg_pg_distribution, show_osd_proc_info, show_osd_proc_info_agg,\n show_osd_pg_histo)\nfrom .plot_data import plot_crush_rules, show_osd_used_space_histo\n\n\nlogger = logging.getLogger('report')\n\n\ndef prepare_path(path: pathlib.Path) -> Tuple[bool, pathlib.Path]:\n if path.is_file():\n folder = tempfile.mkdtemp(prefix=\"ceph_report_\")\n logger.info(\"Unpacking %s to temporary folder %r\", path, folder)\n subprocess.call(f\"tar -zxvf {path} -C {folder} >/dev/null 2>&1\", shell=True)\n return True, pathlib.Path(folder)\n elif not path.is_dir():\n logger.error(\"Path argument (%r) should be a folder with data or path to archive\", path)\n raise ValueError()\n else:\n return False, path\n\n\ndef make_report(name: str, d1_path: pathlib.Path, d2_path: pathlib.Path = None, plot: bool = True) -> Report:\n logger.info(\"Loading collected data into RAM\")\n if d2_path:\n cluster, ceph = load_all(TypedStorage(make_storage(str(d1_path), existing=True)))\n cluster2, ceph2 = load_all(TypedStorage(make_storage(str(d2_path), existing=True)))\n fill_usage(cluster, cluster2, ceph, ceph2)\n cluster.has_second_report = True\n else:\n cluster, ceph = load_all(TypedStorage(make_storage(str(d1_path), existing=True)))\n\n fill_cluster_nets_roles(cluster, ceph)\n\n logger.info(\"Done\")\n\n report = Report(name, \"index.html\")\n\n cluster_reporters: List[Callable[..., AnyXML]] = [\n show_cluster_summary,\n show_issues_table,\n show_primary_settings,\n show_pools_info,\n show_pools_lifetime_load,\n show_pools_curr_load,\n show_ruleset_info,\n show_io_status,\n show_hosts_config,\n show_hosts_status,\n show_hosts_pg_info,\n # show_mons_info,\n show_osd_state,\n show_osd_info,\n show_osd_proc_info,\n show_osd_proc_info_agg,\n show_osd_perf_info,\n show_pg_state,\n show_cluster_err_warn_summary,\n show_cluster_err_warn,\n (show_osd_pool_agg_pg_distribution if len(ceph.osds) > 20 else show_osd_pool_pg_distribution),\n show_host_io_load_in_color,\n show_host_network_load_in_color,\n show_whole_cluster_nets,\n show_osd_used_space_histo,\n show_osd_pg_histo,\n show_pg_size_kde,\n plot_crush_rules\n ]\n\n params = {\"ceph\": ceph, \"cluster\": cluster, \"report\": report, \"uptime\": not cluster.has_second_report}\n\n for reporter in cluster_reporters:\n if getattr(reporter, \"perf_info_required\", False) and not cluster.has_second_report:\n continue\n if getattr(reporter, 'plot', False) and not plot:\n continue\n\n sig = inspect.signature(reporter)\n curr_params = {name: params[name] for name in sig.parameters}\n new_block = reporter(**curr_params)\n\n if new_block:\n assert 'report' not in sig.parameters\n rname = reporter.__name__.replace(\"show_\", \"\")\n report.add_block(rname, reporter.report_name, new_block) # type: ignore\n\n # for _, host in sorted(cluster.hosts.items()):\n # report.add_block(host_link(host.name).id, None, host_info(host, ceph))\n\n return report\n\n\ndef parse_args(argv):\n p = argparse.ArgumentParser()\n p.add_argument(\"-l\", \"--log-level\", choices=[\"DEBUG\", \"INFO\", \"WARNING\", \"ERROR\", \"CRITICAL\"],\n default=None, help=\"Console log level\")\n p.add_argument(\"--plot\", help=\"Draw all plots\", action=\"store_true\")\n p.add_argument(\"-N\", \"--name\", help=\"Report name\", default=\"Nemo\")\n p.add_argument(\"-o\", '--out', help=\"report output folder\", required=True)\n p.add_argument(\"-w\", '--overwrite', action='store_true', help=\"Overwrite result folder data\")\n p.add_argument(\"-p\", \"--pretty-html\", help=\"Prettify index.html\", action=\"store_true\")\n p.add_argument(\"--profile\", help=\"Profile report creation\", action=\"store_true\")\n p.add_argument(\"-e\", \"--embed\", action='store_true', help=\"Embed js/css files into report to make it stand-alone\")\n p.add_argument(\"path\", help=\"Folder with data, or .tar.gz archive\")\n p.add_argument(\"old_path\", nargs='?', help=\"Older folder with data, or .tar.gz archive to calculate load\")\n return p.parse_args(argv[1:])\n\n\ndef main(argv: List[str]):\n opts = parse_args(argv)\n setup_logging(log_level=opts.log_level,\n log_config_file=get_file('logging.json'),\n out_folder=None)\n\n logger.info(\"Generating report from %r to %r\", opts.path, opts.out)\n\n if opts.profile:\n prof: Optional[cProfile.Profile] = cProfile.Profile()\n prof.enable()\n else:\n prof = None\n\n remove_d1, d1_path = prepare_path(pathlib.Path(opts.path))\n\n if opts.old_path:\n remove_d2, d2_path = prepare_path(pathlib.Path(opts.old_path))\n else:\n remove_d2 = False\n d2_path = None # type: ignore\n\n out_p = Path(opts.out)\n index_path = out_p / 'index.html'\n if index_path.exists():\n if not opts.overwrite:\n logger.error(\"%r already exists. Pass -w/--overwrite to overwrite files\", str(index_path))\n return 1\n elif not out_p.exists():\n out_p.mkdir(parents=True, exist_ok=True)\n\n try:\n report = make_report(name=opts.name, d1_path=d1_path, d2_path=d2_path, plot=opts.plot)\n index_path.open(\"w\").write(report.render(pretty_html=opts.pretty_html, embed=opts.embed))\n logger.info(\"Report successfully stored to %r\", str(index_path))\n except StopError:\n pass\n finally:\n if remove_d1:\n shutil.rmtree(str(d1_path))\n if remove_d2:\n assert d2_path\n shutil.rmtree(str(d2_path))\n\n if prof:\n prof.disable() # type: ignore\n s = io.StringIO()\n ps = pstats.Stats(prof, stream=s).sort_stats('time', 'calls')\n ps.print_stats(40)\n print(s.getvalue())\n\n\nif __name__ == \"__main__\":\n exit(main(sys.argv))\n","sub_path":"ceph_report/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":7362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"491099886","text":"import random\n\ncategorys = [\"Caring for others and the environment\", \"Participation\", \"Excellence (in any area)\", \"Correct uniform\", \"Attitude towards attendance\", \"Commitment to Tutor Group\", \"Courtesy\"]\n\noutFile = open(\"dataset.csv\", \"w\")\n\nfor i in range(100):\n output = \"\"\n person = \"\"\n catNum = random.randint(0, len(categorys)-1)\n \n if catNum > 0 and catNum < 3: person = \"Peer {0}\".format(random.randint(1, 100))\n elif catNum > 2 and catNum < 5: person = \"Tutor {0}\".format(random.randint(1, 25))\n else:\n if random.randint(0, 1) == 0: person = \"Peer {0}\".format(random.randint(1, 100))\n else: person = \"Tutor {0}\".format(random.randint(1, 25))\n\n output += \"Person {0},{1},{2}\\n\".format(i+1, categorys[catNum], person)\n outFile.write(output)\n\noutFile.close()\n","sub_path":"Helpful Programs/random input genorator.py","file_name":"random input genorator.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"145677011","text":"# in the file myanalysis.py, I attempt to use some basic python functionality and hopefully\n# agonizingly continue my transition away from matlab\n\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.stats import spearmanr\nfrom scipy.stats import pearsonr\nsns.set(style='white', font_scale=1.2)\n\n# PART ONE: REPRODUCE PREVIOUS STUDY\n# https://www3.nd.edu/~busiforc/handouts/Data%20and%20Stories/correlation/Brain%20Size/brainsize.html\n# load provided data\ndata = pd.read_csv('practical/brainsize.csv', sep=';', index_col=0)\ndata = data.replace('.', np.nan)\n\n# divide data by gender\ngroup_by_gender = data.groupby('Gender')\ndata_male = group_by_gender.get_group('Male')\ndata_female = group_by_gender.get_group('Female')\n\n# compute correlation coefficient between brain volume and FSIQ\ncorr_m, pval_m = pearsonr(data_male['FSIQ'], data_male['MRI_Count'])\ncorr_f, pval_f = pearsonr(data_female['FSIQ'], data_female['MRI_Count'])\n\n# show output\nprint('PART ONE: CORRELATION OF FSIQ AND BRAIN SIZE')\nprint('Correlation in males:')\nprint('rho = %f' % corr_m)\nprint('p = %f \\n' % pval_m)\nprint('Correlation in females:')\nprint('rho = %f' % corr_f)\nprint('p = %f \\n' % pval_f)\n\n# PART TWO: CORRELATE WITH NEW MEASURE\n# introduce new variable\nnp.random.seed(3)\npartY = np.random.normal(100, 10, len(data))\ndata['Reaction_Time'] = partY\n\n# compute correlation with random noise\ncorr_2, pval_2 = spearmanr(data['FSIQ'], partY)\n\n# show output\nprint('PART TWO: CORRELATION OF FSIQ AND REACTION TIME')\nprint('Reaction time, day one = %f +/- %f milliseconds.' % (partY.mean(), partY.std()))\nprint('rho = %f' % corr_2)\nprint('p = %f \\n' % pval_2)\n\n# make a plot\ng = sns.JointGrid(data=data, x='FSIQ', y='Reaction_Time', xlim=(70, 150), ylim=(70, 130), height=5)\ng = g.plot_joint(sns.regplot, color=\"xkcd:muted blue\")\ng = g.plot_marginals(sns.distplot, kde=False, bins=12, color=\"xkcd:bluey grey\")\nplt.tight_layout()\nplt.show()\n\n# now find another correlation with a second random variable\nnp.random.seed(17)\npartY2 = np.random.normal(100, 10, len(data))\ncorr_3, pval_3 = spearmanr(data['FSIQ'], partY2)\nprint('Reaction time, day two = %f +/- %f milliseconds.' % (partY2.mean(), partY2.std()))\nprint('rho = %f' % corr_3)\nprint('p = %f \\n' % pval_3)\n","sub_path":"myanalysis.py","file_name":"myanalysis.py","file_ext":"py","file_size_in_byte":2283,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"154739547","text":"from Acquisition import aq_inner\nfrom five import grok\nfrom plone.directives import dexterity, form\n\nfrom zope import schema\nfrom zope.schema.vocabulary import getVocabularyRegistry\n\nfrom z3c.form import group, field\nfrom zope.component import getMultiAdapter\n\nfrom plone.namedfile.interfaces import IImageScaleTraversable\nfrom Products.CMFCore.utils import getToolByName\nfrom plone.app.contentlisting.interfaces import IContentListing\n\nfrom chromsystems.globalcontacts.contact import IContact\nfrom chromsystems.globalcontacts import MessageFactory as _\n\n\n# Interface class; used to define content-type schema.\n\nclass IGlobalContacts(form.Schema, IImageScaleTraversable):\n \"\"\"\n A collection of contact persons.\n \"\"\"\n\n\nclass GlobalContacts(dexterity.Container):\n grok.implements(IGlobalContacts)\n\n\nclass View(grok.View):\n grok.context(IGlobalContacts)\n grok.require('zope2.View')\n grok.name('view')\n \n def update(self):\n self.has_contacts = len(self.contained_contacts()) > 0\n\n def contacts(self):\n contacts = self.contained_contacts()\n results = []\n for contact in contacts:\n obj = contact.getObject()\n scales = getMultiAdapter((obj, self.request), name='images')\n scale = scales.scale('image', scale='tile')\n imageTag = None\n if scale is not None:\n imageTag = scale.tag()\n results.append({\n 'url': obj.absolute_url(),\n 'title': obj.Title(),\n 'salutation': obj.salutation,\n 'countries': self.countries_vocab(obj.countries),\n 'phone': obj.phone,\n 'email': obj.email,\n 'imageTag': imageTag,\n })\n return results\n\n def contained_contacts(self):\n context = aq_inner(self.context)\n catalog = getToolByName(context, 'portal_catalog')\n results = catalog(object_provides=IContact.__identifier__,\n path='/'.join(context.getPhysicalPath()),\n review_state='published',)\n contacts = IContentListing(results)\n return results\n\n def countries_vocab(self, countries):\n context = aq_inner(self.context)\n vr = getVocabularyRegistry()\n countries_vocabulary = vr.get(context,\n 'chromsystems.userdata.CountryList')\n countrylist = []\n for country in countries:\n countryinfo = {}\n term = countries_vocabulary.getTerm(country)\n countryinfo['title'] = term.title\n countryinfo['value'] = term.value\n countrylist.append(countryinfo)\n return countrylist\n","sub_path":"src/chromsystems.globalcontacts/chromsystems/globalcontacts/globalcontacts.py","file_name":"globalcontacts.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"572774211","text":"import nltk\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import PorterStemmer\nfrom nltk.stem import LancasterStemmer\nimport numpy as np\nfrom os.path import dirname, join\nimport re\nimport math\n\n\nclass PlagiarismChecker:\n def __init__(self, file_a, file_b):\n self.file_a = file_a\n self.file_b = file_b\n self.hash_table = {\"a\": [], \"b\": []}\n self.k_gram = 3\n content_a = (self.file_a)\n content_b = (self.file_b)\n self.calculate_hash(content_a, \"a\")\n self.calculate_hash(content_b, \"b\")\n\n # calaculate hash value of the file content\n # and add it to the document type hash table\n def calculate_hash(self, content, doc_type):\n text = self.prepare_content(content)\n text = \"\".join(text)\n # print(text)\n\n text = rolling_hash(text, self.k_gram)\n for _ in range(len(content) - self.k_gram + 1):\n self.hash_table[doc_type].append(text.hash)\n if text.next_window() == False:\n break\n # print(self.hash_table)\n\n def get_rate(self):\n return self.calaculate_plagiarism_rate(self.hash_table)\n\n # calculate the plagiarism rate using the plagiarism rate formula\n def calaculate_plagiarism_rate(self, hash_table):\n th_a = len(hash_table[\"a\"])\n th_b = len(hash_table[\"b\"])\n a = hash_table[\"a\"]\n b = hash_table[\"b\"]\n sh = len(np.intersect1d(a, b))\n # print(sh, a, b)\n # print(sh, th_a, th_b)\n\n # Formular for plagiarism rate\n # P = (2 * SH / THA * THB ) 100%\n p = (float( sh)/(th_a)) * 100\n if(p>1):\n p=1\n return p\n\n # get content from file\n def get_file_content(self, filename):\n file = open(filename, 'r+', encoding=\"utf-8\")\n return file.read()\n\n # Prepare content by removing stopwords, steemming and tokenizing\n def prepare_content(self, content):\n # STOP WORDS\n stop_words = set(stopwords.words('english'))\n # TOKENIZE\n word_tokens = word_tokenize(content)\n\n filtered_content = []\n # STEMMING\n porter = PorterStemmer()\n for w in word_tokens:\n if w not in stop_words:\n w = w.lower()\n word = porter.stem(w)\n filtered_content.append(word)\n\n return filtered_content\n\n# print('kartik')\n\n# current_dir = dirname(__file__)\n# checker = PlagiarismChecker(\n# join(current_dir, \"../docs/document_a.txt\"),\n# join(current_dir, \"../docs/document_b.txt\")\n# )\n\n# print('The percentage of plagiarism held by both documents is {0}%'.format(\n# checker.get_rate()))\n\n\n\n\nclass rolling_hash:\n def __init__(self, text, patternSize):\n self.text = text\n self.patternSize = patternSize\n self.base = 26\n self.window_start = 0\n self.window_end = 0\n self.mod = 5807\n self.primes = (23,209,3007,40007,500007,6000007,70000007,800007,97,100007,10017,127,10000037,1407,10057)\n self.hash = self.get_hash(text, patternSize)\n\n def get_hash(self, text, patternSize):\n hash_value = 0\n for i in range(0, patternSize):\n # temp = (ord(self.text[i]) - 96)*(self.base*(patternSize - i - 1))% self.mod\n # temp=(temp) % self.mod\n # hash_value = (hash_value + (temp) )% self.mod\n hash_value = (\n hash_value * math.pow(77,i) + (self.primes[i]* (ord(self.text[i]) - 96)*(self.base**(patternSize - i - 1)))) % self.mod\n # hash_value = (\n # hash_value + math.pow(7,i) * self.primes[i]* (ord(self.text[i]) - 96)*(self.base**(patternSize - i - 1))) % self.mod\n\n self.window_start = 0\n self.window_end = patternSize\n\n return hash_value\n \n # def rolling_hash(self,prev,next,present_hash):\n # next_hash=present_hash\n # for i,prime in enumerate(prime_nos):\n # next_hash[i]=next_hash[i]-prev\n # next_hash[i]=next_hash[i]/prime\n # next_hash[i]=next_hash[i]+next*math.pow(prime,2)\n # return next_hash\n\n def next_window(self):\n if self.window_end <= len(self.text) - 1:\n self.hash -= (ord(self.text[self.window_start]) -\n 96)*self.base**(self.patternSize-1)\n\n self.hash *= self.base\n self.hash += ord(self.text[self.window_end]) - 96\n # print( ord(self.text[self.window_end])- 96)\n self.hash %= self.mod\n self.window_start += 1\n self.window_end += 1\n return True\n return False\n\n def current_window_text(self):\n return self.text[self.window_start:self.window_end]\n\n\ndef checker(text, pattern):\n if text == \"\" or pattern == \"\":\n return None\n if len(pattern) > len(pattern):\n return None\n\n text_rolling = rolling_hash(text.lower(), len(pattern))\n pattern_rolling = rolling_hash(pattern.lower(), len(pattern))\n\n for _ in range(len(text)-len(pattern)+1):\n # print(pattern_rolling.hash, text_rolling.hash)\n if text_rolling.hash == pattern_rolling.hash:\n return \"Found\"\n text_rolling.next_window()\n return \"Not Found\"\n\n\n# if __name__ == \"__main__\":\n # print(checker(\"ABDCCEAGmsslslsosspps\", \"agkalallaa\"))\n \n# text1 = \"Kartik Vyas studies at IITJ\"\n# text2 = \"Kartik Vyas is an Undergrad student at IITJ\"\n \ndef create_rabin_karp_2_features(df): \n rabin_karp_2_values = []\n\n for i in df.index:\n if df.loc[i,'Class'] > -1:\n # get texts to compare\n answer_text = df.loc[i, 'Text']\n answer_filename = df.loc[i, 'File'] \n source_filename = answer_filename\n source_filename = \"source\" + answer_filename[6:] \n source_df = df.query('File == @source_filename')\n source_text = source_df.iloc[0].at['Text']\n\n # value = similarity(answer_text, source_text, False)\n try : \n object = PlagiarismChecker(answer_text,source_text)\n value = object.get_rate()\n except : \n value = 0\n \n if value > 1 :\n value =1\n if value < 0 :\n value = 0\n \n rabin_karp_2_values.append(value)\n else:\n rabin_karp_2_values.append(-1)\n\n print('rabin_karp_2 features created!')\n return rabin_karp_2_values\n\ndef similarity_individual(text1,text2) :\n object = PlagiarismChecker(text1,text2)\n value = object.get_rate()\n return value\n\n# checker = PlagiarismChecker(text1,text2)\n\n# print('The percentage of plagiarism held by both documents is {0}%'.format(\n# checker.get_rate()))","sub_path":"semantic/both_models_new_dataset/source_pytorch/distinctFeatures/rabin_karp_2.py","file_name":"rabin_karp_2.py","file_ext":"py","file_size_in_byte":6765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"217795507","text":"# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n def zigzagLevelOrder(self, root):\n if not root:\n return []\n\n result = []\n current_level = [root]\n level = 1\n\n while current_level:\n next_level = []\n if level % 2 == 0:\n result.append([node.val for node in reversed(current_level)])\n else:\n result.append([node.val for node in current_level])\n\n for node in current_level:\n if node.left:\n next_level.append(node.left)\n if node.right:\n next_level.append(node.right)\n\n current_level = next_level\n level += 1\n\n return result\n\n\nclass TestSolution:\n\n def __init__(self):\n self.solution = Solution()\n\n def test_one_node(self):\n root = TreeNode(1)\n\n expected = [[1]]\n actual = self.solution.zigzagLevelOrder(root)\n\n assert actual == expected\n\n def test_two_levels(self):\n root = TreeNode(3)\n root.left = TreeNode(9)\n root.left.left = TreeNode(8)\n root.left.right = TreeNode(10)\n root.right = TreeNode(20)\n root.right.left = TreeNode(15)\n root.right.right = TreeNode(7)\n\n expected = [\n [3],\n [20, 9],\n [8, 10, 15, 7]]\n actual = self.solution.zigzagLevelOrder(root)\n\n assert actual == expected\n\n def test_leetcode_example(self):\n root = TreeNode(3)\n root.left = TreeNode(9)\n root.right = TreeNode(20)\n root.right.left = TreeNode(15)\n root.right.right = TreeNode(7)\n\n expected = [\n [3],\n [20, 9],\n [15, 7]]\n actual = self.solution.zigzagLevelOrder(root)\n\n assert actual == expected\n\n def run_test(self, test, test_name):\n print('Running {}...'.format(test_name))\n\n try:\n test()\n except:\n print('Failed {}'.format(test_name))\n else:\n print('Passed {}!'.format(test_name))\n\n def run_tests(self):\n self.run_test(self.test_one_node, 'test_one_node')\n self.run_test(self.test_two_levels, 'test_two_levels')\n self.run_test(self.test_leetcode_example, 'test_leetcode_example')\n\n\ntester = TestSolution()\ntester.run_tests()\n","sub_path":"103_binary_tree_zigzag_level_order_traversal.py","file_name":"103_binary_tree_zigzag_level_order_traversal.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"175270957","text":"import pytest\nfrom decouple import config, Csv\nfrom django.contrib.auth import get_user_model\nfrom mixer.backend.django import mixer\nfrom places.models import InferredPlaceImage\n\n\npytestmark = pytest.mark.django_db\nname_list = (\n \"공원\",\n \"공항\",\n \"놀이공원\",\n \"다리\",\n \"미술관\",\n \"볼링장\",\n \"산\",\n \"아이스링크\",\n \"아쿠아리움\",\n \"호텔\",\n \"궁궐\",\n \"지하철역\",\n \"놀이터\",\n \"수영장\",\n \"폭포\",\n \"동물원\",\n \"절\",\n \"교회\",\n \"성당\",\n \"시장\",\n \"쇼핑몰\",\n \"클럽\",\n \"박물관\",\n \"축구장\",\n \"야구장\",\n \"농구장\",\n \"공연장\",\n \"베이커리\",\n \"키즈카페\",\n \"숲\",\n \"캠핑장\",\n \"식물원\",\n \"해수욕장\",\n \"수상레포츠\",\n \"미용실\",\n \"PC방\",\n \"도서관\",\n \"컨벤션센터\",\n \"대학교\",\n \"패스트푸드점\",\n \"골프장\",\n \"헬스장\",\n \"병원\",\n \"빨래방\",\n \"찜질방\",\n \"스키장\",\n \"워터파크\",\n \"한옥마을\",\n \"롯데월드타워\",\n \"남산서울타워\",\n \"동대문디자인플라자\",\n \"63빌딩\",\n \"국회의사당\",\n \"청와대\",\n \"세빛섬\",\n)\n\n\ndef get_admin_client_login(client):\n user_model = get_user_model()\n user_1 = mixer.blend(user_model, is_admin=True)\n client.force_login(user_1)\n return client\n\n\ndef create_test_infferd_images(count):\n mixer.cycle(count).blend(\n InferredPlaceImage,\n predicted_place_name=(name for name in name_list),\n )\n","sub_path":"common/utils/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"147448717","text":"# file ACTG/src/constants.py\n\"\"\"\nConstants and settings for ACTG\n\"\"\"\nfrom __future__ import division\n\nimport string\n\n__modname__ = _mn_ = \"constants.py\"\n__author__ = \"Kyle McChesney\"\n__version__ = \"0.1\"\n__maintainer__ = \"Kyle McChesney\"\n__status__ = \"Development\"\n\n\nTRANSTABLE = string.maketrans('ATGCUatgcuNn', 'TACGAtacgaNn')\n","sub_path":"src/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"183734620","text":"import arcpy,arcgisscripting,os\nfrom arcpy import env\narcpy.env.outputMFlag=\"Disabled\"\narcpy.env.outputZFlag=\"Disabled\"\ngp = arcgisscripting.create(10.0)\nin_DWG = gp.GetParameterAsText(0)\nout_SHP = gp.GetParameterAsText(1)\nenv.workspace=in_DWG\n#输入文件\nfilename_list=[]\nfor file in arcpy.ListFiles(\"*.dwg\"):\n\tfilename_list.append(file)\nwhere_clause='\"Layer\" = \\'JZD\\''\nfor filename in filename_list:\n\tarcpy.MakeFeatureLayer_management(u\"{0}\\\\Polygon\".format(filename),filename,where_clause)\n#输出文件\narcpy.Merge_management(filename_list,out_SHP)\narcpy.CalculateField_management(out_SHP,\"DocName\",\"!DocName![:-4]\",\"PYTHON_9.3\")\n#删除不必要字段\nfiled_list =(\"EntLinetype\",\"BlkLinetype\",\"Handle\",\"LyrFrzn\",\"LyrLock\",\"LyrOn\",\"LyrVPFrzn\",\"LyrHandle\",\"Color\",\"EntColor\",\"LyrColor\",\"BlkColor\",\"Linetype\",\"EntLinetyp\",\"LyrLnType\",\"BlkLinetyp\",\"Elevation\",\"Thickness\",\"LineWt\",\"EntLineWt\",\"LyrLineWt\",\"BlkLineWt\",\"RefName\",\"LTScale\",\"ExtX\",\"ExtY\",\"ExtZ\",\"DocType\",\"DocVer\",\"DocPath\")\narcpy.DeleteField_management(out_SHP,filed_list)","sub_path":"脚本/dwg面合并.py","file_name":"dwg面合并.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"4744168","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n# OpenERP, Open Source Management Solution\n# Copyright (C) 2004-2010 Tiny SPRL ().\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (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 Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n##############################################################################\n\nfrom openerp.osv import fields, osv\n\nclass oper_cola(osv.osv):\n _name = 'oper.cola'\n _columns = { \n 'name': fields.char('Nombre'),\n 'period_id': fields.many2one('account.period','Periodo'),\n 'model': fields.char('Modelo'),\n #'state': fields.selection([\n # ('draft','Borrador'),\n # ('in_process','En Proceso'),\n # ('finish','Terminado'),\n # ], 'Estado'),\n \n }\noper_cola()\n\nclass oper_cola_type(osv.osv):\n _name = 'oper.cola.type'\n _columns = { \n 'name': fields.char('Nombre'),\n 'active': fields.boolean('Activo'),\n 'period_id': fields.many2one('account.period','Periodo'),\n 'model': fields.selection([\n ('contrato','Contrato'),\n ('mandato','Mandato'),\n ('cuota','Cuota'),\n ('cuota_recaudada','Cuota Recaudada'),\n ], 'Modelo'),\n }\n \n\n #def create(self, cr, uid, args, context=None):\n #period=self.pool.get('account.period').browse(cr, uid, args['period_id'])\n #args['name']=str(args['model'])+' '+str(period.name)\n #res = super(oper_cola_type, self).create(cr, uid, args, context=context)\n #return res\n \n def write(self, cr, uid, ids, data, context=None):\n period=model=False\n cola_type=self.browse(cr, uid, ids[0])\n if 'period_id' in data: period=self.pool.get('account.period').browse(cr, uid, data['period_id'])\n if 'model' in data: model=True\n if period and model:data['name']=str(data['model'])+' '+str(period.name)\n if period and not model:data['name']=str(cola_type.model)+' '+str(period.name)\n if not period and model:data['name']=str(data['model'])+' '+str(cola_type.period_id.name)\n\n res = super(oper_cola_type, self).write(cr, uid, ids, data, context=context)\n return res\n \noper_cola_type()\n\nclass oper_cola_item(osv.osv):\n _name = 'oper.cola.item'\n _columns = { \n 'name': fields.char('Nombre'),\n 'cola_id': fields.many2one('oper.cola','Cola'),\n 'socio_id': fields.many2one('res.partner','Socio', domain=[('is_socio','=',True)]),\n 'contrato_id': fields.many2one('oper.contrato','Contrato'),\n 'cuota_id': fields.many2one('oper.cuota','Cuota'),\n 'mandato_id': fields.many2one('oper.mandato','Mandato'),\n 'cuota_recaudada_id': fields.many2one('oper.cuota.recaudada','Cuota Reacaudada'),\n 'date_last_quota_send': fields.date('Fecha ultima cuota emitida'),\n 'date_last_quota_collected': fields.date('Fecha ultima cuota recaudada'),\n 'state': fields.selection([\n ('draft','Borrador'),\n ('in_process','En Gestion'),\n ('finish','Gestionada'),\n ], 'Estado'),\n \n }\noper_cola_item()\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:#","sub_path":"econube_operaciones/models/oper_cola.py","file_name":"oper_cola.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"445471248","text":"# Module for parsing and determining best Kernel Debug Kit for host OS\n# Copyright (C) 2022-2023, Dhinak G, Mykola Grymalyuk\n\nimport datetime\nfrom pathlib import Path\nfrom typing import cast\n\nimport packaging.version\nimport requests\n\nimport subprocess\nimport os\n\nimport logging\n\nfrom resources import utilities, network_handler\nfrom resources.constants import Constants\n\nKDK_INSTALL_PATH = \"/Library/Developer/KDKs\"\n\n\nclass KernelDebugKitObject:\n \"\"\"\n Library for querying and downloading Kernel Debug Kits (KDK) for macOS\n\n Usage:\n >>> kdk_object = KernelDebugKitObject(constants, host_build, host_version)\n\n >>> if kdk_object.success:\n\n >>> # Query whether a KDK is already installed\n >>> if kdk_object.kdk_already_installed:\n >>> # Use the installed KDK\n >>> kdk_path = kdk_object.kdk_installed_path\n\n >>> else:\n >>> # Get DownloadObject for the KDK\n >>> # See network_handler.py's DownloadObject documentation for usage\n >>> kdk_download_object = kdk_object.retrieve_download()\n\n >>> # Once downloaded, recommend verifying KDK's checksum\n >>> valid = kdk_object.validate_kdk_checksum()\n\n \"\"\"\n\n def __init__(self, constants: Constants, host_build: str, host_version: str, ignore_installed: bool = False):\n self.constants: Constants = constants\n\n self.host_build: str = host_build # ex. 20A5384c\n self.host_version: str = host_version # ex. 11.0.1\n\n self.ignore_installed: bool = ignore_installed # If True, will ignore any installed KDKs and download the latest\n self.kdk_already_installed: bool = False\n\n self.kdk_installed_path: str = \"\"\n\n self.kdk_url: str = \"\"\n self.kdk_url_build: str = \"\"\n self.kdk_url_version: str = \"\"\n\n self.kdk_url_expected_size: int = 0\n\n self.kdk_url_is_exactly_match: bool = False\n\n self.kdk_closest_match_url: str = \"\"\n self.kdk_closest_match_url_build: str = \"\"\n self.kdk_closest_match_url_version: str = \"\"\n\n self.kdk_closest_match_url_expected_size: int = 0\n\n self.success: bool = False\n\n self.error_msg: str = \"\"\n\n self._get_latest_kdk()\n\n\n def _get_available_kdks(self):\n \"\"\"\n Fetches a list of available KDKs from the KdkSupportPkg API\n\n Returns:\n list: A list of KDKs, sorted by version and date if available. Returns None if the API is unreachable\n \"\"\"\n\n KDK_API_LINK = \"https://raw.githubusercontent.com/dortania/KdkSupportPkg/gh-pages/manifest.json\"\n\n logging.info(\"- Pulling KDK list from KdkSupportPkg API\")\n\n try:\n results = network_handler.SESSION.get(\n KDK_API_LINK,\n headers={\n \"User-Agent\": f\"OCLP/{self.constants.patcher_version}\"\n },\n timeout=10\n )\n except (requests.exceptions.Timeout, requests.exceptions.TooManyRedirects, requests.exceptions.ConnectionError):\n logging.info(\"- Could not contact KDK API\")\n return None\n\n if results.status_code != 200:\n logging.info(\"- Could not fetch KDK list\")\n return None\n\n return sorted(results.json(), key=lambda x: (packaging.version.parse(x[\"version\"]), datetime.datetime.fromisoformat(x[\"date\"])), reverse=True)\n\n\n def _get_latest_kdk(self, host_build: str = None, host_version: str = None):\n \"\"\"\n Fetches the latest KDK for the current macOS version\n\n Args:\n host_build (str, optional): The build version of the current macOS version.\n If empty, will use the host_build from the class. Defaults to None.\n host_version (str, optional): The version of the current macOS version.\n If empty, will use the host_version from the class. Defaults to None.\n \"\"\"\n\n if host_build is None and host_version is None:\n host_build = self.host_build\n host_version = self.host_version\n\n logging.info(f\"- Fetching latest KDK for {host_build} ({host_version})\")\n self.kdk_installed_path = self._local_kdk_installed_build()\n if self.kdk_installed_path:\n logging.info(f\"- KDK already installed ({Path(self.kdk_installed_path).name}), skipping\")\n self.kdk_already_installed = True\n self.success = True\n return\n\n remote_kdk_version = self._get_available_kdks()\n\n parsed_version = cast(packaging.version.Version, packaging.version.parse(host_version))\n\n if remote_kdk_version is None:\n logging.warning(\"- Failed to fetch KDK list, falling back to local KDK matching\")\n\n # First check if a KDK matching the current macOS version is installed\n # ex. 13.0.1 vs 13.0\n loose_version = f\"{parsed_version.major}.{parsed_version.minor}\"\n logging.info(f\"- Checking for KDKs loosely matching {loose_version}\")\n self.kdk_installed_path = self._local_kdk_installed_version(loose_version)\n if self.kdk_installed_path:\n logging.info(f\"- Found matching KDK: {Path(self.kdk_installed_path).name}\")\n self.success = True\n return\n\n older_version = f\"{parsed_version.major}.{parsed_version.minor - 1 if parsed_version.minor > 0 else 0}\"\n logging.info(f\"- Checking for KDKs matching {older_version}\")\n self.kdk_installed_path = self._local_kdk_installed_version(older_version)\n if self.kdk_installed_path:\n logging.info(f\"- Found matching KDK: {Path(self.kdk_installed_path).name}\")\n self.success = True\n return\n\n logging.warning(f\"- Couldn't find KDK matching {host_version} or {older_version}, please install one manually\")\n\n self.error_msg = f\"Could not contact KdkSupportPkg API, and no KDK matching {host_version} ({host_build}) or {older_version} was installed.\\nPlease ensure you have a network connection or manually install a KDK.\"\n\n return\n\n for kdk in remote_kdk_version:\n kdk_version = cast(packaging.version.Version, packaging.version.parse(kdk[\"version\"]))\n if (kdk[\"build\"] == host_build):\n self.kdk_url = kdk[\"url\"]\n self.kdk_url_build = kdk[\"build\"]\n self.kdk_url_version = kdk[\"version\"]\n self.kdk_url_expected_size = kdk[\"fileSize\"]\n self.kdk_url_is_exactly_match = True\n break\n if kdk_version <= parsed_version and kdk_version.major == parsed_version.major and (kdk_version.minor in range(parsed_version.minor - 1, parsed_version.minor + 1)):\n # The KDK list is already sorted by version then date, so the first match is the closest\n self.kdk_closest_match_url = kdk[\"url\"]\n self.kdk_closest_match_url_build = kdk[\"build\"]\n self.kdk_closest_match_url_version = kdk[\"version\"]\n self.kdk_closest_match_url_expected_size = kdk[\"fileSize\"]\n self.kdk_url_is_exactly_match = False\n break\n\n if self.kdk_url == \"\":\n if self.kdk_closest_match_url == \"\":\n logging.warning(f\"- No KDKs found for {host_build} ({host_version})\")\n self.error_msg = f\"No KDKs found for {host_build} ({host_version})\"\n return\n logging.info(f\"- No direct match found for {host_build}, falling back to closest match\")\n logging.info(f\"- Closest Match: {self.kdk_closest_match_url_build} ({self.kdk_closest_match_url_version})\")\n\n self.kdk_url = self.kdk_closest_match_url\n self.kdk_url_build = self.kdk_closest_match_url_build\n self.kdk_url_version = self.kdk_closest_match_url_version\n self.kdk_url_expected_size = self.kdk_closest_match_url_expected_size\n else:\n logging.info(f\"- Direct match found for {host_build} ({host_version})\")\n\n\n # Check if this KDK is already installed\n self.kdk_installed_path = self._local_kdk_installed_build(self.kdk_url_build)\n if self.kdk_installed_path:\n logging.info(f\"- KDK already installed ({Path(self.kdk_installed_path).name}), skipping\")\n self.kdk_already_installed = True\n self.success = True\n return\n\n logging.info(\"- Following KDK is recommended:\")\n logging.info(f\"- KDK Build: {self.kdk_url_build}\")\n logging.info(f\"- KDK Version: {self.kdk_url_version}\")\n logging.info(f\"- KDK URL: {self.kdk_url}\")\n\n self.success = True\n\n\n def retrieve_download(self, override_path: str = \"\"):\n \"\"\"\n Returns a DownloadObject for the KDK\n\n Parameters:\n override_path (str): Override the default download path\n\n Returns:\n DownloadObject: DownloadObject for the KDK, None if no download required\n \"\"\"\n\n self.success = False\n self.error_msg = \"\"\n\n if self.kdk_already_installed:\n logging.info(\"- No download required, KDK already installed\")\n self.success = True\n return None\n\n if self.kdk_url == \"\":\n self.error_msg = \"Could not retrieve KDK catalog, no KDK to download\"\n logging.error(self.error_msg)\n return None\n\n logging.info(f\"- Returning DownloadObject for KDK: {Path(self.kdk_url).name}\")\n self.success = True\n return network_handler.DownloadObject(self.kdk_url, self.constants.kdk_download_path if override_path == \"\" else Path(override_path))\n\n\n def _local_kdk_valid(self, kdk_path: str):\n \"\"\"\n Validates provided KDK, ensure no corruption\n\n The reason for this is due to macOS deleting files from the KDK during OS updates,\n similar to how Install macOS.app is deleted during OS updates\n\n Args:\n kdk_path (str): Path to KDK\n\n Returns:\n bool: True if valid, False if invalid\n \"\"\"\n\n KEXT_CATALOG = [\n \"System.kext/PlugIns/Libkern.kext/Libkern\",\n \"apfs.kext/Contents/MacOS/apfs\",\n \"IOUSBHostFamily.kext/Contents/MacOS/IOUSBHostFamily\",\n \"AMDRadeonX6000.kext/Contents/MacOS/AMDRadeonX6000\",\n ]\n\n kdk_path = Path(kdk_path)\n\n for kext in KEXT_CATALOG:\n if not Path(f\"{kdk_path}/System/Library/Extensions/{kext}\").exists():\n logging.info(f\"- Corrupted KDK found, removing due to missing: {kdk_path}/System/Library/Extensions/{kext}\")\n self._remove_kdk(kdk_path)\n return False\n\n return True\n\n\n def _local_kdk_installed_build(self, build: str = None):\n \"\"\"\n Checks if KDK matching build is installed\n If so, validates it has not been corrupted\n\n Returns:\n str: Path to KDK if valid, None if not\n \"\"\"\n\n if self.ignore_installed is True:\n return None\n\n if build is None:\n build = self.host_build\n\n if not Path(KDK_INSTALL_PATH).exists():\n return None\n\n for kdk_folder in Path(KDK_INSTALL_PATH).iterdir():\n if not kdk_folder.is_dir():\n continue\n if not kdk_folder.name.endswith(f\"{build}.kdk\"):\n continue\n\n if self._local_kdk_valid(kdk_folder):\n return kdk_folder\n\n return None\n\n def _local_kdk_installed_version(self, version: str = None):\n \"\"\"\n Checks if KDK matching version is installed\n If so, validates it has not been corrupted\n\n Returns:\n str: Path to KDK if valid, None if not\n \"\"\"\n\n if self.ignore_installed is True:\n return None\n\n if version is None:\n version = self.host_version\n\n if not Path(KDK_INSTALL_PATH).exists():\n return None\n\n for kdk_folder in Path(KDK_INSTALL_PATH).iterdir():\n if not kdk_folder.is_dir():\n continue\n if version not in kdk_folder.name:\n continue\n\n if self._local_kdk_valid(kdk_folder):\n return kdk_folder\n\n return None\n\n\n def _remove_kdk(self, kdk_path: str):\n \"\"\"\n Removes provided KDK\n\n Args:\n kdk_path (str): Path to KDK\n \"\"\"\n\n if os.getuid() != 0:\n logging.warning(\"- Cannot remove KDK, not running as root\")\n return\n\n result = utilities.elevated([\"rm\", \"-rf\", kdk_path], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n if result.returncode != 0:\n logging.warning(f\"- Failed to remove KDK: {kdk_path}\")\n logging.warning(f\"- {result.stdout.decode('utf-8')}\")\n\n logging.info(f\"- Successfully removed KDK: {kdk_path}\")\n\n\n def _remove_unused_kdks(self, exclude_builds: list = None):\n \"\"\"\n Removes KDKs that are not in use\n\n Args:\n exclude_builds (list, optional): Builds to exclude from removal.\n If None, defaults to host and closest match builds.\n \"\"\"\n\n\n if exclude_builds is None:\n exclude_builds = [\n self.kdk_url_build,\n self.kdk_closest_match_url_build,\n ]\n\n if self.constants.should_nuke_kdks is False:\n return\n\n if not Path(KDK_INSTALL_PATH).exists():\n return\n\n logging.info(\"- Cleaning unused KDKs\")\n for kdk_folder in Path(KDK_INSTALL_PATH).iterdir():\n if kdk_folder.is_dir():\n if kdk_folder.name.endswith(\".kdk\"):\n should_remove = True\n for build in exclude_builds:\n if build != \"\" and kdk_folder.name.endswith(f\"{build}.kdk\"):\n should_remove = False\n break\n if should_remove is False:\n continue\n self._remove_kdk(kdk_folder)\n\n\n def validate_kdk_checksum(self, kdk_dmg_path: str = None):\n \"\"\"\n Validates KDK DMG checksum\n\n Args:\n kdk_dmg_path (str, optional): Path to KDK DMG. Defaults to None.\n\n Returns:\n bool: True if valid, False if invalid\n \"\"\"\n\n self.success = False\n self.error_msg = \"\"\n\n if kdk_dmg_path is None:\n kdk_dmg_path = self.constants.kdk_download_path\n\n if not Path(kdk_dmg_path).exists():\n logging.error(f\"KDK DMG does not exist: {kdk_dmg_path}\")\n return False\n\n # TODO: should we use the checksum from the API?\n result = subprocess.run([\"hdiutil\", \"verify\", self.constants.kdk_download_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n if result.returncode != 0:\n logging.info(\"- Error: Kernel Debug Kit checksum verification failed!\")\n logging.info(f\"- Output: {result.stderr.decode('utf-8')}\")\n msg = \"Kernel Debug Kit checksum verification failed, please try again.\\n\\nIf this continues to fail, ensure you're downloading on a stable network connection (ie. Ethernet)\"\n logging.info(f\"- {msg}\")\n\n self.error_msg = msg\n\n self._remove_unused_kdks()\n\n self.success = True\n","sub_path":"resources/kdk_handler.py","file_name":"kdk_handler.py","file_ext":"py","file_size_in_byte":15549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"495877163","text":"#!/usr/bin/env python\n# vim:fileencoding=utf-8\n# License: GPLv3 Copyright: 2019, Kovid Goyal \n\nimport importlib\nimport os\nimport sys\nfrom operator import itemgetter\n\nfrom .constants import PKG, PREFIX, SOURCES, build_dir, ismacos, mkdtemp\nfrom .download_sources import download, read_deps\nfrom .utils import (RunFailure, create_package, ensure_clear_dir,\n extract_source_and_chdir, fix_install_names,\n install_package, python_build, python_install, qt_build,\n rmtree, run_shell, set_title, simple_build)\n\n\ndef pkg_path(dep):\n return os.path.join(PKG, dep['name'])\n\n\ndef make_build_dir(dep_name):\n ans = None\n if ans is None:\n ans = mkdtemp(prefix=f'{dep_name}-')\n return ans\n\n\ndef build_dep(dep, args, dest_dir=PREFIX):\n dep_name = dep['name']\n set_title('Building ' + dep_name)\n owd = os.getcwd()\n output_dir = todir = make_build_dir(dep_name)\n build_dir(output_dir)\n idep = dep_name.replace('-', '_')\n try:\n m = importlib.import_module('bypy.pkgs.' + idep)\n except ImportError:\n module_dir = os.path.join(os.path.dirname(\n os.path.abspath(__file__)), 'pkgs')\n if os.path.exists(os.path.join(module_dir, f'{idep}.py')):\n raise\n m = None\n tsdir = extract_source_and_chdir(os.path.join(SOURCES, dep['filename']))\n try:\n if hasattr(m, 'main'):\n m.main(args)\n else:\n if 'python' in dep:\n python_build()\n python_install()\n elif dep['name'].startswith('qt-'):\n qt_build()\n else:\n simple_build()\n if ismacos:\n fix_install_names(m, output_dir)\n except RunFailure as e:\n print('\\nRunning the following command failed:', file=sys.stderr)\n print(e.cmd)\n print('Dropping you into a shell', file=sys.stderr)\n sys.stdout.flush(), sys.stderr.flush()\n run_shell(env=e.env, cwd=e.cwd)\n raise SystemExit(1)\n except (Exception, SystemExit):\n import traceback\n traceback.print_exc()\n print('\\nDropping you into a shell')\n sys.stdout.flush(), sys.stderr.flush()\n run_shell()\n raise SystemExit(1)\n create_package(m, output_dir, pkg_path(dep))\n install_package(pkg_path(dep), dest_dir)\n if hasattr(m, 'post_install_check'):\n try:\n m.post_install_check()\n except (Exception, SystemExit):\n import traceback\n traceback.print_exc()\n print('\\nDropping you into a shell')\n sys.stdout.flush(), sys.stderr.flush()\n run_shell()\n raise SystemExit(1)\n os.chdir(owd)\n rmtree(todir)\n rmtree(tsdir)\n\n\ndef unbuilt(dep):\n return not os.path.exists(pkg_path(dep))\n\n\ndef install_packages(which_deps, dest_dir=PREFIX):\n ensure_clear_dir(dest_dir)\n if not which_deps:\n return\n print(f'Installing {len(which_deps)} previously compiled packages:',\n end=' ')\n sys.stdout.flush()\n for dep in which_deps:\n pkg = pkg_path(dep)\n if os.path.exists(pkg):\n print(dep['name'], end=', ')\n sys.stdout.flush()\n install_package(pkg, dest_dir)\n print()\n sys.stdout.flush()\n\n\ndef init_env(which_deps=None):\n if which_deps is None:\n which_deps = read_deps()\n install_packages(which_deps)\n\n\ndef accept_func_from_names(names):\n names = frozenset(names)\n wants_qt = 'qt' in names\n\n def ffunc(dep):\n return dep['name'] in names or (\n wants_qt and dep['name'].startswith('qt-'))\n return ffunc\n\n\ndef main(parsed_args):\n accept_func = unbuilt\n all_deps = read_deps()\n all_dep_names = frozenset(map(itemgetter('name'), all_deps))\n if parsed_args.deps:\n accept_func = accept_func_from_names(parsed_args.deps)\n if (frozenset(parsed_args.deps) - {'qt'}) - all_dep_names:\n raise SystemExit('Unknown dependencies: {}'.format(\n frozenset(parsed_args.deps) - all_dep_names))\n deps_to_build = tuple(filter(accept_func, all_deps))\n if not deps_to_build:\n raise SystemExit('No buildable deps were specified')\n names_of_deps_to_build = frozenset(map(itemgetter('name'), deps_to_build))\n other_deps = [\n dep for dep in all_deps if dep['name'] not in names_of_deps_to_build]\n init_env(other_deps)\n download(deps_to_build)\n\n built_names = set()\n for dep in deps_to_build:\n try:\n build_dep(dep, parsed_args)\n built_names.add(dep['name'])\n print(f'{dep[\"name\"]} successfully built!')\n finally:\n remaining = tuple(\n d['name'] for d in deps_to_build\n if d['name'] not in built_names)\n if remaining:\n print('Remaining deps:', ', '.join(remaining))\n\n # After a successful build, remove the unneeded sw dir\n rmtree(PREFIX)\n","sub_path":"bypy/deps.py","file_name":"deps.py","file_ext":"py","file_size_in_byte":5002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"550536919","text":"#!/usr/bin/env python3\n\nimport sys\n\nif not len(sys.argv) > 1:\n exit()\n\nif sys.argv[1] == '-h':\n help_msg = \"\"\"Get all links in json link of subreddit\n{script} [options] subreddit_json_links\n\noptions:\n-n links in saperate line each, instead of in a single line\n-h display the help\"\"\".format(script=sys.argv[0])\n print(help_msg)\n exit()\n\nrlinks = []\nsplitt = ' '\n\nfor index, link in enumerate(sys.argv, start=1):\n if index > 1:\n import time\n time.sleep(3)\n\n if link == '-n':\n splitt = '\\n'\n\n if 'reddit.com/r/' not in link and '.json' not in link:\n continue\n\n import urllib.request as request\n resp = request.urlopen(link)\n data = resp.read()\n subr = data.decode('utf-8')\n\n import json\n subrjson = json.loads(subr)\n for item in subrjson['data']['children']:\n if item['data']['is_self']:\n continue\n rlinks.append(item['data']['url'])\n\nprint(splitt.join(rlinks))\n","sub_path":"mpv/.local/bin/rlinks.py","file_name":"rlinks.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"128102155","text":"class Solution(object):\n def diameterOfBinaryTree(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: int\n \"\"\"\n self.result=-1\n def height(node):\n if node is None:\n return -1\n left=height(node.left)\n right=height(node.right)\n self.result=max(self.result,left+right+2)\n return max(left,right)+1\n height(root)\n return 0 if self.result==-1 else self.result\n","sub_path":"diameterOfBinaryTree.py","file_name":"diameterOfBinaryTree.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"47988699","text":"#coding:utf-8\n#从Token列表中生成表达式二叉树\n'''\nToken列表的第一个元素必然是整数，提取Token作为第一个叶子节点\n依次循环，从Tokens列表中提取Token\n如果是操作符，将先前的Token存为左节点\n如果是整数，将该Token保存为前面一个节点的右节点\n'''\ndef parse(tokens):\n if token[0][0] != 'int':#如果数组第一个元素不是‘int‘，输入错误提示\n raise ValueError('Must start with an int')\n node = IntNode(tokens[0][1])\n nbo = None\n last = tokens[0][0]\n#在提取Tokens列表的时候，分成三种情况讨论 \n for token in tokens[1:]:\n#当相邻的两个Token类型一样，提示错误\n if token[0] == last:\n raise ValueError(\"Error in syntax\")\n last = token[0]\n#如果Token是操作符，则保存为操作符号节点，将前面一个整数的Token作为其左子节点\n if token[0] =='ope':\n nbo = BinaryOpNode(token[1])\n nbo.left =node\n#如果Token是整数，则将该Token保存为右节点\n if token[0] =='int':\n nbo.right = IntNode(token[1])\n node = nbo\n return node\n","sub_path":"cal05.py","file_name":"cal05.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"276778363","text":"\"\"\"\n单例模式：目的，让一个类在内存中只能产生一个对象\n 核心：对象的创建过程\n step1：开辟内存，创建对象--->new\n step2：自动执行init方法，用于初始化实例变量\n\n 单例的操作步骤：\n 1.重写new方法\n A：在类中添加类属性__instance，用于控制对象的的产生\n 如果__instance为空，创建对象：object类的new：object.__new__(cls)，并且将值返回给__instance\n 如果不为空，直接返回__instance\n\n B：让init，初始化实例变量时，仅依次\n 定义一个类属性：__first_init = False\n 如果__first_init 为False，证明没有初始化属性，那么就执行，修改为True\n 如果__first_init 为True，直接return。\n\"\"\"\n\n\nclass MusicPlayer:\n\n # 定义一个类属性：\n __instance = None\n # 定义一个类属性：用于控制init是否是初次执行\n __first_init = False\n\n # 重写new方法\n def __new__(cls, *args, **kwargs):\n # 1.判断类属性是否是None，证明内存中没有创建过，那么就创建\n if cls.__instance is None:\n cls.__instance = object.__new__(cls)\n # 2.返回\n return cls.__instance\n\n def __init__(self, name):\n # if MusicPlayer.__first_init:\n # return\n\n print(\"init方法。。。\")\n\n self.name = name\n\n # MusicPlayer.__first_init = True\n\n\nplayer1 = MusicPlayer(\"网易云\")\n\nplayer2 = MusicPlayer(\"网\")\nprint(player1)\nprint(player1.name)\nprint(player2)\nprint(player2.name)\n\n","sub_path":"20171123/exercise/demo05_单例模式init调用一次.py","file_name":"demo05_单例模式init调用一次.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"141380877","text":"\"\"\"Wiring container injection example.\"\"\"\n\nimport sys\n\nfrom dependency_injector import containers, providers\nfrom dependency_injector.wiring import inject, Provide\n\n\nclass Service:\n ...\n\n\nclass Container(containers.DeclarativeContainer):\n\n service = providers.Factory(Service)\n\n\n@inject\ndef main(container: Container = Provide[Container]):\n service = container.service()\n ...\n\n\nif __name__ == '__main__':\n container = Container()\n container.wire(modules=[sys.modules[__name__]])\n\n main()\n","sub_path":"examples/wiring/example_container.py","file_name":"example_container.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"507407915","text":"import requests\nfrom bs4 import BeautifulSoup\nimport os\nfrom queue import Queue\nimport time\nimport threading\n\npic_html_queue = Queue()\ntoday = time.strftime(\"%Y-%m-%d\", time.localtime())\nbase_dir = r'D:\\pictures'\nif not os.path.exists(base_dir):\n try:\n os.mkdir(base_dir)\n except:\n pass\n\n\ndef get_main_page_urls():\n \"\"\"\n 获取主页中 每一组图片的url\n \"\"\"\n result = []\n for i in range(1, 6):\n url = 'http://www.win4000.com/meinvtag4_%s.html' % str(i)\n res = requests.get(url)\n print(res.status_code)\n # print(res.text)\n if res.status_code == 200:\n soup = BeautifulSoup(res.text, 'html.parser')\n li_list = soup.select('.Left_bar .tab_tj .tab_box .clearfix li')\n for li in li_list:\n page_url = li.select('a')[0].attrs.get('href')\n title = li.select('p')[0].text\n print(title, page_url)\n result.append((title, page_url))\n\n else:\n print('请求失败[get_main_page_urls](url:%s)' % url)\n return result\n\n\ndef get_single_page_url(result):\n \"\"\"\n 获取每一组图片html页面的url中，每一张图对应的html的url链接\n \"\"\"\n for item in result:\n group_name = item[0] # 每一组图片的名称\n pages_url = item[1] # 每一组图片的html对应的url链接\n res = requests.get(pages_url)\n if res.status_code == 200:\n soup = BeautifulSoup(res.text, 'html.parser')\n a_list = soup.select('#scroll li a')\n for a in a_list:\n page_url = a.attrs.get('href') # 获取这组图片中，每一张图片对应的html的url链接\n pic_html_queue.put((group_name, page_url))\n else:\n print('请求失败[get_single_page_url](url:%s)' % pages_url)\n\n\ndef download_pic():\n \"\"\"\n 下载图片到本地\n \"\"\"\n while True:\n try:\n item = pic_html_queue.get(block=True, timeout=180)\n except:\n break\n group_name = item[0]\n page_url = item[1]\n res = requests.get(page_url)\n soup = BeautifulSoup(res.text, 'html.parser')\n pic_url = soup.select('.pic-large')[0].attrs.get('url') # 图片链接\n pic_name = pic_url.split(r'/')[-1] # 图片名称\n pic_dir = os.path.join(base_dir, today, group_name)\n pic_path = os.path.join(pic_dir, pic_name) # 图片存放路径\n\n print(pic_url, pic_path)\n\n if not os.path.exists(pic_dir):\n try:\n os.makedirs(pic_dir)\n except:\n pass\n try:\n res_pic = requests.get(pic_url)\n if res_pic.status_code == 200:\n with open(pic_path, 'wb') as f:\n f.write(res_pic.content)\n except:\n print('error, pic_url: %s' % page_url)\n continue\n\n print('图片爬取结束。')\n\n\ndef main():\n result = get_main_page_urls()\n get_single_page_url(result)\n for i in range(5):\n td = threading.Thread(target=download_pic)\n td.start()\n\n\nif __name__ == '__main__':\n main()","sub_path":"spider/meizhuo_spider/meizhuo_spider.py","file_name":"meizhuo_spider.py","file_ext":"py","file_size_in_byte":3172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"154996490","text":"import bs4\n\nfrom selenium import webdriver\n\nimport time\nimport os\n\n\ndef getWFSlot(productUrl):\n driver = webdriver.Firefox(executable_path=\"/home/ptt5566/Downloads/geckodriver-v0.26.0-linux64/geckodriver\")\n driver.get(productUrl) \n html = driver.page_source\n soup = bs4.BeautifulSoup(html)\n time.sleep(50)\n no_open_slots = True\n\n while no_open_slots:\n driver.refresh()\n print(\"refreshed\")\n html = driver.page_source\n soup = bs4.BeautifulSoup(html)\n time.sleep(6)\n\n slot_pattern = 'Next available'\n try:\n print(\" part1 try 1\")\n next_slot_text = soup.find('h4', class_ ='ufss-slotgroup-heading-text a-text-normal').text\n print(\" part1 try 2\")\n if slot_pattern in next_slot_text:\n print(\" part1 try 3\")\n print('SLOTS OPEN!')\n #os.system('say \"Slots for delivery opened!\"')\n os.system('totem --play ~/Music/05.\\ merry-go-round.mp3')\n no_open_slots = False\n time.sleep(1400*10)\n else:\n print(\" part1 try 4\")\n except AttributeError:\n print(\" part1 except\")\n continue\n \n try:\n print(\" part2 try 1\")\n no_slot_pattern = 'No delivery windows available. New windows are released throughout the day.'\n print(\" part2 try 2\")\n if no_slot_pattern == soup.find('h4', class_ ='a-alert-heading').text:\n print(\" part2 try 3\")\n print(\"NO SLOTS!\")\n else:\n # I succeeded to reach HERE and buy my food!\n # the log was: \n # part1 try 1\n # part1 try 2\n # part1 try 4\n # part2 try 1\n # part2 try 2\n # part2 try 4\n \n print(\" part2 try 4\")\n os.system('totem --play ~/Music/05.\\ merry-go-round.mp3')\n time.sleep(12000)\n\n except AttributeError: \n print(\" part2 except\")\n print('SLOTS OPEN!')\n #os.system('say \"Slots for delivery opened!\"')\n os.system('totem --play ~/Music/05.\\ merry-go-round.mp3')\n time.sleep(12000)\n no_open_slots = False\n\n\ngetWFSlot('https://www.amazon.com/gp/buy/shipoptionselect/handlers/display.html?hasWorkingJavascript=1')\n\n\n","sub_path":"whole_foods_delivery_slot_firefox.py","file_name":"whole_foods_delivery_slot_firefox.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"122443494","text":"#!/usr/bin/env python\n# encoding: utf-8\n# filename: scores.py\n\n## @package utils.scores\n# Contains highscores saving\n\n# Import json and os\nimport json\nimport os\n\n## Represents the scores\nclass Scores(object):\n\n ## The list of scores\n scores = []\n\n ## Loads scores from a file\n @staticmethod\n def load():\n if os.path.exists(\"scores\"):\n file = open(\"scores\", \"r\")\n\n string = \"\"\n for line in file:\n string += line\n\n Scores.scores = json.loads(string)\n\n ## Adds a new score\n #\n # @param name the player's name\n # @param score the score\n @staticmethod\n def add_score(name, score):\n Scores.scores.append((name, score))\n\n Scores.scores = sorted(Scores.scores, key = lambda score: score[1], reverse = True)[:9]\n\n ## Saves the scores to a file\n @staticmethod\n def save():\n file = open(\"scores\", \"w\")\n\n file.write(json.dumps(Scores.scores, indent = 4, sort_keys = True))\n\n file.close()\n","sub_path":"src/utils/scores.py","file_name":"scores.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"372447849","text":"import random\nimport matplotlib.pyplot as plt\n\ndef sample():\n t = [random.choice([0, 1]) for _ in range(10)]\n return t\n\ndef fb(s):\n sum = 0.0\n for ind,val in enumerate(s):\n sum += pow(2,ind)*val\n return sum\n\nprev_p = []\nnum = 0.0\ndeno = 0.0\ni = 1 #1/3/5/7/9\nfor _ in range(10000000):\n s = sample()\n pz_bs = 0.666667 * pow(0.2, abs(128.0 - fb(s)))\n num += (pz_bs * s[i])\n deno += pz_bs\n if not deno:\n continue\n pq_e = num/deno\n prev_p.append(pq_e)\n\nplt.plot(prev_p)\nplt.xlabel('sample #')\nplt.ylabel('Probability i = '+str(i+1))\nplt.show()\nplt.savefig('prob'+str(i+1))","sub_path":"hw3.py","file_name":"hw3.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"593279286","text":"#ans of qsn21 submitted by NAIRITA MITRA\r\n\r\nlists=[]\r\nlist1=[]\r\nstudent=int(input(\"enter the no of students\"))\r\nfor x in range(student):\r\n roll_no=int(input(\"enter the rollno\"))\r\n lists.append(roll_no)\r\n stud_name=input(\"enter the name\")\r\n lists.append(stud_name)\r\n marks1=int(input(\"enter the marks of the first subject\"))\r\n list1.append(marks1)\r\n marks2=int(input(\"enter the marks of the 2nd subject\"))\r\n list1.append(marks2)\r\n marks3=int(input(\"enter the marks of the 3rd subject\"))\r\n list1.append(marks3)\r\n Total=0\r\n Average=0\r\n Total=marks1+marks2+marks3\r\n Average=Total/3\r\n print(\"the total marks of the student is \")\r\n print(Total)\r\n print(\"the average marks of the student\")\r\n print(Average)\r\nlist1.sort(reverse=True)\r\nprint(\"the descending order of marks is :\")\r\nprint(list1)\r\nprint(\"the names and roll no of students are:\")\r\nprint(lists)\r\n\r\n","sub_path":"qsn21.py","file_name":"qsn21.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"136732386","text":"__all__ = ['HAVECOLORS', 'STYLES', 'COLORCOLORS', 'RARITYCOLORS', 'TYPECOLORS', 'cf', 'mkRatingColor']\n\nFore = None\nBack = None\nStyle = None\nHAVECOLORS = False\n\ntry:\n import colorama\n colorama.init(autoreset = True)\n Fore = colorama.Fore\n Back = colorama.Back\n Style = colorama.Style\n HAVECOLORS = True\nexcept ImportError:\n class ColDummy(object):\n def __getattr__(self, key):\n if key and isinstance(key, str) and key[0] != '_':\n return ''\n raise AttributeError('ColDummy: Not here')\n Fore = ColDummy()\n Back = ColDummy()\n Style = ColDummy()\n\n\nSTYLES = {\n 'r': Style.RESET_ALL,\n 'd': Style.DIM,\n 'b': Style.BRIGHT,\n 'n': Style.NORMAL,\n 'fblack': Fore.BLACK,\n 'fred': Fore.RED,\n 'fgreen': Fore.GREEN,\n 'fyellow': Fore.YELLOW,\n 'fblue': Fore.BLUE,\n 'fmagenta': Fore.MAGENTA,\n 'fcyan': Fore.CYAN,\n 'fwhite': Fore.WHITE,\n 'bblack': Back.BLACK,\n 'bred': Back.RED,\n 'bgreen': Back.GREEN,\n 'byellow': Back.YELLOW,\n 'bblue': Back.BLUE,\n 'bmagenta': Back.MAGENTA,\n 'bcyan': Back.CYAN,\n 'bwhite': Back.WHITE,\n}\n\ndef cf(fmt, *args, **fkwargs):\n kwargs = { **fkwargs, **STYLES }\n return fmt.format(*args, **kwargs)\n\n\nRARITYCOLORS = {\n 'C': Style.NORMAL + Fore.WHITE,\n 'U': Style.BRIGHT + Fore.GREEN,\n 'P': Style.BRIGHT + Fore.MAGENTA,\n 'R': Style.BRIGHT + Fore.BLUE,\n 'L': Style.BRIGHT + Fore.YELLOW\n}\n\nCOLORCOLORS = {\n 'F': Style.NORMAL + Fore.RED,\n 'T': Style.NORMAL + Fore.YELLOW,\n 'J': Style.NORMAL + Fore.GREEN,\n 'P': Style.NORMAL + Fore.BLUE,\n 'S': Style.NORMAL + Fore.MAGENTA\n}\n\nTYPECOLORS = {\n 'Unit': Style.RESET_ALL,\n 'Attachment': Style.BRIGHT + Fore.WHITE,\n 'Spell': Style.NORMAL + Fore.CYAN,\n 'Fast Spell': Style.BRIGHT + Fore.CYAN,\n 'Power': Style.NORMAL + Fore.YELLOW,\n 'Sigil': Style.DIM + Fore.YELLOW\n}\n\n\ndef mkRatingColor(rating):\n if not isinstance(rating, float):\n return ''\n if rating > 3.5:\n return cf('{b}{fgreen}')\n elif rating > 3:\n return cf('{n}{fgreen}')\n elif rating > 2.5:\n return cf('{d}{fgreen}')\n elif rating > 2:\n return cf('{n}{fyellow}')\n elif rating > 1.5:\n return cf('{b}{fyellow}')\n elif rating > 1:\n return cf('{d}{fred}')\n elif rating > 0.5:\n return cf('{n}{fred}')\n return cf('{b}{fred}')\n","sub_path":"dhelper/styling.py","file_name":"styling.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"528845926","text":"#!/usr/bin/env python\n\"\"\"\nCreated on Mon Mar 30 15:38:40 2015\n\n@author: westonanderson\n\n#THE PORTAL CHANGED AS OF 08/17/16. NEW PROCEDURE NEEDED FOR OPENDAP BELOW\n(Haven't updated this script yet)\n\nusername: westonand, sLin32ns\nNeed .netrc file with each Earthdata Login host, user name and password credentials.\nGo to http://www.pydap.org/client.html#basic-digest and copy and paste code into a function called install_cas_client.py \nStart python.\n >>> import install_cas_client\n\n >>> from pydap.client import open_url\n\n >>> dataset = open_url('http://:@server[:port]/path/file[.format[?subset]]')\n\n\"\"\"\n\nimport netCDF4\nimport numpy as np\n\n\nyrs=range(1948,2011)\nmos = ['01','02','03','04','05','06','07','08','09','10','11','12']\nvarName='SoilM'\n\nncPath = '/Volumes/Data_Archive/Results/AmENSO/IntegratedVars/monthlyGlobal/'#'/home/werston/SM/'\n\nurl = 'http://agdisc.gsfc.nasa.gov:80/dods/GLDAS_NOAH10_M.020'\n \nf = netCDF4.Dataset(url,'r',format='NETCDF4')\n\n#Create the NetCDF dataset\nncdfVar = netCDF4.Dataset(str(ncPath+'SM.nc'),'w',format='NETCDF4')\ntime = ncdfVar.createDimension('time',None)\nmon = ncdfVar.createDimension('mon',12)\ndep = ncdfVar.createDimension('dep',4)\nlon = ncdfVar.createDimension('lon',np.size(f.variables['lon']))\nlat = ncdfVar.createDimension('lat',np.size(f.variables['lat']))\n#create variables\nyear = ncdfVar.createVariable('year','i4',('time',))\nmonth = ncdfVar.createVariable('month','S3',('mon'))\ndepth = ncdfVar.createVariable('depth','i4',('dep'))\nlatitude = ncdfVar.createVariable('latitude','f4',('lat'))\nlongitude = ncdfVar.createVariable('longitude','f4',('lon'))\nncVar = ncdfVar.createVariable(varName,'f4',('time','mon','dep','lat','lon'),fill_value=0.0)\n#fill variables lat/lon\nncdfVar.variables['latitude'][:]=f.variables['lat'][:]\nncdfVar.variables['longitude'][:]=f.variables['lon'][:]\nncdfVar.variables['month']=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\nncdfVar.variables['depth'][:]=[10,40,100,200]\n\n#fill the variable itself, as well as the years\nfor i in range(0,np.size(yrs)):\n for mo in range(12):\n ncdfVar.variables['year'][i]=yrs[i]\n ncdfVar.variables[varName][i,mo,0,:,:]=f.variables['soilm0_10cm'][i*12+mo,:,:]\n ncdfVar.variables[varName][i,mo,1,:,:]=f.variables['soilm10_40cm'][i*12+mo,:,:]\n ncdfVar.variables[varName][i,mo,2,:,:]=f.variables['soilm40_100cm'][i*12+mo,:,:]\n ncdfVar.variables[varName][i,mo,3,:,:]=f.variables['soilm100_200cm'][i*12+mo,:,:]\n\nncdfVar.close()","sub_path":"GLDAS/soilMoisture_Noah10.py","file_name":"soilMoisture_Noah10.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"476318637","text":"from importCOVID19 import COVID19\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nplt.rcParams['font.size'] = 10\nplt.rcParams['xtick.labelsize'] = 8\nplt.rcParams['ytick.labelsize'] = 8\nplt.rc('figure', figsize=(10, 5))\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\n# For statistical plots\nimport seaborn as sns\nimport scipy.stats as st\nfrom scipy.stats import norm\n\n# Reading parameters fit up to 2020/05/15\nfitpars = pd.read_csv('20200525fits.csv')\n# Excluded states\ntoexclude = ['TOT','CMX','MEX','GR0','GR1','GR2','COA','BCS','DUR','ZAC','COL','ROO','AGU']\n# Excluding further states for high R0 values\n#toexclude.extend(['OAX','SON','GRO','HID','SLP','QUE','CAM']) # From data 15/05\ntoexclude.extend(['OAX','GRO','SLP','CHH','MOR','BCN','CHP']) # From data 25/05\nmainpars = fitpars.loc[(fitpars['ID'] == 'TOT') | (fitpars['ID'] == 'CMX') | (fitpars['ID'] == 'MEX')]\nprint(mainpars)\nfitpars = fitpars[~fitpars['ID'].isin(toexclude)]\n# Sorting pars in terms of the maximum infected value\nfitpars.sort_values(by=['maxI'], inplace=True,ascending=False)\nprint('Number of states to plot:', len(fitpars.index))\n\n# Get the parVals for a given fitpars row\ndef getPars(row,stat=False):\n if stat:\n return (row[1],row[7]*row[5],row[7],row[6],row[2],row[8])\n # row = [ID, beta, eta]\n # beta, tau, q, delta, eta, epsilon\n q = 1; epsilon = 0.2; delta = 1/10\n return (row[1],q*2/3,q,delta,row[2],epsilon)\n\n# General definitions\nmaxX = 600; startV = 1\nxtime = np.linspace(startV, maxX + startV -1,maxX)\nxdate = pd.to_datetime(pd.Series(pd.date_range('20200315', periods=maxX)))\nrawData = '20200525_Start1503.csv'\nrandData = '20200526_LAST/'\ncolors = sns.cubehelix_palette(10) #02\nposcol = 6\nmxCOVID19 = COVID19('SEIRfull', rawData)\nmxCOVID19.readStat(randData)\nplt.rc('figure', figsize=(12, 12))\nfig, axs = plt.subplots(4, 4, gridspec_kw={'wspace': 0.2, 'hspace' : 0.08}, sharex=True, sharey=True)\nuseconf = True; conf = 0.95\n\nnstate = 0; axins = []; source = mainpars; maxstates = len(source.index)\noutres = []\nfor irow in range(4):\n for icol in range(4):\n print(irow,icol,nstate,maxstates)\n for ps in range(maxstates+1):\n if (nstate<(len(source.index)+maxstates)):\n if ps==maxstates:\n source = fitpars\n maxstates = 0\n print(ps,maxstates)\n row = source.iloc[nstate if maxstates==0 else ps].values\n state = row[0]\n print('Computing ',state,'(',irow,',',icol,')')\n # Getting the scatter data\n data = mxCOVID19.getFitData(state)\n # Getting the best fitted curve under the given hypothesis\n # Setting pars\n mxCOVID19.parVals = getPars(row)\n yValsModel = mxCOVID19.getModel(xtime)[:,1]\n # Analysis of the stat data\n mxCOVID19.getStatData(state)\n allPars = mxCOVID19.actStatData.copy()\n statData = allPars['eta'].values\n limitsETA = st.t.interval(conf, len(statData)-1, loc=np.mean(statData), scale=st.sem(statData))\n # Initializing the min and max y limits for the time range\n ymin = np.zeros(maxX)+1e8; ymax = np.zeros(maxX)\n minR0 = 1e5; maxR0 = 0; cnt = 0\n # Cycle over the statpars\n for i in range(len(allPars.index)):\n statpars = allPars.iloc[i].values\n if ((not useconf) or (statpars[2]>=limitsETA[0]) and (statpars[2]<=limitsETA[1])):\n minR0 = minR0 if (minR0statpars[3]) else statpars[3]\n mxCOVID19.parVals = getPars(statpars,True)\n mxCOVID19.fE0 = statpars[9]\n yVals = mxCOVID19.getModel(xtime)[:,1]\n # Running over x to update max and min values\n cnt+=1\n for xindx in range(maxX):\n ymin[xindx] = ymin[xindx] if (ymin[xindx]yVals[xindx]) else yVals[xindx]\n print(conf*100,'% on eta of '+state+':',limitsETA,'. Min R0=',minR0,', Max R0=',maxR0,' useconf=', useconf,' pars used: ',cnt)\n outres.append([state,np.max(ymin),xdate.iloc[np.argmax(ymin, axis=0)],minR0,maxR0])\n\n if (nstate<(len(source.index)+maxstates)):\n # The main axis\n # Plotting the best fit\n axs[irow,icol].plot(xdate,yValsModel,linewidth=0.8,color=colors[poscol])\n # Plotting the shadow probabilities\n axs[irow,icol].fill_between(xdate, ymin, ymax, alpha=0.3,color=colors[poscol])\n axs[irow,icol].tick_params(labelrotation=22)\n axs[irow,icol].ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n upv = 0.05; starttext = 0.10; sz = 6; xpos = 0.67\n axs[irow,icol].text(xpos,starttext+upv*2, r'$\\beta$='+'{:.2f}'.format(row[1]), transform=axs[irow,icol].transAxes,size = sz)\n axs[irow,icol].text(xpos,starttext+upv*1, r'$\\eta$='+'{:.2f}'.format(row[2]), transform=axs[irow,icol].transAxes,size = sz)\n axs[irow,icol].text(xpos,starttext+upv*0, r'$R_0$='+'{:.1f}'.format(row[3]), transform=axs[irow,icol].transAxes,size = sz)\n axs[irow,icol].text(xpos,starttext+upv*4, r'$I_{m}$='+format(int(row[5]), ',d'), transform=axs[irow,icol].transAxes,size = sz)\n axs[irow,icol].text(xpos,starttext+upv*3, r'$D_{m}$='+row[6], transform=axs[irow,icol].transAxes,size = sz)\n axs[irow,icol].text(0.45,0.95, state, transform=axs[irow,icol].transAxes)#,color='blue', transform=ax.transAxes)\n\n # The inset axis\n axins.append(inset_axes(axs[irow,icol], width=\"30%\", height=\"40%\" ,borderpad=1.1))#, loc='lower left', bbox_to_anchor=(0.8, 0.8, 1, 1)))\n # Plotting the data\n axins[-1].scatter(mxCOVID19.realDays.values,data['Infected'],color='gray',s=2)\n # Plotting the shadow probabilities\n axins[-1].fill_between(xtime, ymin, ymax, alpha=0.3,color=colors[poscol])\n # Plotting the best fit\n axins[-1].plot(xtime,yValsModel,linewidth=0.8,color=colors[poscol])\n axins[-1].set_xlim(mxCOVID19.realDays.values[0],mxCOVID19.realDays.values[-1]) #self.fitData.iloc[0].values\n axins[-1].set_ylim(0,np.max(data['Infected'].values)) #self.fitData.iloc[0].values\n axins[-1].tick_params(direction='out', labelsize = 6)\n axins[-1].ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n print('Maximum for the most optimistic scenario: ', np.max(ymin), \" achieved on \", xdate.iloc[np.argmax(ymin, axis=0)])\n\n # Moving to the next state\n nstate += 1\n\noutres = pd.DataFrame(outres,columns = ['ID','MaxInf','Date','R0min','R0max'])\noutres.to_csv (rawData[:8]+'_bestScenario.csv', index = False, header=True)\n \n# add a big axis, hide frame\nfig.add_subplot(111, frameon=False)\n# hide tick and tick label of the big axis\nplt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\nplt.ylabel('Infected')\nplt.tight_layout()\nplt.savefig('FigureFITstates.png',dpi=600)\nplt.show()\n\n","sub_path":"genFigu03.py","file_name":"genFigu03.py","file_ext":"py","file_size_in_byte":7442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"519992080","text":"import cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('sub_1.jpg')\nimg2 = cv2.imread('sub_2.jpg')\n\n#Obtenemos el largo y ancho de la imagen original\nheight = np.size(img, 0)\nwidth = np.size(img, 1)\n\n#Aplicamos Contrast Stretching \nfor i in range (width-1):\n for j in range (height-1):\n a,x,y = img[j,i]\n b,w,z = img2[j,i]\n temp = abs(a/2 - b/2)\n if(temp < 0):\n temp = 0\n if(temp > 255):\n temp = 255\n img[j,i] = temp\n\ncv2.imshow('resta', img)\ncv2.waitKey(0)","sub_path":"adicionSustraccion/resta.py","file_name":"resta.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"650021100","text":"'''\r\npython item 36\r\n'''\r\n\r\n\r\n# contains integar, string, float variable\r\n'''\r\nint_var = 100 #integer variable\r\nstring_var = \"Hello world!\" #String variable\r\nfloat_var = 1.5 #float variable\r\n\r\nprint int_var\r\nprint string_var\r\nprint float_var\r\n'''\r\n\r\n\r\n#contains a format notation,if,elif,else\r\n\r\ndef bla(name= \"\"):\r\n name = office(name)\r\n\r\ndef office(name):\r\n if name != \"\":\r\n print ('Welcome to {} office').format(name)\r\n else:\r\n while name == \"\":\r\n name = raw_input('\\nYour new here! What is your name? ').capitalize()\r\n print ('\\nWelcome to the office {}').format(name)\r\n print ('\\nSee you around!').capitalize()\r\n end_day(name)\r\n return name\r\ndef end_day(name):\r\n out = raw_input(\"ready to go home? yes/no \")\r\n if out == \"yes\":\r\n print(\"okay lets get out of here\")\r\n elif out == \"no\":\r\n print(\"To bad its time to go\")\r\n elif out == \"quit\":\r\n print(\"Sad to se you leave\") \r\n exit()\r\n \r\n \r\nbla()\r\n\r\n#math operators\r\n'''\r\nx = 5\r\nx += 1\r\ny = 7\r\na = 1\r\nb = 3\r\nc = 5\r\nd = 9\r\ne = 10\r\nt = 0\r\n'''\r\n'''\r\nprint (a+b)\r\nprint(e-b)\r\nprint(d*e)\r\nprint(d/b)\r\nprint(b%d)\r\nprint(x)\r\nprint(y)\r\n'''\r\n# and or not operators(uses values for math operators)\r\n'''\r\nif x and y > 1:\r\n print True\r\nelse:\r\n print False\r\n\r\nif y or t:\r\n print True\r\nelse:\r\n print False\r\n \r\nif not y > 1:\r\n print True\r\nelse:\r\n print False\r\n '''\r\n\r\n#The loops\r\n'''\r\nstring = \"the panda\"\r\n\r\nfor x in string:\r\n print x\r\nwhile string == \"the panda\":\r\n print ('its a panda')\r\n string = string + \" is red\"\r\nprint(string)\r\n'''\r\n# The list iteration\r\n'''\r\nanimals = [\"cat\", \"dog\", \"pig\", \"racoon\"]\r\n\r\nfor animal in animals:\r\n print \"Look at the \", animal\r\nprint \"So cute!\"\r\n'''\r\n\r\n# The tuple iteration\r\n'''\r\ncolors = (\"red\", \"blue\", \"green\", \"orange\")\r\n\r\nfor color in colors:\r\n print \"My favorite color is\",color\r\nprint ('yes all of them')\r\n'''\r\n\r\n\r\n\r\n\r\n \r\n \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"python/py_drill.py","file_name":"py_drill.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"414133724","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api , _\nfrom datetime import date, datetime, timedelta\nfrom odoo.exceptions import UserError\n\n\nclass BudgetReportComparison(models.TransientModel):\n _name = 'budget.custom.report.comparison'\n _inherit = 'budget.custom.report'\n\n # default date is first day of the previous year\n date_from_s = fields.Date(required=1,string='Date From', default=lambda self: date(date.today().year-1 , 1, 1))\n # default date is last day of the previous year\n date_to_s = fields.Date(required=1,string='Date To', default=lambda self: date(date.today().year-1 , 12, 31))\n\n def print_report(self,data):\n\n if self.date_from >= self.date_to:\n raise UserError(_('Start Date must be equal to or less than Date To'))\n\n if len(self.analytic_account_ids) == 0:\n raise UserError(_('You must atleast select one analytic account'))\n\n data = data\n\n # Get all filter in data Dict\n data.update({'date_from': self.date_from})\n data.update({'date_to': self.date_to})\n data.update({'date_from_s': self.date_from_s})\n data.update({'date_to_s': self.date_to_s})\n\n # read_group filters and we pass it to read_group\n # first period\n filters = [('date_from', '>=', self.date_from),\n ('date_to', '<=', self.date_to),\n ('general_budget_id.type', '=', self.budget_type),\n ]\n\n # read_group filters and we pass it to read_group\n # second period\n filters_2 = [\n ('date_from', '>=', self.date_from_s),\n ('date_to', '<=', self.date_to_s),\n ('general_budget_id.type', '=', self.budget_type),\n ]\n\n data.update({'filters':filters})\n data.update({'filters_sec':filters_2})\n\n # read_group fields , we pass it to read_group\n budget_fields = ['general_budget_id', 'general_budget_id.code', 'analytic_account_id', 'planned_amount',\n 'practical_amount', 'total_operation', 'transfer_amount', 'confirm', 'residual', 'percentage',\n 'deviation']\n\n data.update({'fields': budget_fields})\n\n #get all child ids\n analytic_ids = tuple(\n [line.id for line in self.env['account.analytic.account'].search(\n [('id', 'child_of', tuple(self.mapped('analytic_account_ids').ids))]\n )])\n\n data.update({'analytic_ids': analytic_ids})\n data2= data.copy()\n data2.update({'filters' : [('date_from', '>=', self.date_from_s),\n ('date_to', '<=', self.date_to_s) ]})\n\n\n\n\n\n data.update({'analytic_ids': analytic_ids})\n\n\n data.update({'budget_type': self.budget_type})\n # print(\">>>>>>>>>>>>>>>>>>>>>>>>>>\")\n\n return self.env.ref('budget_custom_report.action_budget_comparison_report').with_context(\n landscape=True).report_action(\n self, data=data)\n\n\n\n\n\n\n\n\n\n\nclass budgetCustomReport(models.AbstractModel):\n _name = 'report.budget_custom_report.budget_comparison_report_tamplate'\n\n @api.model\n def get_report_values(self, docids, data=None):\n return {\n 'data': data,\n 'get':self.env['budget.custom.report'],\n\n }","sub_path":"v_11/masa_project/branches/common/budget_custom_report/wizard/budget_comparison.py","file_name":"budget_comparison.py","file_ext":"py","file_size_in_byte":3307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"515350911","text":"# таксата за участие на велосипедистите. Според възрастовата група и вида на трасето на което ще се провежда състезанието, таксата е различна.\n# Ако в \"cross-country\" състезанието се съберат 50 или повече участника(общо младши и старши), таксата намалява с 25%. Организаторите отделят 5% процента от събраната сума за разходи.\n# Изход\n# Да се отпечата на конзолата едно число:\n# \"{дарената сума}\" - форматирана с точност до 2 знака след десетичната запетая.\n\njunior_count = int(input()) #броят младши велосипедисти. Цяло число\nsenior_count = int(input()) # старши велосипедисти. Цяло число\ntrack_type = input() # вид трасе – \"trail\", \"cross-country\", \"downhill\" или \"road\"\nsum = 0\n\nif track_type == \"trail\":\n sum = ((junior_count * 5.5) + (senior_count * 7)) * 0.95\nelif track_type == \"cross-country\":\n sum = ((junior_count * 8) + (senior_count * 9.5)) * 0.95\n if junior_count + senior_count >= 50:\n sum = ((junior_count * (8 * 0.75)) + (senior_count * (9.5 * 0.75))) * 0.95\nelif track_type == \"downhill\":\n sum = ((junior_count * 12.25) + (senior_count * 13.75)) * 0.95\nelif track_type == \"road\":\n sum = ((junior_count * 20) + (senior_count * 21.5)) * 0.95\nprint(f\"{sum:.2f}\")\n","sub_path":"Python Basics June 2020/3 conditional_statements_more_ex/3.3.2. bike_race.py","file_name":"3.3.2. bike_race.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"53692669","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.utils import timezone\nfrom django.contrib import messages\nfrom .forms import EventoForm, PersonaForm, TipoForm\nfrom eventos.models import Evento, Inscripcion, Persona, TipoEvento\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\n# Create your views here.\ndef lista_eventos(request):\n eventos = Evento.objects.filter(fecha__lte=timezone.now()).order_by('fecha')\n return render(request, 'eventos/evento_list.html', {'eventos':eventos})\n@login_required\ndef evento_detalle(request, pk):\n evento = get_object_or_404(Evento, pk=pk)\n return render(request, 'eventos/evento_detalle.html', {'evento': evento})\n@login_required\ndef evento_editar(request, pk):\n evento = get_object_or_404(Evento, pk=pk)\n if request.method == \"POST\":\n form = EventoForm(request.POST, instance=evento)\n if form.is_valid():\n evento = form.save(commit=False)\n evento.save()\n return redirect('lista_eventos')\n else:\n form = EventoForm(instance=evento)\n return render(request, 'eventos/evento_editar.html', {'form': form})\n@login_required\ndef evento_eliminar(request, pk):\n evento = get_object_or_404(Evento, pk=pk)\n evento.delete()\n return redirect('lista_eventos')\n@login_required\ndef evento_nuevo(request):\n if request.method == \"POST\":\n formulario = EventoForm(request.POST)\n if formulario.is_valid():\n evento = Evento.objects.create(nombre=formulario.cleaned_data['nombre'], descripcion=formulario.cleaned_data['descripcion'], fecha=formulario.cleaned_data['fecha'], tipo=formulario.cleaned_data['tipo'])\n for persona_id in request.POST.getlist('personas'):\n inscripcion = Inscripcion(persona_id=persona_id, evento_id = evento.id)\n inscripcion.save()\n messages.add_message(request, messages.SUCCESS, 'Evento guardado exitosamente')\n else:\n formulario = EventoForm()\n return render(request, 'eventos/evento_nuevo.html', {'formulario': formulario})\n@login_required\ndef lista_personas(request):\n personas = Persona.objects.order_by('id')\n return render(request, 'personas/persona_list.html', {'personas':personas})\n@login_required\ndef persona_nueva(request):\n if request.method == \"POST\":\n form = PersonaForm(request.POST)\n if form.is_valid():\n persona = form.save(commit=False)\n persona.save()\n return redirect('lista_personas')\n else:\n form = PersonaForm()\n return render(request, 'personas/persona_nueva.html', {'form': form})\n@login_required\ndef persona_detalle(request, pk):\n persona = get_object_or_404(Persona, pk=pk)\n return render(request, 'personas/persona_detalle.html', {'persona': persona})\n@login_required\ndef persona_editar(request, pk):\n persona = get_object_or_404(Persona, pk=pk)\n if request.method == \"POST\":\n form = PersonaForm(request.POST, instance=persona)\n if form.is_valid():\n persona = form.save(commit=False)\n persona.save()\n return redirect('lista_personas')\n else:\n form = PersonaForm(instance=persona)\n return render(request, 'personas/persona_editar.html', {'form': form})\n@login_required\ndef persona_eliminar(request, pk):\n persona = get_object_or_404(Persona, pk=pk)\n persona.delete()\n return redirect('lista_personas')\n@login_required\ndef lista_tipo(request):\n tipos = TipoEvento.objects.order_by('id')\n return render(request, 'tipos/tipo_list.html', {'tipos':tipos})\n@login_required\ndef tipo_nuevo(request):\n if request.method == \"POST\":\n form = TipoForm(request.POST)\n if form.is_valid():\n tipo = form.save(commit=False)\n tipo.save()\n return redirect('lista_tipo')\n else:\n form = TipoForm()\n return render(request, 'tipos/tipo_nuevo.html', {'form': form})\n@login_required\ndef tipo_detalle(request, pk):\n tipo = get_object_or_404(TipoEvento, pk=pk)\n return render(request, 'tipos/tipo_detalle.html', {'tipo': tipo})\n@login_required\ndef tipo_editar(request, pk):\n tipo = get_object_or_404(TipoEvento, pk=pk)\n if request.method == \"POST\":\n form = TipoForm(request.POST, instance=tipo)\n if form.is_valid():\n tipo = form.save(commit=False)\n tipo.save()\n return redirect('lista_tipo')\n else:\n form = TipoForm(instance=tipo)\n return render(request, 'tipos/tipo_editar.html', {'form': form})\n@login_required\ndef tipo_eliminar(request, pk):\n tipo = get_object_or_404(TipoEvento, pk=pk)\n tipo.delete()\n return redirect('lista_tipo')\n","sub_path":"eventos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"171852215","text":"\"\"\"Fly zone model.\"\"\"\n\nimport datetime\nimport logging\nimport numpy as np\nfrom auvsi_suas.models import aerial_position\nfrom auvsi_suas.models.waypoint import Waypoint\nfrom django.contrib import admin\nfrom django.core import exceptions\nfrom django.core import validators\nfrom django.db import models\nfrom matplotlib import path as mplpath\n\nlogger = logging.getLogger(__name__)\n\n# The time window (in seconds) in which a plane cannot be counted as going out\n# of bounds multiple times. This prevents noisy input data from recording\n# significant more violations than a human observer.\nOUT_OF_BOUNDS_DEBOUNCE_SEC = 10.0\n\n\nclass FlyZone(models.Model):\n \"\"\"An approved area for UAS flight. UAS shall be in at least one zone.\"\"\"\n\n # The polygon defining the boundary of the zone.\n boundary_pts = models.ManyToManyField(Waypoint)\n\n # The minimum altitude of the zone (MSL) in feet.\n altitude_msl_min = models.FloatField(\n validators=aerial_position.ALTITUDE_VALIDATORS)\n\n # The maximum altitude of the zone (MSL) in feet.\n altitude_msl_max = models.FloatField(\n validators=aerial_position.ALTITUDE_VALIDATORS)\n\n def clean(self):\n \"\"\"Validates the model.\"\"\"\n super(FlyZone, self).clean()\n if self.altitude_msl_min > self.altitude_msl_max:\n raise exceptions.ValidationError(\n 'Altitude min must be less than altitude max.')\n\n def contains_pos(self, aerial_pos):\n \"\"\"Whether the given pos is inside the zone.\n\n Args:\n aerial_pos: The AerialPosition to test.\n Returns:\n Whether the given position is inside the flight boundary.\n \"\"\"\n return self.contains_many_pos([aerial_pos])[0]\n\n def contains_many_pos(self, aerial_pos_list):\n \"\"\"Evaluates a list of positions more efficiently than inidividually.\n\n Args:\n aerial_pos_list: A list of AerialPositions to test.\n Returns:\n A list storing whether each position is inside the boundary.\n \"\"\"\n # Get boundary points\n ordered_pts = self.boundary_pts.order_by('order')\n path_pts = [[wpt.latitude, wpt.longitude] for wpt in ordered_pts]\n # First check enough points to define a polygon\n if len(path_pts) < 3:\n return [False] * len(aerial_pos_list)\n\n # Create path to use for testing polygon inclusion\n path_pts.append(path_pts[0])\n path = mplpath.Path(np.array(path_pts))\n\n # Test each aerial position for altitude\n results = []\n for aerial_pos in aerial_pos_list:\n # Check altitude bounds\n alt = aerial_pos.altitude_msl\n altitude_check = (alt <= self.altitude_msl_max\n and alt >= self.altitude_msl_min)\n results.append(altitude_check)\n\n # Create a list of positions to test whether inside polygon\n polygon_test_point_ids = [\n cur_id for cur_id in range(len(aerial_pos_list)) if results[cur_id]\n ]\n if len(polygon_test_point_ids) == 0:\n return results\n polygon_test_points = [[\n aerial_pos_list[cur_id].latitude, aerial_pos_list[cur_id].longitude\n ] for cur_id in polygon_test_point_ids]\n\n # Test each point for inside polygon\n polygon_test_results = path.contains_points(\n np.array(polygon_test_points))\n for test_id in range(len(polygon_test_point_ids)):\n cur_id = polygon_test_point_ids[test_id]\n results[cur_id] = polygon_test_results[test_id]\n\n return results\n\n @classmethod\n def out_of_bounds(cls, fly_zones, uas_telemetry_logs):\n \"\"\"Determines amount of time spent out of bounds.\n\n Args:\n fly_zones: The list of FlyZone that the UAS must be in.\n uas_telemetry_logs: A list of UasTelemetry logs sorted by timestamp\n which demonstrate the flight of the UAS.\n Returns:\n num_violations: The number of times fly zone boundaries violated.\n total_time: The timedelta for time spent out of bounds\n as indicated by the telemetry logs.\n \"\"\"\n # Get the aerial positions for the logs\n aerial_pos_list = uas_telemetry_logs\n log_ids_to_process = range(len(aerial_pos_list))\n\n # Evaluate zones against the logs, eliminating satisfied ones, until\n # only the out of boundary ids remain\n for zone in fly_zones:\n # Stop processing if no ids\n if len(log_ids_to_process) == 0:\n break\n # Evaluate the positions still not satisfied\n cur_positions = [\n aerial_pos_list[cur_id] for cur_id in log_ids_to_process\n ]\n satisfied_positions = zone.contains_many_pos(cur_positions)\n # Retain those which were not satisfied in this pass\n log_ids_to_process = [\n log_ids_to_process[cur_id]\n for cur_id in range(len(log_ids_to_process))\n if not satisfied_positions[cur_id]\n ]\n\n out_of_bounds_time = datetime.timedelta()\n violations = 0\n prev_event_id = -1\n currently_in_bounds = True\n out_of_bounds_ids = set(log_ids_to_process)\n for i in range(len(aerial_pos_list)):\n i_in_bounds = i not in out_of_bounds_ids\n if currently_in_bounds and not i_in_bounds:\n # As soon as there is one telemetry log out of bounds, we count\n # it as a violation.\n currently_in_bounds = False\n violations += 1\n prev_event_id = i\n elif not currently_in_bounds and i_in_bounds:\n # A switch of state needs to happen. But first make sure\n # enough time has passed.\n time_diff = (uas_telemetry_logs[i].timestamp -\n uas_telemetry_logs[prev_event_id].timestamp)\n currently_in_bounds = (time_diff.total_seconds() >=\n OUT_OF_BOUNDS_DEBOUNCE_SEC)\n\n if not currently_in_bounds and i > 0:\n time_diff = (uas_telemetry_logs[i].timestamp -\n uas_telemetry_logs[i - 1].timestamp)\n out_of_bounds_time += time_diff\n\n return (violations, out_of_bounds_time)\n\n\n@admin.register(FlyZone)\nclass FlyZoneModelAdmin(admin.ModelAdmin):\n filter_horizontal = (\"boundary_pts\", )\n list_display = ('pk', 'altitude_msl_min', 'altitude_msl_max')\n","sub_path":"server/auvsi_suas/models/fly_zone.py","file_name":"fly_zone.py","file_ext":"py","file_size_in_byte":6570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"646557261","text":"# this is the toy experiment for Virtual Adversarial Training.\n\nimport numpy as np, matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nimport pandas as pd\nimport sklearn, torch\nimport torch.nn as nn\nfrom sklearn import datasets\nfrom utils import vat_loss\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\ndef initial_plot(y):\n markers = ('s','x','o','^')\n colors = ('red','blue','lightgreen','gray')\n cmap = ListedColormap(colors[:len(np.unique(y))])\n return markers,colors,cmap\n\ndef entropy_loss(ul_y):\n p = F.softmax(ul_y, dim=1)\n return -(p*F.log_softmax(ul_y, dim=1)).sum(dim=1).mean(dim=0)\n\ndef kl_div_with_logit(q_logit, p_logit):\n\n q = F.softmax(q_logit, dim=1)\n logq = F.log_softmax(q_logit, dim=1)\n logp = F.log_softmax(p_logit, dim=1)\n\n qlogq = ( q *logq).sum(dim=1).mean(dim=0)\n qlogp = ( q *logp).sum(dim=1).mean(dim=0)\n\n return qlogq - qlogp\n\ndef _l2_normalize(d):\n\n d = d.numpy()\n d /= (np.sqrt(np.sum(d ** 2)) + 1e-16)\n return torch.from_numpy(d)\n\ndef vat_loss(model, ul_x, ul_y, xi=1e-3, eps=2.5, num_iters=1):\n\n # find r_adv\n\n d = torch.Tensor(ul_x.size()).normal_() # 所有元素的std =1, average = 0\n for i in range(num_iters):\n d = xi *_l2_normalize(d)\n d = Variable(d.cuda(), requires_grad=True)\n y_hat = model(ul_x + d)\n delta_kl = kl_div_with_logit(ul_y.detach(), y_hat)\n delta_kl.backward()\n\n d = d.grad.data.clone().cpu()\n model.zero_grad()\n\n d = _l2_normalize(d)\n d = Variable(d.cuda())\n r_adv = eps *d\n # compute lds\n y_hat = model(ul_x + r_adv.detach())\n delta_kl = kl_div_with_logit(ul_y.detach(), y_hat)\n return delta_kl\n\ndef train(model, x, y, ul_x, optimizer,opt):\n# x is the labeled data with y being the target. ul_x is the unlabeled data.\n model.train()\n ce = nn.CrossEntropyLoss()\n y_pred = model(x)\n ce_loss = ce(y_pred, y)\n\n ul_y = model(ul_x)\n v_loss = vat_loss(model, ul_x, ul_y, eps=2.5)\n loss = v_loss + ce_loss\n # loss = ce_loss\n if opt.method == 'vatent':\n loss += entropy_loss(ul_y)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n return v_loss, ce_loss\n # return 1, ce_loss\n\ndef val(model, x_validate,y_target):\n model.eval()\n y_pred = model(x_validate)\n y_prob = F.softmax(y_pred,dim=1)\n y_pred = y_pred.max(1)[1]\n acc = (y_pred==y_target).cpu().data.numpy().mean()\n return acc, y_prob[:,1]\n\ndef visualization_decision_boundary(model, x_labeled, y_labeled, x_unlabeled,y_unlabeled):\n# def plot_decision_regions(X, y, net, resolution, cm):\n # cm = plt.cm.RdBu\n cm_bright = ListedColormap(['#FF0000', '#00FF00','#0000FF',])\n X = np.concatenate([x_labeled,x_unlabeled])\n resolution = 0.01\n X_numpy = x_labeled\n y_numpy = y_labeled\n markers, colors, cm = initial_plot(y_numpy)\n\n x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n\n xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n np.arange(x2_min, x2_max, resolution)\n )\n X_mesh = Variable(torch.FloatTensor(np.array([xx1.ravel(), xx2.ravel()]).T).cuda(), requires_grad=False)\n predicts = F.softmax(model(X_mesh),dim=1).cpu().data.numpy()[:,1]\n predicts = predicts.reshape(xx1.shape)\n\n plt.figure(2)\n plt.clf()\n plt.contourf(xx1, xx2, predicts, cmap=plt.cm.RdBu, alpha=.1)\n plt.colorbar()\n\n plt.scatter(unlabeled_data_train[:, 0], unlabeled_data_train[:, 1], c=y_unlabeled, cmap=cm_bright,alpha=0.2)\n plt.scatter(x_labeled[:, 0], x_labeled[:, 1], cmap=cm_bright, c=y_labeled, alpha=0.5)\n plt.xlim(xx1.min(), xx1.max())\n plt.ylim(xx2.min(), xx2.max())\n plt.pause(0.0001)\n\n\n ul_y = model(torch.FloatTensor(x_unlabeled).float().cuda())\n v_loss = vat_loss(model, torch.FloatTensor(x_unlabeled).float().cuda(), ul_y, eps=2.5)\n # pass\n\nclass Net (nn.Module):\n def __init__(self):\n super().__init__()\n self.main = nn.Sequential(\n nn.Linear(2,100),\n nn.ReLU(True),\n nn.Linear(100,50),\n nn.ReLU(True),\n # nn.Dropout(),\n nn.Linear(50,2)\n )\n def forward(self, input):\n output = self.main(input)\n return output\n\nclass config():\n method = 'vatent'\n # method = None\n\nopt = config()\n\nnp.random.seed(1)\ntorch.manual_seed(1)\ndataset = datasets.make_moons(n_samples=2010,noise=0.12 ,shuffle=True,random_state=1)\n(X, y) = dataset\n\nlabeled_number = 10\nvalid_number = 800\nnum_iter_per_epoch = 200\nbatch_size = 5\nunlabeled_batch_size = 128\nmax_epochs = 102\n\n\nlabeled_data_train, labeled_target = X[:labeled_number], y[:labeled_number]\nunlabeled_data_train,unlabeled_data_target = X[labeled_number:-valid_number],y[labeled_number:-valid_number]\nvalid_data, valid_target = X[-valid_number:],y[-valid_number:]\n\n# visualize the dataset\ncolor_class = np.zeros(y.shape)\ncolor_class[labeled_number:-valid_number]=1\ncolor_class[-valid_number:]=2\n\nnet = Net().cuda()\ncriterion = nn.CrossEntropyLoss()\noptimiser = torch.optim.Adam(net.parameters(),lr =1e-3)\n\nfor epoch in range(max_epochs):\n for i in range(num_iter_per_epoch):\n batch_indices = torch.LongTensor(np.random.choice(labeled_data_train.shape[0], batch_size, replace=False))\n x = labeled_data_train[batch_indices]\n y = labeled_target[batch_indices]\n batch_indices_unlabeled = torch.LongTensor(\n np.random.choice(unlabeled_data_train.shape[0], unlabeled_batch_size, replace=False))\n ul_x = unlabeled_data_train[batch_indices_unlabeled]\n\n v_loss, ce_loss = train(net.train(), torch.FloatTensor(x).float().cuda(), torch.LongTensor(y).cuda(), torch.FloatTensor(ul_x).float().cuda(),\n optimiser,opt=opt)\n if i % 10 == 0:\n visualization_decision_boundary(net,labeled_data_train,labeled_target,unlabeled_data_train,unlabeled_data_target)\n # print(\"Epoch :\", epoch, \"Iter :\", i, \"VAT Loss :\", v_loss.item(), \"CE Loss :\", ce_loss.item())\n\n valid_acc = val(model=net, x_validate= torch.FloatTensor(valid_data).float().cuda(),y_target=torch.LongTensor(valid_target).cuda())\n print(valid_acc[0])\n","sub_path":"toy_experiment.py","file_name":"toy_experiment.py","file_ext":"py","file_size_in_byte":6286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"214263365","text":"'''\nCreated on Feb 17, 2014\n\n@author: Yehis\n'''\nfrom urllib import request\nif __name__ == '__main__':\n\n\n REQUEST = urllib.request\n ARTIST_ID = [\"7\",\"328\",\"523\"]\n FOLDER_NAME = [\"Amr Diab\",\"Samira Said\",\"Souad Massi\"]\n \n ST_STR = \"table_songs\"\n ED_STR = \"\"\n ENCODING = \"utf-8\"\n LOGGING_PATTERN = \"******** {0} ********\"\n ANCHOR_PATTERN = \".*?[^<].*?\"\n HREF_PATTERN = \"songid=\\d\\d*\"\n NAME_PATTERN = \"title=\\\".*\"\n for x in range(len(ARTIST_ID)):\n URL = \"http://www.6arabyon.com/artist.asp?artistid=\"+ARTIST_ID[x] \n DOWNLOAD_DIR = \"E:/Music/\"+FOLDER_NAME[x]+\"/\"\n \n print(LOGGING_PATTERN.format(\"Reading URL \" + URL))\n req = REQUEST.urlopen(URL) \n content = str(req.read()) \n print(LOGGING_PATTERN.format(\"Done: Reading URL\" + URL))\n \n print(LOGGING_PATTERN.format(\"Extracting Download Pages Link\")) \n s = content.index(ST_STR)\n e = content.index(ED_STR)\n content = content[s:e]\n \n songs = re.findall(ANCHOR_PATTERN , content, flags=0)\n \n print(LOGGING_PATTERN.format(str(len(songs)) + \" Songs Found\"))\n counter = 0 \n href = re.compile(HREF_PATTERN)\n name = re.compile(NAME_PATTERN)\n try:\n for song in songs:\n counter += 1\n if x ==0 and counter < 146:\n continue\n download = href.search(song)\n songName = name.search(song)\n if download is None:\n continue\n \n download = \"download.asp?type=mp3&mp3id=\" + download.group().split(\"=\")[1]\n \n SongURL = urljoin(URL, download) \n if songName is None:\n songName = counter\n songName = songName.group().split('\"')[1] + \".mp3\"\n print(songName + \" : \" + SongURL + \" : \" + song);\n print(LOGGING_PATTERN.format(\"Downloading: '\" + songName + \"' from '\" + SongURL + \"' [\" + str(counter) + \"/\" + str(len(songs)) + \"]\")) \n try:\n webfile = REQUEST.urlretrieve(SongURL, DOWNLOAD_DIR + songName)\n except:\n print (\"Error\") \n continue\n print(LOGGING_PATTERN.format(\"Done: Downloading: '\" + songName + \"' from '\" + SongURL + \"'\"))\n \n except:\n print (\"Error\") \n \n req.close()\n print(LOGGING_PATTERN.format(\"Done: Extracting Download Pages Link\")) ","sub_path":"Downloader/com/sedera/6arabyon/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":2675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"584651542","text":"import numpy as np\nnp.random.seed(1)\nimport tkinter as tk\nimport time\nfrom PIL import ImageTk,Image\nfrom agent import PolicyIterationAgent\n\n\n\nUNIT = 100 # pixels\nMAZE_H = 5 # grid height\nMAZE_W = 5 # grid width\n\nclass GraphicDisplay(tk.Tk):\n def __init__(self):\n super(GraphicEnv, self).__init__()\n self.action_space = ['u', 'd', 'l', 'r']\n self.n_actions = len(self.action_space)\n self.title('PolicyIteration')\n self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT+50))\n self.texts = []\n self.arrows = []\n self.agent = PolicyIterationAgent()\n self._build_env()\n\n def _build_env(self):\n self.canvas = tk.Canvas(self, bg='white',\n height=MAZE_H * UNIT,\n width=MAZE_W * UNIT)\n\n #Buttons\n iterationButton = tk.Button(self, text=\"Iterate\", command=self.doIteration)\n iterationButton.configure(width=10, activebackground=\"#33B5E5\")\n button1_window = self.canvas.create_window(MAZE_W*UNIT*0.13,(MAZE_H*UNIT)+10,window=iterationButton)\n\n policyButton = tk.Button(self, text=\"Policy Update\", command=self.updatePolicy)\n policyButton.configure(width=10, activebackground=\"#33B5E5\")\n button2_window = self.canvas.create_window(MAZE_W*UNIT*0.37,(MAZE_H*UNIT)+10,window=policyButton)\n\n policyButton = tk.Button(self, text=\"move\", command=self.moveByPolicy)\n policyButton.configure(width=10, activebackground=\"#33B5E5\")\n button3_window = self.canvas.create_window(MAZE_W * UNIT*0.62, (MAZE_H * UNIT) + 10, window=policyButton)\n\n policyButton = tk.Button(self, text=\"clear\", command=self.clear)\n policyButton.configure(width=10, activebackground=\"#33B5E5\")\n button4_window = self.canvas.create_window(MAZE_W * UNIT * 0.87, (MAZE_H * UNIT) + 10, window=policyButton)\n\n # create grids\n for c in range(0, MAZE_W * UNIT, UNIT): # 0~400 by 80\n x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT\n self.canvas.create_line(x0, y0, x1, y1)\n for r in range(0, MAZE_H * UNIT, UNIT): # 0~400 by 80\n x0, y0, x1, y1 = 0, r, MAZE_H * UNIT, r\n self.canvas.create_line(x0, y0, x1, y1)\n\n #image_load\n self.up_image = ImageTk.PhotoImage(Image.open(\"../resources/up.png\").resize((13,13)))\n self.right_image = ImageTk.PhotoImage(Image.open(\"../resources/right.png\").resize((13, 13)))\n self.left_image = ImageTk.PhotoImage(Image.open(\"../resources/left.png\").resize((13, 13)))\n self.down_image = ImageTk.PhotoImage(Image.open(\"../resources/down.png\").resize((13, 13)))\n self.cat_image = ImageTk.PhotoImage(Image.open(\"../resources/cat.png\").resize((50, 50), Image.ANTIALIAS))\n self.fire_image = ImageTk.PhotoImage(Image.open(\"../resources/fire.png\").resize((50, 50)))\n self.fish_image = ImageTk.PhotoImage(Image.open(\"../resources/fish.png\").resize((50, 50)))\n\n\n #add image to canvas\n self.cat =self.canvas.create_image(50, 50, image=self.cat_image)\n self.hell1 = self.canvas.create_image(250, 150, image=self.fire_image)\n self.hell2 = self.canvas.create_image(150, 250, image=self.fire_image)\n self.fish = self.canvas.create_image(250, 250, image=self.fish_image)\n\n # add reward text\n self.text_reward(2, 2, \"R : 1.0\")\n self.text_reward(1, 2, \"R : -1.0\")\n self.text_reward(2, 1, \"R : -1.0\")\n\n # pack all\n self.canvas.pack()\n\n def clear(self):\n for i in self.texts:\n self.canvas.delete(i)\n\n for i in self.arrows:\n self.canvas.delete(i)\n\n self.canvas.delete(self.cat)\n self.cat = self.canvas.create_image(50, 50, image=self.cat_image)\n self.agent = PolicyIterationAgent()\n\n def reset(self):\n self.update()\n time.sleep(0.5)\n self.canvas.delete(self.cat)\n origin = np.array([UNIT/2, UNIT/2])\n self.cat = self.canvas.create_image(50, 50, image=self.cat_image)\n # return observation\n return self.canvas.coords(self.cat)\n\n def text_value(self, row , col , contents, font='Helvetica', size=12, style='normal', anchor=\"nw\"):\n origin_x, origin_y = 85, 70\n x , y = origin_y+(UNIT*(col)),origin_x+(UNIT*(row))\n font = (font, str(size), style)\n return self.texts.append(self.canvas.create_text(x, y, fill=\"black\", text=contents, font=font, anchor=anchor))\n\n def text_reward(self, row, col, contents, font='Helvetica', size=12, style='normal', anchor=\"nw\"):\n origin_x, origin_y = 5, 5\n x, y = origin_y + (UNIT * (col)),origin_x + (UNIT * (row))\n font = (font, str(size), style)\n return self.canvas.create_text(x, y, fill=\"black\", text=contents, font=font, anchor=anchor)\n\n def step(self, action):\n s = self.canvas.coords(self.cat)\n\n base_action = np.array([0, 0])\n if action == 0: # up\n if s[1] > UNIT:\n base_action[1] -= UNIT\n elif action == 1: # down\n if s[1] < (MAZE_H - 1) * UNIT:\n base_action[1] += UNIT\n elif action == 2: # right\n if s[0] < (MAZE_W - 1) * UNIT:\n base_action[0] += UNIT\n elif action == 3: # left\n if s[0] > UNIT:\n base_action[0] -= UNIT\n\n self.canvas.move(self.cat, base_action[0], base_action[1]) # move agent\n s_ = self.canvas.coords(self.cat) # next state\n # reward function\n if s_== self.canvas.coords(self.fish):\n reward = 1\n done = True\n elif s_ in [self.canvas.coords(self.hell1), self.canvas.coords(self.hell2)]:\n reward = -1\n done = True\n else:\n reward = 0\n done = False\n\n return s_, reward, done\n\n def catMove(self,action):\n s = self.canvas.coords(self.cat)\n base_action = np.array([0, 0])\n self.render()\n if action[0]==1: #down\n base_action[1] += UNIT\n elif action[0] == -1: #up\n base_action[1] -= UNIT\n elif action[1] == 1: #right\n base_action[0] += UNIT\n elif action[1] == -1: #left\n base_action[0] -= UNIT\n\n self.canvas.move(self.cat, base_action[0], base_action[1]) # move agent\n\n def catLocation(self):\n temp = self.canvas.coords(self.cat)\n x = (temp[0]/100)-0.5\n y = (temp[1]/100) - 0.5\n return (int(y),int(x))\n\n def moveByPolicy(self):\n self.canvas.delete(self.cat)\n self.cat = self.canvas.create_image(50, 50, image=self.cat_image)\n while len(self.agent.getPolicies()[self.catLocation()[0]][self.catLocation()[1]])!=0:\n self.after(100,self.catMove(self.agent.getPolicies()[self.catLocation()[0]][self.catLocation()[1]][0]))\n\n def drawOneArrow(self,col,row,action):\n if action[0]==1: #down\n origin_x, origin_y = 50+(UNIT*row), 90+(UNIT*col)\n self.arrows.append(self.canvas.create_image(origin_x,origin_y, image=self.down_image))\n\n elif action[0] == -1: #up\n origin_x, origin_y = 50 + (UNIT * row), 10 + (UNIT * col)\n self.arrows.append(self.canvas.create_image(origin_x, origin_y, image=self.up_image))\n\n elif action[1] == 1: #right\n origin_x, origin_y = 90 + (UNIT * row), 50 + (UNIT * col)\n self.arrows.append(self.canvas.create_image(origin_x, origin_y, image=self.right_image))\n\n elif action[1] == -1: #left\n origin_x, origin_y = 10 + (UNIT * row), 50 + (UNIT * col)\n self.arrows.append(self.canvas.create_image(origin_x, origin_y, image=self.left_image))\n else :\n print(\"Not proper action \")\n\n def drawFromPolicy(self,policies):\n\n for i in range(MAZE_H):\n for j in range(MAZE_W):\n for k in policies[i][j]:\n self.drawOneArrow(i,j,k)\n\n def printValues(self, values):\n for i in range(MAZE_W):\n for j in range(MAZE_H):\n self.text_value(i, j, values[i][j])\n\n def render(self):\n time.sleep(0.1)\n self.canvas.tag_raise(self.cat)\n self.update()\n\n def doIteration(self):\n for i in self.texts:\n self.canvas.delete(i)\n self.agent.doIteration(1)\n self.printValues(self.agent.getValues())\n\n def updatePolicy(self):\n for i in self.arrows:\n self.canvas.delete(i)\n self.agent.updatePolicy()\n self.drawFromPolicy(self.agent.getPolicies())\n\n\n\nif __name__ == \"__main__\":\n gridworld = GraphicDisplay()\n gridworld.mainloop()\n","sub_path":"grid-world/policy_iteration/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":8663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"483251311","text":"# -*-coding:utf-8-*-\n# Author: alphadl\n# seq2seq_att.py 21/11/18 17:52\nfrom __future__ import unicode_literals, print_function, division\n\n\"\"\"\nTranslation with a Sequence to Sequence Network and Attention\n\nThis implementation is heavily based on NMT tutorial of Pytorch\n\"\"\"\n\n\"\"\"\nRequirements\n\"\"\"\nimport unicodedata\nimport string\nimport re\nimport os\nimport random\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nfrom torch import optim\nimport torch.nn.functional as F\n\nfrom logger import Logger\n\nlogger = Logger(\"./runs\")\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\"\"\"\nLoad the data\n\"\"\"\nSOS_token = 0\nEOS_token = 1\n\n\nclass Langugae_Process:\n def __init__(self, name):\n self.name = name\n self.w2i = {} # word to index\n self.w2c = {} # word to count\n self.i2w = {0: \"SOS\", 1: \"EOS\"} # index to word\n self.n_words = 2 # count SOS and EOS\n\n def addSent(self, sent):\n for w in sent.split(' '):\n self.addWord(w)\n\n def addWord(self, word):\n if word not in self.w2i:\n self.w2i[word] = self.n_words\n self.w2c[word] = 1\n self.i2w[self.n_words] = word\n self.n_words += 1\n else:\n self.w2c[word] += 1\n\n\n# Turn a Unicode string to plain ASCII\ndef unicode2ascii(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn')\n\n\n# Lowercase trim and remove the non-letter characters\ndef normlizaString(s):\n s = unicode2ascii(s.lower().strip())\n # s = re.sub(r\"([.!?])\", r\" \\1\", s)\n # s = re.sub(r\"[^a-zA-Z.!?，。！？]+\", r\" \", s)\n rule = re.compile(r\"[^a-zA-Z0-9\\u4e00-\\u9fa5 ,.!?，。！？、-]\")\n s = rule.sub('', s)\n return s\n\n\n\"\"\"\nTo read the data file we will split the file into lines, and then split lines into pairs.\n\"\"\"\n\n\ndef readLangs(srcLang, tgtLang):\n print(\"Reading lines...\")\n\n # Read file and split into lines\n with open(\"../data/train.%s\" % (srcLang), encoding=\"utf-8\") as srcT, \\\n open(\"../data/train.%s\" % (tgtLang), encoding=\"utf-8\") as tgtT, \\\n open(\"../data/val.%s\" % (srcLang), encoding=\"utf-8\") as srcV, \\\n open(\"../data/val.%s\" % (tgtLang), encoding=\"utf-8\") as tgtV:\n srcT = [line.strip() for line in srcT.readlines()]\n srcV = [line.strip() for line in srcV.readlines()]\n tgtT = [line.strip() for line in tgtT.readlines()]\n tgtV = [line.strip() for line in tgtV.readlines()]\n lines = np.array([srcT + srcV, tgtT + tgtV]).transpose()\n # print(\"raw input>>>>\",len(lines),lines[0])\n pairs = [[normlizaString(ll) for ll in l] for l in lines]\n # print(\"normlized>>>>\",len(pairs),pairs[0])\n # make Lang instances\n input_lang = Langugae_Process(srcLang)\n output_lang = Langugae_Process(tgtLang)\n\n return input_lang, output_lang, pairs\n\n\n\"\"\"\nFiltering seq length to simple the model \n\"\"\"\nMAX_LENGTH = 80\n\n\ndef filterPair(p):\n return len(p[0].split(\" \")) < MAX_LENGTH and \\\n len(p[1].split(\" \")) < MAX_LENGTH\n\n\ndef fileterPairs(pairs):\n return [pair for pair in pairs if filterPair(pair)]\n\n\n\"\"\"\nFull process for preparing the data is :\n1) Read text file and split into lines , split lines into pairs\n2) Normalize text, filter by length and content\n3) Make Vocab from sentences in pairs\n\"\"\"\n\n\ndef preprocess(srcLang, tgtLang):\n src_Lang, tgt_Lang, pairs = readLangs(srcLang, tgtLang)\n print(\"Totally read %s sentences pairs\" % len(pairs))\n pairs = fileterPairs(pairs)\n print(\"Trimmed to %s sentence pairs\" % len(pairs))\n print(\"Counting words...\")\n for pair in pairs:\n src_Lang.addSent(pair[0])\n tgt_Lang.addSent(pair[1])\n print(\"Counted words:\")\n print(\"vocab scale of\", src_Lang.name, \":\", src_Lang.n_words)\n print(\"vocab scale of\", tgt_Lang.name, \":\", tgt_Lang.n_words)\n return src_Lang, tgt_Lang, pairs\n\n\nprint(\"-\" * 20 + \"starting pre-process\" + \"-\" * 20)\n\nsrc_lang, tgt_lang, pairs = preprocess('en', 'cn')\n# print(random.choice(pairs))\n\n\"\"\"\ndefine encoder\n\"\"\"\n\n\nclass Encoder(nn.Module):\n def __init__(self, input_size, hidden_size):\n super(Encoder, self).__init__()\n self.h_size = hidden_size\n self.i_size = input_size\n\n self.embedding = nn.Embedding(input_size, hidden_size)\n self.lstm = nn.GRU(hidden_size, hidden_size)\n\n def forward(self, input, hidden):\n embedded = self.embedding(input).view(1, 1, -1)\n output = embedded\n # print(hidden[0].size(),\"+++\",hidden[1].size())\n output, hidden = self.lstm(output, hidden)\n return output, hidden\n\n def initHidden(self):\n # return (torch.zeros(1, 1, self.h_size, device=device), \\\n # torch.zeros(1, 1, self.h_size, device=device))\n return torch.zeros(1, 1, self.h_size, device=device)\n\n\"\"\"\ndefine decoder\n\"\"\"\n\nclass AttnDecoder(nn.Module):\n def __init__(self, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n super(AttnDecoder, self).__init__()\n self.hidden_size = hidden_size\n self.output_size = output_size\n self.dropout_p = dropout_p\n self.max_length = max_length\n\n self.embedding = nn.Embedding(self.output_size, self.hidden_size)\n self.attn = nn.Linear(self.hidden_size * 2, self.max_length)\n self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)\n self.dropout = nn.Dropout(self.dropout_p)\n self.gru = nn.GRU(self.hidden_size, self.hidden_size)\n self.out = nn.Linear(self.hidden_size, self.output_size)\n\n def forward(self, input, hidden, encoder_outputs):\n embedded = self.embedding(input).view(1, 1, -1)\n embedded = self.dropout(embedded)\n\n attn_weights = F.softmax(\n self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n encoder_outputs.unsqueeze(0))\n\n output = torch.cat((embedded[0], attn_applied[0]), 1)\n output = self.attn_combine(output).unsqueeze(0)\n\n output = F.relu(output)\n output, hidden = self.gru(output, hidden)\n\n output = F.log_softmax(self.out(output[0]), dim=1)\n return output, hidden, attn_weights\n\n def initHidden(self):\n return torch.zeros(1, 1, self.hidden_size, device=device)\n\n\"\"\"\nTraining\n\"\"\"\n\n\n# preparing\ndef s2i(lang, sentence): # sentence to index\n return [lang.w2i[word] for word in sentence.split(\" \")]\n\n\ndef s2t(lang, sentence): # sentence to tensor\n indexes = s2i(lang, sentence)\n indexes.append(EOS_token)\n return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n\n\ndef p2t(pair): # pair to tensors\n src_tensor = s2t(src_lang, pair[0])\n tgt_tensor = s2t(tgt_lang, pair[1])\n return (src_tensor, tgt_tensor)\n\n\n# training\nteacher_forcing_ratio = 1.0\n\n\ndef train(src_tensor, tgt_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length=MAX_LENGTH):\n encoder_hidden = encoder.initHidden()\n\n encoder_optimizer.zero_grad()\n decoder_optimizer.zero_grad()\n\n src_len = src_tensor.size(0)\n tgt_len = tgt_tensor.size(0)\n\n encoder_outputs = torch.zeros(max_length, encoder.h_size, device=device)\n\n loss = 0\n\n for ei in range(src_len):\n encoder_output, encoder_hidden = encoder(src_tensor[ei], encoder_hidden)\n # encoder_output[ei] = encoder_output[0, 0]\n encoder_outputs[ei] = encoder_output[0, 0]\n\n decoder_input = torch.tensor([[SOS_token]], device=device)\n\n decoder_hidden = encoder_hidden\n\n use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n\n if use_teacher_forcing:\n # Feed the target as the next input\n for di in range(tgt_len):\n decoder_output, decoder_hidden,decoder_attention = decoder(decoder_input, decoder_hidden,\\\n encoder_outputs)\n loss += criterion(decoder_output, tgt_tensor[di])\n decoder_input = tgt_tensor[di]\n else:\n # without teach forcing: use its own predictions as the next input\n for di in range(tgt_len):\n decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden,\\\n encoder_outputs)\n topv, topi = decoder_output.topk(1)\n decoder_input = topi.squeeze().detach() # detach from history as input\n\n loss += criterion(decoder_output, tgt_tensor[di])\n if decoder_input.item() == EOS_token:\n break\n\n loss.backward()\n\n encoder_optimizer.step()\n decoder_optimizer.step()\n\n return loss.item() / tgt_len\n\n\n# helper function to print the time\nimport time\nimport math\n\n\ndef toMintutes(s):\n m = math.floor(s / 60)\n s -= m * 60\n return \"%dm %ds\" % (m, s)\n\n\ndef timeSince(since, percent):\n now = time.time()\n s = now - since\n es = s / (percent)\n rs = es - s\n return \"%s (- %s)\" % (toMintutes(s), toMintutes(rs))\n\n\n\"\"\"\nFull process for training the model is :\n1) Start a timer\n2) Initialize Optimizers and criterion \n3) Create set of training pairs\n4) Start empty losses array for plotting\n\"\"\"\n\n\ndef trainIters(encoder, decoder, n_iters, print_every=100, learning_rate=0.01, save_every=100):\n start = time.time()\n plot_losses = []\n print_loss_total = 0 # Reset every print_every\n # plot_loss_total = 0 # Reset every plot_every\n\n encoder_optimizer = optim.SGD(encoder.parameters(), lr=learning_rate)\n decoder_optimizer = optim.SGD(decoder.parameters(), lr=learning_rate)\n training_pairs = [p2t(random.choice(pairs))\n for i in range(n_iters)]\n criterion = nn.NLLLoss()\n\n for iter in range(1, n_iters + 1):\n training_pair = training_pairs[iter - 1]\n input_tensor = training_pair[0]\n target_tensor = training_pair[1]\n\n loss = train(input_tensor, target_tensor, encoder,\n decoder, encoder_optimizer, decoder_optimizer, criterion)\n print_loss_total += loss\n # plot_loss_total += loss\n\n if iter % print_every == 0:\n print_loss_avg = print_loss_total / print_every\n print_loss_total = 0\n print('%s (Now is:%d,Finished %d%%) //average loss:%.4f' % (timeSince(start, iter / n_iters),\n iter, (iter / n_iters) * 100, print_loss_avg))\n # plot the loss\n # if iter % plot_every == 0:\n # plot_loss_avg = plot_loss_total / plot_every\n # plot_losses.append(plot_loss_avg)\n # plot_loss_total = 0\n\n # save the model checkpoint\n if iter % 10000 == 0:\n torch.save(decoder.state_dict(), os.path.join(\"./model\", 'decoder-{}.pt'.format(iter + 1)))\n torch.save(encoder.state_dict(), os.path.join(\"./model\", 'encoder-{}.pt'.format(iter + 1)))\n print(\"Save the %dth step checkpoint at ./model\" % (iter + 1))\n\n # (1) log the scalar values\n info = {'loss_+att': loss}\n for tag, value in info.items():\n logger.scalar_summary(tag, value, iter)\n\n\n # showPlot(plot_losses) #this is not enable in the server\n\n\n# plot function\nimport matplotlib.pyplot as plt\n\nplt.switch_backend('Agg')\nimport matplotlib.ticker as ticker\nimport numpy as np\n\n\ndef showPlot(points):\n print(\"in plot\")\n plt.figure()\n fig, ax = plt.subplots()\n # this locator puts ticks at regular intervals\n loc = ticker.MultipleLocator(base=0.2)\n ax.yaxis.set_major_locator(loc)\n plt.plot(points)\n plt.savefig(\"./loss.jpg\")\n\n\n\"\"\"\nparameters for training\n\"\"\"\nprint(\"-\" * 20 + \"starting training\" + \"-\" * 20)\nhidden_size = 256\n\nencoder1 = Encoder(src_lang.n_words, hidden_size).to(device)\ndecoder1 = AttnDecoder(hidden_size, tgt_lang.n_words,dropout_p=0.1).to(device)\n\ntrainIters(encoder1, decoder1, 120000, print_every=50)\n","sub_path":"seq2seq/seq2seq_att.py","file_name":"seq2seq_att.py","file_ext":"py","file_size_in_byte":12021,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"182073213","text":"from selenium import webdriver\nimport os\n\nclass findingElements():\n\n def findElements(self):\n driverLocation = 'D:\\Shishu Raj Pandey\\Software\\Browser Drivers\\chromedriver\\chromedriver.exe'\n os.environ['webdriver.chrome.driver'] = driverLocation\n driver=webdriver.Chrome(driverLocation)\n driver.get('https://learn.letskodeit.com/p/practice')\n bmwbutton=driver.find_element_by_id(\"bmwradio\")\n if bmwbutton is not None:\n print('BMW Found')\n driver.find_element_by_name(\"cars\").click()\n\nfe=findingElements()\nfe.findElements()\n","sub_path":"findingElements/FindingElementsByLocators.py","file_name":"FindingElementsByLocators.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"46451160","text":"import requests\nimport json\nimport webbrowser\n\nCACHE_DICTION2=[]\n\n# Fetch json data from Itunes \ndef Fetch_Itunes_Json(query=\"Hey+Jude\", number=0):\n\tbase_itunes_url=\"https://itunes.apple.com/search?\" \n\tquery=query.replace(\" \",\"+\") \n\tbase_itunes_url+= \"term=\" + query\n\tbase_itunes_url+= \"&limit=\" + str(number)\n\tjson_string = requests.get(base_itunes_url).text #using requests to access web API and fetch json data \n\tresults_list = json.loads(json_string)['results'] #understanding the layers by examining the structure of json file\n\t#initiate lists\n\tmedias = []\n\tmedia=[]\n\tsong=[]\n\tmovie=[]\n\t# classify dictionaries into lists of same type \n\tfor r in results_list:\n\t\tif r.__contains__('kind'): #make notes of __contains__() \n\t\t\tif \"movie\" in r['kind']:\n\t\t\t\tm = Movie(json_dict=r)\n\t\t\t\tmedia.append(r)\n\t\t\telif \"song\" in r['kind']:\n\t\t\t\tm = Song(json_dict=r)\n\t\t\t\tsong.append(r)\n\t\t\telse:\n\t\t\t\tm = Media(json_dict=r)\n\t\t\t\tmedia.append(r)\n\t\telse:\n\t\t\tm = Media(json_dict=r)\n\t\t\tmedia.append(r)\n\t\tmedias.append(m)\n\tglobal CACHE_DICTION2 #global dictionary\n\tCACHE_DICTION2 = song+movie+media\n\treturn medias\n\n#cache\nCACHE_FRAME = 'sample_json.json'\ntry:\n\tcache_file = open(CACHE_FRAME,'r')\n\tcache_contents = cache_file.read()\n\tCACHE_DICTION = json.loads(cache_contents)\n\tcache_file.close()\nexcept:\n\tCACHE_DICTION = {}\n\nclass Media:\n\n\tdef __init__(self, title=\"No Title\", author=\"No Author\", release_year=\"Unknown\", json_dict=None): #initiate json_dict none\n\t\tif json_dict is None:\n\t\t\tself.title = title\n\t\t\tself.author = author\n\t\t\tself.release_year = release_year\n\t\telse:\n\t\t\tself.process_json(json_dict)\n\n\tdef process_json(self,json_dict):\n\t\tif json_dict.__contains__('trackName'):\n\t\t\tself.title = json_dict['trackName']\n\t\telif json_dict.__contains__('collectionCensoredName'):\n\t\t\tself.title = json_dict['collectionCensoredName']\n\t\telse:\n\t\t\tself.title = \"No Title\"\n\t\tif json_dict.__contains__('artistName'):\n\t\t\tself.author = json_dict['artistName']\n\t\telse:\n\t\t\tself.author = \"No Author\"\n\t\tif json_dict.__contains__('releaseDate'):\n\t\t\tself.release_year = json_dict['releaseDate'][0:4]\n\t\telse:\n\t\t\tself.release_year = \"Unknown\"\n\n\n\tdef __str__(self):\n\t\tstate = self.title + \" by \"\n\t\tstate+= self.author + \"(\"\n\t\tstate+= self.release_year + \")\"\n\t\treturn state\n\n\tdef __len__(self):\n\t\treturn 0\n\n\n## Other classes, functions, etc. should go here\nclass Song(Media): #inheritance\n\n\tdef __init__(self, title=\"No Title\", author=\"No Author\", release_year=\"Unknown\", album=\"Unknown\", genre=\"Unknown\", track_length=0 ,json_dict=None):\n\t\tif json_dict is None:\n\t\t\tsuper().__init__(title,author,release_year) #super()\n\t\t\tself.album = album\n\t\t\tself.genre = genre\n\t\t\tself.track_length = track_length\n\t\telse:\n\t\t\tself.process_json(json_dict)\n\n\tdef process_json(self,json_dict):\n\t\tsuper().process_json(json_dict) \n\t\tif json_dict.__contains__('trackCensoredName'):\n\t\t\tself.album = json_dict['trackCensoredName']\n\t\telse:\n\t\t\tself.album = \"Unknown\"\n\n\t\tif json_dict.__contains__('primaryGenreName'):\n\t\t\tself.genre = json_dict['primaryGenreName']\n\t\telse:\n\t\t\tself.genre = \"Unknown\"\n\n\t\tif json_dict.__contains__('trackTimeMillis'):\n\t\t\tself.track_length = int(json_dict['trackTimeMillis']/1000)\n\t\telse:\n\t\t\tself.track_length = 0\n\n\tdef __str__(self):\n\t\tstate = self.title + \" by \"\n\t\tstate+= self.author + \"(\"\n\t\tstate+= self.release_year + \")[\"\n\t\tstate+= self.genre + \"]\"\n\t\treturn state\n\n\tdef __len__(self):\n\t\treturn self.track_length\n\nclass Movie(Media):\n\n\tdef __init__(self, title=\"No Title\", author=\"No Author\", release_year=\"Unknown\", rating=\"Unknown\", movie_length=0, json_dict=None):\n\t\tif json_dict is None:\n\t\t\tsuper().__init__(title,author,release_year)\n\t\t\tself.rating = rating\n\t\t\tself.movie_length = movie_length\n\t\telse:\n\t\t\tself.process_json(json_dict)\n\n\tdef process_json(self,json_dict):\n\t\tsuper().process_json(json_dict)\n\t\tif json_dict.__contains__('contentAdvisoryRating'):\n\t\t\tself.rating = json_dict['contentAdvisoryRating']\n\t\telse:\n\t\t\tself.rating = \"Unknown\"\n\n\t\tif json_dict.__contains__('trackTimeMillis'):\n\t\t\tself.movie_length = int(json_dict['trackTimeMillis']/60000)\n\t\telse:\n\t\t\tself.movie_length = 0\n\n\n\tdef __str__(self):\n\t\tstate = self.title + \" by \"\n\t\tstate+= self.author + \"(\"\n\t\tstate+= self.release_year + \")[\"\n\t\tstate+= self.rating + \"]\"\n\t\treturn state\n\n\tdef __len__(self):\n\t\treturn self.movie_length\n\nif __name__ == \"__main__\":\n\t# your control code for Part 4 (interactive search) should go here\n\tterm = input('Enter a search term, or \"exit\" to quit:')\n\twhile (term!=\"exit\"):\n\t\tmedias=FetchItunesJson(term)\n\t\tmedia=[]\n\t\tsong=[]\n\t\tmovie=[]\n\t\tfor m in medias:\n\t\t\tif isinstance(m,Song):\n\t\t\t\tsong.append(m)\n\t\t\telif isinstance(m,Movie):\n\t\t\t\tmovie.append(m)\n\t\t\telse:\n\t\t\t\tmedia.append(m)\n\t\tprint ('\\nSONGS')\n\t\tn=0\n\t\tfor s in song:\n\t\t\tn+=1\n\t\t\tprint(str(n),end=\" \")\n\t\t\tprint (s)\n\t\tprint ('\\nMOVIES')\n\t\tfor m in movie:\n\t\t\tn+=1\n\t\t\tprint(str(n),end=\" \")\n\t\t\tprint (m)\n\t\tprint ('\\nOTHER MEDIA')\n\t\tfor m in movie:\n\t\t\tn+=1\n\t\t\tprint(str(n),end=\" \")\n\t\t\tprint (m)\n\n\t\tsecond_term = input('a number for more info, or another search term, or exit:')\n\t\twhile second_term.isdigit():\n\t\t\tprint('Launching')\n\t\t\tn=int(second_term)-1\n\t\t\tresult = CACHE_DICTION2[n]\n\t\t\twebbrowser.open_new(result['trackViewUrl'])\n\t\t\tprint(result['trackViewUrl'])\n\t\t\tprint('in web browser...')\n\t\t\tsecond_term = input('a number for more info, or another search term, or exit:')\n\t\tterm=second_term\n\tprint (\"Bye\")","sub_path":"proj1_w18.py","file_name":"proj1_w18.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"303368718","text":"__author__ = 'remillet'\nfrom django.conf.urls import patterns, url\nfrom intakes import views\n\nurlpatterns = patterns('',\n # ex: /intakes/\n url(r'^$', views.get_intakes, name='get_intakes'),\n # ex: /intakes/5/\n url(r'^(?P[\\w-]+)/$', views.get_intake_detail, name='get_intake_detail'),\n )","sub_path":"webapps/django/ucb_deployment_site/intakes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"606351640","text":"import info\n\n\nclass subinfo(info.infoclass):\n def setTargets(self):\n for ver in ['1.8.7', '1.8.9.1', '1.8.11', '1.8.14']:\n self.targets[ver] = 'ftp://ftp.stack.nl/pub/users/dimitri/doxygen-%s.windows.bin.zip' % ver\n self.targetInstallPath[ver] = \"dev-utils/bin\"\n\n self.targetDigests['1.8.7'] = 'ca9640fbb28695f16521e5eacf49f278ff192d1c'\n self.targetDigests['1.8.9.1'] = '942a40755c537ad31cc18c8e519377db66edff29'\n self.targetDigests['1.8.11'] = (\n ['f25964e0203739d77e79d74bafdbef212bd97748e20fdafad078a8e2d315a7ff'], CraftHash.HashAlgorithm.SHA256)\n self.targetDigests['1.8.14'] = (\n ['c08900ffda8ed911746c86ad3354ad86084715cfd39ceca938f7bc2ead7988fc'], CraftHash.HashAlgorithm.SHA256)\n\n self.description = 'Automated C, C++, and Java Documentation Generator'\n self.defaultTarget = '1.8.14'\n\n\nfrom Package.BinaryPackageBase import *\n\n\nclass Package(BinaryPackageBase):\n def __init__(self):\n BinaryPackageBase.__init__(self)\n","sub_path":"dev-utils/_windows/doxygen/doxygen.py","file_name":"doxygen.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"643473051","text":"import json\n\nfrom boto3 import client as boto3_client\n\n\ndynamodb_client = boto3_client('dynamodb')\n\n\ndef lambda_handler(event, context):\n\n connection_id = event[\"requestContext\"].get(\"connectionId\")\n dynamodb_client.put_item(\n TableName='connections',\n Item={\n \"id\": {'S': connection_id},\n \"rooms\": {'SS': ['lobby']}\n }\n )\n\n return {\n 'statusCode': 200,\n 'body': json.dumps('Helloooo from Lambda!')\n }\n","sub_path":"src/chat_socket/connect.py","file_name":"connect.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"633373804","text":"from sys import maxsize\nfrom Queue import Queue as Q\nfrom PIL import Image\nimport numpy\n\ndef find_first_white(pixels):\n for i in range(28):\n for j in range(28):\n if pixels[j,i] <= 100:\n return [j,i]\n return [-1,-1]\n\ndef pix_density(img):\n density = 0\n pixels = img.load()\n for i in range(img.size[0]):\n for j in range(img.size[1]):\n if pixels[j,i] > 100:\n density += 1\n return density\n\ndef density_ul(img):\n density = 0\n pixels = img.load()\n for i in range(img.size[0]/2):\n for j in range(img.size[1]/2):\n if pixels[j,i] > 100:\n density += 1\n return density\n\ndef density_ur(img):\n density = 0\n pixels = img.load()\n for i in range(img.size[0]/2):\n for j in range(img.size[1]/2, img.size[1]):\n if pixels[j,i] > 100:\n density += 1\n return density\n\ndef density_bl(img):\n density = 0\n pixels = img.load()\n for i in range(img.size[0]/2, img.size[0]):\n for j in range(img.size[1]/2):\n if pixels[j,i] > 100:\n density += 1\n return density\ndef density_br(img):\n density = 0\n pixels = img.load()\n for i in range(img.size[0]/2, img.size[0]):\n for j in range(img.size[1]/2, img.size[1]):\n if pixels[j,i] > 100:\n density += 1\n return density\n\ndef hgt_to_wdth(img):\n l_hgt = maxsize\n l_wdth = maxsize\n m_hgt = 0\n m_wdth = 0\n pixels = img.load()\n for i in range(img.size[0]):\n for j in range(img.size[1]):\n if pixels[j,i] > 100:\n if j < l_wdth:\n l_wdth = j\n if j > m_wdth:\n m_wdth = j\n if i < l_hgt:\n l_hgt = j\n if i > m_hgt:\n m_hgt = i\n height = float(m_hgt - l_hgt)\n width = float(m_wdth - l_wdth)\n return float(width/height)\n\ndef grey_out(pixels, x, y):\n pixels[x,y] = 150\n if (x+1 <=27) and pixels[x+1,y] <= 100:\n grey_out(pixels, x+1, y)\n if (y+1 <=27) and pixels[x,y+1] <= 100:\n grey_out(pixels, x, y+1)\n if (x-1 >= 0) and pixels[x-1, y] <= 100:\n grey_out(pixels, x-1, y)\n if (y-1 >= 0) and pixels[x,y-1] <= 100:\n grey_out(pixels, x, y-1)\n return pixels\n\ndef num_holes(img):\n pixels = img.load()\n num_holes = 0\n fw = find_first_white(pixels)\n while fw != [-1,-1]:\n pixels = grey_out(pixels, fw[0], fw[1])\n num_holes += 1\n fw = find_first_white(pixels)\n return num_holes\n\ndef horiz_symmetry(img):\n pixels = img.load()\n symmetry = 0\n for i in range(img.size[0]/2):\n for j in range(img.size[1]):\n if pixels[j,i] > 100 and pixels[28-j,i]:\n symmetry += 1\n return symmetry\n\ndef num_intersections(img):\n pixels = img.load()\n intersections = 0\n for i in range(img.size[0]):\n for j in range(img.size[1]):\n if j is not 0:\n if pixels[j,i] != pixels[j-1,i]:\n intersections += 1\n return intersections\n\n\n\n\n\n\n","sub_path":"features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"153388978","text":"def collatz(number):\n number = int(number)\n if number%2:\n number = 3*number +1\n else:\n number = number//2\n print(number)\n return number\n\nwhile True:\n print(\"Enter a number:\")\n number = input()\n if number == 'finish':\n break\n while number != 1:\n number = collatz(number)\n print(\"finish\")\n","sub_path":"3.11.1Collatz序列.py","file_name":"3.11.1Collatz序列.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"578822085","text":"import pandas as pd\n\ndef transform_data(transfer):\n transfer = transfer.split('class=\"text\">')[1]\n transfer = transfer.split('')[0]\n transfer = transfer.replace(\n '', ' - ').replace(\n '
De ', ' - ').replace(\n ' a ', ' - ')\n\n # At this point every transfer should look like: \n # Antoñín - 17/09/2020 - 10:10 - 800.000 € - Equipo ESPAÑA - futmondo\n \n transfer_data = transfer.split(' - ')\n transfer_dict = {\n 'player': transfer_data[0],\n 'date': transfer_data[1],\n 'time': transfer_data[2],\n 'amount': transfer_data[3].replace('.', '').replace(' €', ''),\n 'from': transfer_data[4],\n 'to': transfer_data[5],\n }\n return transfer_dict\n \ndef main():\n with open('futmondo.html', 'r', encoding=\"utf8\") as f:\n transfers = f.readlines()\n transfers = [x.strip() for x in transfers]\n\n transfers = [transform_data(t) for t in transfers]\n\n pd.DataFrame(transfers.items(), columns=['Player', 'Date', 'Time', 'Amount', 'From', 'To'])\n pd.to_csv('test.csv')\n\n #with open('futmondo_clean.html', 'a', encoding=\"utf8\") as f:\n # for t in transfers:\n # f.write(f'{t}\\n')\n\nmain()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"492557435","text":"def transformar_int(x):\n \"\"\"Funcion que trasforma un objeto a int \n Args:\n x: objeto a transformar\n \n Returns:\n Valor del objeto en int\n \n Raises:\n ValueError: En caso de que sea str\n TypeError: en caso de que sea un objeto contenedor\n \"\"\"\n \n try:\n return (int(x))\n except (ValueError, TypeError) as e:\n raise e\n\ndef main():\n numero = input('Ingrese un numero: ')\n print(transformar_int(numero))\n\ndef test_funcion():\n entero = 34\n cadena = '56'\n lista = [1, 2, 34]\n palabra = \"hola\"\n try: \n print(transformar_int(entero))\n print(transformar_int(cadena))\n print(transformar_int(lista))\n print(transformar_int(palabra))\n print ('esto nunca va a mostrarse')\n except (ValueError, TypeError):\n print('error')\n print ('esto siempre se ejecuta')\n \n\nif __name__ == '__main__':\n test_funcion()","sub_path":"Labs/error1.py","file_name":"error1.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"236051965","text":"from PyQt5.QtWidgets import QApplication, QMainWindow\nfrom PyQt5.QtCore import QThread, pyqtSignal, Qt\nfrom window import Ui_MainWindow\nfrom base import SharedBase\nimport sys\n\n\nclass MyMainWindow(QMainWindow, Ui_MainWindow):\n def __init__(self):\n super(MyMainWindow, self).__init__()\n self.setupUi(self)\n self.pushButton.clicked.connect(self.do)\n self.lineEdit.returnPressed.connect(self.do)\n\n def do(self):\n try:\n self.lineEdit.setDisabled(True)\n self.user_input_url = self.lineEdit.text()\n self.pushButton.setDisabled(True)\n self.base = SharedBase(self.user_input_url)\n self.site_name = self.base.get_site_name()\n self.work = WorkingThread(self.site_name, self.user_input_url)\n self.work.status_report_signal.connect(self.status_receive_signal)\n self.work.progress_report_signal.connect(self.progress_receive_signal)\n self.work.start()\n except NameError as e:\n self.statusBar().showMessage('Website %s illegal or not supported' % e)\n self.pushButton.setDisabled(False)\n\n def status_receive_signal(self, text):\n self.statusBar().showMessage(text)\n if text == 'All Done!':\n self.pushButton.setDisabled(False)\n self.lineEdit.setDisabled(False)\n\n def progress_receive_signal(self, progress):\n self.progressBar.setProperty(\"value\", progress)\n\n\nclass WorkingThread(QThread):\n status_report_signal = pyqtSignal(str)\n progress_report_signal = pyqtSignal(float)\n\n def __init__(self, site_name, url):\n super(WorkingThread, self).__init__()\n self.site_name = site_name\n self.user_input_url = url\n\n def run(self):\n if self.site_name == 'dm5':\n from dm5 import DM5 as SiteClass\n elif self.site_name == 'ck101':\n from ck101 import Ck101 as SiteClass\n self.website_object = SiteClass(self.user_input_url)\n self.comic_name = self.website_object.get_name()\n self.ref_box = self.website_object.get_chapter_info()\n self.status_report_signal.emit('%s, total %d chapters detected.' % (self.comic_name, max(self.ref_box.keys())))\n self.main_loop(self.ref_box)\n if self.website_object.is_volume() is True:\n self.main_loop(self.website_object.get_volume_info(), True)\n\n def main_loop(self, refer_box, is_volume=False):\n total_parents = max(refer_box.keys())\n if is_volume is True:\n parent_str = 'Volume'\n else:\n parent_str = 'Chapter'\n for parent in range(1, total_parents + 1):\n if parent in refer_box.keys():\n cid = refer_box[parent]\n for page in range(1, self.website_object.get_page_info(cid) + 1):\n link = self.website_object.get_image_link(cid, page)\n try:\n self.website_object.down(self.comic_name, cid, link, parent, page, is_volume)\n self.status_report_signal.emit(\n '%s %d page %d has been downloaded successfully' % (parent_str, parent, page))\n progress = page / self.website_object.get_page_info(cid)\n self.progress_report_signal.emit(progress * 100)\n except:\n self.status_report_signal.emit(\n 'Error occurred when downloading %s %d, Page %d.' % (parent_str, parent, page))\n else:\n self.status_report_signal.emit('Chapter %d cannot be found.' % parent)\n self.status_report_signal.emit('All Done!')\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n window = MyMainWindow()\n window.show()\n sys.exit(app.exec_())\n","sub_path":"driveit-gui.py","file_name":"driveit-gui.py","file_ext":"py","file_size_in_byte":3823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"226897257","text":"\"\"\"\nGiven the participants' score sheet for your University Sports Day, \nyou are required to find the runner-up score. You are given n scores. \nStore them in a list and find the score of the runner-up.\n\"\"\"\n\nn = int(input())\narr = list(map(int, input().split()))\nmax=max(arr)\narr.sort(reverse=True)\ni=0;\nwhile i 256:\n # 256 + 32 + 1\n data = conn.recv(289)\n recv_size += 256\n control_byte = data[0:1]\n byte_array += bytearray(data[1:257])\n hmac_array += bytearray(data[257:289])\n print(hmac_array)\n sig = hmac.new(h_key, byte_array, hashlib.sha256).digest()\n if(sig != hmac_array):\n print(\"MAC ERROR\")\n break\n else:\n array_offset = 0\n for i in range(0, 16):\n Decrypted_array += AES_decrypt(\n byte_array[array_offset:array_offset+16], key, 16)\n array_offset += 16\n byte_array.clear()\n hmac_array.clear()\n\n else:\n data = conn.recv(file_size - recv_size + 32 + 1)\n remain_size = file_size - recv_size\n recv_size += file_size - recv_size\n control_byte = data[0:1]\n byte_array += bytearray(data[1:remain_size + 1])\n hmac_array += bytearray(data[remain_size + 1:remain_size + 1 + 32])\n sig = hmac.new(h_key, byte_array, hashlib.sha256).digest()\n if(sig != hmac_array):\n print(\"MAC ERROR\")\n break\n else:\n array_offset = 0\n while(1):\n if(remain_size < 16):\n Decrypted_array += AES_decrypt(\n byte_array[array_offset:array_offset+remain_size], key, remain_size)\n break\n else:\n Decrypted_array += AES_decrypt(\n byte_array[array_offset:array_offset+16], key, 16)\n array_offset += 16\n remain_size -= 16\n byte_array.clear()\n hmac_array.clear()\n\n # Write data to file\n f.write(Decrypted_array)\n conn.close()\n\n print(\"Completed receiving\")\n print(\"Filename: \", file_name)\n print(\"Filesize: \", os.stat(file_name)[stat.ST_SIZE])\n print(\"File from \" + str(addr))\n print(\"Consecution time: %s second\" % (time.time() - start_time))\n\n f.close()\n receiver.close()\n","sub_path":"Client/file_receiver_without_NXP.py","file_name":"file_receiver_without_NXP.py","file_ext":"py","file_size_in_byte":7579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"181255662","text":"from django.shortcuts import render,redirect,HttpResponse\nfrom django.views import View\nimport pymysql\nfrom .page import *\nfrom django import forms\ndb = pymysql.connect(\"localhost\",\"root\",\"root\",database=\"yishuo\",cursorclass=pymysql.cursors.DictCursor)\n\nclass types(View):\n def get(self,request):\n page = request.GET.get(\"page\") if request.GET.get(\"page\") else 0\n page = int(page)\n num = 3\n cursor = db.cursor()\n sql = \"select * from types limit %s,%s\"\n cursor.execute(sql,(page*num,num))\n result = cursor.fetchall()\n sqls = \"select count(*) as t from types\"\n cursor.execute(sqls)\n nums = cursor.fetchone()\n nums = nums[\"t\"]\n nums = math.ceil(nums/num)\n return render(request, \"types/types.html\", {\"data\": result,\"page\":getpages(nums,page,\"/types\")})\n def post(self,request):\n pass\nclass typeadd(View):\n def get(self,request):\n return render(request,\"types/typeadd.html\")\n def post(self,request):\n tname = request.POST.get(\"tname\")\n ttid = request.POST.get(\"ttid\")\n cursor = db.cursor()\n sql = \"insert into types(tname,ttid) VALUES (%s,%s)\"\n cursor.execute(sql,[tname,ttid])\n db.commit()\n return redirect(\"/types/\")\nclass typedel(View):\n def get(self,request):\n tid = request.GET.get(\"tid\")\n cursor = db.cursor()\n sql = \"delete from types WHERE tid=%s\"\n cursor.execute(sql,[tid])\n db.commit()\n return redirect(\"/types/\")\nclass typeedit(View):\n def get(self,request):\n tid = request.GET.get(\"tid\")\n cursor = db.cursor()\n sql = \"select * from types WHERE tid=%s\"\n cursor.execute(sql,[tid])\n result = cursor.fetchone()\n return render(request,\"types/typeedit.html\",{\"data\":result})\n def post(self,request):\n tid = request.POST.get(\"tid\")\n tname = request.POST.get(\"tname\")\n ttid = request.POST.get(\"ttid\")\n print(tname,ttid,tid)\n cursor = db.cursor()\n sql = \"update types set tname=%s,ttid=%s where tid=%s\"\n cursor.execute(sql,[tname,ttid,tid])\n db.commit()\n return redirect(\"/types/\")","sub_path":"yishuoserver/xueshengguanlixitong/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"640927967","text":"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. 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,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n# pylint: disable=invalid-name,arguments-differ,no-else-return,unused-argument,missing-docstring\n\"\"\"\nRelay TensorRT codegen.\n\"\"\"\nimport tvm\nfrom tvm import relay\nfrom tvm.relay.expr import Call, Constant\n\nfrom . import _transform\nfrom .expr_functor import ExprMutator\n\ndef _bind_params(func, params):\n \"\"\"\n Bind the params to the expression as constants.\n \"\"\"\n name_dict = {}\n for arg in func.params:\n name = arg.name_hint\n if name in name_dict:\n name_dict[name] = None\n else:\n name_dict[name] = arg\n bind_dict = {}\n for k, v in params.items():\n if k not in name_dict:\n continue\n arg = name_dict[k]\n if arg is None:\n raise ValueError(\"Multiple args in the function have name %s\" % k)\n bind_dict[arg] = relay.expr.const(v)\n return relay.expr.bind(func, bind_dict)\n\nclass LegalizeLayoutTranform(ExprMutator):\n \"\"\"\n Legalize Relay layout transforms to transpose ops to simplify TensorRT conversion.\n \"\"\"\n def visit_call(self, expr):\n visit = super().visit_call(expr)\n if expr.op == tvm.relay.op.get(\"layout_transform\"):\n src_layout = expr.attrs['src_layout']\n dst_layout = expr.attrs['dst_layout']\n if src_layout == \"NCHW\" and dst_layout == \"NHWC\":\n return relay.transpose(visit.args[0], axes=[0, 2, 3, 1])\n elif src_layout == \"NHWC\" and dst_layout == \"NCHW\":\n return relay.transpose(visit.args[0], axes=[0, 3, 1, 2])\n elif src_layout == \"HWIO\" and dst_layout == \"OIHW\":\n return relay.transpose(visit.args[0], axes=[3, 2, 0, 1])\n elif src_layout == \"HWOI\" and dst_layout == \"OIHW\":\n return relay.transpose(visit.args[0], axes=[2, 3, 0, 1])\n elif src_layout == \"HWIO\" and dst_layout == \"IOHW\":\n return relay.transpose(visit.args[0], axes=[2, 3, 0, 1])\n return visit\n\nclass RemoveDropout(ExprMutator):\n \"\"\"\n Removes all nn.dropout from an expr.\n \"\"\"\n def visit_tuple_getitem(self, expr):\n visit = super().visit_tuple_getitem(expr)\n if visit.index != 0:\n return visit\n elif isinstance(visit.tuple_value, Call) and visit.tuple_value.op.name == \"nn.dropout\":\n return visit.tuple_value.args[0]\n return visit\n\nclass RemoveMultiplyByOne(ExprMutator):\n \"\"\"\n Removes multiply by 1.0f. This pass when followed by\n RemoveRedundantTranspose is intended to remove a pattern of\n Transpose([1, 0]) -> Scale(1.0f) -> Transpose([1, 0]) produced by\n PyTorch's addmm operator.\n \"\"\"\n def visit_call(self, expr):\n if expr.op.name == \"multiply\":\n if isinstance(expr.args[1], Constant):\n data = expr.args[1].data.asnumpy()\n if data.shape == () and data.item() == 1.0:\n return expr.args[0]\n return super().visit_call(expr)\n\nclass RemoveRedundantTranspose(ExprMutator):\n \"\"\"\n Removes Transpose([1, 0]) followed by Transpose([1, 0]). This pass, when\n preceded by with RemoveMultiplyByOne is intended to remove a pattern of\n Transpose([1, 0]) -> Scale(1.0f) -> Transpose([1, 0]) produced by\n PyTorch's addmm operator.\n \"\"\"\n def check_axes(self, axes):\n return len(axes) == 2 and int(axes[0].value) == 1 and int(axes[1].value) == 0\n\n def visit_call(self, expr):\n if expr.op.name == \"transpose\":\n if self.check_axes(expr.attrs['axes']):\n if isinstance(expr.args[0], Call) and expr.args[0].op.name == \"transpose\":\n if self.check_axes(expr.args[0].attrs['axes']):\n return expr.args[0].args[0]\n return super().visit_call(expr)\n\ndef PreprocessForTrt(mod):\n \"\"\"Applies passes to prepare main function for TensorRT conversion.\n\n Parameters\n ----------\n mod: Module\n The original module.\n\n Returns\n -------\n mod: Module\n The module modified for TensorRT.\n \"\"\"\n mod['main'] = LegalizeLayoutTranform().visit(mod['main'])\n mod['main'] = RemoveDropout().visit(mod['main'])\n mod['main'] = RemoveMultiplyByOne().visit(mod['main'])\n mod['main'] = RemoveRedundantTranspose().visit(mod['main'])\n return mod\n\ndef GetTrtVersion():\n \"\"\"Gets the version of TensorRT that TVM is built against.\n\n Returns\n -------\n ret: Tuple[int]\n TensorRT version as a tuple of major, minor, and patch number. If TVM\n is not built with TensorRT, an empty tuple is returned instead.\n \"\"\"\n return tuple(map(int, _transform.GetTrtVersion()))\n\ndef IsTrtRuntimeAvailable():\n if not tvm.get_global_func(\"relay._transform.GetTrtVersion\", True):\n return False\n return GetTrtVersion() != ()\n\ndef EnableTrt(mod, params=None, trt_version=None):\n \"\"\"Converts the \"main\" function in the module into one that can be executed using\n TensorRT. If any of the operators are not supported by the TensorRT\n conversion, the unmodified program will be returned instead.\n\n Parameters\n ----------\n mod: Module\n The original module.\n\n params : dict of str to NDArray\n Input parameters to the graph that do not change\n during inference time. Used for constant folding.\n\n trt_version : Optional[Tuple[int]]\n Which version of TensorRT to target for partitioning as a tuple of\n (major, minor, patch). If not specified, will attempt to get using\n GetTrtVersion.\n\n Returns\n -------\n mod: Module\n The modified module which will use the TensorRT runtime if compatible.\n \"\"\"\n if not trt_version:\n trt_version = GetTrtVersion()\n # If TVM wasn't built against TRT, default to target TRT 6. Since the\n # actual conversion to TRT is done at runtime, building against TRT is\n # not required for compilation.\n if not trt_version:\n trt_version = (6, 0, 1)\n assert isinstance(trt_version, (list, tuple))\n assert len(trt_version) == 3\n\n # Apply passes required for TRT\n mod = relay.transform.RemoveUnusedFunctions()(mod)\n mod = relay.transform.InferType()(mod)\n mod = relay.transform.ConvertLayout('NCHW')(mod)\n mod = PreprocessForTrt(mod)\n if params:\n # Bind params so that we can use FoldConstant.\n mod['main'] = _bind_params(mod['main'], params)\n mod = relay.transform.FoldConstant()(mod)\n return _transform.EnableTrt(*trt_version)(mod)\n","sub_path":"python/tvm/relay/tensorrt.py","file_name":"tensorrt.py","file_ext":"py","file_size_in_byte":7260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"344797370","text":"#!/usr/bin/env python3\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pylab as plt\nimport matplotlib\nimport matplotlib.animation as animation\nfrom matplotlib import style\nfrom matplotlib.colors import ListedColormap\nfrom matplotlib.pyplot import figure\nfrom matplotlib.widgets import Slider\nstyle.use('seaborn-colorblind')\n\npd.set_option(\"display.max_rows\", None)\n\nbox = 'BOX2'\npir = 'PIR02'\nled = 'LED02'\n#filename = 'BOX1-3-20181018.txt'\n#filename = 'BOX2-COM4-20181018.txt'\nfilename = 'BOX1-COM3-20181012.txt'\n\ndf = pd.read_table(filename, sep='\\s+',\n skiprows=23, index_col=None)\ndf.index = pd.to_datetime(df['MO/DY/YEAR']+' ' + df['HH:MM:SS'],\n format=\"%m/%d/%Y %H:%M:%S\")\ndf0 = pd.DataFrame(\n {'HH:MM:SS': ['00:00:00'], 'MO/DY/YEAR': [df['MO/DY/YEAR'][0]],\n 'LED01': [0], 'PIR01': [0], 'LED02': [0], 'PIR02': [0], 'LED03': [0], 'PIR03': [0], 'LED04': [0], 'PIR04': [0], 'LED05': [0], 'PIR05': [0], 'LED06': [0], 'PIR06': [0], 'LED07': [0], 'PIR07': [0], 'LED08': [0], 'PIR08': [0], 'LED09': [0], 'PIR09': [0], 'LED10': [0], 'PIR10': [0]\n })\ndf0.index = pd.to_datetime(df0['MO/DY/YEAR']+' ' + df0['HH:MM:SS'],\n format=\"%m/%d/%Y %H:%M:%S\")\n\ndf1 = pd.DataFrame(\n {'HH:MM:SS': df['HH:MM:SS'][0], 'MO/DY/YEAR': [df['MO/DY/YEAR'][0]],\n 'LED01': [0], 'PIR01': [0], 'LED02': [0], 'PIR02': [0], 'LED03': [0], 'PIR03': [0], 'LED04': [0], 'PIR04': [0], 'LED05': [0], 'PIR05': [0], 'LED06': [0], 'PIR06': [0], 'LED07': [0], 'PIR07': [0], 'LED08': [0], 'PIR08': [0], 'LED09': [0], 'PIR09': [0], 'LED10': [0], 'PIR10': [0]\n })\ndf1.index = pd.to_datetime(df1['MO/DY/YEAR']+' ' + df1['HH:MM:SS'],\n format=\"%m/%d/%Y %H:%M:%S\")\ndf1.index.set_value(df1.index, df1.index[0], pd.Timestamp(\n df1.index.date[0].year, df1.index.date[0].month, df1.index.date[0].day, df1.index.time[0].hour, df1.index.time[0].minute-1, df1.index.time[0].second))\n\ndf2 = pd.DataFrame(\n {'HH:MM:SS': ['00:00:00'], 'MO/DY/YEAR': [df['MO/DY/YEAR'][-1]],\n 'LED01': [0], 'PIR01': [0], 'LED02': [0], 'PIR02': [0], 'LED03': [0], 'PIR03': [0], 'LED04': [0], 'PIR04': [0], 'LED05': [0], 'PIR05': [0], 'LED06': [0], 'PIR06': [0], 'LED07': [0], 'PIR07': [0], 'LED08': [0], 'PIR08': [0], 'LED09': [0], 'PIR09': [0], 'LED10': [0], 'PIR10': [0]\n })\ndf2.index = pd.to_datetime(df2['MO/DY/YEAR']+' ' + df2['HH:MM:SS'],\n format=\"%m/%d/%Y %H:%M:%S\")\ndf2.index.set_value(df2.index, df2.index[0], pd.Timestamp(\n df2.index.date[0].year, df2.index.date[0].month, df2.index.date[0].day, 23, 59, 0))\n\ndf3 = pd.DataFrame(\n {'HH:MM:SS': df['HH:MM:SS'][-1], 'MO/DY/YEAR': [df['MO/DY/YEAR'][-1]],\n 'LED01': [0], 'PIR01': [0], 'LED02': [0], 'PIR02': [0], 'LED03': [0], 'PIR03': [0], 'LED04': [0], 'PIR04': [0], 'LED05': [0], 'PIR05': [0], 'LED06': [0], 'PIR06': [0], 'LED07': [0], 'PIR07': [0], 'LED08': [0], 'PIR08': [0], 'LED09': [0], 'PIR09': [0], 'LED10': [0], 'PIR10': [0]\n })\ndf3.index = pd.to_datetime(df3['MO/DY/YEAR']+' ' + df3['HH:MM:SS'],\n format=\"%m/%d/%Y %H:%M:%S\")\ndf3.index.set_value(df3.index, df3.index[0], pd.Timestamp(\n df3.index.date[0].year, df3.index.date[0].month, df3.index.date[0].day, df3.index.time[0].hour, df3.index.time[0].minute+1, df3.index.time[0].second))\n\n\ndf = pd.concat([df0, df1, df, df3, df2])\ndategroup = df.groupby(pd.Grouper(freq='D'))\n\nk = 0\ndf2 = pd.DataFrame()\nfor name, group in dategroup:\n if k >= 1:\n a = group\n df2 = pd.concat([df2, a], axis=0)\n k = k+1\n\n# Remove the margins\ndategroup2 = df2.groupby(pd.Grouper(freq='D'))\nplt.rcParams['axes.autolimit_mode'] = 'round_numbers'\nplt.rcParams['axes.xmargin'] = 0.\nplt.rcParams['axes.ymargin'] = 0.1\n#plt.rcParams['xtick.direction'] = 'out'\nplt.rcParams['axes.linewidth'] = 0.5 # axis thickness\nplt.rcParams['font.family'] = ['sans serif']\nplt.rcParams['font.size'] = 10\n\nn_group = dategroup.ngroups\n\nfig, axes = plt.subplots(nrows=n_group, ncols=2)\n\n# Half-opaque grayscale colormap \n# by Bart https://stackoverflow.com/questions/37327308/add-alpha-to-an-existing-matplotlib-colormap\ncmap = plt.cm.gray\nmy_cmap = cmap(np.arange(cmap.N))\nmy_cmap[:,-1] = np.linspace(0.2, 1, cmap.N)\nmy_cmap = ListedColormap(my_cmap)\n\n# scale to 1000 if max PIR is 60\nscale = 1000/max(group[pir])\n\n# Double-plot actogram\n# Plot the 1st column\nj = 0\nfor name, group in dategroup:\n (group[pir]*scale).plot.area(ax=axes[j, 0], sharey=True, cmap='gray', figsize=(4.5, 0.2*n_group))\n ((1-group[led])*800).plot.area(linewidth=0, ax=axes[j, 0],\n cmap=my_cmap, sharey=True)\n axes[j, 0].axes.set_yticklabels([])\n axes[j, 0].axes.set_yticks([])\n axes[j, 0].axes.set_xticklabels([0, 3, 6, 9, 12, 15, 18, 21, 24], rotation=0, size=8.5)\n axes[j, 0].axes.set_ylim(1,800)\n axes[j, 0].axes.set_xlabel('Hour of day', rotation=0, size=9)\n axes[j, 0].axes.set_ylabel(\n str(group[pir].index.date[0].month) + '/' + str(group[pir].index.date[0].day) + ' ', rotation=0, size=9)\n axes[j, 0].yaxis.set_label_coords(-0.125,0.0)\n if j < n_group-1:\n x_axis = axes[j, 0].axes.get_xaxis()\n x_axis.set_visible(False)\n j = j+1\n# Plot the 2nd column\ni = 0\nfor name, group in dategroup2:\n (group[pir]*scale).plot.area(ax=axes[i, 1], sharey=True, cmap='gray', figsize=(4.3, 0.2*n_group))\n ((1-group[led])*800).plot.area(linewidth=0,\n cmap=my_cmap, ax=axes[i, 1], sharey=True)\n x_axis = axes[i, 1].axes.get_xaxis()\n x_axis.set_visible(False)\n axes[i, 1].axes.set_ylim(1,800)\n y_axis = axes[i, 1].axes.get_yaxis()\n y_axis.set_visible(False)\n i = i+1\n\nfig.subplots_adjust(left=0.12, right=0.9, bottom=0.3, wspace=0, hspace=0)\nplt.axis('off')\nplt.suptitle(box, size=9)\nplt.savefig(box+'.png')\nplt.show()\n","sub_path":"actogram/actogram.py","file_name":"actogram.py","file_ext":"py","file_size_in_byte":5847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"488699395","text":"from flask import Flask, render_template, request, redirect\r\nfrom flask import jsonify, url_for, flash\r\nfrom sqlalchemy import create_engine, asc\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom database_setup import Base, Categories, CategoryItem, User\r\nfrom flask import session as login_session\r\nfrom functools import wraps\r\nimport random\r\nimport string\r\nfrom oauth2client.client import flow_from_clientsecrets\r\nfrom oauth2client.client import FlowExchangeError\r\nimport httplib2\r\nimport json\r\nfrom flask import make_response\r\nimport requests\r\n\r\napp = Flask(__name__)\r\nCLIENT_ID = json.loads(\r\n open('client_secrets.json', 'r').read())['web']['client_id']\r\nAPPLICATION_NAME = \"Movies Catalog Application\"\r\n\r\nengine = create_engine('sqlite:///moviecatalog.db',\r\n connect_args={'check_same_thread': False}, echo=True)\r\nBase.metadata.bind = engine\r\n\r\nDBSession = sessionmaker(bind=engine)\r\nsession = DBSession()\r\n\r\nmovielist = session.query(Categories)\r\n\r\n# Create anti-forgery state token for the login session\r\n\r\n\r\n@app.route('/login')\r\ndef showLogin():\r\n state = ''.join(random.choice(string.ascii_uppercase + string.digits)\r\n for x in xrange(32))\r\n login_session['state'] = state\r\n # return \"The current session state is %s\" % login_session['state']\r\n return render_template('login.html', STATE=state, Categories=movielist)\r\n\r\n\r\n@app.route('/gconnect', methods=['POST'])\r\ndef gconnect():\r\n # Validate state token\r\n if request.args.get('state') != login_session['state']:\r\n response = make_response(json.dumps('Invalid state parameter.'), 401)\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n # Obtain authorization code\r\n code = request.data\r\n\r\n try:\r\n # Upgrade the authorization code into a credentials object\r\n oauth_flow = flow_from_clientsecrets('client_secrets.json', scope='')\r\n oauth_flow.redirect_uri = 'postmessage'\r\n credentials = oauth_flow.step2_exchange(code)\r\n except FlowExchangeError:\r\n response = make_response(\r\n json.dumps('Failed to upgrade the authorization code.'), 401)\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n\r\n # Check that the access token is valid.\r\n access_token = credentials.access_token\r\n url = ('https://www.googleapis.com/oauth2/v1/tokeninfo?access_token=%s'\r\n % access_token)\r\n h = httplib2.Http()\r\n result = json.loads(h.request(url, 'GET')[1])\r\n # If there was an error in the access token info, abort.\r\n if result.get('error') is not None:\r\n response = make_response(json.dumps(result.get('error')), 500)\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n\r\n # Verify that the access token is used for the intended user.\r\n gplus_id = credentials.id_token['sub']\r\n if result['user_id'] != gplus_id:\r\n response = make_response(\r\n json.dumps(\"Token's user ID doesn't match given user ID.\"), 401)\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n\r\n # Verify that the access token is valid for this app.\r\n if result['issued_to'] != CLIENT_ID:\r\n response = make_response(\r\n json.dumps(\"Token's client ID does not match app's.\"), 401)\r\n print(\"Token's client ID does not match app's.\")\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n\r\n stored_access_token = login_session.get('access_token')\r\n stored_gplus_id = login_session.get('gplus_id')\r\n if stored_access_token is not None and gplus_id == stored_gplus_id:\r\n response = make_response(json.dumps('Current user is already \\\r\n connected.'), 200)\r\n response.headers['Content-Type'] = 'application/json'\r\n return response\r\n\r\n # Store the access token in the session for later use.\r\n login_session['access_token'] = credentials.access_token\r\n login_session['gplus_id'] = gplus_id\r\n\r\n # Get user info\r\n userinfo_url = \"https://www.googleapis.com/oauth2/v1/userinfo\"\r\n params = {'access_token': credentials.access_token, 'alt': 'json'}\r\n answer = requests.get(userinfo_url, params=params)\r\n\r\n data = answer.json()\r\n\r\n login_session['username'] = data['name']\r\n login_session['picture'] = data['picture']\r\n login_session['email'] = data['email']\r\n\r\n # see if user exists, if it doesn't make a new one\r\n user_id = getUserID(data[\"email\"])\r\n if not user_id:\r\n user_id = createUser(login_session)\r\n login_session['user_id'] = user_id\r\n\r\n output = ''\r\n output += '

Welcome, '\r\n output += login_session['username']\r\n output += '!

'\r\n output += '/edit/', methods=['GET', 'POST'])\r\ndef editCategory(category_id):\r\n if 'username' not in login_session:\r\n return redirect('/login')\r\n editedCategory = session.query(\r\n Categories).filter_by(id=category_id).one()\r\n # See if the logged in user is not the owner of book\r\n creator = getUserInfo(editedCategory.user_id)\r\n user = getUserInfo(login_session['user_id'])\r\n # If logged in user != item owner redirect them\r\n if creator.id != login_session['user_id']:\r\n flash(\"You can't edit this category\"\r\n \"This is belongs to %s\" % creator.name)\r\n return redirect(url_for('showCategories'))\r\n if request.method == 'POST':\r\n if request.form['name']:\r\n editedCategory.name = request.form['name']\r\n flash('Category Successfully Edited %s' % editedCategory.name)\r\n return redirect(url_for('showCategories'))\r\n else:\r\n return render_template('editcategory.html',\r\n category=editedCategory, Categories=movielist)\r\n\r\n# delete a category\r\n\r\n\r\n@app.route('/category//delete/', methods=['GET', 'POST'])\r\ndef deleteCategory(category_id):\r\n if 'username' not in login_session:\r\n return redirect('/login')\r\n categoryToDelete = session.query(Categories).filter_by(\r\n id=category_id).one()\r\n creator = getUserInfo(categoryToDelete.user_id)\r\n user = getUserInfo(login_session['user_id'])\r\n # If logged in user != item owner redirect them\r\n if creator.id != login_session['user_id']:\r\n flash(\"You can't delete this category\"\r\n \"This is belongs to %s\" % creator.name)\r\n return redirect(url_for('showCategories'))\r\n if request.method == 'POST':\r\n session.delete(categoryToDelete)\r\n flash('%s Successfully Deleted' % categoryToDelete.name)\r\n session.commit()\r\n return redirect(url_for('showCategories', category_id=category_id))\r\n else:\r\n return render_template('deletecategory.html',\r\n category=categoryToDelete, Categories=movielist)\r\n\r\n\r\n# Show a category items\r\n\r\n\r\n@app.route('/categories//')\r\n@app.route('/categories//items/')\r\ndef showcategoryitems(categories_id):\r\n categories = session.query(Categories).filter_by(id=categories_id).one()\r\n categoryitems = session.query(CategoryItem).filter_by(\r\n categories_id=categories.id).all()\r\n\r\n return render_template('categoryitems.html', categoryitems=categoryitems,\r\n categories=categories, Categories=movielist)\r\n# Create a new category item\r\n\r\n\r\n@app.route('/categories//items/new/',\r\n methods=['GET', 'POST'])\r\ndef newcategoryItem(categories_id):\r\n if 'username' not in login_session:\r\n return redirect('/login')\r\n categories = session.query(Categories).filter_by(id=categories_id).one()\r\n # See if the logged in user is not the owner of book\r\n creator = getUserInfo(categories.user_id)\r\n user = getUserInfo(login_session['user_id'])\r\n # If logged in user != item owner redirect them\r\n if creator.id != login_session['user_id']:\r\n flash(\"You can't add new category items\"\r\n \" This belongs to %s\" % creator.name)\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories.id))\r\n if request.method == 'POST':\r\n newcategoryItem = CategoryItem(name=request.form['name'],\r\n likes=request.form['likes'],\r\n dislikes=request.form['dislikes'],\r\n views=request.form['views'],\r\n img_url=request.form['img_url'],\r\n categories_id=categories_id,\r\n user_id=categories.user_id)\r\n session.add(newcategoryItem)\r\n session.commit()\r\n flash('New category item %s Successfully Created' %\r\n (newcategoryItem.name))\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories_id))\r\n else:\r\n return render_template('newcategoryitems.html',\r\n categories_id=categories_id,\r\n Categories=movielist)\r\n\r\n# Edit a category item\r\n\r\n\r\n@app.route('/category//items//edit',\r\n methods=['GET', 'POST'])\r\ndef editcategoryItem(categories_id, categoryitem_id):\r\n if 'username' not in login_session:\r\n return redirect('/login')\r\n editedcategoryItem = session.query(CategoryItem).filter_by(\r\n id=categoryitem_id).one()\r\n categories = session.query(Categories).filter_by(id=categories_id).one()\r\n # See if the logged in user is not the owner of category\r\n creator = getUserInfo(categories.user_id)\r\n user = getUserInfo(login_session['user_id'])\r\n # If logged in user != item owner redirect them\r\n if creator.id != login_session['user_id']:\r\n flash(\"You can't edit this category items\"\r\n \"This is belongs to %s\" % creator.name)\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories.id))\r\n if request.method == 'POST':\r\n if request.form['name']:\r\n editedcategoryItem.name = request.form['name']\r\n if request.form['likes']:\r\n editedcategoryItem.likes = request.form['likes']\r\n if request.form['dislikes']:\r\n editedcategoryItem.dislikes = request.form['dislikes']\r\n if request.form['views']:\r\n editedcategoryItem.likes = request.form['views']\r\n if request.form['img_url']:\r\n editedcategoryItem.img_url = request.form['img_url']\r\n session.add(editedcategoryItem)\r\n session.commit()\r\n flash('category Item Successfully Edited')\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories_id))\r\n else:\r\n return render_template('editcategoryitems.html',\r\n categories_id=categories_id,\r\n categoryitem_id=categoryitem_id,\r\n categoryitem=editedcategoryItem,\r\n Categories=movielist)\r\n\r\n# Delete a menu item\r\n\r\n\r\n@app.route('/category//items//delete',\r\n methods=['GET', 'POST'])\r\ndef deletecategoryItem(categories_id, categoryitem_id):\r\n if 'username' not in login_session:\r\n return redirect('/login')\r\n categories = session.query(Categories).filter_by(id=categories_id).one()\r\n itemToDelete = session.query(CategoryItem).filter_by(\r\n id=categoryitem_id).one()\r\n # See if the logged in user is not the owner of book\r\n creator = getUserInfo(categories.user_id)\r\n user = getUserInfo(login_session['user_id'])\r\n # If logged in user != item owner redirect them\r\n if creator.id != login_session['user_id']:\r\n flash(\"You can't delete this category items\"\r\n \"This is belongs to %s\" % creator.name)\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories.id))\r\n if request.method == 'POST':\r\n session.delete(itemToDelete)\r\n session.commit()\r\n flash('Category Item Successfully Deleted')\r\n return redirect(url_for('showcategoryitems',\r\n categories_id=categories_id))\r\n else:\r\n return render_template('deletecategoryitems.html',\r\n categoryItem=itemToDelete, Categories=movielist)\r\n\r\n\r\n@app.route('/category//items/JSON')\r\ndef categoryitemJSON(categories_id):\r\n categories = session.query(Categories).filter_by(id=categories_id).one()\r\n categoryitems = session.query(CategoryItem).filter_by(\r\n categories_id=categories.id).all()\r\n return jsonify(categoryitems=[i.serialize for i in categoryitems])\r\n\r\n\r\n@app.route('/category//item//JSON')\r\ndef categoryItemJSON(categories_id, categoryitem_id):\r\n categoryitems = session.query(CategoryItem).filter_by(\r\n id=categoryitem_id).one()\r\n return jsonify(categoryitems=categoryitems.serialize)\r\n\r\n\r\n@app.route('/category/items/JSON')\r\ndef itemsJSON():\r\n categoryitems = session.query(CategoryItem).all()\r\n return jsonify(categoryitems=[i.serialize for i in categoryitems])\r\n\r\n\r\n@app.route('/category/JSON')\r\ndef categoriesJSON():\r\n categories = session.query(Categories).all()\r\n return jsonify(categories=[r.serialize for r in categories])\r\n\r\nif __name__ == '__main__':\r\n global movielist\r\n app.secret_key = 'super_secret_key'\r\n app.debug = True\r\n app.run(host='0.0.0.0', port=5000) \r\n","sub_path":"catalog/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":16955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"550440150","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mai 15 11:09:22 2018\n\n@author: Diogo Leite\n\"\"\"\n\nfrom SQL_obj_new.Datasets_configurations_types_sql_new import _Datasets_config_types_SQL\n\nclass DS_configurations_types(object):\n \"\"\"\n This class treat the configurations type available for the datasets creation\n\n By default, all FK are in the lasts positions in the parameters declaration\n \"\"\" \n\n def __init__(self, id_dataset_conf_type = -1, designation = \"\"):\n \"\"\"\n Constructor of the DS_configurations_types object. All the parameters have a default value\n\n :param id_dataset_conf_type: id of dataset_configuration_type - -1 if unknown\n :param designation: designation of the configuration type - \"\" if unknown\n\n :type id_dataset_conf_type: int - not required\n :type designation: text - required \n \"\"\"\n\n self.id_dataset_conf_type = id_dataset_conf_type\n self.designation = designation\n\n def get_all_configurations_ds_types():\n \"\"\"\n return an array with all the dataset configurations types in the database\n\n :return: array of dataset configuration type\n :rtype: array(DS_configurations_types)\n \"\"\"\n listOfdsConfType = []\n sqlObj = _Datasets_config_types_SQL()\n results = sqlObj.select_all_Datasets_conf_type()\n for element in results:\n listOfdsConfType.append(DS_configurations_types(element[0], element[1]))\n return listOfdsConfType \n\n\n def __str__(self):\n \"\"\"\n Overwrite of the str method\n \"\"\"\n message_str = \"ID: {0:d} Configuration type: {1}\".format(self.id_dataset_conf_type, self.designation)\n return message_str","sub_path":"objects_new/Datasets_configurations_types_new.py","file_name":"Datasets_configurations_types_new.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"635404149","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Terada\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport os\nfrom pandas.io.parsers import read_csv\nfrom sklearn.utils import shuffle\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nfrom collections import OrderedDict\n\nimport glob\nimport cv2\nfrom collections import deque\nimport csv\nimport copy\nfrom natsort import natsorted\n\n## Translation x4\ndef doTranslation(_img):\n rows, cols = _img.shape\n x_value = [-cols / 10, cols / 10]\n y_value = [-rows / 10, rows / 10]\n img_list = deque([])\n for xv in x_value:\n for yv in y_value:\n M = np.float32([[1, 0, xv], [0, 1, yv]])\n dst = cv2.warpAffine(_img, M, (cols, rows))\n img_list.append(dst)\n return img_list\n\n## Scale x8\ndef doScale(_img):\n rows, cols = _img.shape\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n img_list = deque([])\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n M = np.float32([[xv, 0, 0], [0, yv, 0]])\n dst = cv2.warpAffine(_img, M, (cols, rows))\n img_list.append(dst)\n return img_list\n\n## Scale -> Flip x8\ndef doScale_yFlip(_img):\n rows, cols = _img.shape\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n img_list = deque([])\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n M = np.float32([[xv, 0, 0], [0, yv, 0]])\n dst = cv2.warpAffine(_img, M, (cols, rows))\n dst_yflip = cv2.flip(dst, 1)\n img_list.append(dst_yflip)\n return img_list\n\ndef getFullPathFiles(_dir, _ext = \"*\"):\n files = None\n if type(_dir) == str:\n # get fullpath files\n files = glob.glob(_dir + \"//\" + _ext)\n files = natsorted(files)\n elif type(_dir) == list:\n files = _dir\n return files\n\ndef writeImageList(_list, _dir, _filename, _name):\n # get filename\n fname = os.path.basename(_filename).split('.')[0]\n # get root directory\n rootDir = os.path.dirname(os.path.abspath(_dir))\n # set directory name\n dir = rootDir + '/' + os.path.basename(_dir) + _name\n # create directory\n os.makedirs(dir, exist_ok=True)\n # write image\n for ino, img in enumerate(_list):\n cv2.imwrite(\"{0}/{1}{2}_{3}.jpg\".format(dir, fname, _name, ino), img)\n\ndef generateImages(_srcPath, _dstPath = None, _isDraw = False, _isWrite = False):\n if _dstPath == None:\n _dstPath = _srcPath\n files = getFullPathFiles(_srcPath, _ext=\"*.jpg\")\n\n for i, filename in enumerate(files):\n # read image\n img = cv2.imread(filename, 0)\n\n # pre-processing\n img_yflip = cv2.flip(img, 1) #x:0, y:1, xy:-1\n img_trans_list = doTranslation(img)\n img_scale_list = doScale(img)\n img_scale_ylip_list = doScale_yFlip(img)\n\n # draw\n if _isDraw:\n cv2.imshow(\"Image\", img)\n cv2.imshow(\"yFlip\", img_yflip)\n for ino, pimg in enumerate(img_trans_list):\n cv2.imshow(\"Translation {0}\".format(ino), pimg)\n for ino, pimg in enumerate(img_scale_list):\n cv2.imshow(\"Scale {0}\".format(ino), pimg)\n for ino, pimg in enumerate(img_scale_ylip_list):\n cv2.imshow(\"Scale Flip {0}\".format(ino), pimg)\n cv2.waitKey(0)\n\n # write\n if _isWrite:\n writeImageList(list([img_yflip]), _dstPath, filename, '_yflip')\n writeImageList(img_trans_list, _dstPath, filename, '_trans')\n writeImageList(img_scale_list, _dstPath, filename, '_scale')\n writeImageList(img_scale_ylip_list, _dstPath, filename, '_scale_yflip')\n\n###---------------------------------------------\n\ndef readText(filename):\n data = deque([])\n with open(filename, 'r') as f:\n reader = csv.reader(f)\n header = next(reader)\n for row in reader:\n data.append(row)\n return header, data\n\n### x, y, z -> d, x, y\ndef read3DText(filename):\n data = deque([])\n with open(filename, 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n data.append(row)\n return data\n\n## Flip\ndef doTextFlip(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n for i in range(len(_list_data)):\n i_flip = yflipID[i]\n _list_data[i_flip][2] = str(cols - int(_list_data[i][2]))\n _list_data[i_flip][3] = str(int(_list_data[i][3]))\n return _list_data\n\n### x, y, z -> d, x, y\ndef do3DTextFlip(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n for i in range(len(_list_data)):\n i_flip = yflipID[i]\n _list_data[i_flip][1] = str(cols - float(_list_data[i][1]))\n _list_data[i_flip][2] = str(float(_list_data[i][2]))\n return _list_data\n\n## Translation\ndef doTextTranslation(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [-cols / 10, cols / 10]\n y_value = [-rows / 10, rows / 10]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n dst[i][2] = str(float(_list_data[i][2]) + float(xv))\n dst[i][3] = str(float(_list_data[i][3]) + float(yv))\n txt_list.append(dst)\n return txt_list\n\n### x, y, z -> d, x, y\ndef do3DTextTranslation(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [-cols / 10, cols / 10]\n y_value = [-rows / 10, rows / 10]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n dst[i][1] = str(float(_list_data[i][1]) + float(xv))\n dst[i][2] = str(float(_list_data[i][2]) + float(yv))\n txt_list.append(dst)\n return txt_list\n\n## Scale\ndef doTextScale(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n dst[i][2] = str(int(float(_list_data[i][2]) * xv))\n dst[i][3] = str(int(float(_list_data[i][3]) * yv))\n txt_list.append(dst)\n return txt_list\n\n### x, y, z -> d, x, y\ndef do3DTextScale(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n dst[i][1] = str(float(float(_list_data[i][1]) * xv))\n dst[i][2] = str(float(float(_list_data[i][2]) * yv))\n txt_list.append(dst)\n return txt_list\n\n## Scale -> Flip x8\ndef doTextScale_yFlip(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n i_flip = yflipID[i]\n dst[i_flip][2] = str(cols - float(float(_list_data[i][2]) * xv))\n dst[i_flip][3] = str(float(float(_list_data[i][3]) * yv))\n txt_list.append(dst)\n return txt_list\n\n### x, y, z -> d, x, y\ndef do3DTextScale_yFlip(_data, cols, rows):\n _list_data = copy.deepcopy(list(_data))\n x_value = [1.1, 1.0, 0.9]\n y_value = [1.1, 1.0, 0.9]\n txt_list = deque([])\n\n for xv in x_value:\n for yv in y_value:\n if xv == 1.0 and yv == 1.0:\n continue\n dst = copy.deepcopy(_list_data) #\n for i in range(len(_list_data)):\n i_flip = yflipID[i]\n dst[i_flip][1] = str(cols - float(float(_list_data[i][1]) * xv))\n dst[i_flip][2] = str(float(float(_list_data[i][2]) * yv))\n txt_list.append(dst)\n return txt_list\n\ndef writeTextList(header, _list, _dir, _filename, _name):\n # get filename\n fname = os.path.basename(_filename).split('.')[0]\n # get root directory\n rootDir = os.path.dirname(os.path.abspath(_dir))\n # set directory name\n dir = rootDir + '/' + os.path.basename(_dir) + _name\n # create directory\n os.makedirs(dir, exist_ok=True)\n\n for ino, txt in enumerate(_list):\n with open(\"{0}/{1}{2}_{3}.txt\".format(dir, fname, _name, ino), 'w') as f:\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerow(header)\n writer.writerows(txt)\n\n### \ndef write3DTextList(_list, _dir, _filename, _name):\n # get filename\n fname = os.path.basename(_filename).split('.')[0]\n # get root directory\n rootDir = os.path.dirname(os.path.abspath(_dir))\n # set directory name\n dir = rootDir + '/' + os.path.basename(_dir) + _name\n # create directory\n os.makedirs(dir, exist_ok=True)\n\n for ino, txt in enumerate(_list):\n with open(\"{0}/{1}{2}_{3}.txt\".format(dir, fname, _name, ino), 'w') as f:\n writer = csv.writer(f, lineterminator='\\n')\n writer.writerows(txt)\n\ndef generateTexts(_srcPath, _dstPath = None, _isDraw = False, _isWrite = False):\n if _dstPath == None:\n _dstPath = _srcPath\n files = getFullPathFiles(_srcPath, _ext=\"*.txt\")\n\n for i, filename in enumerate(files):\n # read text\n header, data = readText(filename)\n width = 360\n height = 400\n\n # pre-processing\n txt_yflip = doTextFlip(data, width, height)\n txt_trans_list = doTextTranslation(data, width, height)\n txt_scale_list = doTextScale(data, width, height)\n txt_scale_yflip_list = doTextScale_yFlip(data, width, height)\n\n writeTextList(header, list([txt_yflip]), _dstPath, filename, \"_yflip\")\n writeTextList(header, txt_trans_list, _dstPath, filename, \"_trans\")\n writeTextList(header, txt_scale_list, _dstPath, filename, \"_scale\")\n writeTextList(header, txt_scale_yflip_list, _dstPath, filename, \"_scale_yflip\")\n\ndef generateTexts3D(_srcPath, _dstPath = None, _isDraw = False, _isWrite = False):\n if _dstPath == None:\n _dstPath = _srcPath\n files = getFullPathFiles(_srcPath, _ext=\"*.txt\")\n\n for i, filename in enumerate(files):\n # read text\n data = read3DText(filename)\n width = 360\n height = 400\n\n # pre-processing\n txt_yflip = do3DTextFlip(data, width, height)\n txt_trans_list = do3DTextTranslation(data, width, height)\n txt_scale_list = do3DTextScale(data, width, height)\n txt_scale_yflip_list = do3DTextScale_yFlip(data, width, height)\n\n write3DTextList(list([txt_yflip]), _dstPath, filename, \"_yflip\")\n write3DTextList(txt_trans_list, _dstPath, filename, \"_trans\")\n write3DTextList(txt_scale_list, _dstPath, filename, \"_scale\")\n write3DTextList(txt_scale_yflip_list, _dstPath, filename, \"_scale_yflip\")\n\n\n# parts swap\nyflipID = {0:3, 1:2, 2:1, 3:0, 4:6, 5:7, 6:4, 7:5, 8:8, 9:9, 10:12, 11:11, 12:10, 13:13}\n\n\n# usage: python .\\tool\\expand_data.py\nif __name__ == \"__main__\":\n\n datasetNames = [\"set1\", \"set2\", \"set3\"]\n\n for datasetName in datasetNames:\n src_path = 'data/images/edge/{0}'.format(datasetName)\n generateImages(_srcPath=src_path, _isDraw=False, _isWrite=True)\n\n src_path = 'data/images/before/{0}'.format(datasetName)\n #generateImages(_srcPath=src_path, _isDraw=False, _isWrite=True)\n\n src_path = 'data/images/after/{0}'.format(datasetName)\n #generateImages(_srcPath=src_path, _isDraw=False, _isWrite=True)\n\n src_path = 'data/labels/before/{0}'.format(datasetName)\n #generateTexts(_srcPath=src_path, _isDraw=False, _isWrite=True)\n\n src_path = 'data/labels/after/{0}'.format(datasetName)\n #generateTexts(_srcPath=src_path, _isDraw=False, _isWrite=True)\n\n src_path = 'data/labels3d/{0}'.format(datasetName)\n #generateTexts3D(_srcPath=src_path, _isDraw=False, _isWrite=True)\n","sub_path":"tool/expand_data.py","file_name":"expand_data.py","file_ext":"py","file_size_in_byte":12493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"142438474","text":"#!/usr/bin/env python3\n\n#puzzle input: 171309-643603\n\nfrom itertools import groupby\nimport numpy\nfrom collections import Counter\nfrom itertools import dropwhile\nstart = 171309\nend = 643603\n\nvalues = []\n\nfor i in range(start, end):\n values.append(i)\n\ndigits = []\n\nfor elem in values:\n digits.append([int(d) for d in str(elem)])\n\n\ncount = 0\nlista = []\nfor elem in digits:\n if(all(elem[i] <= elem[i + 1] for i in range(len(elem)-1)) and (any(sum(1 for _ in g) > 1 for _, g in groupby(elem)) == True)):\n lista.append(elem)\n\"\"\"\nfor elem in lista:\n if len(elem) < 6:\n lista.remove(elem)\n\"\"\"\n\nprint(len(lista))\n\ndef unique(list1):\n # intilize a null list \n unique_list = []\n # traverse for all elements \n for x in list1:\n # check if exists in unique_list or not \n if x not in unique_list:\n unique_list.append(x)\n return unique_list\n\nthree = []\ntwo = []\nnew_lista = []\nfor elem in lista:\n c = Counter(elem)\n for k,value in c.items():\n if value > 4:\n new_lista.append(elem)\n\nunique_gfive = unique(new_lista)\nprint(len(new_lista))\nprint(len(unique_gfive))\nlista = unique(lista)\n\n\nfour = []\n\n\nfor elem in lista:\n c = Counter(elem)\n for key, value in c.items():\n if value == 4:\n four.append(elem)\nfour = unique(four)\nfor elem in lista:\n c = Counter(elem)\n for key, value in c.items():\n if value == 3:\n three.append(elem)\n\ncounting = 0\n\nnew_four = []\nnew_three = []\nthree = unique(three)\nfor elem in three:\n c = Counter(elem)\n for key, value in c.items():\n if value == 2:\n new_three.append(elem)\n\nused_three = []\nunique_three = unique(new_three)\n\nfor elem in four:\n c = Counter(elem)\n for key, value in c.items():\n if value == 2:\n new_four.append(elem)\n\n\nused_four = []\nunique_four = unique(new_four)\n\nprint(len(lista) - len(unique_gfive) - (len(three)-len(unique_three)) - (len(four) - len(unique_four)))\n\n","sub_path":"advent-of-code-19/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"436643269","text":"\"\"\"\nmerge_dev_with_master.py\nwritten in Python3\nauthor: C. Lockhart \n\"\"\"\n\nfrom _scripts.increment_version import *\nfrom gitpipe import Git\n\n# Connect to git repository\ngit = Git()\n\n# We must be on dev branch\nbranch = git.get_branch()\nassert branch == 'dev', branch\n\n# Increment version; print out string\nversion = increment_version()\nprint('package version: {}\\n'.format(version))\n\n# Add, commit, and push any uncommitted changes\ngit.add('-A')\ngit.commit(input('Commit message: '))\ngit.push(remote='origin', branch='dev')\n\n# Checkout master, merge, tag, and push\ngit.checkout('master')\ngit.merge(branch='dev', options='--no-edit')\ngit.tag('v' + version)\ngit.push(remote='origin', branch='master', options='--tags')\n\n","sub_path":"_scripts/merge_dev_with_master.py","file_name":"merge_dev_with_master.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"418494603","text":"# Time: O(nlogn)\n# Space: O(n)\n\n# Given two arrays A and B of equal size, the advantage of A with respect to B\n# is the number of indices i for which A[i] > B[i].\n#\n# Return any permutation of A that maximizes its advantage with respect to B.\n#\n# Example 1:\n#\n# Input: A = [2,7,11,15], B = [1,10,4,11]\n# Output: [2,11,7,15]\n# Example 2:\n#\n# Input: A = [12,24,8,32], B = [13,25,32,11]\n# Output: [24,32,8,12]\n#\n# Note:\n# - 1 <= A.length = B.length <= 10000\n# - 0 <= A[i] <= 10^9\n# - 0 <= B[i] <= 10^9\n\nclass Solution(object):\n '''\n Greedy Algorithm:\n If the smallest card a in A beats the smallest card b in B, we should pair them. Because every card in A is larger than b,\n any card we place in front of b will score a point. We should use the weakest card to pair with b as it makes the rest cards in A strictly larger.\n If smallest a cannot beat smallest b, a can't beat any cards and we pair it to largest b.\n\n We sort the 2 lists and create the assignments for each b. Then use our annotations assigned to reconstruct the answer.\n '''\n def advantageCount(self, A, B):\n \"\"\"\n :type A: List[int]\n :type B: List[int]\n :rtype: List[int]\n \"\"\"\n sortedA = sorted(A)\n sortedB = sorted(B)\n\n candidates = {b: [] for b in B} # or use collections.defaultdict; b may duplicate, so cannot use a simple dict\n j, k = 0, -1\n for a in sortedA:\n if a > sortedB[j]:\n candidates[sortedB[j]].append(a)\n j += 1\n else:\n candidates[sortedB[k]].append(a)\n k -= 1\n return [candidates[b].pop() for b in B]\n\n # TLE for input [8,2,4,4,5,6,6,0,4,7], [0,8,7,4,4,2,8,5,2,0]\n # time complexity n!*n, n is length of A\n def advantageCount_permutation(self, A, B):\n import itertools\n def gen(a):\n if not a:\n yield []\n for i in xrange(len(a)):\n for sub in gen(a[:i]+a[i+1:]):\n yield [a[i]] + sub\n\n score, ans = 0, A\n# for P in gen(A): # my own permutations\n for P in itertools.permutations(A):\n cur = sum(p > b for p, b in itertools.izip(P, B))\n if cur > score:\n score, ans = cur, P\n if score == len(A): break\n\n return ans\n","sub_path":"Python/advantage-shuffle.py","file_name":"advantage-shuffle.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"633786142","text":"\"\"\"\nmlp1d.network\n=============\nCreate a network for 1 dimensional Riemann problems, essentially 2 inputs, and 1 output.\nThe number of inputs and outputs are chosen by user, but code is created to work on\nnetworks which have trainingdata of 2 in 1 out. \n\nExample code\n------------\n # set N, dimensions, dfdu\n net = network.Network(\n N=N, \n dimensions=dimensions,\n dfdu=dfdu\n )\n # set epochs, batchsize, data_train, data_val, destination, name\n net.backward(\n epochs = epochs,\n batchsize = batchsize,\n data_train=data_trn,\n data_val=data_val,\n destination=destination,\n name=name\n )\n # plot training and result using flatplotlib.netplot\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nin_jupy = False\ntry:\n cfg = get_ipython().__class__.__name__\n if cfg == 'ZMQInteractiveShell':\n in_jupy = True\nexcept NameError:\n in_jupy = False\n\nif in_jupy:\n from tqdm import tqdm_notebook as tqdm # for notebook\nelse:\n from tqdm import tqdm # for terminal\n\n\nclass Cell(nn.Module):\n def __init__(self, dimensions, activation=F.relu, final_activation=False):\n \"\"\"\n Inputs:\n dimensions - number of node in network layers,\n type: torch.tensor,\n structure: [dim(input), ..., dim(hidden layers), ..., dim(output)]\n activation - activation layer to use in all layers,\n default: torch.relu\n final_activation - whether or not to have activation i last layer,\n type: boolean,\n default: False\n \"\"\"\n super(Cell, self).__init__()\n\n self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # then use obj.to(self.device) to cast to GPU/CPU memory\n\n self.dimensions = dimensions\n self.size = len(dimensions)\n self.activation = activation\n self.final_activation = final_activation\n\n self.layer_inp = nn.Linear(dimensions[0], dimensions[1])\n self.layer_hid = nn.ModuleList([\n nn.Linear(dimensions[i], dimensions[i+1]) for i in range(1, self.size - 2)\n ])\n self.layer_out = nn.Linear(dimensions[-2], dimensions[-1])\n\n #self.drop = nn.Dropout(p=0.05)\n \n def forward(self, inp):\n \"\"\"\n Feed forward method of network. \n Input:\n inp - input tensor to insert into network,\n type: torch.tensor,\n size: self.dimension_layers[0]\n Output:\n out - output of network,\n type: torch.tensor,\n size: self.dimension_layers[-1]\n \"\"\"\n inp = inp.to(self.device)\n out = self.layer_inp(inp)\n out = self.activation(out)\n for layer in self.layer_hid:\n out = self.activation(layer(out).to(self.device))\n #out = self.drop(out)\n out = self.layer_out(out)\n return out if not self.final_activation else self.activation(out)\n\n\nclass Network(nn.Module):\n def __init__(self, \n N, dimensions, dfdu,\n activation=torch.relu,\n final_activation=False,\n optimizer=torch.optim.Adam):\n \"\"\"\n The outer structure of the network with the new loss function. This function \n takes dataset of M x 2 x N dimension and uses N data points a basis for each \n run through of the training process. Then we calculate the loss with respect \n to all N outputs and back propagate through the network structure called Cell, \n to correct errors.\n Input:\n N - number of data points to throw at the network each time,\n type: int\n dimensions - list of dimensions in layers of the inner network structure,\n type: tuple, list, numpy.array, torch.tensor\n loss - the loss function to use when training the inner network,\n default: torch.nn.L1Loss\n activation - activation layer to use in all layers,\n default: torch.relu\n final_activation - whether or not to have activation i last layer,\n type: boolean,\n default: False\n Additional:\n cells - list of N objects, for training with N dimensional data.\n type: Cell\n inp_ind - N x 2 dimensional tensor of cycle indexes for training data.\n structure: [ [N, 0], [0, 1], ..., [N-1, N] ]\n \"\"\"\n super().__init__()\n\n # seed to obtain consistent results\n torch.random.manual_seed(42)\n torch.manual_seed(42)\n torch.cuda.manual_seed(42)\n np.random.seed(42)\n\n self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\" # then use obj.to(self.device) to cast to GPU memory\n\n self.dfdu = dfdu\n self.N = N \n self.dimensions = dimensions\n\n self.trained_epochs = 0\n self.epochs = None\n self.batchsize = None\n \n self.activation = activation \n self.final_activation = final_activation\n\n self.network = Cell(dimensions, activation=activation).to(self.device)\n\n self.opt = optimizer(self.network.parameters())\n self.loss_train = None\n self.loss_val = None\n self.weights = None\n\n def backward(self, data_train, data_val, epochs, batchsize, destination, name):\n \"\"\"\n Inputs:\n data_train - data for training\n type: tensor\n data_val - data for validation\n type: tensor\n epochs - number of epochs to train \n batchsize - batchsize to train simultaneously\n destination - destination to save model\n name - name to save model as\n \"\"\"\n self.trained_epochs += epochs\n self.epochs, self.batchsize = epochs, batchsize\n inp_train_l = data_train.data[:,0].to(self.device)\n inp_train_l = inp_train_l.reshape((len(inp_train_l),1))\n inp_train_r = data_train.data[:,1].to(self.device)\n inp_train_r = inp_train_r.reshape((len(inp_train_r),1))\n inp_train = torch.cat((inp_train_l,inp_train_r),dim=1)\n out_train = data_train.data[:,2].to(self.device)\n\n inp_val_l = data_val.data[:,0].to(self.device)\n inp_val_l = inp_val_l.reshape((len(inp_val_l),1))\n inp_val_r = data_val.data[:,1].to(self.device)\n inp_val_r = inp_val_r.reshape((len(inp_val_r),1))\n inp_val = torch.cat((inp_val_l,inp_val_r),dim=1)\n out_val = data_val.data[:,2].to(self.device)\n\n # Make a 'data-feeder'\n sampler = torch.utils.data.DataLoader(\n range(self.N), \n batch_size=self.batchsize,\n shuffle=True\n )\n # Create variables for saving history\n if self.loss_train is None and self.loss_val is None and self.weights is None:\n self.loss_train = []\n self.loss_val = []\n self.weights = []\n val_loss = self.network.forward(inp_val).squeeze().to(self.device)\n val_loss = nn.MSELoss()(val_loss, out_val)\n self.loss_val.append(val_loss)\n else:\n self.loss_train = list(self.loss_train)\n self.loss_val = list(self.loss_val)\n self.weights = list(self.weights)\n best_loss = np.inf\n cur_loss = np.inf\n pbar = tqdm(\n total=self.epochs, \n desc='Training progress (loss: )', \n bar_format = '{desc}: {percentage:3.0f}%{bar}Epoch: {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'\n )\n for epoch in range(self.epochs):\n if self.device=='cuda': torch.cuda.empty_cache()\n loss_tmp = []\n for i, batch in enumerate(sampler):\n self.opt.zero_grad()\n loss_out = self.network.forward(inp_train[batch]).to(self.device).squeeze()\n loss_tar = out_train[batch].to(self.device)\n cur_loss = nn.MSELoss()(loss_out.to(self.device), loss_tar.to(self.device))\n del loss_out, loss_tar\n cur_loss.backward(retain_graph=True)\n self.opt.step()\n self.loss_train.append(cur_loss.data)\n # Save model if it is better than the previous best\n if cur_loss < best_loss:\n best_loss = cur_loss\n destination = '.' if (destination == '') else destination\n torch.save(self.network, destination+'/'+'model_'+name+'.pt')\n del cur_loss\n # Save validation loss and average of training loss\n val_loss = self.network.forward(inp_val).squeeze().to(self.device)\n val_loss = nn.MSELoss()(val_loss, out_val)\n self.loss_val.append(val_loss)\n weight_beg = [self.network.layer_inp.weight.cpu().detach().numpy()]\n weight_mid = [l.weight.cpu().detach().numpy() for l in self.network.layer_hid]\n weight_end = [self.network.layer_out.weight.cpu().detach().numpy()]\n self.weights.append(weight_beg + weight_mid + weight_end)\n pbar.set_description(desc='Training progress (best loss: {0:.2e})'.format(best_loss))\n pbar.update(1)\n pbar.close()\n self.loss_train = torch.tensor(self.loss_train)\n self.loss_val = torch.tensor(self.loss_val)\n \n def backward_newLoss(self, data_train, data_val, epochs, batchsize, destination, name):\n \"\"\"\n Inputs:\n data_train - data for training\n type: tensor\n data_val - data for validation\n type: tensor\n epochs - number of epochs to train \n batchsize - batchsize to train simultaneously\n destination - destination to save model\n name - name to save model as\n \"\"\"\n def loss_var(inp, out):\n \"\"\"\n Calculates the loss wrt. the numeric solution to conservation law.\n Inputs:\n inp - input of network\n out - output of network\n Output:\n loss - out-value to send into actual loss function\n \"\"\"\n inp = inp.to(self.device)\n out = out.to(self.device)\n out = out.squeeze(-1)\n loss = torch.zeros_like(inp, requires_grad=False)\n\n dx = 1/self.N\n dt = dx/(torch.max(torch.abs(self.dfdu(inp)))) # TODO: Why correct without multiplying with Courant coefficient?!\n C = dt/dx\n\n loss[:,:-1] = inp[:,:-1] - C*(out[:,1:] - out[:,:-1])\n loss[:,-1] = inp[:,-1] - C*(out[:,0] - out[:,-1])\n return loss\n # set index tensor\n N_ind = torch.arange(0,self.N).reshape((self.N,1))\n inp_ind = torch.cat((N_ind.roll(1),N_ind),1) # call: data[inp_ind]\n # set epochs and batchsize\n self.trained_epochs += epochs\n self.epochs, self.batchsize = epochs, batchsize\n # extract data from input\n inp_train, out_train = data_train[:,0,:], data_train[:,1,:]\n inp_val, out_val = data_val[:,0,:], data_val[:,1,:]\n # Make a 'data-feeder'\n sampler = torch.utils.data.DataLoader(\n range(inp_train.shape[0]), \n batch_size=self.batchsize,\n shuffle=True\n )\n # Create variables for saving history\n if self.loss_train is None and self.loss_val is None and self.weights is None:\n self.loss_train = []\n self.loss_val = []\n self.weights = []\n val_loss = (self.network.forward(inp_val[:,inp_ind].to(self.device)).data) #[:,inp_ind] of inp?\n val_loss = loss_var(inp_val, val_loss)\n val_loss = nn.L1Loss()(val_loss, out_val.to(self.device))\n self.loss_val.append(val_loss)\n else:\n self.loss_train = list(self.loss_train)\n self.loss_val = list(self.loss_val)\n self.weights = list(self.weights)\n best_loss = np.inf\n cur_loss = np.inf\n pbar = tqdm(\n total=self.epochs, \n desc='Training progress (loss: )', \n bar_format = '{desc}: {percentage:3.0f}%{bar}Epoch: {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'\n )\n for epoch in range(self.epochs):\n torch.cuda.empty_cache()\n loss_tmp = []\n for i, batch in enumerate(sampler):\n self.opt.zero_grad()\n out = self.network.forward(inp_train[batch][:,inp_ind].to(self.device))\n loss_out = loss_var(inp_train[batch], out)\n loss_tar = out_train[batch].to(self.device)\n cur_loss = nn.L1Loss()(loss_out, loss_tar)\n cur_loss.backward(retain_graph=True)\n self.opt.step()\n self.loss_train.append(cur_loss.data)\n # Save model if it is better than the previous best\n if cur_loss < best_loss:\n best_loss = cur_loss\n destination = '.' if (destination == '') else destination\n torch.save(self.network, destination+'/'+'model_'+name+'.pt')\n # Save validation loss and average of training loss\n val_loss = (self.network.forward(inp_val[:,inp_ind].to(self.device)).data) #[:,inp_ind] of inp?\n val_loss = loss_var(inp_val, val_loss)\n val_loss = nn.L1Loss()(val_loss, out_val.to(self.device))\n self.loss_val.append(val_loss)\n weight_beg = [self.network.layer_inp.weight.cpu().detach().numpy()]\n weight_mid = [l.weight.cpu().detach().numpy() for l in self.network.layer_hid]\n weight_end = [self.network.layer_out.weight.cpu().detach().numpy()]\n self.weights.append(weight_beg + weight_mid + weight_end)\n pbar.update(1)\n pbar.set_description(desc='Training progress (loss: {0:.2e})'.format(cur_loss))\n # print(f'epoch: {epoch:5d} - loss: {cur_loss.item():9.6e}')\n pbar.close()\n self.loss_train = torch.tensor(self.loss_train)\n self.loss_val = torch.tensor(self.loss_val)\n\n @property\n def history(self):\n hist = []\n if self.loss_train is not None:\n hist.append(self.loss_train)\n if self.loss_val is not None:\n hist.append(self.loss_val)\n return hist\n\n @property\n def history_weight(self):\n hist = []\n if self.weights is None:\n return (hist)\n return self.weights\n\n","sub_path":"PACKAGE/riemannsolver/dnn1d/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":14790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"575761942","text":"class Setting():\n \"\"\"a class to store all settings for Alien Invation\"\"\"\n\n def __init__(self):\n \"\"\"initialize the game's static settings.\"\"\"\n self.screen_width = 720\n self.screen_height = 540\n self.bg_color = (230, 230, 230)\n\n # Ship srtting\n self.ship_speed_factor = 1.5\n self.ship_limit = 3\n\n # snip\n self.bullet_speed_factor = 1\n self.bullet_width = 3\n self.bullet_height = 15\n self.bullet_color = 60, 60, 60\n self.bullet_allowed = 3\n\n # alien setting\n self.alien_speed_factor = 1\n self.fleet_drop_speed = 10\n # fleet direction of 1 represents right; -1 represents left\n self.fleet_direction = 1\n\n # how quickly the speeds up\n self.speedup_scale = 1.1\n # how quickly the alien pont values increase\n self.score_scale = 1.5\n\n self.initialize_dynamic_setting()\n\n def initialize_dynamic_setting(self):\n \"\"\"initialize settings that change throughout the game.\"\"\"\n self.ship_speed_factor = 1.5\n self.bullet_speed_factor = 3\n self.alien_speed_factor = 1\n\n # fleet_direction of 1 represents right; -1 represents left.\n self.fleet_direction = 1\n\n # scoring\n self.alien_points = 10\n\n def increase_speed(self):\n \"\"\"increase speed settings and aliens point values.\"\"\"\n self.ship_speed_factor *= self.speedup_scale\n self.bullet_speed_factor *= self.speedup_scale\n self.alien_speed_factor *= self.speedup_scale\n\n self.alien_points = int(self.alien_points * self.score_scale)\n","sub_path":"alien_invasion/setting.py","file_name":"setting.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"194113824","text":"from django.shortcuts import render, redirect, reverse\nfrom django.contrib import auth, messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom accounts.forms import UserLoginForm, UserRegistrationForm\nimport sweetify\n\n\ndef registration(request):\n \"\"\"Return the registration.html file\"\"\"\n if request.user.is_authenticated:\n return redirect(reverse('index'))\n\n if request.method == \"POST\":\n registration_form = UserRegistrationForm(request.POST)\n\n if registration_form.is_valid():\n registration_form.save()\n\n user = auth.authenticate(\n username=request.POST['username'],\n password=request.POST['password1']\n )\n\n if user:\n auth.login(user=user, request=request)\n sweetify.success(\n request, \"You have successfully registered!\",\n icon=\"success\"\n )\n return redirect(reverse('index'))\n\n else:\n sweetify.error(\n request,\n \"\"\"We're truly sorry. We are unable\n to register your account at this time.\"\"\",\n icon=\"error\"\n )\n\n else:\n registration_form = UserRegistrationForm()\n\n return render(request, 'registration.html', {\n \"registration_form\": registration_form})\n\n\ndef login(request):\n \"\"\"Return the login.html file\"\"\"\n if request.user.is_authenticated:\n return redirect(reverse('index'))\n\n if request.method == \"POST\":\n login_form = UserLoginForm(request.POST)\n\n if login_form.is_valid():\n user = auth.authenticate(username=request.POST['username'],\n password=request.POST['password'])\n\n if user:\n auth.login(user=user, request=request)\n\n sweetify.sweetalert(\n request,\n \"\"\"You have successfully logged in!\"\"\",\n icon=\"success\"\n )\n return redirect(reverse('index'))\n\n else:\n login_form.add_error(\n None,\n \"Your username or password is incorrect\"\n )\n\n else:\n login_form = UserLoginForm()\n\n return render(request, 'login.html', {\"login_form\": login_form})\n\n\ndef userprofile(request):\n \"\"\"Return the profile.html file\"\"\"\n user = User.objects.get(email=request.user.email)\n return render(request, 'profile.html', {\"profile\": user})\n\n\n@login_required\ndef logout(request):\n \"\"\"Logs out the user\"\"\"\n auth.logout(request)\n sweetify.sweetalert(\n request, \"You have successfully been logged out!\",\n icon=\"success\"\n )\n return redirect(reverse('index'))\n","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"163973510","text":"# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\nfrom smo.common.node_object import NodeCollection, NodeObject\nfrom smo.common.scripting_mixins import ScriptableCreate, ScriptableDelete\nfrom smo.utils import templating\n\n\nclass Procedure(NodeObject, ScriptableCreate, ScriptableDelete):\n\n TEMPLATE_ROOT = templating.get_template_root(__file__, 'templates')\n\n @classmethod\n def _from_node_query(cls, server: 's.Server', parent: None, **kwargs) -> 'Procedure':\n \"\"\"\n Creates a new Database object based on the results from a query to lookup databases\n :param server: Server that owns the database\n :param parent: Parent object of the database. Should always be None\n :param kwargs: Optional parameters for the database. Values that can be provided:\n Kwargs:\n oid int: Object ID of the database\n name str: Name of the database\n spcname str: Name of the tablespace for the database\n datallowconn bool: Whether or not the database can be connected to\n cancreate bool: Whether or not the database can be created by the current user\n owner int: Object ID of the user that owns the database\n datistemplate bool: Whether or not the database is a template database\n canconnect bool: Whether or not the database is accessbile to current user\n :return: Instance of the Database\n \"\"\"\n proc = cls(server, kwargs[\"name\"], kwargs[\"dbname\"])\n return proc\n\n def __init__(self, server: 's.Server', name: str, dbname: str):\n \"\"\"\n Initializes a new instance of a database\n \"\"\"\n NodeObject.__init__(self, server, None, name)\n ScriptableCreate.__init__(self, self._template_root(self.server), self._macro_root(), self.server.version)\n ScriptableDelete.__init__(self, self._template_root(self.server), self._macro_root(), self.server.version)\n\n self._dbname = dbname\n self._server_version = server.version\n\n @classmethod\n def _template_root(cls, server: 's.Server') -> str:\n return cls.TEMPLATE_ROOT\n\n def _create_query_data(self) -> dict:\n \"\"\" Return the data input for create query \"\"\"\n return {\n \"dbname\": self._dbname,\n \"proc_name\": self._name\n }\n\n def _delete_query_data(self) -> dict:\n \"\"\" Return the data input for delete query \"\"\"\n return {\n \"dbname\": self._dbname,\n \"proc_name\": self._name\n }\n\n def create_script(self):\n \"\"\"Generates a script that creates an object of the inheriting type\"\"\"\n data = self._create_query_data()\n template_root = self._template_root(self._server)\n sql = templating.render_template(\n templating.get_template_path(template_root, 'create.sql', self._server_version),\n macro_roots=self._macro_root(),\n **data\n )\n\n cols, rows = self._server.connection.execute_dict(sql)\n script = rows[0][\"Create Procedure\"]\n return script\n","sub_path":"mysqlsmo/objects/procedure/procedure.py","file_name":"procedure.py","file_ext":"py","file_size_in_byte":3352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"298946206","text":"\n\ndef test_fixdate(longdate):\n import datetime\n\n examples = {\n \"Year too young\" : \"Mon, 08 Jan 2025 00:00:00 -0800\",\n \"Year too old\" : \"Mon, 08 Jan 952 00:00:00 -0800\",\n \"Day too low\" : \"Mon, 00 Jan 2019 00:00:00 -0800\",\n \"Day too high\" : \"Mon, 38 Jan 2019 00:00:00 -0800\",\n \"Month cameled\" : \"Mon, 08 jUl 2005 00:00:00 -0800\",\n \"Long Month\" : \"Mon, 08 October 2015 00:00:00 -0800\"\n }\n\n\n for test_date in examples:\n assert datetime.isodate( fixdate(examples[test_date]) )\n\n \"\"\" Many podcasts use a super long date format that I hate. This will\n cut them down to a simple format. Here's the original:\n\n \"Mon, 08 Jul 2019 00:00:00 -0800\"\n\n \"\"\"\n\n tdate = longdate.split(\" \")\n months = {\"JAN\" : \"01\", \"FEB\" : \"02\", \"MAR\" : \"03\", \"APR\" : \"04\",\n \"MAY\" : \"05\", \"JUN\" : \"06\", \"JUL\" : \"07\", \"AUG\" : \"08\",\n \"SEP\" : \"09\", \"OCT\" : \"10\", \"NOV\" : \"11\", \"DEC\" : \"12\"}\n\n return \"%s-%s-%s\" % (tdate[3], months[tdate[2].upper()], tdate[1])\n","sub_path":"tests/test_feedparse.py","file_name":"test_feedparse.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"339692072","text":"#!/usr/bin/env python3\n'''\nPostgreSQL query plan JSON looks like this (SELECT * FROM foo, bar):\n\n[{u'Plan': {u'Join Type': u'Inner',\n u'Node Type': u'Nested Loop',\n u'Plan Rows': 3468000,\n u'Plan Width': 36,\n u'Plans': [{u'Alias': u'foo',\n u'Node Type': u'Seq Scan',\n u'Parent Relationship': u'Outer',\n u'Plan Rows': 2550,\n u'Plan Width': 4,\n u'Relation Name': u'foo',\n u'Startup Cost': 0.0,\n u'Total Cost': 35.5},\n {u'Node Type': u'Materialize',\n u'Parent Relationship': u'Inner',\n u'Plan Rows': 1360,\n u'Plan Width': 32,\n u'Plans': [{u'Alias': u'bar',\n u'Node Type': u'Seq Scan',\n u'Parent Relationship': u'Outer',\n u'Plan Rows': 1360,\n u'Plan Width': 32,\n u'Relation Name': u'bar',\n u'Startup Cost': 0.0,\n u'Total Cost': 23.6}],\n u'Startup Cost': 0.0,\n u'Total Cost': 30.4}],\n u'Startup Cost': 0.0,\n u'Total Cost': 43412.5}}]\n'''\n\nclass PostgresSplainer(object):\n def explain(self):\n self.cursor.execute(\n 'EXPLAIN (FORMAT JSON) %(query)s' % {'query': self.query})\n\n def get_tables(self):\n self.explain()\n\n all_relations = []\n def recurse(plan):\n if 'Relation Name' in plan:\n all_relations.append(plan['Relation Name'].lower())\n for plan in plan.get('Plans', []):\n recurse(plan)\n\n for json, in self.cursor.fetchall():\n recurse(json[0]['Plan'])\n return all_relations\n","sub_path":"plansplain/backends/postgres.py","file_name":"postgres.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"103061509","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 30 09:11:12 2018\n\n@author: philipp krah, jiahan wang\n\"\"\"\n\n###############################################################################\n# IMPORTED MODULES\n###############################################################################\nimport sys\nsys.path.append('../lib')\nimport numpy as np\nfrom numpy import exp, mod,meshgrid\nimport matplotlib.pyplot as plt\nfrom sPOD_tools import frame, sPOD\n###############################################################################\n\n##########################################\n#%% Define your DATA:\n##########################################\nplt.close(\"all\")\nNx = 200 # number of grid points in x\nNt = 250 # numer of time intervalls\ndt = 0.01 # size of time intervall\nT = Nt*dt # total time\nL = 2*np.pi # total domain size\nx0 = L*0.5 # starting point of the gauss puls\nsigma = L/50 # standard diviation of the puls\nnmodes = 1 # reduction of singular values\nx = np.linspace(0, L, Nx)\nt = np.linspace(0, T, Nt)\ndx = x[1]-x[0]\ndt = t[1]-t[0]\nc = dx/dt\n[T, X] = meshgrid(t, x)\n\nfun = lambda x, t: 0.5 * exp(-(mod((x-c*t), L)-x0)**2/sigma**2) + \\\n 0.5 * exp(-(mod((x+c*t), L)-x0)**2/sigma**2)\n\n# Define your field as a list of fields:\n# For example the first element in the list can be the density of\n# a flow quantity and the second element could be the velocity in 1D\ndensity = fun(X, T)\nvelocity = fun(X, T)\nfields = [density] #, velocity]\n\n\n#######################################\n# %% CALL THE SPOD algorithm\n######################################\nn_velocities = 2 # number of velocities to be detected\n\nsPOD_frames, rel_err = sPOD(fields, n_velocities, dx, dt,\n nmodes=1, eps=1e-7, Niter=30, visualize=False)\n\n###########################################\n# %% 1. visualize your results: sPOD frames\n##########################################\n# the result is a list of the decomposed field.\n# each element of the list contains a frame of the decomposition.\n# If you want to plot the k-th frame use:\n# 1. frame\nk_frame = 0\nplt.subplot(121)\nsPOD_frames[k_frame].plot_field()\nplt.suptitle(\"sPOD Frames\")\nplt.xlabel(r'$N_x$')\nplt.ylabel(r'$N_t$')\nplt.title(r\"$q^\"+str(k_frame)+\"(x,t)$\")\n# 2. frame\nk_frame = 1\nplt.subplot(122)\nsPOD_frames[k_frame].plot_field()\nplt.xlabel(r'$N_x$')\nplt.ylabel(r'$N_t$')\nplt.title(r\"$q^\"+str(k_frame)+\"(x,t)$\")\n# by default this will plot the field in the first component\n# of your field list (here: density)\n\n###########################################\n# 2. visualize your results: relative error\n##########################################\n\nplt.figure()\nplt.semilogy(rel_err)\nplt.title(\"relative error\")\nplt.ylabel(r\"$\\frac{||X - \\tilde{X}_i||_2}{||X||_2}$\")\nplt.xlabel(r\"$i$\")\n","sub_path":"examples/simple_example.py","file_name":"simple_example.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"113099917","text":"\n\n#calss header\nclass _GADFLY():\n\tdef __init__(self,): \n\t\tself.name = \"GADFLY\"\n\t\tself.definitions = [u'someone who is always annoying or criticizing other people: ', u'a fly that bites horses and cows']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_gadfly.py","file_name":"_gadfly.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"74620779","text":"from SubLib.mp3player import Mp3Player\r\nfrom SubLib.GUI import gui\r\n\r\ng = gui()\r\nmp3 = Mp3Player()\r\nmp3.display_to_get_event(800, 600)\r\nloop = 1\r\n# mp3.set_position(140000)\r\nwhile True:\r\n\r\n # mp3.play_mp3('file.mp3')\r\n #print('start')\r\n # mp3.change_speed('0.5')\r\n # t = 0\r\n #print(mp3.get_position())\r\n mp3.control_player_by_keyboard()\r\n if g.button(mp3.display, 'play', 150, 450, 100, 50, g.green, g.blue):\r\n mp3.play_mp3_button('Kalimba.mp3', loop)\r\n if mp3.playing_mp3():\r\n loop = 0\r\n else:\r\n loop = 1\r\n if g.button(mp3.display, 'stop', 350, 450, 100, 50, g.blue, g.red):\r\n mp3.stop_mp3()\r\n\r\n g.update_display()\r\n #clock.tick(15)\r\n #mp3.repeat()\r\n\r\nprint('End')","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"208119659","text":"\n\n#calss header\nclass _MUG():\n\tdef __init__(self,): \n\t\tself.name = \"MUG\"\n\t\tself.definitions = [u'a large cup with straight sides used for hot drinks: ', u'a heavy glass with a handle and usually with patterns cut into its side, out of which you drink beer', u'a person who is stupid and easily deceived: ', u\"someone's face: \"]\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_mug.py","file_name":"_mug.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"156698699","text":"from OpenGL.GL import *\nfrom OpenGL.GLUT import *\nfrom OpenGL.GLU import *\nimport numpy\nimport sys\nimport math\n\n# vertex array object\nclass VAObject:\n def __init__( self, dataArrays, indices = [], type = GL_TRIANGLES, patch_vertices = 3 ):\n self.__obj = glGenVertexArrays( 1 )\n self.__noOfIndices = len( indices )\n self.__indexArr = numpy.array( indices, dtype='uint' )\n self.__type = type\n self.__patch_vertices = patch_vertices\n self.__vertexSize = []\n self.__dataLength = []\n self.__noOfBuffers = len( dataArrays )\n self.__buffers = glGenBuffers( self.__noOfBuffers )\n glBindVertexArray( self.__obj )\n for i_buffer in range( 0, self.__noOfBuffers ):\n vertexSize, dataArr = dataArrays[i_buffer]\n self.__vertexSize.append( vertexSize )\n self.__dataLength.append( len( dataArr ) )\n glBindBuffer( GL_ARRAY_BUFFER, self.__buffers if self.__noOfBuffers == 1 else self.__buffers[i_buffer] )\n glBufferData( GL_ARRAY_BUFFER, numpy.array( dataArr, dtype='float32' ), GL_STATIC_DRAW )\n glEnableVertexAttribArray( i_buffer )\n glVertexAttribPointer( i_buffer, self.__vertexSize[i_buffer], GL_FLOAT, GL_FALSE, 0, None )\n self.__iBuffer = glGenBuffers( 1 )\n glBindBuffer( GL_ARRAY_BUFFER, 0 )\n if self.__noOfIndices > 0:\n glBindBuffer( GL_ELEMENT_ARRAY_BUFFER, self.__iBuffer )\n glBufferData( GL_ELEMENT_ARRAY_BUFFER, self.__indexArr, GL_STATIC_DRAW )\n glBindVertexArray( 0 )\n def DrawArray(self):\n glBindVertexArray( self.__obj )\n if self.__type == GL_PATCHES:\n glPatchParameteri( GL_PATCH_VERTICES, self.__patch_vertices )\n glDrawArrays( GL_PATCHES, 0, self.__dataLength[0] )\n glBindVertexArray( 0 )\n def Draw(self):\n if self.__noOfIndices == 0:\n self.DrawArray()\n return\n glBindVertexArray( self.__obj )\n #for i_buffer in range( 0, self.__noOfBuffers ):\n # glBindBuffer( GL_ARRAY_BUFFER, self.__buffers if self.__noOfBuffers == 1 else self.__buffers[i_buffer] )\n # glEnableVertexAttribArray( i_buffer )\n # glVertexAttribPointer( i_buffer, self.__vertexSize[i_buffer], GL_FLOAT, GL_FALSE, 0, None )\n #glDrawElements( self.__type, self.__noOfIndices, GL_UNSIGNED_INT, self.__indexArr )\n if self.__type == GL_PATCHES:\n glPatchParameteri( GL_PATCH_VERTICES, self.__patch_vertices )\n glDrawElements( self.__type, self.__noOfIndices, GL_UNSIGNED_INT, None )\n glBindVertexArray( 0 )\n\n# define screenspace quad vertex array opject\nquadVAO = VAObject( [ (2, [ -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, 1.0 ]) ], [ 0, 1, 2, 0, 2, 3 ] )\n\n# define tetrahedron vertex array opject\nsin120 = 0.8660254\ntetVAO = VAObject(\n [ (3, [ 0.0, 0.0, 1.0, 0.0, -sin120, -0.5, sin120 * sin120, 0.5 * sin120, -0.5, -sin120 * sin120, 0.5 * sin120, -0.5 ]),\n (3, [ 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ])\n ], \n [ 0, 1, 2, 0, 2, 3, 0, 3, 1, 1, 3, 2 ]\n)\n\n# define icosahedron patch object\nicoPts = [\n ( 0.000, 0.000, 1.000), ( 0.894, 0.000, 0.447), ( 0.276, 0.851, 0.447), (-0.724, 0.526, 0.447),\n (-0.724, -0.526, 0.447), ( 0.276, -0.851, 0.447), ( 0.724, 0.526, -0.447), (-0.276, 0.851, -0.447), \n (-0.894, 0.000, -0.447), (-0.276, -0.851, -0.447), ( 0.724, -0.526, -0.447), ( 0.000, 0.000, -1.000) ]\nicoCol = [ [1.0, 0.0, 0.0], [0.0, 0.0, 1.0], [1.0, 1.0, 0.0], [0.0, 1.0, 0.0], [1.0, 0.5, 0.0], [1.0, 0.0, 1.0] ]\nicoIndices = [\n 2, 0, 1, 3, 0, 2, 4, 0, 3, 5, 0, 4, 1, 0, 5, 11, 7, 6, 11, 8, 7, 11, 9, 8, 11, 10, 9, 11, 6, 10, \n 1, 6, 2, 2, 7, 3, 3, 8, 4, 4, 9, 5, 5, 10, 1, 2, 6, 7, 3, 7, 8, 4, 8, 9, 5, 9, 10, 1, 10, 6 ]\nicoPosData = []\nfor inx in icoIndices: AddToBuffer( icoPosData, icoPts[inx] )\nicoNVData = []\nfor inx_nv in range(0, len(icoIndices) // 3):\n nv = [0.0, 0.0, 0.0]\n for inx_p in range(0, 3): \n for inx_s in range(0, 3): nv[inx_s] += icoPts[ icoIndices[inx_nv*3 + inx_p] ][inx_s]\n AddToBuffer( icoNVData, nv, 3 )\nicoVAO = VAObject( [ (3, icoPosData), (3, icoNVData)], [], GL_PATCHES, 3 )\n","sub_path":"dox/ogl/_library/ogl_vertex_array_object.py","file_name":"ogl_vertex_array_object.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"346431049","text":"import torch\nimport torch.nn as nn\nimport torch.distributions as dist\nfrom torch.distributions.kl import kl_divergence\n\nfrom .gp_utils import vec2tril, cholesky, rev_cholesky, gp_cond, linear_joint, linear_marginal_diag\nfrom .kernels import RBFKernel\nfrom .likelihoods import MulticlassSoftmax\n\n\nclass VARGPRetrain(nn.Module):\n def __init__(self, z_init, kernel, likelihood, n_var_samples=1, prev_params=None):\n super().__init__()\n\n self.prev_params = prev_params\n self.retrain_params = prev_params\n if prev_params:\n self.retrain_params = nn.ModuleList([\n nn.ParameterDict(dict(\n z=nn.Parameter(p['z']),\n u_mean=nn.Parameter(p['u_mean']),\n u_tril_vec=nn.Parameter(p['u_tril_vec']),\n ))\n for p in prev_params\n ])\n\n self.M = z_init.size(-2)\n\n self.kernel = kernel\n self.n_v = n_var_samples\n self.likelihood = likelihood\n\n self.z = nn.Parameter(z_init.detach())\n\n out_size = self.z.size(0)\n self.u_mean = nn.Parameter(torch.Tensor(out_size, self.M, 1).normal_(0., .5))\n self.u_tril_vec = nn.Parameter(torch.ones(out_size, (self.M * (self.M + 1)) // 2))\n\n def compute_q(self, theta, prev_params, cache=None):\n '''\n Compute variational auto-regressive distributions.\n\n Arguments:\n theta: n_hypers x (D + 1)\n\n Returns\n mu_lt: n_hypers x out_size x (\\sum M_t - M_T) x 1\n S_lt: n_hypers x out_size x (\\sum M_t - M_T) x (\\sum M_t - M_T)\n mu_leq_t: n_hypers x out_size x (\\sum M_t) x 1\n S_leq_t: n_hypers x out_size x (\\sum M_t) x (\\sum M_t)\n z_leq_t: out_size x (\\sum M_t) x D\n '''\n n_hypers = theta.size(0)\n\n ## Compute q(u_{2?\n if prev_params:\n prior_log_mean = prev_params[-1].get('kernel.log_mean')\n prior_log_logvar = prev_params[-1].get('kernel.log_logvar')\n\n def process(p):\n for k in list(p.keys()):\n if k.startswith('kernel'):\n p.pop(k)\n return p\n\n prev_params = [process(p) for p in prev_params]\n\n kernel = RBFKernel(z.size(-1), prior_log_mean=prior_log_mean, prior_log_logvar=prior_log_logvar)\n likelihood = MulticlassSoftmax(n_f=n_f)\n gp = VARGPRetrain(z, kernel, likelihood, n_var_samples=n_var_samples, prev_params=prev_params)\n return gp\n","sub_path":"var_gp/vargp_retrain.py","file_name":"vargp_retrain.py","file_ext":"py","file_size_in_byte":9713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"289660531","text":"#! /usr/bin/env python3\nimport requests\n\nimport config\n\nimport os\n\nfrom bs4 import BeautifulSoup\n\nimport re\n\n\ndata = {'login':config.user, 'password':config.password}\n\nbegin = requests.Session()\nheaders = {'Content-type':'application/json'}\nreq = begin.post(config.login_url, json = data, headers=headers)\nprint(req.text)\ni = 0\nf = open('/home/worker/vendingautm/finally', 'w')\nf.write(\"\")\nf.close\nwhile i!=config.pages:\n r = begin.get(config.get_list_url + str(i))\n soup = BeautifulSoup(str(r.text), 'html.parser')\n r = r.text\n table = soup.find('tbody')\n r = str(table)\n final = BeautifulSoup(str(r),'html.parser')\n for a in final(\"a\"):\n a.decompose()\n for script in final([\"script\", \"style\"]):\n script.decompose()\n for td in final(\"td\"):\n td.find_all('td')\n all_text = ''.join(final.findAll(text=True))\n r = str(all_text)\n file = open('/home/worker/vendingautm/finally', 'a')\n file.write(r) \n file.close()\n i+=1\n\nos.system(\"sed -i '/^$/d' /home/worker/vendingautm/finally \")\n\n\n","sub_path":"getlist.py","file_name":"getlist.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"349051387","text":"from plenum.client.client import Client\nfrom plenum.common.util import getMaxFailures\nfrom plenum.test.eventually import eventually\nfrom plenum.test.helper import randomOperation, checkSufficientRepliesRecvd, \\\n sendRandomRequest\n\n\ndef testMerkleProofForFirstLeaf(client1: Client, replied1):\n replies = client1.getRepliesFromAllNodes(1).values()\n assert Client.verifyMerkleProof(*replies)\n\n\ndef testMerkleProofForNonFirstLeaf(looper, nodeSet, client1: Client, replied1):\n req2 = sendRandomRequest(client1)\n f = nodeSet.f\n looper.run(eventually(checkSufficientRepliesRecvd, client1.inBox, req2.reqId, f, retryWait=1, timeout=15))\n replies = client1.getRepliesFromAllNodes(req2.reqId).values()\n assert Client.verifyMerkleProof(*replies)\n","sub_path":"plenum/test/test_verif_merkle_proof.py","file_name":"test_verif_merkle_proof.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"379787642","text":"import requests\nimport json\nfrom bottle import request, response\nfrom MyHTMLParser import MyHTMLParser\nfrom chatterbot import ChatBot\nfrom chatterbot.trainers import ListTrainer\nfrom chatterbot.trainers import ChatterBotCorpusTrainer\n\nCURSES = [\n \"fuck\",\n \"shit\",\n \"suck\",\n \"piss\",\n \"puss\",\n \"pussy\",\n \"dick\",\n \"cunt\",\n \"wanker\",\n \"d1ck\",\n \"bastard\",\n]\n\n\nJOKES_API_BASE_URL = \"https://sv443.net/jokeapi/category/Any\"\nmy_bot = ChatBot(name='boto', read_only=True,\n logic_adapters=['chatterbot.logic.BestMatch'])\n\nsmall_talk = ['hi there!',\n 'hi!',\n 'how do you do?',\n 'how are you?',\n 'I\\'m cool.',\n 'fine, you?',\n 'always cool.',\n 'I\\'m ok',\n 'glad to hear that.',\n 'I\\'m fine',\n 'glad to hear that.',\n 'i feel awesome',\n 'excellent, glad to hear that.',\n 'not so good',\n 'sorry to hear that.',\n 'what\\'s your name?',\n 'I\\'m boto. What\\'s up?.']\n\n# list_trainer = ListTrainer(my_bot)\n# list_trainer.train(small_talk)\n\ncorpus_trainer = ChatterBotCorpusTrainer(my_bot)\ncorpus_trainer.train('chatterbot.corpus.english')\n\n\ndef check_user_response(msg):\n msg_lower_case = msg.lower()\n if not request.get_cookie(\"username\"):\n print(\">>>>>first if: \", request.get_cookie(\n \"username\"), \">>>>\", request.cookies.username)\n response.set_cookie(\"username\", \"Guest\")\n return construct_response(\"dancing\", \"Hi, my name is Boto. What is your name?\")\n\n elif \"my\" in msg_lower_case and \"name\" in msg_lower_case:\n print(\">>>>> second if\")\n name = msg.split()[-1]\n response.set_cookie(\"username\", name)\n # username = request.cookies.username\n return construct_response(\"excited\", f\"Hello {name}, nice to meet you. I'll remember your name now!\")\n\n elif \"__first*\" in msg:\n print(\">>>> third if\")\n username = request.cookies.username\n return construct_response(\"excited\", f\"Welcome back {username}, if you want to know what to ask, type 'help'\")\n\n if did_user_curse(msg_lower_case):\n return did_user_curse(msg_lower_case)\n\n elif msg_lower_case == \"help\":\n return show_help()\n\n elif \"joke\" in msg_lower_case:\n return get_joke()\n\n elif \"distance\" in msg_lower_case and \"between\" in msg_lower_case and \"and\" in msg_lower_case:\n print(\">>>>>>> in distance\")\n distance, unused = get_distance(msg_lower_case)\n return construct_response(\"takeoff\", distance)\n\n elif \"directions\" in msg_lower_case and \"from\" in msg_lower_case and \"to\" in msg_lower_case:\n return get_directions(msg_lower_case)\n\n # elif msg_lower_case.endswith(\"?\"):\n # return handle_question(msg_lower_case)\n\n else:\n print(\">>>>>>>> else statement\")\n bot_response = my_bot.get_response(msg_lower_case)\n print(bot_response)\n return construct_response(\"confused\", str(bot_response))\n # return construct_response(\"confused\", \"I did not understand that, sorry. Try rephrasing or asking for help by typing 'help'.\")\n\n\ndef did_user_curse(msg):\n split_msg = msg.split(\" \")\n if any(word in CURSES for word in split_msg):\n return construct_response(\"crying\", \"This is not nice! I will not tolerate such language.\")\n else:\n return False\n\n\ndef get_distance(msg):\n split_msg = msg.split(\" \")\n fromIndex = int(split_msg.index(\"from\")) + 1\n toIndex = int(split_msg.index(\"to\"))\n\n params = {\n \"origin\": \"+\".join(split_msg[fromIndex:toIndex]),\n \"destination\": \"+\".join(split_msg[toIndex+1:]),\n \"key\": \"#GOOGLE_API_KEY_HERE\"\n }\n\n GOOGLE_MAPS_BASE_URL = \"https://maps.googleapis.com/maps/api/directions/json?\"\n\n data = requests.get(url=GOOGLE_MAPS_BASE_URL, params=params).json()\n # >>>>>>>>>>>>>>>>>>\n legs = data['routes'][0]['legs']\n journey_directions = \"\"\n\n journey_directions += f\"The distance to your destination is {legs[0]['distance']['text']}.\"\n journey_directions += f\" The time it will take to your destination is {legs[0]['duration']['text']}. >>>>>>>>\"\n print(journey_directions)\n return journey_directions, legs\n\n\ndef get_directions(msg):\n journey_directions, legs = get_distance(msg)\n journey_directions += create_journey_instructions(legs[0]['steps'])\n\n return construct_response(\"takeoff\", journey_directions)\n\n\ndef create_journey_instructions(steps):\n parser = MyHTMLParser() # HTML parser for directions API data\n instruct = \"\"\n for step in steps:\n parser.feed(step['html_instructions'])\n instruct += parser.get_data() + \">>>>>\"\n print(instruct)\n return instruct\n\n\ndef handle_question(question):\n # if \"weather\" in question:\n # get_weather()\n # else:\n return construct_response(\"confused\", \"I don't understand your question. Sorry.\")\n\n\ndef get_weather():\n return construct_response(\"ok\", \"not implemented yet\")\n\n\ndef get_joke():\n print(\">>> Getting joke from api\")\n return request_joke_from_api(JOKES_API_BASE_URL)\n\n\ndef show_help():\n msg = \"Questions you can ask me: 1. 'tell me a joke'. 2. 'tell me the distance between New York and Washington'. 3. 'give me directions from Tel Aviv to Ramat Gan'. You can tell me your name like this: 'my name is Boto'\"\n return construct_response(\"ok\", msg)\n\n\ndef construct_response(animation, msg):\n return {\"animation\": animation, \"msg\": msg}\n\n\ndef request_joke_from_api(base_url, params=None):\n if params is None:\n response = requests.get(url=base_url)\n data = response.json()\n print(\">>> received response \", data)\n print(data['type'])\n if data['type'] == \"twopart\":\n joke = data['setup'] + ' ' + data['delivery']\n else:\n joke = data['joke']\n return construct_response(\"laughing\", joke)\n\n\ndef set_cookie():\n print(\"not implemented yet\")\n","sub_path":"boto_funcs.py","file_name":"boto_funcs.py","file_ext":"py","file_size_in_byte":6023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"212727500","text":"#!/usr/bin/env python\n'''\nThis example implements an example application that includes an aioprometheus\nbased metrics collecting and exporting capability as well as a client function\nthat emulates Prometheus scraping the metrics server. The fetching client\nrequests metrics from the server in two different formats; text and binary.\n'''\n\nimport asyncio\nimport logging\nimport psutil\nimport random\nimport socket\n\nimport aiohttp\nfrom aiohttp.hdrs import ACCEPT, CONTENT_TYPE\n\nfrom aioprometheus import (\n Counter,\n Gauge,\n Histogram,\n Registry,\n Service,\n Summary,\n)\n\n\nUPDATE_INTERVAL = 1.0\n\n\ndef on_timer_expiry(loop, ram_metric, cpu_metric,\n requests_metric, payload_metric,\n latency_metric):\n ''' Update metrics at regular interval '''\n\n # Add ram metrics\n ram = psutil.virtual_memory()\n swap = psutil.swap_memory()\n\n ram_metric.set({'type': \"virtual\", }, ram.used)\n ram_metric.set({'type': \"swap\"}, swap.used)\n\n # Add cpu metrics\n for c, p in enumerate(psutil.cpu_percent(interval=1, percpu=True)):\n cpu_metric.set({'core': c}, p)\n\n # increment a counter\n requests_metric.inc({'path': \"/\"})\n\n payload_metric.add({'path': \"/data\"}, random.random() * 2**10)\n\n # add a random request latency\n latency_metric.add({'path': \"/data\"}, random.random() * 5)\n\n # schedule another update\n loop.call_later(\n UPDATE_INTERVAL, on_timer_expiry, loop,\n ram_metric, cpu_metric, requests_metric,\n payload_metric, latency_metric)\n\n\nasync def fetch_metrics(url, loop):\n ''' Fetch metrics from the service endpoint using different formats.\n\n This coroutine runs 'n' times, with a brief interval in between, before\n exiting.\n '''\n\n n = 3\n while n > 0:\n n -= 1\n\n with aiohttp.ClientSession(loop=loop) as session:\n print('fetching metrics, requesting text format')\n headers = {\n ACCEPT: 'text/plain'}\n async with session.get(url, headers=headers) as resp:\n assert resp.status == 200\n content = await resp.read()\n content_type = resp.headers.get(CONTENT_TYPE)\n print('Content-Type: {}'.format(content_type))\n print('size: {}'.format(len(content)))\n print(content.decode())\n\n await asyncio.sleep(1.0)\n\n print('fetching metrics, requesting binary format')\n headers = {\n ACCEPT: 'application/vnd.google.protobuf; '\n 'proto=io.prometheus.client.MetricFamily; '\n 'encoding=delimited'}\n async with session.get(url, headers=headers) as resp:\n assert resp.status == 200\n content = await resp.read()\n content_type = resp.headers.get(CONTENT_TYPE)\n print('Content-Type: {}'.format(content_type))\n print('size: {}'.format(len(content)))\n print(content)\n\n await asyncio.sleep(2.0)\n\n\ndef fetch_task(url, loop):\n asyncio.ensure_future(fetch_metrics(url, loop))\n\n\nif __name__ == '__main__':\n\n logging.basicConfig(level=logging.DEBUG)\n logging.getLogger('asyncio').setLevel(logging.ERROR)\n logging.getLogger('aiohttp').setLevel(logging.ERROR)\n logger = logging.getLogger(__name__)\n\n loop = asyncio.get_event_loop()\n\n # create a metrics server with the default registry\n svr = Service(loop=loop)\n\n # Get the host name of the machine to use in metrics\n host = socket.gethostname()\n\n # Create our collectors\n requests_metric = Counter(\n \"requests\", \"Number of requests.\", {'host': host})\n svr.registry.register(requests_metric)\n\n ram_metric = Gauge(\n \"memory_usage_bytes\",\n \"Memory usage in bytes.\",\n {'host': host})\n svr.registry.register(ram_metric)\n cpu_metric = Gauge(\n \"cpu_usage_percent\",\n \"CPU usage percent.\",\n {'host': host})\n svr.registry.register(cpu_metric)\n\n payload_metric = Summary(\n \"request_payload_size_bytes\",\n \"Request payload size in bytes.\",\n {'host': host},\n invariants=[(0.50, 0.05), (0.99, 0.001)])\n svr.registry.register(payload_metric)\n\n latency_metric = Histogram(\n \"request_latency_seconds\", \"Request latency in seconds\",\n {'host': host}, buckets=[0.1, 0.5, 1.0, 5.0])\n svr.registry.register(latency_metric)\n\n loop.run_until_complete(svr.start())\n logger.debug('serving prometheus metrics on: %s', svr.url)\n\n # schedule the first update, which will continue to re-schedule itself.\n loop.call_later(\n UPDATE_INTERVAL, on_timer_expiry, loop,\n ram_metric, cpu_metric, requests_metric,\n payload_metric, latency_metric)\n\n # initiate the client task\n loop.call_later(1.5, fetch_task, svr.url, loop)\n\n try:\n loop.run_forever()\n except KeyboardInterrupt:\n pass\n finally:\n loop.run_until_complete(svr.stop())\n loop.close()\n","sub_path":"examples/example-metrics.py","file_name":"example-metrics.py","file_ext":"py","file_size_in_byte":5031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"325235698","text":"#!/usr/bin/env python3\n\"\"\"\nTeleop: Human control of the robot\n\nA simple CLI-based remote control for the robot.\n\nControls (from stdin) are:\n- w/a/s/d = forward/left/backward/right (Dvorak bindings of ,/a/o/e are accepted too).\n- [Space] = stop the robot\n- q = quit\n- ? = show this help message\n\nParameters:\n\n- forward_vel: forward (and backward) speed of the robot\n- angular_vel: angular speed of the robot\n\nReasonable defaults, determined through experimentation in the simulator, have been set for all parameters.\n\"\"\"\nfrom geometry_msgs.msg import Twist, Vector3\nimport rospy\nimport tty\nimport select\nimport sys\nimport termios\n\n\nclass AsyncInputReader:\n \"\"\"\n Get non-blocking input from stdin. Based on code provided by the teaching team\n \"\"\"\n\n def __init__(self):\n self.settings = termios.tcgetattr(sys.stdin)\n\n def read(self):\n tty.setraw(sys.stdin.fileno())\n select.select([sys.stdin], [], [], 0)\n key = sys.stdin.read(1)\n termios.tcsetattr(sys.stdin, termios.TCSADRAIN, self.settings)\n\n if key == '\\x03':\n raise KeyboardInterrupt()\n\n return key\n\n\nclass TeleopNode:\n def __init__(self, name='teleop'):\n rospy.init_node(name)\n self.cmd_vel = rospy.Publisher('/cmd_vel', Twist, queue_size=10)\n self.input = AsyncInputReader()\n self.forward_vel = rospy.get_param('~forward_vel', 0.5)\n self.angular_vel = rospy.get_param('~angular_vel', 1)\n\n def set_speed(self, forward, angular):\n twist = Twist(\n linear=Vector3(forward, 0, 0),\n angular=Vector3(0, 0, angular)\n )\n self.cmd_vel.publish(twist)\n\n def handle_input(self, key: str):\n if key == ' ':\n self.set_speed(0, 0)\n elif key == 'q':\n self.set_speed(0, 0)\n raise KeyboardInterrupt(\"Exit requested!\")\n elif key == '?':\n print(\"\"\"\nNeato Teleop:\nw/a/s/d = forward/left/backward/right (Dvorak bindings of ,/a/o/e are accepted too).\n[Space] = stop the robot\nq = quit\n? = show this help message\n\t\t\t\"\"\")\n\n elif key in [',', 'w']:\n self.set_speed(self.forward_vel, 0)\n elif key in ['o', 's']:\n self.set_speed(-self.forward_vel, 0)\n elif key in ['a']:\n self.set_speed(0, self.angular_vel)\n elif key in ['e', 'd']:\n self.set_speed(0, -self.angular_vel)\n\n def run(self):\n r = rospy.Rate(10)\n while not rospy.is_shutdown():\n try:\n self.handle_input(self.input.read())\n r.sleep()\n except KeyboardInterrupt:\n if not rospy.is_shutdown():\n self.set_speed(0, 0)\n raise\n\n\nif __name__ == '__main__':\n TeleopNode().run()\n","sub_path":"scripts/teleop.py","file_name":"teleop.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"373863052","text":"import requests\nimport os\nimport json\n# from base64 import b64encode, b64decode\n\nif __name__ == '__main__':\n wav_path = r'wav/test1.wav'\n # with open(wav_path, 'rb') as wav_file:\n # # wav_data = wav_file.read()\n # requests.post(url=\"http://127.0.0.3:5000/\", data=wav_file)\n files = {'file': open(wav_path, 'rb')}\n print(files)\n req = requests.post(url=\"http://127.0.0.3:5000/\", data={'wav': wav_path, 'way': 'real_time'}, files=files)\n wenben = req.text\n print(wenben)\n","sub_path":"send_realtime.py","file_name":"send_realtime.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"332764483","text":"import sys\nimport os.path\nimport re\nimport time\nfrom model import *\nfrom utils import *\n\ndef load_data():\n data = []\n inputs = []\n outputs = []\n batch_len = 0\n print(\"loading data...\")\n fo = open(sys.argv[4], \"r\")\n for line in fo:\n line = line.strip()\n tokens = [int(i) for i in line.split(\" \")]\n z = tokens.pop()\n if len(inputs) == 0:\n batch_len = z\n pad = [0] * (batch_len - z)\n inputs.append(tokens[:z] + pad)\n outputs.append(tokens[z:] + pad)\n if len(inputs) == BATCH_SIZE:\n data.append((Var(LongTensor(inputs)), LongTensor(outputs)))\n inputs = []\n outputs = []\n fo.close()\n print(\"data size: %d\" % (len(data) * BATCH_SIZE))\n print(\"batch size: %d\" % BATCH_SIZE)\n return data\n\ndef train():\n print(\"cuda: %s\" % CUDA)\n num_epochs = int(sys.argv[5])\n data = load_data()\n word_to_idx = load_word_to_idx(sys.argv[2])\n tag_to_idx = load_tag_to_idx(sys.argv[3])\n model = lstm_crf(len(word_to_idx), tag_to_idx)\n optim = torch.optim.SGD(model.parameters(), lr = LEARNING_RATE, weight_decay = WEIGHT_DECAY)\n epoch = load_checkpoint(sys.argv[1], model) if os.path.isfile(sys.argv[1]) else 0\n if CUDA:\n model = model.cuda()\n filename = re.sub(\"\\.epoch[0-9]+$\", \"\", sys.argv[1])\n print(model)\n print(\"training model...\")\n for i in range(epoch + 1, epoch + num_epochs + 1):\n avrg_loss = 0\n for j, (x, y) in enumerate(data):\n model.zero_grad()\n loss = model.loss(x, y)\n loss.backward()\n optim.step()\n loss = scalar(loss)\n avrg_loss += loss\n print(\"epoch = %d, iteration = %d, loss = %f\" % (i, j + 1, loss))\n avrg_loss /= len(data)\n save_checkpoint(filename, model, i, avrg_loss)\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 6:\n sys.exit(\"Usage: %s model word_to_idx tag_to_idx training_data num_epoch\" % sys.argv[0])\n train()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"492337585","text":"#!/usr/bin/env python3\n\nimport os\nimport aiohttp\nimport aiounittest\nfrom kaiterra_async_client.tests import skip_online_tests\nfrom kaiterra_async_client import KaiterraAPIClient, Units\n\n\ndef create_client(session):\n if \"KAITERRA_APIV1_URL_KEY\" not in os.environ:\n raise Exception(\n \"Missing Kaiterra API key in environment variable KAITERRA_APIV1_URL_KEY\"\n )\n api_key = os.environ[\"KAITERRA_APIV1_URL_KEY\"]\n\n return KaiterraAPIClient(\n session, api_key=api_key, preferred_units=[Units.DegreesFahrenheit]\n )\n\n\n@skip_online_tests\nclass GetSensorDataTests(aiounittest.AsyncTestCase):\n \"\"\"\n Tests for retrieving sensor data.\n \"\"\"\n\n async def test_simple(self):\n async with aiohttp.ClientSession() as session:\n client = create_client(session)\n readings = await client.get_latest_sensor_readings(\n [\n \"/lasereggs/00000000-0001-0001-0000-00007e57c0de\",\n \"/lasereggs/00000000-ffff-0001-ffff-00007e57c0de\",\n ]\n )\n self.assertEqual(2, len(readings))\n\n # First sensor exists, and should have readings\n self.assertIsNotNone(readings[0])\n self.assertIn(\"rpm25c\", readings[0])\n self.assertIsNone(readings[1])\n\n async def test_simple_devices(self):\n async with aiohttp.ClientSession() as session:\n client = create_client(session)\n readings = await client.get_latest_sensor_readings(\n [\n \"/devices/00000000-0001-0001-0000-00007e57c0de/top\",\n \"/devices/00000000-0031-0001-0000-00007e57c0de/top\",\n ]\n )\n self.assertEqual(2, len(readings))\n\n self.assertIsNotNone(readings[0])\n self.assertIsNotNone(readings[1])\n\n async def test_validate_sensor_ids(self):\n async with aiohttp.ClientSession() as session:\n client = create_client(session)\n # Malformed UDID\n with self.assertRaises(ValueError):\n await client.get_latest_sensor_readings(\n [\"/lasereggs/0000000-0001-0001-0000-00007e57c0de\"]\n )\n\n # Should be a list, not a string\n with self.assertRaises(ValueError):\n await client.get_latest_sensor_readings(\n \"/lasereggs/00000000-0001-0001-0000-00007e57c0de\"\n )\n","sub_path":"kaiterra_async_client/tests/online_tests/test_sensors.py","file_name":"test_sensors.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"181231793","text":"import sqlite3\n\n\nclass DataBase:\n def __init__(self, filename):\n self.conn = sqlite3.connect(filename)\n self.cursor = self.conn.cursor()\n\n def create_table(self, tablename):\n self.cursor.execute(\"CREATE TABLE IF NOT EXISTS {0:s}(\"\n \"'type' TEXT,\"\n \"'id' TEXT,\"\n \"'time' INTEGER,\"\n \"'price' TEXT,\"\n \"'quantity' TEXT)\".format(tablename))\n\n def fill_row(self, tablename, *args):\n with self.conn:\n self.cursor.executemany(\"INSERT INTO {0:s} VALUES(?, ?, ?, ?, ?)\".format(tablename), (args,))\n","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"605018193","text":"\"\"\"\nTic Tac Toe Player\n\"\"\"\n\nimport math\nimport copy\n\nX = \"X\"\nO = \"O\"\nEMPTY = None\n\n\ndef initial_state():\n \"\"\"\n Returns starting state of the board.\n \"\"\"\n return [[EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY],\n [EMPTY, EMPTY, EMPTY]]\n # return [[EMPTY, O, X],\n # [X, O, EMPTY],\n # [O, EMPTY, X]]\n\n\ndef player(board):\n \"\"\"\n Returns player who has the next turn on a board.\n \"\"\"\n total_moves = 0\n for i in range(len(board)):\n for j in range(len(board[i])):\n if board[i][j] == 'X' or board[i][j] == 'O':\n total_moves += 1\n\n if total_moves % 2 == 0:\n return 'X'\n else:\n return 'O'\n\n\ndef actions(board):\n \"\"\"\n Returns set of all possible actions (i, j) available on the board.\n \"\"\"\n all_possible_moves = []\n for i in range(len(board)):\n for j in range(len(board[i])):\n if board[i][j] == EMPTY:\n all_possible_moves.append((i,j))\n\n if len(all_possible_moves) > 0:\n return set(all_possible_moves)\n else:\n return None\n\ndef result(board, action):\n \"\"\"\n Returns the board that results from making move (i, j) on the board.\n \"\"\"\n copy_of_board = copy.deepcopy(board)\n possible_moves = actions(board)\n # If action is not a valid action for the board, your program should raise an exception.\n if possible_moves == None:\n raise Exception('Not a possible move')\n else:\n copy_of_board[action[0]][action[1]] = player(copy_of_board)\n return copy_of_board\n\n\ndef winner(board):\n \"\"\"\n Returns the winner of the game, if there is one.\n \"\"\"\n # If the X player has won the game, your function should return X. \n # If the O player has won the game, your function should return O.\n for i in range(len(board)):\n for j in range(len(board[i])):\n if board[i][j] is not EMPTY:\n current_player = board[i][j]\n\n # Check horizontally \n if board[i][0] == board[i][1] == board[i][2]:\n return current_player\n\n # Check vertically\n if board[0][j] == board[1][j] == board[2][j]:\n return current_player\n\n # Check diagonally\n if board[0][0] == current_player and board[0][0] == board[1][1] == board[2][2]:\n return current_player\n elif board[2][0] == current_player and board[2][0] == board[1][1] == board[0][2]:\n return current_player\n\n return None\n\n\ndef terminal(board):\n \"\"\"\n Returns True if game is over, False otherwise.\n \"\"\"\n if winner(board) or actions(board) == None:\n return True\n else:\n return False\n\n\ndef utility(board):\n \"\"\"\n Returns 1 if X has won the game, -1 if O has won, 0 otherwise.\n \"\"\"\n board_winner = winner(board)\n if board_winner == 'X':\n return 1\n elif board_winner == 'O':\n return -1\n else: \n return 0\n\n\n\ndef minimax(board):\n \"\"\"\n Returns the optimal action for the current player on the board.\n \"\"\"\n if terminal(board):\n return None\n\n if player(board) == 'X':\n m = max_value(board)\n return m[1]\n else:\n m = min_value(board)\n return m[1]\n \n\ndef max_value(board):\n # This will end the recursion\n if terminal(board):\n return [utility(board), None]\n\n # Since this is the max value we need to start at -Infinity so that \n # as we loop through all possible actions we can pick an action that brings us\n # the best value. Therefor value will only ever increase\n value = -math.inf\n best_action = None\n for action in actions(board):\n value_for_current_action = max(value, min_value(result(board, action))[0])\n if value_for_current_action > value:\n value = value_for_current_action\n best_action = action\n # Best possible outcome for the maximizer is 1 so if our value is == 1\n # then we can just take that action and break\n if value == 1:\n break\n\n return [value, best_action]\n\ndef min_value(board):\n # End the recursion\n\n # this at the end of the recursion this returns a number but if it is not the\n # end of the recursion it returns an array []\n if terminal(board):\n return [utility(board), None]\n \n # Min values goal is to minimize and choose the most negative action found\n value = math.inf\n best_action = None\n for action in actions(board):\n value_for_current_action = min(value, max_value(result(board, action))[0])\n if value_for_current_action < value:\n value = value_for_current_action\n best_action = action\n\n # Best possible outcome for the minimizer is -1 so if our value is == -1\n # then we can just take that action and break\n if value == -1:\n break\n return [value, best_action]\n","sub_path":"CS50_TicTacToe_Minimax_Algorithm/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":4970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"117460308","text":"from __future__ import print_function, division\nimport argparse\nimport copy\nimport numpy as np\nimport os\nimport pandas as pd\nfrom PIL import Image\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nfrom torchvision import datasets, transforms, utils, models\nfrom torch.utils.data.sampler import SubsetRandomSampler\nfrom torch.utils.data.sampler import WeightedRandomSampler\nimport time\n\nfrom collections import OrderedDict\n\nnp.random.seed(148)\ntorch.manual_seed(148)\n\nos.environ['KMP_DUPLICATE_LIB_OK'] = 'True' # solve some MacOS specific problems\n\n'''\nAll pre-trained models expect input images normalized in the same way, i.e. mini-batches of 3-channel RGB images \nof shape (3 x H x W), where H and W are expected to be at least 224. The images have to be loaded in to a \nrange of [0, 1] and then normalized using mean = [0.485, 0.456, 0.406] and std = [0.229, 0.224, 0.225].\n'''\n\n'''\nThis code is adapted from the official PyTorch Fine-Tune example:\n(https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html)\nand official PyTorch Dataset example:\nhttps://pytorch.org/tutorials/beginner/data_loading_tutorial.html\n'''\n\n\nclass CovidDataset(Dataset):\n\n def __init__(self, txt_file, root_dir, transform=None):\n \"\"\"\n Args:\n txt_file (string): Path to the txt file with annotations.\n root_dir (string): Directory with all the images.\n transform (callable, optional): Optional transform to be applied\n on a sample.\n \"\"\"\n self.df = pd.read_csv(txt_file, sep=\" \", header=None)\n self.root_dir = root_dir\n self.transform = transform\n self.classes = len(set(self.df[2]))\n\n def __len__(self):\n return len(self.df)\n\n def __getitem__(self, idx):\n if torch.is_tensor(idx):\n idx = idx.tolist()\n\n img_name = os.path.join(self.root_dir, self.df.iloc[idx, 1])\n image = Image.open(img_name).convert('RGB')\n label = self.df.iloc[idx, 2]\n label = ['COVID-19', 'pneumonia', 'normal'].index(label)\n label = np.array(label, dtype=np.int64)\n sample = {'image': image, 'label': label}\n if self.transform:\n if label == 0:\n sample['image'] = self.transform[1](sample['image'])\n else:\n sample['image'] = self.transform[0](sample['image'])\n\n return sample\n\n\ndef train_model(model, dataloaders, criterion, optimizer, device, model_save_path, num_epochs=25):\n since = time.time()\n\n val_acc_history = []\n\n best_model_wts = copy.deepcopy(model.state_dict())\n best_loss = 99.99\n prev = time.time()\n for epoch in range(num_epochs):\n t = time.time() - prev\n prev = time.time()\n print(t)\n print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n print('-' * 10)\n\n # Each epoch has a training and validation phase\n for phase in ['train', 'val']:\n if phase == 'train':\n model.train() # Set model to training mode\n else:\n model.eval() # Set model to evaluate mode\n\n running_loss = 0.0\n running_corrects = 0\n tp = 0\n num_covid = 0\n\n # Iterate over data.\n for i_batch, sample_batched in enumerate(dataloaders[phase]):\n inputs = sample_batched['image'].to(device)\n labels = sample_batched['label'].to(device)\n # zero the parameter gradients\n optimizer.zero_grad()\n\n # forward\n # track history if only in train\n with torch.set_grad_enabled(phase == 'train'):\n # Get model outputs and calculate loss\n # Special case for inception because in training it has an auxiliary output. In train\n # mode we calculate the loss by summing the final output and the auxiliary output\n # but in testing we only consider the final output.\n outputs = model(inputs)\n loss = criterion(outputs, labels)\n\n _, preds = torch.max(outputs, 1)\n\n # backward + optimize only if in training phase\n if phase == 'train':\n loss.backward()\n optimizer.step()\n\n # statistics\n running_loss += loss.item() * inputs.size(0)\n running_corrects += torch.sum(preds == labels.data)\n for i, gt in enumerate(labels.data):\n if gt == 0:\n num_covid += 1\n if preds[i] == 0:\n tp += 1\n epoch_loss = running_loss / train_num if phase == 'train' else running_loss / val_num\n epoch_acc = running_corrects.double() / train_num if phase == 'train' else running_corrects.double() / val_num\n epoch_recall = float(tp) / num_covid\n print('{} Loss: {:.4f} Acc: {:.4f} recall: {:.4f}'.format(phase, epoch_loss, epoch_acc, epoch_recall))\n\n # deep copy the model\n if phase == 'val' and epoch_loss < best_loss:\n best_loss = epoch_loss\n best_model_wts = copy.deepcopy(model.state_dict())\n torch.save(model.state_dict(), model_save_path)\n if phase == 'val':\n torch.save(model.state_dict(), \"ckpts/\" + str(epoch))\n val_acc_history.append(epoch_acc)\n\n print()\n\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n print('Best val loss: {:4f}'.format(best_loss))\n\n # load best model weights\n model.load_state_dict(best_model_wts)\n return model, val_acc_history\n\n\ndef initialize_model(model_name, num_classes, use_pretrained=True):\n # Initialize these variables which will be set in this if statement. Each of these\n # variables is model specific.\n model_ft = None\n input_size = 0\n\n if model_name == \"resnet18\":\n \"\"\" Resnet18\n \"\"\"\n model_ft = models.resnet18(pretrained=use_pretrained)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n input_size = 224\n\n elif model_name == \"resnet50\":\n \"\"\" Resnet50\n \"\"\"\n model_ft = models.resnet50(pretrained=use_pretrained)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n input_size = 224\n\n elif model_name == \"resnet152\":\n \"\"\" Resnet152\n \"\"\"\n model_ft = models.resnet152(pretrained=use_pretrained)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n input_size = 224\n\n elif model_name == \"resnext\":\n \"\"\" Resnext101\n \"\"\"\n model_ft = models.resnext101_32x8d(pretrained=True)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n input_size = 224\n elif model_name == \"densenet\":\n \"\"\" Densenet121\n \"\"\"\n model_ft = models.densenet121(True)\n num_ftrs = model_ft.classifier.in_features\n model_ft.classifier = nn.Linear(num_ftrs, 14)\n modelCheckpoint = torch.load(\"model.pth.tar\")\n state_dict = modelCheckpoint['state_dict']\n new_state_dict = OrderedDict()\n for k, v in state_dict.items():\n name = k[7:] # remove 'module.' of DataParallel\n new_state_dict[name] = v\n model_ft.load_state_dict(new_state_dict, strict=False)\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\n input_size = 224\n else:\n print(\"Invalid model name, exiting...\")\n exit()\n\n return model_ft, input_size\n\n\ndef test(model, device, test_loader):\n model.eval() # Set the model to inference mode\n test_loss = 0\n correct = 0\n test_num = 0\n tp = np.array([0, 0, 0])\n num = np.array([0, 0, 0])\n tn = np.array([0, 0, 0])\n negative = np.array([0, 0, 0])\n with torch.no_grad(): # For the inference step, gradient is not computed\n for sample_batched in test_loader:\n data = sample_batched['image'].to(device)\n target = sample_batched['label'].to(device)\n output = model(data)\n _, preds = torch.max(output, 1)\n print(output.shape, target.shape)\n criterion = nn.CrossEntropyLoss() # sum up batch loss\n loss = criterion(output, target)\n test_loss += loss.item() * data.size(0)\n pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability\n correct += pred.eq(target.view_as(pred)).sum().item()\n test_num += len(data)\n for i, gt in enumerate(target.data):\n num[gt] += 1\n if pred[i] == gt:\n tp[gt] += 1\n tn += 1\n negative += 1\n tn[pred[i]] -= 1\n negative[pred[i]] -= 1\n if pred[i] != gt:\n tn[gt] -= 1\n\n print(np.divide(tp, num))\n print(np.divide(tn, negative))\n\n test_loss /= test_num\n print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.6f}%)\\n'.format(\n test_loss, correct, test_num,\n 100. * correct / test_num))\n\n\ndef main():\n parser = argparse.ArgumentParser(description='PyTorch Baseline')\n parser.add_argument('--mode', type=str, default='train',\n help='train mode or test mode')\n parser.add_argument('--model-type', type=str, default='densenet',\n help='model type')\n parser.add_argument('--train-img-path', type=str, default='./data/train',\n help='training data path')\n parser.add_argument('--test-img-path', type=str, default='./data/test',\n help='test data path')\n parser.add_argument('--train-txt-path', type=str, default='./data/train_split_v3.txt',\n help='train txt path')\n parser.add_argument('--test-txt-path', type=str, default='./data/test_split_v3.txt',\n help='test txt path')\n parser.add_argument('--model-save-path', type=str, default='./resnet121_new_data_aug.pth',\n help='model save path')\n parser.add_argument('--model-load-path', type=str, default='./baseline.pth',\n help='model load path')\n parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n help='input batch size for training (default: 64)')\n parser.add_argument('--epochs', type=int, default=30, metavar='N',\n help='number of epochs to train (default: 30)')\n parser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n help='learning rate (default: 0.01)')\n args = parser.parse_args()\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n print(device)\n training_image_path = args.train_img_path\n test_image_path = args.test_img_path\n train_txt_path = args.train_txt_path\n test_txt_path = args.test_txt_path\n\n if args.mode == \"test\":\n assert os.path.exists(args.model_load_path)\n test_transform = transforms.Compose([\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n ])\n test_dataset = CovidDataset(txt_file=test_txt_path, root_dir=test_image_path,\n transform=[test_transform, test_transform])\n # do train_val_split\n\n covid_index = []\n normal_index = []\n pneumonia_index = []\n\n test_txt_df = pd.read_csv(args.test_txt_path, sep=\" \", header=None)\n for i in range(test_txt_df.shape[0]):\n label = test_txt_df.iloc[i, -2]\n if label == 'COVID-19':\n covid_index.append(i)\n elif label == 'pneumonia':\n pneumonia_index.append(i)\n elif label == 'normal':\n normal_index.append(i)\n\n test_covid_index = np.random.choice(covid_index, 100, replace=False)\n test_normal_index = np.random.choice(normal_index, 100, replace=False)\n test_pneumonia_index = np.random.choice(pneumonia_index, 100, replace=False)\n test_index = []\n test_index.extend(test_covid_index)\n test_index.extend(test_normal_index)\n test_index.extend(test_pneumonia_index)\n\n test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.batch_size,\n sampler=SubsetRandomSampler(test_index), num_workers=8)\n model, input_size = initialize_model(args.model_type, 3, use_pretrained=True)\n model = model.to(device)\n model.load_state_dict(torch.load(args.model_load_path))\n test(model, device, test_loader)\n return\n\n train_transform = transforms.Compose([\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n ])\n\n covid_transform1 = transforms.Compose([\n transforms.Resize((256, 256)),\n transforms.RandomHorizontalFlip(),\n transforms.RandomResizedCrop(size=(224, 224), scale=(0.7, 1.0)),\n # transforms.RandomVerticalFlip(),\n # transforms.RandomRotation(30),\n # transforms.RandomAffine(0, (0.25, 0.25), scale=(0.8, 1.2)),\n # transforms.ColorJitter(brightness=0.3),\n # transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n ])\n\n covid_transform2 = transforms.Compose([\n transforms.RandomHorizontalFlip(),\n transforms.RandomRotation(15),\n transforms.RandomAffine(0, (0.25, 0.25), scale=(0.8, 1.2)),\n transforms.ColorJitter(brightness=0.3),\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n ])\n\n val_transform = transforms.Compose([\n transforms.Resize((224, 224)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n ])\n\n train_dataset = CovidDataset(txt_file=train_txt_path, root_dir=training_image_path,\n transform=[covid_transform1, covid_transform1])\n\n val_dataset = CovidDataset(txt_file=train_txt_path, root_dir=training_image_path,\n transform=[covid_transform1, covid_transform1])\n\n # do train_val_split\n\n covid_index = []\n normal_index = []\n pneumonia_index = []\n\n train_txt_df = pd.read_csv(args.train_txt_path, sep=\" \", header=None)\n for i in range(train_txt_df.shape[0]):\n label = train_txt_df.iloc[i, -2]\n if label == 'COVID-19':\n covid_index.append(i)\n elif label == 'pneumonia':\n pneumonia_index.append(i)\n elif label == 'normal':\n normal_index.append(i)\n\n train_covid_index = np.random.choice(covid_index, len(covid_index) - 50, replace=False)\n train_normal_index = np.random.choice(normal_index, len(normal_index) - 50, replace=False)\n train_pneumonia_index = np.random.choice(pneumonia_index, len(pneumonia_index) - 50, replace=False)\n train_index = []\n train_index.extend(train_covid_index)\n train_index.extend(train_normal_index)\n train_index.extend(train_pneumonia_index)\n\n global train_num\n train_num = len(train_index)\n train_num = 3 * len(train_normal_index)\n print('train_num', train_num)\n val_index = np.setdiff1d(range(len(train_dataset)), train_index)\n global val_num\n val_num = len(val_index)\n print('val_num', val_num)\n\n target = train_txt_df.iloc[:, -2].tolist()\n target = [['COVID-19', 'pneumonia', 'normal'].index(i) for i in target]\n class_sample_count = np.unique(target, return_counts=True)[1]\n print(class_sample_count)\n class_sample_count[0] = class_sample_count[0] * 2\n weight = 1. / class_sample_count\n samples_weight = weight[target]\n print(samples_weight[50])\n for index in val_index:\n samples_weight[index] = 0\n samples_weight = torch.from_numpy(samples_weight)\n\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size,\n sampler=SubsetRandomSampler(train_index), num_workers=8)\n train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size,\n sampler=WeightedRandomSampler(samples_weight, train_num), num_workers=8)\n val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=args.batch_size,\n sampler=SubsetRandomSampler(val_index), num_workers=8)\n dataloaders_dict = {}\n dataloaders_dict['train'] = train_loader\n dataloaders_dict['val'] = val_loader\n\n # Initialize the model for this run\n model_name = args.model_type\n num_classes = 3\n model_ft, input_size = initialize_model(model_name, num_classes, use_pretrained=True)\n\n # Print the model we just instantiated\n # print(model_ft)\n\n # Send the model to GPU\n model_ft = model_ft.to(device)\n\n params_to_update = model_ft.parameters()\n '''\n print(\"Params to learn:\")\n for name, param in model_ft.named_parameters():\n if param.requires_grad == True:\n print(\"\\t\", name)\n '''\n base_parameters = list(model_ft.parameters())[:-2]\n fc_parameters = list(model_ft.parameters())[-2:]\n # print(fc_parameters)\n\n # Observe that all parameters are being optimized\n optimizer_ft = optim.SGD([\n {'params': base_parameters},\n {'params': fc_parameters, 'lr': args.lr}\n ], lr=0.1 * args.lr, momentum=0.9, weight_decay=1e-5)\n\n # adam\n optimizer_ft_adam = optim.Adam([\n {'params': base_parameters},\n {'params': fc_parameters, 'lr': 3e-4}\n ], lr=3e-5)\n # Setup the loss fxn\n criterion = nn.CrossEntropyLoss()\n\n # Train and evaluate\n num_epochs = args.epochs\n model_ft, hist = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, device=device,\n model_save_path=args.model_save_path, num_epochs=num_epochs)\n\n # save model\n torch.save(model_ft.state_dict(), args.model_save_path)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"chexnet.py","file_name":"chexnet.py","file_ext":"py","file_size_in_byte":18746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"484492681","text":"U0080 = chr(0x0080)\nU0081 = chr(0x0081)\nBREAK_PERMITTED_HERE = chr(0x0082)\nNO_BREAK_HERE = chr(0x0083)\nINDEX = chr(0x0084)\nNEXT_LINE = chr(0x0085)\nSTART_OF_SELECTED_AREA = chr(0x0086)\nEND_OF_SELECTED_AREA = chr(0x0087)\nCHARACTER_TABULATION_SET = chr(0x0088)\nCHARACTER_TABULATION_WITHJUSTIFICATION = chr(0x0089)\nLINE_TABULATION_SET = chr(0x008A)\nPARTIAL_LINE_FORWARD = chr(0x008B)\nPARTIAL_LINE_BACKWARD = chr(0x008C)\nREVERSE_LINE_FEED = chr(0x008D)\nSINGLE_SHIFT_TWO = chr(0x008E)\nSINGLE_SHIFT_THREE = chr(0x008F)\nDEVICE_CONTROL_STRING = chr(0x0090)\nPRIVATE_USE_ONE = chr(0x0091)\nPRIVATE_USE_TWO = chr(0x0092)\nSET_TRANSMIT_STATE = chr(0x0093)\nCANCEL_CHARACTER = chr(0x0094)\nMESSAGE_WAITING = chr(0x0095)\nSTART_OF_GUARDED_AREA = chr(0x0096)\nEND_OF_GUARDED_AREA = chr(0x0097)\nSTART_OF_STRING = chr(0x0098)\nU0099 = chr(0x0099)\nSINGLE_CHARACTER_INTRODUCER = chr(0x009A)\nCONTROL_SEQUENCE_INTRODUCER = chr(0x009B)\nSTRING_TERMINATOR = chr(0x009C)\nOPERATING_SYSTEM_COMMAND = chr(0x009D)\nPRIVACY_MESSAGE = chr(0x009E)\nAPPLICATION_PROGRAM_COMMAND = chr(0x009F)\nNO_BREAK_SPACE = chr(0x00A0)\nINVERTED_EXCLAMATION_MARK = chr(0x00A1)\nCENT_SIGN = chr(0x00A2)\nPOUND_SIGN = chr(0x00A3)\nCURRENCY_SIGN = chr(0x00A4)\nYEN_SIGN = chr(0x00A5)\nBROKEN_BAR = chr(0x00A6)\nSECTION_SIGN = chr(0x00A7)\nDIAERESIS = chr(0x00A8)\nCOPYRIGHT_SIGN = chr(0x00A9)\nFEMININE_ORDINAL_INDICATOR = chr(0x00AA)\nLEFT_POINTING_DOUBLE_ANGLE_QUOTATIONMARK = chr(0x00AB)\nNOT_SIGN = chr(0x00AC)\nSOFT_HYPHEN = chr(0x00AD)\nREGISTERED_SIGN = chr(0x00AE)\nMACRON = chr(0x00AF)\nDEGREE_SIGN = chr(0x00B0)\nPLUS_MINUS_SIGN = chr(0x00B1)\nSUPERSCRIPT_TWO = chr(0x00B2)\nSUPERSCRIPT_THREE = chr(0x00B3)\nACUTE_ACCENT = chr(0x00B4)\nMICRO_SIGN = chr(0x00B5)\nPILCROW_SIGN = chr(0x00B6)\nMIDDLE_DOT = chr(0x00B7)\nCEDILLA = chr(0x00B8)\nSUPERSCRIPT_ONE = chr(0x00B9)\nMASCULINE_ORDINAL_INDICATOR = chr(0x00BA)\nRIGHT_POINTING_DOUBLE_ANGLE_QUOTATIONMARK = chr(0x00BB)\nVULGAR_FRACTION_ONE_QUARTER = chr(0x00BC)\nVULGAR_FRACTION_ONE_HALF = chr(0x00BD)\nVULGAR_FRACTION_THREE_QUARTERS = chr(0x00BE)\nINVERTED_QUESTION_MARK = chr(0x00BF)\nLATIN_CAPITAL_LETTER_A_WITH_GRAVE = chr(0x00C0)\nLATIN_CAPITAL_LETTER_A_WITH_ACUTE = chr(0x00C1)\nLATIN_CAPITAL_LETTER_A_WITH_CIRCUMFLEX = chr(0x00C2)\nLATIN_CAPITAL_LETTER_A_WITH_TILDE = chr(0x00C3)\nLATIN_CAPITAL_LETTER_A_WITH_DIAERESIS = chr(0x00C4)\nLATIN_CAPITAL_LETTER_A_WITH_RING_ABOVE = chr(0x00C5)\nLATIN_CAPITAL_LETTER_AE = chr(0x00C6)\nLATIN_CAPITAL_LETTER_C_WITH_CEDILLA = chr(0x00C7)\nLATIN_CAPITAL_LETTER_E_WITH_GRAVE = chr(0x00C8)\nLATIN_CAPITAL_LETTER_E_WITH_ACUTE = chr(0x00C9)\nLATIN_CAPITAL_LETTER_E_WITH_CIRCUMFLEX = chr(0x00CA)\nLATIN_CAPITAL_LETTER_E_WITH_DIAERESIS = chr(0x00CB)\nLATIN_CAPITAL_LETTER_I_WITH_GRAVE = chr(0x00CC)\nLATIN_CAPITAL_LETTER_I_WITH_ACUTE = chr(0x00CD)\nLATIN_CAPITAL_LETTER_I_WITH_CIRCUMFLEX = chr(0x00CE)\nLATIN_CAPITAL_LETTER_I_WITH_DIAERESIS = chr(0x00CF)\nLATIN_CAPITAL_LETTER_ETH = chr(0x00B0)\nLATIN_CAPITAL_LETTER_N_WITH_TILDE = chr(0x00B1)\nLATIN_CAPITAL_LETTER_O_WITH_GRAVE = chr(0x00B2)\nLATIN_CAPITAL_LETTER_O_WITH_ACUTE = chr(0x00B3)\nLATIN_CAPITAL_LETTER_O_WITH_CIRCUMFLEX = chr(0x00B4)\nLATIN_CAPITAL_LETTER_O_WITH_TILDE = chr(0x00B5)\nLATIN_CAPITAL_LETTER_O_WITH_DIAERESIS = chr(0x00B6)\nMULTIPLICATION_SIGN = chr(0x00B7)\nLATIN_CAPITAL_LETTER_O_WITH_STROKE = chr(0x00B8)\nLATIN_CAPITAL_LETTER_U_WITH_GRAVE = chr(0x00B9)\nLATIN_CAPITAL_LETTER_U_WITH_ACUTE = chr(0x00BA)\nLATIN_CAPITAL_LETTER_U_WITH_CIRCUMFLEX = chr(0x00BB)\nLATIN_CAPITAL_LETTER_U_WITH_DIAERESIS = chr(0x00BC)\nLATIN_CAPITAL_LETTER_Y_WITH_ACUTE = chr(0x00BD)\nLATIN_CAPITAL_LETTER_THORN = chr(0x00BE)\nLATIN_SMALL_LETTER_SHARP_S = chr(0x00BF)\nLATIN_SMALL_LETTER_A_WITH_GRAVE = chr(0x00E0)\nLATIN_SMALL_LETTER_A_WITH_ACUTE = chr(0x00E1)\nLATIN_SMALL_LETTER_A_WITH_CIRCUMFLEX = chr(0x00E2)\nLATIN_SMALL_LETTER_A_WITH_TILDE = chr(0x00E3)\nLATIN_SMALL_LETTER_A_WITH_DIAERESIS = chr(0x00E4)\nLATIN_SMALL_LETTER_A_WITH_RING_ABOVE = chr(0x00E5)\nLATIN_SMALL_LETTER_AE = chr(0x00E6)\nLATIN_SMALL_LETTER_C_WITH_CEDILLA = chr(0x00E7)\nLATIN_SMALL_LETTER_E_WITH_GRAVE = chr(0x00E8)\nLATIN_SMALL_LETTER_E_WITH_ACUTE = chr(0x00E9)\nLATIN_SMALL_LETTER_E_WITH_CIRCUMFLEX = chr(0x00EA)\nLATIN_SMALL_LETTER_E_WITH_DIAERESIS = chr(0x00EB)\nLATIN_SMALL_LETTER_I_WITH_GRAVE = chr(0x00EC)\nLATIN_SMALL_LETTER_I_WITH_ACUTE = chr(0x00ED)\nLATIN_SMALL_LETTER_I_WITH_CIRCUMFLEX = chr(0x00EE)\nLATIN_SMALL_LETTER_I_WITH_DIAERESIS = chr(0x00EF)\nLATIN_SMALL_LETTER_ETH = chr(0x00F0)\nLATIN_SMALL_LETTER_N_WITH_TILDE = chr(0x00F1)\nLATIN_SMALL_LETTER_O_WITH_GRAVE = chr(0x00F2)\nLATIN_SMALL_LETTER_O_WITH_ACUTE = chr(0x00F3)\nLATIN_SMALL_LETTER_O_WITH_CIRCUMFLEX = chr(0x00F4)\nLATIN_SMALL_LETTER_O_WITH_TILDE = chr(0x00F5)\nLATIN_SMALL_LETTER_O_WITH_DIAERESIS = chr(0x00F6)\nDIVISION_SIGN = chr(0x00F7)\nLATIN_SMALL_LETTER_O_WITH_STROKE = chr(0x00F8)\nLATIN_SMALL_LETTER_U_WITH_GRAVE = chr(0x00F9)\nLATIN_SMALL_LETTER_U_WITH_ACUTE = chr(0x00FA)\nLATIN_SMALL_LETTER_U_WITH_CIRCUMFLEX = chr(0x00FB)\nLATIN_SMALL_LETTER_U_WITH_DIAERESIS = chr(0x00FC)\nLATIN_SMALL_LETTER_Y_WITH_ACUTE = chr(0x00FD)\nLATIN_SMALL_LETTER_THORN = chr(0x00FE)\nLATIN_SMALL_LETTER_Y_WITH_DIAERESIS = chr(0x00FF)\n","sub_path":"gui/mem_dixy/Unicode/U0080.py","file_name":"U0080.py","file_ext":"py","file_size_in_byte":5081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"305753996","text":"\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nimport pickle\nfrom sklearn import metrics\nimport time\n\n#48000 bad queries\n#8700 good queries\n\ni = 200\n\nwhile i < 8700:\n\n start = time.time()\n #print('started')\n badQueries = open('badQueries_full_url.txt', 'r', encoding='utf-8')\n validQueries = open('goodQueries_full_url.txt', 'r', encoding='utf-8')\n\n\n badQueries = list(set(badQueries))\n validQueries = list(set(validQueries))\n\n num_samples = i\n badQueries = badQueries[:num_samples]\n validQueries = validQueries[:num_samples]\n\n #print(len(badQueries))\n #print(len(validQueries))\n allQueries = badQueries + validQueries\n yBad = [1 for i in range(0, len(badQueries))]\n yGood = [0 for i in range(0, len(validQueries))]\n y = yBad + yGood\n queries = allQueries\n\n\n #print('vectorizing')\n vectorizer = TfidfVectorizer(min_df=0.0, analyzer=\"char\", sublinear_tf=True, ngram_range=(1, 3))\n X = vectorizer.fit_transform(queries)\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n badCount = len(badQueries)\n validCount = len(validQueries)\n\n #print('training')\n lgs = LogisticRegression(class_weight={1: 2 * validCount / badCount, 0: 1.0}, solver='lbfgs') # class_weight='balanced')\n lgs.fit(X_train, y_train)\n\n #print('saving')\n with open('model_full_url.pkl', 'wb') as model_file:\n pickle.dump(lgs, model_file)\n\n with open('vectorizer_full_url.pkl', 'wb') as vectorizer_file:\n pickle.dump(vectorizer, vectorizer_file)\n\n end = time.time()\n\n predicted = lgs.predict(X_test)\n\n fpr, tpr, _ = metrics.roc_curve(y_test, (lgs.predict_proba(X_test)[:, 1]))\n auc = metrics.auc(fpr, tpr)\n\n print(\"%d\" % badCount, end=',')\n print(\"%d\" % validCount, end=',')\n print(\"%.6f\" % (validCount / (validCount + badCount)), end=',')\n print(\"%f\" % lgs.score(X_test, y_test), end=',') # checking the accuracy\n print(\"%f\" % metrics.precision_score(y_test, predicted), end=',')\n print(\"%f\" % metrics.recall_score(y_test, predicted), end=',')\n print(\"%f\" % metrics.f1_score(y_test, predicted), end=',')\n print(\"%f\" % auc, end=',')\n print(\"{0}\".format(end - start))\n\n i += 200\n","sub_path":"model_full_url.py","file_name":"model_full_url.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"24230214","text":"def distancia(ang,vel):\n import math\n dist=((vel**2)*(math.sin(2*ang)))/9.8\n \n if 98<=dist and dist<=102:\n \treturn( 'Acertou!')\n elif dist>102:\n \treturn('Muito longe')\n elif dist<98:\n return('Muito perto')\n\nang=float(input(\"insira o angulo de lançamento: \"))\nvel=float(input(\"insira a velocidade inicial do lançamento: \"))\nprint(distancia(ang,vel))\n","sub_path":"backup/user_189/ch30_2019_08_26_19_18_54_592478.py","file_name":"ch30_2019_08_26_19_18_54_592478.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"41686272","text":"import uuid\nimport random\nimport json\nfrom aiohttp import web\nfrom panini import app as panini_app\nfrom examples.simple_examples._wss_manager import WSSManager, html\n\n\napp = panini_app.App(\n service_name=\"async_NATS_WSS_bridge\",\n # host='nats-server' if 'HOSTNAME' in os.environ else '127.0.0.1',\n host=\"54.36.108.188\",\n port=4222,\n app_strategy=\"asyncio\",\n web_server=True,\n web_port=1111,\n)\nlog = app.logger\ntest_msg = {\n \"key1\": \"value1\",\n \"key2\": 2,\n \"key3\": 3.0,\n \"key4\": [1, 2, 3, 4],\n \"key5\": {\"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 5},\n \"key6\": {\"subkey1\": \"1\", \"subkey2\": 2, \"3\": 3, \"4\": 4, \"5\": 5},\n \"key7\": None,\n}\n\nmanager = WSSManager(app)\n\n\n@app.timer_task(interval=1)\nasync def publish_periodically_for_test():\n test_msg[\"key3\"] = random.random()\n await app.publish(\"test.subject\", test_msg)\n\n\n@app.http.get(\"/\")\nasync def web_endpoint_listener(request):\n \"\"\"\n Web client to view NATS stream. Displays messages from subjects that an user is following\n\n Example of request\n subscribe:\n {\"subjects\":[\"*.>\"],\"action\":\"subscribe\"}\n unsubscribe:\n {\"subjects\":[\"*.>\"],\"action\":\"unsubscribe\"}\n\n \"\"\"\n return web.Response(text=html, content_type=\"text/html\")\n\n\n@app.http.get(\"/stream\")\nasync def web_endpoint_listener(request):\n ws = web.WebSocketResponse()\n await ws.prepare(request)\n connection_id = str(uuid.uuid4())[:10]\n await ws.send_str(json.dumps({\"success\": True, \"data\": \"Successfully connected\"}))\n await manager.client_listener(ws, connection_id)\n try:\n await ws.close()\n except Exception as e:\n log.error(str(e))\n return ws\n\n\nasync def incoming_messages_callback(subscriber, msg, **kwargs):\n try:\n await subscriber.send_str(\n json.dumps({\"subject\": msg.subject, \"data\": msg.data})\n )\n except Exception as e:\n log.error(f\"error: {str(e)}\")\n\n\nif __name__ == \"__main__\":\n manager.callback = incoming_messages_callback\n app.http_server.web_app[\"subscribers\"] = {}\n app.start()\n","sub_path":"examples/simple_examples/async_wss_web_server.py","file_name":"async_wss_web_server.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"245725621","text":"from unittest import TestCase\nfrom PDM import PDM\nfrom Etat import *\n\n__author__ = 'Clement'\n\n\nclass TestPDM(TestCase):\n def setUp(self):\n etat_initial = Etat(0, 0)\n etat_final = Etat(2, 2)\n recompenses = [\n [0, -1, -3],\n [0, -1, -1],\n [0, 0, 1],\n ]\n self.probleme = PDM(recompenses, etat_initial, etat_final, True)\n\n def test_get_actions_possibles(self):\n actions_attendues = [\n [['Bas', 'Droite'], ['Bas', 'Gauche', 'Droite'], ['Bas', 'Gauche']],\n [['Haut', 'Bas', 'Droite'], ['Haut', 'Bas', 'Gauche', 'Droite'], ['Haut', 'Bas', 'Gauche']],\n [['Haut', 'Droite'], ['Haut', 'Gauche', 'Droite'], ['Haut', 'Gauche']]\n ]\n for etat in self.probleme.get_etats():\n actions_possibles = self.probleme.get_actions_possibles(etat)\n self.assertListEqual(actions_possibles, actions_attendues[etat.get_ligne()][etat.get_colonne()])\n\n def test_iteration_valeurs(self):\n actions_attendues = [\n ['Bas', 'Gauche', 'Bas'],\n ['Bas', 'Bas', 'Bas'],\n ['Droite', 'Droite', 'Gauche']\n ]\n actions_optimales = self.probleme.iteration_valeurs()\n self.assertListEqual(actions_optimales, actions_attendues)\n\n def test_get_matrice_transition(self):\n matrices_obtenues = [\n self.probleme.get_matrice_transition(Etat(0, 0), 'Bas'),\n self.probleme.get_matrice_transition(Etat(0, 0), 'Droite'),\n self.probleme.get_matrice_transition(Etat(1, 1), 'Haut'),\n self.probleme.get_matrice_transition(Etat(1, 1), 'Bas'),\n self.probleme.get_matrice_transition(Etat(1, 1), 'Gauche'),\n self.probleme.get_matrice_transition(Etat(1, 1), 'Droite'),\n ]\n matrices_attendues = [\n [\n [0, 0, 0],\n [0.9, 0.1, 0],\n [0, 0, 0]\n ],\n [\n [0, 0.9, 0],\n [0, 0.1, 0],\n [0, 0, 0]\n ],\n [\n [0.1, 0.8, 0.1],\n [0, 0, 0],\n [0, 0, 0]\n ],\n [\n [0, 0, 0],\n [0, 0, 0],\n [0.1, 0.8, 0.1]\n ],\n [\n [0.1, 0, 0],\n [0.8, 0, 0],\n [0.1, 0, 0]\n ],\n [\n [0, 0, 0.1],\n [0, 0, 0.8],\n [0, 0, 0.1]\n ]\n ]\n self.assertListEqual(matrices_obtenues, matrices_attendues)\n\n def test_c(self):\n recompenses_obtenues = [\n self.probleme.c(Etat(0, 0), 'Bas'),\n self.probleme.c(Etat(0, 0), 'Droite'),\n self.probleme.c(Etat(1, 1), 'Haut'),\n self.probleme.c(Etat(1, 1), 'Bas'),\n self.probleme.c(Etat(1, 1), 'Gauche'),\n self.probleme.c(Etat(1, 1), 'Droite'),\n self.probleme.c(Etat(2, 2), 'Haut'),\n self.probleme.c(Etat(2, 2), 'Gauche'),\n ]\n recompenses_attendues = [-0.1, -1, -1.1, 0.1, 0, -1, -1, -0.1]\n self.assertListEqual(recompenses_obtenues, recompenses_attendues)\n\n","sub_path":"PDM-valeurs/test_PDM.py","file_name":"test_PDM.py","file_ext":"py","file_size_in_byte":3205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"540744281","text":"import acm\nfrom DealPackageDevKit import DealPackageDefinition, Text, Int, Settings\nfrom inspect import cleandoc\n\n@Settings(SheetApplicable=False)\nclass DealPackageGraph(DealPackageDefinition):\n \"\"\"\n Enter values in \n * Minimum X value\n * Maximum X value\n * Gradient\n to see the graph change.\n \"\"\"\n\n minX = Int( defaultValue=0,\n tick=True,\n label='Minimum X value')\n \n maxX = Int( defaultValue=10,\n tick=True,\n label='Maximum X value')\n \n gradient = Int( defaultValue=1,\n tick=2,\n label='Gradient')\n \n doc = Text( defaultValue=cleandoc(__doc__),\n editable=False,\n width=150,\n height=120)\n\n # ####################### #\n # Interface Overrides #\n # ####################### #\n\n def CustomPanes(self):\n return [ \n {'General' : \"\"\"\n minX;\n maxX;\n gradient;\n fill;\n hbox{DESCRIPTION;\n doc;\n );\n \"\"\"\n }\n ] \n\n def GraphYValues(self, xValues):\n yValues = [self.gradient*x for x in xValues]\n return yValues\n \n def GraphXValues(self):\n step = int((self.maxX-self.minX)/10)\n step = max(1, step)\n r = list(range(self.minX, self.maxX+step, step))\n return r\n\n def IsValid(self, exceptionAccumulator, aspect):\n exceptionAccumulator('This example is used for demonstration and can not be saved.')\n","sub_path":"Extensions/Deal Package Examples/FPythonCode/Graph_DPE.py","file_name":"Graph_DPE.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"214321541","text":"import math\nfrom typing import re\n\n\ndef getTopWord(filename, n):\n '''\n Hàm trả lại danh sách (list) n từ có số lần nhiều nhất trong file văn bản filename.\n Trong file văn bản filename, mỗi từ phân cách nhau bởi dấu cách\n Danh sách kết quả được sắp xếp giảm dần theo thứ tự từ điển của các từ.\n Nếu 2 từ có tần số xuất hiện bằng nhau thì ưu tiên từ có thứ tự từ điển lớn hơn\n (ví dụ 'd' > 'c' vì vậy nếu 'd' và 'c' có cùng số lần xuất hiện thì lấy 'd')\n Ví dụ, file văn bản có nội dung như sau:\n \"\n a b c a a a b c d f d a d a f g s g h f s s a\n a g h b c e f g m n j s a r t y y u v z x k l a\n \"\n\n danh sách các từ cùng số lần xuất hiện sắp xếp theo số lần xuất hiện giảm dần, từ giảm dần theo thứ tự từ điển như sau:\n\n [('a', 10), ('s', 4), ('g', 4), ('f', 4), ('d', 3), ('c', 3), ('b', 3), ('y', 2)\n , ('h', 2), ('z', 1), ('x', 1), ('v', 1), ('u', 1), ('t', 1), ('r', 1), ('n', 1)\n , ('m', 1), ('l', 1), ('k', 1), ('j', 1), ('e', 1)]\n\n Như vậy với n = 6, kết quả là\n\n ['s', 'g', 'f', 'd', 'c', 'a']\n\n\n Chú ý, file văn bản có nhiều dòng và không có ký tự unicode\n Nếu n > số từ thì kết quả là toàn bộ danh sách các từ.\n '''\n f = open(filename,'r', encoding='utf-8')\n token = f.read().split()\n w = [] #dict\n c = []\n res = []\n counts = ()\n for t in token:\n if t not in w:\n w.append(t)\n for j in w:\n count = 0\n for w in token:\n if(w == j):\n count += 1\n counts = (j,count)\n c.append(counts)\n c.sort(key= lambda x: x[1], reverse= False)\n c.reverse()\n if(n > len(c)):\n res = c\n else:\n for i in range(n):\n res.append(c[i])\n Top = []\n for counts in c:\n Top.append(counts[0])\n Top.sort(reverse=True)\n return Top\n\n\n # pass\n\n\ndef getVector(filename, topword):\n '''\n Hàm này trả lại danh sách (list) số nguyên tương ứng với vector biểu diễn văn bản trong file filename.\n phần tử thứ i trong danh sách là số lần từ topword[i] xuất hiện trong văn bản.\n\n ví dụ văn bản là\n \"\n a b c a a a b c d f d a d a f g s g h f s s a\n a g h b c e f g m n j s a r t y y u v z x k l a\n \"\n\n\n topword = ['s', 'g', 'f', 'd', 'a']\n\n kết quả là: [4, 4, 4, 3, 10]\n '''\n\n\n pass\n\ndef numerator(u,v):\n n = sum([u[i]*v[i] for i in range(0,len(u))])\n return n\n\ndef denominator(u):\n x = sum([i**2 for i in u])\n return math.sqrt(x)\n\n\ndef getCosineSim(u, v):\n '''\n Phương thức tính cosine góc tạo bởi hai vector u, v\n\n cosine(u,v) = (u.v)/(||u||x||v||)\n\n ví dụ với u = [1,2,3,4], v = [1,2,1,1], kết quả làm tròn đến 5 chữ số là: 0.82808\n '''\n num = numerator(u,v)\n den = denominator(u)*denominator(v)\n cosin = num/den\n return cosin\n\n # pass\n\ndef sinhTaylor(x, e):\n '''\n Viết chương trình tính sinh(x) theo khai triển Taylor,\n trong đó e là sai số để xác định thời điểm dừng thuật toán,\n Thuật toán dừng lại tại số hạng Pi nếu |Pi - Pi-1| <= e\n\n ví dụ x = 5.5, e = 0.00001 kết quả làm tròn đến 5 chữ số là: 122.34392\n nhưng với e = 0.5 kết quả làm tròn đến 5 chữ số là: 122.34289\n\n '''\n result = x\n a = x\n i = 1\n while(a>e):\n a = a * (pow(x,2)) / (2 * i * (2 * i + 1))\n result += a\n i = i+1\n a = a * (pow(x, 2)) / (2 * i * (2 * i + 1))\n result += a\n return result\n # pass\n\n\n'''\n Chú ý, các phương thức sẽ được gọi đến để chấm điểm, \n do vậy bài nộp của sinh viên cần phải bỏ hết (hoặc comment #) các lệnh in ra màn hình\n\n'''\n\n\ndef testDemo():\n print(getTopWord('text.txt', 5))\n print(getVector('text.txt', getTopWord('text.txt', 5)))\n\n print(round(getCosineSim([1, 2, 3, 4], [1, 2, 1, 1]), 5))\n\n print(round(sinhTaylor(5.5, 0.5), 5))\n\n\n'''\nBỏ comment lệnh testDemo() dưới đây để test chương trình, và comment lại lệnh đó khi nộp bài\n'''\ntestDemo()\n","sub_path":"PythonProjects/Submit/sub6/Mid1.py","file_name":"Mid1.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"340360599","text":"\"\"\"\nотображает изображение с помощью альтернативного объекта из пакета PIL\nподдерживает множество форматов изображений; предварительно установите пакет\nPIL: поместите его в каталог Lib\\site-packages\n\"\"\"\nimport os, sys\nfrom tkinter import *\nfrom PIL.ImageTk import PhotoImage\n\nimgdir = 'images'\nimgfile = 'spb_backyard.jpg' # поддерживает gif, jpg, png, tiff, и др.\nif len(sys.argv) > 1:\n imgfile = sys.argv[1]\nimgpath = os.path.join(imgdir, imgfile)\n\nwin = Tk()\nwin.title(imgfile)\nimgobj = PhotoImage(file=imgpath) # теперь поддерживает и JPEG!\nLabel(win, image=imgobj).pack()\nwin.mainloop()\nprint(imgobj.width(), imgobj.height()) # показать размер в пикселях при выходе\n","sub_path":"Gui/PIL/viewer-pil.py","file_name":"viewer-pil.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"479136244","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 16 09:37:32 2017\n\nEPI 6.6: Remove duplicates from a sorted array\n\nA: <2,3,3,5,5,7,7,11,13> --> A: <2,3,5,7,11,13,0,0,0>\nA: <2,3,5,5,7,11,11,11,13> --> A:<2,3,5,7,11,13,0,0,0>\n\nsee also variant question \n\n\"\"\"\n\ndef RemoveDuplicates_FirstImplementation(A, size):\n \n i = 0\n inew = 0\n while i < size-1:\n# print('i:{} A: {}'.format(i,A))\n if A[i] != A[i+1]:\n A[inew] = A[i] \n inew += 1\n \n i += 1\n \n# print('i: {} A[i]:{} inew:{} A[inew]:{}'.format(i, A[i], inew, A[inew]))\n \n # Handle the Last element\n if inew != 0:\n if A[inew-1] != A[i]:\n A[inew] = A[i]\n else:\n A[inew] = A[i]\n inew += 1\n i += 1\n \n # Set the shifted sections to zero\n for j in range(inew, i):\n A[j] = 0\n \n size = size - (i-inew)\n \n return A, size\n\ndef RemoveDuplicates(A, size):\n\n inew = 0\n for i in range(1, size):\n print('i: {} A[i]: {}'.format(i, A[i]))\n if A[i-1] != A[i]:\n inew += 1\n A[inew] = A[i]\n \n\n print('inew : {} i: {}'.format(inew, i))\n for i in range(inew+1, i+1):\n A[i] = 0\n\n size = size - (i-inew)\n\n \n return A, size\n \n\ndef main():\n print('Remove duplicates from a sorted array')\n\n A= [2,3,3,5,5,7,7,11,13, 13, 13]\n A= [2,3,3,5,5,7,7,11,13]\n# A= [0,1,2,3,4,5,5]\n# A= [1,1,1,1,1]\n A= [1,1,1,2,2,2,3,3,3]\n A = [2,3,5,5,7,11,11,11,13]\n \n print('Original array A: {}'.format(A))\n size = len(A)\n# res, size = RemoveDuplicates_FirstImplementation(A, size)\n res, size = RemoveDuplicates(A, size)\n print('res: {}, size:{}'.format(res, size))\n \n\nif __name__ == '__main__':\n main() ","sub_path":"mulakat/EPI06_Arrays/removeDuplicatesFromArray.py","file_name":"removeDuplicatesFromArray.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"182566607","text":"\n\n#calss header\nclass _RECLAIM():\n\tdef __init__(self,): \n\t\tself.name = \"RECLAIM\"\n\t\tself.definitions = [u'to take back something that was yours: ', u'to make land, such as desert or areas covered by water, suitable for farming or building', u'to treat waste materials in order to get useful materials, such as glass or paper, that can be used again']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_reclaim.py","file_name":"_reclaim.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"219840967","text":"# Import necessary libraries\nimport numpy as np\nfrom scipy.sparse import lil_matrix, csr_matrix\nimport networkx as nx\nimport re\n\n\nfilms = {}\n\nactors = open(\"Data/actors.list\", \"r\", encoding=\"ISO-8859-1\")\n\nactor_index = 0\nrecent = False\n\nstart_year = 2007\n\ntemp_films = []\n\ndef add_actor(film, actor):\n if film in films:\n films[film].append(actor)\n else:\n films[film] = [actor]\n\nfor line in actors:\n # Line is an actor and film\n match = re.match(r'([^\\t]+)[\\t]+([^\"]*) \\(([0-9]{4})\\)', line)\n if match:\n # add temp films and increment actor index\n if len(temp_films) >= 3:\n for film in temp_films:\n add_actor(film, actor_index)\n temp_films.clear()\n actor_index += 1\n if int(match.group(3)) >= start_year:\n temp_films.append(match.group(2))\n else:\n # Line is an actor and TV show\n match = re.match(r'([^\\t]+)[\\t]+(.*) \\(([0-9]{4})\\)', line)\n if match:\n # add temp films and increment actor index\n if len(temp_films) >= 3:\n for film in temp_films:\n add_actor(film, actor_index)\n temp_films.clear()\n actor_index += 1\n else:\n # Line is just a film (actor defined earlier)\n match = re.match(r'[\\t]+([^\"]*) \\(([0-9]{4})\\)', line)\n if match:\n if int(match.group(2)) >= start_year:\n temp_films.append(match.group(1))\n\nactors.close()\n\nprint(len(films))\n\nprint(actor_index)\nprint(actor_index**2)\n\nadj = lil_matrix((actor_index + 1, actor_index + 1), dtype=np.int8)\n\nfor film, actors in films.items():\n for actor1 in actors:\n for actor2 in actors:\n adj[actor1, actor2] += 1\n\nout = adj.tocsr()\n\nnp.savez(\"Data/imdb_processed\", data = out.data, indices = out.indices,\n indptr = out.indptr, shape = out.shape)\n","sub_path":"imdb.py","file_name":"imdb.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"540867012","text":"# Copyright (c) 2020, Cerebras Systems, Inc. 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\nimport sys\nimport os\nsys.path.insert(0, os.path.join(os.getcwd(), \"pytorch\"))\n\nimport array\nimport numpy as np\nimport toml\nimport mlperf_loadgen as lg\nfrom tqdm import tqdm\n\nfrom QSL import AudioQSL, AudioQSLInMemory\nfrom decoders import ScriptGreedyDecoder\nfrom helpers import add_blank_label\nfrom preprocessing import AudioPreprocessing\nfrom model_separable_rnnt import RNNT\n\nimport multiprocessing as mp\nimport threading\nimport time\nimport torch\nimport torch.autograd.profiler as profiler\n\nquery_count = 0\nfinish_count = 0\nstart_time = time.time()\ndebug = False\n\ndef get_num_cores():\n cmd = \"lscpu | awk '/^Core\\(s\\) per socket:/ {cores=$NF}; /^Socket\\(s\\):/ {sockets=$NF}; END{print cores*sockets}'\"\n lscpu = os.popen(cmd).readlines()\n lscpu = int(lscpu[0])\n return lscpu\n\ndef block_until(counter, num_ins, t):\n while counter.value < num_ins:\n time.sleep(t)\n\nclass Input(object):\n def __init__(self, id_list, idx_list):\n assert isinstance(id_list, list)\n assert isinstance(idx_list, list)\n assert len(id_list) == len(idx_list)\n self.query_id_list = id_list\n self.query_idx_list = idx_list\n\n\nclass Output(object):\n def __init__(self, query_id, transcript):\n self.query_id = query_id\n self.transcript = transcript\n\n\nclass InQueue():\n def __init__(self, in_queue, batch_size=1):\n self.in_queue = in_queue\n self.batch_size = batch_size\n\n def put(self, query_samples):\n query_len = len(query_samples)\n query_idx = [q.index for q in query_samples]\n query_id = [q.id for q in query_samples]\n\n if query_len == 1:\n input_item = Input(query_id, query_idx)\n self.in_queue.put(input_item)\n else:\n bs = self.batch_size\n for i in range(0, query_len, bs):\n i_end = min(i + bs, query_len)\n input_item = Input(query_id[i:i_end], query_idx[i:i_end])\n self.in_queue.put(input_item)\n\nclass InQueueServer():\n def __init__(self, input_queue_list, qsl, seq_cutoff_list, \n batch_size_list, total_query_count):\n\n self.input_queue_list = input_queue_list\n self.qsl = qsl\n self.seq_cutoff_list = seq_cutoff_list\n self.num_queues = len(input_queue_list) \n self.batch_size_list = batch_size_list \n self.query_batcher = [[] for _ in range(self.num_queues)]\n self.total_query_count = total_query_count\n self.curr_query_count = 0\n\n def put(self, query_samples): \n\n assert len(query_samples) == 1 # server scenario\n self.curr_query_count += 1 \n\n for i in range(self.num_queues): \n idx = query_samples[0].index #BS=1\n waveform = self.qsl[idx] \n if len(waveform) <= self.seq_cutoff_list[i]:\n self.query_batcher[i].append(query_samples[0]) \n # put queries in queue if BS treshold reached\n if len(self.query_batcher[i]) == self.batch_size_list[i]:\n qid_list, qidx_list = [], []\n for q in self.query_batcher[i]:\n qid_list.append(q.id) \n qidx_list.append(q.index)\n input_item = Input(qid_list, qidx_list)\n self.input_queue_list[i].put(input_item)\n self.query_batcher[i] = [] \n break \n\n if self.curr_query_count == self.total_query_count:\n # no more calls to put function\n # submit remaining queries in query batcher to input queues\n # process remaining queries with BS=1 \n for i in range(self.num_queues): \n for q in self.query_batcher[i]: \n input_item = Input([q.id], [q.index])\n self.input_queue_list[i].put(input_item) \n\nclass Consumer(mp.Process):\n def __init__(self, task_queue, result_queue, lock, init_counter,\n rank, start_core, end_core, num_cores,\n qsl, config_toml, checkpoint_path, dataset_dir,\n manifest_filepath, perf_count, cosim, profile, ipex, bf16,\n warmup):\n\n mp.Process.__init__(self)\n\n ### sub process\n self.task_queue = task_queue\n self.result_queue = result_queue\n self.lock = lock\n self.init_counter = init_counter\n self.rank = rank\n self.start_core = start_core\n self.end_core = end_core\n\n self.qsl = qsl\n self.config_toml = config_toml\n self.checkpoint_path = checkpoint_path\n self.dataset_dir = dataset_dir\n self.manifest_filepath = manifest_filepath\n self.perf_count = perf_count\n self.cosim = cosim\n self.profile = profile\n self.ipex = ipex\n self.bf16 = bf16\n self.warmup = warmup\n\n self.model_init = False\n\n # warmup basically go through samples with different feature lengths so\n # all shapes can be prepared\n def do_warmup(self):\n print ('Start warmup...')\n length_list = {}\n count = 0\n idxs = self.qsl.idxs()\n for i in idxs:\n feature_list = []\n feature_length_list = []\n waveform = self.qsl[i]\n feature_element, feature_length = self.audio_preprocessor.forward(\n (torch.from_numpy(waveform).unsqueeze(0),\n torch.tensor(len(waveform)).unsqueeze(0)))\n feature_list.append(feature_element.squeeze(0).transpose_(0, 1))\n feature_length_list.append(feature_length.squeeze(0))\n feature = torch.nn.utils.rnn.pad_sequence(feature_list, batch_first=True)\n feature_length = torch.tensor(feature_length_list)\n\n if feature_length[0].item() in length_list:\n continue\n length_list[feature_length[0].item()] = True\n\n assert feature.ndim == 3\n assert feature_length.ndim == 1\n if self.ipex:\n import intel_pytorch_extension as ipex\n if self.bf16:\n ipex.enable_auto_mixed_precision(mixed_dtype=torch.bfloat16)\n ipex.core.enable_auto_dnnl()\n feature = feature.to(ipex.DEVICE)\n feature_length = feature_length.to(ipex.DEVICE)\n feature_ = feature.permute(1, 0, 2)\n _, _, transcripts = self.greedy_decoder.forward_batch(feature_, feature_length, self.rank)\n\n count += 1\n if self.rank==0 and count % 10 == 0:\n print ('Warmup {} samples'.format(count))\n print ('Warmup done')\n\n def run_queue(self, debug = False):\n next_task = self.task_queue.get()\n if next_task is None:\n self.task_queue.task_done()\n return False\n\n query_id_list = next_task.query_id_list\n query_idx_list = next_task.query_idx_list\n query_len = len(query_id_list)\n with torch.no_grad():\n t1 = time.time()\n serial_audio_processor = True\n if serial_audio_processor:\n feature_list = []\n feature_length_list = []\n for idx in query_idx_list:\n waveform = self.qsl[idx]\n feature_element, feature_length = self.audio_preprocessor.forward(\n (torch.from_numpy(waveform).unsqueeze(0),\n torch.tensor(len(waveform)).unsqueeze(0)))\n feature_list.append(feature_element.squeeze(0).transpose_(0, 1))\n feature_length_list.append(feature_length.squeeze(0))\n feature = torch.nn.utils.rnn.pad_sequence(feature_list, batch_first=True)\n feature_length = torch.tensor(feature_length_list)\n else:\n waveform_list = []\n for idx in query_idx_list:\n waveform = self.qsl[idx]\n waveform_list.append(torch.from_numpy(waveform))\n waveform_batch = torch.nn.utils.rnn.pad_sequence(waveform_list, batch_first=True)\n waveform_lengths = torch.tensor([waveform.shape[0] for waveform in waveform_list],\n dtype=torch.int64)\n\n feature, feature_length = self.audio_preprocessor.forward((waveform_batch, waveform_lengths))\n\n assert feature.ndim == 3\n assert feature_length.ndim == 1\n if self.ipex:\n import intel_pytorch_extension as ipex\n if self.bf16:\n ipex.enable_auto_mixed_precision(mixed_dtype=torch.bfloat16)\n ipex.core.enable_auto_dnnl()\n feature = feature.to(ipex.DEVICE)\n feature_length = feature_length.to(ipex.DEVICE)\n if serial_audio_processor:\n feature_ = feature.permute(1, 0, 2)\n else:\n feature_ = feature.permute(2, 0, 1)\n t3 = time.time()\n if query_len == 1:\n _, _, transcripts = self.greedy_decoder.forward_single_batch(feature_, feature_length, self.ipex, self.rank)\n else:\n _, _, transcripts = self.greedy_decoder.forward_batch(feature_, feature_length, self.ipex, self.rank)\n t4 = time.time()\n # cosim\n if self.cosim:\n _, _, transcripts0 = self.greedy_decoder.forward(feature, feature_length)\n if transcripts0 != transcripts:\n print ('vvvvvv difference between reference and batch impl. vvvvvv')\n for i in range(query_len):\n if transcripts0[i] != transcripts[i]:\n for j in range(len(transcripts0[i])):\n if transcripts0[i][j] != transcripts[i][j]:\n break\n print ('[{}] reference'.format(i))\n print ('{} diff {}'.format(transcripts0[i][0:j], transcripts0[i][j:]))\n print ('[{}] batch'.format(i))\n print ('{} diff {}'.format(transcripts[i][0:j], transcripts[i][j:]))\n print ('')\n print ('^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^')\n else:\n print ('.', end='', flush=True)\n\n t6 = time.time()\n assert len(transcripts) == query_len\n for id, trans in zip(query_id_list, transcripts):\n self.result_queue.put(Output(id, trans))\n t2 = time.time()\n dur = t2 - t1\n if debug:\n print ('Audio {} Infer {} Total {}'.format(t3-t1, t4-t3, t2-t1))\n if query_len > 1:\n print(\"#### rank {} finish {} sample in {:.3f} sec\".format(self.rank, query_len, dur))\n else:\n print(\"#### rank {} finish sample of feature_len={} in {:.3f} sec\".format(self.rank, feature_length[0].item(), dur))\n\n self.task_queue.task_done()\n return True\n\n def run(self):\n core_list = range(self.start_core, self.end_core + 1)\n num_cores = len(core_list)\n os.sched_setaffinity(self.pid, core_list)\n cmd = \"taskset -p -c %d-%d %d\" % (self.start_core, self.end_core, self.pid)\n print (cmd)\n os.system(cmd)\n os.environ['OMP_NUM_THREADS'] = '{}'.format(self.end_core-self.start_core+1)\n print(\"### set rank {} to cores [{}:{}]; omp num threads = {}\"\n .format(self.rank, self.start_core, self.end_core, num_cores))\n\n torch.set_num_threads(num_cores)\n\n if not self.model_init:\n print(\"lazy_init rank {}\".format(self.rank))\n config = toml.load(self.config_toml)\n dataset_vocab = config['labels']['labels']\n rnnt_vocab = add_blank_label(dataset_vocab)\n featurizer_config = config['input_eval']\n self.audio_preprocessor = AudioPreprocessing(**featurizer_config)\n self.audio_preprocessor.eval()\n self.audio_preprocessor = torch.jit.script(self.audio_preprocessor)\n self.audio_preprocessor = torch.jit._recursive.wrap_cpp_module(\n torch._C._freeze_module(self.audio_preprocessor._c))\n\n model = RNNT(\n feature_config=featurizer_config,\n rnnt=config['rnnt'],\n num_classes=len(rnnt_vocab)\n )\n checkpoint = torch.load(self.checkpoint_path, map_location=\"cpu\")\n migrated_state_dict = {}\n for key, value in checkpoint['state_dict'].items():\n key = key.replace(\"joint_net\", \"joint.net\")\n migrated_state_dict[key] = value\n del migrated_state_dict[\"audio_preprocessor.featurizer.fb\"]\n del migrated_state_dict[\"audio_preprocessor.featurizer.window\"]\n model.load_state_dict(migrated_state_dict, strict=True)\n\n if self.ipex:\n import intel_pytorch_extension as ipex\n if self.bf16:\n ipex.enable_auto_mixed_precision(mixed_dtype=torch.bfloat16)\n ipex.core.enable_auto_dnnl()\n model = model.to(ipex.DEVICE)\n\n model.eval()\n if not self.ipex:\n model.encoder = torch.jit.script(model.encoder)\n model.encoder = torch.jit._recursive.wrap_cpp_module(\n torch._C._freeze_module(model.encoder._c))\n model.prediction = torch.jit.script(model.prediction)\n model.prediction = torch.jit._recursive.wrap_cpp_module(\n torch._C._freeze_module(model.prediction._c))\n model.joint = torch.jit.script(model.joint)\n model.joint = torch.jit._recursive.wrap_cpp_module(\n torch._C._freeze_module(model.joint._c))\n if not self.ipex:\n model = torch.jit.script(model)\n\n self.greedy_decoder = ScriptGreedyDecoder(len(rnnt_vocab) - 1, model)\n\n self.model_init = True\n\n if self.warmup:\n self.do_warmup()\n\n self.lock.acquire()\n self.init_counter.value += 1\n self.lock.release()\n\n if self.rank == 0 and self.cosim:\n print ('Running with cosim mode, performance will be slow!!!')\n if self.rank == 0 and self.profile:\n print ('Start profiler')\n with profiler.profile(record_shapes=True) as prof:\n self.run_queue(debug=True)\n print(prof.key_averages().table(sort_by=\"self_cpu_time_total\", row_limit=20))\n print(prof.key_averages().table(sort_by=\"cpu_time_total\", row_limit=20))\n print(prof.key_averages(group_by_input_shape=True).table(sort_by=\"self_cpu_time_total\", row_limit=40))\n print(prof.key_averages(group_by_input_shape=True).table(sort_by=\"cpu_time_total\", row_limit=40))\n while self.run_queue():\n pass\n else:\n while self.run_queue():\n pass\n\ndef response_loadgen(out_queue):\n global finish_count\n out_queue_cnt = 0\n while True:\n next_task = out_queue.get()\n if next_task is None:\n print(\"Exiting response thread\")\n break\n\n query_id = next_task.query_id\n transcript = next_task.transcript\n response_array = array.array('q', transcript)\n bi = response_array.buffer_info()\n response = lg.QuerySampleResponse(query_id, bi[0],\n bi[1] * response_array.itemsize)\n lg.QuerySamplesComplete([response])\n out_queue_cnt += 1\n finish_count += 1\n if debug:\n print(\"#### finish {} samples\".format(finish_count))\n\n print(\"Finish processing {} samples\".format(out_queue_cnt))\n\n\nclass PytorchSUT:\n def __init__(self, config_toml, checkpoint_path, dataset_dir,\n manifest_filepath, perf_count, total_query_count, scenario, machine_conf, batch_size=1,\n cores_for_loadgen=0, cores_per_instance=1, enable_debug=False,\n cosim=False, profile=False, ipex=False, bf16=False, warmup=False):\n ### multi instance attributes\n self.batch_size = batch_size\n self.cores_for_loadgen = cores_for_loadgen\n self.cores_per_instance = cores_per_instance\n self.num_cores = get_num_cores()\n self.lock = mp.Lock()\n self.init_counter = mp.Value(\"i\", 0)\n self.output_queue = mp.Queue()\n self.input_queue = mp.JoinableQueue()\n self.cosim = cosim\n self.ipex = ipex\n self.bf16 = bf16\n self.warmup = warmup\n self.scenario = scenario\n\n #server-specific\n self.num_queues = None \n self.core_count_list = [] \n self.num_instance_list = [] \n self.seq_cutoff_list = []\n self.batch_size_list = []\n self.input_queue_list = []\n self.total_query_count = total_query_count\n\n if self.scenario == \"Server\": \n # read config\n self.read_machine_conf(machine_conf) \n # create queue list\n for _ in range(self.num_queues):\n self.input_queue_list.append(mp.JoinableQueue())\n\n config = toml.load(config_toml)\n\n dataset_vocab = config['labels']['labels']\n rnnt_vocab = add_blank_label(dataset_vocab)\n featurizer_config = config['input_eval']\n\n self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries,\n self.process_latencies)\n self.qsl = AudioQSLInMemory(dataset_dir,\n manifest_filepath,\n dataset_vocab,\n featurizer_config[\"sample_rate\"],\n perf_count)\n \n if self.scenario == \"Offline\":\n self.issue_queue = InQueue(self.input_queue, batch_size)\n elif self.scenario == \"Server\":\n self.issue_queue = InQueueServer(self.input_queue_list, self.qsl, \n self.seq_cutoff_list, self.batch_size_list,\n self.total_query_count)\n\n ### worker process\n self.consumers = []\n cur_core_idx = self.cores_for_loadgen\n rank = 0\n if self.scenario == \"Offline\":\n while cur_core_idx + self.cores_per_instance <= self.num_cores:\n self.consumers.append(\n Consumer(self.input_queue, self.output_queue,\n self.lock, self.init_counter, rank, cur_core_idx,\n cur_core_idx+self.cores_per_instance-1, self.num_cores,\n self.qsl, config_toml, checkpoint_path, dataset_dir, manifest_filepath,\n perf_count, cosim, profile, ipex, bf16, warmup))\n rank += 1\n cur_core_idx += self.cores_per_instance\n elif self.scenario == \"Server\":\n for i in range(self.num_queues): \n curr_cores_per_instance = self.core_count_list[i] \n for _ in range(self.num_instance_list[i]): \n \n self.consumers.append(\n Consumer(self.input_queue_list[i], self.output_queue,\n self.lock, self.init_counter, rank, cur_core_idx,\n cur_core_idx + curr_cores_per_instance-1, self.num_cores,\n self.qsl, config_toml, checkpoint_path, dataset_dir, manifest_filepath,\n perf_count, cosim, profile, ipex, bf16, warmup))\n rank += 1\n cur_core_idx += curr_cores_per_instance\n self.num_instances = len(self.consumers)\n\n ### start worker process\n for c in self.consumers:\n c.start()\n\n ### wait until all sub processes are ready\n block_until(self.init_counter, self.num_instances, 2)\n\n ### start response thread\n self.response_worker = threading.Thread(\n target=response_loadgen, args=(self.output_queue,))\n self.response_worker.daemon = True\n self.response_worker.start()\n\n ### debug\n global debug\n debug = enable_debug\n\n\n def read_machine_conf(self, machine_conf):\n\n # machine conf format: core_per_instance, num_instances, seq_len_cutoff\n # assuming seq_len_cutoff in increasing order \n infile = open(machine_conf, \"r\")\n data = infile.read().splitlines()\n \n self.num_queues = len(data) \n for d in data:\n core_count, num_instance, cutoff, batch_size = map(int, d.split())\n self.core_count_list.append(core_count) \n self.num_instance_list.append(num_instance)\n self.seq_cutoff_list.append(cutoff) \n self.batch_size_list.append(batch_size)\n infile.close()\n #TO DO: validate config\n\n def issue_queries(self, query_samples):\n global start_time\n global query_count\n if self.batch_size != 1:\n ### make sure samples in the same batch are about the same length\n # qsl must be reversed sorted for best performance\n query_samples.sort(key=lambda k: self.qsl[k.index].shape[0], reverse=True)\n self.issue_queue.put(query_samples)\n end_time = time.time()\n dur = end_time - start_time\n start_time = end_time\n query_count += len(query_samples)\n if debug:\n print('\\n#### issue {} samples in {:.3f} sec: total {} samples'.format(len(query_samples), dur, query_count))\n\n def flush_queries(self):\n pass\n\n def process_latencies(self, latencies_ns):\n print(\"Average latency (ms) per query:\")\n print(np.mean(latencies_ns)/1000000.0)\n print(\"Median latency (ms): \")\n print(np.percentile(latencies_ns, 50)/1000000.0)\n print(\"90 percentile latency (ms): \")\n print(np.percentile(latencies_ns, 90)/1000000.0)\n\n def __del__(self):\n ### clear up sub processes\n\n if self.scenario == \"Offline\":\n self.input_queue.join()\n for i in range(self.num_instances):\n self.input_queue.put(None)\n\n elif self.scenario == \"Server\":\n\n for i in range(self.num_queues):\n self.input_queue_list[i].join()\n for _ in range(self.num_instance_list[i]): \n self.input_queue_list[i].put(None) \n\n for c in self.consumers:\n c.join()\n self.output_queue.put(None)\n\n print(\"Finished destroying SUT.\")\n","sub_path":"closed/Intel/code/rnnt/pytorch_SUT.py","file_name":"pytorch_SUT.py","file_ext":"py","file_size_in_byte":23540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"256556123","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport pickle\nimport transform\n\n# Define a function that applies Sobel x or y,\n# then takes an absolute value and applies a threshold.\ndef abs_sobel_thresh(img, orient='x', thresh_min=20, thresh_max=100):\n # Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # Apply x or y gradient with the OpenCV Sobel() function\n # and take the absolute value\n if orient == 'x':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))\n if orient == 'y':\n abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))\n # Rescale back to 8 bit integer\n scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))\n # Create a copy and apply the threshold\n binary_output = np.zeros_like(scaled_sobel)\n # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too\n binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1\n\n # Return the result\n return binary_output\n\n# Define a function that applies Sobel x and y,\n# then computes the magnitude of the gradient\n# and applies a threshold\ndef mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 100)):\n # Convert to grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # Take both Sobel x and y gradients\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n # Calculate the gradient magnitude\n gradmag = np.sqrt(sobelx**2 + sobely**2)\n # Rescale to 8 bit\n scale_factor = np.max(gradmag)/255\n gradmag = (gradmag/scale_factor).astype(np.uint8)\n # Create a binary image of ones where threshold is met, zeros otherwise\n binary_output = np.zeros_like(gradmag)\n binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1\n\n # Return the binary image\n return binary_output\n\n# Define a function that applies Sobel x and y,\n# then computes the direction of the gradient\n# and applies a threshold.\n# Define a function to threshold an image for a given range and Sobel kernel\ndef dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):\n # Grayscale\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n # Calculate the x and y gradients\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n # Take the absolute value of the gradient direction,\n # apply a threshold, and create a binary image result\n absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))\n binary_output = np.zeros_like(absgraddir)\n binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1\n\n # Return the binary image\n return binary_output\n\ndef combined_sobel(image):\n # Apply each of the thresholding functions\n gradx = abs_sobel_thresh(image, orient='x', thresh_min=10, thresh_max=100)\n grady = abs_sobel_thresh(image, orient='y', thresh_min=10, thresh_max=100)\n mag_binary = mag_thresh(image, sobel_kernel=3, mag_thresh=(30, 100))\n dir_binary = dir_threshold(image, sobel_kernel=11, thresh=(0.7, 1.3))\n\n combined = np.zeros_like(dir_binary)\n combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1\n return combined\n\n# Ref https://stackoverflow.com/questions/22588146/tracking-white-color-using-python-opencv\n# Ref https://stackoverflow.com/questions/9179189/detect-yellow-color-in-opencv\ndef color_mask_selection(image):\n hls_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n\n lower_white = np.array([0,200,0])\n upper_white = np.array([255,230,255])\n\n lower_yellow = np.array([20,0,100])\n upper_yellow = np.array([30,255,255])\n\n # Threshold the HLS image to get only white colors\n white_mask = cv2.inRange(hls_image, lower_white, upper_white)\n yellow_mask = cv2.inRange(hls_image, lower_yellow, upper_yellow)\n mask = cv2.bitwise_or(white_mask, yellow_mask)\n # Bitwise-AND mask and original image\n\n result = cv2.bitwise_and(image,image, mask= mask)\n return result\n\ndef luv_l(img, thresh=(225, 255)):\n # 1) Convert to LUV color space\n luv = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)\n luv_l = luv[:,:,0]\n # 2) Apply a threshold to the L channel\n binary_output = np.zeros_like(luv_l)\n binary_output[(luv_l > thresh[0]) & (luv_l <= thresh[1])] = 1\n\n return binary_output\n\ndef lab_b(img, thresh=(150,220)):\n # 1) Convert to LAB color space\n lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)\n lab_b = lab[:,:,2]\n\n # 2) Apply a threshold to the B channel\n binary_output = np.zeros_like(lab_b)\n binary_output[((lab_b > thresh[0]) & (lab_b <= thresh[1]))] = 1\n\n return binary_output\n\ndef hls_S(image, thresh=(170, 255)):\n # 1) Convert to HLS color space\n hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n\n hls_s = hls[:,:,2]\n\n # 2) Apply a threshold to the S channel\n binary_output = np.zeros_like(hls_s)\n binary_output[(hls_s > thresh[0]) & (hls_s <= thresh[1])] = 1\n\n return binary_output\n\ndef pipeline(image):\n undistort = transform.undistort(image)\n warp, Minv = transform.warp(undistort)\n # HSL S channel\n hls_s_channel = hls_S(warp)\n # LUV L-channel\n luv_l_channel = luv_l(warp)\n\n # Lab B channel\n lab_b_channel = lab_b(warp)\n # Combine LUV L-channel, S channel and Lab B channel thresholds\n combined = np.zeros_like(hls_s_channel)\n combined[(luv_l_channel == 1) | (lab_b_channel == 1)] = 1\n return combined, Minv, undistort\n\n\n\ndef pipeline2(image, s_thresh=(170, 255), sx_thresh=(20, 100)):\n image = image.copy()\n undistort = transform.undistort(image)\n warp, Minv = transform.warp(undistort)\n image = warp.copy()\n hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n s_channel = hls[:,:,2]\n gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n # Sobel x\n sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x\n abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal\n scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))\n\n # Threshold x gradient\n thresh_min = sx_thresh[0]\n thresh_max = sx_thresh[1]\n sxbinary = np.zeros_like(scaled_sobel)\n sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1\n\n # Threshold color channel\n s_thresh_min = s_thresh[0]\n s_thresh_max = s_thresh[1]\n s_binary = np.zeros_like(s_channel)\n s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1\n\n # Stack each channel to view their individual contributions in green and blue respectively\n # This returns a stack of the two binary images, whose components you can see as different colors\n color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary)) * 255\n\n # Combine the two binary thresholds\n combined_binary = np.zeros_like(sxbinary)\n combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1\n\n return combined_binary\n\ndef pipeline_yellow_white_mask(image):\n undistort = transform.undistort(image)\n warp, Minv = transform.warp(undistort)\n\n color_mask = color_mask_selection(warp)\n\n # color_mask = color_mask*(255/np.max(color_mask))\n\n return color_mask, Minv, undistort\n","sub_path":"Term-1/P4-Advanced-Lane-Lines/color_and_gradient_transformation.py","file_name":"color_and_gradient_transformation.py","file_ext":"py","file_size_in_byte":7272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"653563377","text":"\"\"\" DATA \"\"\"\nSCORE_DECAY_FACTOR = 0.5 # How much a goal from the first year is worth\nUSE_TEST_DATA = False\n\n\"\"\" NETWORK ARCHITECTURE \"\"\"\nINPUT_SHAPE = 40 # = 2 * number of unique teams in train/test data\nDROPOUT = 0.4\nLEAKY_RELU_ALPHA = 0.1\n\n\"\"\" TRAINING \"\"\"\nLEARNING_RATE = 0.00005\nEPOCHS = 500\nBATCH_SIZE = 100\n\nWRONG_WINNER_PENALTY = 10\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"350158627","text":"#!/usr/bin/env python3\n#Time-stamp: <2018-10-23 17:44:17 hamada>\nimport random\n\nrandom.seed(2)\n\nwith open(\"log.txt\", \"r\") as f:\n lines = f.read()\n\nlines = lines.split(\"\\n\")\nlines.pop(-1)\nlines.reverse()\n\nbudget_JPY = 8000000\nnmax = 60\nc = budget_JPY / (nmax * 1.)\nx = c\n\ncommits = []\nn = 1\nfor s in lines:\n commits.append({'n': n, 'x': x, 'c': c, 'hash': s})\n x = int(c + 0.5)\n n += 1\n\n\n\nmd = ''\nmd += \"Initial Budget from a Client = %d JPY (example case)\" % budget_JPY +\"\\n\\n\"\nmd += '| n | X(n) | C(n) | commit hash | balance (MAK) | Client\\'s Budget (JPY) |' + \"\\n\"\nmd += '|---:|---:|---:|:---| ---:|---:|' + \"\\n\"\n\n\naccum = 0\n\n\nplots = []\n\nfor s in commits:\n salary = s['x']\n\n if budget_JPY >= salary:\n budget_JPY -= salary\n accum += salary\n else:\n salary = budget_JPY\n accum += salary\n budget_JPY = 0\n\n md += \"| %d | %d | %1.3f | %s | %d | %d|\\n\" % (s['n'], salary, s['c'], s['hash'], accum, budget_JPY)\n plots.append({'n': s['n'], 'c': s['c'], 'salary': salary, 'accum': accum, 'budget': budget_JPY})\n\nprint (md)\n\n\nwith open(\"./plotdata.log\", \"w\") as f:\n lines = []\n for p in plots:\n lines.append(\"%d\\t%d\\t%1.4f\\t%d\\t%d\\n\"%(p['n'], p['salary'], p['c'], p['accum'], p['budget']) )\n\n for line in lines:\n f.write(line)\n\n","sub_path":"model.002/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"59025075","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# \n# Copyright (c) 2017 Nothing, Inc. All Rights Reserved\n# \n\n\"\"\"\nFile: first_missing_positive.py\nAuthor: guyu(dsgdtc@163.com)\nDate: 2019/1/3 16:37\n\"\"\"\nclass Solution:\n def firstMissingPositive(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n #第一个循环去遍历Nums里的每一个元素\n for i in range(len(nums)):\n #如果当前这个数字在我们能够进行转移的范围内\n if 0 < nums[i] and nums[i]< len(nums):\n # if nums[i] != nums[nums[i]-1]:\n #如果当前这个位置上的数字与它的index对应不上的话，我们会把它放到他应该在的正确的位置上去\n #同时为了保证数组元素信息不丢失，那个正确位置上的信息我们也要拿过来。\n while nums[i] != i + 1:\n nums[nums[i]-1], nums[i] = nums[i], nums[nums[i] - 1]\n #一旦换完后当前位置i上的元素出现以下任意一种情况都说明我们不用再换了\n if nums[i] <= 0 or nums[i] >= len(nums) or nums[i]==nums[nums[i]-1]:\n break\n #上一段代码在干的事情就是我们让大于0小于len(nums)的元素全部到对应的位置上去\n #比如nums为[1,3,-1,5,2],那么经过上面之后就变成[1,2,3,-1,5],能够对应上的数字全部已经正确排序，缺陷的(-1)的index+1就是缺失的第一个正数。\n\n jet = 1\n\n #再次遍历数组\n #jet是一个标识符，用来标志我们找没找到不符合的项\n for i in range(len(nums)):\n #找到的第一个大小与index不符的项，我们的答案就已经产生，就是当前index+1\n if nums[i] != i + 1:\n jet = 0\n ans = i + 1\n break\n #如果数组是[1,2,3]这种，每一项都符合，则缺失的是4,也就是len(nums) + 1\n if jet == 1:\n ans = len(nums)+1\n return ans","sub_path":"leetcode/lc-all-solutions-master/041.first-missing-positive/first_missing_positive.py","file_name":"first_missing_positive.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"117989107","text":"'''Driver for the perceptron class. This module handles all the file\n input and output.'''\n\n\nfrom Perceptron import Perceptron\ndef main():\n '''Main driver module.'''\n training_data = [ ( (1.0,0.0,0.0), 1.0),\\\n ( (1.0,1.0,0.0), 1.0),\\\n ( (1.0,0.0,1.0), 1.0),\\\n ( (1.0,1.0,1.0), 0.0) ] \n\n perceptron = Perceptron(training_data)\n perceptron.retrieve_training_data() \n perceptron.run()\n\nif __name__ == '__main__':\n main()\n","sub_path":"Perceptron/Driver_Perceptron.py","file_name":"Driver_Perceptron.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"621353408","text":"#!/usr/bin/python3\n\"\"\" Reddit API client \"\"\"\n\n\nimport pprint\nimport requests\nfrom requests.models import Response\n\nURL = 'http://reddit.com/r/{}/hot.json'\n\n\ndef recurse(subreddit, hot_list=[], after=None):\n \"\"\" GET all hot post \"\"\"\n\n headers = {'User-agent': 'Unix:0-subs:v1'}\n params = {'limit': 100}\n if isinstance(after, str):\n if after != \"STOP\":\n params['after'] = after\n else:\n return hot_list\n resp = requests.get(URL.format(subreddit),\n headers=headers, params=params)\n if resp.status_code != 200:\n return None\n data = resp.json().get('data', {})\n after = data.get('after', 'STOP')\n if not after:\n after = \"STOP\"\n hot_list = hot_list + [post.get('data', {}).get('title')\n for post in data.get('children', [])]\n return recurse(subreddit, hot_list, after)\n","sub_path":"0x16-api_advanced/2-recurse.py","file_name":"2-recurse.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"607738131","text":"#coding=UTF-8\nfrom django.db.models import Q\nfrom django.http import JsonResponse, HttpResponse\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.template import loader\nfrom django.urls import reverse\n\nfrom app.models import MainWheel, MainNav, MainMustBuy, MainShop, MainShow, FoodType, Goods, UserModel, CartModel, \\\n OrderGoods, Order\n\n\ndef home(request):\n wheels = MainWheel.objects.all()\n navs = MainNav.objects.all()\n mustbuys = MainMustBuy.objects.all()\n shops = MainShop.objects.all()\n shop1 = shops[0:1]\n shop2 = shops[1:3]\n shop3 = shops[3:7]\n shop4 = shops[7:11]\n mainshows = MainShow.objects.all()\n\n data = {\n \"title\":\"首页\",\n 'wheels':wheels,\n 'navs':navs,\n 'mustbuys':mustbuys,\n 'shop1':shop1,\n 'shop2':shop2,\n 'shop3':shop3,\n 'shop4':shop4,\n 'mainshows':mainshows,\n }\n return render(request,'app/main/home/Home.html',context=data)\n\ndef market(request):\n return redirect(reverse(\"app:marketWithParams\", kwargs={\"typeid\": \"104749\", \"childcid\": \"0\", \"order_rule\": \"0\"}))\n\n\ndef marketWithParams(request, typeid, childcid, order_rule):\n\n foodtypes = FoodType.objects.all()\n if childcid == \"0\":\n goods_list = Goods.objects.filter(categoryid=typeid)\n else:\n goods_list = Goods.objects.filter(categoryid=typeid).filter(childcid=childcid)\n\n if order_rule == \"1\":\n goods_list = goods_list.order_by(\"price\")\n elif order_rule == \"2\":\n goods_list = goods_list.order_by(\"-price\")\n elif order_rule == \"3\":\n goods_list = goods_list.order_by(\"-productnum\")\n\n foodtype = FoodType.objects.get(typeid=typeid)\n childtypenames = foodtype.childtypenames\n childtype_list = childtypenames.split(\"#\")\n child_type_list = []\n for childtype in childtype_list:\n child_type_list.append(childtype.split(\":\"))\n\n data = {\n \"title\":\"闪购\",\n 'foodtypes': foodtypes,\n 'foodtype':foodtype,\n \"goods_list\": goods_list,\n \"typeid\": typeid,\n \"childcid\": childcid,\n \"child_type_list\": child_type_list\n }\n return render(request, 'app/main/market/Market.html',context=data)\n\n\ndef mine(request):\n\n username = request.session.get(\"username\")\n\n data = {\n \"title\": \"我的\",\n \"is_login\": False\n }\n\n if username:\n user = UserModel.objects.get(u_name=username)\n data[\"is_login\"] = True\n data[\"username\"] = user.u_name\n data[\"usericon\"] = \"/static/uploads/\" + user.u_icon.url\n\n\n data[\"order_wait_service\"] = Order.objects.filter(o_user=user).filter(Q(o_status=4)|Q(o_status=5)).count()\n data[\"order_finished\"] = Order.objects.filter(o_user=user).filter(o_status=3).count()\n data[\"order_wait_receive\"] = Order.objects.filter(o_user=user).filter(o_status=2).count()\n data[\"order_wait_pay\"] = Order.objects.filter(o_user=user).filter(o_status=1).count()\n\n return render(request, 'app/main/mine/Mine.html', context=data)\n\n\ndef cart(request):\n\n username = request.session.get(\"username\")\n\n if not username:\n return redirect(reverse(\"app:user_login\"))\n\n user = UserModel.objects.get(u_name=username)\n\n carts = CartModel.objects.filter(c_user=user)\n\n total = cal_total(username)\n\n is_all_select = True\n\n for cart_obj in carts:\n if not cart_obj.c_is_select:\n is_all_select = False\n break\n\n data = {\n \"title\": \"购物车\",\n \"carts\": carts,\n \"is_all_select\":is_all_select,\n \"total\":total\n }\n\n return render(request, 'app/main/cart/Cart.html', context=data)\n\n\ndef add_to_cart(request):\n username = request.session.get(\"username\")\n data = {\n \"status\": \"200\",\n \"good_num\":0,\n }\n if username:\n print(request.GET.get(\"goodsid\"))\n goodsid = request.GET.get(\"goodsid\")\n\n user =UserModel.objects.get(u_name=username)\n goods = Goods.objects.get(pk=goodsid)\n\n cart_objs = CartModel.objects.filter(c_user=user).filter(c_goods=goods)\n\n if cart_objs.exists():\n cart_obj = cart_objs.first()\n cart_obj.c_goods_num = cart_obj.c_goods_num + 1\n cart_obj.save()\n else:\n cart_obj = CartModel()\n cart_obj.c_user = user\n cart_obj.c_goods = goods\n cart_obj.save()\n\n data[\"goods_num\"] = cart_obj.c_goods_num\n\n else:\n data = {\n \"status\": \"302\",\n \"good_num\": 0,\n }\n return JsonResponse(data)\n\ndef subgoods(request):\n cartid = request.GET.get(\"cartid\")\n\n cart_obj = CartModel.objects.get(pk=cartid)\n\n data = {\n \"msg\": \"ok\",\n \"status\": \"200\",\n \"goods_num\": 0\n }\n\n if cart_obj.c_goods_num == 1:\n cart_obj.delete()\n\n else:\n cart_obj.c_goods_num = cart_obj.c_goods_num - 1\n cart_obj.save()\n data[\"goods_num\"] = cart_obj.c_goods_num\n\n username = request.session.get(\"username\")\n\n total = cal_total(username)\n\n data[\"total_money\"] = total\n\n return JsonResponse(data)\n\ndef addgoods(request):\n cartid = request.GET.get(\"cartid\")\n\n cart_obj = CartModel.objects.get(pk=cartid)\n\n data = {\n \"msg\": \"ok\",\n \"status\": \"200\",\n \"goods_num\": 0\n }\n\n print(cart_obj.c_goods_num)\n cart_obj.c_goods_num = cart_obj.c_goods_num + 1\n cart_obj.save()\n print(cart_obj.c_goods_num)\n data[\"goods_num\"] = cart_obj.c_goods_num\n\n username = request.session.get(\"username\")\n\n total = cal_total(username)\n\n data[\"total_money\"] = total\n\n return JsonResponse(data)\n\n\ndef user_register(request):\n if request.method == \"GET\":\n return render(request, 'app/user/user_register.html')\n elif request.method == \"POST\":\n username = request.POST.get(\"username\")\n password = request.POST.get(\"password\")\n email = request.POST.get(\"email\")\n icon = request.FILES.get(\"icon\")\n\n user = UserModel()\n user.u_name = username\n user.u_email = email\n print(password)\n # user.u_password = password\n user.set_password(password)\n user.u_icon = icon\n\n user.save()\n\n \n request.session[\"username\"] = username\n return redirect(reverse(\"app:mine\"))\n\n\ndef user_logout(request):\n request.session.flush()\n return redirect(reverse(\"app:mine\"))\n\n\ndef check_user(request):\n username = request.GET.get(\"username\")\n\n users = UserModel.objects.filter(u_name=username)\n\n data = {\n \"msg\": \"ok\",\n \"status\": \"200\",\n }\n\n if users.exists():\n data[\"msg\"] = \"user exist\"\n data[\"status\"] = \"901\"\n\n return JsonResponse(data=data)\n\n\ndef user_login(request):\n if request.method == \"GET\":\n return render(request, 'app/user/user_login.html')\n elif request.method == \"POST\":\n username = request.POST.get(\"username\")\n password = request.POST.get(\"password\")\n\n users = UserModel.objects.filter(u_name=username)\n if users.exists():\n user = users.first()\n if user.verify_password(password):\n # if user.is_active:\n request.session[\"username\"] = username\n return redirect(reverse(\"app:mine\"))\n # else:\n # return HttpResponse(\"请激活账号使用\")\n else:\n return redirect(reverse(\"app:user_login\"))\n else:\n return redirect(reverse(\"app:user_login\"))\n\n\n\ndef change_cart_status(request):\n\n cartid = request.GET.get(\"cartid\")\n\n cart_obj = CartModel.objects.get(pk=cartid)\n\n cart_obj.c_is_select = not cart_obj.c_is_select\n\n cart_obj.save()\n\n data = {\n \"msg\": \"ok\",\n \"status\": \"200\",\n \"is_select\": cart_obj.c_is_select,\n }\n is_all_select = True\n total_money = 0\n\n user = UserModel.objects.get(u_name=request.session.get(\"username\"))\n carts = CartModel.objects.filter(c_user=user)\n for car in carts:\n if not car.c_is_select:\n is_all_select = False\n else:\n total_money += car.c_goods_num * car.c_goods.price\n\n data[\"is_all_select\"] = is_all_select\n data[\"total_money\"] = total_money\n\n return JsonResponse(data)\n\n\ndef change_cart_status_multi(request):\n\n change_list = request.GET.get(\"cart_select\")\n\n print(change_list)\n\n changelist = change_list.split(\"#\")\n\n print(changelist)\n\n for change in changelist:\n car_obj = CartModel.objects.get(pk=change)\n car_obj.c_is_select = not car_obj.c_is_select\n car_obj.save()\n\n data = {\n \"msg\": \"ok\",\n \"status\": \"200\",\n \"change_list\": change_list\n }\n\n return JsonResponse(data)\n\n\ndef cal_total(username):\n total_money = 0\n\n user = UserModel.objects.get(u_name=username)\n carts = CartModel.objects.filter(c_user=user).filter(c_is_select=True)\n for car in carts:\n total_money += car.c_goods_num * car.c_goods.price\n\n return total_money\n\n\ndef generate_order(request):\n\n cart_list = request.GET.get(\"goods_list\")\n\n cart_list = cart_list.split(\"#\")\n\n \"\"\"\n 生成订单\n 移除购物车中的数据\n 将移除的数据添加到订单商品表中\n \"\"\"\n user = UserModel.objects.get(u_name=request.session.get(\"username\"))\n order = Order()\n order.o_user = user\n order.save()\n\n for cart_id in cart_list:\n ordergoods = OrderGoods()\n ordergoods.order = order\n\n cart_obj = CartModel.objects.get(pk=cart_id)\n ordergoods.goods_num = cart_obj.c_goods_num\n ordergoods.goods = cart_obj.c_goods\n\n ordergoods.save()\n cart_obj.delete()\n\n data = {\n \"status\": \"200\",\n \"msg\": \"ok\",\n \"order_id\": order.id\n }\n return JsonResponse(data)\n\n\ndef order_detail(request):\n order_id = request.GET.get(\"orderid\")\n\n order = Order.objects.get(pk=order_id)\n username = order.o_user.u_name\n user = UserModel.objects.filter(u_name=username).first()\n\n data = {\n \"order\": order,\n \"user\": user\n }\n\n return render(request, 'app/order/order_detail.html', context=data)\n\n\ndef alipay(request):\n orderid = request.GET.get(\"orderid\")\n order = Order.objects.get(pk=orderid)\n username = order.o_user.u_name\n user = UserModel.objects.filter(u_name=username).first()\n user.u_telephone = request.GET.get(\"telephone\")\n user.u_address = request.GET.get(\"address\")\n user.save()\n\n\n order.o_status = 2\n order.o_telephone = request.GET.get(\"telephone\")\n order.o_address = request.GET.get(\"address\")\n order.save()\n return redirect(reverse(\"app:mine\"))\n\n\ndef order_list(request,type):\n\n user = UserModel.objects.get(u_name=request.session.get(\"username\"))\n if type == 'wait_pay':\n orders = Order.objects.filter(o_user=user).filter(o_status=1).order_by(\"-id\")\n text = '继续支付'\n elif type == 'wait_receive':\n orders = Order.objects.filter(o_user=user).filter(o_status=2).order_by(\"-id\")\n text = '确认收货'\n elif type == 'finished':\n orders = Order.objects.filter(o_user=user).filter(o_status=3).order_by(\"-id\")\n text = '申请售后'\n elif type == 'wait_service':\n orders = Order.objects.filter(o_user=user).filter(Q(o_status=4)|Q(o_status=5)).order_by(\"-id\")\n text = '确认售后'\n\n data = {\n 'text':text,\n \"type\":type,\n \"orders\": orders,\n }\n\n return render(request, 'app/order/order_list.html', context=data)\n\n\ndef order_change(request):\n order_id = request.GET.get(\"orderid\")\n order = Order.objects.get(pk=order_id)\n if order.o_status < 5:\n order.o_status += 1\n data = {'msg': True}\n else:\n data = {'msg': False}\n order.save()\n return JsonResponse(data)\n","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"603635891","text":"# Das Script holt sich die Nachrichten eines Raumes (50 per Default) und löscht diese - Hierzu ist allerdings das \"ProPack\", da hier ein User mit Compliance Rechten notwendig ist.\nfrom dotenv import load_dotenv\nload_dotenv()\nimport requests\nimport os\nimport json\n\n\n\n# Das Script arbeitet mit der Library \"requests\". Gegebenenfalls muss diese mit \"pip install requests\" nachinstalliert werden.\nroomId=os.getenv(\"ROOMID\")\napiUrl = \"https://api.ciscospark.com/v1/messages?roomId=\"+roomId\naccess_token = os.getenv(\"ACCESSTOKEN\")\nhttpHeaders = {\"Content-type\" : \"application/json\", \"Authorization\" : \"Bearer \" + access_token}\nbody = {}\n# Nachdem wir die Get Nachrichteninhalte zusammengefügt haben, senden wir das GET los und speichern die Rückgabe ind der Variable Response\nresponse = requests.get(url=apiUrl, json=body, headers=httpHeaders)\ndata = json.loads(response.text)\n\n# print(response.status_code)\n# print(response.text)\n# print(data)\n\n#print (type(data))\n\nfor key in data['items']:\n datablock=str(key['id'])\n apiUrl = \"https://api.ciscospark.com/v1/messages/\" + datablock\n response = requests.delete(url=apiUrl, json=body, headers=httpHeaders)\n print (response.status_code)\n\n\n\n\n\n","sub_path":"SimpleDeleteAllMesages.py","file_name":"SimpleDeleteAllMesages.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"385247563","text":"# Seed value\nseed_value= 7\n\n# Set the `PYTHONHASHSEED` environment variable at a fixed value\nimport os\nos.environ['PYTHONHASHSEED']=str(seed_value)\n\n# Set the `python` built-in pseudo-random generator at a fixed value\nimport random\nrandom.seed(seed_value)\n\n# Set the `numpy` pseudo-random generator at a fixed value\nimport numpy as np\nnp.random.seed(seed_value)\n\n# Set the `tensorflow` pseudo-random generator at a fixed value\nimport tensorflow as tf\ntf.random.set_seed(seed_value)\n\n# gpus = tf.config.list_physical_devices('GPU')\n# tf.config.set_visible_devices(gpus[0], 'GPU')\n\n# os.environ['CUDA_VISIBLE_DEVICES'] = '-1'\n# tf.config.set_visible_devices([], 'GPU')\n\nimport pandas as pd\nfrom tensorflow.keras import Sequential\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow_addons.layers import MultiHeadAttention\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.initializers import Constant, RandomNormal\nfrom tensorflow.keras.layers import Layer, concatenate, Convolution1D, Embedding, Dense, Flatten, MaxPooling1D, Input, Bidirectional, SimpleRNN, LSTM, GlobalAveragePooling1D, LayerNormalization, Dropout\nfrom tensorflow.keras.callbacks import EarlyStopping\n\nclass TransformerBlock(Layer):\n def __init__(self, embed_dim, num_heads, ff_dim, rate=0.8):\n super(TransformerBlock, self).__init__()\n self.att = MultiHeadAttention(head_size=embed_dim, num_heads=num_heads)\n self.ffn = Sequential(\n [Dense(ff_dim, activation=\"relu\"), Dense(embed_dim),]\n )\n self.layernorm1 = LayerNormalization(epsilon=1e-6)\n self.layernorm2 = LayerNormalization(epsilon=1e-6)\n self.dropout1 = Dropout(rate)\n self.dropout2 = Dropout(rate)\n\n def call(self, inputs, training):\n attn_output = self.att([inputs, inputs])\n attn_output = self.dropout1(attn_output, training=training)\n out1 = self.layernorm1(inputs + attn_output)\n ffn_output = self.ffn(out1)\n ffn_output = self.dropout2(ffn_output, training=training)\n return self.layernorm2(out1 + ffn_output)\n\nclass TokenAndPositionEmbedding(Layer):\n def __init__(self, maxlen, vocab_size, embed_dim, weights):\n super(TokenAndPositionEmbedding, self).__init__()\n self.token_emb = Embedding(input_dim=vocab_size, output_dim=embed_dim, embeddings_initializer=weights)\n self.pos_emb = Embedding(input_dim=maxlen, output_dim=embed_dim)\n\n def call(self, x):\n maxlen = tf.shape(x)[-1]\n positions = tf.range(start=0, limit=maxlen, delta=1)\n positions = self.pos_emb(positions)\n x = self.token_emb(x)\n return x + positions\n \ndef run(task, input_concat, model_type, i):\n \n '''\n task: string\n binary: Dataset with sentiment of postive and negative.\n multiclass: Dataset with sentiment of positive, neutral, and negative.\n \n input_concat: boolean\n True: The input is concatenated.\n False: The input is seperated.\n \n model_type: string\n CNN\n BiLSTM\n Transformer\n \n '''\n \n ### Dataset selection\n \n if task == 'multiclass':\n # dataset for multiclass\n df = pd.read_csv('data/geo_microblog.csv')\n else:\n # dataset for binary\n df = pd.read_csv('data/geo_microblog.csv')\n df = df[df.sentiment != 1]\n df.sentiment.replace(2, 1, inplace=True) # neutral\n\n ### Text processing\n \n # prepare tokenizer\n \n t = Tokenizer()\n t.fit_on_texts(df['text'])\n vocab_size = len(t.word_index) + 1\n \n # integer encode the documents\n \n encoded_docs = t.texts_to_sequences(df['text'])\n txtlen = 30\n loclen = 0\n \n # pad documents to a max length \n \n padded_docs = pad_sequences(encoded_docs, txtlen, padding='post')\n\n ### Location processing\n \n # location = df['geonames'].apply(lambda x: pd.Series([i for i in x.split(',') if i not in [\"\"]]).value_counts())\n # location = location.reindex(sorted(location.columns), axis=1).fillna(0)\n # location = location.values\n # np.save('microblog_multiclass_location.npy', location)\n if task == 'binary':\n location = np.load('variable/microblog_binary_location.npy')\n else:\n location = np.load('variable/microblog_multiclass_location.npy')\n loclen = location.shape[1]\n \n ### Prepare train and test set\n \n # merge txt and loc\n \n merge = np.concatenate((padded_docs, location),axis=1)\n \n # divide dataset to train and test set\n \n x_train, x_test, y_train, y_test = train_test_split(merge, df['sentiment'], test_size=0.3, random_state=100)\n \n if input_concat == False:\n # split train set to text and location\n x_train1 = x_train[:,:txtlen]\n x_train2 = x_train[:,-loclen:]\n \n # # split test set to text and location\n x_test1 = x_test[:,:txtlen]\n x_test2 = x_test[:,-loclen:]\n \n ### Pretrained word embedding\n \n # load the whole embedding into memory\n \n #embeddings_index = dict()\n #f = open('../glove.twitter.27B.200d.txt')\n #for line in f:\n #\tvalues = line.split()\n #\tword = values[0]\n #\tcoefs = asarray(values[1:], dtype='float32')\n #\tembeddings_index[word] = coefs\n #f.close()\n #print('Loaded %s word vectors.' % len(embeddings_index))\n \n # create a weight matrix for words in training docs\n \n vector_dimension = 200\n #embedding_matrix = zeros((vocab_size, vector_dimension))\n #for word, i in t.word_index.items():\n #\tembedding_vector = embeddings_index.get(word)\n #\tif embedding_vector is not None:\n #\t\tembedding_matrix[i] = embedding_vector\n \n if task == 'binary':\n embedding_matrix = np.load('variable/microblog_binary_embedding_matrix.npy')\n else:\n embedding_matrix = np.load('variable/microblog_multiclass_embedding_matrix.npy')\n \n ### Deep Learning model\n \n if input_concat == True:\n input_dimension = txtlen+loclen\n inputs = Input(shape=(input_dimension,))\n embedding_layer = Embedding(vocab_size, vector_dimension, embeddings_initializer=Constant(embedding_matrix), input_length=input_dimension)(inputs)\n else:\n inputText = Input(shape=(txtlen,))\n x = Embedding(vocab_size, vector_dimension, embeddings_initializer=Constant(embedding_matrix), input_length=txtlen)(inputText)\n inputLocation = Input(shape=(loclen,))\n y = Embedding(vocab_size, vector_dimension, embeddings_initializer=RandomNormal(), input_length=loclen)(inputLocation)\n embedding_layer = concatenate([x, y], axis=1)\n \n if model_type == \"CNN\":\n # CNN\n convolution_first = Convolution1D(filters=100, kernel_size=5, activation='relu')(embedding_layer)\n convolution_second = Convolution1D(filters=100, kernel_size=4, activation='relu')(convolution_first)\n convolution_third = Convolution1D(filters=100, kernel_size=3, activation='relu')(convolution_second)\n pooling_max = MaxPooling1D(pool_size=2)(convolution_third)\n flatten_layer = Flatten()(pooling_max)\n dense = Dense(20, activation=\"relu\")(flatten_layer)\n if task == 'binary':\n outputs = Dense(units=1, activation='sigmoid')(dense)\n else:\n outputs = Dense(units=3, activation='softmax')(dense)\n \n if model_type == \"BiLSTM\":\n ### BiLSTM\n lstm_first = Bidirectional(LSTM(units=100))(embedding_layer)\n dense = Dense(20, activation=\"relu\")(lstm_first)\n if task == 'binary':\n outputs = Dense(1, activation='sigmoid')(dense)\n else:\n outputs = Dense(3, activation='softmax')(dense)\n \n if model_type == \"RNN\":\n ### BiLSTM\n rnn_first = Bidirectional(SimpleRNN(units=100))(embedding_layer)\n dense = Dense(20, activation=\"relu\")(rnn_first)\n if task == 'binary':\n outputs = Dense(1, activation='sigmoid')(dense)\n else:\n outputs = Dense(3, activation='softmax')(dense)\n \n if model_type == \"Transformer\":\n ### Transformer\n num_heads = 2 # Number of attention heads\n ff_dim = 32 # Hidden layer size in feed forward network inside transformer\n \n if input_concat == True:\n embedding_layer_weighted = TokenAndPositionEmbedding(input_dimension, vocab_size, vector_dimension, Constant(embedding_matrix))\n x = embedding_layer_weighted(inputs)\n else:\n embedding_layer_weighted = TokenAndPositionEmbedding(txtlen, vocab_size, vector_dimension, Constant(embedding_matrix))\n x = embedding_layer_weighted(inputText)\n embedding_layer = TokenAndPositionEmbedding(loclen, vocab_size, vector_dimension, RandomNormal())\n y = embedding_layer(inputLocation)\n x = concatenate([x, y], axis=1)\n \n transformer_block = TransformerBlock(vector_dimension, num_heads, ff_dim)\n x = transformer_block(x)\n x = GlobalAveragePooling1D()(x)\n x = Dense(20, activation=\"relu\")(x)\n if task == 'binary':\n outputs = Dense(1, activation='sigmoid')(x)\n else:\n outputs = Dense(3, activation='softmax')(x)\n \n \n # build model\n \n if input_concat == True: \n model = Model(inputs, outputs)\n else:\n model = Model(inputs=[inputText, inputLocation], outputs=outputs)\n \n # compile model\n \n if task == 'binary':\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n else:\n model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy']) \n\n # early stopping\n \n early_stopping_monitor = EarlyStopping(patience=3) #patience: epochs the model can go without improving before we stop training\n\n \n if input_concat == True:\n # fit model\n \n history = model.fit(x_train, y_train, validation_split=0.3, epochs=20, callbacks=[early_stopping_monitor], verbose=1) \n \n # save model \n \n model.save_weights(f\"saved/{task}/2_loc/concat/{model_type}/{task}_location_True_concat_{model_type}_{i}.h5\", save_format=\"h5\")\n \n # evaluate model\n \n loss, accuracy = model.evaluate(x_test, y_test, verbose=1)\n \n else:\n # fit model\n history = model.fit([x_train1, x_train2], y_train, validation_split=0.3, epochs=20, callbacks=[early_stopping_monitor], verbose=1) \n \n # save model \n \n model.save_weights(f\"saved/{task}/2_loc/both/{model_type}/{task}_location_True_both_{model_type}_{i}.h5\", save_format=\"h5\")\n \n # evaluate model\n loss, accuracy = model.evaluate([x_test1, x_test2], y_test, verbose=1)\n \n return model, history, loss, accuracy\n\n### Run all\n\ntask_list = ['multiclass'] \ninput_concat_list = [True, False]\nmodel_type_list = [\"RNN\"]#\"CNN\", \"BiLSTM\", \nrepeat_num = 10\ndf_new = pd.DataFrame(columns=[\"accuracy\", \"loss\"])\n\nfor task in task_list:\n for input_concat in input_concat_list:\n for model_type in model_type_list:\n acc, ls = [], []\n for i in range(repeat_num):\n model, history, loss, accuracy= run(task, input_concat, model_type, i)\n \n acc.append(accuracy)\n ls.append(loss)\n \n df_temp = pd.DataFrame(columns=[\"accuracy\", \"loss\"], index=range(repeat_num))\n df_temp[\"accuracy\"] = acc\n df_temp[\"loss\"] = ls\n df_temp.at[repeat_num, \"accuracy\"] = f\"{task}_concat_{input_concat}_{model_type}\"\n df_new = df_new.append(df_temp)\n\ndf_new.to_csv(\"saved/2_loc_multiclass_rnn.csv\", index=False)\n \n### Single Run\n# task = 'binary'\n# input_concat = True\n# model_type = \"CNN\"\n# i = 0\n# model, history, loss, accuracy = run(task, input_concat, model_type)\n\n# visualize model\n# tf.keras.utils.plot_model(model, show_shapes=True)\n# reconstructed_model = keras.models.load_model(\"my_h5_model.h5\")\n\n\n\n","sub_path":"Microblog/2_location_vectorization.py","file_name":"2_location_vectorization.py","file_ext":"py","file_size_in_byte":12230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"497182028","text":"# program to print the substring between @ & #\n\ns = \"Go#od@morning#have a nice day\"\n\nstart = s.find('@')\nend = s.find('#', start)\nprint(s[start+1:end])\n\n# Assume a list of email ids\ns1 = \"abc@gmail.com;mail@gmail.com;test@gmail.com\"\n\n\n# Without split function\nstart = 0\nend = s1.find(';')\nwhile end != -1:\n print(s1[start:end])\n start = end + 1\n end = s1.find(';', start)\n\n\na = s1.split(';')\nfor email in a:\n print(email)\n","sub_path":"Module 2/subString.py","file_name":"subString.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"216535958","text":"def completeGarbage(nameOfFile, nameOfFileSecond):\n thisFile = open(nameOfFile, 'r')\n content = thisFile.readlines()\n\n thisFile_Second = open(nameOfFileSecond, 'r')\n content_second = thisFile_Second.readlines()\n\n setOne = set(content)\n setTwo = set(content_second)\n\n print(setTwo)\n\n for element in setOne & setTwo:\n print(element)\n\n\ncompleteGarbage(\"top100moviesAFI.txt\", \"top100moviesRT.txt\")","sub_path":"i210-LAB13.py","file_name":"i210-LAB13.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"114176265","text":"#!/usr/bin/python\n#-*-coding:utf-8-*-\nimport os\nimport threading\nfrom app_engine_project import requests\nimport json\nimport re\nimport sys\nimport time\ntry:\n reload(sys)\n sys.setdefaultencoding(\"utf-8\")\nexcept:\n 'py3 uchun keremas'\n\nl_time = int(time.time())\n\ndef r_input(x):\n try:\n return(raw_input(str(x)))\n except:\n return(input(str(x)))\n\ndef log(tt):\n open(\"./installing_\" + str(l_time) + \".log\",'a').write(\"\\n [\" + str(time.time()) + \"] \" + str(tt))\n\n\nlog(\"O'rnatilish jarayoni boshlandi\")\n\ndef a():\n data = requests.get(\"https://storage.googleapis.com/appengine-sdks/featured/google_appengine_1.9.70.zip\").content\n open('./master.zip',\"wb\").write(data)\n del(data)\n log(\"Fayl yuklandi\")\n os.system(\"unzip -qu master.zip\")\n \n\nthreading.Thread(target=a).start()\n\nlog(\"fayl yuklanmoqda\")\n\ndef wait(instruction):\n while r_input(\"O'xshadimi? y/n:\").lower() != \"y\":\n print(instruction)\n\nprint(\"Salom, bu script sizga google ga bo't joylashga yordam beradi.\\n Reja bo'yicha, Muammolar bo'lsa, \\033[34m@botlarhaqida\\033[0m gruppasida yozip ko'rin \\n1) appengine.google.com ga kiring. Kirganingizda \\\"y\\\" dip yozing.\")\nwhile r_input(\"Kirdingizmi? y/n:\").lower() != \"y\":\n print('https://appengine.google.com/ ga kiring. Bu qiyinamas')\n\nprint(\"Ok. Kirgan bo'lsangiz, u yerda \\033[4m'Create project'\\033[0m yoki \\033[4m'Создать проект'\\033[0m tugmasini topib bosing. Bosgandan keyin yana 'y' deb bosing.\")\nwait(\"Agar topgan bo'lsangiz, 'y' deb yozing. Bu script interactive emas\")\n\nprint(\"Menyu ochiladi, va u yerga project nomini yozasiz. Yaxshisi, bo'tingizni userneymini yozing. Misol uchun, @intelekt_bot ni intelekt-bot deb, shunda chalkashishlar kam bo'ladi.\")\nprint(\"\\033[1m Project nomi bilan id si bir xil bo'lgani yaxshi. Project nomi va id sini eslap qoling!\\033[0m\")\nprint('proektni sozdat qilishni bosing. Pasda \"creating project...\" yoki shunga o\\'xshagan yozuv chiqadi')\nwait(\"Project nomini va id sini eslap qolip, create yoki \\033[95mсоздать\\033[0m ni \\033[95mbosing\\033[0m\")\n\nprint(\"Ok, endi @botfather ga kiring va \\033[95m/newbot\\033[0m buyrug'ini bering. Keyin, bo'tni ismini (nikini) yozing. Undan keyin bo'tni \\033[95m @userneym \\033[0m ini yozing. Agar bot yasalmasa, unda boshqa userneym bilan harakat qiling. Bo't \\033[95m @userneym`i \\033[95m ohiri \\033[95m bot \\033[0m yoki \\033[95m _bot \\033[0m bilan tugashi kerak.\")\np = True\nAPI_TOKEN = \"\"\n\ntry:\n requests.get(\"https://api.telegram.org/bot\", timeout=5).text\n while API_TOKEN==\"\":\n token = r_input(\"token:\")\n res = re.search(r\"([0-9]+:[\\w]+)\", token)\n if res:\n for t in res.groups():\n try:\n data = requests.get(\"https://api.telegram.org/bot\" + t + \"/getMe\", timeout = 10).text\n username = json.loads(data)[\"result\"]['username']\n print(\"\\n\\nbotingiz topildi! username: @\" + username)\n API_TOKEN = t\n del(data)\n break\n except Exception as ex:\n log(\"token bilan muammo: \" + str(ex) + \" token: \" + t) \n if API_TOKEN == \"\":\n print(\"API token topilmadi. Qaytdan kiriting:\")\nexcept:\n print(\"Siz ishlatayotgan muhitdan telegram serverlariga ulanib bo'lmadi. 3 hil variant bor: 1)Bizni serverlar orqali urinib ko'rish\\n2)tokenni qo'lda app_engine_installer/app_engine_project/main.py faylida tog'rilab yozish\\n3)tokenni bir martta yozish. (noto'g'ri yozip qo'ysangiz 2-variantni qilishga to'gri keladi).\\nQaysi variantni tanlaysiz?\")\n i = r_input(\"variant: \")\n while(not i in [\"1\", \"2\", \"3\"]):\n print(\"variant raqamini o'zini yozing\")\n i = r_input(\"variant: \")\n \n if i==\"1\":\n while API_TOKEN==\"\":\n token = r_input(\"token:\")\n res = re.search(r\"([0-9]+:[\\w|-]+)\", token)\n if res:\n for t in res.groups():\n nn = int(time.time())%2+1\n try:\n data = requests.get(\"http://t-checker-\" + str(nn) + \".appspot.com/check?t=\" + str(t)).text\n if data != \"ERROR\":\n username = data\n print(\"\\n\\nbotingiz topildi! username: @\" + username)\n API_TOKEN = t\n del(data)\n break\n else:\n log(\"token bilan muammo. token: \" + t)\n \n except Exception as ex:\n log(\"token bilan muammo: \" + str(ex) + \" token: \" + t) \n \n if API_TOKEN == \"\":\n print(\"API token topilmadi. Qaytdan kiriting:\")\n \n if i == \"2\":\n print(\"OK, tokenni o'ziz yozarsiz\")\n API_TOKEN = \"meni token bilan almashtiring\"\n if i == \"3\":\n API_TOKEN = r_input(\"tokenni kiriting: \")\n \nprint(\"O'zingizni id raqamingizni yozing. Uni @intelekt_bot ga /id buyrug'ini berib bilsa bo'ladi. Agar noto'g'ri id yozsangiz, unda boshqa odam bo'tga admin bo'lip qoladi\")\nadmin_id = 0\nlog(\"admindan id so'ralmoqda...\")\nwhile admin_id == 0:\n try:\n a_id = int(r_input('id: '))\n admin_id = a_id\n log(\"admin id aniqlandi\")\n except:\n log(\"admin parot qivotti\")\n print(\"\\033[1m id raqam bo'ladi \\033[0m\")\n \n\n\nprint(\"Google app engine dagi ochgan projectingizni id sini yozing. Projectingizni nomini yozganingizda tegida chiqishi kerak. Ba'zida project nomi bilan bir xil bo'ladi, ba'zida esa, project nomi dan keyin chiziqcha va raqamlar bo'ladi\")\nproject_id = \"\"\nwhile project_id == \"\":\n project_id = r_input(\"Proekt id si:\")\n l = [' ', \"'\", '.', ',', '_', '\"', \"\\\\\", '/', '@']\n for ll in l:\n if ll in project_id:\n project_id = ''\n if project_id == \"\":\n print(\"Bunday project-id bo'lmaydi\")\n log(\"project id hato\")\n \n\nprint(\"Fayllar sozlanmoqda...\")\nlog(\"fayllar sozlanmoqda\")\ndata = open('app_engine_installer/app_engine_project/app.yaml','r').read()\ndata = data.replace('project_nomi', project_id)\nopen('app_engine_installer/app_engine_project/app.yaml','w').write(data)\ndata = open('app_engine_installer/app_engine_project/main.py','r').read()\ndata = data.replace('project_nomi', project_id)\ndata = data.replace('replace_me_with_token',API_TOKEN)\nopen('app_engine_installer/app_engine_project/main.py','w').write(data)\nlog(\"fayllar sozlandi\")\n\nprint(\"Ok, ohiriga kelib qoldik. Bo't deyali tayyor. https://appengine.google.com saytiga kiring, yangi ochgan projectingizni tanlang. saytda tepada chap tomonda menyu bor. Menyuga kiring. Usha menyudan \\033[95m APP ENGINE \\033[0m ni tanlang.\\n\\nOchilgan stranitsada Choose language yoki Выбрать язык ni bosing. Pasda python ni belgisi chiqib keladi. Ushani tanlang. Karta chiqib kelganda Europe-West (Yevropa) ni tanlang. Pasda next ni bosing. Serverla tayyor bo'lishini kuting. tayyor bo'lganda esa, Tayyor dip yozing.\")\nwhile r_input('//>').lower() != \"tayyor\":\n print(\"So'zingiz ma'nosini tushunmayman\")\n \nprint(\"Agar hammasi tayyor bo'lsa, bo'tni serverga joylimiza. faqat siz avtorizatsiyadan o'tishingiz kerak. Hozir link chiqadi va siz o'sha linkga kirib kod`ni copy qilib kelasiz. Keyin terminalga yozasiz. Ok?\")\nr_input('>')\nlog(\"serverga joylavommiza\")\nos.system('google_appengine/appcfg.py -A '+ project_id + \" update app_engine_installer/app_engine_project/app.yaml --noauth_local_webserver\")\n\ntry:\n requests.get('https://' + project_id + \".appspot.com/set_webhook\").text\n print(\"Agar siz hammasini to'g'ri qilgan bo'lsangiz, bo't ishga tushdi.\")\nexcept Exception as ex:\n log(\"serverga joylagandan keyingi muammo: \" + str(ex))\n print(\"Server ishlamiyopti. Qandaydir hato bo'lgan. Qaytadan harakat qilinmoqda...\")\n os.system(\"google_appengine/appcfg.py set_default_version /app_engine_installer/app_engine_project --noauth_local_webserver\")\n os.system('google_appengine/appcfg.py -A '+ project_id + \" update app_engine_installer/app_engine_project/app.yaml --noauth_local_webserver\")\n\nopen(\"./upload_\" + project_id + \".sh\",'w').write('google_appengine/appcfg.py -A '+ project_id + \" update app_engine_installer/app_engine_project/app.yaml --noauth_local_webserver\") \nprint(\"kodga o'zgarishlar kiritganingizdan keyin upload_\" + project_id + \".sh ni ishga tushirsangiz ham bo'ladi\")\n\nprint(\"Eslatma: --noauth_local_webserver degan narsani keyingi safardan yozish shart emas\")\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"23863226","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May 23 10:52:30 2020\n\n@author: user\n\"\"\"\n\n\n'''\nIn this little assignment you are given a string of space separated \nnumbers, and have to return the highest and lowest number.\n\nExample:\n\nhigh_and_low(\"1 2 3 4 5\") # return \"5 1\"\nhigh_and_low(\"1 2 -3 4 5\") # return \"5 -3\"\nhigh_and_low(\"1 9 3 4 -5\") # return \"9 -5\"\nNotes:\n\nAll numbers are valid Int32, no need to validate them.\nThere will always be at least one number in the input string.\nOutput string must be two numbers separated by a single space, \nand highest number is first.\n'''\n\ndef high_and_low(numbers):\n sort=[]\n x=numbers.split(' ')\n for i in x:\n sort.append(int(i))\n sort2=sorted(sort)\n \n return f'{sort2[-1]} {sort2[0]}'\n\nprint(high_and_low(\"1 2 3 4 5\")) # return \"5 1\"\nprint(high_and_low(\"1 2 -3 4 5\")) # return \"5 -3\"\nprint(high_and_low(\"1 9 3 4 -5\")) # return \"9 -5\"","sub_path":"high-low.py","file_name":"high-low.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"328102966","text":"#!/usr/bin/python3\nfrom collections import Counter\n\nfrom Bot import *\nfrom api import *\n\n# UNTOUCHED = 0\n# MISS = 1\n# HIT = 2\n# SUNK = 3\n\nprint = lambda x: sys.stderr.write(x + '\\n')\nHOR = True\nVER = False\n\n\nclass Asterix(Bot):\n def __init__(self):\n super().__init__()\n # self.activity_map = make_field_with_value(UNTOUCHED)\n self.enemy_ship_type_count = {Ship.CARRIER: 1,\n Ship.BATTLESHIP: 2,\n Ship.CRUISER: 3,\n Ship.DESTROYER: 4}\n self.own_ship_type_count = {Ship.CARRIER: 1,\n Ship.BATTLESHIP: 2,\n Ship.CRUISER: 3,\n Ship.DESTROYER: 4}\n self.enemy_multiplier_map = make_field_with_value(1)\n self.enemy_possible_ships = self.init_possible_ships(self.enemy_ship_type_count)\n self.own_possible_ships = self.init_possible_ships(self.own_ship_type_count)\n self.own_multiplier_map = make_field_with_value(1)\n self.already_picked = []\n\n def init_possible_ships(self, ships):\n poss = []\n for ship_size, ship_count in ships.items():\n for n in range(ship_count):\n for x in range(DIMENSIONS[X]):\n for y in range(DIMENSIONS[Y] - ship_size + 1):\n coords = []\n for i in range(ship_size):\n coords.append((x, y + i))\n poss.append((n, coords))\n for y in range(DIMENSIONS[Y]):\n for x in range(DIMENSIONS[X] - ship_size + 1):\n coords = []\n for i in range(ship_size):\n coords.append((x + i, y))\n poss.append((n, coords))\n return poss\n\n def get_shipcounts(self):\n return Counter([y for _, x in self.enemy_possible_ships for y in x])\n # result = []\n # for id, y in self.enemy_possible_ships:\n # for _ in range(self.enemy_ship_type_count[t]):\n # result += y\n # return Counter(result)\n\n def get_top_of_heatmap(self):\n max_val = 0\n max_coords = []\n counts = self.get_shipcounts()\n for x in range(DIMENSIONS[X]):\n for y in range(DIMENSIONS[Y]):\n xy_multiplier = self.enemy_multiplier_map[x][y]\n cur_val = counts[(x, y)] * xy_multiplier\n if cur_val > max_val:\n max_val = cur_val\n max_coords = [(x, y)]\n elif cur_val == max_val:\n max_coords.append((x, y))\n return random.choice(max_coords)\n\n def get_bottom_of_heat_map(self, heatmap):\n\n # result = []\n # for t, y in heatmap:\n # for _ in range(self.enemy_ship_type_count[t]):\n # result += y\n # counts = Counter(result)\n # # if len(counts) >= 5:\n # # least_likely = counts.most_common()[-5:]\n # # else:\n # # least_likely = counts.most_common()\n # # likely_ = random.choice(least_likely)[0]\n counts = Counter([y for _, x in self.enemy_possible_ships for y in x])\n likely_ = counts.most_common()[-1][0]\n # print(str(likely_))\n return likely_\n\n def remove_ship_from_heatmap(self, sunk):\n # self.possible_ships = list(filter(lambda pos: sunk is not pos[0], self.possible_ships))\n # self.enemy_ship_type_count[sunk] -= 1 # FIXME: Kan dit onder de 0?\n return list(filter(lambda x: x[0] != self.enemy_ship_type_count[sunk] or len(x[1]) != sunk,\n self.enemy_possible_ships))\n\n def remove_location_from_heatmap(self, x, y):\n self.enemy_possible_ships = list(filter(lambda pos: (x, y) not in pos[1], self.enemy_possible_ships))\n\n def increase_likelihood_around(self, x, y, prev=HOR):\n if x - 1 >= 0: self.enemy_multiplier_map[x - 1][y] *= 1000\n if x + 1 < DIMENSIONS[X]: self.enemy_multiplier_map[x + 1][y] *= 1000\n if y - 1 >= 0: self.enemy_multiplier_map[x][y - 1] *= 1000\n if y + 1 < DIMENSIONS[Y]: self.enemy_multiplier_map[x][y + 1] *= 1000\n\n def choose_ship_location(self):\n ship_type = self.choose_ship_size()\n possible_ship_locations = list(filter(lambda pos: ship_type == len(pos[1]), self.own_possible_ships))\n possible_ship_locations = list(map(lambda x: (self.accumulated_heat(x), x), possible_ship_locations))\n possible_ship_locations.sort()\n # pref = []\n # first = possible_ship_locations[0][0]\n # for pos in possible_ship_locations:\n # if first != pos[0]:\n # break\n # pref.append(pos)\n loc = possible_ship_locations[1][1][1]\n print(str(loc))\n for coords in loc:\n self.own_possible_ships = list(filter(lambda pos: coords not in pos[1], self.own_possible_ships))\n for (x, y) in loc:\n for (nx, ny) in [(x - 1, y), (x, y - 1), (x + 1, y), (x, y + 1)]:\n if (nx, ny) not in loc:\n if nx >= 0 and nx < DIMENSIONS[X] and ny >= 0 and ny < DIMENSIONS[Y]:\n self.own_multiplier_map[nx][ny] *= 10\n return loc[0], loc[-1]\n\n # def choose_ship_location(self):\n # ship_type = self.choose_ship_size()\n # possible_locations = list(filter(lambda pos: ship_type == pos[0], self.own_possible_ships))\n # loc = random.choice(possible_locations)[1]\n # # unlikely = self.get_bottom_of_heat_map(possible_locations)\n # # loc = random.choice(list(filter(lambda pos: unlikely in pos[1], possible_locations)))[1]\n # for coord in loc:\n # self.own_possible_ships = list(filter(lambda pos: coord not in pos[1], self.own_possible_ships))\n # return loc[0], loc[-1]\n\n def choose_island_location(self):\n coords = self.get_top_of_heatmap()\n\n self.remove_location_from_heatmap(*coords)\n return coords\n\n def choose_shot_location(self):\n shot_loc = self.get_top_of_heatmap()\n if shot_loc in self.already_picked:\n print(\"Already picked {}\".format(str(shot_loc)))\n all_coords = []\n for x in range(DIMENSIONS[X]):\n for y in range(DIMENSIONS[Y]):\n all_coords.append((x, y))\n pickone = list(filter(lambda x: x not in self.already_picked, all_coords))\n already = random.choice(pickone)\n self.already_picked.append(already)\n return already\n else:\n self.already_picked.append(shot_loc)\n return shot_loc\n\n def choose_ship_size(self):\n return super().choose_ship_size()\n # You may want to extend this method, but it is not required.\n\n def handle_result(self, text):\n super().handle_result(text)\n\n def parse(t):\n if \"HIT\" in t:\n if \"YOUSUNKMY\" in t:\n print(str(t))\n return True, Ship[t.split()[-1]]\n else:\n return True, None\n else:\n return False, None\n\n hit, sunk = parse(text)\n x, y = self.lastCoord\n if hit:\n if sunk is not None:\n self.remove_location_from_heatmap(x, y)\n self.remove_ship_from_heatmap(sunk)\n self.enemy_multiplier_map[x][y] = 0\n self.increase_likelihood_around(x, y, self.to_prev(x, y)) # HIERRRR\n print('---------------------------------')\n print('Result was SUNK')\n print('---------------------------------')\n else:\n self.increase_likelihood_around(x, y, self.to_prev(x, y))\n self.enemy_multiplier_map[x][y] = 0\n print('---------------------------------')\n print('Result was HIT')\n print('---------------------------------')\n else:\n self.remove_location_from_heatmap(x, y)\n self.enemy_multiplier_map[x][y] = 0\n print('---------------------------------')\n print('Result was MISS')\n print('---------------------------------')\n print('')\n print('Multiplier map:')\n print('\\n'.join(list(map(str, self.enemy_multiplier_map))))\n print('')\n print('Heatmap')\n print('\\n'.join(list(map(str, self.to_heatmap()))))\n\n def handle_update(self, text):\n super().handle_update(text)\n if text.find(\"RESULT GOTISLAND\"):\n tokens = text.strip().split()\n coord = (int(re.sub(\"\\D\", \"\", tokens[2])),\n int(re.sub(\"\\D\", \"\", tokens[3])))\n self.own_possible_ships = list(filter(lambda pos: coord not in pos[1], self.own_possible_ships))\n\n # You may want to extend this method, but it is not required.\n\n def to_heatmap(self):\n start = make_field_with_value(0)\n counts = self.get_shipcounts()\n for x in range(DIMENSIONS[X]):\n for y in range(DIMENSIONS[Y]):\n start[x][y] = counts[(x, y)] * self.enemy_multiplier_map[x][y]\n return start\n\n def to_prev(self, _, y):\n return self.already_picked[-1][1] != y\n\n def accumulated_heat(self, ship_type_and_loc):\n ship_type, ship_coords = ship_type_and_loc\n\n def get_counts():\n return Counter([y for _, x in self.own_possible_ships for y in x])\n # result = []\n # for t, y in self.own_possible_ships:\n # for _ in range(self.own_ship_type_count[t]):\n # result += y\n # return Counter(result)\n\n counts = get_counts()\n acc_heat = 0\n for coord in ship_coords:\n acc_heat += counts[coord] * self.own_multiplier_map[coord[0]][coord[1]] # HIERRR\n # acc_heat += (100 - counts[coord]) * self.own_multiplier_map[coord[0]][coord[1]]\n return acc_heat\n\n\nif __name__ == \"__main__\":\n Asterix().run()\n","sub_path":"Asterix.py","file_name":"Asterix.py","file_ext":"py","file_size_in_byte":10118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"512052669","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 26 13:50:31 2017\n\n@author: 54326\n\"\"\"\n\nfrom math import sqrt, log, pi, cos\nimport numpy as np\nimport pandas as pd\nfrom pandas import Series, DataFrame\nimport matplotlib.pyplot as plt\nfrom scipy import stats\n\n# parameters, example from John C.Hull, Page 273\nS0 = 49; K = 50; T = 0.3846; r = 0.05; u = 0.13; sigma = 0.2; \nM = 20; dt = T / M; N = 5000; h = 1000;\n\n# Box-Muller transform\ndef std_norm(n):\n x = []\n for i in range(n):\n u1 = np.random.uniform()\n u2 = np.random.uniform()\n p_r = sqrt(-2*log(u1))\n p_theta = 2*pi*u2\n xv = p_r * cos(p_theta)\n x.append(xv)\n return x\n\nfig = plt.figure()\nplt.hist(std_norm(N), bins=25)\nplt.title('Box-Muller Samples from Standard Normal Distribution')\n\n# variables for dynamic hedging\nS = np.zeros((M + 1, N))\nd1 = np.zeros((M + 1, N))\nd2 = np.zeros((M + 1, N))\nNd1 = np.zeros((M + 1, N))\ndelta = np.zeros((M + 1, N))\ntrade = np.zeros((M + 1, N))\ncost = np.zeros((M + 1, N))\nCF = np.zeros((M + 1, N))\ninterest = np.zeros((M + 1, N))\n\nS[0] = S0\nd1[0] = (np.log(S[0] / K) + \\\n (r + 0.5 * sigma ** 2) * T) / (sigma * sqrt(T))\nd2[0] = d1[0] - sigma * sqrt(T)\nNd1[0] = stats.norm.cdf(d1[0])\ndelta[0] = Nd1[0]\ntrade[0] = Nd1[0] * h\ncost[0] = trade[0] * S0\nCF[0] = cost[0]\ninterest[0] = CF[0] * r * dt\n\n# Black-Scholes-Meoton Price\nNd2 = np.zeros((M + 1, N))\nBS = np.zeros((M + 1 , N))\n\nNd2[0] = stats.norm.cdf(d2[0])\nBS[0] = S[0] * Nd1[0] - K * np.exp(-r * T) * Nd2[0]\n\n# Greeks\ngamma = np.zeros((M + 1, N))\ntheta = np.zeros((M + 1, N))\nvega = np.zeros((M +1, N))\nrho = np.zeros((M +1, N))\n\ngamma[0] = np.exp(-0.5 * d1[0] ** 2) / (sqrt(2 * pi * T) * S[0] * sigma)\ntheta[0] = np.exp(-0.5 * d1[0] ** 2) * (-S[0]) * sigma / (2 * \\\n sqrt(2 * pi * T)) - r * K * np.exp(-r * T) * Nd2[0]\nvega[0] = S[0] * sqrt(T) * np.exp(-0.5 * d1[0] ** 2) / sqrt (2 * pi)\nrho[0] = K * T * np.exp(-r * T) * Nd2[0]\n\n# Simulating N paths with M time steps\nfor t in range(1, M + 1):\n z = np.array(std_norm(N))\n S[t] = S[t-1] * np.exp((u - 0.5 * sigma ** 2) * dt + sigma * sqrt(dt) * z)\n d1[t] = (np.log(S[t] / K) + (r + 0.5 * sigma ** 2) * \\\n (T- t * dt))/ (sigma * sqrt(T - t * dt))\n d2[t] = d1[t] - sigma * sqrt(T - t * dt)\n Nd1[t] = stats.norm.cdf(d1[t])\n delta[t] = Nd1[t]\n trade[t] = (delta[t] - delta[t-1]) * h\n cost[t] = trade[t] * S[t] \n CF[t] = CF[t-1] + interest[t-1] + cost[t]\n interest[t] = CF[t] * r * dt\n \n Nd2[t] = stats.norm.cdf(d2[t])\n BS[t] = S[t] * Nd1[t] - K * np.exp(-r * (T - t *dt)) * Nd2[t]\n \n gamma[t] = np.exp(-0.5 * d1[t] ** 2) / (sqrt(2 * pi * \\\n (T - t * dt)) * S[t] * sigma)\n theta[t] = np.exp(-0.5 * d1[t] ** 2) * (-S[t]) * sigma / (2 * sqrt(2 * \\\n pi * (T - t * dt))) - r * K * np.exp(-r * (T - t * dt)) * Nd2[t]\n vega[t] = S[t] * sqrt(T - t * dt) * np.exp(-0.5 * d1[t] ** 2) / sqrt (2 * pi)\n rho[t] = K * (T - t * dt) * np.exp(-r * (T - t * dt)) * Nd2[t]\n \nfig = plt.figure()\nplt.plot(S[:, :40])\nplt.title('stock price paths with M steps')\n\nfig = plt.figure()\nplt.hist(np.log(S[M, :]), bins=25)\nplt.title('distribution of S(T)')\n \n# dynamic hedging process\ndata = {}\ngreeks = {}\nfor i in range(5):\n s = DataFrame(Series(S[:,i]))\n dlt = Series(delta[:, i])\n trd = Series(trade[:, i])\n cst = Series(cost[:, i])\n cf = Series(CF[:, i])\n ist = Series(interest[:, i])\n data['m'+str(i)] = pd.concat([s, dlt, trd, cst, cf, ist], axis=1)\n data['m' + str(i)].columns = ['stock price', 'delta', 'trade', \\\n 'cost', 'cash flow', 'insterest']\n \n gma = Series(gamma[:, i])\n tht = Series(theta[:, i])\n vga = Series(vega[:, i])\n ro = Series(rho[:, i])\n greeks['m' + str(i)] = pd.concat([s, dlt, gma, tht, vga, ro], axis=1)\n greeks['m' +str(i)].columns = ['stock price', 'delta', 'gamma', 'theta', \\\n 'vega', 'rho']\n\n#data['m0']\n#greeks['m0']\n\n# hedge performance \nPL = []\nfor i in range(N):\n if S[M, i] > K:\n pl = CF[M, i] / h - K\n else:\n pl = CF[M, i] / h\n PL.append(pl)\nPL = np.array(PL) * np.exp(-r * T)\nhp = PL.std() / BS[0][0]\nprint('Hedge performance: standard deviation of hedge cost / BS price =', hp)\n\nfig = plt.figure()\nplt.plot(PL, label='hedge cost')\nplt.plot(BS[0], label='BS price')\nplt.legend()\nplt.title('delta neutral dynamic hedging')\n\nfig, axes = plt.subplots(2, 3)\naxes[0, 0].plot(data['m0']['stock price'], label='stock price'), axes[0, 0].legend()\naxes[0, 1].plot(greeks['m0']['delta'], label='delta'), axes[0, 1].legend()\naxes[0, 2].plot(greeks['m0']['gamma'], label='gamma'), axes[0, 2].legend()\naxes[1, 0].plot(greeks['m0']['theta'], label='theta'), axes[1, 0].legend()\naxes[1, 1].plot(greeks['m0']['vega'], label='vega'), axes[1, 1].legend()\naxes[1, 2].plot(greeks['m0']['rho'], label='rho'), axes[1, 2].legend() \n\n#\nfig = plt.figure()\npd.Series(PL).plot(kind='kde', style='b--', label='sigma=0.2', legend=True)\n#pd.Series(PL2).plot(kind='kde', style='g--',label='sigma=0.3', legend=True)\nplt.title('hedge costs of different sigams')","sub_path":"delta_neutral.py","file_name":"delta_neutral.py","file_ext":"py","file_size_in_byte":5062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"492941190","text":"# Enter your code here. Read input from STDIN. Print output to STDOUT\nimport math\nt = int(input())\n\nwhile t>0:\n\n n = int(input())\n c = 0\n for i in range(2, math.floor(math.sqrt(n))+1):\n if n%i==0:\n c += 1\n if n==1:\n print(\"Not prime\")\n t -= 1\n continue\n if c==0:\n print(\"Prime\")\n else:\n print(\"Not prime\")\n t -= 1\n\n\n","sub_path":"Day 25/day25.py","file_name":"day25.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"627908048","text":"from OpenGL.GL import *\nfrom OpenGL.GLU import *\nimport pygame, sys, math, random\n\npygame.init()\n\nFPS = 60\n\nWIDTH = 1280 \nHEIGHT = 760\n\nBLACK = (0,0,0)\nWHITE = (255,255,255)\nRED = (255, 0, 0)\nCYAN = (0, 255,255)\nYELLOW = (255,255,0)\n\nLEFT_BUTTON = 1\nMIDDLE = 2\nRIGHT_BUTTON = 3\nSCROLL_UP = 4\nSCROLL_DOWN = 5\n\nNEIGHBOR_DIST = 50\nAVOID_DIST = 10\nOBSTACLE_DIST = 40\nTARGET_DIST = 50\nVISITED = 20\n\nMAX_SPEED = 5\n\nBOID_SIZE = 5\nOBSTACLE_SIZE = 10\nTARGET_SIZE = 10\nNODE_SIZE = 10\n\nCOHESION_FACTOR = 100\nALIGN_FACTOR = 20\nAVOID_FACTOR = 50\n\nclock = pygame.time.Clock()\n\nmode = 1\nboids = []\nobstacles = []\ntargets = []\nnodes = []\n\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\n\nclass Boid:\n\n def __init__(self, x, y):\n\n self.x = x\n self.y = y\n\n self.velocity = [random.randint(-5,6),random.randint(-5,6)]\n\n self.neighbors = []\n self.neighbor_obstacles = []\n self.current_target = None\n\n self.current_node = None\n self.visited_nodes = []\n \n def display(self):\n pygame.draw.circle(screen, WHITE, (int(self.x), int(self.y)), BOID_SIZE)\n \n def wrap(self):\n\n if self.x > WIDTH:\n self.x = 0\n \n if self.x < 0:\n self.x = WIDTH\n\n if self.y > HEIGHT:\n self.y = 0\n \n if self.y < 0:\n self.y = HEIGHT\n\n def move(self): \n\n if abs(self.velocity[0]) > MAX_SPEED:\n self.velocity[0] /= MAX_SPEED\n \n changeDir = random.randint(-1,1)\n\n if changeDir == -1:\n self.velocity[0] *= -1\n\n if abs(self.velocity[1]) > MAX_SPEED:\n self.velocity[1] /= MAX_SPEED\n \n changeDir = random.randint(-1,1)\n\n if changeDir == -1:\n self.velocity[1] *= -1\n \n self.x += self.velocity[0] \n self.y += self.velocity[1] \n \n def closestNeighbor(self):\n \n self.neighbors = []\n for b in boids:\n\n if b != self:\n \n _, _, dist = calculateDist(b.x, b.y, self.x, self.y)\n\n if dist < NEIGHBOR_DIST:\n\n self.neighbors.append(b)\n\n def closestObstacle(self):\n self.neighbor_obstacles = []\n \n for o in obstacles:\n \n _, _, dist = calculateDist(o.x, o.y, self.x, self.y)\n\n if dist < OBSTACLE_DIST:\n\n self.neighbor_obstacles.append(o)\n \n def closestTarget(self):\n self.current_target = None\n current_dist = float('inf')\n for t in targets:\n \n if t != self.current_target:\n _, _, dist = calculateDist(t.x, t.y, self.x, self.y)\n \n if dist < TARGET_DIST and dist < current_dist:\n current_dist = dist\n self.current_target = t\n\n def closestNode(self):\n\n if len(self.visited_nodes) >= len(nodes):\n self.visited_nodes = []\n self.current_node = None\n current_dist = float('inf')\n for n in nodes:\n if n in self.visited_nodes:\n continue\n if n != self.current_node:\n _, _, dist = calculateDist(n.x, n.y, self.x, self.y)\n \n if dist < VISITED:\n self.visited_nodes.append(n)\n continue\n if dist < TARGET_DIST and dist < current_dist:\n current_dist = dist\n self.current_node = n\n\n def separation(self):\n\n sumX = 0\n sumY = 0\n length = len(self.neighbors)\n if length > 0:\n for boid in self.neighbors:\n \n dx, dy, dist = calculateDist(boid.x, boid.y, self.x, self.y)\n \n if dist < AVOID_DIST:\n sumX -= dx\n sumY -= dy\n \n self.velocity[0] += sumX / AVOID_FACTOR\n self.velocity[1] += sumY / AVOID_FACTOR\n\n def avoidObstacle(self):\n\n sumX = 0\n sumY = 0\n length = len(self.neighbor_obstacles)\n if length > 0:\n for obstacle in self.neighbor_obstacles:\n x = obstacle.x\n y = obstacle.y\n\n dx = x - self.x\n dy = y - self.y\n \n sumX -= dx\n sumY -= dy\n \n self.velocity[0] += sumX / AVOID_FACTOR\n self.velocity[1] += sumY / AVOID_FACTOR\n\n def align(self):\n\n sumX = 0\n sumY = 0\n length = len(self.neighbors)\n if length > 0:\n \n for boid in self.neighbors:\n \n sumX += boid.velocity[0]\n sumY += boid.velocity[1]\n\n sumX = sumX / length\n sumY = sumY / length\n\n sumX = sumX - self.velocity[0]\n sumY = sumY - self.velocity[1]\n \n self.velocity[0] += sumX / ALIGN_FACTOR\n self.velocity[1] += sumY / ALIGN_FACTOR\n\n def cohesion(self):\n\n sumX = 0\n sumY = 0\n\n length = len(self.neighbors)\n if length > 0:\n\n for boid in self.neighbors:\n \n sumX += boid.x\n sumY += boid.y\n\n \n sumX = sumX / length\n sumY = sumY / length\n \n sumX -= self.x\n sumY -= self.y\n\n self.velocity[0] += sumX/COHESION_FACTOR\n self.velocity[1] += sumY/COHESION_FACTOR\n\n def towardTarget(self):\n\n if self.current_target != None:\n\n sumX = self.current_target.x - self.x\n sumY = self.current_target.y - self.y\n\n self.velocity[0] += sumX/COHESION_FACTOR\n self.velocity[1] += sumY/COHESION_FACTOR\n\n def followPath(self):\n\n if self.current_node != None:\n\n sumX = self.current_node.x - self.x\n sumY = self.current_node.y - self.y\n\n self.velocity[0] += sumX/COHESION_FACTOR\n self.velocity[1] += sumY/COHESION_FACTOR\n\n def flocking(self):\n \n self.align()\n self.cohesion()\n self.separation()\n self.avoidObstacle()\n self.towardTarget()\n self.followPath()\n \nclass Obstacle():\n\n def __init__(self, x, y):\n\n self.x = x\n self.y = y\n\n def display(self):\n pygame.draw.circle(screen, RED, (int(self.x), int(self.y)), OBSTACLE_SIZE) \n\nclass Target():\n\n def __init__(self, x, y):\n\n self.x = x\n self.y = y\n\n def display(self):\n pygame.draw.circle(screen, CYAN, (int(self.x), int(self.y)), TARGET_SIZE) \n\nclass Node():\n\n def __init__(self, x, y):\n\n self.x = x\n self.y = y\n\n def display(self):\n pygame.draw.circle(screen, YELLOW, (int(self.x), int(self.y)), NODE_SIZE) \n\ndef calculateDist(x1, y1, x2, y2):\n\n dx = x1 - x2\n dy = y1 - y2\n\n return dx, dy, math.sqrt(dx**2 + dy**2)\n\nfor i in range(100):\n b = Boid(random.randint(0 , WIDTH),random.randint(0, HEIGHT))\n boids.append(b)\n\nif __name__ == '__main__':\n\n while True:\n \n screen.fill(BLACK)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n \n if event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == LEFT_BUTTON:\n x,y = pygame.mouse.get_pos()\n boid = Boid(x,y)\n boids.append(boid)\n \n if event.button == MIDDLE:\n mode = (mode + 1) % 3\n \n if event.button == RIGHT_BUTTON:\n x,y = pygame.mouse.get_pos()\n removed = False\n \n if mode == 1:\n \n for o in obstacles:\n\n _, _, dist = calculateDist(x, y, o.x, o.y)\n\n if dist < OBSTACLE_SIZE / 2:\n obstacles.remove(o)\n removed = True\n\n if not removed:\n obstacle = Obstacle(x, y)\n obstacles.append(obstacle)\n \n elif mode == 2:\n\n for t in targets:\n\n _, _, dist = calculateDist(x, y, t.x, t.y)\n\n if dist < TARGET_SIZE / 2:\n targets.remove(t)\n removed = True\n\n if not removed:\n target = Target(x, y)\n targets.append(target)\n \n elif mode == 0:\n\n for n in nodes:\n\n _, _, dist = calculateDist(x, y, n.x, n.y)\n\n if dist < NODE_SIZE / 2:\n nodes.remove(n)\n removed = True\n\n if not removed:\n node = Node(x, y)\n nodes.append(node)\n\n for n in nodes:\n n.display()\n\n for t in targets:\n t.display()\n\n for o in obstacles:\n o.display()\n\n for b in boids:\n b.wrap()\n b.display()\n b.closestNeighbor()\n b.closestObstacle()\n b.closestTarget()\n b.closestNode()\n b.flocking()\n b.move()\n \n pygame.display.flip()\n clock.tick(FPS)","sub_path":"boids.py","file_name":"boids.py","file_ext":"py","file_size_in_byte":9652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"146757009","text":"from typing import Optional\n\nfrom fastapi import Depends, HTTPException\nfrom sqlalchemy.orm import Session\nfrom starlette.responses import Response\nfrom starlette.status import HTTP_204_NO_CONTENT\n\nfrom empire.server.api.api_router import APIRouter\nfrom empire.server.api.jwt_auth import get_current_active_user\nfrom empire.server.api.v2.credential.credential_dto import (\n Credential,\n CredentialPostRequest,\n Credentials,\n CredentialUpdateRequest,\n domain_to_dto_credential,\n)\nfrom empire.server.api.v2.shared_dependencies import get_db\nfrom empire.server.api.v2.shared_dto import BadRequestResponse, NotFoundResponse\nfrom empire.server.api.v2.tag import tag_api\nfrom empire.server.core.db import models\nfrom empire.server.server import main\n\ncredential_service = main.credentialsv2\n\nrouter = APIRouter(\n prefix=\"/api/v2/credentials\",\n tags=[\"credentials\"],\n responses={\n 404: {\"description\": \"Not found\", \"model\": NotFoundResponse},\n 400: {\"description\": \"Bad request\", \"model\": BadRequestResponse},\n },\n dependencies=[Depends(get_current_active_user)],\n)\n\n\nasync def get_credential(uid: int, db: Session = Depends(get_db)):\n credential = credential_service.get_by_id(db, uid)\n\n if credential:\n return credential\n\n raise HTTPException(404, f\"Credential not found for id {uid}\")\n\n\ntag_api.add_endpoints_to_taggable(router, \"/{uid}/tags\", get_credential)\n\n\n@router.get(\"/{uid}\", response_model=Credential)\nasync def read_credential(\n uid: int, db_credential: models.Credential = Depends(get_credential)\n):\n return domain_to_dto_credential(db_credential)\n\n\n@router.get(\"/\", response_model=Credentials)\nasync def read_credentials(\n db: Session = Depends(get_db),\n search: Optional[str] = None,\n credtype: Optional[str] = None,\n):\n credentials = list(\n map(\n lambda x: domain_to_dto_credential(x),\n credential_service.get_all(db, search, credtype),\n )\n )\n\n return {\"records\": credentials}\n\n\n@router.post(\n \"/\",\n status_code=201,\n response_model=Credential,\n)\nasync def create_credential(\n credential_req: CredentialPostRequest, db: Session = Depends(get_db)\n):\n resp, err = credential_service.create_credential(db, credential_req)\n\n if err:\n raise HTTPException(status_code=400, detail=err)\n\n return domain_to_dto_credential(resp)\n\n\n@router.put(\"/{uid}\", response_model=Credential)\nasync def update_credential(\n uid: int,\n credential_req: CredentialUpdateRequest,\n db: Session = Depends(get_db),\n db_credential: models.Credential = Depends(get_credential),\n):\n resp, err = credential_service.update_credential(db, db_credential, credential_req)\n\n if err:\n raise HTTPException(status_code=400, detail=err)\n\n return domain_to_dto_credential(resp)\n\n\n@router.delete(\n \"/{uid}\",\n status_code=HTTP_204_NO_CONTENT,\n response_class=Response,\n)\nasync def delete_credential(\n uid: str,\n db: Session = Depends(get_db),\n db_credential: models.Credential = Depends(get_credential),\n):\n credential_service.delete_credential(db, db_credential)\n","sub_path":"empire/server/api/v2/credential/credential_api.py","file_name":"credential_api.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"78968536","text":"from pmutils.repo_server import post_knowledge\nfrom datetime import datetime\nfrom datetime import date\nfrom pathlib import Path\nimport subprocess\nimport argparse\nimport filecmp\nimport shutil\nimport time\nimport os\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--repo', default='/Users/posh/knowledge_repo/analytics/knowledge_repo', help='directory to populate notebooks')\n\nargs = parser.parse_args()\nREPO = args.repo\n\nNOW = datetime.now()\nWEEKDAY = date(NOW.year, NOW.month, NOW.day).weekday()\nREPO = args.repo\n\n\n\ndef sleep(t=None):\n if t:\n sleep(t)\n return\n \n # check for weekend\n if WEEKDAY < 5:\n if NOW.hour < 6 or NOW.hour > 22:\n time.sleep(3600)\n else:\n time.sleep(30)\n\n else:\n if NOW.hour < 6 or NOW.hour > 22:\n time.sleep(7200)\n else:\n time.sleep(1800)\n\n\n\n# makes a directory and waits up to 10 seconds for it to exists\ndef create_project(path=None, wait=10):\n os.makedirs(path, exist_ok=True)\n loops = int(wait/0.1)\n for i in range(loops):\n time.sleep(0.1)\n if os.path.exists(path):\n break\n\n\n\n# TODO: add option for reference path outside of cwd\n# checks if files exist in mirror, if they do compares file for any differences in source\n# returns true if missing or different, returns false if files are identical\ndef is_updated(project=None, filename=None, repo=None, ref='mirror', ref_path=None):\n if project=='stash':\n source_path = '/'.join([repo, filename])\n else:\n source_path = '/'.join([repo, project, filename])\n mirror_path = '/'.join([os.getcwd(), ref, project, filename])\n source_file, mirror_file = Path(source_path), Path(mirror_path)\n\n # copy file to the mirror if it doesn't exist.\n if mirror_file.exists() and mirror_file.is_file():\n return not filecmp.cmp(source_file, mirror_file, shallow=False)\n else:\n destination = '/'.join([os.getcwd(), ref, project])\n create_project(path=destination)\n #with open(source_file, 'rb') as src:\n # with open(mirror_file, 'wb+') as mir:\n # mir.write(src.read())\n shutil.copy2(source_path, destination)\n return True\n\n\n\n\ndef update_repo():\n for item in os.walk(REPO):\n if item[0].endswith('.ipynb_checkpoints')\\\n or len(item[2]) < 1:\n continue\n\n for notebook in item[2]:\n if '_kr.' not in notebook:\n continue\n\n if item[0] == REPO:\n project = 'stash'\n else:\n project = item[0].split('/')[-1]\n\n filename = item[0] + '/' + notebook\n\n post_knowledge(filename=filename, project=project)\n\n\nwhile True:\n update_repo()\n break#sleep(t=1800)\n","sub_path":"pm_scripts/reload_repo.py","file_name":"reload_repo.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"36285375","text":"from . import STDR\n\nclass ATR(STDR):\n def __init__(self, version=None, endian=None, record = None):\n self.id = 'ATR'\n self.local_debug = False\n # Version\n if version==None or version=='V4':\n self.version = 'V4'\n self.info= '''\nAudit Trail Record\n------------------\n\nFunction:\n Used to record any operation that alters the contents of the STDF file. The name of the\n program and all its parameters should be recorded in the ASCII field provided in this\n record. Typically, this record will be used to track filter programs that have been\n applied to the data.\n\nFrequency:\n * Optional\n * One for each filter or other data transformation program applied to the STDF data.\n\nLocation:\n Between the File Attributes Record (FAR) and the Master Information Record (MIR).\n The filter program that writes the altered STDF file must write its ATR immediately\n after the FAR (and hence before any other ATRs that may be in the file). In this way,\n multiple ATRs will be in reverse chronological order.\n'''\n self.fields = {\n 'REC_LEN' : {'#' : 0, 'Type' : 'U*2' , 'Ref' : None, 'Value' : None, 'Text' : 'Bytes of data following header ', 'Missing' : None, 'Note' : ''},\n 'REC_TYP' : {'#' : 1, 'Type' : 'U*1' , 'Ref' : None, 'Value' : 0, 'Text' : 'Record type ', 'Missing' : None, 'Note' : ''},\n 'REC_SUB' : {'#' : 2, 'Type' : 'U*1' , 'Ref' : None, 'Value' : 20, 'Text' : 'Record sub-type ', 'Missing' : None, 'Note' : ''},\n 'MOD_TIM' : {'#' : 3, 'Type' : 'U*4' , 'Ref' : None, 'Value' : None, 'Text' : 'Date & time of STDF file modification ', 'Missing' : 0, 'Note' : ''},\n 'CMD_LINE' : {'#' : 4, 'Type' : 'C*n' , 'Ref' : None, 'Value' : None, 'Text' : 'Command line of program ', 'Missing' : '', 'Note' : ''}\n }\n else:\n raise STDR.STDFError(\"%s object creation error: unsupported version '%s'\" % (self.id, version))\n self._default_init(endian, record)","sub_path":"Semi_ATE/STDF/ATR.py","file_name":"ATR.py","file_ext":"py","file_size_in_byte":2145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"281813289","text":"#!/usr/bin/env python3\n# assign.py : Given n number of students with their preferences, assign them to teams such that overall time for\n# grading the assignment is minimum.\n# Team - Ankita Alshi, Bhargavi Chalasa, Dheeraj Singh, 2017\n#\n# Comments:\n# Search Problem: write a program to find an assignment of students to teams that minimizes the total amount of \n# work the course staff needs to do, subject to the constraint that no team may have more than 3 students.\n#\n# (1)State Space: Maximum N teams with combination of not more than 3 students per team. (where N is number of students)\n# Initial State: board arrangement having N teams, each team having one student. (where N is number of students)\n# Goal State: Set of teams such that all students belong to a team and every team contains max 3 students with least amount of work required.\n# Successor: It creates new combination teams removing one student from a team and adds it to others. \n# Cost: Cost is total cost to grade all the teams for given arrangement of teams\n#\n# (2) Search Algorithm: We have used beam search algorithm to solve this problem. Instaed of randomly initializing first set of states, we are \n# starting with a board having N teams with one student per team. For this beam search the value of k for the loop we have taken as N (where N is\n# number of students). We are also using fringe in form of heap queue and selecting 2 states from heapqueue with least cost. Then the successors\n# of those two states are created and added to heapqueue. And we are not checking goal state in each loop. the goal state is the arrangement of teams\n# with least cost present in heap queue after N loops are done.\n#\n# (3) As number of student increases (N>100) the algorithm was taking a lot of time if we take 2 nsmallest states from heap queue, so for larger\n# number students we are taking only 1 smallest cost state to pass on to the successor function.\n#\n# References:\n# We have referenced following link to understand heapqueue implementation:\n# https://docs.python.org/3.0/library/heapq.html#heapq.nsmallest\n# We have referenced following links for List and dictionary methods:\n# https://www.tutorialspoint.com/python/python_lists.htm\n# https://www.tutorialspoint.com/python/python_dictionary.htm\n#\n\nfrom heapq import *\nimport sys\nimport datetime\n\n# Create result string to print the final teams in required output format\ndef print_sol(board):\n result = \"\"\n for row in board:\n for col in row:\n result += col\n result += \" \"\n if (not (len(row) == 0 or (len(board) == board.index(row) + 1) )):\n result += \"\\n\"\n return result\n\ndef trim(board):\n s = []\n for each_row in board:\n if (not each_row == []):\n s.append(each_row)\n return s\n\n# Remove student from ith team and add him to nth team\ndef add_student(board, n, i, student):\n s = [[val for val in row] for row in board]\n s[n].append(student)\n s[i].remove(student)\n return s\n\n# Calculate number of students assign to team size other than their preference\ndef wrong_team_size(team):\n count = 0\n for mem in team:\n index = listuserid.index(mem)\n prefteam = listdict[index]['teamnum']\n if (not (prefteam == \"0\")):\n if (not (str(len(team)) == prefteam)):\n count += 1\n return count\n\n# Calculate number of students who did not get team members of their choice\ndef notgotwanted(team):\n count = 0\n for memi in team:\n indexi = listuserid.index(memi)\n for memj in listdict[indexi]['wantedmem']:\n if (memj not in team):\n count += 1 \n return count\n\n# Calculate number of student who got team member that they did not wanted to work with\ndef gotunwanted(team):\n count = 0\n for memi in team:\n indexi = listuserid.index(memi)\n for memj in listdict[indexi]['unwantedmem']:\n if (memj in team):\n count += 1\n return count\n\n# Define initial team arrangement as one student per team\ndef initial_board():\n for student in listuserid:\n initial.append([student])\n return initial\n\n# Calculate cost for a team\ndef cost_team(team):\n if (len(team) == 0):\n return 0\n else:\n cost = k + (1 * wrong_team_size(team)) + (n * notgotwanted(team)) + (m * gotunwanted(team))\n return cost\n\n# Calculate total cost for given arrangement of teams\ndef tot_cost(board):\n total_cost = 0\n for row in board:\n if (not len(row) == 0):\n total_cost += cost_team(row)\n return total_cost\n\n# Create successors by adding students to different teams\ndef successor(board, i):\n move_student = board[i][0]\n succ = []\n for n in range(len(board)):\n if (not i == n):\n curr_size = len(board[n])\n if (curr_size > 0 and curr_size < 3):\n temp = add_student(board, n, i, move_student)\n succ_cost = tot_cost(temp)\n if (succ_cost <= curr_cost):\n succ.append([succ_cost, temp])\n return succ\n\n# Solve the problem recursively till all the team members of ith team are assigned to other teams\ndef recur_solve(s, i):\n if(not len(s[i]) == 0):\n for x in successor(s, i):\n heappush(fringe, (x[0], x[1]))\n recur_solve(x[1], i)\n return\n\n# Solve the problem by adding team member from ith team to other teams to find minimum cost\ndef solve():\n global fringe, curr_cost\n smin = []\n fringe = []\n s = initial_board()\n curr_cost = tot_cost(s)\n heappush(fringe, (curr_cost, s))\n for i in range(rows):\n if (i == 0):\n recur_solve(s, i)\n else:\n for each in smin:\n curr_cost = each[0]\n s = each[1]\n heappush(fringe, (curr_cost, s))\n recur_solve(s, i)\n \n if (i == rows-1):\n curr_cost, s = heappop(fringe)\n else:\n smin = nsmallest(nsmall, fringe)\n return s \n\n# Storin input data from file to dictionary\ndef filetodict():\n for a in ifile:\n line = a.split()\n d = {'userid': line[0], 'teamnum': line[1], 'wantedmem': line[2].split(\",\"), 'unwantedmem': line[3].split(\",\")}\n if (line[2] == \"_\"):\n d['wantedmem'] = []\n if (line[3] == \"_\"):\n d['unwantedmem'] = []\n listdict.append(d)\n return\n\n# Input from command line: 1. input file1 2. Time taken to grade one team \n# 3. time taken to answer mail query 4. time for a meeting to resolve issue\nfilename = sys.argv[1]\nk = int(sys.argv[2])\nn = int(sys.argv[3]) \nm = int(sys.argv[4])\n\n# Open input file containing student userid with their preferences\nifile = open(filename, \"r\")\n\n# List of Dictionaries to store input values\nlistdict = []\nfiletodict()\n\n# List of userids given\nlistuserid = [a['userid'] for a in listdict]\nrows = cols = len(listdict)\nif (rows > 50):\n nsmall = 1\nelse:\n nsmall = 2\n\n# Solve the the problem and print final list of team with the time required to grade them.\ninitial = []\ngoal = solve()\nprint (print_sol(trim(goal)))\nprint (tot_cost(goal))\n","sub_path":"team-assign.py","file_name":"team-assign.py","file_ext":"py","file_size_in_byte":7204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"287278455","text":"# Copyright 2018 ARICENT HOLDINGS LUXEMBOURG SARL and Cable Television\n# Laboratories, Inc.\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# This script is responsible for deploying Aricent_Iaas environments and\n# Kubernetes Services\n\n\nimport argparse\nimport logging\nimport sys\nimport os\n\nfrom snaps.provisioning import ansible_utils\n\nfrom snaps_k8s.common.utils import file_utils\nfrom snaps_k8s.common.consts import consts\nfrom snaps_k8s.common.utils.validation_utils import validate_deployment_file\nfrom snaps_k8s.provision.kubernetes.deployment import deploy_infra\n\n__author__ = '_ARICENT'\n\nlogger = logging.getLogger('launch_provisioning')\n\n\ndef __installation_logs(cmdln_args):\n \"\"\"\n This will initialize the logging for Kubernetes installation\n :param cmdln_args : the command line arguments\n \"\"\"\n level_value = cmdln_args.log_level\n\n log_file_name = consts.K8_INSTALLATION_LOGS\n if level_value.upper() == 'INFO':\n level_value = logging.INFO\n elif level_value.upper() == 'ERROR':\n level_value = logging.ERROR\n elif level_value.upper() == 'DEBUG':\n level_value = logging.DEBUG\n elif level_value.upper() == 'WARNING':\n level_value = logging.WARNING\n elif level_value.upper() == 'CRITICAL':\n level_value = logging.CRITICAL\n else:\n print(\"Incorrect log level %s received as input from user\" %\n level_value)\n exit(1)\n\n logger.setLevel(level_value)\n\n log_output = cmdln_args.log_out\n if log_output == 'stderr':\n logging.basicConfig(level=logging.DEBUG)\n elif log_output == 'stdout':\n logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n else:\n logging.basicConfig(\n format='%(asctime)s %(levelname)s [%(filename)s:'\n '%(lineno)s - %(funcName)2s() ] %(message)s ',\n datefmt='%b %d %H:%M', filename=log_file_name, filemode='w',\n level=level_value)\n logging.getLogger().addHandler(logging.StreamHandler())\n\n\ndef __launcher_conf():\n \"\"\"\n Performs build server setup\n \"\"\"\n logger.info('Setting up build server with playbook [%s]',\n consts.BUILD_PREREQS)\n ansible_utils.apply_playbook(consts.BUILD_PREREQS)\n\n\ndef __manage_operation(config, operation, deploy_file):\n \"\"\"\n This will launch the provisioning of kubernetes setup on the cluster node\n which are defined in the deployment.yaml.\n :param config : This configuration data extracted from the provided yaml\n file.\n \"\"\"\n ret_value = False\n if config and isinstance(config, dict):\n if config.get('kubernetes'):\n logger.info(\"Yaml Configuration %s\", config)\n logger.info(\"Read & Validate functionality for Kubernetes %s\",\n operation)\n ret_value = deploy_infra(config, operation, deploy_file)\n else:\n logger.error(\"Configuration Error \")\n else:\n logger.info(\"Installation of additional services\")\n ret_value = deploy_infra(config, operation, deploy_file)\n\n return ret_value\n\n\ndef run(arguments):\n \"\"\"\n This will launch the provisioning of Bare metal & IaaS.\n There is pxe based configuration defined to provision the bare metal.\n For IaaS provisioning different deployment models are supported.\n Relevant conf files related to PXE based Hw provisioning & IaaS must be\n present in ./conf folder.\n :param arguments: This expects command line options to be entered by user\n for relevant operations.\n :return: To the OS\n \"\"\"\n ret_value = False\n __installation_logs(arguments)\n\n logger.info('Launching Operation Starts ........')\n\n dir_path = os.path.dirname(os.path.realpath(__file__))\n export_path = dir_path + \"/\"\n os.environ['CWD_IAAS'] = export_path\n logger.info('Current Exported Relevant Path - %s', export_path)\n\n config = file_utils.read_yaml(arguments.config)\n logger.info('Read configuration file - %s', arguments.config)\n\n if arguments.deploy_kubernetes:\n __launcher_conf()\n validate_deployment_file(config)\n ret_value = __manage_operation(config, \"deploy_k8\", arguments.config)\n if arguments.clean_kubernetes:\n ret_value = __manage_operation(config, \"clean_k8\", arguments.config)\n if ret_value:\n logger.info('Completed operation successfully')\n else:\n logger.info('Operation unsuccessful')\n\n\nif __name__ == '__main__':\n # To ensure any files referenced via a relative path will begin from the\n # directory in which this file resides\n os.chdir(os.path.dirname(os.path.realpath(__file__)))\n\n parser = argparse.ArgumentParser()\n parser_group = parser.add_mutually_exclusive_group()\n required_group = parser.add_mutually_exclusive_group(required=True)\n required_group.add_argument('-f', '--file', dest='config',\n help='The configuration file in YAML format',\n metavar=\"FILE\")\n parser_group.add_argument('-k8_d', '--deploy_kubernetes',\n action='store_true',\n help='When used, deployment of kubernetes '\n 'will be started')\n parser_group.add_argument('-k8_c', '--clean_kubernetes',\n action='store_true',\n help='When used, the kubernetes cluster '\n 'will be removed')\n parser.add_argument('-l', '--log-level', default='INFO',\n help='Logging Level (INFO|DEBUG|ERROR)')\n parser.add_argument('-o', '--log-out', default='file', dest='log_out',\n help='Logging output (file(default)|stdout|stderr)')\n args = parser.parse_args()\n\n if (args.deploy_kubernetes or args.clean_kubernetes) and not args.config:\n logger.info(\n \"Cannot start Kubernetes related operations without filename. \"\n \"Choose the option -f/--file\")\n exit(1)\n\n run(args)\n","sub_path":"iaas_launch.py","file_name":"iaas_launch.py","file_ext":"py","file_size_in_byte":6556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"12721935","text":"# Copyright (c) 2015, Alphamonak Solutions Ltd. \n# License: GNU General Public License v3. See license.txt\n\nfrom __future__ import unicode_literals\n\nimport redapp\nimport unittest\n\ntest_records = redapp.get_test_records('Lead')\n\nclass TestLead(unittest.TestCase):\n\tdef test_make_customer(self):\n\t\tfrom redapple.crm.doctype.lead.lead import make_customer\n\n\t\tredapp.delete_doc_if_exists(\"Customer\", \"_Test Lead\")\n\n\t\tcustomer = make_customer(\"_T-Lead-00001\")\n\t\tself.assertEquals(customer.doctype, \"Customer\")\n\t\tself.assertEquals(customer.lead_name, \"_T-Lead-00001\")\n\n\t\tcustomer.company = \"_Test Company\"\n\t\tcustomer.customer_group = \"_Test Customer Group\"\n\t\tcustomer.insert()\n","sub_path":"redapple/crm/doctype/lead/test_lead.py","file_name":"test_lead.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"113524065","text":"from fractions import Fraction\nimport math\n\n\ndef decompose_pi_fraction(f:Fraction):\n \"\"\"\n Break a fraction that has a power of two as denominator into a sequence of fractions \n that add up to it, all of which have 1 for numerator.\n\n Adjusts the fraction to be in the (2,0] range\n \"\"\"\n\n # Check that f's denominator is a power of two\n assert(f.denominator & (f.denominator - 1) == 0)\n\n # Adjust to the the (2*pi,0] range\n f = Fraction(f.numerator % (2*f.denominator), f.denominator)\n\n if f.numerator == 0: return [f]\n elif f.numerator == 1: return [f]\n else:\n p = int(math.log(f.numerator, 2))\n largest_pow_of_2 = int(pow(2, p)) \n return [Fraction(largest_pow_of_2, f.denominator)] \\\n + decompose_pi_fraction(Fraction(f.numerator - largest_pow_of_2,f.denominator))\n\n\ndef phase_frac_to_latex(phi : Fraction):\n \"\"\"Assumes phi is multiplied by pi\"\"\"\n if phi.numerator== 0: return \"0\"\n\n sign = \"\" if phi.numerator > 0 else \"-\"\n num = \"\" if abs(phi.numerator) == 1 else str(abs(phi.numerator))\n\n if phi.denominator == 1:\n return \"%s%s\\\\pi\"%(sign,num)\n\n den = str(phi.denominator)\n\n return \"%s\\\\frac{%s\\\\pi}{%s}\"%(sign,num,den)\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"372976581","text":"# -*- coding: utf-8 -*-\n#\n# This file is part of Invenio.\n# Copyright (C) 2015-2018 CERN.\n#\n# Invenio is free software; you can redistribute it and/or modify it\n# under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Utility functions for search engine.\"\"\"\n\nimport os\n\nfrom flask import current_app\n\n\ndef prefix_index(app, index):\n \"\"\"Prefixes the given index if needed.\n\n :param app: Flask app to get the config from.\n :param index: Name of the index to prefix.\n :returns: A string with the new index name prefixed if needed.\n \"\"\"\n index_prefix = app.config['SEARCH_INDEX_PREFIX'] or ''\n return index_prefix + index\n\n\ndef build_index_name(app, *parts):\n \"\"\"Build an index name from parts.\n\n :param parts: Parts that should be combined to make an index name.\n \"\"\"\n base_index = os.path.splitext(\n '-'.join([part for part in parts if part])\n )[0]\n\n return prefix_index(app=app, index=base_index)\n\n\ndef schema_to_index(schema, index_names=None):\n \"\"\"Get index/doc_type given a schema URL.\n\n :param schema: The schema name\n :param index_names: A list of index name.\n :returns: A tuple containing (index, doc_type).\n \"\"\"\n parts = schema.split('/')\n doc_type = os.path.splitext(parts[-1])\n\n if doc_type[1] not in {'.json', }:\n return (None, None)\n\n if index_names is None:\n return (build_index_name(current_app, *parts), doc_type[0])\n\n for start in range(len(parts)):\n index_name = build_index_name(current_app, *parts[start:])\n if index_name in index_names:\n return (index_name, doc_type[0])\n\n return (None, None)\n","sub_path":"invenio_search/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"205499868","text":"from typing import Dict, List, Optional, Tuple, Type\n\nimport networkx\nfrom networkx import Graph\nfrom networkx.drawing.nx_agraph import graphviz_layout\n\nfrom streams_explorer.core.config import settings\nfrom streams_explorer.core.k8s_app import K8sApp\nfrom streams_explorer.core.services.metric_providers import MetricProvider\nfrom streams_explorer.models.graph import Metric\nfrom streams_explorer.models.kafka_connector import (\n KafkaConnector,\n KafkaConnectorTypesEnum,\n)\nfrom streams_explorer.models.node_types import NodeTypesEnum\nfrom streams_explorer.models.sink import Sink\nfrom streams_explorer.models.source import Source\n\n\nclass NodeNotFound(Exception):\n pass\n\n\nclass DataFlowGraph:\n def __init__(self, metric_provider: Type[MetricProvider]):\n self.graph = networkx.DiGraph()\n self.independent_graphs: Dict[str, networkx.DiGraph] = {}\n self.metric_provider_class = metric_provider\n\n def add_streaming_app(self, app: K8sApp):\n self.graph.add_node(\n app.name,\n label=app.name,\n node_type=NodeTypesEnum.STREAMING_APP,\n **app.attributes,\n )\n if app.output_topic:\n self._add_topic(app.output_topic)\n self._add_output_topic(app.name, app.output_topic)\n if app.error_topic:\n self._add_error_topic(app.name, app.error_topic)\n for input_topic in app.input_topics:\n self._add_topic(input_topic)\n self._add_input_topic(app.name, input_topic)\n for extra_input in app.extra_input_topics:\n self._add_topic(extra_input)\n self._add_input_topic(app.name, extra_input)\n for extra_output in app.extra_output_topics:\n self._add_topic(extra_output)\n self._add_output_topic(app.name, extra_output)\n\n def add_connector(self, connector: KafkaConnector):\n self.graph.add_node(\n connector.name,\n label=connector.name,\n node_type=NodeTypesEnum.CONNECTOR,\n )\n for topic in connector.topics:\n self._add_topic(topic)\n if connector.type == KafkaConnectorTypesEnum.SINK:\n self.graph.add_edge(topic, connector.name)\n if connector.type == KafkaConnectorTypesEnum.SOURCE:\n self.graph.add_edge(connector.name, topic)\n if connector.error_topic:\n self._add_error_topic(connector.name, connector.error_topic)\n\n def add_source(self, source: Source):\n self.graph.add_node(\n source.name,\n label=source.name,\n node_type=source.node_type,\n )\n self.graph.add_edge(source.name, source.target)\n\n def add_sink(self, sink: Sink):\n self.graph.add_node(\n sink.name,\n label=sink.name,\n node_type=sink.node_type,\n )\n self.graph.add_edge(sink.source, sink.name)\n\n def get_positioned_pipeline_graph(self, pipeline_name: str) -> dict:\n return self.__get_positioned_json_graph(self.independent_graphs[pipeline_name])\n\n def get_positioned_graph(self) -> dict:\n return self.__get_positioned_json_graph(self.graph)\n\n def get_metrics(self) -> List[Metric]:\n if self.metric_provider is not None:\n return self.metric_provider.get()\n return []\n\n def get_node_type(self, id: str) -> str:\n try:\n return self.graph.nodes[id].get(\"node_type\")\n except KeyError:\n raise NodeNotFound()\n\n def extract_independent_pipelines(self):\n undirected_graph = self.graph.to_undirected()\n independent_pipeline_nodes = list(\n networkx.connected_components(undirected_graph)\n )\n for pipeline in independent_pipeline_nodes:\n pipeline_graph = self.graph.subgraph(pipeline)\n pipeline_name = self.__extract_pipeline_name(pipeline_graph)\n self.independent_graphs[pipeline_name] = pipeline_graph\n\n def _add_topic(self, name: str):\n self.graph.add_node(\n name,\n label=name,\n node_type=NodeTypesEnum.TOPIC,\n )\n\n def _add_input_topic(self, streaming_app, topic_name):\n self.graph.add_edge(topic_name, streaming_app)\n\n def _add_output_topic(self, streaming_app, topic_name):\n self._add_topic(topic_name)\n self.graph.add_edge(streaming_app, topic_name)\n\n def _add_error_topic(self, streaming_app, topic_name):\n self.graph.add_node(\n topic_name,\n label=topic_name,\n node_type=NodeTypesEnum.ERROR_TOPIC,\n )\n self.graph.add_edge(streaming_app, topic_name)\n\n def __extract_pipeline_name(self, pipeline_graph):\n streaming_apps = list(\n filter(self.__filter_streaming_apps, pipeline_graph.nodes(data=True))\n )\n if len(streaming_apps) < 1:\n return list(pipeline_graph.nodes)[0]\n\n name = unique_name = self.__get_streaming_app_pipeline(streaming_apps[0])\n\n index = 0\n while self.independent_graphs.get(unique_name) is not None:\n index += 1\n unique_name = f\"{name}{index}\"\n return unique_name\n\n def reset(self):\n self.graph = networkx.DiGraph()\n self.independent_graphs = {}\n self.metric_provider = self.metric_provider_class(self.graph.nodes(data=True))\n\n @staticmethod\n def __filter_streaming_apps(node: Tuple[str, dict]) -> bool:\n return node[1].get(\"node_type\") == NodeTypesEnum.STREAMING_APP\n\n @staticmethod\n def __get_streaming_app_pipeline(streaming_app: Tuple[str, dict]) -> str:\n streaming_app_name, streaming_app_labels = streaming_app\n pipeline: Optional[str] = None\n if (\n settings.k8s.independent_graph\n and settings.k8s.independent_graph.label is not None\n ):\n pipeline = streaming_app_labels.get(settings.k8s.independent_graph.label)\n if pipeline is None:\n pipeline = streaming_app_name\n return pipeline\n\n @staticmethod\n def __get_json_graph(graph: Graph) -> dict:\n json_graph: dict = networkx.node_link_data(graph)\n json_graph[\"edges\"] = json_graph.pop(\"links\")\n return json_graph\n\n @staticmethod\n def __get_positioned_json_graph(graph: Graph) -> dict:\n pos = graphviz_layout(graph, prog=\"dot\", args=settings.graph_layout_arguments)\n x = {n: p[0] for n, p in pos.items()}\n y = {n: p[1] for n, p in pos.items()}\n networkx.set_node_attributes(graph, x, \"x\")\n networkx.set_node_attributes(graph, y, \"y\")\n return DataFlowGraph.__get_json_graph(graph)\n","sub_path":"backend/streams_explorer/core/services/dataflow_graph.py","file_name":"dataflow_graph.py","file_ext":"py","file_size_in_byte":6645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"416079052","text":"\ndef create_bow(rawtext):\n dict = {}\n rawtext = rawtext.lower()\n text = \"\"\n for letter in rawtext:\n if letter.isalpha() or letter == '\\n' or letter == ' ':\n text = text + letter\n for word in text.split():\n dict.setdefault(word,0)\n dict[word] = dict[word] + 1\n return dict\n\n\ndef file_to_bow(filename):\n f = open(filename)\n text = f.read()\n d = create_bow(text)\n return d\n\n# Example\n# find all the words (and counts) that appear more than twice\n#[(word, d[word]) for word in d.keys() if d[word] > 2]\n","sub_path":"classcode/bow/bow.py","file_name":"bow.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"58108402","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\n# [START functions_pubsub_setup]\nimport base64\nimport json\nimport os\n\nimport googleapiclient.discovery\nimport pandas as pd\n\n\ndef _sample_instances(data_file, num_rows):\n \"\"\"Samples instance from a CSV file.\"\"\"\n\n df = pd.read_csv(data_file).sample(frac=1).drop('Cover_Type', axis=1)\n \n instances = []\n for row in df.head(num_rows).iterrows():\n feature_dict = {key: [value] for key, value in row[1].to_dict().items()}\n instances.append(feature_dict)\n\n return instances\n\ndef _call_caip_predict(service_name, signature_name, model_output_key, instances):\n \n service = googleapiclient.discovery.build('ml', 'v1')\n \n request_body={\n 'signature_name': signature_name,\n 'instances': instances}\n\n response = service.projects().predict(\n name=service_name,\n body=request_body\n\n ).execute()\n\n if 'error' in response:\n raise RuntimeError(response['error'])\n\n return [output[model_output_key] for output in response['predictions']]\n \ndef run_predictions(event, context):\n \"\"\"Background Cloud Function to be triggered by Pub/Sub.\n Args:\n event (dict): The dictionary with data specific to this type of\n event. The `data` field contains the PubsubMessage message. The\n `attributes` field will contain custom attributes if there are any.\n context (google.cloud.functions.Context): The Cloud Functions event\n metadata. The `event_id` field contains the Pub/Sub message ID. The\n `timestamp` field contains the publish time.\n \"\"\"\n \n json_str = base64.b64decode(event['data']).decode('utf-8')\n params = json.loads(json_str)\n instances = _sample_instances(\n params['data_file'], \n params['num_examples'])\n \n predictions = _call_caip_predict(\n params['service_name'], \n params['signature_name'], \n params['model_output_key'], \n instances)\n \n return predictions\n \n ","sub_path":"archive/prediction_robot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"130729883","text":"from flask import Flask, render_template, request\nimport sqlite3 as sql\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n return render_template('home.html')\n\n@app.route('/enternew')\ndef new_student():\n return render_template('salary.html')\n\n@app.route('/addrec',methods = ['POST', 'GET'])\ndef addrec():\n if request.method == 'POST':\n try:\n id = request.form['id']\n name = request.form['name']\n city = request.form['city']\n salary = request.form['salary']\n if id == '':\n err = True\n return render_template(\"result.html\", msg = \"ID is empty\")\n if name == '':\n err = True\n return render_template(\"result.html\", msg = \"Name is empty\")\n if city == '':\n err = True\n return render_template(\"result.html\", msg = \"City is empty\")\n if salary == '':\n err = True\n return render_template(\"result.html\", msg = \"Salary is empty\")\n err = False\n with sql.connect(\"database.db\") as con:\n cur = con.cursor()\n \n cur.execute('''INSERT INTO salarydata (id,name,city,salary) \n VALUES (?,?,?,?)''',(id,name,city,salary) )\n \n con.commit()\n msg = \"Record with ID : {} Created\".format(id)\n except:\n con.rollback()\n msg = \"Create Database First\"\n \n finally:\n if not err:\n return render_template(\"result.html\",msg = msg)\n con.close()\n\n@app.route('/list')\ndef list():\n con = sql.connect(\"database.db\")\n con.row_factory = sql.Row\n \n cur = con.cursor()\n try:\n cur.execute(\"select * from salarydata\")\n \n rows = cur.fetchall();\n return render_template(\"list.html\",rows = rows)\n except:\n return render_template(\"result.html\",msg = \"Database Not Found\")\n\n@app.route('/delete')\ndef delete_database():\n import os\n os.remove('./database.db')\n return render_template(\"result.html\",msg = \"Deletion Success\")\n \n@app.route('/create_db')\ndef create_db():\n conn = sql.connect('database.db')\n print (\"Opened database successfully\")\n \n try:\n conn.execute('CREATE TABLE salarydata (id TEXT NOT NULL, name TEXT NOT NULL, city TEXT NOT NULL, salary TEXT NOT NULL)')\n msg = \"Table created successfully\"\n conn.close()\n except:\n msg = \"Delete Database First\"\n conn.close()\n return render_template(\"result.html\",msg = msg)\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', debug = True, port=5804)\n","sub_path":"05-sql/flask_api.py","file_name":"flask_api.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"573676078","text":"#coding=utf8\n\nimport pandas as pd\nimport talib as tb\nimport numpy as np\n\nfrom copy import copy\nfrom datetime import datetime\n\n\nclass indicatorBase(object):\n def __init__(self):\n self.minperiod = None\n self.instrument = None\n\n self.preload_bar_list = []\n\n def set_dtformat(self,bar):\n # 目前只设置支持int和str\n date = bar['date']\n dt = \"%Y-%m-%d %H:%M:%S\"\n if self.timeindex:\n date = datetime.strptime(str(date), self.dtformat).strftime('%Y-%m-%d')\n return date + ' ' + bar[self.timeindex.lower()]\n else:\n return datetime.strptime(str(date), self.dtformat).strftime(dt)\n\n def _set_feed(self,marketevent):\n m = marketevent\n self.instrument = m.instrument\n self.iteral_data2 = m.info['iteral_data2']\n\n self.fromdate = m.info['fromdate']\n self.dtformat = m.info['dtformat']\n self.tmformat = m.info['tmformat']\n self.timeindex = m.info['timeindex']\n\n self.bar_list = copy(m.info['bar_dict'][m.instrument])\n self.bar_list2 = copy(self.bar_list)\n self.preload_bar_list = m.info['preload_bar_list']\n\n def _insert_preload_bar(self,minperiod):\n \"\"\"直接将preload的dict一个一个插到bardict前面，然后开始计算\"\"\"\n \"\"\"若preload_bar_list为空或者不够，则前进\"\"\"\n self.preload_limit = self.preload_bar_list[:minperiod]\n self.bar_list = copy(self.bar_list2)\n [self.bar_list.insert(0,i) for i in self.preload_limit] # load to bar_list\n\n\nclass indicator(indicatorBase):\n def __init__(self):\n super(indicator,self).__init__()\n\n # shortcut\n self.SMA = self.SimpleMovingAverage\n\n def SimpleMovingAverage(self,period,index=-1):\n self.minperiod = period\n self._insert_preload_bar(period)\n\n data = [i['close'] for i in self.bar_list][-period+index:]\n sma = tb.SMA(np.array(data),period)\n if np.isnan(sma[index]):\n raise Warning\n else:\n return sma[index]\n","sub_path":"OnePy/indicator.py","file_name":"indicator.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"390488771","text":"from nes_py.wrappers import BinarySpaceToDiscreteSpaceEnv\nimport gym_super_mario_bros\n#from gym_super.gym_super_mario_bros_wra.actions import SIMPLE_MOVEMENT\n\n\"\"\"Static action sets for binary to discrete action space wrappers.\"\"\"\n\n\n# actions for the simple run right environment\nRIGHT_ONLY = [\n ['NOP'],\n ['right'],\n ['right', 'A'],\n ['right', 'B'],\n ['right', 'A', 'B'],\n]\n\n\n# actions for very simple movement\nSIMPLE_MOVEMENT = [\n ['NOP'],\n ['right'],\n ['right', 'A'],\n ['right', 'B'],\n ['right', 'A', 'B'],\n ['A'],\n ['left'],\n]\n\n\n# actions for more complex movement\nCOMPLEX_MOVEMENT = [\n ['NOP'],\n ['right'],\n ['right', 'A'],\n ['right', 'B'],\n ['right', 'A', 'B'],\n ['A'],\n ['left'],\n ['left', 'A'],\n ['left', 'B'],\n ['left', 'A', 'B'],\n ['down'],\n]\n\n\n\"\"\"\nTemplate\n\nSuperMArioBros- world - level - V\n\nworld : {1,2,3,4,5,6,7,8}\n\nlevel : {1,2,3,4}\n\nversion : {0,1,2,3}\n\nversion 0 : standard , 1: downsampling , 2: pixel, 3:rectangle\n\nNoFrameskip : added before the first hyphen in order to disable frame skip\n\nexample\n\nto play world 3 and level 4 using downsample\n\nSuperMarioBros-3-4-v1\n\"\"\"\nenv = gym_super_mario_bros.make('SuperMarioBros-8-4-v0')\nenv = BinarySpaceToDiscreteSpaceEnv(env, COMPLEX_MOVEMENT)\ndone = True\nfor step in range(5000):\n if done:\n state = env.reset()\n state, reward, done, indfo = env.step(env.action_space.sample())\n print(reward)\n env.render()\n\nenv.close()\n","sub_path":"supermario_v3_try_other_levels.py","file_name":"supermario_v3_try_other_levels.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"603893552","text":"import os\nimport json\nfrom pprint import pprint\n\n\nclass Save():\n \"\"\"\n Unfortunately django sets up a lot of stuff to be able to load the models.\n Since it's a pain in the butt to do that ourselves, we can't instantiate models and save.\n Here we'll transform the data into json objects in the format:\n {\n \"model\": \"playlist.<>\",\n \"fields\": {\n \"<>: <>, \n ...\n }\n }\n and save it as a file to the fixtures directory.\n When we call `python manage.py loaddata <>`, it will load the data into the database\n \"\"\"\n\n def __init__(self):\n self.parent_dir = os.path.dirname(os.path.realpath(__file__))\n\n def create_user(self, username):\n user = {\n \"model\": \"playlist.spotifyuser\",\n \"fields\": {\n \"username\": username\n }\n }\n return user\n\n def create_playlist(self, playlist):\n if playlist[\"name\"] == None:\n raise NullValueError(\"No playlist name!\")\n new_playlist = {\n \"model\": \"playlist.playlist\",\n \"fields\": {\n \"url\": playlist['href'],\n \"sp_playlist_id\": playlist['id'],\n \"name\": playlist['name'],\n \"owner\": playlist['owner']\n }\n }\n return new_playlist\n\n def create_track(self, track, playlist_id):\n if track[\"artist_id\"] == None:\n raise NullValueError(\"No artist ID!\")\n new_track = {\n \"model\": \"playlist.track\",\n \"fields\": {\n \"sp_track_id\": track[\"track_id\"],\n \"track_name\": track[\"track_name\"],\n \"sp_artist_id\": track[\"artist_id\"],\n \"popularity\": track[\"popularity\"],\n \"preview_url\": track[\"preview_url\"],\n \"playlists\": [playlist_id]\n }\n }\n return new_track\n\n def create_artist(self, artist_id, name):\n if not artist_id:\n raise NullValueError(\"no artist ID!\")\n if not name:\n raise NullValueError(\"no artist name!\")\n artist = {\n \"model\": \"playlist.artist\",\n \"fields\": {\n \"sp_artist_id\": artist_id,\n \"name\": name\n }\n }\n return artist\n\n def reformat_spotify_users(self):\n users = []\n for playlist_file in os.listdir(\"{}/data/user_playlists\".format(self.parent_dir)):\n username = playlist_file.split(\".json\")[0]\n json_user = self.create_user(username=username)\n users.append(json_user)\n json_users = json.dumps(users, indent=4)\n with open(\"{}/fixtures/users.json\".format(self.parent_dir), 'w+') as f:\n f.write(json_users)\n\n def reformat_playlists(self):\n playlists = []\n for playlist_file in os.listdir(\"{}/data/user_playlists\".format(self.parent_dir)):\n with open(\"{}/data/user_playlists/{}\".format(self.parent_dir, playlist_file), 'r') as f:\n content = f.read()\n playlists_for_user = json.loads(content)\n for playlist in playlists_for_user:\n try:\n new_playlist = self.create_playlist(playlist)\n except NullValueError as e:\n print(\"Can't have None value for playlist name. Skipping\")\n continue\n playlists.append(new_playlist)\n json_playlists = json.dumps(playlists, indent=4)\n with open(\"{}/fixtures/playlists.json\".format(self.parent_dir), 'w+') as f:\n f.write(json_playlists)\n\n def reformat_artists_tracks(self):\n \"\"\"\n Artists are extracted from the track objects returned from the Spotify API so we do them both at once\n Also, since a track can belong to multiple playlists use a tracks dictionary to keep track\n of all playlists a track belongs to before outputting json\n \"\"\"\n file_num = 0\n section = 1\n tracks = {}\n artists = {}\n for track_file in os.listdir(\"{}/data/playlist_tracks\".format(self.parent_dir)):\n file_num = file_num + 1\n playlist_id = track_file.split(\"-\")[1].split(\".json\")[0]\n with open(\"{}/data/playlist_tracks/{}\".format(self.parent_dir, track_file), 'r') as f:\n content = f.read()\n playlist_content = json.loads(content)\n url = playlist_content[\"href\"]\n for track in playlist_content[\"items\"]:\n # artist\n artist_id = track[\"artist_id\"]\n if not artist_id in artists.items():\n try:\n artists[artist_id] = self.create_artist(artist_id, track[\"artist_name\"])\n except NullValueError as e:\n print(\"Can't have None value for artist ID or artist name. Skipping\")\n continue\n # track\n track_id = track[\"track_id\"]\n if track_id in tracks.items():\n # a track can belong to multiple playlists\n tracks[\"track_id\"][\"playlists\"].append(playlist_id)\n else:\n try:\n new_track = self.create_track(track, playlist_id)\n tracks[track_id] = new_track\n except NullValueError as e:\n print(\"Can't have None value for artist ID. Skipping\")\n continue\n\n # periodically save to fixtures so we don't run out of memory\n if file_num % 100 == 0:\n # save artists\n artists = json.dumps(list(artists.values()), indent=4)\n with open(\"{}/fixtures/artists-{}.json\".format(self.parent_dir, section), 'w+') as f:\n f.write(artists)\n # save tracks\n # reformat tracks from dictionary to list and convert to json\n tracks = json.dumps(list(tracks.values()), indent=4)\n with open(\"{}/fixtures/tracks-{}.json\".format(self.parent_dir, section), 'w+') as f:\n f.write(tracks)\n # refresh\n tracks = {}\n artists = {}\n section = section + 1\n\nclass NullValueError(Exception):\n pass\n\ndef main():\n s = Save()\n # s.reformat_spotify_users()\n # s.reformat_playlists()\n s.reformat_artists_tracks()\n\nif __name__ == \"__main__\":\n main()","sub_path":"ruimusic/playlist/reformat_to_save_to_db.py","file_name":"reformat_to_save_to_db.py","file_ext":"py","file_size_in_byte":6531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"569793622","text":"rows = []\r\n\r\nfor i in range(5):\r\n x = input()\r\n if \"FBI\" in x:\r\n rows.append(i+1)\r\n\r\nif len(rows) == 0:\r\n print(\"HE GOT AWAY!\")\r\nelse:\r\n print(' '.join(map(str,rows)))","sub_path":"Avion.py","file_name":"Avion.py","file_ext":"py","file_size_in_byte":174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"304058253","text":"from database.db import Database\ndb = Database()\n\ndef upsert_provider(params):\n sql_params = [params['provider_name'], params['provider_name']]\n sql_stmt = '''insert into providers\n (provider) values (%s)\n ON CONFLICT (provider) DO UPDATE\n SET provider = %s RETURNING id'''\n return db.run_query(sql_stmt, sql_params, 'one')\n\n\ndef update_provider(params, provider_id):\n sql_params = [params['provider_name']]\n sql_str = ''\n if 'phone' in params:\n sql_str += ', phone = %s'\n sql_params.append(params['phone'])\n if 'specialty' in params:\n sql_str += ', specialty = %s'\n sql_params.append(params['specialty'])\n if 'critical_access_provider' in params:\n sql_str += ', critical_access_provider = %s'\n sql_params.append(params['critical_access_provider'])\n if 'notes' in params:\n sql_str += ', notes = %s'\n sql_params.append(params['notes'])\n\n sql_params.append(provider_id)\n sql_stmt = '''update providers set provider = %s '''\n sql_stmt += sql_str + ''' where id = %s returning *'''\n return db.run_query(sql_stmt, sql_params, 'one')\n","sub_path":"scripts/database/providers.py","file_name":"providers.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"354466730","text":"import numpy as np\nimport tensorflow as tf\nimport pandas as pd\nimport os\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nimport time\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef MLP_EML(X_train, X_test, y_train, y_test, n_hidden, rho, lr):\n n_input = 2\n\n n_output = 1\n\n\n\n weights = {'w': tf.Variable(tf.random_uniform(shape=[n_input, n_hidden], \n minval=-2, maxval=2, dtype=tf.float32, seed=36),\n trainable = False, name='w'),\n 'v': tf.Variable(tf.random_uniform(shape=[n_hidden, n_output], minval=0, maxval=2,\n dtype=tf.float32, seed=36), name='v')}\n bias = {'b': tf.Variable(tf.random_uniform(shape=[n_hidden], minval=0, maxval=3, dtype=tf.float32, seed=36),\n trainable=False, name='b')}\n x=tf.placeholder(tf.float32, [None,n_input])\n y=tf.placeholder(tf.float32, [None, n_output])\n\n layer1 = tf.subtract(tf.matmul(x, weights['w']), bias['b'])\n layer1_act = tf.tanh(layer1 / 2)\n\n out_l = tf.matmul(layer1_act, weights['v'])\n\n cost = tf.reduce_mean(tf.squared_difference(y, out_l))\n reg_cost = cost + rho * (tf.nn.l2_loss(weights['w']) + tf.nn.l2_loss(weights['v']) + tf.nn.l2_loss(bias['b']))\n\n train_step = tf.train.AdamOptimizer(learning_rate=lr).minimize(reg_cost)\n grad = tf.gradients(reg_cost, [weights['w'], weights['v'], bias['b']])\n start_time = time.time()\n \n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n for epoch in range(8000):\n sess.run(train_step, feed_dict={x: X_train, y: y_train})\n if epoch == 0:\n pred_initial = sess.run(out_l,feed_dict={x:X_train})\n pred_y_test = sess.run(out_l, feed_dict={x: X_test})\n pred_y_train = sess.run(out_l, feed_dict={x: X_train})\n \n grad_opt = sess.run(grad, feed_dict={x: X_train, y: y_train})\n \n fnormgrad=[*grad_opt]\n fnormgrad=np.array(fnormgrad)\n arrng=fnormgrad[fnormgrad != np.array(None)]\n eng=np.concatenate( arrng, axis=0 )\n hz=(sum(eng[0]**2)+sum(eng[1]**2))**(0.5)\n norm_grad_opt = np.sqrt(np.sum(hz))\n \n x1=np.linspace(-2, 2 ,50)\n x2=np.linspace(-2,2,50)\n X_1, X_2=np.meshgrid(x1,x2)\n XX=np.vstack([ X_1.reshape(-1), X_2.reshape(-1) ]).T\n ygen= sess.run(out_l, feed_dict={x:XX})\n \n end_time = time.time()\n \n mse_initial = mean_squared_error(y_train, pred_initial)/2\n mse_train = mean_squared_error(y_train,pred_y_train)/2\n mse_test = mean_squared_error(y_test, pred_y_test)/2\n\n \n \n end_time = time.time()\n \n \n print('Number of neurons N: ', n_hidden)\n print('Initial Training Error: ', mse_initial)\n print('Final Train Error: ', mse_train)\n print('Final Test Error: ', mse_test)\n print('Optimization solver chosen: AdamOptimizer')\n print('Norm of the gradient at the optimal point: ', norm_grad_opt)\n print('Time for optimizing the network: %s seconds' % round(end_time - start_time))\n print('value of sigma: 1')\n print('value of rho: 0.00001')\n print('Other hyperparameters:(number of epochs)): 8000')\n \n return ygen\n \n \ndef fun_plot(Z): \n \n x1=np.linspace(-2, 2 ,50)\n x2=np.linspace(-2,2,50)\n X1, X2=np.meshgrid(x1,x2)\n Z=np.reshape(Z, (50,50))\n plt.figure(figsize=(20,10))\n\n ax = plt.axes(projection='3d')\n ax.plot_surface(X1, X2, Z, cmap='viridis')\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('z')\n ax.view_init(40, 305)\n \n return(plt.show())\n \n \n \n \n \n ","sub_path":"2_1/Functions_21.py","file_name":"Functions_21.py","file_ext":"py","file_size_in_byte":3737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"464292781","text":"# -*- encoding: UTF-8 -*-\nimport sublime\nimport sublime_plugin\nimport os\nimport collections\nfrom xml.dom import minidom\nimport sys\n\nclass QvdField:\n fieldName = ''\n uniqValues = 0\n memoryUsage = 0\nclass QvdTable:\n noOfRecords = 0\n tableName = ''\n creatorDoc = ''\n createdTime = ''\n fields = []\n\nclass QlikviewQvdFileListener(sublime_plugin.EventListener):\n\n EXT_QLIKVIEW_QVD = \".QVD\"\n def on_activated(self,view):\n if not self.is_ST3():\n return\n fn = view.file_name()\n if (fn is None):\n return\n if (fn.upper().endswith(self.EXT_QLIKVIEW_QVD)):\n view.run_command('qvd_viewer',{'cmd':''})\n def is_ST3(self):\n ''' check if ST3 based on python version '''\n version = sys.version_info\n if isinstance(version, tuple):\n version = version[0]\n elif getattr(version, 'major', None):\n version = version.major\n return (version >= 3)\n\nclass QvdViewerCommand(sublime_plugin.TextCommand):\n moduleSettings = None\n edit = None\n path = ''\n def run(self, edit, cmd=''):\n self.edit = edit\n self.path = self.view.file_name()\n sublime.active_window().run_command('close')\n # view.run_command('close')\n self.view = sublime.active_window().new_file()\n view = self.view\n view.set_scratch(True)\n token = collections.deque()\n tokenMarker = collections.deque([b'<',b'/',b'Q',b'v',b'd',b'T',b'a',b'b',b'l',b'e',b'H',b'e',b'a',b'd',b'e',b'r',b'>'])\n token = collections.deque(tokenMarker)\n tokenStr = collections.deque()\n buff = collections.deque()\n n = 0\n headerFound = False\n with open(self.path, 'rb') as f:\n while True:\n char = f.read(1)\n n = n + 1\n if n > 100000:\n break\n if char =='':\n break\n buff.append(char)\n token.append(char)\n token.popleft()\n if token == tokenMarker:\n headerFound = True\n break\n if not headerFound:\n self.addLine('ERROR: QvdFile header have not been recognized')\n return\n buffString = b''.join(buff)\n xml = minidom.parseString(buffString)\n self.parseHeader(xml)\n def parseHeader(self, xml):\n table = QvdTable()\n table.fields = []\n table.tableName = self.getValue(xml,\"TableName\")\n table.noOfRecords = self.getValue(xml,\"NoOfRecords\")\n table.createdTime = self.getValue(xml,\"CreateUtcTime\")\n for fieldXml in xml.getElementsByTagName(\"QvdFieldHeader\"):\n field = QvdField()\n field.fieldName = self.getValue(fieldXml,\"FieldName\")\n field.uniqValues = self.getValue(fieldXml,\"NoOfSymbols\")\n field.memoryUsage = self.getValue(fieldXml,\"Length\")\n field.fieldType = 'Number'\n if self.getValue(fieldXml,\"NumberFormat/Type\") == \"UNKNOWN\":\n field.fieldType = \"String\"\n table.fields.append(field)\n viewHeader = table.tableName + '.MD'\n self.addLine(viewHeader)\n self.addLine('---')\n self.addLine('')\n line = '%s records. QVD created at %s' % (table.noOfRecords,table.createdTime)\n self.addLine(line)\n self.addLine('')\n self.addLine('###Fields:')\n self.addLine('')\n for field in table.fields:\n line = \"- **%s**. Unique values: %s, Memory usage: %s\" % (field.fieldName, field.uniqValues, field.memoryUsage)\n self.addLine(line)\n self.addLine('')\n self.addLine('####Sample load statement:')\n self.addLine('')\n self.addLine('```QlikView')\n self.addLine('')\n self.addLine('LOAD')\n comma = ','\n for field in table.fields:\n if field.fieldName == table.fields[-1].fieldName:\n comma = ''\n self.addLine(' ' + field.fieldName + comma)\n self.addLine(' FROM [' + self.path+'] (QVD);')\n self.addLine('')\n self.addLine('```')\n self.closeOthers(viewHeader)\n def addLine(self,line):\n self.view.insert(self.edit, self.view.size(), line + '\\n')\n def getValue(self,xml,tagName):\n nodeList = xml.getElementsByTagName(tagName)\n if len(nodeList) == 0:\n return ''\n tag = nodeList[0].toxml()\n xmlData=tag.replace('<'+tagName+'>','').replace('','')\n return xmlData\n def closeOthers(self,viewHeader):\n window = self.view.window()\n myId = self.view.id()\n for v in window.views():\n if v.id() == myId:\n continue\n l = v.line(sublime.Region(0,0))\n line = v.substr(l)\n if (line == viewHeader):\n window.focus_view(v)\n window.run_command('close')\n window.focus_view(self.view)\n","sub_path":"qvd_viewer.py","file_name":"qvd_viewer.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"333090166","text":"from django.conf.urls import patterns, include, url\nfrom django.views.generic import TemplateView\nfrom django.contrib import admin\n\n\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^', include('home.urls', namespace='home')),\n url(r'^download/', include('download.urls', namespace='download')),\n url(r'^feedback/', include('feedback.urls', namespace='feedback')),\n url(r'^api/', include('api.urls', namespace='api')),\n url(r'^apps/', include('apps.urls', namespace='apps')),\n)\n","sub_path":"data_justice/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"309142551","text":"from django.shortcuts import render,redirect\nfrom .models import Complaints,Poll,Lecturer\nfrom complaint.forms import RegistrationForm,Complaintform\nfrom django.views.generic import TemplateView\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import F\nfrom django.shortcuts import get_object_or_404\n# Create your views here.\n@login_required\ndef view_home(request):\n\tc=Complaints.objects.all().order_by('-created_date')\n\targs={'c':c}\n\treturn render(request,'complaint/home.html',args)\n\n@login_required\ndef view_poll(request):\n c=Poll.objects.all().order_by('-created_date')\n lts = Lecturer.objects.all().distinct()\n args={'c':c,'lts':lts}\n return render(request,'complaint/add_poll.html',args)\n\n@login_required\ndef give_poll(request,pk=None):\n\tc = get_object_or_404(Poll,pk=pk)\n\treturn render(request,'complaint/give_poll.html',{'c':c})\n\n\n@login_required\ndef cont(request):\n\treturn render(request,'complaint/cont.html')\n\n@login_required\ndef status(request):\n c=Poll.objects.all()\n lect = Lecturer.objects.all()\n count=0\n for x in lect:\n count = count + x.votes\n args={'c':c,'count':count,'lect':lect}\n return render(request,'complaint/poll_status.html',args)\n\ndef register(request):\n if request.method == 'POST':\n form = RegistrationForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect('/home/')\n else:\n form = RegistrationForm()\n\n\n return render(request,'complaint/register.html',{'form':form})\n\n@login_required\ndef update_vote(request,pk=None):\n Lecturer.objects.filter(pk=pk).update(votes=F('votes')+1)\n return redirect('/add_poll/')\n\nclass HomeView(TemplateView):\n\ttemplate_name = 'complaint/complaint.html'\n\tdef get(self,request):\n\t\tcomp=Complaintform()\n\t\targs={'comp':comp}\n\t\treturn render(request,self.template_name,args)\n\tdef post(self,request):\n\t\tcomp=Complaintform\n\t\tif request.method== 'POST':\n\t\t\tcomp=Complaintform(request.POST)\n\t\t\tif comp.is_valid():\n\t\t\t\tpost=comp.save(commit=False)\n\t\t\t\tpost.user = request.user\n\t\t\t\tpost.save()\n\t\t\t\ttitle = comp.cleaned_data['title']\n\t\t\t\tdescription = comp.cleaned_data['description']\n\t\t\t\tcategory= comp.cleaned_data['category']\n\t\t\t\tcomp=Complaintform(request.GET)\n\t\t\t\treturn redirect('/home/')\n\t\treturn render(request,self.template_name,{'title':title,'comp':comp,'description':description,'category':category})\n# class pollview(TemplateView):\n# template_name = 'complaint/add_poll.html'\n\n# def get(self, request):\n# survey = PollForm\n# args = {'survey':survey}\n# return render(request, self.template_name,args)\n\n# def post(self,r\n","sub_path":"sai/complaint/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"268654448","text":"import sys\nsys.path.append('/home/PyJan/myproject/Bootstrap_base')\nfrom flask import Flask, request, render_template, redirect, url_for, flash\nfrom flask_bootstrap import Bootstrap\nfrom flask_login import LoginManager, UserMixin, login_user, logout_user, current_user, login_required\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_wtf import FlaskForm\nfrom flask_wtf.file import FileField, FileRequired\nfrom wtforms import StringField, PasswordField, BooleanField, SubmitField, TextAreaField\nfrom wtforms.validators import length, email\nimport datetime\nfrom flask_migrate import Migrate\nfrom flask_script import Manager, Shell\nfrom werkzeug import secure_filename\nimport os\nfrom flask_mail import Mail, Message\nfrom config.config import USE_MAIL\nfrom logging import FileHandler, WARNING\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'fuckoffyouhackers112'\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://sammy:sammy@localhost:5432/sammy'\napp.config['MAIL_SERVER'] = 'smtp.gmail.com'\napp.config['MAIL_PORT'] = 587\napp.config['MAIL_USE_TLS'] = True\napp.config['MAIL_USERNAME'] = os.environ.get('MAIL_USERNAME')\napp.config['MAIL_PASSWORD'] = os.environ.get('MAIL_PASSWORD')\n\nbootstrap = Bootstrap(app)\nloginmanager = LoginManager()\nloginmanager.init_app(app)\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\nmanager = Manager(app)\nmail = Mail(app)\n\nif app.debug == False:\n file_handler = FileHandler('errors_file.txt')\n file_handler.setLevel(WARNING)\n app.logger.addHandler(file_handler)\n\nclass User(UserMixin, db.Model):\n __tablename__ = 'user'\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(30), unique=True)\n email = db.Column(db.String(50))\n password = db.Column(db.String(50))\n\n orders = db.relationship('Orders', backref='user')\n\n def __repr__(self):\n return ''.format(\n self.username,\n self.id,\n self.email,\n self.password\n )\n\nclass Orders(db.Model):\n __tablename__ = 'orders'\n id = db.Column(db.Integer, primary_key=True)\n userid = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n itemid = db.Column(db.Integer, db.ForeignKey('items.id'), nullable=False)\n volume = db.Column(db.Integer)\n ordered = db.Column(db.Boolean)\n paid = db.Column(db.Boolean)\n delivered = db.Column(db.Boolean)\n orderdate = db.Column(db.TIMESTAMP)\n paydate = db.Column(db.TIMESTAMP)\n deliverydate = db.Column(db.TIMESTAMP)\n\n\n def __repr__(self):\n return ''.format(\n self.id,\n self.userid,\n self.itemid,\n self.volume\n )\n\nclass Items(db.Model):\n __tablename__ = 'items'\n id = db.Column(db.Integer, primary_key=True)\n name = db.Column(db.String)\n\n orders = db.relationship('Orders', backref='item')\n\n def __repr__(self):\n return ''.format(self.name, self.id)\n\nclass ItemsDesc(db.Model):\n __tablename__ = 'itemsdesc'\n id = db.Column(db.Integer, primary_key=True)\n itemid = db.Column(db.Integer, db.ForeignKey('items.id'), nullable=False)\n name = db.Column(db.String, nullable=False)\n desc = db.Column(db.String)\n imgref = db.Column(db.String)\n\n item = db.relationship('Items', backref=db.backref('itemdesc', uselist=False))\n\nclass Prices(db.Model):\n __tablename__ = 'prices'\n id = db.Column(db.Integer, primary_key=True)\n itemid = db.Column(db.Integer, db.ForeignKey('items.id'), nullable=False)\n price = db.Column(db.Integer)\n validfrom = db.Column(db.TIMESTAMP)\n validto = db.Column(db.TIMESTAMP)\n\n item = db.relationship('Items', backref='pricetags')\n\nclass LoginForm(FlaskForm):\n username = StringField('User name: ', validators=[length(min=3, max=30)])\n password = PasswordField('Password: ', validators=[length(min=5, max=30)])\n submit = SubmitField('Log in')\n\nclass SignupForm(FlaskForm):\n username = StringField('User name: ', validators=[length(min=3, max=30)])\n email = StringField('Email: ', validators=[email(message='Not valid email address')])\n password = PasswordField('Password: ', validators=[length(min=5, max=30)])\n submit = SubmitField('Sign up')\n\nclass InsertItem(FlaskForm):\n internalname = StringField('Internal name: ', validators=[length(min=3)])\n pagename = StringField('Page name: ', validators=[length(min=3)])\n description = TextAreaField('Description: ')\n price = StringField('Price: ')\n picture = FileField('Load picture', validators=[FileRequired('No picture provided')])\n submit = SubmitField('Save')\n\n@loginmanager.user_loader\ndef loaduser(userid):\n return User.query.get(int(userid))\n\n@app.route('/')\ndef main():\n items = ItemsDesc.query.all()\n return render_template('vlab.html',items=items)\n\n@app.route('/selection/')\ndef selection(product):\n item = ItemsDesc.query.get(int(product))\n price = Prices.query.filter_by(itemid=item.id).first()\n return render_template('product.html', item=item, price=price, \n items = ItemsDesc.query.all())\n\n@app.route('/basket', methods=['GET', 'POST'])\n@login_required\ndef basket():\n if request.args.get('productid'):\n chosen_product = Items.query.get(int(request.args.get('productid')))\n user = User.query.filter_by(id=current_user.id).first()\n issued_order = Orders(item=chosen_product, user=user, volume=1)\n db.session.add(issued_order)\n db.session.commit()\n if request.args.get('deletion'):\n product_for_removal = Orders.query.get(int(request.args.get('deletion')))\n db.session.delete(product_for_removal)\n db.session.commit()\n basket = Orders.query.filter(\n Orders.userid==current_user.id,\n db.or_(Orders.ordered.is_(None),\n Orders.ordered.is_(False))).all()\n return render_template('basket.html', basket=basket, totalprice=totalprice(basket), \n items = ItemsDesc.query.all())\n\ndef totalprice(basket):\n totalprice = 0\n for b in basket:\n totalprice += b.item.pricetags[0].price\n return totalprice\n\n@app.route('/myorder', methods=['GET', 'POST'])\ndef myorder():\n myorder = Orders.query.filter(\n Orders.userid==current_user.id,\n db.or_(Orders.ordered.is_(None),\n Orders.ordered.is_(False))).all()\n for item in myorder:\n item.ordered = True\n item.orderdate = datetime.datetime.now()\n db.session.commit()\n user = User.query.get(current_user.id)\n greetings = user.username\n if USE_MAIL:\n sendmail(user.email, myorder=myorder, totalprice=totalprice(myorder),\n greetings=greetings)\n return render_template('myorder.html', myorder=myorder, \n totalprice=totalprice(myorder), items = ItemsDesc.query.all())\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n loginform = LoginForm()\n if loginform.validate_on_submit():\n if loginform.username.data == 'admin' and loginform.password.data == 'vikinka':\n user = User(username='admin', password='vikinka', email='vikinka@seznam.cz')\n return redirect(url_for('adminpage')) \n user = User.query.filter_by(username=loginform.username.data).first()\n if user is None:\n loginform.username.errors.append('Wrong user name')\n else:\n if user.password == loginform.password.data:\n login_user(user)\n return redirect(url_for('main'))\n else:\n loginform.password.errors.append('Wrong password') \n return render_template('login.html', loginform=loginform)\n\n@app.route('/signup', methods=['GET', 'POST'])\ndef signup():\n signupform = SignupForm()\n if signupform.validate_on_submit():\n user = User.query.filter_by(username=signupform.username.data).first()\n if user is None:\n user = User(username=signupform.username.data,\n email=signupform.email.data,\n password=signupform.password.data)\n db.session.add(user)\n db.session.commit()\n login_user(user)\n return redirect('/')\n else:\n signupform.username.errors.append('This user already exists')\n return render_template('signup.html', signupform=signupform)\n\n@app.route('/logout')\n@login_required\ndef logout():\n logout_user()\n return redirect(url_for('main'))\n\n@app.route('/loggedin')\n@login_required\ndef loggedin():\n return 'current user is {0}'.format(current_user.username)\n\n@app.route('/showbase')\ndef showbase():\n signupform = SignupForm()\n return render_template('base.html', userform=signupform)\n\n@app.route('/adminpage')\ndef adminpage():\n return render_template('adminpage.html')\n\n@app.route('/payments', methods=['GET','POST'])\ndef payments():\n if request.method == 'POST':\n to_update_paid = db.session.query(Orders).filter(\n Orders.id.in_(request.form.getlist('paid'))).all()\n for order in to_update_paid:\n order.paid = True\n order.paydate = datetime.datetime.now()\n db.session.commit()\n to_update_delivered = db.session.query(Orders).filter(\n Orders.id.in_(request.form.getlist('delivered'))).all()\n for order in to_update_delivered:\n order.delivered = True\n order.deliverydate = datetime.datetime.now()\n db.session.commit()\n orders = Orders.query.filter(db.or_(~Orders.paid.is_(True),\n ~Orders.delivered.is_(True))).all()\n return render_template('payments.html', orders=orders)\n\n@app.route('/insertitem', methods=['GET','POST'])\ndef insertitem():\n print('in insert item')\n insertitem = InsertItem()\n print(insertitem)\n if insertitem.validate_on_submit():\n filename = secure_filename(insertitem.picture.data.filename)\n insertitem.picture.data.save('static/' + filename) \n item = Items(name=insertitem.internalname.data)\n itemdesc = ItemsDesc(item=item, name=insertitem.pagename.data, \n desc=insertitem.description.data, imgref=filename)\n price = Prices(item=item, price=int(insertitem.price.data))\n db.session.add(item)\n db.session.add(itemdesc)\n db.session.add(price)\n db.session.commit()\n return redirect(url_for('insertitem'))\n return render_template('insertitem.html', form=insertitem)\n\n@app.route('/deleteitem', methods=['GET','POST'])\ndef deleteitem():\n if 'deletion' in request.args:\n itemdesc = ItemsDesc.query.get(request.args['deletion'])\n item = Items.query.get(itemdesc.item.id)\n price = Prices.query.get(itemdesc.item.id)\n db.session.delete(price)\n db.session.commit()\n db.session.delete(itemdesc)\n db.session.commit()\n db.session.delete(item)\n db.session.commit()\n return redirect(url_for('deleteitem'))\n itemdescs = ItemsDesc.query.all()\n return render_template('deleteitem.html', itemdescs=itemdescs)\n\ndef sendmail(to, myorder, totalprice, greetings):\n msg = Message(\"Viki's lab - order confimation\", sender='pyjan3@gmail.com',\n recipients=[to])\n msg.body = 'text body'\n msg.html = render_template('emailtemplate.html', myorder=myorder, \n totalprice=totalprice, greetings=greetings)\n mail.send(msg)\n\ndef make_shell_context():\n return dict(app=app, db=db, User=User, Orders=Orders, Items=Items, \n ItemsDesc=ItemsDesc)\n\nmanager.add_command(\"shell\", Shell(make_context=make_shell_context))\n\nif __name__ == \"__main__\":\n #app.run(debug=True)\n manager.run()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"133264186","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan 30 10:36:18 2019\r\n\r\n@author: YUNG-CHUN\r\n\"\"\"\r\n\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\nurl = \"https://www.ptt.cc/bbs/joke/index.html\"\r\nfor i in range(2): #爬2頁\r\n res = requests.get(url)\r\n soup = BeautifulSoup(res.text,\"lxml\")\r\n u = soup.select(\"div.btn-group.btn-group-paging a\") \r\n for entry in soup.select('.r-ent'):\r\n for s in entry.select('div.title a'):\r\n entry_url = \"https://www.ptt.cc\"+s['href']\r\n entry_res = requests.get(entry_url)\r\n entry_soup = BeautifulSoup(entry_res.text,\"html.parser\")\r\n main_content = entry_soup.find(id=\"main-content\")\r\n metas = main_content.select('div.article-metaline')\r\n filtered = [ v for v in main_content.stripped_strings if v[0] not in [u'※', u'◆'] and v[:2] not in [u'--'] ]\r\n author = filtered[1]\r\n title = filtered[5]\r\n date = filtered[7]\r\n tag = filtered[3]\r\n content = filtered[8]\r\n print(\"日期: \"+date+\"\\n\"+\"作者: \"+author+\"\\n\"+\"標題: \"+title+\"\\n\"+\"內容:\\n\"+content+\"\\n\\n\"+\"看板名稱: \"+tag+\"\\n\")\r\n url = \"https://www.ptt.cc\"+ u[1][\"href\"] #上一頁\r\n\r\n\r\n","sub_path":"ptt爬蟲.py","file_name":"ptt爬蟲.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"127345403","text":"from bs4 import BeautifulSoup\r\nfrom selenium import webdriver\r\nfrom pathlib import Path\r\n\r\nimport pandas as pd\r\nimport argparse\r\nimport os\r\n\r\n\r\ndef google_crawler(origin_keyword, start, end):\r\n # parser = argparse.ArgumentParser()\r\n\r\n # parser.add_argument('--keyword', required=True, help=\"검색할 키워드\")\r\n # parser.add_argument('--start', required=True, help=\"시작할 날짜 ex. 3/15/2021\")\r\n # parser.add_argument('--end', required=True, help=\"끝나는 날짜 ex. 3/16/2021\")\r\n\r\n # args = parser.parse_args()\r\n\r\n keyword = origin_keyword.replace(\" \", \"+\")\r\n start = list(start.split('-'))\r\n end = list(end.split('-'))\r\n\r\n start_date = start[1]+'/'+start[2]+'/'+start[0]\r\n end_date = end[1]+'/'+end[2]+'/'+end[0]\r\n\r\n # print(start_date, end_date)\r\n path = os.getcwd() + \"/chromedriver\"\r\n\r\n options = webdriver.ChromeOptions()\r\n options.add_argument('headless')\r\n\r\n driver = webdriver.Chrome(path, chrome_options=options)\r\n driver.get(\r\n f\"https://www.google.com/search?q={keyword}&hl=en&tbas=0&biw=1920&bih=760&source=lnt&tbs=cdr%3A1%2Ccd_min%3A{start_date}%2Ccd_max%3A{end_date}&tbm=nws\")\r\n\r\n html = driver.page_source\r\n soup = BeautifulSoup(html, 'html.parser')\r\n dbsr = soup.select('.dbsr')\r\n\r\n titles = []\r\n links = []\r\n dates = []\r\n sites = []\r\n cnt = 0\r\n while True:\r\n html = driver.page_source\r\n soup = BeautifulSoup(html, 'html.parser')\r\n dbsr = soup.select('.dbsr')\r\n for i in dbsr:\r\n title = i.select_one('.JheGif.nDgy9d').text\r\n link = i.a.attrs['href']\r\n date = i.select_one('.WG9SHc').text\r\n site = i.select_one('.XTjFC.WF4CUc').text\r\n titles.append(title)\r\n links.append(link)\r\n dates.append(date)\r\n sites.append(site)\r\n cnt += 1\r\n try:\r\n driver.find_element_by_link_text(\"Next\").click()\r\n except:\r\n driver.quit()\r\n break\r\n driver.implicitly_wait(1)\r\n\r\n driver.quit()\r\n\r\n dir_keyword = origin_keyword.replace(\" \", \"\")\r\n\r\n def toKoreanStyle(temp_str):\r\n temp_list = temp_str.split(\"/\")\r\n for i, v in enumerate(temp_list):\r\n if len(v) == 1:\r\n temp_list[i] = \"0\" + temp_list[i]\r\n temp = temp_list[-1] + \"\".join(temp_list[:-1])\r\n return temp\r\n\r\n dir_start = toKoreanStyle(start_date)\r\n dir_end = toKoreanStyle(end_date)\r\n BASE_DIR = os.path.dirname(os.path.abspath(__file__))\r\n\r\n dir_keyword = origin_keyword.replace(\" \", \"\")\r\n dir_name = BASE_DIR+f\"./media/{dir_keyword}\"\r\n Path(dir_name).mkdir(parents=True, exist_ok=True)\r\n\r\n data = {'title': titles, 'link': links, 'site': sites, 'date': dates}\r\n df = pd.DataFrame(data, columns=['title', 'link', 'site', 'date'])\r\n df.to_csv(os.path.join(\r\n BASE_DIR, f'media/{dir_keyword}/google_{dir_start}_{dir_end}.csv'))\r\n # print(\"Complete\")\r\n return ((f'google_{dir_start}_{dir_end}.csv'), os.path.join(BASE_DIR, f'media/{dir_keyword}/google_{dir_start}_{dir_end}.csv'))\r\n","sub_path":"google_news_crawler.py","file_name":"google_news_crawler.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"22666012","text":"# bot.py\r\nimport asyncio\r\nfrom logging import error\r\nimport os\r\nimport random\r\nimport itertools\r\nimport aiohttp\r\nfrom aiohttp import payload\r\nimport discord\r\nimport youtube_dl\r\nimport json\r\nimport urllib\r\nimport simplejson\r\nimport re\r\nimport time\r\nimport lxml\r\nimport ffmpeg\r\nfrom youtubesearchpython.__future__ import VideosSearch\r\nfrom lxml import etree\r\nfrom discord import message\r\nfrom discord.flags import Intents\r\nfrom discord.ext import commands\r\nintents = discord.Intents.default()\r\nintents.members = True\r\nintents.reactions = True\r\n\r\nprefix = '$'\r\ntoken = 'put token here'\r\n\r\nbot = commands.Bot(command_prefix = prefix, intents=intents, activity=discord.Game(name=f'{prefix}help'), help_command=None)\r\n@bot.event\r\nasync def on_guild_join(guild):\r\n await guild.create_text_channel('logs')\r\n muted = discord.utils.get(guild.roles, name=\"muted\")\r\n if muted == None:\r\n await guild.create_role(name=\"muted\")\r\n muted = discord.utils.get(guild.roles, name=\"muted\")\r\n for channel in guild.channels:\r\n await channel.set_permissions(muted, speak=False, send_messages=False, read_message_history=True, read_messages=True)\r\n print(f'initial setup complete for {guild}')\r\n return\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def mute(ctx, arg):\r\n print(f'command mute used by {ctx.author}')\r\n numeric_filter = filter(str.isdigit, arg)\r\n numeric_string = \"\".join(numeric_filter)\r\n mute = discord.utils.get(ctx.guild.roles,name=\"muted\")\r\n member = ctx.guild.get_member(int(numeric_string))\r\n await member.add_roles(mute)\r\n await ctx.channel.send(f'{member.mention} has been muted.')\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def unmute(ctx, arg):\r\n print(f'command unmute used by {ctx.author}')\r\n numeric_filter = filter(str.isdigit, arg)\r\n numeric_string = \"\".join(numeric_filter)\r\n mute = discord.utils.get(ctx.guild.roles,name=\"muted\")\r\n member = ctx.guild.get_member(int(numeric_string))\r\n await member.remove_roles(mute)\r\n await ctx.send(f'{member.mention} has been unmuted.')\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def purge(ctx, arg: int):\r\n print(f'command purge used by {ctx.author}. the last {arg} messages will be attempted to be deleated.')\r\n channel = ctx.channel\r\n messages = []\r\n async for message in channel.history(limit=arg + 1):\r\n messages.append(message)\r\n await channel.delete_messages(messages)\r\n await channel.send(f\"Successfully removed the last {arg} messages\")\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def kick(ctx, member: discord.Member, *, reason=None):\r\n print(f'command kick used by {ctx.author}')\r\n await member.kick(reason=reason)\r\n await ctx.send(f'User {member} has been kicked')\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def ban(ctx, member: discord.Member, *, reason=None):\r\n print(f'command ban used by {ctx.author}')\r\n if member == None:\r\n await ctx.channel.send(\"Could not find user.\")\r\n return\r\n if member == ctx.message.author:\r\n await ctx.channel.send('You cant ban yourself!')\r\n return\r\n if reason == None:\r\n reason = \"Repeated rule violations\"\r\n message = f\"You have been banned from {ctx.guild.name} for {reason}\"\r\n await member.send(message)\r\n await member.ban(reason=reason)\r\n await ctx.channel.send(f\"{member} is banned for {reason}\")\r\n\r\n@bot.command()\r\n@commands.has_permissions(administrator = True)\r\nasync def unban(ctx, *, member):\r\n print(f'command unban used by {ctx.author}')\r\n banned_users = await ctx.guild.bans()\r\n member_name, member_discriminator = member.split(\"#\")\r\n for ban_entry in banned_users:\r\n user = ban_entry.user\r\n if (user.name, user.discriminator) == (member_name, member_discriminator):\r\n await ctx.guild.unban(user)\r\n await ctx.channel.send(f'Unbanned {user.mention}')\r\n return\r\n\r\n@bot.command()\r\nasync def stop(ctx):\r\n print(f'user {ctx.author} has used the stop command')\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await voice.disconnect(force=True)\r\n await ctx.send('stopped')\r\n\r\n@bot.command()\r\nasync def search(ctx, *, arg):\r\n print(f'command search used by {ctx.author}')\r\n message = await ctx.send('searching... \\n*this may take a while as this feature is in beta. other commands will not work until this is complete*')\r\n message\r\n ydl_opts = {'format': 'bestaudio', 'noplaylist':'True'}\r\n videosSearch = VideosSearch(arg, limit = 5)\r\n videosResult = await videosSearch.next()\r\n id1 = videosResult['result'][0]['link'].replace('https://www.youtube.com/watch?v=', '')\r\n id2 = videosResult['result'][1]['link'].replace('https://www.youtube.com/watch?v=', '')\r\n id3 = videosResult['result'][2]['link'].replace('https://www.youtube.com/watch?v=', '')\r\n id4 = videosResult['result'][3]['link'].replace('https://www.youtube.com/watch?v=', '')\r\n id5 = videosResult['result'][4]['link'].replace('https://www.youtube.com/watch?v=', '')\r\n video_ids = [id1,id2,id3,id4,id5]\r\n\r\n # will convert above two lines to not break async in the future\r\n num = 0\r\n list = ''\r\n\r\n while num < 5:\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n list = list + str(num + 1) + ' ' + '`' + video_title + '`' + '\\n'\r\n num += 1\r\n await message.edit(content=list)\r\n await message.add_reaction('1\\N{variation selector-16}\\N{combining enclosing keycap}')\r\n await message.add_reaction('2\\N{variation selector-16}\\N{combining enclosing keycap}')\r\n await message.add_reaction('3\\N{variation selector-16}\\N{combining enclosing keycap}')\r\n await message.add_reaction('4\\N{variation selector-16}\\N{combining enclosing keycap}')\r\n await message.add_reaction('5\\N{variation selector-16}\\N{combining enclosing keycap}')\r\n await message.add_reaction('\\U0001f6d1')\r\n def check(reaction, user):\r\n return user == ctx.author\r\n try:\r\n reaction, user = await bot.wait_for('reaction_add', timeout=120.0, check=check)\r\n except asyncio.TimeoutError:\r\n await ctx.channel.send(f'{ctx.author.mention}, Your request has timed out.')\r\n else:\r\n print(reaction)\r\n if str(reaction) == '1\\N{variation selector-16}\\N{combining enclosing keycap}':\r\n num = 0\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n await message.edit(content='Selected: \\n `' + video_title + '`' + '\\n*this may take a while to start as this feature is in beta*')\r\n member = ctx.guild.get_member(int(ctx.author.id))\r\n if member.voice.channel.id != None:\r\n print(member.voice.channel.id)\r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n 'outtmpl': './video0.mp3'\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n print(video_id)\r\n ydl.download(['https://www.youtube.com/watch?v=' + str(video_id)])\r\n channel = member.voice.channel\r\n vc = await channel.connect()\r\n audio = discord.FFmpegPCMAudio(source=\"./video0.mp3\")\r\n vc.play(audio)\r\n while vc.is_playing():\r\n await asyncio.sleep(.1)\r\n vc.stop()\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await vc.disconnect(force=True)\r\n audio.cleanup()\r\n await asyncio.sleep(1)\r\n os.remove('./video0.mp3')\r\n else:\r\n ctx.send(f\"silly {member.mention}, you must be in a voice channel to do this!\")\r\n if str(reaction) == '2\\N{variation selector-16}\\N{combining enclosing keycap}':\r\n num = 1\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n await message.edit(content='Selected: \\n `' + video_title + '`' + '\\n*this may take a while to start as this feature is in beta*')\r\n member = ctx.guild.get_member(int(ctx.author.id))\r\n if member.voice.channel.id != None:\r\n print(member.voice.channel.id)\r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n 'outtmpl': './video0.mp3'\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n print(video_id)\r\n ydl.download(['https://www.youtube.com/watch?v=' + str(video_id)])\r\n channel = member.voice.channel\r\n vc = await channel.connect()\r\n audio = discord.FFmpegPCMAudio(source=\"./video0.mp3\")\r\n vc.play(audio)\r\n while vc.is_playing():\r\n await asyncio.sleep(.1)\r\n vc.stop()\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await vc.disconnect(force=True)\r\n audio.cleanup()\r\n await asyncio.sleep(1)\r\n os.remove('./video0.mp3')\r\n else:\r\n ctx.send(f\"silly {member.mention}, you must be in a voice channel to do this!\")\r\n if str(reaction) == '3\\N{variation selector-16}\\N{combining enclosing keycap}':\r\n num = 2\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n await message.edit(content='Selected: \\n `' + video_title + '`' + '\\n*this may take a while to start as this feature is in beta*')\r\n member = ctx.guild.get_member(int(ctx.author.id))\r\n if member.voice.channel.id != None:\r\n print(member.voice.channel.id)\r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n 'outtmpl': './video0.mp3'\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n print(video_id)\r\n ydl.download(['https://www.youtube.com/watch?v=' + str(video_id)])\r\n channel = member.voice.channel\r\n vc = await channel.connect()\r\n audio = discord.FFmpegPCMAudio(source=\"./video0.mp3\")\r\n vc.play(audio)\r\n while vc.is_playing():\r\n await asyncio.sleep(.1)\r\n vc.stop()\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await vc.disconnect(force=True)\r\n audio.cleanup()\r\n await asyncio.sleep(1)\r\n os.remove('./video0.mp3')\r\n else:\r\n ctx.send(f\"silly {member.mention}, you must be in a voice channel to do this!\")\r\n if str(reaction) == '4\\N{variation selector-16}\\N{combining enclosing keycap}':\r\n num = 3\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n await message.edit(content='Selected: \\n `' + video_title + '`' + '\\n*this may take a while to start as this feature is in beta*')\r\n member = ctx.guild.get_member(int(ctx.author.id))\r\n if member.voice.channel.id != None:\r\n print(member.voice.channel.id)\r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n 'outtmpl': './video0.mp3'\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n print(video_id)\r\n ydl.download(['https://www.youtube.com/watch?v=' + str(video_id)])\r\n channel = member.voice.channel\r\n vc = await channel.connect()\r\n audio = discord.FFmpegPCMAudio(source=\"./video0.mp3\")\r\n vc.play(audio)\r\n while vc.is_playing():\r\n await asyncio.sleep(.1)\r\n vc.stop()\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await vc.disconnect(force=True)\r\n audio.cleanup()\r\n await asyncio.sleep(1)\r\n os.remove('./video0.mp3')\r\n else:\r\n ctx.send(f\"silly {member.mention}, you must be in a voice channel to do this!\")\r\n if str(reaction) == '5\\N{variation selector-16}\\N{combining enclosing keycap}':\r\n num = 4\r\n video = \"https://www.youtube.com/watch?v=\" + video_ids[num]\r\n ydl_opts = {}\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n info_dict = ydl.extract_info(video, download=False)\r\n video_url = info_dict.get(\"url\", None)\r\n video_id = info_dict.get(\"id\", None)\r\n video_title = info_dict.get('title', None)\r\n await message.edit(content='Selected: \\n `' + video_title + '`' + '\\n*this may take a while to start as this feature is in beta*')\r\n member = ctx.guild.get_member(int(ctx.author.id))\r\n if member.voice.channel.id != None:\r\n print(member.voice.channel.id)\r\n ydl_opts = {\r\n 'format': 'bestaudio/best',\r\n 'postprocessors': [{\r\n 'key': 'FFmpegExtractAudio',\r\n 'preferredcodec': 'mp3',\r\n 'preferredquality': '192',\r\n }],\r\n 'outtmpl': './video0.mp3'\r\n }\r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n print(video_id)\r\n ydl.download(['https://www.youtube.com/watch?v=' + str(video_id)])\r\n channel = member.voice.channel\r\n vc = await channel.connect()\r\n audio = discord.FFmpegPCMAudio(source=\"./video0.mp3\")\r\n vc.play(audio)\r\n while vc.is_playing():\r\n await asyncio.sleep(.1)\r\n vc.stop()\r\n voice = discord.utils.get(bot.voice_clients, guild=ctx.guild)\r\n if voice != None:\r\n await vc.disconnect(force=True)\r\n audio.cleanup()\r\n await asyncio.sleep(1)\r\n os.remove('./video0.mp3')\r\n else:\r\n ctx.send(f\"silly {member.mention}, you must be in a voice channel to do this!\")\r\n if str(reaction) == '\\U0001f6d1':\r\n await ctx.send(f'{ctx.author.mention}, this request has been canceled.')\r\n#logs\r\n\r\n@bot.listen('on_message')\r\nasync def on_message(message):\r\n if message.author.id != bot.user.id:\r\n channel = discord.utils.get(message.guild.text_channels, name=\"logs\")\r\n await channel.send(f'`{message.channel}` : `{message.author}` : {message.content}')\r\n\r\n\r\n@bot.command()\r\nasync def ping(ctx):\r\n ping_ = bot.latency\r\n ping = round(ping_ * 1000)\r\n await ctx.send(f\"my ping is {ping}ms\")\r\n\r\n \r\n\r\n\r\n@bot.command()\r\nasync def help(ctx):\r\n await ctx.channel.send(f'how the bot works: \\n*{prefix}mute * mutes a user. must have vc mute perms \\n*{prefix}unmute * unmutes a user. must have vc mute perms \\n*{prefix}purge* deleats the most recent messages in a channel. must have manage message perms \\n*{prefix}kick * kicks a user. must have kick perms \\n*{prefix}ban * bans a user for the reason specified. must have ban perms. \\n*{prefix}unban * unbans a user. must have ban perms.\\n*{prefix}search* allows a user to playa youtube videos audio in a vc.\\n*{prefix}stop* stops all audio playback')\r\n return\r\n\r\nbot.run(token)\r\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":18334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"154283879","text":"# Created by taka the 14-02-2021 at 16:14\n\nimport pandas as pd\nimport config as cfg\nimport os\n\nif __name__ == '__main__':\n ratings_df = pd.DataFrame({}, columns=['ID', '17', '18', '19', '20', '21'])\n\n for i in range(17, 22):\n print('Starting the collect of players rating in fifa {} csv.'.format(i))\n dataset_info = cfg.players_dataset_info[str(i)]\n delim = dataset_info[-2]\n id_col = dataset_info[-1]\n rating = dataset_info[2]\n fifa_df = pd.read_csv(os.path.join(cfg.data_path, 'players_{}.csv'.format(i)), sep=delim)\n\n for idx, row in fifa_df.iterrows():\n\n if len(ratings_df.loc[ratings_df['ID'] == int(row[id_col])]) == 0:\n ratings_df = ratings_df.append({c: (int(row[id_col]) if c == 'ID' else 0) for c in ratings_df.columns},\n ignore_index=True)\n\n ratings_df.loc[ratings_df.ID == int(row[id_col]), str(i)] = float(row[rating])\n print('Fifa {} csv rating collected.'.format(i))\n\n ratings_df.to_csv(os.path.join(cfg.data_path, 'players_rating.csv'), sep=',')\n","sub_path":"Scripts/players_rating_processing.py","file_name":"players_rating_processing.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"544961785","text":"import torch.nn as nn\nfrom .human_model_layer import KinematicLayer\n\nclass KinematicAvgPoolHead(nn.Module):\n def __init__(self, in_channels, out_channels, fea_map_size):\n super(KinematicAvgPoolHead, self).__init__()\n self.avgpool = nn.AvgPool2d(fea_map_size, stride=1)\n self.fc = nn.Linear(in_channels, out_channels)\n self.kin_layer = KinematicLayer()\n\n def forward(self, x):\n x = self.avgpool(x)\n x = x.view(x.size(0), -1)\n x = self.fc(x)\n x = self.kin_layer(x)\n return x\n","sub_path":"pytorch_projects/common_pytorch/base_modules/kinematic_avg_pool_head.py","file_name":"kinematic_avg_pool_head.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"283562884","text":"import new_bot.sheets as sheets\nimport logging\n\n\nlogging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n filename=\"sample.log\")\nlogger = logging.getLogger(\"TeleBot\")\nlogger.setLevel(level=logging.INFO)\n\n\nsheet = sheets.Sheet(logger)\n\nf = open('answers.txt', 'r')\n\nlines = f.readlines()\ncount = 0\nfor line in lines:\n circle = 1\n splitted = line[:-1].split(' ')\n p = int(splitted[0])\n t = int(splitted[2])\n if p > 18:\n p -= 18\n t -= 18\n circle = 2\n m1 = splitted[3]\n m2 = splitted[4]\n\n count += 1\n\n sheet.new_batch_update(p, t, circle, int(m2), splitted[1])\n\nprint(\"Read \" + str(count) + \" lines\")\nsheet.write_batch()\n","sub_path":"final_processor.py","file_name":"final_processor.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"242548148","text":"from django import forms\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit\n\n\nclass UpdateTaskForm(forms.Form):\n name = forms.CharField(max_length = 200)\n description = forms.CharField(widget = forms.Textarea())\n completed = forms.BooleanField()\n expected_at = forms.DateTimeField()\n\n def __init__(self, *args, **kwargs):\n super(NewForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_id = 'upate_task_form'\n self.helper.form_method = 'post'\n self.helper.form_action = 'submit_task'\n self.helper.add_input(Submit('submit', 'Update Task'))","sub_path":"tasks/lib/forms/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"51524897","text":"\nfrom sklearn.manifold import TSNE\nfrom sklearn.externals import joblib\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\n\n\ndef tsne():\n vec = joblib.load('country_vec.pkl')\n country = joblib.load('country_dic.pkl')\n result = TSNE(n_components=2, random_state=0).fit_transform(vec)\n cls = KMeans(n_clusters=6).fit(vec)\n colors = ['red', 'blue', 'yellow', 'pink', 'green', 'cyan']\n plt.figure(num=None, figsize=(16, 12), dpi=300)\n\n name = []\n for v in country.values():\n name.append(f'${v}$')\n for i, label in enumerate(cls.labels_):\n plt.scatter(result[i, 0], result[i, 1], marker=name[i], s=2000, c=colors[label])\n plt.show()\n\n\nif __name__ == '__main__':\n tsne()","sub_path":"hotate/chapter10/knock99.py","file_name":"knock99.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"550948910","text":"# coding: utf-8\n\n\"\"\"\nCopyright 2016 SmartBear Software\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 Ref: https://github.com/swagger-api/swagger-codegen\n\"\"\"\n\nfrom pprint import pformat\nfrom six import iteritems\nimport re\n\n\nclass JobJobChangelistcreateParams(object):\n \"\"\"\n NOTE: This class is auto generated by the swagger code generator program.\n Do not edit the class manually.\n \"\"\"\n def __init__(self):\n \"\"\"\n JobJobChangelistcreateParams - a model defined in Swagger\n\n :param dict swaggerTypes: The key is attribute name\n and the value is attribute type.\n :param dict attributeMap: The key is attribute name\n and the value is json key in definition.\n \"\"\"\n self.swagger_types = {\n 'newer_snapid': 'int',\n 'older_snapid': 'int',\n 'retain_repstate': 'bool'\n }\n\n self.attribute_map = {\n 'newer_snapid': 'newer_snapid',\n 'older_snapid': 'older_snapid',\n 'retain_repstate': 'retain_repstate'\n }\n\n self._newer_snapid = None\n self._older_snapid = None\n self._retain_repstate = None\n\n @property\n def newer_snapid(self):\n \"\"\"\n Gets the newer_snapid of this JobJobChangelistcreateParams.\n Newer snapshot ID.\n\n :return: The newer_snapid of this JobJobChangelistcreateParams.\n :rtype: int\n \"\"\"\n return self._newer_snapid\n\n @newer_snapid.setter\n def newer_snapid(self, newer_snapid):\n \"\"\"\n Sets the newer_snapid of this JobJobChangelistcreateParams.\n Newer snapshot ID.\n\n :param newer_snapid: The newer_snapid of this JobJobChangelistcreateParams.\n :type: int\n \"\"\"\n \n if not newer_snapid:\n raise ValueError(\"Invalid value for `newer_snapid`, must not be `None`\")\n if newer_snapid < 1.0:\n raise ValueError(\"Invalid value for `newer_snapid`, must be a value greater than or equal to `1.0`\")\n\n self._newer_snapid = newer_snapid\n\n @property\n def older_snapid(self):\n \"\"\"\n Gets the older_snapid of this JobJobChangelistcreateParams.\n Older snapshot ID.\n\n :return: The older_snapid of this JobJobChangelistcreateParams.\n :rtype: int\n \"\"\"\n return self._older_snapid\n\n @older_snapid.setter\n def older_snapid(self, older_snapid):\n \"\"\"\n Sets the older_snapid of this JobJobChangelistcreateParams.\n Older snapshot ID.\n\n :param older_snapid: The older_snapid of this JobJobChangelistcreateParams.\n :type: int\n \"\"\"\n \n if not older_snapid:\n raise ValueError(\"Invalid value for `older_snapid`, must not be `None`\")\n if older_snapid < 1.0:\n raise ValueError(\"Invalid value for `older_snapid`, must be a value greater than or equal to `1.0`\")\n\n self._older_snapid = older_snapid\n\n @property\n def retain_repstate(self):\n \"\"\"\n Gets the retain_repstate of this JobJobChangelistcreateParams.\n Whether to retain the replication record after a changelist is created. Retaining a replication record allows a changelist to be recreated later.\n\n :return: The retain_repstate of this JobJobChangelistcreateParams.\n :rtype: bool\n \"\"\"\n return self._retain_repstate\n\n @retain_repstate.setter\n def retain_repstate(self, retain_repstate):\n \"\"\"\n Sets the retain_repstate of this JobJobChangelistcreateParams.\n Whether to retain the replication record after a changelist is created. Retaining a replication record allows a changelist to be recreated later.\n\n :param retain_repstate: The retain_repstate of this JobJobChangelistcreateParams.\n :type: bool\n \"\"\"\n \n self._retain_repstate = retain_repstate\n\n def to_dict(self):\n \"\"\"\n Returns the model properties as a dict\n \"\"\"\n result = {}\n\n for attr, _ in iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n\n return result\n\n def to_str(self):\n \"\"\"\n Returns the string representation of the model\n \"\"\"\n return pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"\n For `print` and `pprint`\n \"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"\n Returns true if both objects are equal\n \"\"\"\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"\n Returns true if both objects are not equal\n \"\"\"\n return not self == other\n\n","sub_path":"isi_sdk/models/job_job_changelistcreate_params.py","file_name":"job_job_changelistcreate_params.py","file_ext":"py","file_size_in_byte":5810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"417381349","text":"from common import common\r\n\r\n\r\ndef __getPlc(race_date_No, horse_code, plc_dict):\r\n if (race_date_No in plc_dict.keys()) and (horse_code in plc_dict[race_date_No].keys()):\r\n return plc_dict[race_date_No][horse_code].replace('DH', '')\r\n return -1\r\n\r\n\r\n# plc_dict: race_date_No & {horse_code & plc}\r\ndef getHorseJockeyRecord(raceCard_rows, plc_dict):\r\n temp_raceCard_rows = {} # race_date_No & [rows]\r\n for row in raceCard_rows:\r\n race_date_No = row['race_date'] + common.toDoubleDigitStr(row['race_No'])\r\n if race_date_No not in temp_raceCard_rows.keys():\r\n temp_raceCard_rows[race_date_No] = []\r\n temp_raceCard_rows[race_date_No].append(row)\r\n\r\n jockey_record_dict = {} # race_date_No & {horse_code & [No1, No2, No3, No4, All]}\r\n temp_plc_record = {} # horse_code & {jockey & [No1, No2, No3, No4, All]}\r\n sort_date_No_list = sorted(temp_raceCard_rows.keys())\r\n for race_date_No in sort_date_No_list:\r\n if race_date_No not in jockey_record_dict.keys():\r\n jockey_record_dict[race_date_No] = {}\r\n rows = temp_raceCard_rows[race_date_No]\r\n for row in rows:\r\n horse_code = row['horse_code'].strip()\r\n jockey = row['jockey'].strip()\r\n\r\n # ÈüÇ°\r\n if (horse_code in temp_plc_record.keys()) and (jockey in temp_plc_record[horse_code].keys()):\r\n record = temp_plc_record[horse_code][jockey]\r\n jockey_record_dict[race_date_No][horse_code] = [record[0], record[1], record[2], record[3], record[4]]\r\n else:\r\n jockey_record_dict[race_date_No][horse_code] = [0, 0, 0, 0, 0]\r\n\r\n # Èüºó\r\n if horse_code not in temp_plc_record.keys():\r\n temp_plc_record[horse_code] = {}\r\n if jockey not in temp_plc_record[horse_code].keys():\r\n temp_plc_record[horse_code][jockey] = [0, 0, 0, 0, 0]\r\n plc = __getPlc(race_date_No, horse_code, plc_dict)\r\n if plc not in common.words:\r\n if int(plc) == 1:\r\n temp_plc_record[horse_code][jockey][0] += 1\r\n elif int(plc) == 2:\r\n temp_plc_record[horse_code][jockey][1] += 1\r\n elif int(plc) == 3:\r\n temp_plc_record[horse_code][jockey][2] += 1\r\n elif int(plc) == 4:\r\n temp_plc_record[horse_code][jockey][3] += 1\r\n temp_plc_record[horse_code][jockey][4] += 1\r\n\r\n # if 'V082' == horse_code:\r\n # print('\\n', race_date_No, jockey, plc)\r\n # print(temp_plc_record[horse_code])\r\n # print(jockey_record_dict[race_date_No][horse_code])\r\n return jockey_record_dict\r\n\r\n","sub_path":"20190413/historyData_model3/horse_record/horse_jockey_record.py","file_name":"horse_jockey_record.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"329466327","text":"# Tien Phan\n# list practice with Python\n\nlast_semester_gradebook = [(\"politics\", 80), (\"latin\", 96), (\"dance\", 97), (\"architecture\", 65)]\n\nsubjects = [\"physics\", \"calculus\", \"poetry\", \"history\"]\ngrades = [98, 87, 85, 88]\nsubjects.append(\"computer science\")\ngrades.append(100)\n\ngradebook = list(zip(subjects, grades))\n\ngradebook.append((\"visual arts\", 93))\n\nfull_gradebook = gradebook + last_semester_gradebook\nprint(list(gradebook))\nprint(\"\\n\")\nprint(list(full_gradebook))\n","sub_path":"ListPractice.py","file_name":"ListPractice.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"435738188","text":"#!/usr/bin/env python\r\n\r\nimport socket\r\nimport time\r\n\r\nTCP_IP = '127.0.0.1'\r\nTCP_PORT = 6789\r\nBUFFER_SIZE = 1024\r\n\r\n\r\n#Below are the test cases\r\n#Simply comment out the ones not being used\r\n\r\n#Test case 1 - Valid ID\r\nGPS = \"P 35 TF 98.800 98.800\" \r\n\r\n#Test case 2 - Not Valid ID\r\n#GPS = \"100000000 98.765 98.765\" \r\n\r\nprint('CLIENT:')\r\n\r\nGPSByte = GPS.encode()\r\n\r\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\ntry:\r\n s.connect((TCP_IP, TCP_PORT))\r\nexcept IOError:\r\n print('couldnt connect. Retrying')\r\n time.sleep(3)\r\n try: \r\n\r\n s.connect((TCP_IP, TCP_PORT))\r\n except IOError:\r\n print('Could not connect. Retry later.')\r\n s.close()\r\n exit()\r\n\r\ns.send(GPSByte)\r\n\r\ns.close()\r\nprint('\\n Connection closed')","sub_path":"Test_Client2.py","file_name":"Test_Client2.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"239206989","text":"\"\"\"\r\nTesting locations\r\n\r\nped_loc = \"C:/Users/Mitch V/Documents/UChicago/Research/\"\r\npheno_loc = \"C:/Users/Mitch V/Documents/UChicago/Research/Sex-specific/processed_data/males.Pheno.Rready.nullLPS.7M.031520.txt\"\r\nwrite_path = \"C:/Users/Mitch V/Documents/UChicago/Research/\"\r\n\r\n\"\"\"\r\n\r\n#import sys module\r\nimport sys\r\n\r\ndef ped_prep(ped_loc, pheno_loc, write_path):\r\n #Parse the ped file into a list\r\n ped_raw = []\r\n with open(ped_loc) as UTR_file:\r\n for line in UTR_file.readlines():\r\n ped_raw.append(line.strip().split(\" \"))\r\n del line\r\n #Parse the pheno file into a list\r\n pheno_raw = []\r\n with open(pheno_loc) as pheno_file:\r\n for line in pheno_file.readlines():\r\n pheno_raw.append(line.strip().split(\"\\t\"))\r\n del line\r\n\r\n #make ref list of ids to convert ped ids to pheno ids\r\n #Create list of extract ids from pheno file\r\n ref_list = []\r\n for entry in pheno_raw[1:]:\r\n ref_list.append([int(entry[2]), int(entry[1])])\r\n del entry\r\n\r\n #sort ped_final to match GRM order\r\n ref_list.sort(key = lambda x:x[1])\r\n \r\n #cycle through entries in ref_list and ped_raw to create ped_final\r\n #Also orders correctly to match pheno file\r\n ped_final = []\r\n temp_list = []\r\n for i in range(len(ref_list)):\r\n for j in range(len(ped_raw)):\r\n #Check if findivs match\r\n if ref_list[i][0] == int(ped_raw[j][1]):\r\n #create temp list to modify iid column and then append to ped_final\r\n temp_list = ped_raw[j][:]\r\n temp_list[1] = ref_list[i][1]\r\n ped_final.append(temp_list)\r\n #clean-up\r\n del i\r\n del j\r\n del pheno_raw\r\n del ref_list\r\n del temp_list\r\n del ped_raw\r\n \r\n #Write pheno to file\r\n import csv\r\n with open(write_path, \"w\") as writeFile:\r\n writer = csv.writer(writeFile, delimiter = ' ')\r\n for i in ped_final:\r\n writer.writerow(i)\r\n writeFile.close()\r\n\r\n#call function with system arguments input from command shell\r\nped_prep(sys.argv[1], sys.argv[2], sys.argv[3])","sub_path":"processing_scripts/ped_prep_QC.py","file_name":"ped_prep_QC.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"350339996","text":"\n\n#calss header\nclass _VICIOUS():\n\tdef __init__(self,): \n\t\tself.name = \"VICIOUS\"\n\t\tself.definitions = [u'Viscious people or actions show an intention or wish to hurt someone or something very badly: ', u'used to describe an object, condition, or remark that causes great physical or emotional pain: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_vicious.py","file_name":"_vicious.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"616397054","text":"import tensorflow as tf\nimport os\nimport sys\nimport pathlib\nimport time\nimport datetime\n\n\nabs_path = pathlib.Path(__file__).parent.absolute()\nsys.path.append(sys.path.append(abs_path))\n\n\npredata = __import__(\"preprocess\")\nmydataset = predata.mydataset\ntext_cnn = __import__(\"model\")\nTextCNN = text_cnn.TextCNN\n\n\n#指定样本文件\npositive_data_file = \"./data/rt-polaritydata/rt-polarity.pos\"\nnegative_data_file = \"./data/rt-polaritydata/rt-polarity.neg\"\n#设置训练参数\nnum_steps = 2000 #定义训练次数\ndisplay_every = 20 #定义训练中的显示间隔\ncheckpoint_every = 100 #定义训练中保存模型的间隔\nSaveFileName = \"text_cnn_model\" #定义保存模型文件夹名称\n#设置模型参数\nnum_classes = 2 #设置模型分类\ndropout_keep_prob = 0.8 #定义dropout系数\nl2_reg_lambda = 0.1 #定义正则化系数\nfilter_sizes = \"3,4,5\" #定义多通道卷积核\nnum_filters = 64 #定义每通道的输出个数\n\n\ndef train_step(x_batch, y_batch, cnn, train_summary_writer, sess): #训练步骤\n feed_dict = {\n cnn.input_x: x_batch,\n cnn.input_y: y_batch,\n cnn.dropout_keep_prob: dropout_keep_prob\n }\n _, step, summaries, loss, accuracy = sess.run([cnn.train_op, cnn.global_step, cnn.train_summary_op, cnn.loss, cnn.accuracy], feed_dict)\n time_str = datetime.datetime.now().isoformat()\n train_summary_writer.add_summary(summaries, step)\n return (time_str, step, loss, accuracy)\n\n\ndef train():\n tf.reset_default_graph() #清空图\n\n #预处理生成字典及数据集\n data, vocab_processor, max_document_length = mydataset(\n positive_data_file, negative_data_file)\n iterator = data.make_one_shot_iterator()\n next_element = iterator.get_next()\n\n #定义TextCnn网络\n cnn = TextCNN(sequence_length=max_document_length,\n num_classes=num_classes,\n vocab_size=len(vocab_processor.vocabulary_),\n embedding_size=128,\n filter_sizes=list(map(int, filter_sizes.split(\",\"))),\n num_filters=num_filters,\n l2_reg_lambda=l2_reg_lambda)\n #构建网络\n cnn.build_mode()\n\n #打开session，准备训练\n session_conf = tf.ConfigProto(allow_soft_placement=True,\n log_device_placement=False)\n with tf.Session(config=session_conf) as sess:\n sess.run(tf.global_variables_initializer())\n\n #准备输出模型路径\n timestamp = str(int(time.time()))\n out_dir = os.path.abspath(os.path.join(os.path.curdir, SaveFileName, timestamp))\n print(\"Writing to {}\\n\".format(out_dir))\n\n #准备输出摘要路径\n train_summary_dir = os.path.join(out_dir, \"summaries\", \"train\")\n train_summary_writer = tf.summary.FileWriter(train_summary_dir, sess.graph)\n\n #准备检查点名称\n checkpoint_dir = os.path.abspath(os.path.join(out_dir, \"checkpoints\"))\n checkpoint_prefix = os.path.join(checkpoint_dir, \"model\")\n if not os.path.exists(checkpoint_dir):\n os.makedirs(checkpoint_dir)\n #定义保存检查点的saver\n saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)\n\n #保存字典\n vocab_processor.save(os.path.join(out_dir, \"vocab\"))\n\n i = 0\n while tf.train.global_step(sess, cnn.global_step) < num_steps:\n x_batch, y_batch = sess.run(next_element)\n i = i + 1\n time_str, step, loss, accuracy = train_step(x_batch, y_batch, cnn, train_summary_writer, sess)\n\n current_step = tf.train.global_step(sess, cnn.global_step)\n if current_step % display_every == 0:\n print(\"{}: step {}, loss {:g}, acc {:g}\".format(time_str, step, loss, accuracy))\n\n if current_step % checkpoint_every == 0:\n path = saver.save(sess,\n checkpoint_prefix,\n global_step=current_step)\n print(\"Saved model checkpoint to {}\\n\".format(path))\n\n\ndef main(argv=None):\n train()\n\n\nif __name__ == '__main__':\n tf.app.run()","sub_path":"[NLP]-文本分类textCNN/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"329175445","text":"from cc3d.core.PySteppables import *\nimport numpy as np\nimport os\nimport Parameters\n\nplot_ODEModel = False\nplot_CellModel = False\nplot_PlaqueAssay = False\nOutputData = False\n\nIFNWash = False #Whether the plate is prestimulated with IFNe before infection\n\nhow_to_determine_IFNe = 3 # Determines the IFNe from the ODE model (1) from Cell model as scalar (2) or from field (3)\nhow_to_determine_V = 3 # Determines the Virus from the ODE model (1) or from field (3)\n\nmin_to_mcs = 10.0 # min/mcs\nhours_to_mcs = min_to_mcs / 60.0 # hours/mcs\ndays_to_mcs = min_to_mcs / 1440.0 # day/mcs\nhours_to_simulate = 80.0 # 10 in the original model\n\nvirus_diffusion_coefficient = 1.0/10.0 #vl^2 / min\nIFNe_diffusion_coefficient = 1.0/10.0 #vl^2 / min\n\nReplicate = Parameters.R\nMultiplier = 50.0\nParameter = 'k31'\n# virus_diffusion_coefficient *= Multiplier\n# IFNe_diffusion_coefficient *= 15.0 * Multiplier\n\n'''Smith AP, Moquin DJ, Bernhauerova V, Smith AM. Influenza virus infection model with density dependence \nsupports biphasic viral decay. Frontiers in microbiology. 2018 Jul 10;9:1554.'''\n\nFluModel_string = ''' \n model FluModel()\n\n //State Variables and Transitions\n V1: -> T ; -beta * V * T ; // Susceptible Cells\n V2: -> I1 ; beta * V * T - k * I1 ; // Early Infected Cells\n V3: -> I2 ; k * I1 - delta_d * I2 / (K_delta + I2) ; // Late Infected Cells\n V4: -> V ; p * I2 - c * V ; // Extracellular Virus\n V5: -> D ; delta_d * I2 / (K_delta + I2) ; // Cleared Infected Cells (for Bookkeeping)\n\n //Parameters\n beta = 2.4* 10^(-4) ; // Virus Infective\n p = 1.6 ; // Virus Production\n c = 13.0 ; // Virus Clearance\n k = 4.0 ; // Eclipse phase\n delta_d = 1.6 * 10^6 ; // Infected Cell Clearance\n K_delta = 4.5 * 10^5 ; // Half Saturation Constant \n\n // Initial Conditions ;\n T0 = 1.0*10^7;\n T = T0 ; // Initial Number of Uninfected Cells\n I1 = 75.0 ; // Initial Number of Infected Cells\nend'''\n\n'''Jordan J. A. Weaver and Jason E. Shoemaker. Mathematical Modeling of RNA Virus Sensing Pathways Reveal Paracrine Signaling as the Primary Factor \nRegulating Excessive Cytokine Production'''\n\nIFNModel_string = '''\n //Equations\n E2a: -> IFN ; P*(k11*RIGI*V+k12*(V^n)/(k13+(V^n))+k14*IRF7P) ;\n E2b: IFN -> IFNe ; k21*IFN ;\n E3a: IFNe -> ; t2*IFNe ;\n E4a: -> STATP ; P*k31*IFNe/(k32+k33*IFNe) ;\n E4b: STATP -> ; t3*STATP ;\n E5a: -> IRF7 ; P*(k41*STATP+k42*IRF7P) ;\n E5b: IRF7 -> ; t4*IRF7 ;\n E6a: -> IRF7P ; P*k51*IRF7 ;\n E6b: IRF7P -> ; t5*IRF7P ;\n E7a: P -> ; P*k61*V ;\n E8a: -> V ; P*(k71*V)/(1.0+k72*IFNe*7E-5) ;\n E8b: V -> ; k73*V ;\n\n //Parameters\n // k11 = 10.0^5 ; \n k11 = 0.0 ;\n k12 = 9.746 ; \n k13 = 12.511 ; \n k14 = 13.562 ;\n k21 = 10.385 ;\n t2 = 3.481 ;\n k31 = 45.922 ;\n k32 = 5.464 ;\n k33 = 0.068 ;\n t3 = 0.3 ;\n k41 = 0.115 ;\n k42 = 1.053 ;\n t4 = 0.75 ;\n k51 = 0.202 ;\n t5 = 0.3 ;\n k61 = 0.635 ;\n k71 = 1.537 ;\n k72 = 47.883 ;\n k73 = 0.197 ;\n n = 3.0 ;\n\n //Initial Conditions\n P = 1.0 ;\n RIGI = 1.0 ;\n IRF7 = 0.0 ;\n V = 6.9e-8 ;\n'''\n\n# Viral Replication Model\nviral_model_string = '''\n E7a: H -> ; H*k61*V ;\n E8a: -> V ; H*k71*V/(1.0+k72*IFNe*7E-5) ;\n E8b: V -> ; k73*V ;\n\n //Parameters\n k61 = 0.635 ;\n k71 = 1.537 ;\n k72 = 47.883 ;\n k73 = 0.197 ;\n\n //Initial Conditions\n V = 0.0 ; \n H = 1.0 ;\n \n //Inputs\n IFNe = 0.0 ;\n'''\n\nIFN_model_string = '''\n //Equations\n E2a: -> IFN ; H*(k11*RIGI*V+k12*(V^n)/(k13+(V^n))+k14*IRF7P) ;\n E2b: IFN -> ; k21*IFN ;\n E4a: -> STATP ; H*k31*IFNe/(k32+k33*IFNe) ;\n E4b: STATP -> ; t3*STATP ;\n E5a: -> IRF7 ; H*(k41*STATP+k42*IRF7P) ;\n E5b: IRF7 -> ; t4*IRF7 ;\n E6a: -> IRF7P ; H*k51*IRF7 ;\n E6b: IRF7P -> ; t5*IRF7P ;\n\n //Parameters\n //k11 = 10.0^(6) ; \n k11 = 0.0 ; \n k12 = 9.746 ; \n k13 = 12.511 ; \n k14 = 13.562 ;\n k21 = 10.385 ;\n k31 = 45.922 ;\n k32 = 5.464 ;\n k33 = 0.068 ;\n t3 = 0.3 ;\n k41 = 0.115 ;\n k42 = 1.053 ;\n t4 = 0.75 ;\n k51 = 0.202 ;\n t5 = 0.3 ;\n n = 3.0 ;\n RIGI = 1.0 ;\n \n // Inputs\n H = 0.0 ;\n IFNe = 0.0 ;\n V = 0.0 ;\n'''\n\nclass ODEModelSteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n # Store Initial Number of Cells\n self.shared_steppable_vars['InitialNumberCells'] = len(self.cell_list)\n\n # Set Max Simulation Steps\n self.get_xml_element('simulation_steps').cdata = hours_to_simulate / hours_to_mcs\n\n # Load Original IFN ODE Model\n self.add_free_floating_antimony(model_string=IFNModel_string, model_name='IFNModel',\n step_size=hours_to_mcs)\n\n # Load Original FLU ODE Model\n self.add_free_floating_antimony(model_string=FluModel_string, model_name='FluModel',\n step_size=days_to_mcs)\n\n # Load Viral Model inside Cells\n self.add_antimony_to_cell_types(model_string=viral_model_string, model_name='VModel',\n cell_types=[self.U], step_size=hours_to_mcs)\n\n # Load IFN Model inside Cells\n self.add_antimony_to_cell_types(model_string=IFN_model_string, model_name='IModel',\n cell_types=[self.U], step_size=hours_to_mcs)\n\n # Parameter Scan\n # self.sbml.FluModel['beta'] *= Multiplier\n # self.sbml.FluModel['c'] *= Multiplier\n # self.sbml.FluModel['k'] *= Multiplier\n self.sbml.IFNModel['k31'] *= Multiplier\n for cell in self.cell_list:\n cell.sbml.IModel['k31'] *= Multiplier\n # cell.sbml.VModel['k73'] *= Multiplier\n # cell.sbml.VModel['t5'] *= Multiplier\n\n # Initial conditions: infected cell in the center\n cell = self.cell_field[self.dim.x // 2, self.dim.y // 2, 0]\n cell.type = self.I1\n cell.sbml.VModel['V'] = 6.9e-8\n self.sbml.FluModel['I1'] = 1.0 / self.shared_steppable_vars['InitialNumberCells']\n self.sbml.FluModel['V'] = 0.0\n \n #Set prestimulated internal protein values\n if IFNWash:\n for cell in self.cell_list_by_type(self.U,self.I1):\n cell.sbml.IModel['IFN'] = 0.035\n cell.sbml.IModel['IRF7'] = 0.097\n cell.sbml.IModel['IRF7P'] = 0.028\n cell.sbml.IModel['STATP'] = 0.714 \n\nclass CellularModelSteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n # Set IFNe diffusion parameters\n self.get_xml_element('IFNe_dc').cdata = IFNe_diffusion_coefficient * min_to_mcs\n self.get_xml_element('IFNe_decay').cdata = self.sbml.IFNModel['t2'] * hours_to_mcs\n self.shared_steppable_vars['ExtracellularIFN_Scalar'] = self.sbml.IFNModel['IFNe']\n\n # Set Virus diffusion parameters\n self.get_xml_element('virus_dc').cdata = virus_diffusion_coefficient * min_to_mcs\n self.get_xml_element('virus_decay').cdata = self.sbml.FluModel['c'] * days_to_mcs\n self.shared_steppable_vars['ExtracellularVirus_Scalar'] = self.sbml.FluModel['V']\n\n # Set secretors\n self.secretorIFN = self.get_field_secretor(\"IFNe\")\n self.secretorV = self.get_field_secretor(\"Virus\")\n\n def step(self, mcs):\n ## Measure amount of IFNe in the Field\n self.shared_steppable_vars['ExtracellularIFN_Field'] = 0\n for cell in self.cell_list_by_type(self.U,self.I1,self.I2):\n self.shared_steppable_vars['ExtracellularIFN_Field'] += self.secretorIFN.amountSeenByCell(cell)\n\n ## Production of IFNe\n # E2b: IFN -> IFNe; k21 * IFN ;\n self.total_IFNe_production = 0.0\n k21 = self.sbml.IFNModel['k21'] * hours_to_mcs\n for cell in self.cell_list_by_type(self.U,self.I1,self.I2):\n intracellularIFN = cell.sbml.IModel['IFN']\n p = k21 * intracellularIFN\n release = self.secretorIFN.secreteInsideCellTotalCount(cell, p / cell.volume)\n self.total_IFNe_production += release.tot_amount\n\n ## Decay of IFNe\n # E3a: IFNe -> ; t2*IFNe ;\n I = self.shared_steppable_vars['ExtracellularIFN_Scalar']\n t2 = self.sbml.IFNModel['t2'] * hours_to_mcs\n self.total_IFNe_decay = t2 * I\n ## Update Scalar IFNe\n self.shared_steppable_vars['ExtracellularIFN_Scalar'] += self.total_IFNe_production - self.total_IFNe_decay\n\n ## Measure amount of extracellular virus field\n self.shared_steppable_vars['ExtracellularVirus_Field'] = 0\n for cell in self.cell_list:\n V = self.secretorV.amountSeenByCell(cell)\n self.shared_steppable_vars['ExtracellularVirus_Field'] += V\n\n ## Production of extracellular virus\n # E8b: V -> ; k73 * V\n self.total_virus_production = 0.0\n k73 = self.sbml.IFNModel['k73'] * hours_to_mcs\n for cell in self.cell_list_by_type(self.I2):\n Virus = cell.sbml.VModel['V']\n p = k73 * Virus * 1094460.28\n release = self.secretorV.secreteInsideCellTotalCount(cell, p / cell.volume)\n self.total_virus_production += release.tot_amount\n\n ## Decay of IFNe\n # V -> ; c * V\n V = self.shared_steppable_vars['ExtracellularVirus_Scalar']\n c = self.sbml.FluModel['c'] * days_to_mcs\n self.total_virus_decay = c * V\n ## Update Scalar Virus\n self.shared_steppable_vars['ExtracellularVirus_Scalar'] += self.total_virus_production - self.total_virus_decay\n\n ## P to D transition\n # E7a: P -> ; P * k61 * V;\n for cell in self.cell_list_by_type(self.I2):\n k61 = cell.sbml.VModel['k61'] * hours_to_mcs\n H = cell.sbml.VModel['H']\n V = cell.sbml.VModel['V']\n r = k61 * V * (1-H)\n p_I2toD = 1.0 - np.exp(-r)\n if np.random.random() < p_I2toD:\n cell.type = self.DEAD\n\n ## I1 to I2 transition\n # E2: I1 -> I2 ; k * I1\n for cell in self.cell_list_by_type(self.I1):\n k = self.sbml.FluModel['k'] * days_to_mcs\n r = k\n p_T1oI2 = 1.0 - np.exp(-r)\n if np.random.random() < p_T1oI2:\n cell.type = self.I2\n\n ## U to I1 transition\n # E1: T -> I1 ; beta * V * T\n for cell in self.cell_list_by_type(self.U):\n # Determine Virus from the ODE\n if how_to_determine_V == 1:\n b = self.sbml.FluModel['beta'] * self.sbml.FluModel['T0'] * days_to_mcs\n V = self.sbml.FluModel['V'] / self.sbml.FluModel['T0']\n # Determine Virus from field\n if how_to_determine_V == 3:\n b = self.sbml.FluModel['beta'] * self.shared_steppable_vars['InitialNumberCells'] * days_to_mcs\n V = self.secretorV.amountSeenByCell(cell)\n r = b * V\n p_UtoI1 = 1.0 - np.exp(-r)\n if np.random.random() < p_UtoI1:\n cell.type = self.I1\n cell.sbml.VModel['V'] = 6.9e-8\n\n ## Updating Cellular Models\n for cell in self.cell_list:\n # Determine IFNe from the ODE\n if how_to_determine_IFNe == 1:\n cell.sbml.VModel['IFNe'] = self.sbml.IFNModel['IFNe']\n cell.sbml.IModel['IFNe'] = self.sbml.IFNModel['IFNe']\n # Determine IFNe from scalar from cell model\n if how_to_determine_IFNe == 2:\n cell.sbml.VModel['IFNe'] = self.shared_steppable_vars['ExtracellularIFN_Scalar'] / self.shared_steppable_vars['InitialNumberCells']\n cell.sbml.IModel['IFNe'] = self.shared_steppable_vars['ExtracellularIFN_Scalar'] / self.shared_steppable_vars['InitialNumberCells']\n # Determine IFNe from field\n if how_to_determine_IFNe == 3:\n cell.sbml.VModel['IFNe'] = self.secretorIFN.amountSeenByCell(cell)\n cell.sbml.IModel['IFNe'] = self.secretorIFN.amountSeenByCell(cell)\n cell.sbml.IModel['H'] = cell.sbml.VModel['H']\n cell.sbml.IModel['V'] = cell.sbml.VModel['V']\n\n self.timestep_sbml()\n\nclass IFNPlotSteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n self.initial_infected = len(self.cell_list_by_type(self.U))\n # Initialize Graphic Window for Jordan IFN model\n if (plot_ODEModel == True) or (plot_CellModel == True):\n self.plot_win1 = self.add_new_plot_window(title='V',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win2 = self.add_new_plot_window(title='H',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n\n self.plot_win3 = self.add_new_plot_window(title='P',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win4 = self.add_new_plot_window(title='IFNe',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win5 = self.add_new_plot_window(title='STATP',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win6 = self.add_new_plot_window(title='IRF7',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win7 = self.add_new_plot_window(title='IRF7P',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win8 = self.add_new_plot_window(title='INF',\n x_axis_title='Hours',\n y_axis_title='Variable', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n if plot_ODEModel:\n self.plot_win1.add_plot(\"ODEV\", style='Dots', color='yellow', size=5)\n self.plot_win2.add_plot(\"ODEH\", style='Dots', color='white', size=5)\n self.plot_win3.add_plot(\"ODEP\", style='Dots', color='red', size=5)\n self.plot_win4.add_plot(\"ODEIFNe\", style='Dots', color='orange', size=5)\n self.plot_win5.add_plot(\"ODESTATP\", style='Dots', color='blue', size=5)\n self.plot_win6.add_plot(\"ODEIRF7\", style='Dots', color='green', size=5)\n self.plot_win7.add_plot(\"ODEIRF7P\", style='Dots', color='purple', size=5)\n self.plot_win8.add_plot(\"ODEIFN\", style='Dots', color='magenta', size=5)\n\n if plot_CellModel:\n self.plot_win1.add_plot(\"CC3DV\", style='Lines', color='yellow', size=5)\n self.plot_win2.add_plot(\"CC3DH\", style='Lines', color='white', size=5)\n self.plot_win3.add_plot(\"CC3DP\", style='Lines', color='red', size=5)\n self.plot_win4.add_plot(\"CC3DIFNe_Scalar\", style='Lines', color='orange', size=5)\n self.plot_win4.add_plot(\"CC3DIFNe_Field\", style='Lines', color='yellow', size=5)\n self.plot_win5.add_plot(\"CC3DSTATP\", style='Lines', color='blue', size=5)\n self.plot_win6.add_plot(\"CC3DIRF7\", style='Lines', color='green', size=5)\n self.plot_win7.add_plot(\"CC3DIRF7P\", style='Lines', color='purple', size=5)\n self.plot_win8.add_plot(\"CC3DIFN\", style='Lines', color='magenta', size=5)\n\n def step(self, mcs):\n if plot_ODEModel:\n P = len(self.cell_list_by_type(self.U,self.I1,self.I2))/self.shared_steppable_vars['InitialNumberCells']\n self.plot_win1.add_data_point(\"ODEV\", mcs * hours_to_mcs, self.sbml.IFNModel['V'])\n self.plot_win2.add_data_point(\"ODEH\", mcs * hours_to_mcs, self.sbml.IFNModel['P'])\n self.plot_win3.add_data_point(\"ODEP\", mcs * hours_to_mcs, self.sbml.IFNModel['P'])\n self.plot_win4.add_data_point(\"ODEIFNe\", mcs * hours_to_mcs, self.sbml.IFNModel['IFNe'] * P)\n self.plot_win5.add_data_point(\"ODESTATP\", mcs * hours_to_mcs, self.sbml.IFNModel['STATP'])\n self.plot_win6.add_data_point(\"ODEIRF7\", mcs * hours_to_mcs, self.sbml.IFNModel['IRF7'])\n self.plot_win7.add_data_point(\"ODEIRF7P\", mcs * hours_to_mcs, self.sbml.IFNModel['IRF7P'])\n self.plot_win8.add_data_point(\"ODEIFN\", mcs * hours_to_mcs, self.sbml.IFNModel['IFN'])\n\n if plot_CellModel:\n L = len(self.cell_list_by_type(self.U,self.I1,self.I2))\n avgV = 0.0\n avgH = 0.0\n avgSTATP = 0.0\n avgIRF7 = 0.0\n avgIRF7P = 0.0\n avgIFN = 0.0\n for cell in self.cell_list_by_type(self.U,self.I1,self.I2):\n avgV += cell.sbml.VModel['V'] / L\n avgH += cell.sbml.VModel['H'] / L\n avgSTATP += cell.sbml.IModel['STATP'] / L\n avgIRF7 += cell.sbml.IModel['IRF7'] / L\n avgIRF7P += cell.sbml.IModel['IRF7P'] / L\n avgIFN += cell.sbml.IModel['IFN'] / L\n\n self.plot_win1.add_data_point(\"CC3DV\", mcs * hours_to_mcs, avgV)\n self.plot_win2.add_data_point(\"CC3DH\", mcs * hours_to_mcs, avgH)\n self.plot_win3.add_data_point(\"CC3DP\", mcs * hours_to_mcs,\n L / self.shared_steppable_vars['InitialNumberCells'])\n self.plot_win4.add_data_point(\"CC3DIFNe_Scalar\", mcs * hours_to_mcs,\n self.shared_steppable_vars['ExtracellularIFN_Scalar']\n / self.shared_steppable_vars['InitialNumberCells'])\n self.plot_win4.add_data_point(\"CC3DIFNe_Field\", mcs * hours_to_mcs,\n self.shared_steppable_vars['ExtracellularIFN_Field']\n / self.shared_steppable_vars['InitialNumberCells'])\n self.plot_win5.add_data_point(\"CC3DSTATP\", mcs * hours_to_mcs, avgSTATP)\n self.plot_win6.add_data_point(\"CC3DIRF7\", mcs * hours_to_mcs, avgIRF7)\n self.plot_win7.add_data_point(\"CC3DIRF7P\", mcs * hours_to_mcs, avgIRF7P)\n self.plot_win8.add_data_point(\"CC3DIFN\", mcs * hours_to_mcs, avgIFN)\n\nclass FluPlotSteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n self.initial_uninfected = len(self.cell_list_by_type(self.U))\n if (plot_ODEModel == True) or (plot_CellModel == True):\n self.plot_win9 = self.add_new_plot_window(title='Flu Model Cells',\n x_axis_title='Hours',\n y_axis_title='Variables', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n self.plot_win10 = self.add_new_plot_window(title='Flu Model Virus',\n x_axis_title='Hours',\n y_axis_title='Virus', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n\n if plot_ODEModel == True:\n self.plot_win9.add_plot(\"ODET\", style='Dots', color='blue', size=5)\n self.plot_win9.add_plot(\"ODEI1\", style='Dots', color='orange', size=5)\n self.plot_win9.add_plot(\"ODEI2\", style='Dots', color='red', size=5)\n self.plot_win9.add_plot(\"ODED\", style='Dots', color='purple', size=5)\n self.plot_win10.add_plot(\"ODEV\", style='Dots', color='blue', size=5)\n\n if plot_CellModel == True:\n self.plot_win9.add_plot(\"CC3DT\", style='Lines', color='blue', size=5)\n self.plot_win9.add_plot(\"CC3DI1\", style='Lines', color='orange', size=5)\n self.plot_win9.add_plot(\"CC3DI2\", style='Lines', color='red', size=5)\n self.plot_win9.add_plot(\"CC3DD\", style='Lines', color='purple', size=5)\n self.plot_win10.add_plot(\"CC3DV\", style='Lines', color='blue', size=5)\n\n def step(self, mcs):\n if (plot_ODEModel == True) or (plot_CellModel == True):\n if plot_ODEModel == True:\n self.plot_win9.add_data_point(\"ODET\", mcs * days_to_mcs * 24.0,\n self.sbml.FluModel['T'] / self.sbml.FluModel['T0'])\n self.plot_win9.add_data_point(\"ODEI1\", mcs * days_to_mcs * 24.0,\n self.sbml.FluModel['I1'] / self.sbml.FluModel['T0'])\n self.plot_win9.add_data_point(\"ODEI2\", mcs * days_to_mcs * 24.0,\n self.sbml.FluModel['I2'] / self.sbml.FluModel['T0'])\n self.plot_win9.add_data_point(\"ODED\", mcs * days_to_mcs * 24.0,\n self.sbml.FluModel['D'] / self.sbml.FluModel['T0'])\n self.plot_win10.add_data_point(\"ODEV\", mcs * days_to_mcs * 24.0,\n np.log10(self.sbml.FluModel['V']))\n\n if plot_CellModel == True:\n self.plot_win9.add_data_point(\"CC3DT\", mcs * days_to_mcs * 24.0,\n len(self.cell_list_by_type(self.U)) / self.initial_uninfected)\n self.plot_win9.add_data_point(\"CC3DI1\", mcs * days_to_mcs * 24.0,\n len(self.cell_list_by_type(self.I1)) / self.initial_uninfected)\n self.plot_win9.add_data_point(\"CC3DI2\", mcs * days_to_mcs * 24.0,\n len(self.cell_list_by_type(self.I2)) / self.initial_uninfected)\n self.plot_win9.add_data_point(\"CC3DD\", mcs * days_to_mcs * 24.0,\n len(self.cell_list_by_type(self.DEAD)) / self.initial_uninfected)\n self.plot_win10.add_data_point(\"CC3DV\", mcs * days_to_mcs * 24.0,\n np.log10(self.shared_steppable_vars['ExtracellularVirus_Field']))\n\nclass OutputSteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n if OutputData:\n folder_path = '/Users/Josua/Data/'\n # folder_path = '/N/u/joaponte/Carbonate/IFNParameterSweep/Output/'\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)\n\n # Output ODE Data\n file_name1 = 'FullModelCellular_%s_%.2f_%i.txt' % (Parameter,Multiplier,Replicate)\n self.output1 = open(folder_path + file_name1, 'w')\n self.output1.write(\"%s,%s,%s,%s,%s,%s,%s\\n\" %('Time','U','I1','I2','D','Ve','IFNe'))\n self.output1.flush()\n\n # Output CC3D Data\n file_name2 = 'FullModelIntracellular_%s_%.2f_%i.txt' % (Parameter,Multiplier,Replicate)\n self.output2 = open(folder_path + file_name2, 'w')\n self.output2.write(\"%s,%s,%s,%s,%s,%s,%s,%s,%s,%s\\n\" %\n ('Time','CC3DV','CC3DH','CC3DP','CC3DIFNe_Scalar','CC3DIFNe_Field','CC3DSTATP','CC3DIRF7',\n 'CC3DIRF7P','CC3DIFN'))\n self.output2.flush()\n\n #IFNe secretor\n self.secretorIFNe = self.get_field_secretor(\"IFNe\")\n \n def step(self, mcs):\n if OutputData:\n Time = mcs * hours_to_mcs\n U = len(self.cell_list_by_type(self.U)) / self.shared_steppable_vars['InitialNumberCells']\n I1 = len(self.cell_list_by_type(self.I1)) / self.shared_steppable_vars['InitialNumberCells']\n I2 = len(self.cell_list_by_type(self.I2)) / self.shared_steppable_vars['InitialNumberCells']\n D = len(self.cell_list_by_type(self.DEAD)) / self.shared_steppable_vars['InitialNumberCells']\n Ve = self.shared_steppable_vars['ExtracellularVirus_Field']\n IFNe = 0.0\n for cell in self.cell_list:\n IFNe += self.secretorIFNe.amountSeenByCell(cell)\n\n self.output1.write(\"%e,%e,%e,%e,%e,%e,%e\\n\" % (Time,U,I1,I2,D,Ve,IFNe))\n self.output1.flush()\n\n L = len(self.cell_list_by_type(self.U,self.I1,self.I2))\n CC3DP = L / self.shared_steppable_vars['InitialNumberCells']\n CC3DV = 0.0\n CC3DH = 0.0\n CC3DSTATP = 0.0\n CC3DIRF7 = 0.0\n CC3DIRF7P = 0.0\n CC3DIFN = 0.0\n for cell in self.cell_list_by_type(self.U,self.I1,self.I2):\n CC3DV += cell.sbml.VModel['V'] / L\n CC3DH += cell.sbml.VModel['H'] / L\n CC3DSTATP += cell.sbml.IModel['STATP'] / L\n CC3DIRF7 += cell.sbml.IModel['IRF7'] / L\n CC3DIRF7P += cell.sbml.IModel['IRF7P'] / L\n CC3DIFN += cell.sbml.IModel['IFN'] / L\n CC3DIFNe_Scalar = self.shared_steppable_vars['ExtracellularIFN_Scalar']\\\n / self.shared_steppable_vars['InitialNumberCells']\n CC3DIFNe_Field = self.shared_steppable_vars['ExtracellularIFN_Field'] \\\n / self.shared_steppable_vars['InitialNumberCells']\n self.output2.write(\"%e,%e,%e,%e,%e,%e,%e,%e,%e,%e\\n\" %\n (Time,CC3DV,CC3DH,CC3DP,CC3DIFNe_Scalar,CC3DIFNe_Field,CC3DSTATP,CC3DIRF7,\n CC3DIRF7P,CC3DIFN))\n self.output2.flush()\n\nclass PlaqueAssaySteppable(SteppableBasePy):\n def __init__(self, frequency=1):\n SteppableBasePy.__init__(self, frequency)\n\n def start(self):\n if OutputData:\n folder_path = '/Users/Josua/Data/'\n # folder_path = '/N/u/joaponte/Carbonate/IFNParameterSweep/Output/'\n if not os.path.exists(folder_path):\n os.makedirs(folder_path)\n file_name3 = 'PlaqueAssay_%s_%.2f_%i.txt' % (Parameter,Multiplier,Replicate)\n self.output3 = open(folder_path + file_name3, 'w')\n self.output3.write(\"%s,%s,%s,%s,%s,%s\\n\" % ('Time', 'avgI1rd', 'avgI2rd', 'avgDrd', 'Beta','Beff'))\n self.output3.flush()\n\n # Parameters for Measuring Effective Infectivity\n self.previousT = 0.0\n\n if plot_PlaqueAssay == True:\n self.plot_win11 = self.add_new_plot_window(title='Plaque Growth',\n x_axis_title='Hours',\n y_axis_title='Avg Radial Distance', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n self.plot_win11.add_plot(\"rdI1\", style='Lines', color='orange', size=5)\n self.plot_win11.add_plot(\"rdI2\", style='Lines', color='red', size=5)\n self.plot_win11.add_plot(\"rdD\", style='Lines', color='purple', size=5)\n\n self.plot_win12 = self.add_new_plot_window(title='Effective Infectivity',\n x_axis_title='Hours',\n y_axis_title='Effective Infectivity', x_scale_type='linear',\n y_scale_type='linear',\n grid=False, config_options={'legend': True})\n self.plot_win12.add_plot(\"ODEB\", style='Dots', color='blue', size=5)\n self.plot_win12.add_plot(\"CC3DBeff\", style='Lines', color='blue', size=5)\n\n # Parameters for Measuring Effective Infectivity\n self.previousT = 0.0\n\n def step(self, mcs):\n if (plot_PlaqueAssay == True) or (OutputData == True):\n # Measure area occupied by D cells and assume its a circle\n volume_D = 0.0\n for cell in self.cell_list_by_type(self.DEAD):\n volume_D += cell.volume\n avgDrd = np.sqrt(volume_D/np.pi)\n\n volume_I2 = 0.0\n for cell in self.cell_list_by_type(self.I2):\n volume_I2 += cell.volume\n avgI2rd = np.sqrt((volume_D+volume_I2)/np.pi)\n\n volume_I1 = 0.0\n for cell in self.cell_list_by_type(self.I1):\n volume_I1 += cell.volume\n avgI1rd = np.sqrt((volume_D+volume_I2+volume_I1)/np.pi)\n\n Beff = 0.0\n num_U = len(self.cell_list_by_type(self.U))\n dT = abs(num_U - self.previousT)\n self.previousT = num_U\n if self.shared_steppable_vars['ExtracellularVirus_Field']:\n if num_U:\n Beff = dT / (num_U*self.shared_steppable_vars['ExtracellularVirus_Field']*hours_to_mcs)\n\n if plot_PlaqueAssay == True:\n self.plot_win11.add_data_point(\"rdI1\", mcs * hours_to_mcs, avgI1rd)\n self.plot_win11.add_data_point(\"rdI2\", mcs * hours_to_mcs, avgI2rd)\n self.plot_win11.add_data_point(\"rdD\", mcs * hours_to_mcs, avgDrd)\n\n self.plot_win12.add_data_point(\"ODEB\", mcs * hours_to_mcs,self.sbml.FluModel['beta']\n * days_to_mcs / hours_to_mcs)\n self.plot_win12.add_data_point(\"CC3DBeff\", mcs * hours_to_mcs, Beff)\n\n if OutputData:\n Time = mcs * hours_to_mcs\n Beta = self.sbml.FluModel['beta'] * days_to_mcs / hours_to_mcs\n self.output3.write(\"%e,%e,%e,%e,%e,%e\\n\" % (Time, avgI1rd, avgI2rd, avgDrd, Beta, Beff))\n self.output3.flush()\n","sub_path":"IFNModels/PlaqueAssay/Simulation/JordanAmberModelSteppables.py","file_name":"JordanAmberModelSteppables.py","file_ext":"py","file_size_in_byte":33669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"83498961","text":"# sequence_manipulation.py\n\ncodon_table = {\n 'UUU': 'F', 'CUU': 'L', 'AUU': 'I', 'GUU': 'V', 'UUC': 'F', 'CUC': 'L', 'AUC': 'I', 'GUC': 'V', 'UUA': 'L',\n 'CUA': 'L', 'AUA': 'I', 'GUA': 'V', 'UUG': 'L', 'CUG': 'L', 'AUG': 'M', 'GUG': 'V', 'UCU': 'S', 'CCU': 'P',\n 'ACU': 'T', 'GCU': 'A', 'UCC': 'S', 'CCC': 'P', 'ACC': 'T', 'GCC': 'A', 'UCA': 'S', 'CCA': 'P', 'ACA': 'T',\n 'GCA': 'A', 'UCG': 'S', 'CCG': 'P', 'ACG': 'T', 'GCG': 'A', 'UAU': 'Y', 'CAU': 'H', 'AAU': 'N', 'GAU': 'D',\n 'UAC': 'Y', 'CAC': 'H', 'AAC': 'N', 'GAC': 'D', 'CAA': 'Q', 'AAA': 'K', 'GAA': 'E', 'CAG': 'Q', 'AAG': 'K',\n 'GAG': 'E', 'UGU': 'C', 'CGU': 'R', 'AGU': 'S', 'GGU': 'G', 'UGC': 'C', 'CGC': 'R', 'AGC': 'S', 'GGC': 'G',\n 'CGA': 'R', 'AGA': 'R', 'GGA': 'G', 'UGG': 'W', 'CGG': 'R', 'AGG': 'R', 'GGG': 'G',\n 'UAA': 'Stop', 'UAG': 'Stop', 'UGA': 'Stop'\n}\n\n\ndef rna_to_protein(rna_sequence):\n '''takes an rna string as input and returns the corresponding protein string\n '''\n protein_sequence = ''\n for start in range(len(rna_sequence))[::3]:\n codon = rna_sequence[start:start + 3].upper()\n if codon_table[codon] != 'Stop':\n protein_sequence += codon_table[codon]\n return protein_sequence\n\n\ndef dna_to_rna(dna_sequence):\n '''takes a dna string as input and returns the corresponding rna string\n '''\n rna_sequence = dna_sequence.upper().replace('T', 'U')\n return rna_sequence\n\n\ndef rna_to_dna(rna_sequence):\n '''takes an rna string as input and returns the corresponding dna string\n '''\n dna_sequence = rna_sequence.upper().replace('U', 'T')\n return dna_sequence\n\n\ndef reverse_compliment(sequence, dna=True):\n '''returns the reverse compliment of a dna (default) or rna (if dna != True) strand\n '''\n def replace_all(replacements, sequence):\n return ''.join(replacements[x] for x in sequence if x in replacements.keys())\n\n reversed_sequence = sequence[::-1].upper()\n\n if dna == True:\n return replace_all({'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G'}, reversed_sequence)\n else:\n return replace_all({'A': 'U', 'U': 'A', 'G': 'C', 'C': 'G'}, reversed_sequence)\n\n\ndef alignment_score(sequence_a, sequence_b):\n '''takes two sequences as input and returns the number of point mutations between the two of them\n '''\n sequences = zip(sequence_a, sequence_b)\n local_score = 0\n fitting_score = 0\n total_mutations = 0\n\n current_length = 0\n for a, b in sequences:\n if a == b:\n fitting_score -= 1\n current_length += 1\n else:\n fitting_score += 1\n total_mutations += 1\n if current_length > local_score:\n local_score = current_length\n current_length = 0\n if current_length > local_score:\n local_score = current_length\n\n return (total_mutations, fitting_score, local_score)\n","sub_path":"tools/sequence_manipulation.py","file_name":"sequence_manipulation.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"363654979","text":"import pandas as pd\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import svm\nfrom sklearn.naive_bayes import GaussianNB\n#from xgboost import XGBClassifier, plot_importance\n\ndf = pd.read_csv('./KSJR_Car_Hacking_D_training-1(DS_CV).csv')\ndf_x = df[['Data0','Data1','Data2','Data3','Data4','Data5','Data6','Data7']]\ndf_y = df['Class']\n\nmodel1 = LogisticRegression() \nmodel2 = KNeighborsClassifier(n_neighbors = 6) \nmodel3 = RandomForestClassifier (n_estimators =10) \nmodel4 = svm.SVC (kernel ='linear') \nmodel5 = svm.SVC (kernel ='poly') \nmodel6 = svm.SVC (kernel ='rbf') \nmodel7 = svm.SVC (kernel ='sigmoid') \nmodel8 = GaussianNB() \n#model9 = XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\n\nkfold = KFold(n_splits=2, shuffle = True, random_state = 0) #10-fold cross validation \nscores1 = cross_val_score(model1, df_x, df_y, cv=kfold) \nscores2 = cross_val_score(model2, df_x, df_y, cv=kfold) \nscores3 = cross_val_score(model3, df_x, df_y, cv=kfold) \nscores4 = cross_val_score(model4, df_x, df_y, cv=kfold) \nscores5 = cross_val_score(model5, df_x, df_y, cv=kfold) \nscores6 = cross_val_score(model6, df_x, df_y, cv=kfold)\nscores7 = cross_val_score(model7, df_x, df_y, cv=kfold) \nscores8 = cross_val_score(model8, df_x, df_y, cv=kfold) \n#scores9 = cross_val_score(model9, df_x, df_y, cv=kfold) \n\nprint(\"LogisticRefression Acc: \"+str(scores1.mean())) \nprint(\"KNeighborsClassifier Acc: \"+str(scores2.mean())) \nprint(\"RandomForestClassifier Acc: \"+str(scores3.mean())) \nprint(\"SVC_linear Acc: \"+str(scores4.mean())) \nprint(\"SVC_poly Acc: \"+str(scores5.mean())) \nprint(\"SVC_rbf Acc: \"+str(scores6.mean())) \nprint(\"SVC_sigmoid Acc: \"+str(scores7.mean())) \nprint(\"GaussianNB Acc: \"+str(scores8.mean())) \n#print(\"Gradient Boosting Acc: \"+str(scores9.mean()))\n","sub_path":"모델선택하기/모델 선택하기.py","file_name":"모델 선택하기.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"82538832","text":"import json\nimport requests\nfrom threading import Thread\n\n\nclass PictureLoad(Thread):\n\n def __init__(self, url):\n super(PictureLoad, self).__init__()\n self._url = url\n\n def run(self):\n\n resp = requests.get(self._url)\n filename = self._url[self._url.rfind('/') + 1:]\n try:\n with open(filename, 'wb') as fs:\n fs.write(resp.content)\n except IOError as e:\n print(e)\n\n\ndef main():\n resp = requests.get('http://api.tianapi.com/meinv/?key=81085f5747a59581327b29d1bccfb925&num=10')\n my_dict = json.loads(resp.text)\n threads = []\n for temp in my_dict['newslist']:\n pic_url = temp['picUrl']\n pictureload = PictureLoad(pic_url)\n pictureload.start()\n #resp = PictureLoad(pic_url).start()\n threads.append(pictureload)\n for thread in threads:\n thread.join()\n print('图片下载完成')\n\n\n\nif __name__ == '__main__':\n main()","sub_path":"day18_/text7.py","file_name":"text7.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"400099126","text":"# Copyright 2015 Cisco Systems, Inc.\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\"\"\"Extensions Driver for Cisco Nexus1000V.\"\"\"\n\nfrom oslo_config import cfg\nfrom oslo_log import log\nfrom oslo_utils import uuidutils\n\nfrom networking_cisco.plugins.ml2.drivers.cisco.n1kv import (\n constants)\nfrom networking_cisco.plugins.ml2.drivers.cisco.n1kv import (\n exceptions as n1kv_exc)\nfrom networking_cisco.plugins.ml2.drivers.cisco.n1kv import (\n extensions)\nfrom networking_cisco.plugins.ml2.drivers.cisco.n1kv import (\n n1kv_db)\n\nfrom neutron.api import extensions as api_extensions\nfrom neutron.api.v2 import attributes\nfrom neutron.extensions import providernet\nfrom neutron.i18n import _LE\nfrom neutron.plugins.common import constants as p_const\nfrom neutron.plugins.ml2.common import exceptions as ml2_exc\nfrom neutron.plugins.ml2 import driver_api as api\n\nLOG = log.getLogger(__name__)\n\n\nclass CiscoN1kvExtensionDriver(api.ExtensionDriver):\n \"\"\"Cisco N1KV ML2 Extension Driver.\"\"\"\n\n # List of supported extensions for cisco Nexus1000V.\n _supported_extension_alias = \"n1kv\"\n\n def initialize(self):\n api_extensions.append_api_extensions_path(extensions.__path__)\n\n @property\n def extension_alias(self):\n \"\"\"\n Supported extension alias.\n\n :returns: alias identifying the core API extension supported\n by this driver\n \"\"\"\n return self._supported_extension_alias\n\n def process_create_port(self, context, data, result):\n \"\"\"Implementation of abstract method from ExtensionDriver class.\"\"\"\n port_id = result.get('id')\n policy_profile_attr = data.get(constants.N1KV_PROFILE)\n if not attributes.is_attr_set(policy_profile_attr):\n policy_profile_attr = (cfg.CONF.ml2_cisco_n1kv.\n default_policy_profile)\n with context.session.begin(subtransactions=True):\n try:\n n1kv_db.get_policy_binding(port_id, context.session)\n except n1kv_exc.PortBindingNotFound:\n if not uuidutils.is_uuid_like(policy_profile_attr):\n policy_profile = n1kv_db.get_policy_profile_by_name(\n policy_profile_attr,\n context.session)\n if policy_profile:\n policy_profile_attr = policy_profile.id\n else:\n LOG.error(_LE(\"Policy Profile %(profile)s does \"\n \"not exist.\"),\n {\"profile\": policy_profile_attr})\n raise ml2_exc.ExtensionDriverError()\n elif not (n1kv_db.get_policy_profile_by_uuid(\n context.session,\n policy_profile_attr)):\n LOG.error(_LE(\"Policy Profile %(profile)s does not \"\n \"exist.\"),\n {\"profile\": policy_profile_attr})\n raise ml2_exc.ExtensionDriverError()\n n1kv_db.add_policy_binding(port_id,\n policy_profile_attr,\n context.session)\n result[constants.N1KV_PROFILE] = policy_profile_attr\n\n def extend_port_dict(self, session, model, result):\n \"\"\"Implementation of abstract method from ExtensionDriver class.\"\"\"\n port_id = result.get('id')\n with session.begin(subtransactions=True):\n try:\n res = n1kv_db.get_policy_binding(port_id, session)\n result[constants.N1KV_PROFILE] = res.profile_id\n except n1kv_exc.PortBindingNotFound:\n # Do nothing if the port binding is not found.\n pass\n\n def process_create_network(self, context, data, result):\n \"\"\"Implementation of abstract method from ExtensionDriver class.\"\"\"\n net_id = result.get('id')\n prov_net_type = data.get(providernet.NETWORK_TYPE)\n net_prof_attr = data.get(constants.N1KV_PROFILE)\n if not attributes.is_attr_set(net_prof_attr):\n if not attributes.is_attr_set(prov_net_type):\n network_type = cfg.CONF.ml2.tenant_network_types[0]\n else:\n network_type = prov_net_type\n if network_type == p_const.TYPE_VLAN:\n net_prof_attr = constants.DEFAULT_VLAN_NETWORK_PROFILE_NAME\n elif network_type == p_const.TYPE_VXLAN:\n net_prof_attr = constants.DEFAULT_VXLAN_NETWORK_PROFILE_NAME\n else:\n # This network type is not supported with network profiles\n return\n with context.session.begin(subtransactions=True):\n try:\n if not uuidutils.is_uuid_like(net_prof_attr):\n net_prof_attr = n1kv_db.get_network_profile_by_name(\n net_prof_attr, context.session)\n else:\n net_prof_attr = n1kv_db.get_network_profile_by_uuid(\n net_prof_attr, context.session)\n # TODO(sopatwar) Handle restrict_network_profiles = True\n # Add logic to check for network profile :: tenant binding\n except n1kv_exc.NetworkProfileNotFound:\n LOG.error(_LE(\"Network Profile %(profile)s does \"\n \"not exist.\"), {\"profile\":\n net_prof_attr})\n raise ml2_exc.ExtensionDriverError()\n segment_type = net_prof_attr.segment_type\n n1kv_db.add_network_binding(net_id, segment_type,\n 0,\n net_prof_attr.id,\n context.session)\n data[providernet.NETWORK_TYPE] = segment_type\n result[constants.N1KV_PROFILE] = net_prof_attr.id\n\n def extend_network_dict(self, session, model, result):\n \"\"\"Implementation of abstract method from ExtensionDriver class.\"\"\"\n net_id = result.get('id')\n with session.begin(subtransactions=True):\n try:\n res = n1kv_db.get_network_binding(net_id, session)\n result[constants.N1KV_PROFILE] = res.profile_id\n except n1kv_exc.NetworkBindingNotFound:\n # Do nothing if the network binding is not found.\n pass\n","sub_path":"networking_cisco/plugins/ml2/drivers/cisco/n1kv/n1kv_ext_driver.py","file_name":"n1kv_ext_driver.py","file_ext":"py","file_size_in_byte":6991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"428419469","text":"__author__ = 'walzer'\nimport json\nimport operator\nfrom datetime import datetime\nfrom typing import List,Dict,Union,Any,Tuple\nimport numpy as np \n\n#int\n#str\n#float\nFloatVector = List[float]\nIntVector = List[int]\nStringVector = List[str]\nFloatMatrix = List[FloatVector]\nIntMatrix = List[IntVector]\nStringMatrix = List[StringVector]\n#Table = Dict[str,Union(FloatVector,IntVector,StringVector)]\nTable = Dict[str,List]\n\nclass JsonSerialisable(object):\n \"\"\"\n JsonSerialisable Main structure template for mzQC objects\n\n Sets the foundation for a mzQC object to be readily (de-)serialisable with standard python json handling code.\n Facilitates reading and writing of complex objects.\n\n \"\"\"\n mappings: Dict[str, Any] = dict()\n\n @staticmethod\n def time_helper(da:str) -> datetime:\n \"\"\"\n time_helper Helper method for ISO8601 string of various length consumption \n\n Used on JSON datetime object string representation will handle length and return python datetime objects.\n\n Parameters\n ----------\n da : str\n JSON datetime object string representation\n\n Returns\n -------\n datetime\n Python datetime object including the same amount detail provided \n \"\"\"\n if len(da) > 19:\n return datetime.strptime(da, '%Y-%m-%dT%H:%M:%S.%f')\n #elif len(da) <= 19:\n return datetime.strptime(da, '%Y-%m-%dT%H:%M:%S')\n\n @classmethod\n def class_mapper(classself, d):\n \"\"\"\n class_mapper Maps incoming objects to their respective definition\n\n Allows every registered object to 'know' its type map incuding recursing into its attributes. \n Can be used as object_hook in the json load process.\n \n Parameters\n ----------\n classself : self\n The objects class self\n d : dict\n The dictionary mapping attributes\n\n Returns\n -------\n class object\n Returns an object of the 'outer-most' class \n\n Raises\n ------\n ValueError\n If expected date strings are invalid.\n \"\"\"\n maxcls: Any = None\n exmax: int = 0 \n for keys, cls in classself.mappings.items():\n if keys.issuperset(d.keys()):\n nx = len(set(d.keys()).intersection(set(keys)))\n if nx > exmax:\n maxcls = cls\n exmax = nx\n \n if maxcls != None:\n return maxcls(**d)\n else:\n if {'creationDate': None}.keys() == d.keys():\n try:\n return JsonSerialisable.time_helper(d['creationDate'])\n except ValueError:\n raise ValueError(\"It appears the creationDate of your file is not of ISO 8601 format including time to the second: {}\".format(d['creationDate']))\n else:\n # raise ValueError('Unable to find a matching class for object: {d} (keys: {k})' .format(d=d,k=d.keys()))\n return d\n\n @classmethod\n def complex_handler(classself, obj):\n \"\"\"\n complex_handler Handles the in-depth serialisations necessary\n\n Facilitates the correct serialisation for each type of object (within the registered mzQC JsonSerialisable context).\n\n Parameters\n ----------\n classself : self\n The objects class self\n obj : object\n The object to be deserialised\n\n Returns\n -------\n obj\n The correct object deconstruction into its deserialisable bits\n\n Raises\n ------\n TypeError\n In case a given object cannot be serialised with the given set of functionalities.\n \"\"\" \n if hasattr(obj, '__dict__'):\n return {k:v for k,v in obj.__dict__.items() if v is not None and v is not \"\"}\n\n elif 'numpy' in str(type(obj)):\n if isinstance(obj,np.ndarray):\n return obj.tolist() \n return obj.item()\n\n elif isinstance(obj, datetime):\n return obj.replace(microsecond=0).isoformat()\n \n else:\n raise TypeError('Object of type {ty} with value {val} is not JSON (de)serializable'.format(ty=type(obj), val=repr(obj)))\n\n @classmethod\n def register(classself, cls):\n \"\"\"\n register The method for class registration in the class mapping process\n\n Each registered class gets mapped.\n\n Parameters\n ----------\n classself : self\n the objects class self\n cls : object\n the class type\n\n Returns\n -------\n cls\n the class type\n \"\"\" \n classself.mappings[frozenset(tuple([attr for attr, val in cls().__dict__.items()]))] = cls\n return cls\n\n @classmethod\n def ToJson(classself, obj, readability=0):\n \"\"\"\n ToJson Main method for serialisation\n\n Parameters\n ----------\n classself : self\n The objects class self\n obj : object\n The object to be serialised\n readability : int, optional\n The indentation level, by default 0 (=no indentation, \n 1=minor indentation on MZQC objects, >1 heavy indentation for max. human readability)\n\n Returns\n -------\n str\n The serialisation result\n \"\"\" \n if readability==0:\n return json.dumps(obj.__dict__, default=classself.complex_handler)\n elif readability == 1:\n return json.dumps(obj.__dict__, default=classself.complex_handler, indent=2, cls=MzqcJSONEncoder)\n else:\n return json.dumps(obj.__dict__, default=classself.complex_handler, indent=4)\n\n @classmethod\n def FromJson(classself, json_str):\n \"\"\"\n FromJson Main method for deserialisation\n\n Accounts for neccessary object rectification due to same-attribute class footprints.\n N.B.: for this to work the class init variables must be same name as the corresponding member attributes (self).\n\n Parameters\n ----------\n classself : self\n The objects class self\n json_str : str\n The JSON string to be deserialised\n\n Returns\n -------\n [type]\n [description]\n \"\"\" \n j = json.loads(json_str, object_hook=classself.class_mapper)\n return rectify(j)\n\n\ndef rectify(obj):\n \"\"\"\n rectify Rectifies objects according to their position in the local hierarchy\n\n Carries out the neccessary object rectification due to same-attribute class footprints.\n Rectification depends on the object position in the local object hierarchy.\n\n Parameters\n ----------\n obj : object\n The object to be rectified\n\n Returns\n -------\n object\n The rectified object\n \"\"\" \n static_list_typemap = {'runQualities': RunQuality, 'setQualities': SetQuality, 'controlledVocabularies': ControlledVocabulary, \n 'qualityMetrics': QualityMetric, 'inputFiles': InputFile, 'analysisSoftware': AnalysisSoftware, 'fileProperties': CvParameter}\n static_singlet_typemap = {'fileFormat': CvParameter, 'metadata': MetaDataParameters}\n if hasattr(obj, '__dict__'):\n for k,v in obj.__dict__.items():\n if k in static_list_typemap.keys():\n v = [rectify((static_list_typemap[k])(**i.__dict__ if hasattr(i, '__dict__') else i)) for i in v]\n elif k in static_singlet_typemap.keys():\n k = rectify((static_singlet_typemap[k])(**v.__dict__ if hasattr(v, '__dict__') else v))\n return obj\n\n\nclass MzqcJSONEncoder(json.JSONEncoder):\n \"\"\"\n MzqcJSONEncoder The encoder used to facilitate indented encoding \n\n Handles the string encoding and formatting of the serialised objects.\n\n \"\"\"\n def iterencode(self, o, _one_shot=False):\n indent_level = 0\n value_scope = False\n for s in super(MzqcJSONEncoder, self).iterencode(o, _one_shot=_one_shot):\n if value_scope and indent_level == 0 and s.startswith('}'):\n value_scope = False\n elif s.startswith('\"value\"'):\n value_scope = True\n if 0 < indent_level:\n s = s.replace('\\n', '').rstrip().lstrip()\n if s.startswith(','):\n s = ',' + s[1:].lstrip()\n if s.startswith('[') and value_scope:\n indent_level += 1\n if s.endswith(']') and value_scope:\n indent_level -= 1\n s = s.replace(']', '\\n'+' '*self.indent*6+']').rstrip()\n yield s\n\n\nclass jsonobject(object):\n \"\"\"\n jsonobject Proxy object for better integration of mzQC objects\n\n Useful for testing and validity checks as __eq__ is overridden to compare all attributes as well.\n\n \"\"\" \n def __eq__(self, other):\n \"\"\"\n __eq__ Overrides the default implementation\n\n Compare all attributes as well.\n\n Parameters\n ----------\n other : object\n\n Returns\n -------\n bool\n False if the two objects are not of the same class or any of the attributes differ\n \"\"\" \n if isinstance(other, __class__):\n # TODO find difference in keys and check whether they are None or \"\" in the other or vice versa\n snn = [k for k,v in self.__dict__.items() if (not v == None and not v == \"\")]\n onn = [k for k,v in other.__dict__.items() if (not v == None and not v == \"\")]\n if set(snn) == set(onn):\n return all([self.__getattribute__(attr) == other.__getattribute__(attr) for attr in self.__dict__.keys()])\n return False\n\n@JsonSerialisable.register\nclass ControlledVocabulary(jsonobject):\n \"\"\"\n ControlledVocabulary Object representation for mzQC schema type ControlledVocabulary\n\n \"\"\" \n def __init__(self, name: str=\"\", uri: str=\"\", version: str=\"\"):\n self.name = name # required\n self.uri = uri # required\n self.version = version # optional\n\n@JsonSerialisable.register\nclass CvParameter(jsonobject):\n \"\"\"\n CvParameter Object representation for mzQC schema type CvParameter\n\n \"\"\" \n def __init__(self, accession: str=\"\", \n name: str=\"\", \n description: str=\"\", \n value: Union[int,str,float,IntVector,StringVector,FloatVector,IntMatrix,StringMatrix,FloatMatrix,Table, None]=None,\n unit: str=\"\"):\n self.accession = accession # required \"pattern\": \"^[A-Z]+:[0-9]{7}$\"\n self.name = name # required\n self.description = description # optional, \"pattern\": \"^[A-Z]+$\"\n self.value = value # optional\n self.unit = unit # optional, IMO this should be accession only, not annother cvParam\n\n@JsonSerialisable.register\nclass AnalysisSoftware(CvParameter):\n \"\"\"\n AnalysisSoftware Object representation for mzQC schema type AnalysisSoftware\n\n \"\"\" \n def __init__(self, accession: str=\"\", \n name: str=\"\", \n description: str=\"\", \n value: str=\"\", \n unit: str=\"\", \n version: str = \"\", \n uri: str = \"\"):\n super().__init__(accession, name, description, value, unit) # optional, this will set None to optional omitted arguments\n self.version = version # required\n self.uri = uri # required\n\n@JsonSerialisable.register\nclass InputFile(jsonobject):\n \"\"\"\n InputFile Object representation for mzQC schema type InputFile\n\n \"\"\" \n def __init__(self, location: str = \"\", \n name: str = \"\", \n fileFormat: CvParameter = None, \n fileProperties: List[CvParameter] = None):\n self.location = location # required , uri\n self.name = name # required , string (doubles as internal and external ref anchor?)\n self.fileFormat = fileFormat # required , cvParam\n self.fileProperties = [] if fileProperties is None else fileProperties # optional, cvParam, at least one item\n\n@JsonSerialisable.register\nclass MetaDataParameters(jsonobject):\n \"\"\"\n MetaDataParameters Object representation for mzQC schema type MetaDataParameters\n\n \"\"\" \n def __init__(self, \n # fileProvenance: str=\"\", \n # cv_params: List[CvParameter] = None ,\n inputFiles: List[InputFile] = None, \n analysisSoftware: List[AnalysisSoftware]=None \n ):\n # self.fileProvenance = fileProvenance # not in schema\n # self.cv_params = [] if cv_params is None else cv_params # not in schema, IMO should be in there\n self.inputFiles = [] if inputFiles is None else inputFiles # required\n self.analysisSoftware = [] if analysisSoftware is None else analysisSoftware # required\n \n # schema: at least one input_file in input_files\n # schema: at least one analysis_software in analysis_software \n\n@JsonSerialisable.register\nclass QualityMetric(CvParameter):\n \"\"\"\n QualityMetric Object representation is passed for its more concrete derivatives\n\n \"\"\" \n pass\n # def __init__(self, cvRef: str=\"\", \n # accession: str=\"\", \n # name: str=\"\", \n # description: str=\"\", \n # value: Union[int,str,float,IntVector,StringVector,FloatVector,IntMatrix,StringMatrix,FloatMatrix,Table, None]=None, # here we could clamp down on allowed value types\n # unit: str=\"\"):\n # super().__init__(cvRef, accession, name, description, value, unit) # optional, this will set None to optional omitted arguments\n # schema: is cvParam object \n # schema: do we allow no-value metrics? cvParam value attribute is optional\n # implementation: this is a different object class because we want to make semantical distinctions between pure metrics and generic CvParams\n\n@JsonSerialisable.register\nclass BaseQuality(jsonobject):\n \"\"\"\n BaseQuality Object representation for mzQC schema type BaseQuality\n\n \"\"\" \n def __init__(self, metadata: MetaDataParameters=None, \n qualityMetrics: List[QualityMetric]=None):\n self.metadata = metadata # required\n self.qualityMetrics = [] if qualityMetrics is None else qualityMetrics # required,\n # schema: at least one item in quality_metrics\n\n@JsonSerialisable.register\nclass RunQuality(BaseQuality):\n \"\"\"\n QualityMetric Object representation is passed for its more general basis\n\n \"\"\" \n pass\n\n@JsonSerialisable.register\nclass SetQuality(BaseQuality):\n \"\"\"\n SetQuality Object representation is passed for its more general basis\n\n \"\"\" \n pass\n \n@JsonSerialisable.register\nclass MzQcFile(jsonobject):\n \"\"\"\n MzQcFile Object representation for mzQC schema type MzQcFile\n\n \"\"\" \n def __init__(self, creationDate: Union[datetime,str] = datetime.now().replace(microsecond=0), version: str = \"0.0.11\", \n runQualities: List[RunQuality]=None, \n setQualities: List[SetQuality]=None, \n controlledVocabularies: List[ControlledVocabulary]=None \n ):\n self.creationDate = JsonSerialisable.time_helper(creationDate) if isinstance(creationDate, str) else creationDate # not in schema, IMO should be\n self.version = version\n self.runQualities = [] if runQualities is None else runQualities\n self.setQualities = [] if setQualities is None else setQualities\n self.controlledVocabularies = [] if controlledVocabularies is None else controlledVocabularies # required\n # schema: at least one cv in controlled_vocabularies\n # schema: at least one of run_qualities or set_qualities\n # schema: at least one item in run_qualities or set_qualities\n\n\n","sub_path":"mzqc/MZQCFile.py","file_name":"MZQCFile.py","file_ext":"py","file_size_in_byte":16008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"613108123","text":"#\n# (c) 2018 elias/vanissoft\n#\n'''\n\n\n'''\n\nimport pandas as pd\nimport numpy as np\nimport os\nimport json\nimport pickle\nimport arrow\nfrom config import *\n\n\n\n\nclass MarketDataFeeder:\n\t\"\"\"\n\tRead static historic data and calls consumers.\n\tRefreshes with new data.\n\tRaw data for Account filtered movs.\n\tTimeframe data for token/pair filtered.\n\n\tConsumer samples:\n\t\t* BTS/USD, USD/CNY price with 1day timeframe\n\t\t* account trades\n\t\t* last 24h statistics\n\t\"\"\"\n\tRequests_account = {}\n\tRequests_token = {}\n\tRequests_pair = {}\n\tParquet_files = []\n\tFiles_to_process = []\n\tDf_raw_current = None\n\tCurrent_file = None\n\tLast_file = None\n\tLast_file_date = None\n\tCurrent_range = None\n\tDatastores_account = {}\n\tDatastores_token = {}\n\tDatastores_pair = {}\n\tDate_ranges = []\n\tAssets_id = {}\n\tAssets_name = {}\n\n\n\t@classmethod\n\tdef update(cls):\n\t\tpass\n\n\tdef __init__(self):\n\t\tcls = self.__class__\n\t\tif len(cls.Parquet_files) == 0:\n\t\t\tos.chdir('../data')\n\t\t\tfrom glob import glob\n\t\t\tcls.Parquet_files = glob('bts_trades_*.parquet')\n\t\t\tcls.Parquet_files.sort(reverse=True)\n\t\twith open('assets.pickle', 'rb') as h:\n\t\t\ttmp = pickle.load(h)\n\t\t\tcls.Assets_id = {k: v for (k, v) in [(k, v[0]) for (k, v) in tmp.items()]}\n\t\t\tcls.Assets_name = {v: k for (k, v) in [(k, v[0]) for (k, v) in tmp.items()]}\n\n\n\t@classmethod\n\tdef precharge_df(cls):\n\t\tprint(\"precharge\")\n\t\tfor req in cls.Requests_account.items():\n\t\t\tfor acc in req[1]['accounts']:\n\t\t\t\tif cls.Datastores_account[acc]['precharged']:\n\t\t\t\t\tcontinue\n\t\t\t\tdf_file = 'datastores_account_{}.parquet'.format(acc)\n\t\t\t\trtn = Redisdb.hget('datastores_account', df_file)\n\t\t\t\tif rtn is not None:\n\t\t\t\t\tcls.Datastores_account[acc]['files'] = json.loads(rtn.decode('utf8'))['files']\n\t\t\t\t\tprint(\"datastores account files readed\", json.loads(rtn.decode('utf8'))['files'])\n\t\t\t\tif os.path.isfile(df_file):\n\t\t\t\t\tprint(\"read\", df_file)\n\t\t\t\t\tdf = pd.read_parquet(df_file)\n\t\t\t\t\tcls.Datastores_account[acc]['df'] = df\n\t\t\t\t\tcls.Datastores_account[acc]['range'] = [arrow.get(df.block_time.min()), arrow.get(df.block_time.max())]\n\t\t\t\t\tcls.Datastores_account[acc]['precharged'] = True\n\t\tfor req in cls.Requests_pair.items():\n\t\t\tfor pair in req[1]['pairs']:\n\t\t\t\tif cls.Datastores_pair[pair]['precharged']:\n\t\t\t\t\tcontinue\n\t\t\t\tdf_file = 'datastores_pair_{}_{}.parquet'.format(req[1]['name'], pair.replace('/', '_'))\n\t\t\t\trtn = Redisdb.hget('datastores_pair', df_file)\n\t\t\t\tif rtn is not None:\n\t\t\t\t\tcls.Datastores_pair[pair]['files'] = json.loads(rtn.decode('utf8'))['files']\n\t\t\t\t\tprint(\"datastores pair files readed\", json.loads(rtn.decode('utf8'))['files'])\n\t\t\t\tif os.path.isfile(df_file):\n\t\t\t\t\tprint(\"read\", df_file)\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdf = pd.read_parquet(df_file)\n\t\t\t\t\t\tcls.Datastores_pair[pair]['df'] = df\n\t\t\t\t\t\tcls.Datastores_pair[pair]['range'] = [arrow.get(df.index.min()), arrow.get(df.index.max())]\n\t\t\t\t\t\tcls.Datastores_pair[pair]['precharged'] = True\n\t\t\t\t\texcept Exception as err:\n\t\t\t\t\t\tprint('error while reading', df_file)\n\t\tprint('precharge')\n\n\t@classmethod\n\tdef _files_range(cls, range, excl_list=[]):\n\t\t\"\"\"\n\t\tsetup the files to read\n\t\t:return:\n\t\t\"\"\"\n\t\ttp = []\n\t\tfor f in cls.Parquet_files:\n\t\t\tf0 = f.split('.')[0]\n\t\t\tif f0[11:15] < '2018':\n\t\t\t\tf0 = f0.split('.')[0]+\"99\"\n\t\t\tf1 = \"bts_trades_\" + range[0].isoformat().replace('-', '')[:8]\n\t\t\tf2 = \"bts_trades_\" + range[1].isoformat().replace('-', '')[:8]\n\t\t\tif f0 >= f1 and f0 <= f2:\n\t\t\t\tif f not in cls.Files_to_process and f not in excl_list:\n\t\t\t\t\tcls.Files_to_process.append(f)\n\n\t@classmethod\n\tdef step(cls):\n\t\tif len(cls.Files_to_process) > 0:\n\t\t\t# read 1 file\n\t\t\tcls.readfile(cls.Files_to_process.pop(0))\n\t\t\t# and request another call\n\t\t\tRedisdb.rpush('operations_bg', json.dumps({'call': 'marketdatafeeder_step', 'module': 'general'}))\n\t\t\treturn True\n\t\treturn False\n\n\t@classmethod\n\tdef request(cls, data):\n\t\t\"\"\"\n\t\tdata.keys = name, type, callback, daterange, pair=None, token=None\n\t\t:return: pandas.DataFrame\n\t\t\"\"\"\n\t\treqr = data['daterange']\n\t\tif reqr[0] is None:\n\t\t\treqr[0] = arrow.utcnow().shift(years=-10)\n\t\tif reqr[1] is None:\n\t\t\treqr[1] = arrow.utcnow().shift(years=+10)\n\n\t\tif data['type'] == 'account_trades':\n\t\t\tcls.Requests_account[data['name']] = data\n\t\t\tfor acc in data['accounts']:\n\t\t\t\tif acc not in cls.Datastores_account:\n\t\t\t\t\tcls.Datastores_account[acc] = {'df': None, 'files': [], 'range': [None, None], 'df_file': None, 'precharged': False}\n\n\t\telif data['type'] == 'token':\n\t\t\tcls.Requests_token[data['name']] = data\n\n\t\telif data['type'] == 'pair':\n\t\t\tcls.Requests_pair[data['name']] = data\n\t\t\tfor pair in data['pairs']:\n\t\t\t\tif pair not in cls.Datastores_pair:\n\t\t\t\t\tcls.Datastores_pair[pair] = {'df': None, 'files': [], 'range': [None, None], 'df_file': None, 'precharged': False}\n\n\t\tcls.precharge_df()\n\n\t\t# exclusion list\n\t\texcl_list = set()\n\t\tif data['type'] == 'account_trades':\n\t\t\tfor acc in data['accounts']:\n\t\t\t\tfor f in cls.Datastores_account[acc]['files']:\n\t\t\t\t\texcl_list.add(f)\n\n\t\telif data['type'] == 'token':\n\t\t\tpass\n\t\telif data['type'] == 'pair':\n\t\t\tfor pair in data['pairs']:\n\t\t\t\tfor f in cls.Datastores_pair[pair]['files']:\n\t\t\t\t\tif f not in excl_list:\n\t\t\t\t\t\texcl_list.add(f)\n\n\t\tcls._reqlastdata() # refresh more recent data\n\t\tcls._files_range(reqr, excl_list)\n\n\n\t@classmethod\n\tdef _consolidate(cls):\n\t\t\"\"\"\n\t\tFills the Datastores with cls.Df_raw_current data and serve requests.\n\n\t\t:return:\n\t\t\"\"\"\n\t\tfor req in cls.Requests_account.items():\n\t\t\tdf = cls.Df_raw_current\n\t\t\tfor acc in req[1]['accounts']:\n\t\t\t\tif cls.Last_file != cls.Current_file and cls.Current_file in cls.Datastores_account[acc]['files']:\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tcls.Datastores_account[acc]['files'].append(cls.Current_file)\n\t\t\t\tdf2 = df.loc[(df.account_id == acc)]\n\t\t\t\tif len(df2) == 0:\n\t\t\t\t\tcontinue\n\t\t\t\tif cls.Datastores_account[acc]['df'] is not None and len(cls.Datastores_account[acc]) > 0:\n\t\t\t\t\tdf3 = pd.concat([cls.Datastores_account[acc]['df'], df2])\n\t\t\t\t\tdf3 = df3.drop_duplicates()\n\t\t\t\telse:\n\t\t\t\t\tdf3 = df2\n\t\t\t\tcls.Datastores_account[acc]['df'] = df3\n\t\t\t\tcls.Datastores_account[acc]['range'] = [arrow.get(df3.block_time.min()), arrow.get(df3.block_time.max())]\n\t\t\t\tprint(\"len Datastores_account\", len(df3))\n\n\t\tfor req in cls.Requests_pair.items():\n\t\t\tdf = cls.Df_raw_current\n\t\t\tfor pair in req[1]['pairs']:\n\t\t\t\tif cls.Last_file != cls.Current_file and cls.Current_file in cls.Datastores_pair[pair]['files']:\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\tcls.Datastores_pair[pair]['files'].append(cls.Current_file)\n\t\t\t\tpair_id = cls.Assets_name[pair.split('/')[0]]+':'+cls.Assets_name[pair.split('/')[1]]\n\t\t\t\tdf2 = df.loc[(df.pair == pair_id)]\n\t\t\t\ttry:\n\t\t\t\t\tdf2 = df2.loc[(df2.pays_amount > 0.00001) & (df2.receives_amount > 0.00001)]\n\t\t\t\texcept:\n\t\t\t\t\tprint('old version?', cls.Current_file)\n\t\t\t\t\tdf2['pays_amount'] = df2.quote_amount\n\t\t\t\t\tdf2['receives_amount'] = df2.base_amount\n\t\t\t\tdf2['type'] = 'SELL'\n\t\t\t\tdf2['amount_quote'] = df2['pays_amount']\n\t\t\t\tdf2['amount_base'] = df2['receives_amount']\n\n\t\t\t\tpair_id_inv = cls.Assets_name[pair.split('/')[1]] + ':' + cls.Assets_name[pair.split('/')[0]]\n\t\t\t\tdf3 = df.loc[(df.pair == pair_id_inv)]\n\t\t\t\ttry:\n\t\t\t\t\tdf3 = df3.loc[(df3.pays_amount > 0.00001) & (df3.receives_amount > 0.00001)]\n\t\t\t\texcept:\n\t\t\t\t\tprint('old version?', cls.Current_file)\n\t\t\t\t\tdf3['pays_amount'] = df3.quote_amount\n\t\t\t\t\tdf3['receives_amount'] = df3.base_amount\n\t\t\t\tdf3['type'] = 'BUY'\n\t\t\t\tdf3['amount_quote'] = df3['pays_amount']\n\t\t\t\tdf3['amount_base'] = df3['receives_amount']\n\t\t\t\tdf3['price'] = 1/df3['price']\n\n\t\t\t\tdf4 = pd.concat([df2,df3])\n\t\t\t\tdf4 = df4.set_index(['block_time'], drop=False)\n\t\t\t\tdf4['pair_text'] = df4['pair'].apply(lambda x: cls.Assets_id[x.split(':')[0]] + \"/\" + cls.Assets_id[x.split(':')[1]])\n\n\t\t\t\tdf5 = df4.resample('5min', how={'price': 'ohlc', 'amount_base': 'sum', 'amount_quote': 'sum'}).ffill()\n\t\t\t\tdf5.columns = df5.columns.map(''.join)\n\t\t\t\t#df5['block_time'] = df5.index\n\n\t\t\t\tif len(df5) == 0:\n\t\t\t\t\tcontinue\n\t\t\t\tif cls.Datastores_pair[pair]['df'] is not None and len(cls.Datastores_pair[pair]) > 0:\n\t\t\t\t\tdf6 = pd.concat([cls.Datastores_pair[pair]['df'], df5])\n\t\t\t\t\tdf6 = df6.drop_duplicates()\n\t\t\t\telse:\n\t\t\t\t\tdf6 = df5\n\t\t\t\t#df6 = df6.rename(index=str, columns={'amount_baseamount_base': 'amount_base', 'amount_quoteamount_quote': 'amount_quote'})\n\t\t\t\tcls.Datastores_pair[pair]['df'] = df6\n\t\t\t\tcls.Datastores_pair[pair]['range'] = [arrow.get(df6.index.min()), arrow.get(df6.index.max())]\n\t\t\t\tprint(\"len Datastores token\", len(df6))\n\n\n\t\tcls.resprequesters()\n\n\t@classmethod\n\tdef resprequesters(cls):\n\t\tfor req in cls.Requests_account.items():\n\t\t\tfor acc in req[1]['accounts']:\n\t\t\t\tif cls.Datastores_account[acc]['range'][0] is None:\n\t\t\t\t\tdts_range = [arrow.get('20000101'), arrow.get('20000101')]\n\t\t\t\telse:\n\t\t\t\t\tdts_range = cls.Datastores_account[acc]['range']\n\t\t\t\tif (len(cls.Files_to_process) == 0) or \\\n\t\t\t\t\t(len(cls.Parquet_files) == len(cls.Datastores_account[acc])) or \\\n\t\t\t\t\t(dts_range[0] <= req[1]['daterange'][0] and dts_range[1] >= req[1]['daterange'][1]):\n\t\t\t\t\tif cls.Datastores_account[acc]['df'] is not None:\n\t\t\t\t\t\treq[1]['callback'](cls.Datastores_account[acc]['df'])\n\t\t\t\t\t\t# persistence. saving when request is fully served\n\t\t\t\t\t\tdf_file = 'datastores_account_{}.parquet'.format(acc)\n\t\t\t\t\t\tcls.Datastores_account[acc]['df'].to_parquet(df_file, 'fastparquet', 'GZIP')\n\t\t\t\t\t\tRedisdb.hset('datastores_account', df_file, json.dumps({'files': cls.Datastores_account[acc]['files']}))\n\t\tfor req in cls.Requests_pair.items():\n\t\t\tfor pair in req[1]['pairs']:\n\t\t\t\tif cls.Datastores_pair[pair]['range'][0] is None:\n\t\t\t\t\tdts_range = [arrow.get('20000101'), arrow.get('20000101')]\n\t\t\t\telse:\n\t\t\t\t\tdts_range = cls.Datastores_pair[pair]['range']\n\t\t\t\tif (len(cls.Files_to_process) == 0) or \\\n\t\t\t\t\t(len(cls.Parquet_files) == len(cls.Datastores_pair[pair])) or \\\n\t\t\t\t\t(dts_range[0] <= req[1]['daterange'][0] and dts_range[1] >= req[1]['daterange'][1]):\n\t\t\t\t\tif cls.Datastores_pair[pair]['df'] is not None:\n\t\t\t\t\t\tdf = cls.Datastores_pair[pair]['df'].rename(columns={'amount_baseamount_base': 'amount_base',\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t 'amount_quoteamount_quote': 'amount_quote'})\n\t\t\t\t\t\tdf = df.loc[(df.index > req[1]['daterange'][0].isoformat())]\n\t\t\t\t\t\tdf = df.sort_index()\n\t\t\t\t\t\treq[1]['callback'](pair, df)\n\t\t\t\t\t\t# persistence. saving when request is fully served\n\t\t\t\t\t\tdf_file = 'datastores_pair_{}_{}.parquet'.format(req[1]['name'], pair.replace('/','_'))\n\t\t\t\t\t\tcls.Datastores_pair[pair]['df'].to_parquet(df_file, 'fastparquet', 'GZIP')\n\t\t\t\t\t\tRedisdb.hset('datastores_pair', df_file, json.dumps({'files': cls.Datastores_pair[pair]['files']}))\n\n\t@classmethod\n\tdef readfile(cls, file):\n\t\tif os.path.isfile(file):\n\t\t\tprint(\"reading\", file)\n\t\t\tdf = pd.read_parquet(file)\n\t\t\tcls.Current_file = file\n\t\t\tcls.Current_range = [arrow.get(df.block_time.min()), arrow.get(df.block_time.max())]\n\t\t\tcls.Df_raw_current = df\n\t\t\tcls._consolidate()\n\t\telse:\n\t\t\treturn None\n\n\n\t@classmethod\n\tdef _reqlastdata(cls):\n\t\tfnow = arrow.utcnow().isoformat().replace('-', '')[:8]\n\t\tcls.Last_file = 'bts_trades_' + fnow + '.parquet'\n\t\tif cls.Last_file_date is None:\n\t\t\tisnew = True\n\t\telif cls.Last_file_date <= arrow.get(os.stat(cls.Last_file).st_mtime):\n\t\t\tisnew = True\n\t\telse:\n\t\t\tisnew = False\n\t\tif isnew:\n\t\t\tcls.Last_file_date = arrow.get(os.stat(cls.Last_file).st_mtime)\n\t\t\tif cls.Last_file not in cls.Files_to_process:\n\t\t\t\tcls.Files_to_process.insert(0, cls.Last_file)\n\n\n\nclass Account_data:\n\t\"\"\"\n\tTrades done by accounts.\n\tReads historic data harvested by tradehistory.py\n\n\tExample:\n\tTo obtain all trades from accounts:\n\t\td = Account_data(['account1', 'account2', ...])\n\n\t\"\"\"\n\tDataframe = None\n\tAccounts_id = {}\n\tAccounts_name = {}\n\tFile_list = []\n\tMarketDataFeeder = None\n\n\n\t@classmethod\n\tdef data_received(cls, df):\n\t\tprint(\"data received from MDF\")\n\t\tprint(df.block_time.min(), df.block_time.max())\n\t\tif cls.Callback is not None:\n\t\t\tcls.Callback(df)\n\n\n\t@classmethod\n\tdef _save(cls):\n\t\tcls.Dataframe.to_parquet('bts_account_movs.parquet', 'fastparquet', 'GZIP')\n\n\tdef __init__(self, accounts, days=30, MDF=None, callback=None):\n\t\tcls = self.__class__\n\t\tfrom bitshares.account import Account\n\t\tfor acc in accounts:\n\t\t\tid = Account(acc).identifier\n\t\t\tcls.Accounts_id[id] = acc\n\t\t\tcls.Accounts_name[acc] = id\n\t\tcls.Dataframe = None\n\t\tcls.Callback = callback\n\n\t\tdr = [arrow.utcnow().shift(days=days*-1), arrow.utcnow()]\n\t\tMDF.request({'name': 'account', 'type': 'account_trades', 'callback': cls.data_received,\n\t\t\t\t\t\t\t\t\t 'accounts': cls.Accounts_id.keys(), 'daterange': dr})\n\t\tprint(\"request account_trades\", dr)\n\n\n\n\n\nclass Pair_data:\n\t\"\"\"\n\tTrades by pairs.\n\tReads historic data harvested by tradehistory.py\n\n\tExample:\n\tTo obtain all trades from accounts:\n\t\td = Pair_data(['pair1', 'pair2', ...])\n\n\t\"\"\"\n\tDataframe = None\n\tAccounts_id = {}\n\tAccounts_name = {}\n\tFile_list = []\n\tMarketDataFeeder = None\n\n\n\t@classmethod\n\tdef data_received(cls, pair, df):\n\t\tprint(\"data received from MDF\")\n\t\tprint(df.index.min(), df.index.max())\n\t\tif cls.Callback is not None:\n\t\t\tcls.Callback(pair, df)\n\n\n\t@classmethod\n\tdef _save(cls):\n\t\tcls.Dataframe.to_parquet('bts_account_movs.parquet', 'fastparquet', 'GZIP')\n\n\tdef __init__(self, pairs, timeframe='1h', days=30, MDF=None, callback=None):\n\t\tcls = self.__class__\n\t\tcls.Dataframe = None\n\t\tcls.Callback = callback\n\n\t\tdr = [arrow.utcnow().shift(days=days*-1), arrow.utcnow()]\n\t\tMDF.request({'name': timeframe, 'type': 'pair', 'callback': cls.data_received,\n\t\t\t\t\t\t\t\t\t 'pairs': pairs, 'daterange': dr})\n\t\tprint(\"request \", {'name': timeframe, 'type': 'pair', 'callback': cls.data_received,\n\t\t\t\t\t\t\t\t\t 'pairs': pairs, 'daterange': dr})\n\n\n\n\nclass Stats:\n\t#TODO: mechanism for invalidate cache\n\t#TODO: make use of MarketDataFeeder instead of direct read of parquet files\n\tCache = None\n\n\tdef _load_last_data(self):\n\t\tos.chdir('../data')\n\t\tfrom glob import glob\n\t\tfiles = glob('bts_trades_*.parquet')\n\t\tfiles.sort()\n\t\tself.df = pd.read_parquet(files[-1])\n\t\t#self.df = pq.read_table(files[-1], nthreads=4).to_pandas()\n\t\timport pickle\n\t\twith open('assets.pickle', 'rb') as h:\n\t\t\ttmp = pickle.load(h)\n\t\tself.Assets_id = {k: v for (k, v) in [(k, v[0]) for (k, v) in tmp.items()]}\n\t\tself.Assets_name = {v: k for (k, v) in [(k, v[0]) for (k, v) in tmp.items()]}\n\n\tdef __init__(self):\n\t\tcls = self.__class__\n\t\tself.last = None\n\t\tself.df = None\n\t\tself.Assets_id = None\n\t\tself.Assets_name = None\n\t\tself.stats_by_token = None\n\t\tself.stats_by_pair = None\n\t\tself.stats_by_account = None\n\t\tself.stats_by_account_pair = None\n\n\t\tif cls.Cache is not None:\n\t\t\tself.stats_by_token, self.stats_by_pair, self.stats_by_account, self.stats_by_account_pair = cls.Cache\n\t\t\treturn None\n\t\tself._load_last_data()\n\n\t\tdf = self.df\n\t\ttmp = df.block_time.max() - df.block_time.min()\n\t\tself.data_days = ((tmp.components.days*24) + tmp.components.hours)/24\n\t\td1 = df.groupby('pays_asset').agg({'pays_asset': 'count', 'pays_amount': 'sum'})\n\t\td2 = df.groupby('receives_asset').agg({'receives_asset': 'count', 'receives_amount': 'sum'})\n\t\td1['asset'] = d1.index\n\t\td2['asset'] = d2.index\n\t\td3 = pd.concat([d1, d2], axis=1)\n\t\t#TODO: expurious error about categories\n\t\ttry:\n\t\t\td3 = d3.fillna(0)\n\t\texcept Exception as e:\n\t\t\tprint(e)\n\t\td3['ops'] = d3.pays_asset + d3.receives_asset\n\t\td3['volume'] = d3.pays_amount + d3.receives_amount\n\t\td3.sort_values('ops', ascending=False, inplace=True)\n\t\td3['ops_day'] = d3['ops'] / self.data_days\n\t\td3['volume_day'] = d3['volume'] / self.data_days\n\t\td3['asset_name'] = d3.index\n\t\td3.replace({'asset_name': self.Assets_id}, inplace=True)\n\n\t\tself.stats_by_token = d3\n\n\t\td1 = df.groupby('pair').agg({'pays_amount': 'sum', 'receives_amount': 'sum', 'price': 'mean', 'pair': 'count'}).sort_values('pair', ascending=False)\n\t\td1['pair_id'] = d1.index\n\t\td1['pair_text'] = d1['pair_id'].apply(lambda x: self.Assets_id[x.split(':')[0]] + \"/\" + self.Assets_id[x.split(':')[1]])\n\n\t\trtn = Redisdb.get(\"settings_prefs_bases\")\n\t\tif rtn is None:\n\t\t\tprefs = []\n\t\telse:\n\t\t\tprefs = json.loads(rtn.decode('utf8'))\n\t\tpairs = d1.pair_text.tolist()\n\n\t\td1['invert'] = d1.pair_text.apply(lambda x: 1 if (prefs.index(x.split('/')[0]) if x.split('/')[0] in prefs else 999) < (prefs.index(x.split('/')[1]) if x.split('/')[1] in prefs else 998) else 0)\n\n\t\td2 = d1.loc[(d1.invert==1)]\n\t\td2[['pays_amount', 'receives_amount']] = d2[['receives_amount', 'pays_amount']]\n\t\td2[['price']] = 1/d2[['price']]\n\t\td2.pair_id = d2.pair_id.apply(lambda x: x.split(':')[1]+\":\"+x.split(':')[0])\n\t\td2.pair_text = d2.pair_text.apply(lambda x: x.split('/')[1]+\"/\"+x.split('/')[0])\n\t\td2.index = d2.pair_id\n\n\t\td3 = pd.concat([d2, d1.loc[(d1.invert==0)]])\n\n\t\td1 = d3.groupby('pair_id').agg({'pays_amount': 'sum', 'receives_amount': 'sum', 'price': 'mean', 'pair': 'sum'}).sort_values('pair', ascending=False)\n\t\td1['pair_id'] = d1.index\n\t\td1['pair_text'] = d1['pair_id'].apply(lambda x: self.Assets_id[x.split(':')[0]] + \"/\" + self.Assets_id[x.split(':')[1]])\n\t\tself.stats_by_pair = d1\n\n\n\t\tself.stats_by_account = df.groupby('account_id').agg({'pair': 'count'}).sort_values('pair', ascending=False)\n\t\tself.stats_by_account['account_id'] = self.stats_by_account.index\n\n\t\tself.stats_by_account_pair = df.groupby(['account_id', 'pair']).agg({'pair': 'count'}).sort_values('pair', ascending=False)\n\t\tself.stats_by_account_pair['account_id'] = self.stats_by_account_pair.index.get_level_values(0)\n\t\tself.stats_by_account_pair['pair_id'] = self.stats_by_account_pair.index.get_level_values(1)\n\t\tself.stats_by_account_pair['pair_text'] = self.stats_by_account_pair['pair_id'].apply(lambda x: self.Assets_id[x.split(':')[0]] + \"/\" + self.Assets_id[x.split(':')[1]])\n\n\t\tcls.Cache = (self.stats_by_token, self.stats_by_pair, self.stats_by_account, self.stats_by_account_pair)\n\n\n\n\nif __name__ == \"__main__\":\n\timport os\n\ta=MarketDataFeeder()\n\tprint(\"Starting\")\n\tdef froga(data):\n\t\tprint(\"response\")\n\tb=Pair_data(['BTS/CNY'], '1h', 5, a, froga)\n\twhile a.step():\n\t\tpass\n\n\tprint('end')","sub_path":"app/market_data.py","file_name":"market_data.py","file_ext":"py","file_size_in_byte":17701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"621298754","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 25 09:50:35 2019\n\n@author: Macrobull\n\"\"\"\n\nfrom __future__ import division\n\nimport logging, shutil\n\n__all__ = [\n 'convert',\n]\n\nDEFAULT_ONNX_OPSET_VERSION = 9\n\n\ndef make_var_name(name):\n \"\"\"\n make a valid variable name in Python code and filename in filesystem\n \"\"\"\n\n if name == '':\n return ''\n for s in ' \\\\|/:.-':\n name = name.replace(s, '_')\n if name.startswith('_'):\n name = 'var' + name\n elif name[0].isdigit():\n name = 'var_' + name\n return name\n\n\ndef convert(onnx_model_filename,\n save_dir,\n model_basename='model.py',\n model_func_name='inference',\n embed_params=False,\n onnx_opset_version=None,\n onnx_opset_pedantic=True,\n onnx_skip_optimization=False,\n debug=False,\n **kwargs):\n \"\"\"\n convert an ONNX model to Paddle fluid Python code and desc pb\n \"\"\"\n\n assert isinstance(onnx_model_filename, str)\n assert isinstance(save_dir, str)\n assert isinstance(model_basename, str)\n assert isinstance(model_func_name, str)\n assert onnx_opset_version is None or isinstance(onnx_opset_version, int)\n\n import onnx\n\n from onnx.checker import ValidationError\n from onnx.checker import check_model\n from onnx.version_converter import convert_version\n\n from .onnx_utils import DEFAULT_OP_DOMAIN\n from .onnx_utils import graph_ops, graph_weights\n from .onnx_utils import inferred_model_value_info\n from .onnx_utils import polish_model, optimize_model_strip_initializer\n from .writer import Program, Writer\n\n logger = logging.getLogger('onnx2fluid')\n\n # prepare onnx model\n logger.info('loading model: %s ...', onnx_model_filename)\n onnx_model = onnx.load(onnx_model_filename)\n\n try:\n logger.info('checking model ...')\n check_model(onnx_model)\n if onnx_opset_version is None: # WORKAROUND: RuntimeError: No Adapter For OP\n logger.warning(\n 'opset conversion skipped for onnx_opset_pedantic is OFF')\n logger.info('assumed opset version: %d', DEFAULT_ONNX_OPSET_VERSION)\n else:\n logger.info('using opset version: %d', onnx_opset_version)\n onnx_model = convert_version(onnx_model, onnx_opset_version)\n except ValidationError as e:\n if onnx_opset_pedantic:\n raise e\n else:\n logger.warning('due to onnx_opset_pedantic is OFF')\n logger.warning('the ONNX model sanity checking error is suppressed')\n logger.warning('value_info inferring may be uncompleted')\n\n # onnx model optimization\n logger.info('model has %d ops', len(onnx_model.graph.node))\n if onnx_skip_optimization:\n logger.info('stripping model ...')\n onnx_model = optimize_model_strip_initializer(onnx_model)\n else:\n logger.info('optimizing model ...')\n onnx_model = polish_model(onnx_model, checking=onnx_opset_pedantic)\n\n # prepare filesystem\n shutil.rmtree(save_dir, ignore_errors=True)\n shutil.os.makedirs(save_dir, exist_ok=True)\n logger.info('folder %s cleared', save_dir)\n\n # DEBUG:\n if debug:\n debug_model_filename, _ = shutil.os.path.splitext(onnx_model_filename)\n onnx.save(onnx_model, debug_model_filename + '.polished.onnx')\n\n # I/O instances\n onnx_graph = onnx_model.graph\n fluid_program = Program()\n fluid_writer = Writer()\n\n # model components\n inp_vars = [make_var_name(value.name) for value in onnx_graph.input]\n out_vars = [make_var_name(value.name) for value in onnx_graph.output]\n par_vars = []\n value_infos = inferred_model_value_info(onnx_model)\n value_infos = {\n make_var_name(key): value\n for key, value in value_infos.items()\n }\n\n # prepare additional value_info\n # for weights\n for name, weight in graph_weights(onnx_graph):\n var_name = make_var_name(name)\n value_info = value_infos[var_name]\n value_info['lod'] = []\n value_info['embedded_as'] = []\n value_info['get_weight'] = (lambda w: lambda: w.tolist())(\n weight) # lazy getter\n\n logger.info('conversion started')\n # op set conversion\n # topo = 'backward' if embed_params else 'forward'\n topo = 'forward'\n for name, domain, op_type, inputs, outputs, attrs in graph_ops(onnx_graph,\n topo=topo):\n op_name = make_var_name(name)\n inputs = list(map(make_var_name, inputs))\n outputs = list(map(make_var_name, outputs))\n logger.debug('translating op %s(%s) %s::%s ...', name, op_name, domain,\n op_type)\n if domain == DEFAULT_OP_DOMAIN:\n domain = ''\n try:\n fluid_writer.emit_op(\n fluid_program,\n op_name,\n domain,\n op_type,\n inputs,\n outputs,\n attrs,\n value_infos,\n embed_params=embed_params,\n )\n except BaseException as e:\n logger.fatal('conversion failed for:\\n\\t%s -> %s::%s -> %s', inputs,\n domain, op_type, outputs)\n raise e\n op_codes = fluid_program.codes\n fluid_program.codes = []\n logger.info('%d ops in, %d ops out', len(onnx_graph.node),\n len(fluid_program.op_descs))\n\n # type-shape info copy\n for var_name, value_info in value_infos.items():\n fluid_program.VarTypeShapeInfo(var_name, value_info,\n remove_batch=False) #\n bad_vars = []\n for var_name, var_desc in fluid_program.var_descs.items():\n if not var_desc.type.lod_tensor.HasField('tensor'):\n bad_vars.append(var_name)\n if bad_vars:\n logger.warning('type-shape not infered for var %s ...',\n ', '.join(bad_vars[:5]))\n logger.warning('this causes little problem for PaddlePaddle, '\n 'but Paddle Mobile may not infer correctly')\n logger.warning('please consider running validation with -i '\n 'to invoke type-shape inference in PaddlePaddle')\n\n # weight writer\n for name, weight in graph_weights(onnx_graph):\n var_name = make_var_name(name)\n par_vars.append(var_name)\n value_info = value_infos[var_name]\n embedded_names = value_info.get('embedded_as', [])\n if embedded_names:\n if len(embedded_names) > 1:\n logger.info(\n 'weight %s is shared between ops, more disk space will be consumed',\n name)\n logger.debug('saving weight %s(%s[%d], %dB) as %s ...', name,\n weight.dtype, weight.size, weight.nbytes,\n embedded_names)\n for embedded_name in embedded_names: # multiple references\n fluid_writer.write_weight(weight,\n shutil.os.path.join(\n save_dir, embedded_name),\n lod=value_info['lod'])\n else:\n logger.debug('saving weight %s(%s[%d], %dB) to %s ...', name,\n weight.dtype, weight.size, weight.nbytes, var_name)\n fluid_writer.write_weight(weight,\n shutil.os.path.join(save_dir, var_name),\n lod=value_info['lod'])\n fluid_writer.emit_param(fluid_program, var_name, value_info)\n param_codes = fluid_program.codes\n fluid_program.codes = []\n logger.info('%d weights converted', len(par_vars))\n\n # input writer\n external_inputs = []\n for var_name in inp_vars:\n if var_name not in par_vars:\n value_info = value_infos[var_name]\n assert value_info['external']\n external_inputs.append(var_name)\n fluid_writer.emit_inputs(fluid_program,\n external_inputs,\n value_infos,\n remove_batch=False) # TODO:\n input_codes = fluid_program.codes\n fluid_program.codes = []\n logger.info('%d inputs converted', len(external_inputs))\n\n # output writer\n external_outputs = []\n for var_name in out_vars:\n if var_name not in par_vars:\n value_info = value_infos[var_name]\n assert value_info['external']\n external_outputs.append(var_name)\n fluid_writer.emit_outputs(fluid_program, external_outputs)\n output_codes = [''] + fluid_program.codes # add an empty line\n fluid_program.codes = []\n logger.info('%d outputs converted', len(external_outputs))\n\n # code generation\n header_codes = fluid_writer.header_code(\n model_func_name,\n 'From: {}'.format(onnx_model_filename),\n )\n code_filename = shutil.os.path.join(save_dir, model_basename)\n fluid_writer.write_code_file(\n code_filename,\n header_codes,\n input_codes,\n param_codes,\n op_codes,\n output_codes,\n )\n logger.info('code saved to %s, factory function: %s', code_filename,\n model_func_name)\n\n # desc generation\n desc_filename = shutil.os.path.join(save_dir, '__model__')\n fluid_writer.write_desc_file(\n desc_filename,\n op_descs=fluid_program.op_descs,\n var_descs=list(fluid_program.var_descs.values()),\n )\n logger.info('program saved to %s', desc_filename)\n\n logger.info('conversion finished')\n\n\ndef main():\n import argparse\n\n parser = argparse.ArgumentParser(\n description='onnx2fluid.convert',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n )\n parser.add_argument(\n 'model',\n nargs=1,\n help='path to model.onnx',\n )\n parser.add_argument(\n '--debug',\n '-d',\n action='store_true',\n help='enable debug logging and checking',\n )\n parser.add_argument(\n '--output_dir',\n '-o',\n type=str,\n default='',\n help='output directory',\n )\n parser.add_argument(\n '--embed_params',\n '-e',\n action='store_true',\n help='try to embed parameters for trainable Paddle fluid layers',\n )\n parser.add_argument(\n '--pedantic',\n action='store_true',\n default=True,\n help='accept and convert only standard ONNX opset',\n )\n parser.add_argument(\n '--no-pedantic',\n '-x',\n action='store_false',\n dest='pedantic',\n help='process non-standard ONNX ops, this may lead to failures',\n )\n parser.add_argument(\n '--naive',\n '-n',\n action='store_true',\n default=False,\n help='bypass ONNX op optimizations, especially for training purpose',\n )\n parser.add_argument(\n '--skip-version-conversion',\n '-y',\n action='store_true',\n default=False,\n help='skip ONNX op version conversion, workaround for RumtimeErrors',\n )\n args = parser.parse_args()\n\n logging_format = '[%(levelname)8s]%(name)s::%(funcName)s:%(lineno)04d: %(message)s'\n logging_level = logging.DEBUG if args.debug else logging.INFO\n logging.basicConfig(format=logging_format, level=logging_level)\n\n debug = args.debug\n model_filename = args.model[0]\n basepath, _ = shutil.os.path.splitext(model_filename)\n save_dir = args.output_dir\n save_dir = (save_dir.rstrip(shutil.os.sep)\n if save_dir else basepath) + shutil.os.sep\n embed_params = args.embed_params\n pedantic = args.pedantic\n skip_optimization = args.naive\n onnx_opset_version = None if args.skip_version_conversion else DEFAULT_ONNX_OPSET_VERSION\n\n convert(model_filename,\n save_dir,\n embed_params=embed_params,\n onnx_opset_version=onnx_opset_version,\n onnx_opset_pedantic=pedantic,\n onnx_skip_optimization=skip_optimization,\n debug=debug)\n\n\nif __name__ == '__main__':\n del convert\n\n from onnx2fluid.conversion import convert\n\n main()\n","sub_path":"onnx2fluid/onnx2fluid/conversion.py","file_name":"conversion.py","file_ext":"py","file_size_in_byte":12235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"561520044","text":"# -*- encoding: utf-8 -*-\r\r\n#################################################################################\r\r\n# #\r\r\n# Copyright (C) 2009 Renato Lima - Akretion #\r\r\n# Copyright (C) 2012 Raphaël Valyi - Akretion #\r\r\n# #\r\r\n#This program is free software: you can redistribute it and/or modify #\r\r\n#it under the terms of the GNU Affero General Public License as published by #\r\r\n#the Free Software Foundation, either version 3 of the License, or #\r\r\n#(at your option) any later version. #\r\r\n# #\r\r\n#This program is distributed in the hope that it will be useful, #\r\r\n#but WITHOUT ANY WARRANTY; without even the implied warranty of #\r\r\n#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #\r\r\n#GNU Affero General Public License for more details. #\r\r\n# #\r\r\n#You should have received a copy of the GNU Affero General Public License #\r\r\n#along with this program. If not, see . #\r\r\n#################################################################################\r\r\n\r\r\nimport time \r\r\nimport netsvc\r\r\nimport decimal_precision as dp\r\r\nfrom osv import fields, osv\r\r\nimport pooler\r\r\nfrom tools import config\r\r\nfrom tools.translate import _\r\r\n\r\r\n\r\r\nclass sale_shop(osv.osv):\r\r\n _inherit = 'sale.shop'\r\r\n \r\r\n _columns = {\r\r\n 'default_fo_category_id': fields.many2one('l10n_br_account.fiscal.operation.category', 'Categoria Fiscal Padrão'),\r\r\n }\r\r\n\r\r\nsale_shop()\r\r\n\r\r\n\r\r\nclass sale_order(osv.osv):\r\r\n _inherit = 'sale.order'\r\r\n\r\r\n def _get_order(self, cr, uid, ids, context={}):\r\r\n result = super(sale_order, self)._get_order(cr, uid, ids, context)\r\r\n return result.keys()\r\r\n\r\r\n def _invoiced_rate(self, cursor, user, ids, name, arg, context=None):\r\r\n res = {}\r\r\n for sale in self.browse(cursor, user, ids, context=context):\r\r\n if sale.invoiced:\r\r\n res[sale.id] = 100.0\r\r\n continue\r\r\n tot = 0.0\r\r\n for invoice in sale.invoice_ids:\r\r\n if invoice.state not in ('draft', 'cancel') and invoice.fiscal_operation_id.id == sale.fiscal_operation_id.id:\r\r\n tot += invoice.amount_untaxed\r\r\n if tot:\r\r\n res[sale.id] = min(100.0, tot * 100.0 / (sale.amount_untaxed or 1.00))\r\r\n else:\r\r\n res[sale.id] = 0.0\r\r\n return res\r\r\n\r\r\n _columns = {\r\r\n 'fiscal_operation_category_id': fields.many2one('l10n_br_account.fiscal.operation.category', 'Categoria',\r\r\n domain=\"[('type','=','output'),('use_sale','=',True)]\", \r\r\n readonly=True, states={'draft': [('readonly', False)]}),\r\r\n 'fiscal_operation_id': fields.many2one('l10n_br_account.fiscal.operation', 'Operação Fiscal',\r\r\n readonly=True, states={'draft': [('readonly', False)]},\r\r\n domain=\"[('fiscal_operation_category_id','=',fiscal_operation_category_id),('type','=','output'),('use_sale','=',True)]\"),\r\r\n 'fiscal_position': fields.many2one('account.fiscal.position', 'Fiscal Position',readonly=True,\r\r\n states={'draft': [('readonly', False)]}),\r\r\n 'invoiced_rate': fields.function(_invoiced_rate, method=True, string='Invoiced', type='float'),\r\r\n }\r\r\n\r\r\n def onchange_partner_id(self, cr, uid, ids, partner_id=False, partner_invoice_id=False, \r\r\n shop_id=False, fiscal_operation_category_id=False):\r\r\n\r\r\n result = super(sale_order, self).onchange_partner_id(cr, uid, ids, partner_id)\r\r\n\r\r\n if not shop_id or not partner_id:\r\r\n return result\r\r\n \r\r\n obj_shop = self.pool.get('sale.shop').browse(cr, uid, shop_id)\r\r\n company_id = obj_shop.company_id.id\r\r\n\r\r\n if not fiscal_operation_category_id:\r\r\n fiscal_operation_category_id = obj_shop.default_fo_category_id.id\r\r\n result['fiscal_operation_category_id'] = fiscal_operation_category_id\r\r\n\r\r\n partner_invoice_id = result['value'].get('partner_invoice_id', False)\r\r\n obj_fiscal_position_rule = self.pool.get('account.fiscal.position.rule')\r\r\n fiscal_result = obj_fiscal_position_rule.fiscal_position_map(cr, uid, partner_id, partner_invoice_id, \r\r\n company_id, fiscal_operation_category_id, \r\r\n context={'use_domain': ('use_sale','=',True)})\r\r\n \r\r\n result['value'].update(fiscal_result)\r\r\n\r\r\n return result\r\r\n\r\r\n def onchange_partner_invoice_id(self, cr, uid, ids, partner_invoice_id=False, partner_id=False, \r\r\n shop_id=False, fiscal_operation_category_id=False):\r\r\n \r\r\n result = {'value': {}}\r\r\n \r\r\n if not shop_id or not partner_id:\r\r\n return result\r\r\n \r\r\n obj_shop = self.pool.get('sale.shop').browse(cr, uid, shop_id)\r\r\n company_id = obj_shop.company_id.id\r\r\n\r\r\n if not fiscal_operation_category_id:\r\r\n fiscal_operation_category_id = obj_shop.default_fo_category_id.id\r\r\n result['fiscal_operation_category_id'] = fiscal_operation_category_id\r\r\n\r\r\n result = super(sale_order, self).onchange_partner_invoice_id(cr, uid, ids, partner_invoice_id, partner_id, shop_id)\r\r\n obj_fiscal_position_rule = self.pool.get('account.fiscal.position.rule')\r\r\n fiscal_result = obj_fiscal_position_rule.fiscal_position_map(cr, uid, partner_id, partner_invoice_id, company_id, fiscal_operation_category_id, context={'use_domain': ('use_sale', '=', True)})\r\r\n\r\r\n result['value'].update(fiscal_result)\r\r\n\r\r\n return result\r\r\n\r\r\n def onchange_shop_id(self, cr, uid, ids, shop_id=False, partner_id=False, partner_invoice_id=False,\r\r\n fiscal_operation_category_id=False):\r\r\n \r\r\n result = super(sale_order, self).onchange_shop_id(cr, uid, ids, shop_id, partner_id, partner_invoice_id)\r\r\n \r\r\n if not shop_id:\r\r\n return result\r\r\n \r\r\n obj_shop = self.pool.get('sale.shop').browse(cr, uid, shop_id)\r\r\n company_id = obj_shop.company_id.id\r\r\n\r\r\n result['value']['fiscal_operation_category_id'] = fiscal_operation_category_id or (obj_shop.default_fo_category_id and obj_shop.default_fo_category_id.id or False)\r\r\n\r\r\n if not partner_id:\r\r\n return result\r\r\n\r\r\n obj_fiscal_position_rule = self.pool.get('account.fiscal.position.rule')\r\r\n fiscal_result = obj_fiscal_position_rule.fiscal_position_map(cr, uid, partner_id, partner_invoice_id, company_id, fiscal_operation_category_id, context={'use_domain': ('use_sale', '=', True)})\r\r\n \r\r\n result['value'].update(fiscal_result)\r\r\n\r\r\n return result\r\r\n\t\r\r\n def onchange_fiscal_operation_category_id(self, cr, uid, ids, partner_id, partner_invoice_id=False, \r\r\n shop_id=False, fiscal_operation_category_id=False):\r\r\n \r\r\n result = {'value': {'fiscal_operation_id': False, 'fiscal_position': False}}\r\r\n \r\r\n if not shop_id or not partner_id or not fiscal_operation_category_id:\r\r\n return result\r\r\n \r\r\n obj_shop = self.pool.get('sale.shop').browse(cr, uid, shop_id)\r\r\n company_id = obj_shop.company_id.id\r\r\n \r\r\n result['value']['fiscal_operation_category_id'] = fiscal_operation_category_id or (obj_shop.default_fo_category_id and obj_shop.default_fo_category_id.id)\r\r\n\r\r\n obj_fiscal_position_rule = self.pool.get('account.fiscal.position.rule')\r\r\n\r\r\n fiscal_result = obj_fiscal_position_rule.fiscal_position_map(cr, uid, partner_id, partner_invoice_id, \r\r\n company_id, fiscal_operation_category_id, \r\r\n context={'use_domain': ('use_sale','=',True)})\r\r\n \r\r\n result['value'].update(fiscal_result)\r\r\n del result['value']['fiscal_operation_category_id']\r\r\n \r\r\n return result\r\r\n\t\t\r\r\n def _make_invoice(self, cr, uid, order, lines, context=None):\r\r\n journal_obj = self.pool.get('account.journal')\r\r\n inv_obj = self.pool.get('account.invoice')\r\r\n obj_invoice_line = self.pool.get('account.invoice.line')\r\r\n lines_service = []\r\r\n lines_product = []\r\r\n inv_ids = []\r\r\n inv_id_product = False\r\r\n inv_id_service = False\r\r\n \r\r\n if context is None:\r\r\n context = {}\r\r\n\r\r\n obj_company = self.pool.get('res.company').browse(cr, uid, order.company_id.id)\r\r\n fiscal_document_serie_ids = [fdoc for fdoc in obj_company.document_serie_product_ids if fdoc.fiscal_document_id.id == order.fiscal_operation_id.fiscal_document_id.id and fdoc.active]\r\r\n \r\r\n if not fiscal_document_serie_ids:\r\r\n raise osv.except_osv(_('No fiscal document serie found !'),_(\"No fiscal document serie found for selected company %s, fiscal operation: '%s' and fiscal documento %s\") % (order.company_id.name, order.fiscal_operation_id.code, order.fiscal_operation_id.fiscal_document_id.name))\r\r\n\r\r\n journal_ids = [jou for jou in order.fiscal_operation_category_id.journal_ids if jou.company_id.id == obj_company.id]\r\r\n if journal_ids:\r\r\n journal_id = journal_ids[0].id\r\r\n else:\r\r\n raise osv.except_osv(_('Error !'),\r\r\n _('There is no sales journal defined for this company in Fiscal Operation Category: \"%s\" (id:%d)') % (order.company_id.name, order.company_id.id))\r\r\n \r\r\n for inv_line in obj_invoice_line.browse(cr, uid, lines, context=context):\r\r\n if inv_line.product_id.fiscal_type == 'service' or inv_line.product_id.is_on_service_invoice:\r\r\n lines_service.append(inv_line.id)\r\r\n \r\r\n if inv_line.product_id.fiscal_type == 'product': \r\r\n lines_product.append(inv_line.id)\r\r\n \r\r\n if lines_service:\r\r\n inv_id_service = super(sale_order, self)._make_invoice(cr, uid, order, lines_service, context=None)\r\r\n inv_ids.append(inv_id_service)\r\r\n\r\r\n if lines_product:\r\r\n inv_id_product = super(sale_order, self)._make_invoice(cr, uid, order, lines_product, context=None)\r\r\n inv_ids.append(inv_id_product)\r\r\n \r\r\n for inv in inv_obj.browse(cr, uid, inv_ids, context=None):\r\r\n \r\r\n service_type_id = False\r\r\n comment = ''\r\r\n fiscal_type = ''\r\r\n fiscal_operation_category_id = order.fiscal_operation_category_id\r\r\n fiscal_operation_id = order.fiscal_operation_id\r\r\n fiscal_position = order.fiscal_position and order.fiscal_position.id\r\r\n \r\r\n inv_line_ids = map(lambda x: x.id, inv.invoice_line)\r\r\n \r\r\n order_lines = self.pool.get('sale.order.line').search(cr, uid, [('order_id', '=', order.id), ('invoice_lines', 'in', inv_line_ids)], context=context)\r\r\n for order_line in self.pool.get('sale.order.line').browse(cr, uid, order_lines, context=context):\r\r\n inv_line_id = [inv_line for inv_line in order_line.invoice_lines if inv_line.id in inv_line_ids]\r\r\n if inv_line_id:\r\r\n cfop_id = \"\"\r\r\n if order_line.fiscal_operation_id and order_line.product_id:\r\r\n fo_ids_ncm = self.pool.get('l10n_br_account.fiscal.operation.line').search(cr, uid, [('company_id','=',order_line.company_id.id),('fiscal_operation_id','=',order_line.fiscal_operation_id.id),('fiscal_classification_id','=',order_line.product_id.property_fiscal_classification.id)])\r\r\n for fo_line_ncm in self.pool.get('l10n_br_account.fiscal.operation.line').browse(cr, uid, fo_ids_ncm):\r\r\n if fo_line_ncm.tax_code_id.domain == 'icms': \r\r\n cfop_id = fo_line_ncm.cfop_id.id\r\r\n \r\r\n \r\r\n if cfop_id ==\"\": \r\r\n obj_invoice_line.write(cr, uid, inv_line_id[0].id, {'fiscal_operation_category_id': order_line.fiscal_operation_category_id.id or order.fiscal_operation_category_id.id, \r\r\n 'fiscal_operation_id': order_line.fiscal_operation_id.id or order.fiscal_operation_id.id, \r\r\n 'cfop_id': (order_line.fiscal_operation_id and order_line.fiscal_operation_id.cfop_id.id) or (order.fiscal_operation_id and order.fiscal_operation_id.cfop_id.id) or False})\r\r\n else:\r\r\n obj_invoice_line.write(cr, uid, inv_line_id[0].id, {'fiscal_operation_category_id': order_line.fiscal_operation_category_id.id or order.fiscal_operation_category_id.id, \r\r\n 'fiscal_operation_id': order_line.fiscal_operation_id.id or order.fiscal_operation_id.id, \r\r\n 'cfop_id': cfop_id})\r\r\n \r\r\n pItem = self.pool.get('account.invoice.line').browse(cr, uid, inv_line_id[0].id, context=context)\r\r\n \r\r\n res = pItem._Calcula_linha(cr, uid, pItem.id)\r\r\n \r\r\n obj_invoice_line.write(cr, uid, inv_line_id[0].id, res)\r\r\n\r\r\n \r\r\n if order_line.product_id.fiscal_type == 'service' or inv_line.product_id.is_on_service_invoice:\r\r\n fiscal_operation_category_id = order_line.fiscal_operation_category_id or order.fiscal_operation_category_id or False\r\r\n fiscal_operation_id = order_line.fiscal_operation_id or order.fiscal_operation_id or False\r\r\n #Em quanto não tem as posições fiscais na linha coloca falso na nota de serviço\r\r\n fiscal_position = False\r\r\n service_type_id = (order_line.fiscal_operation_id and order_line.fiscal_operation_id.service_type_id.id) or (order.fiscal_operation_id and order.fiscal_operation_id.service_type_id.id) or False\r\r\n fiscal_type = order_line.product_id.fiscal_type\r\r\n \r\r\n if fiscal_operation_id or order.fiscal_operation_id.inv_copy_note:\r\r\n comment = fiscal_operation_id and fiscal_operation_id.note or ''\r\r\n \r\r\n if order.note:\r\r\n comment += ' - ' + order.note\r\r\n \r\r\n inv_l10n_br = {'fiscal_operation_category_id': fiscal_operation_category_id and fiscal_operation_category_id.id, \r\r\n 'fiscal_operation_id': fiscal_operation_id and fiscal_operation_id.id, \r\r\n 'fiscal_document_id': order.fiscal_operation_id.fiscal_document_id.id, \r\r\n 'document_serie_id': fiscal_document_serie_ids[0].id,\r\r\n 'service_type_id': service_type_id,\r\r\n 'fiscal_type': fiscal_type or 'product',\r\r\n 'fiscal_position': fiscal_position,\r\r\n 'comment': comment,\r\r\n 'journal_id': journal_id,\r\r\n }\r\r\n \r\r\n inv_obj.write(cr, uid, inv.id, inv_l10n_br , context=context)\r\r\n inv_obj.button_compute(cr, uid, [inv.id])\r\r\n return inv_id_product or inv_id_service\r\r\n \r\r\n def _prepare_order_picking(self, cr, uid, order, context=None):\r\r\n result = super(sale_order, self)._prepare_order_picking(cr, uid, order, context)\r\r\n result['fiscal_operation_category_id'] = order.fiscal_operation_category_id and order.fiscal_operation_category_id.id\r\r\n result['fiscal_operation_id'] = order.fiscal_operation_id and order.fiscal_operation_id.id\r\r\n result['fiscal_position'] = order.fiscal_position and order.fiscal_position.id\r\r\n return result\r\r\n\r\r\n def _amount_line_tax(self, cr, uid, line, context=None):\r\r\n val = 0.0\r\r\n \r\r\n price = line.price_unit * (1-(line.discount or 0.0)/100.0)\r\r\n \r\r\n \r\r\n \r\r\n for c in self.pool.get('account.tax').compute_all(cr, uid, line.tax_id, price, line.product_uom_qty, line.order_id.partner_invoice_id.id, line.product_id, line.order_id.partner_id, fiscal_operation=line.fiscal_operation_id)['taxes']:\r\r\n tax_brw = self.pool.get('account.tax').browse(cr, uid, c['id'])\r\r\n if not tax_brw.tax_code_id.tax_discount:\r\r\n val += c.get('amount', 0.0)\r\r\n return val\r\r\n \r\r\nsale_order()\r\r\n\r\r\n\r\r\nclass sale_order_line(osv.osv):\r\r\n _inherit = 'sale.order.line'\r\r\n \r\r\n def _amount_line(self, cr, uid, ids, field_name, arg, context=None):\r\r\n tax_obj = self.pool.get('account.tax')\r\r\n cur_obj = self.pool.get('res.currency')\r\r\n res = {}\r\r\n \r\r\n if context is None:\r\r\n context = {}\r\r\n for line in self.browse(cr, uid, ids, context=context):\r\r\n \r\r\n price = line.price_unit * (1 - (line.discount or 0.0) / 100.0) \r\r\n \r\r\n \r\r\n taxes = tax_obj.compute_all(cr, uid, line.tax_id, price, line.product_uom_qty, line.order_id.partner_invoice_id.id, line.product_id, line.order_id.partner_id, fiscal_operation=line.fiscal_operation_id)\r\r\n \r\r\n cur = line.order_id.pricelist_id.currency_id\r\r\n \r\r\n res[line.id] = cur_obj.round(cr, uid, cur, taxes['total'])\r\r\n \r\r\n return res\r\r\n \r\r\n _columns = {\r\r\n 'fiscal_operation_category_id': fields.many2one('l10n_br_account.fiscal.operation.category', 'Categoria',\r\r\n domain=\"[('type','=','output'),('use_sale','=',True)]\", \r\r\n readonly=True, states={'draft':[('readonly',False)]}),\r\r\n 'fiscal_operation_id': fields.many2one('l10n_br_account.fiscal.operation', 'Operação Fiscal', \r\r\n readonly=True, states={'draft':[('readonly',False)]},\r\r\n domain=\"[('fiscal_operation_category_id','=',fiscal_operation_category_id),('type','=','output'),('use_sale','=',True)]\"),\r\r\n 'fiscal_position': fields.many2one('account.fiscal.position', 'Fiscal Position', readonly=True,\r\r\n domain=\"[('fiscal_operation_id','=',fiscal_operation_id)]\", \r\r\n states={'draft':[('readonly',False)]}),\r\r\n 'price_subtotal': fields.function(_amount_line, string='Subtotal', digits_compute= dp.get_precision('Sale Price')),\r\r\n }\r\r\n\r\r\n def product_id_change(self, cr, uid, ids, pricelist, product, qty=0,\r\r\n uom=False, qty_uos=0, uos=False, name='', partner_id=False,\r\r\n lang=False, update_tax=True, date_order=False, packaging=False, \r\r\n fiscal_position=False, flag=False, context=None, \r\r\n fiscal_operation_category_id=False, fiscal_operation_id=False, \r\r\n shop_id=False, parent_fiscal_position=False):\t\r\r\n\t\t\r\r\n\t\t\r\r\n\t\t\r\r\n result = super(sale_order_line, self).product_id_change(cr, uid, ids, pricelist, product, qty,uom, qty_uos, uos, name, partner_id,\r\r\n lang, update_tax, date_order, packaging, \r\r\n fiscal_position, flag, context)\r\r\n\r\r\n \r\r\n if not fiscal_operation_category_id or not fiscal_operation_id or not product:\r\r\n return result\r\r\n\r\r\n default_product_category = self.pool.get('l10n_br_account.product.operation.category').search(cr, uid, [('product_tmpl_id','=', product),('fiscal_operation_category_source_id','=',fiscal_operation_category_id)])\r\r\n\r\r\n if not default_product_category:\r\r\n if fiscal_operation_category_id:\r\r\n result['value']['fiscal_operation_category_id'] = fiscal_operation_category_id\r\r\n \r\r\n if fiscal_operation_id:\r\r\n result['value']['fiscal_operation_id'] = fiscal_operation_id\r\r\n \r\r\n return result\r\r\n\r\r\n obj_default_prod_categ = self.pool.get('l10n_br_account.product.operation.category').browse(cr, uid, default_product_category)[0]\r\r\n result['value']['fiscal_operation_category_id'] = obj_default_prod_categ.fiscal_operation_category_destination_id.id\r\r\n result['value']['fiscal_operation_id'] = False\r\r\n \r\r\n #res.parnter address information\r\r\n obj_partner = self.pool.get('res.partner').browse(cr, uid, partner_id)\r\r\n partner_addr = self.pool.get('res.partner').address_get(cr, uid, [obj_partner.id], ['default'])\r\r\n partner_fiscal_type = obj_partner.partner_fiscal_type_id.id\r\r\n partner_addr_default = self.pool.get('res.partner.address').browse(cr, uid, [partner_addr['default']])[0]\r\r\n to_country = partner_addr_default.country_id.id\r\r\n to_state = partner_addr_default.state_id.id\r\r\n\r\r\n #res.company address information\r\r\n obj_shop = self.pool.get('sale.shop').browse(cr, uid, shop_id)\r\r\n company_addr = self.pool.get('res.partner').address_get(cr, uid, [obj_shop.company_id.partner_id.id], ['default'])\r\r\n company_addr_default = self.pool.get('res.partner.address').browse(cr, uid, [company_addr['default']])[0]\r\r\n from_country = company_addr_default.country_id.id\r\r\n from_state = company_addr_default.state_id.id\r\r\n \r\r\n fsc_pos_id = self.pool.get('account.fiscal.position.rule').search(cr, uid, ['&',('company_id','=', obj_shop.company_id.id), ('fiscal_operation_category_id','=',obj_default_prod_categ.fiscal_operation_category_destination_id.id), ('use_sale','=',True),('fiscal_type', '=', obj_shop.company_id.fiscal_type),\r\r\n '|',('from_country','=',from_country),('from_country','=',False),\r\r\n '|',('to_country','=',to_country),('to_country','=',False),\r\r\n '|',('from_state','=',from_state),('from_state','=',False),\r\r\n '|',('to_state','=',to_state),('to_state','=',False),\r\r\n '|',('partner_fiscal_type_id','=',partner_fiscal_type),('partner_fiscal_type_id','=',False),\r\r\n '|',('to_state','=',to_state),('to_state','=',False),\r\r\n '|',('date_start', '=', False),('date_start', '<=', date_order),\r\r\n '|',('date_end', '=', False),('date_end', '>=', date_order),\r\r\n '|',('revenue_start', '=', False),('revenue_start', '<=', obj_shop.company_id.annual_revenue),\r\r\n '|',('revenue_end', '=', False),('revenue_end', '>=', obj_shop.company_id.annual_revenue),\r\r\n ]) \r\r\n \r\r\n if fsc_pos_id:\r\r\n obj_fpo_rule = self.pool.get('account.fiscal.position.rule').browse(cr, uid, fsc_pos_id)[0]\r\r\n #if fiscal_position != obj_fpo_rule.fiscal_position_id.id:\r\r\n # result['tax_id'] = self.pool.get('account.fiscal.position').map_tax(cr, uid, obj_fpo_rule.fiscal_position_id.id, product_obj.taxes_id)\r\r\n # result['value']['fiscal_position'] = obj_fpo_rule.fiscal_position_id.id\r\r\n result['value']['fiscal_operation_id'] = obj_fpo_rule.fiscal_position_id.fiscal_operation_id.id\r\r\n \r\r\n return result\r\r\n\r\r\n def create_sale_order_line_invoice(self, cr, uid, ids, context=None):\r\r\n result = super(sale_order_line, self).create_sale_order_line_invoice(cr, uid, ids, context)\r\r\n inv_ids = []\r\r\n if result:\r\r\n\r\r\n for so_line in self.browse(cr, uid, ids):\r\r\n for inv_line in so_line.invoice_lines:\r\r\n if inv_line.invoice_id.state in ('draft'):\r\r\n company_id = self.pool.get('res.company').browse(cr, uid, order.company_id.id)\r\r\n if not company_id.document_serie_product_ids:\r\r\n raise osv.except_osv(_('No fiscal document serie found !'),_(\"No fiscal document serie found for selected company %s and fiscal operation: '%s'\") % (order.company_id.name, order.fiscal_operation_id.code))\r\r\n if inv_line.invoice_id.id not in inv_ids: \r\r\n inv_ids.append(inv_line.id)\r\r\n self.pool.get('account.invoice').write(cr, uid, inv_line.invoice_id.id, {'fiscal_operation_category_id': so_line.order_id.fiscal_operation_category_id.id,\r\r\n 'fiscal_operation_id': so_line.order_id.fiscal_operation_id.id, \r\r\n 'fiscal_document_id': so_line.order_id.fiscal_operation_id.fiscal_document_id.id,\r\r\n 'document_serie_id': company_id.document_serie_product_ids[0].id})\r\r\n \r\r\n self.pool.get('account.invoice.line').write(cr, uid, inv_line.id, {'cfop_id': so_line.fiscal_operation_id.cfop_id.id, \r\r\n 'fiscal_operation_category_id': so_line.fiscal_operation_category_id.id, \r\r\n 'fiscal_operation_id': so_line.fiscal_operation_id.id})\r\r\n\r\r\n return result\r\r\n\r\r\nsale_order_line()\r\r\n\r\r\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\r\r\n","sub_path":"l10n_br_sale/sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":27914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"232404422","text":"#creating a function to compute pay\ndef compute_pay(a,b):\n#'a' is the hours worked and 'b' is the rate of pay\n#assuming the standard time for working in a day is 8 hours but then an employee works past that time, then we multipy rate by 1.5\n if a <= 8:\n compt = a * b\n elif a > 8:\n compt = a * (b * 1.5)\n else:\n print('No time worked!!')\n\n return compt\ntry:\n hours=float(input('Please enter hours: '))\n rate=float(input('Please enter rate:'))\nexcept:\n print('Invalid input, Please input numerical values.')\ntry:\n x=compute_pay(hours, rate)\nexcept:\n print('Improper usage of function ')\nprint('The pay is : ', x)\n\n\n","sub_path":"src/chapter4/exercise6.py","file_name":"exercise6.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"488483110","text":"# 给定一棵二叉搜索树，请找出其中的第k小的TreeNode结点。\n\n\nclass Solution:\n # 返回对应节点TreeNode\n\n def KthNode(self, pRoot, k):\n # write code here\n if not pRoot: return None\n stack = []\n while pRoot or stack:\n while pRoot:\n stack.append(pRoot)\n pRoot = pRoot.left\n pRoot = stack.pop()\n k -= 1\n if k == 0:\n return pRoot\n pRoot = pRoot.right\n\n\n# 找出第k大的值\nclass Solution:\n def kthLargest(self, root: TreeNode, k: int) -> int:\n def dfs(root):\n if not root: return\n dfs(root.right)\n if self.k == 0: return\n self.k -= 1\n if self.k == 0: self.res = root.val\n dfs(root.left)\n\n self.k = k\n dfs(root)\n return self.res\n\n\n# 找出第k小的值\nclass Solution:\n\n def KthNode(self, root, k):\n def dfs(root):\n if not root: return\n dfs(root.left)\n if self.k == 0: return\n self.k -= 1\n if self.k == 0: self.res = root.val\n dfs(root.right)\n\n self.k = k\n dfs(root)\n return self.res\n","sub_path":"JZ62. 二叉搜索树的第k个节点.py","file_name":"JZ62. 二叉搜索树的第k个节点.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"379810769","text":"import datetime\nimport subprocess\nchanges = open(\"outchange.txt\", \"r\").read().split(\"\\n\")\nx = 0\noutput = open(\"output/output_\"+str(int(open(\"expcount.txt\", \"r\").read())+1)+\".csv\", \"w\")\nfor line in open(\"outraw.txt\", \"r\"):\n date1 = datetime.datetime.strptime(line.split(\" \")[0]+\" \"+line.split(\" \")[1], '%Y-%m-%d %H:%M:%S.%f')\n if x < changes.__len__()-1:\n date2 = datetime.datetime.strptime(changes[x].split(\" \")[0]+\" \"+changes[x].split(\" \")[1], '%Y-%m-%d %H:%M:%S.%f')\n state = not (changes[x].split(\" \")[2] == \"True\")\n if(date1 > date2):\n x+= 1\n else:\n state = False\n #print(date1, date2, state)\n output.write(line.split(\" \")[2].strip()+\", \"+str(state)+\"\\n\")\nprint(\"Experiment merged: found \"+str(x)+\" state changes\")\nsubprocess.call([\"python\", \"serialize.py\"])","sub_path":"led/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"192324762","text":"# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nimport json\nimport requests\n\nfrom odoo import models, fields\nfrom werkzeug.urls import url_join\n\n\nclass SocialLivePostFacebook(models.Model):\n _inherit = 'social.live.post'\n\n facebook_post_id = fields.Char('Actual Facebook ID of the post')\n\n def _refresh_statistics(self):\n super(SocialLivePostFacebook, self)._refresh_statistics()\n accounts = self.env['social.account'].search([('media_type', '=', 'facebook')])\n\n for account in accounts:\n posts_endpoint_url = url_join(self.env['social.media']._FACEBOOK_ENDPOINT, \"/v3.3/%s/%s\" % (account.facebook_account_id, 'feed'))\n result = requests.get(posts_endpoint_url, {\n 'access_token': account.facebook_access_token,\n 'fields': 'id,shares,insights.metric(post_impressions),likes.limit(1).summary(true),comments.summary(true)'\n })\n\n result_posts = result.json().get('data')\n if not result_posts:\n account.sudo().write({'is_media_disconnected': True})\n return\n\n facebook_post_ids = [post.get('id') for post in result_posts]\n existing_live_posts = self.env['social.live.post'].sudo().search([\n ('facebook_post_id', 'in', facebook_post_ids)\n ])\n\n existing_live_posts_by_facebook_post_id = {\n live_post.facebook_post_id: live_post for live_post in existing_live_posts\n }\n\n for post in result_posts:\n existing_live_post = existing_live_posts_by_facebook_post_id.get(post.get('id'))\n if existing_live_post:\n likes_count = post.get('likes', {}).get('summary', {}).get('total_count', 0)\n shares_count = post.get('shares', {}).get('count', 0)\n comments_count = post.get('comments', {}).get('summary', {}).get('total_count', 0)\n existing_live_post.write({\n 'engagement': likes_count + shares_count + comments_count,\n })\n\n def _post(self):\n facebook_live_posts = self.filtered(lambda post: post.account_id.media_type == 'facebook')\n super(SocialLivePostFacebook, (self - facebook_live_posts))._post()\n\n facebook_live_posts._post_facebook()\n\n def _post_facebook(self):\n for live_post in self:\n account = live_post.account_id\n post_endpoint_url = url_join(self.env['social.media']._FACEBOOK_ENDPOINT, \"/v3.3/%s/feed\" % account.facebook_account_id)\n\n post = live_post.post_id\n\n message_with_shortened_urls = self.env['link.tracker'].sudo()._convert_links_text(post.message, live_post._get_utm_values())\n\n params = {\n 'message': message_with_shortened_urls,\n 'access_token': account.facebook_access_token\n }\n\n if post.image_ids and len(post.image_ids) == 1:\n # if you have only 1 image, you have to use another endpoint with different parameters...\n params['caption'] = params['message']\n photos_endpoint_url = url_join(self.env['social.media']._FACEBOOK_ENDPOINT, '/v3.3/%s/photos' % account.facebook_account_id)\n image = post.image_ids[0]\n result = requests.request('POST', photos_endpoint_url, params=params,\n files={'source': ('source', open(image._full_path(image.store_fname), 'rb'), image.mimetype)})\n else:\n if post.image_ids:\n images_attachments = post._format_images_facebook(account.facebook_account_id, account.facebook_access_token)\n if images_attachments:\n for index, image_attachment in enumerate(images_attachments):\n params.update({'attached_media[' + str(index) + ']': json.dumps(image_attachment)})\n\n link_url = self.env['social.post']._extract_url_from_message(message_with_shortened_urls)\n # can't combine with images\n if link_url and not post.image_ids:\n params.update({'link': link_url})\n\n result = requests.post(post_endpoint_url, params)\n\n if (result.status_code == 200):\n live_post.facebook_post_id = result.json().get('id', False)\n values = {\n 'state': 'posted',\n 'failure_reason': False\n }\n else:\n values = {\n 'state': 'failed',\n 'failure_reason': json.loads(result.text or '{}').get('error', {}).get('message', '')\n }\n\n live_post.write(values)\n","sub_path":"webkul_addons/social_facebook/models/social_live_post.py","file_name":"social_live_post.py","file_ext":"py","file_size_in_byte":4805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"8294952","text":"import os\nimport base64\nfrom pwn import *\n\nr = remote('49.4.26.104', 32428)\n\nr.recvuntil('wait...\\n')\n#print repr(r.recv())\n#print repr(r.recv(1024*1024))\nb64asdsad = r.recvuntil('\\n',True)\nf = open('1.bin', 'wb')\nf.write(b64asdsad)\nf.close()\nos.system('base64 -d 1.bin | gunzip > 1.elf')\n\n\n","sub_path":"writeup/2019/qwb/babyaeg/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"289619992","text":"# coding:utf-8\n# 导入相关文件界面的逻辑功能函数\nimport sys,os\n# import pandas as pd\n\nfrom importFileUI import Ui_importFileDialog\nimport myLogging as mylogger\nfrom PyQt5.QtWidgets import QDialog,QWidget,QFileDialog\nfrom PyQt5.QtCore import Qt\n\n\n# Ui_MainWindow\nclass importFileDialog(QDialog,Ui_importFileDialog):\n def __init__(self,win,workPath,parent=None):\n # mylogger.logger.debug(\"QuestionCheckDialog init\")\n super(importFileDialog,self).__init__(parent)\n mylogger.logger.debug(\"importFileDialog init..\")\n self.workPath = workPath\n self.win = win\n\n self.res_path = {\"A\":\"\",\"B\":\"\",\"住户\":\"\",\"住宅\":\"\",\"小区\":\"\"}\n self.setupUi(self)\n self.connectSlot()\n mylogger.logger.debug(\"importFileDialog init ok\")\n\n # self.checkBox = ''\n\n def connectSlot(self):\n self.A_pushButton.clicked.connect(lambda :self.openFile(\"A\"))\n self.B_pushButton.clicked.connect(lambda :self.openFile(\"B\"))\n self.zhuhu_pushButton.clicked.connect(lambda :self.openFile(\"住户\"))\n self.zhuzhai_pushButton.clicked.connect(lambda :self.openFile(\"住宅\"))\n self.xiaoqu_pushButton.clicked.connect(lambda :self.openFile(\"小区\"))\n self.zy_pushButton.clicked.connect(lambda :self.openFile(\"账页表\"))\n\n def openFile(self,info=\"\"):\n print(\"openFile\")\n # print(self.win)\n # print(self.workPath)\n filePath, filetype = QFileDialog.getOpenFileName(parent=self.win,caption=info,directory=self.workPath,filter=\"All Files (*);;Text Files (*.txt)\") # 设置文件扩展名过滤,注意用双分号间隔\n if filePath.strip() != \"\":\n if info == \"A\":\n self.A_path.setText(filePath)\n self.res_path[info] = filePath\n if info == \"B\":\n self.B_path.setText(filePath)\n self.res_path[info] = filePath\n if info == \"住户\":\n self.zhuhu_path.setText(filePath)\n self.res_path[info] = filePath\n if info == \"住宅\":\n self.zhuzhai_path.setText(filePath)\n self.res_path[info] = filePath\n if info == \"小区\":\n self.xiaoqu_path.setText(filePath)\n self.res_path[info] = filePath\n if info == \"账页表\":\n self.zy_path.setText(filePath)\n self.res_path[info] = filePath\n\n def getPath(self):\n return self.res_path\n\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n qcd = QuestionCheckDialog()\n qcd.show()\n sys.exit(app.exec_())","sub_path":"importFileDialog.py","file_name":"importFileDialog.py","file_ext":"py","file_size_in_byte":2625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"185057309","text":"from swagger_server import db\nfrom swagger_server.custom_controllers.base import (\n BaseController,\n BaseConverter,\n BadRequest,\n NotFound,\n handle_exception,\n)\nfrom swagger_server.db_models import Meal, Recipe, Rating\nfrom swagger_server.models import MealWithId, RatingWithId\n\n\nclass MealConverter(BaseConverter):\n def update_object(self, obj, body):\n obj.recipe_id = body.recipe_id\n obj.photo = body.photo\n obj.comment = body.comment\n obj.when = body.when\n return obj\n\n def object_to_object_with_id(self, meal):\n return MealWithId(\n id=meal.id,\n when=meal.when,\n photo=meal.photo,\n comment=meal.comment,\n recipe_id=meal.recipe_id,\n )\n\n def body_to_object(self, body):\n return Meal(\n when=body.when,\n photo=body.photo,\n comment=body.comment,\n recipe_id=body.recipe_id,\n )\n\n\nclass MealController(BaseController):\n Model = Meal\n Converter = MealConverter\n\n def _on_before_add(self, body):\n recipe = Recipe.query.get(body.recipe_id)\n if recipe is None:\n raise BadRequest()\n\n def _on_before_update(self, id, body):\n meal = Meal.query.get(id)\n if meal is None:\n raise NotFound()\n\n\nclass RatingConverter(BaseConverter):\n def object_to_object_with_id(self, obj):\n return RatingWithId(value=obj.value, comment=obj.comment, id=obj.id)\n\n def body_to_object(self, meal_id, body):\n return Rating(value=body.value, comment=body.comment, meal_id=meal_id)\n\n def update_object(self, obj, body):\n obj.value = body.value\n obj.comment = body.comment\n return obj\n\n\nclass RatingController(BaseController):\n Model = Rating\n Converter = RatingConverter\n\n def _on_before_update(self, id, body):\n rating = Rating.query.get(id)\n if rating is None:\n raise NotFound()\n\n def _on_before_add(self, meal_id):\n meal = Meal.query.get(meal_id)\n if meal is None:\n raise NotFound()\n\n def _on_before_list(self, id):\n meal = Meal.query.get(id)\n if meal is None:\n raise NotFound()\n\n @handle_exception\n def list(self, meal_id):\n self._on_before_list(meal_id)\n ratings = Rating.query.filter_by(meal_id=meal_id)\n return [self.Converter().object_to_object_with_id(rating) for rating in ratings]\n\n @handle_exception\n def add(self, meal_id, body):\n self._on_before_add(meal_id)\n rating = self.Converter().body_to_object(meal_id, body)\n self._add_object(rating)\n return self.Converter().object_to_object_with_id(rating), 201\n","sub_path":"python-flask-server/swagger_server/custom_controllers/meals.py","file_name":"meals.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"383851893","text":"import firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import db\n\n\ncred = credentials.Certificate(\"./calmdnd-firebase-adminsdk-5i177-e2b913ce4f.json\")\n\n# Initialize the app with a service account, granting admin privileges\nfirebase_admin.initialize_app(cred, {\n 'databaseURL': 'https://calmdnd.firebaseio.com'\n})\n\nmonsterList = []\ndef monsterListener(event):\n\n monster = str(event.data)\n\n if monster in monsterList:\n\n monsterList.remove(monster)\n print(monsterList)\n else:\n\n monsterList.append(monster)\n print(monsterList)\n\n file = \"monsters.txt\"\n\n with open(file, mode = \"w\") as outfile:\n for s in monsterList:\n if s != 'None':\n outfile.write(\"%s\\n\" % s)\n\nfirebase_admin.db.reference('/monsters').listen(monsterListener)\n\nattackFromList = []\ndef attackFromListener(event):\n\n attackFrom = str(event.data)\n\n if attackFrom in attackFromList:\n\n attackFromList.remove(attackFrom)\n print(attackFrom)\n else:\n\n attackFromList.append(attackFrom)\n print(attackFromList)\n\n file = \"attackFrom.txt\"\n\n with open(file, mode = \"w\") as outfile:\n for s in attackFromList:\n if s != 'None':\n outfile.write(\"%s\\n\" % s)\n\nfirebase_admin.db.reference('/attack/from').listen(attackFromListener)\n\nattackToList = []\ndef attackToListener(event):\n\n attackTo = str(event.data)\n\n if attackTo in attackToList:\n\n attackToList.remove(attackTo)\n print(attackTo)\n else:\n\n attackToList.append(attackTo)\n print(attackToList)\n\n file = \"attackTo.txt\"\n\n with open(file, mode = \"w\") as outfile:\n for s in attackToList:\n if s != 'None':\n outfile.write(\"%s\\n\" % s)\n\nfirebase_admin.db.reference('/attack/to').listen(attackToListener)\n","sub_path":"fire_info.py","file_name":"fire_info.py","file_ext":"py","file_size_in_byte":1847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"166574153","text":"from flask import (\n Flask, request, Response, jsonify\n)\nfrom db import db_session\nfrom models import Task\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom jsonschema import validate, ValidationError\nimport datetime\nimport json\n\napp = Flask(__name__)\n\n\n@app.teardown_appcontext\ndef shutdown_session(exception=None):\n \"\"\"Close database session after each request\"\"\"\n db_session.remove()\n\n\n@app.route(\"/todolist\")\ndef display_all_tasks():\n \"\"\"Return list of all saved tasks as a JSON\"\"\"\n tasks = db_session.query(Task)\n tasks_json_list = [t.as_dict(include_id=True) for t in tasks]\n return Response(json.dumps(tasks_json_list, default=str),\n status=200,\n mimetype=\"application/json\")\n\n\nadd_task_schema = { # Specification of a correct incoming JSON file\n \"type\": \"object\",\n \"properties\": {\n \"title\": {\"type\": \"string\"},\n \"done\": {\"type\": \"boolean\"},\n \"done_date\": {\"type\": [\"string\", \"null\"]}\n },\n \"required\": [\"title\"]\n}\n\nupdate_task_schema = {k: add_task_schema[k] for k in add_task_schema\n if k != \"required\"} # Update does not require any fields\n\n\ndef validate_json_task(task_json, require_title=True):\n \"\"\"Validate incoming JSON and convert date string to datetime\"\"\"\n if require_title:\n schema = add_task_schema\n else:\n schema = update_task_schema\n\n validate(task_json, schema)\n if not task_json.get(\"done\") and task_json.get(\"done_date\") is not None:\n raise ValidationError(\"Contradictive done and done_date\")\n if task_json.get(\"done_date\"):\n # Check if given date is a correct datetime, if not we will catch\n # ValueError in above function\n task_json[\"done_date\"] = datetime.datetime.strptime(\n task_json[\"done_date\"], '%Y-%m-%d %H:%M:%S')\n return task_json\n\n\n@app.route(\"/todolist\", methods=[\"POST\"])\ndef add_task():\n \"\"\"Convert the input JSON to task object and add it to the database\"\"\"\n try:\n task_json_object = validate_json_task(request.json)\n except (ValueError, ValidationError) as validation_error:\n return Response(\"Incorrect JSON input: \" + str(validation_error), 400)\n\n # Model attribute names are the same as JSON keys,\n # so we can use dict unpacking\n current_task = Task(**task_json_object)\n\n if request.access_route:\n current_task.author_ip = request.access_route[0]\n else:\n current_task.author_ip = request.remote_addr\n\n if not current_task.done:\n current_task.done = False\n elif not current_task.done_date:\n current_task.done_date = datetime.datetime.utcnow()\n\n db_session.add(current_task)\n db_session.commit()\n return jsonify(task_id=current_task.id)\n\n\n@app.route(\"/todolist/\", methods=[\"GET\", \"PATCH\", \"DELETE\"])\ndef task(task_id):\n \"\"\"A task endpoint which supports display, edition and deletion\"\"\"\n try:\n task_query = db_session.query(Task).filter(Task.id == task_id)\n # Make sure that task record won't be changed before we actually update\n if request.method == \"PATCH\":\n current_task = task_query.with_for_update().one()\n else:\n current_task = task_query.one()\n except NoResultFound:\n return Response(\"No such task\", 404)\n\n if request.method == \"GET\":\n # We care about order of keys in the result and compatibility with\n # Python 3.5 requires usage of OrderedDict and a manual JSON response\n return Response(json.dumps(current_task.as_dict(), default=str),\n status=200,\n mimetype=\"application/json\")\n\n elif request.method == \"PATCH\":\n try:\n update_json = validate_json_task(request.json,\n require_title=False)\n except (ValueError, ValidationError) as validation_error:\n return Response(\n \"Incorrect JSON input: \" + str(validation_error), 400)\n\n current_task.title = update_json.get(\"title\", current_task.title)\n\n new_done = update_json.get(\"done\")\n\n if new_done:\n if update_json.get(\"done_date\"):\n current_task.done_date = update_json[\"done_date\"]\n else:\n current_task.done_date = datetime.datetime.utcnow()\n else:\n current_task.done_date = None\n\n current_task.done = new_done\n db_session.commit()\n\n elif request.method == \"DELETE\":\n db_session.delete(current_task)\n db_session.commit()\n\n return Response(status=204)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"119706043","text":"from django.shortcuts import get_object_or_404, render\nfrom django.http import Http404\nfrom .models import Person\n\n# Create your views here.\ndef index(request):\n people = Person.objects.all()\n return render(request, 'people/index.html', {\n 'people': people}\n )\n\n# def person(request, person_id):\n# person = get_object_or_404(Person, pk=person_id)\n# return render(request, 'people/person_detail.html', {'person': person})\n\ndef person_detail(request, slug):\n selection = Person.objects.all()\n person = get_object_or_404(selection, slug = slug)\n return render(request, 'people/person_detail.html', {\n 'person': person\n }\n )","sub_path":"people/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"267636079","text":"import csv\nimport os\nimport sys\n\ndoc_name = 'comparison_algo'\n\nnewpath = r'//Users/eniascailliau/Drive/Master/dump/'+doc_name\nif not os.path.exists(newpath):\n os.makedirs(newpath)\n\nnewpath = r'//Users/eniascailliau/Drive/Master/dump/'+doc_name+\"/html\"\nif not os.path.exists(newpath):\n os.makedirs(newpath)\n\nalgorithm1_data = []\nalgorithm2_data = []\n\nwith open('algorithm_1.csv', 'rt', encoding='UTF8') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for row in spamreader:\n algorithm1_data.append([row[1], row[2]])\n\nwith open('algorithm_2.csv', 'rt', encoding='UTF8') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for row in spamreader:\n algorithm2_data.append([row[1], row[2]])\n\n# for i in range(0, len(algorithm1_data)):\n# print(algorithm1_data[i])\n\n\nimport plotly as py\nimport plotly.graph_objs as go\nfrom plotly.graph_objs import Scatter, Layout\n\nf = lambda x: x[0]\nf2 = lambda x: x[1]\ntrace0 = go.Scatter(\n x=list(map(f, algorithm1_data)),\n y=list(map(f2, algorithm1_data)),\n mode='lines+markers',\n name='Algorithm 1'\n)\ntrace1 = go.Scatter(\n x=list(map(f, algorithm2_data)),\n y=list(map(f2, algorithm2_data)),\n mode='lines+markers',\n name='Algorithm 2'\n)\n\ndata = [trace0, trace1]\n\npy.offline.plot({\"data\": data, \"layout\": Layout(title='comparison_algo' + \"_normalized\"\n , xaxis=dict(title='Duration (s)')\n , yaxis=dict(title='Heartrate (BPM)'))},\n filename=\"/Users/eniascailliau/Drive/Master/dump/\" + 'comparison_algo' + '/html/_normalized.html')","sub_path":"compareHWAlgorithms.py","file_name":"compareHWAlgorithms.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"168000938","text":"# -*- coding: utf-8 -*-\n\ndef shot_amulet():\n\tfor i in range(64):\n\t\tshot = EntityShot(WORLD, \"AMULET\", 0xA00000)\n\t\tshot.Pos = randomvec() * random() * 1024\n\t\tshot.LookAtVec = -normalize(shot.Pos)\n\t\tshot.Upward = randomvec()\n\t\t\n\t\tdef move(s = shot): s.Velocity = normalize(s.Pos) * -1\n\t\tshot.AddTask(move, 0, 1, 210 - WORLD.FrameCount)\n\t\t\n\t\tshot.Spawn()\nWORLD.AddTask(shot_amulet, 0, 150, 0)\n","sub_path":"Th08-東方永夜抄/Last Word/「深弾幕結界　-夢幻泡影-」 Phase10.py","file_name":"「深弾幕結界　-夢幻泡影-」 Phase10.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"61892888","text":"import pandas as pd\nimport numpy as np\nfrom scipy.stats import chi2_contingency\n\ndef Chi2Box(df,variable,flag,bins=10,confidenceVal=3.841,sample=None):\n \"\"\"\n 卡方分箱代码实现\n :param df: dataframe,包含两列，一列是variable，一列是flag\n :param variable: 分箱的变量\n :param flag: 分箱的因变量\n :param bins: 分箱个数上限\n :param confidenceVal: 置信值\n :param sample: 抽样百分比；样本过多是否进行抽样，提高分箱效率\n :return:\n \"\"\"\n if sample is not None:\n df = df.sample(sample)\n else:\n pass\n\n # 对数据进行格式化处理\n total_num = df[flag].groupby(df[variable]).count()\n total_num = pd.DataFrame({'total_num': total_num}) #之所以转化为数据框是为了获取索引，增加为新的一列\n negative_num = df[flag].groupby(df[variable]).sum()\n negative_num = pd.DataFrame({'negative_num':negative_num})\n regroup = pd.merge(total_num,negative_num,left_index=True,right_index=True,how='inner')\n regroup['positive_num'] = regroup['total_num']-regroup['negative_num']\n regroup.reset_index(inplace=True)\n\n #转化为np.array数据形式，好进行数据的处理\n np_regroup = np.array(regroup)\n print('数据已经格式化处理，开始进行数据的预处理！')\n\n # 处理连续没有正样本或负样本的区间，并进行区间的合并（以免卡方值计算报错）\n i = 0\n while(i<=np_regroup.shape[0]-2):\n if((np_regroup[i,2]==0 and np_regroup[i+1,2]==0) or (np_regroup[i,3]==0 and np_regroup[i+1,3]==0)):\n np_regroup[i,2]+=np_regroup[i+1,2]\n np_regroup[i,3]+= np_regroup[i+1,3]\n np_regroup[i,0]=np_regroup[i+1,0]\n np_regroup[i,1] += np_regroup[i+1, 1]\n np_regroup = np.delete(np_regroup,i+1,axis=0)\n i = i-1\n i = i+1\n print(\"数据合并完毕，开始计算卡方值\")\n # 对相邻的两个区间计算卡方值\n chi_table = np.array([]) # 创建一个数组保存相邻两个区间的卡方值\n for i in np.arange(np_regroup.shape[0]-1):\n observed = np.array([[np_regroup[i,2], np_regroup[i,3]], [np_regroup[i+1, 2],np_regroup[i+1, 3]]])\n chi = chi2_contingency(observed)[0]\n chi_table = np.append(chi_table, chi)\n print('已完成数据初处理，正在进行卡方分箱核心操作')\n\n # 把卡方值最小的两个区间进行合并（卡方分箱核心）\n while(1):\n if(len(chi_table) <= bins-1 and min(chi_table)>confidenceVal):\n break\n chi_min_index = np.argwhere(chi_table == min(chi_table))[0] # 找到卡方值最小的索引位置\n np_regroup[chi_min_index, 2] += np_regroup[chi_min_index+1, 2]\n np_regroup[chi_min_index, 3] += np_regroup[chi_min_index+1, 3]\n np_regroup[chi_min_index, 1] += np_regroup[chi_min_index+1, 1]\n np_regroup[chi_min_index, 0] = np_regroup[chi_min_index+1, 0]\n np_regroup = np.delete(np_regroup, chi_min_index+1, axis=0)\n\n # 计算合并后的卡方值\n if chi_min_index == np_regroup.shape[0]-1:\n observed = np.array([[np_regroup[chi_min_index-1, 2], np_regroup[chi_min_index-1, 3]], [np_regroup[chi_min_index, 2], np_regroup[chi_min_index, 3]]])\n chi_table[chi_min_index-1] = chi2_contingency(observed)[0]\n chi_table = np.delete(chi_table,chi_min_index,axis=0)\n elif chi_min_index == 0:\n observed = np.array([[np_regroup[chi_min_index, 2], np_regroup[chi_min_index, 3]], [np_regroup[chi_min_index+1, 2], np_regroup[chi_min_index+1, 3]]])\n chi_table[chi_min_index] = chi2_contingency(observed)[0]\n chi_table = np.delete(chi_table,chi_min_index+1,axis=0)\n else:\n observed1 = np.array([[np_regroup[chi_min_index-1, 2],np_regroup[chi_min_index-1, 3]],[np_regroup[chi_min_index, 2],np_regroup[chi_min_index, 3]]])\n observed2 = np.array([[np_regroup[chi_min_index, 2], np_regroup[chi_min_index, 3]], [np_regroup[chi_min_index+1, 2], np_regroup[chi_min_index+1, 3]]])\n chi_table[chi_min_index-1] = chi2_contingency(observed1)[0]\n chi_table[chi_min_index] = chi2_contingency(observed2)[0]\n chi_table = np.delete(chi_table, chi_min_index+1, axis=0)\n\n print(\"已完成卡方分箱核心操作，正在保存结果\")\n # 保存生成的结果\n result_data = pd.DataFrame()\n list_temp = []\n result_data['variable'] = [variable]*np_regroup.shape[0]\n for i in np.arange(np_regroup.shape[0]):\n if i == 0:\n x = '0'+','+str(np_regroup[i,0])\n else:\n x = str(np_regroup[i-1, 0])+','+str(np_regroup[i, 0])\n list_temp.append(x)\n\n result_data['interval'] = list_temp\n result_data['flag_1'] = np_regroup[:, 2]\n result_data['flag_0'] = np_regroup[:, 3]\n return result_data,chi_table\n\nif __name__ == '__main__':\n\n data = pd.read_csv('./dataProcessed.csv')\n # data = pd.DataFrame({'age':[18,19,21,20,25,33,30,70],'label':[1,0,1,1,0,1,0,1]})\n result_box,chi2 = Chi2Box(df=data,variable='age',flag='label',bins=4)\n print(result_box,chi2)","sub_path":"chi2_bins.py","file_name":"chi2_bins.py","file_ext":"py","file_size_in_byte":5150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"509978759","text":"#!/usr/bin/python3\n# from flask_socketio import SocketIO, send, emit\nfrom flask import Flask, flash, request, jsonify, render_template, redirect, url_for, Response\nfrom flask_bootstrap import Bootstrap\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\nimport uuid\nimport configparser\nfrom queue import Queue\nfrom threading import Thread\nimport threading\n\nfrom database import operationsDB\nfrom operations import task, threadPool, status, result\n\napp = Flask(__name__)\nbootstrap = Bootstrap(app)\ncurrentTasks = {}\n\nlogLevel = {'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'ERROR': logging.ERROR,\n 'WARN': logging.WARNING}\n\nconfig = configparser.ConfigParser()\nconfig.read('../etc/server.conf')\n\nbufferSize = int(config['internal']['bufferSize'])\nlimitThread = int(config['internal']['limitThread'])\nworkerQueue = Queue(bufferSize)\npool = threadPool(limitThread=limitThread ,workerQueue=workerQueue, logger=app.logger)\n\ndef runTask(cmd):\n global currentTasks\n if workerQueue.full():\n return jsonify({'err': 'Server is overloading ...'}), 503\n \n taskId = str(uuid.uuid4().hex)\n dbDir = config['internal']['dbDir']\n worker = Thread(target=task().run,name='task-{0}'.format(threading.current_thread().getName()),\n args=(currentTasks,taskId, cmd, app.logger, 'sqlite:///{0}'.format(dbDir)), daemon=True)\n workerQueue.put(worker)\n\n return jsonify({'id': '{}'.format(taskId)})\n\ndef loadTaskFromDB():\n global currentTasks\n dbDir = config['internal']['dbDir']\n if os.path.isfile(dbDir):\n db = operationsDB(db='sqlite:///{0}'.format(dbDir), logger=app.logger)\n code, items = db.read_task()\n if code:\n for it in items: \n item = {'status': it.status,\n 'result': it.result,\n 'console': it.console,\n 'script': it.script,\n 'timestamp': it.timestamp}\n if it.status == status[0]:\n item['result'] = result[1]\n db.update_task(it.taskid, item)\n currentTasks[it.taskid] = item\n else:\n app.logger.error(\"Can't read tasks from database due to err {0}\".format(items))\n\ndef getTaskFromCache(taskId):\n if taskId in currentTasks:\n return jsonify(currentTasks[taskId])\n else:\n return jsonify(\"{0} not found\".format(taskId)), 404\n\ndef clearTask():\n global currentTasks\n dbDir = config['internal']['dbDir']\n db = operationsDB(db='sqlite:///{0}'.format(dbDir))\n currentTasks.clear()\n err, msg = db.delete_task_table()\n if not err:\n app.logger.error(\">> clearTask: {0}\".format(msg))\n\n return err\n\n\n#####################################################################################################\n##################################--------server--------###################################\n#####################################################################################################\n\n@app.route('/', methods=['GET'])\ndef index():\n if request.method == 'GET':\n return render_template('index.html', tasks=currentTasks)\n\n@app.route('/clear-history', methods=['GET'])\ndef clear_history():\n if request.method == 'GET' and clearTask():\n app.logger.debug(\"History was cleaned!\")\n return jsonify('OK')\n \n return jsonify('NOT OK')\n\n@app.route('/status', methods=['GET'])\ndef status():\n if 'id' in request.headers:\n return getTaskFromCache(request.headers['id'])\n\n return jsonify(currentTasks)\n\n@app.route('/test-commands', methods=['POST'])\ndef test_commands():\n if request.method == 'POST' and'cmd' in request.headers:\n return runTask(request.headers['cmd'])\n\n return jsonify({'err': 'Retry with correct headers and method'}), 403\n\n@app.errorhandler(Exception)\ndef handle_error(e):\n app.logger.error(str(e))\n return jsonify(error=str(e)), 500\n\nimport signal, os\ndef graceful_killer(signal, frame):\n app.logger.info(\"====Stopping server====\")\n os._exit(0)\n\ndef logInit():\n formatter = logging.Formatter(\n \"[%(asctime)s] %(threadName)s %(levelname)s - %(message)s\")\n\n handler = RotatingFileHandler(config['logger']['logAppFile'], maxBytes=10000000, backupCount=5)\n handler.setLevel(logLevel[config['logger']['logAppLevel']])\n handler.setFormatter(formatter)\n\n app.logger.setLevel(logging.DEBUG)\n app.logger.addHandler(handler)\n\n logger = logging.getLogger('werkzeug')\n werkzeugHandler = RotatingFileHandler(config['logger']['logwerkzeugFile'], maxBytes=10000000, backupCount=5)\n logger.setLevel(logLevel[config['logger']['logwerkzeugLevel']])\n logger.addHandler(werkzeugHandler)\n\nif __name__ == '__main__':\n signal.signal(signal.SIGINT, graceful_killer)\n signal.signal(signal.SIGTERM, graceful_killer)\n\n logInit()\n loadTaskFromDB()\n port = config['internal']['port']\n app.logger.info(\"====Starting server at port={0}====\".format(port))\n pool.start()\n app.run(host='0.0.0.0', port=port)\n \n","sub_path":"restFull-server/command-server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"112731592","text":"#!/usr/bin/env python\n\n# Although this module has much in common with the metadata.py module, it is\n# best to separate them, since the ASN.1 module definitions may change\n# indepedently of each other.\n\nfrom __future__ import print_function\n\nfrom pyasn1.type import univ, char, namedtype, namedval, tag, constraint, useful\n\nfrom pyasn1.codec.ber import encoder, decoder\n\nfrom timeservermodule import *\n\nfrom datetime import datetime\nimport calendar\nimport hashlib\nimport json\n\n\ndef ber_to_json_metadata(get_json_signed, ber_metadata):\n asn_metadata = decoder.decode(ber_metadata, asn1Spec=CurrentTime())[0]\n\n asn_signed = asn_metadata['signed']\n ber_signed = get_ber_signed(asn_signed)\n ber_signed_digest = hashlib.sha256(ber_signed).hexdigest()\n\n json_signatures = []\n asn_signatures = asn_metadata['signatures']\n\n for i in range(asn_metadata['numberOfSignatures']):\n asn_signature = asn_signatures[i]\n asn_digest = asn_signature['hash']['digest']['hexString']\n # NOTE: Ensure that hash(BER(Metadata.signed)==Metadata.signatures[i].hash).\n assert asn_digest == ber_signed_digest\n\n # Cheap hack.\n method = int(asn_signature['method'])\n assert method == 1\n method = 'ed25519'\n\n json_signature = {\n # NOTE: Check that signatures are for hash instead of signed.\n 'hash': ber_signed_digest,\n 'keyid': str(asn_signature['keyid']),\n 'method': method,\n 'sig': str(asn_signature['value'])\n }\n json_signatures.append(json_signature)\n\n return {\n 'signatures': json_signatures,\n 'signed': get_json_signed(asn_metadata)\n }\n\n\ndef epoch_to_iso8601(timestamp):\n return datetime.utcfromtimestamp(timestamp).isoformat()+'Z'\n\n\ndef get_asn_and_ber_signed(get_asn_signed, json_signed):\n asn_signed = get_asn_signed(json_signed)\n ber_signed = get_ber_signed(asn_signed)\n return asn_signed, ber_signed\n\n\ndef get_ber_signed(asn_signed):\n return encoder.encode(asn_signed)\n\n\ndef identity_update_json_signature(ber_signed_digest, json_signature):\n # NOTE: Replace this signature with sign(private_key, ber_signed_digest).\n json_signature['sig'] = json_signature['sig']\n\n\ndef iso8601_to_epoch(datestring):\n return calendar.timegm(datetime.strptime(datestring,\n \"%Y-%m-%dT%H:%M:%SZ\").timetuple())\n\n\ndef json_to_ber_metadata(asn_signed, ber_signed, json_signatures):\n metadata = CurrentTime()\n metadata['signed'] = asn_signed\n signedDigest = hashlib.sha256(ber_signed).hexdigest()\n\n asn_signatures = Signatures()\\\n .subtype(implicitTag=tag.Tag(tag.tagClassContext,\n tag.tagFormatSimple, 2))\n numberOfSignatures = 0\n\n for json_signature in json_signatures:\n asn_signature = Signature()\n asn_signature['keyid'] = json_signature['keyid']\n asn_signature['method'] = \\\n int(SignatureMethod(json_signature['method'].encode('ascii')))\n asn_hash = Hash().subtype(implicitTag=tag.Tag(tag.tagClassContext,\n tag.tagFormatConstructed, 2))\n asn_hash['function'] = int(HashFunction('sha256'))\n asn_digest = BinaryData()\\\n .subtype(explicitTag=tag.Tag(tag.tagClassContext,\n tag.tagFormatConstructed, 1))\n asn_digest['hexString'] = signedDigest\n asn_hash['digest'] = asn_digest\n asn_signature['hash'] = asn_hash\n asn_signature['value'] = json_signature['sig']\n asn_signatures[numberOfSignatures] = asn_signature\n numberOfSignatures += 1\n\n metadata['numberOfSignatures'] = numberOfSignatures\n metadata['signatures'] = asn_signatures\n return encoder.encode(metadata)\n\n\ndef pretty_print(json_metadata):\n # http://stackoverflow.com/a/493399\n print(json.dumps(json_metadata, sort_keys=True, indent=1,\n separators=(',', ': ')), end='')\n\n\ndef test(json_filename, ber_filename, get_asn_signed, get_json_signed,\n update_json_signature):\n # 1. Read from JSON.\n with open(json_filename, 'rb') as jsonFile:\n before_json = json.load(jsonFile)\n json_signed = before_json['signed']\n json_signatures = before_json['signatures']\n\n # 2. Write the signed encoding.\n asn_signed, ber_signed = get_asn_and_ber_signed(get_asn_signed, json_signed)\n ber_signed_digest = hashlib.sha256(ber_signed).hexdigest()\n # NOTE: Use ber_signed_digest to *MODIFY* json_signatures.\n for json_signature in json_signatures:\n update_json_signature(ber_signed_digest, json_signature)\n with open (ber_filename, 'wb') as berFile:\n ber_metadata = json_to_ber_metadata(asn_signed, ber_signed, json_signatures)\n berFile.write(ber_metadata)\n\n # 3. Read it back to check the signed hash.\n with open(ber_filename, 'rb') as berFile:\n ber_metadata = berFile.read()\n # NOTE: In after_json, check that signatures match signed_hash.\n after_json = ber_to_json_metadata(get_json_signed, ber_metadata)\n pretty_print(after_json)\n","sub_path":"json2ber2json/timeserver.py","file_name":"timeserver.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"228678462","text":"class Guess:\n\n def __init__(self, word):\n ## 단어를 secretword에, \n self.secretWord = word\n self.guessedChars = []\n self.numTries = 0\n self.currentStatus = '_' * len(self.secretWord)\n\n def display(self):\n print('Current : ' + self.currentStatus)\n print('Tries : ' + str(self.numTries))\n\n\n def guess(self, character):\n ## 문자를 이미 입력했었던 경우\n if character in self.guessedChars:\n pass\n ## 문자를 새로 입력하는 경우\n else:\n ## 문자가 단어에 있는경우\n if self.secretWord.find(character) != -1:\n self.guessedChars.append(character)\n ## 문자가 있는 부분의 인덱스만 따로 초기화\n for i in range(len(self.secretWord)):\n if character == self.secretWord[i]:\n self.currentStatus = self.currentStatus[:i] + character + self.currentStatus[i+1:]\n if self.currentStatus == self.secretWord:\n print('Answer is ' + self.secretWord)\n return True\n ##문자가 단어에 없는 경우\n else:\n self.guessedChars.append(character)\n self.numTries += 1\n\n","sub_path":"Week13/guess.py","file_name":"guess.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"540993572","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 30 21:23:59 2018\n\n@author: Owner\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport math\nfrom dfply import *\n\n###############################################################################\n## Data Prep ##\n###############\n\nsales = pd.read_csv('data/sales.csv')\n\nsales = sales.dropna()\n\nsales.head()\nsales.columns\nsales.describe()\n\n# base price per unit statistic (log transformed)\nsales['log_ppu'] = np.log(sales.Val/sales.Quant)\n\n# groupwise mean ppu statistics\nmean_ppu = np.mean(sales.log_ppu)\nmean_prod_ppu = sales.groupby(['Prod'], as_index=False)['log_ppu'] \\\n .mean() \\\n .rename(index=str, columns={\"log_ppu\": \"mean_prod_ppu\"})\nmean_id_ppu = sales.groupby(['ID'], as_index=False)['log_ppu'] \\\n .mean() \\\n .rename(index=str, columns={\"log_ppu\": \"mean_id_ppu\"})\nmean_prod_id_ppu = sales.groupby(['ID', 'Prod'], as_index=False)['log_ppu'] \\\n .mean() \\\n .rename(index=str, columns={\"log_ppu\": \"mean_prod_id_ppu\"})\n\n# merge groupwise mean ppu statistics with original df\nsales['mean_ppu'] = mean_ppu\nsales = sales.merge(mean_prod_ppu, how = 'inner', on = 'Prod')\nsales = sales.merge(mean_id_ppu, how = 'inner', on = 'ID') \nsales = sales.merge(mean_prod_id_ppu, how = 'inner', on = ['ID', 'Prod'])\n\n# squared differences between mean ppu statistics and original ppu\nsales['sq_dist_ppu'] = (sales.log_ppu - sales.mean_ppu)**2\nsales['sq_dist_prod_ppu'] = (sales.log_ppu - sales.mean_prod_ppu)**2\nsales['sq_dist_id_ppu'] = (sales.log_ppu - sales.mean_id_ppu)**2\nsales['sq_dist_prod_id_ppu'] = (sales.log_ppu - sales.mean_prod_id_ppu)**2\n\nsales = sales[['Insp', 'sq_dist_ppu', 'sq_dist_prod_ppu', \n 'sq_dist_id_ppu', 'sq_dist_prod_id_ppu']]\n\n\n\nsales.to_csv('data/sales_preprocessed.csv')\n","sub_path":"python/sales-classifier.py","file_name":"sales-classifier.py","file_ext":"py","file_size_in_byte":1868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"66395773","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path(\"\",views.index,name=\"ShopHome\"),\n path('about',views.about,name=\"About\"),\n path('news',views.news,name=\"News\"),\n path('contact',views.contact,name=\"Contact\"),\n path('tracker',views.tracker,name=\"Tracker\"),\n path('products/',views.productview,name=\"ProductView\"),\n path('search',views.search,name=\"Search\"),\n path('checkout',views.checkout,name=\"Checkout\"),\n path(\"login\",views.handlelogin,name=\"Login\"),\n path(\"logout\",views.handlelogout,name=\"Logout\"),\n path('signup',views.signup,name=\"SignUp\"),\n path('charge',views.charge,name=\"Charge\"),\n path('payment', views.HomePageView.as_view(), name='home'),\n path('history', views.history, name='History'),\n path('orderdetail/', views.orderdetail, name='OrderDetail'),\n path('rating/', views.rating, name='Rating')\n]","sub_path":"shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"435846648","text":"import sys\nsys.path.append(\"../MachineLearning\")\n\nimport numpy as np\nfrom scipy import signal\n\nclass Signal_sin(object):\n\tdef __init__(self, sigma=0.01, freq=1.0, phase=0.0, **kwargs):\n\t\tself.sigma=sigma\n\t\tself.freq=freq\n\t\tself.phase=phase\n\tdef __call__(self, x):\n\t\treturn np.sin(self.freq*(x - self.phase)) + self.sigma*np.random.randn(*x.shape)\n\t\nclass Signal_double_sin(object):\n\tdef __init__(self, sigma=0.01, freq1=1.0, freq2=1.0, phase1=0.0, phase2=0.0, **kwargs):\n\t\tself.sigma = sigma\n\t\tself.freq1 = freq1\n\t\tself.freq2 = freq2\n\t\tself.phase1 = phase1\n\t\tself.phase2 = phase2\n\tdef __call__(self, x):\n\t\treturn np.sin(self.freq1*(x - self.phase1))*np.sin(self.freq2*(x - self.phase2)) + self.sigma*np.random.randn(*x.shape)\n\t\nclass Signal_square(object):\n\tdef __init__(self, duty=0.5, sigma_low=0.01, sigma_high=0.01, freq=1.0, phase=0.0, **kwargs):\n\t\tself.duty = duty\n\t\tself.sigma_low = sigma_low\n\t\tself.sigma_high = sigma_high\n\t\tself.freq = freq\n\t\tself.phase = phase\n\tdef __call__(self, x, duty=None):\n\t\tif duty is not None:\n\t\t\tself.duty = duty\n\t\ts = signal.square(self.freq*(x - self.phase), self.duty)\n\t\ts2 = s + (s+1)*self.sigma_high/2*np.random.randn(*x.shape) + (1-s)*self.sigma_low/2*np.random.randn(*x.shape)\n\t\treturn s2\n\t\nclass Signal_sawtooth(object):\n\tdef __init__(self, sigma=0.01, freq=1.0, phase=0.0, **kwargs):\n\t\tself.sigma = sigma\n\t\tself.freq = freq\n\t\tself.phase = phase\n\tdef __call__(self, x):\n\t\treturn signal.sawtooth(self.freq*(x - self.phase)) + self.sigma*np.random.randn(*x.shape)\n\t\t\n\t\ndefaultSignal = Signal_square()\n\ndef pfg_batch_generator(batch_size, look_back, P, small, reset_rate, sum_losses, modes=[[0,1]], signalFunctions=defaultSignal):\n\t\"\"\"\n\tparam:\n\t\tmodes, list of two-lists, given point in period and corresponding value. Default [[0,1]] yields a 1 at point 0 in the period (0,2pi)\n\t\t\tadditional modes can be given: modes=[[0,1],[pi,-1]] would output a 1 at start and -1 at middle of period.\n\t\"\"\"\n\tif not type(signalFunctions) == list:\n\t\tsignalFunctions = [signalFunctions]\n\tfirst_cycle = True\n\tperiod = np.pi*2 + small*P\n\t\n\tstart = np.linspace(0, 2*np.pi, batch_size, endpoint=False)\n\t\n\tt = 0.\n\twhile True:\n\t\tt_ = np.array([[start[b] + t - (look_back - l - 1)*(period/P) for l in range(look_back)] for b in range(batch_size)]) # [bs, lb]\n\t\t\n\t\ty_all = np.array([func_(t_) for func_ in signalFunctions]) # [features, bs, lb]\n\t\ty_all = np.transpose(y_all, [1,2,0]) # [bs, lb, features]\n\t\ty_ = y_all[:,-sum_losses:] # [bs, sl, features]\n\t\t\n\t\tx_ = np.zeros_like(t_, dtype=np.float32)\n\t\tfor tp, xp in modes:\n\t\t\tx_ += np.float32(np.minimum(np.absolute(t_%period - tp), np.absolute(t_%period - tp - period)) < period/2./P)*xp\n\t\tx_ = np.reshape(x_, (batch_size, look_back, 1))\n\t\t\n\t\tfc = [first_cycle for _ in range(batch_size)]\n\t\tif np.random.rand() < reset_rate:\n\t\t\tfor b in range(batch_size):\n\t\t\t\tif np.minimum(np.absolute(t_[b,0]%period - 0), np.absolute(t_[b,0]%period - 0 - period)) < period/2./P:\n\t\t\t\t\tfc[b] = True\n\t\t\t\t\tbreak\n\t\t\n\t\tyield x_, y_, fc # batch_size, sum_losses, features\n\t\tt += period/P\n\t\t\n\t\t# t = t % 2*np.pi\n\t\tif t > 1.e12:\n\t\t\tbreak\n\t\tfirst_cycle = False","sub_path":"signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":3104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"19329746","text":"import string, json, datetime\nfrom Twitter.Base import Twitter\nfrom Twitter.Candidate import HRC, DJT, TweetAuthor\nfrom Twitter.Analysis.Sentimental import *\n\nimport nltk\n\nnltk.data.path.append('/Volumes/Data/nltk_data')\n\ndef word_freq_analysis():\n connection = Twitter()\n hrc = HRC()\n djt = DJT()\n\n data = {\n 'accounts': {},\n 'last_update': datetime.datetime.utcnow().isoformat()\n }\n\n for candidate in [hrc, djt]:\n candidate.save_new_tweets(connection)\n data['accounts'][candidate.handle] = candidate.analysis()\n\n with open('data/data.json', 'w') as outfile:\n json.dump(data, outfile)\n\n\ndef sentimental_analysis():\n analysis = SentimentalAnalysis()\n analysis.predict()\n\n# word_freq_analysis()\nsentimental_analysis()\n\n\n\n\n","sub_path":"PresElec2016.py","file_name":"PresElec2016.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"277930062","text":"import flask \nfrom keras.models import load_model\nfrom nltk.corpus import stopwords\nimport nltk\nimport re\n\n#loading model\nmodel = load_model('final_lstm.h5')\n\n#f1\ndef wordlist(essay, remove_stopwords):\n \n essay = re.sub(\"[^a-zA-Z]\", \" \", essay)\n words = essay.lower().split()\n if remove_stopwords:\n stops = set(stopwords.words(\"english\"))\n words = [w for w in words if not w in stops]\n return (words)\n\n#f2\ndef Make_sentences(essay, remove_stopwords):\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n raw_sentences = tokenizer.tokenize(essay.strip())\n sentences = []\n for raw_sentence in raw_sentences:\n if len(raw_sentence) > 0:\n sentences.append(wordlist(raw_sentence, remove_stopwords))\n return sentences\n\n#f3\ndef makeFeatureVec(words, model, num_features):\n \n featureVec = np.zeros((num_features),dtype=\"float32\")\n num_words = 0.\n index2word_set = set(model.wv.index_to_key)\n for word in words:\n if word in index2word_set:\n num_words += 1\n featureVec = np.add(featureVec,model.wv[word]) \n featureVec = np.divide(featureVec,num_words)\n return featureVec\n\n#f4\ndef getAvgFeatureVecs(essays, model, num_features):\n\n counter = 0\n essayFeatureVecs = np.zeros((len(essays),num_features),dtype=\"float32\")\n for essay in essays:\n essayFeatureVecs[counter] = makeFeatureVec(essay, model, num_features)\n counter = counter + 1\n return essayFeatureVecs\n\n#cleaning the data\ndef preProcess(text): \n a = Make_sentences(text, remove_stopwords=True)\n\n b = getAvgFeatureVecs(a, )\n\n return b\n\n#defining app\napp = flask.Flask(__name__, template_folder = 'templates')\n\n\n#main route\n@app.route('/', methods=['GET', 'POST'])\ndef main():\n if flask.request.method == 'GET':\n return(flask.render_template('main.html'))\n\n if flask.request.method == 'POST':\n\n essay = flask.request.form['e']\n\n essay_pro = preProcess(essay)\n\n pred = model.predict(essay_pro)\n\n return flask.render_template('main.html', result = pred)\n\n\n","sub_path":"try/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"481033869","text":"import torch\n\nfrom .. import util\n\n\nclass Distribution():\n def __init__(self, name, address_suffix='', batch_shape=torch.Size(), event_shape=torch.Size(), torch_dist=None):\n self.name = name\n self._address_suffix = address_suffix\n self._batch_shape = batch_shape\n self._event_shape = event_shape\n self._torch_dist = torch_dist\n\n @property\n def batch_shape(self):\n if self._torch_dist is not None:\n return self._torch_dist.batch_shape\n else:\n return self._batch_shape\n\n @property\n def event_shape(self):\n if self._torch_dist is not None:\n return self._torch_dist.event_shape\n else:\n return self._event_shape\n\n def sample(self):\n if self._torch_dist is not None:\n s = self._torch_dist.sample()\n return s\n else:\n raise NotImplementedError()\n\n def log_prob(self, value, sum=False):\n if self._torch_dist is not None:\n lp = self._torch_dist.log_prob(util.to_tensor(value))\n return torch.sum(lp) if sum else lp\n else:\n raise NotImplementedError()\n\n def prob(self, value):\n return torch.exp(self.log_prob(util.to_tensor(value)))\n\n @property\n def mean(self):\n if self._torch_dist is not None:\n return self._torch_dist.mean\n else:\n raise NotImplementedError()\n\n @property\n def variance(self):\n if self._torch_dist is not None:\n return self._torch_dist.variance\n else:\n raise NotImplementedError()\n\n @property\n def stddev(self):\n return self.variance.sqrt()\n\n def expectation(self, func):\n raise NotImplementedError()\n\n @staticmethod\n def kl_divergence(distribution_1, distribution_2):\n if distribution_1._torch_dist is None or distribution_2._torch_dist is None:\n raise ValueError('KL divergence is not currently supported for this pair of distributions.')\n return torch.distributions.kl.kl_divergence(distribution_1._torch_dist, distribution_2._torch_dist)\n","sub_path":"pyprob/distributions/distribution.py","file_name":"distribution.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"526787676","text":"import re\nfrom zhon.hanzi import punctuation as zhonpunctuation\nfrom string import punctuation as enpunctuation\n\n\n\ndef get_chn(s,start,end):\n punctuation=zhonpunctuation+enpunctuation\n print(punctuation)\n segments_reg = \"({}.*?{})\".format(start,end)\n chn_reg = '[\\u4e00-\\u9fa5]'\n result=[]\n for line in re.split('\\n',s):\n if re.search('[{}]'.format(''.join(punctuation)), line):\n continue\n segments = re.findall(segments_reg, line,)\n if segments:\n for segment in segments:\n match=re.findall(chn_reg,segment)\n result.append(''.join(match))\n return result\n\n\ns='''一二三五六七一二四一二三一二三五六七四\n八九十四。一二三五六七四。一二三\n别闹了\n一二三,s四\n一二三三四'''\n\nget_chn(s,start='一二三',end='四')\n\n","sub_path":"job/匹配中文的正则.py","file_name":"匹配中文的正则.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"352260791","text":"import os\nimport shutil\n#import xml.etree.ElementTree as ET\nfrom configuration.Constants import Constants\n\n\"\"\"\n utility class\n\"\"\"\nclass Utilities:\n\n @staticmethod\n def mkdir(path):\n \"\"\"\n utility for creating a directory\n :param path:\n :return:\n \"\"\"\n if not os.path.exists(path):\n os.mkdir(path)\n #print(\"making directory : \" + path)\n else:\n print(path+ \" already exists\")\n\n @staticmethod\n def cpy(srcpath,destpath):\n \"\"\"\n utility for copying a file\n :param srcpath:\n :param destpath:\n :return:\n \"\"\"\n if not os.path.exists(destpath):\n shutil.copy(srcpath,destpath)\n # print(\"copying file from : \" + srcpath + \" to : \" + destpath)\n else:\n print(destpath+ \" already exists\")\n\n @staticmethod\n def makejavanames(dbname):\n \"\"\"\n utility to make a Java name from a database name\n :param dbname:\n :return:\n \"\"\"\n javaname = ''\n gettername = ''\n if (dbname.find(\"_\") > -1):\n index = 0\n convertedjavaname = ''\n toUpper = False\n x = range(len(dbname))\n for n in x:\n if (toUpper == True):\n if(dbname[n].isnumeric == False):\n convertedjavaname += dbname[n].upper()\n else:\n convertedjavaname += dbname[n]\n toUpper = False\n elif (dbname[n] == \"_\"):\n toUpper = True\n else:\n convertedjavaname += dbname[n]\n gettername = Utilities.capitalize(convertedjavaname)\n javaname = convertedjavaname\n else:\n gettername = Utilities.capitalize(dbname)\n javaname = dbname\n print(\"making javaname = \" + javaname + \" getter name = \" + gettername + \" from db name = \" + dbname)\n return (javaname, gettername)\n\n @staticmethod\n def capitalize(word):\n \"\"\"\n small function to capitalize the first letter of the javaname\n :param word:\n :return:\n \"\"\"\n letter = word[0].upper()\n restofword = word[1:len(word)]\n totalword = letter + restofword\n return totalword\n\n @staticmethod\n def handleFieldsWithCommas(inputstring):\n \"\"\"\n the input file tha we get may include records that have fields with commas in them\n in this case, the field will be surrounded by double quotes\n this method will check the record for double quotes, and then it will parse through it\n according to double quotes, and, when it finds a pair, it will replace any commas with @#$\n finally, it will recombine the string, split it by comma again, and then replace any items in the\n array that have @#$ in them with a comma\n :param inputstring:\n :return: array of strings\n \"\"\"\n if inputstring.count('\"') == 0:\n return inputstring.split(',')\n elif inputstring.count('\"') % 2 > 0:\n raise Exception(\"# of double quote characters in this file is an odd #: therefore the file is invalid!\")\n else:\n leftstring = ''\n rightstring = ''\n middlestring = inputstring\n while middlestring.find('\"') > -1:\n leftstring += middlestring[0:middlestring.find(\n '\"') + 1] # leftstring equals everything up to and including the odd quote\n middlestring = middlestring[\n middlestring.find('\"') + 1:] # middle string now equals everything after the odd quote\n if middlestring.find('\"') < len(middlestring) - 1: # if the even quote is not at the end of the string\n rightstring = middlestring[middlestring.find(\n '\"') + 1:] # the right string equals everything that is after the even quote\n else:\n rightstring = ''\n middlestring = middlestring[:middlestring.find('\"') + 1].replace(\",\",\n \"@#$\") # now make the middle string everything before and including\n # the even quote, and replace all commas with @#$\n leftstring += middlestring # now add the middlestring to the leftstring\n middlestring = rightstring # and make the right string the new middle string\n leftstring += middlestring\n # print(leftstring)\n temp_array = leftstring.split(',')\n for i in range(0, len(temp_array)):\n item = str(temp_array[i])\n if item.find(\"@#$\") > -1:\n item = item.replace(\"@#$\", \",\").replace('\"', '')\n temp_array[i] = item\n return temp_array\n\n @staticmethod\n def handleFieldsWithCommasAndParens(inputstring):\n \"\"\"\n the input file that we get may include records that have fields with commas in them\n in this case, the field will be surrounded by double quotes\n this method will check the record for double quotes, and then it will parse through it\n according to double quotes, and, when it finds a pair, it will replace any commas with @#$\n finally, it will recombine the string, split it by comma again, and then replace any items in the\n array that have @#$ in them with a comma\n :param inputstring:\n :return: array of strings\n\n quotesfound = False\n parenfound = False\n strlen = len(inputstring)\n outputstr = ''\n counter = 0\n leftstring = ''\n rightstring = ''\n middlestring = inputstring\n while counter < strlen:\n character = inputstring[counter]\n if character == '\"':\n outputstr += character\n quotesfound = not quotesfound\n elif character == \"(\":\n outputstr += character\n parenfound = True\n elif character == \")\":\n outputstr += character\n parenfound = False\n elif character == \",\":\n if (quotesfound == True or parenfound == True):\n outputstr += \"@#$\"\n else:\n outputstr += character\n elif character == \" \":\n if quotesfound == True:\n outputstr += \"^&%\"\n else:\n outputstr += character\n else:\n outputstr += character\n counter +=1\n \"\"\"\n temp_array = inputstring.split(\"decimal\")\n if len(temp_array)>1:\n for i in range(0, len(temp_array)):\n if i>0:\n item = str(temp_array[i])\n if item.find(\",\") > -1:\n item = item.replace(\",\", \"ZYX\",1)\n temp_array[i] = item\n inputstring = \"decimal\".join(temp_array)\n temp_array = inputstring.split(',')\n for i in range(0, len(temp_array)):\n item = str(temp_array[i])\n if item.find(\"@#$\") > -1:\n item = item.replace(\"@#$\", \",\").replace('\"', '')\n temp_array[i] = item\n return temp_array\n\n @staticmethod\n def parseGroupingsTextFile(filename):\n inputfile = open(filename)\n projectnames = []\n projecttables = {}\n for line in inputfile:\n linestr = str(line)\n if linestr.find(\"#\")==-1:\n projectname = linestr.split(\":\")[0].strip()\n tablesstr = linestr.split(\":\")[1].strip()\n tables = tablesstr.split(\",\")\n projectnames.append(projectname)\n projecttables[projectname] = tables\n return (projectnames,projecttables)\n\n @staticmethod\n def remove_datatypes_from_string( inputstring):\n \"\"\"\n this method is for the mid-level business classes; it will remove the data types from an input parameter string\n :param inputstring:\n :return:\n \"\"\"\n tempstring = inputstring[inputstring.find(\"public ResponseEntity\") + 30:]\n methodname = tempstring[0:tempstring.find(\"(\")]\n parameterlist = tempstring[tempstring.find(\"(\"):tempstring.find(\")\")]\n paramsstring = ''\n if(len(parameterlist)==0):\n return methodname+\"()\"\n if(parameterlist.find(\",\") > -1):\n parameterpairs = parameterlist.split(\",\")\n for pair in parameterpairs:\n items = pair.split(\" \")\n oddfield = True\n for item in items:\n if(len(item)>0):\n if(item.find(\"@\")>-1):\n None\n elif(oddfield == False):\n paramsstring += item + \",\"\n oddfield = True\n else:\n oddfield = False\n return methodname+\"(\"+paramsstring[:-1]+\")\"\n else:\n items = parameterlist.split(\" \")\n oddfield = True\n for item in items:\n if (len(item) > 0):\n if (item.find(\"@\") > -1):\n None\n elif (oddfield == False):\n paramsstring += item + \",\"\n oddfield = True\n else:\n oddfield = False\n return methodname + \"(\" + paramsstring[:-1] + \")\"\n\n @staticmethod\n def remove_annotations_from_string( inputstring):\n \"\"\"\n this method is for the mid-level proxies, it will remove annotations from the method declarations\n :param inputstring:\n :return:\n \"\"\"\n stringarray = inputstring.split(\"(\")\n outputstring = stringarray[0]+'('\n remaining = stringarray[1].split(\" \")\n for word in remaining:\n wordstr = str(word)\n if wordstr.find('@') == -1:\n outputstring += wordstr + \" \"\n return outputstring.rstrip()\n\n @staticmethod\n def translateDataType(type):\n \"\"\"\n this method needs to translate Boxed primitives to primitives\n :param type:\n :return:\n \"\"\"\n if type == \"BigDecimal\" or type == \"Float\" or type == \"Double\":\n return \"double\"\n elif type == \"BigInteger\" or type == \"Integer\":\n return \"int\"\n elif type == \"String\":\n return \"String\"\n elif type == \"Boolean\":\n return \"boolean\"\n else:\n return \"None\"\n\n @staticmethod\n def translateAngularDataType(type):\n \"\"\"\n this method needs to translate Boxed primitives to javascript data types\n :param type:\n :return:\n \"\"\"\n if type == \"BigDecimal\" or type == \"Float\" or type == \"Double\":\n return \"number\"\n elif type == \"BigInteger\" or type == \"Integer\":\n return \"number\"\n elif type == \"String\":\n return \"string\"\n elif type == \"Boolean\":\n return \"boolean\"\n else:\n return \"None\"\n\n @staticmethod\n def create_get_pk_stmt(table, test=False):\n \"\"\"\n finds the primary key and adds it into the script\n :param table:\n :param file:\n :return:\n \"\"\"\n tabs = Constants.tab\n # FOR EACH FIELD:\n for field in table.fieldnames:\n fielddata = table.fielddata[field]\n if fielddata.isprimary == True:\n if test == True:\n return fielddata.gettername\n else:\n return \"get\" + fielddata.gettername+\"()\"","sub_path":"utilities/Utilities.py","file_name":"Utilities.py","file_ext":"py","file_size_in_byte":11859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"327381546","text":"import sys\n\n# Library to save LDA model\nimport joblib\n\n# Library to read files format\nimport scanpy as sc\n\n# Library to build pyLDAvis graph\nimport pyLDAvis\nfrom pyLDAvis import sklearn as sklearn_lda\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n# Path to database\npath_to_database = sys.argv[1]\n\n# Path to lda model\npath_to_lda_model = sys.argv[2]\n\n# Path to data, on which lda model was learnt\npath_to_data = sys.argv[3]\n\n# Path to data, on which lda model was learnt\npath_to_output_data = sys.argv[4]\n\nfile_name = sys.argv[5]\n\n# Numer of genes, to get from each model topic\nnumber_of_genes_in_topics = 200\n\n# Opening database\ndata_base = open(path_to_database, \"r\")\n\n# Reading database and parsing it into dictionary\nlines_in_database = data_base.readline()\nlines_in_database = data_base.readline()\nbase_dict = {}\ndata = []\nwhile (lines_in_database):\n data.append(lines_in_database.split('\\t'))\n lines_in_database = data_base.readline()\n\nfor i in range(len(data)):\n cell_type = data[i][4]\n cell_name = data[i][5]\n base_dict[cell_name, cell_type] = []\n\nfor i in range(len(data)):\n genes_array = data[i][8].replace(\" \", \"\").split(',')\n for j in range(len(genes_array)):\n genes_array[j] = genes_array[j].replace(\"[\", \"\")\n genes_array[j] = genes_array[j].replace(\"]\", \"\")\n cell_type = data[i][4]\n cell_name = data[i][5]\n for j in range(len(genes_array)):\n base_dict[cell_name, cell_type].append(genes_array[j])\n\n# End of reading database\n\n# Readind lda model\nlda_model = joblib.load(path_to_lda_model)\nmodel_data = sc.read_10x_mtx(path_to_data + '/')\n\n# Getting top-genes for each theme\n\ngenes_names = []\nfor row in sc.get.var_df(model_data).index:\n genes_names.append(row)\n\ngenes_by_theme = []\nfor topic_idx, topic in enumerate(lda_model.components_):\n topic_genes_names = \"\"\n topic_genes_names += \" \".join([genes_names[i]\n for i in topic.argsort()[:-number_of_genes_in_topics - 1:-1]])\n genes_by_theme.append(topic_genes_names.split(\" \"))\n\n# Genes per topic are now in genes_by_theme array\n\nanswer = []\nfor i in range(lda_model.n_components):\n genes_intersection = {}\n step_answer = []\n for elem in (base_dict):\n cnt = 0\n for k in range(len(base_dict[elem])):\n for j in range(len(genes_by_theme[i])):\n if (base_dict[elem][k] == genes_by_theme[i][j]):\n cnt += 1\n name_and_type = []\n name_and_type.append(elem[0])\n name_and_type.append(elem[1])\n genes_intersection[float(cnt) / float(len(base_dict[elem]))] = name_and_type\n sorted_values = sorted(genes_intersection, reverse=True)\n number = 0\n for j in range(5):\n step_answer.append(genes_intersection[sorted_values[j]])\n answer.append(step_answer)\n\nfile = open(path_to_output_data + \"/\" + file_name + \".txt\", \"w\")\nfile.write(\"Here are predicted cells by genes significance in each topic.\" + '\\n')\nfor i in range(len(answer)):\n file.write(\"Topic \" + str(i) + '\\n')\n for j in range(len(answer[i])):\n file.write(\"Cell name: \" + answer[i][j][0] + \"; Cell type: \" + answer[i][j][1] + '\\n')\nfile.close()\n\n# Building pyLDAvis graph\n\ndata_for_pyLDA = []\n\nfor row in sc.get.var_df(model_data).index:\n data_for_pyLDA.append(row)\n\nvectorizer_n = CountVectorizer(lowercase=False, token_pattern=r\"(?u)\\w+\\.\\w+|\\w+\\-\\w+|\\w+|\\.\\w+|\\w+\\.|\\w+\\-|\\-\\w+\")\ncount_data = vectorizer_n.fit_transform(data_for_pyLDA)\n\nLDAvis_prepared = sklearn_lda.prepare(lda_model, count_data, vectorizer_n)\npyLDAvis.save_html(LDAvis_prepared, path_to_output_data + '/' + file_name + '.html')\n","sub_path":"djagnoBioInformaticsProject/Scripts/database_prepare.py","file_name":"database_prepare.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"103886909","text":"from django import forms\nfrom .models import Ingredient\n\n\nclass StepForm(forms.Form):\n \"\"\"\n Form for individual steps in a recipe\n Used in formset\n \"\"\"\n\n title = forms.CharField(max_length=30,\n widget=forms.TextInput(attrs={\n 'placeholder': 'Step Title',\n 'class': 'input'}), required=True)\n details = forms.CharField(max_length=800,\n widget=forms.Textarea(attrs={\n 'placeholder': 'Step Details',\n 'class': 'textarea',\n 'rows': '2'}), required=True)\n\n\nclass IngredientForm(forms.Form):\n \"\"\"\n Form for each individual ingredient of the recipe\n Used in formset\n \"\"\"\n\n model = forms.ModelChoiceField(queryset=Ingredient.objects.order_by('name'))\n quantity = forms.DecimalField(max_digits=3, decimal_places=1,min_value=0, localize=False,\n widget=forms.NumberInput(attrs={\n 'style': 'width: 5rem',\n 'placeholder': 'Qty.'\n }))\n unit = forms.CharField(max_length=8,\n widget=forms.TextInput(attrs={\n 'placeholder': 'Unit',\n 'class': 'input',\n }), required=False)\n\n\nclass RecipeForm(forms.Form):\n \"\"\"\n Form for user to input details on a new recipe\n excludes steps, which are handled by separate formset\n \"\"\"\n\n name = forms.CharField(max_length=30,\n widget=forms.TextInput(attrs={\n 'placeholder': 'Recipe Name',\n 'class': 'input is-large'}), required=True)\n description = forms.CharField(max_length=400,\n widget=forms.Textarea(attrs={\n 'placeholder': 'Description',\n 'class': 'textarea ',\n 'rows': '2'}), required=True)\n\n\n","sub_path":"project/recipe/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"109035388","text":"from numpy import *\nimport scipy.io\nfrom scipy import interpolate\nfrom scipy.io import loadmat\n\n\n\ndef obstacles_positions():\n\n\tmin_x = -3.0\n\tmin_y = -3.0\n\tmax_x = 3.0\n\tmax_y = 3.0\n\tmin_z = 0.5\n\tmax_z = 1.2\n\n\tmin_allowed_distance = 0.8\n\tnum_points = 8\n\n\tpoints_x_init_obs = []\n\tpoints_x_fin_obs = []\n\n\tpoints_y_init_obs = []\n\tpoints_y_fin_obs = []\n\n\tpoints_z_init_obs = []\n\tpoints_z_fin_obs = []\n\n\n\tfor i in range (0, 1000):\n\n\t\trandom_x_init = random.uniform(min_x, max_x)\n\t\trandom_x_fin = random.uniform(min_x, max_x)\n\t\trandom_y_init = random.uniform(min_y, max_y)\n\t\trandom_y_fin = random.uniform(min_y, max_y)\n\t\trandom_z_init = random.uniform(min_z, max_z)\n\t\trandom_z_fin = random.uniform(min_z, max_z)\n\n\t\tif (i == 0):\n\t\t\tpoints_x_init_obs.append(random_x_init)\n\t\t\tpoints_y_init_obs.append(random_y_init)\n\t\t\tpoints_z_init_obs.append(random_z_init)\n\t\t\n\n\t\t\n\t\tif (i>0):\n\n\t\t\tdistance_init = sqrt((asarray(points_x_init_obs)-random_x_init)**2+(asarray(points_y_init_obs)-random_y_init)**2+(asarray(points_z_init_obs)-random_z_init)**2)\n\t\t\tdistance_init = min(abs(distance_init)) \n\n\n\t\t\tif (distance_init >= min_allowed_distance ):\n\n\t\t\t\tpoints_x_init_obs.append(random_x_init)\n\t\t\t\tpoints_y_init_obs.append(random_y_init)\n\t\t\t\tpoints_z_init_obs.append(random_z_init)\n\n\t\tif (shape(points_x_init_obs)[0]>num_points-1):\n\n\t\t\tbreak\n\n\tpoints_x_init_obs = points_x_init_obs\n\tpoints_y_init_obs = points_y_init_obs\n\tpoints_z_init_obs = points_z_init_obs\n\n\treturn points_x_init_obs, points_y_init_obs, points_z_init_obs\n\n\n\t\t","sub_path":"16_robot_8_obstacles/points.py","file_name":"points.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"434316262","text":"import pandas as pd\nimport matplotlib.pyplot as plt\n\ndf3 = pd.read_csv('data/df3')\nprint(df3.info())\nprint(df3.head())\n\n# Create a scatter plot of 'b' vs 'a'.\ndf3.plot.scatter(x='a', y='b', color='red', figsize=(8, 2))\nplt.show()\n\n# Create a histogram from column 'a'.\ndf3['a'].plot.hist()\nplt.show()\n\n# Redoing the last plot using a different style and more bins\nplt.style.use('ggplot')\ndf3['a'].plot.hist(bins=25)\nplt.show()\n\n# Create a boxplot comparing the a and b columns.\ndf3[['a', 'b']].plot.box()\nplt.show()\n\n# Create a kde plot of the 'd' column.\ndf3['d'].plot.kde(color='red', lw=5, linestyle='--')\nplt.show()\n\n# Create an area plot of all the columns for just the rows up to 30. (hint: use .ix).\ndf3.iloc[:30].plot.area()\nplt.legend(loc='center right', bbox_to_anchor=(1.15, 0.5))\nplt.show()\n","sub_path":"10-Pandas-Data-Visualization/57-Exercise.py","file_name":"57-Exercise.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"502564825","text":"import re\nBRIGHT_REGEX = re.compile('.*bright.*')\n\ndef task_256_to_urwid_256():\n manual_map = {\n 'red': 'dark red',\n 'green': 'dark green',\n 'blue': 'dark blue',\n 'cyan': 'dark cyan',\n 'magenta': 'dark magenta',\n 'gray': 'light gray',\n 'yellow': 'brown',\n 'color0': 'black',\n 'color1': 'dark red',\n 'color2': 'dark green',\n 'color3': 'brown',\n 'color4': 'dark blue',\n 'color5': 'dark magenta',\n 'color6': 'dark cyan',\n 'color7': 'light gray',\n 'color8': 'dark gray',\n 'color9': 'light red',\n 'color10': 'light green',\n 'color11': 'yellow',\n 'color12': 'light blue',\n 'color13': 'light magenta',\n 'color14': 'light cyan',\n 'color15': 'white',\n }\n manual_map.update(task_color_gray_to_g())\n manual_map.update(task_color_to_h())\n manual_map.update(task_rgb_to_h())\n return manual_map\n\ndef task_bright_to_color(color_string):\n color_map = {\n 'bright black': 'color8',\n 'bright red': 'color9',\n 'bright green': 'color10',\n 'bright yellow': 'color11',\n 'bright blue': 'color12',\n 'bright magenta': 'color13',\n 'bright cyan': 'color14',\n 'bright white': 'color15',\n }\n if BRIGHT_REGEX.match(color_string):\n for bright_color in color_map:\n color_string = color_string.replace(bright_color, color_map[bright_color])\n return color_string\n\ndef task_color_gray_to_g():\n color_map = {}\n for i in range(0, 24):\n gray_key = 'gray%d' % i\n color_key = 'color%d' % (i + 232)\n # NOTE: This is an approximation of the conversion, close enough!\n value = 'g%d' % (i * 4)\n color_map[gray_key] = value\n color_map[color_key] = value\n return color_map\n\ndef task_color_to_h():\n color_map = {}\n for i in range(16, 232):\n key = 'color%d' % i\n value = 'h%d' % i\n color_map[key] = value\n return color_map\n\ndef task_rgb_to_h():\n index_to_hex = [\n '0',\n '6',\n '8',\n 'a',\n 'd',\n 'f',\n ]\n color_map = {}\n count = 0\n for r in range(0, 6):\n for g in range(0, 6):\n for b in range(0, 6):\n key = 'rgb%d%d%d' % (r, g, b)\n value = '#%s%s%s' % (index_to_hex[r], index_to_hex[g], index_to_hex[b])\n color_map[key] = value\n count += 1\n return color_map\n","sub_path":"vit/color_mappings.py","file_name":"color_mappings.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"283971603","text":"#\n# Copyright 2021 Ocean Protocol Foundation\n# SPDX-License-Identifier: Apache-2.0\n#\n\n\"\"\"\n Used for deploying fake OCEAN\n isort:skip_file\n\"\"\"\n\nimport json\nimport os\nimport sys\n\nfrom ocean_lib.config_provider import ConfigProvider\n\n# Setup ocean_lib.enforce_typing_shim before importing anything that uses it\nfrom ocean_lib.enforce_typing_shim import setup_enforce_typing_shim\n\nsetup_enforce_typing_shim()\n\nfrom ocean_lib.example_config import ExampleConfig # noqa: E402\nfrom ocean_lib.models.data_token import DataToken # noqa: E402\nfrom ocean_lib.ocean import util # noqa: E402\nfrom ocean_lib.ocean.util import get_web3_connection_provider # noqa: E402\nfrom ocean_lib.web3_internal.contract_handler import ContractHandler # noqa: E402\nfrom ocean_lib.web3_internal.utils import privateKeyToAddress # noqa: E402\nfrom ocean_lib.web3_internal.wallet import Wallet # noqa: E402\nfrom ocean_lib.web3_internal.web3_provider import Web3Provider # noqa: E402\nfrom tests.resources.helper_functions import ( # noqa: E402\n get_ganache_wallet,\n get_publisher_ocean_instance,\n)\n\n\ndef deploy_fake_OCEAN():\n \"\"\"\n Does the following:\n 1. Deploy to ganache a new ERC20 contract having symbol OCEAN\n 2. Mints tokens\n 3. Distributes tokens to TEST_PRIVATE_KEY1 and TEST_PRIVATE_KEY2\n 4. In addresses.json, updates development : Ocean entry with new address\n \"\"\"\n network = \"ganache\"\n config = ExampleConfig.get_config()\n ConfigProvider.set_config(config)\n Web3Provider.init_web3(provider=get_web3_connection_provider(config.network_url))\n ContractHandler.set_artifacts_path(config.artifacts_path)\n\n artifacts_path = ContractHandler.artifacts_path\n addresses_file = config.address_file\n\n ocean = get_publisher_ocean_instance()\n web3 = ocean.web3\n\n addresses = dict()\n\n if os.path.exists(addresses_file):\n with open(addresses_file) as f:\n network_addresses = json.load(f)\n else:\n network_addresses = {network: {}}\n\n if network not in network_addresses:\n network = \"development\"\n\n # ****SET ENVT****\n deployer_private_key = get_ganache_wallet().private_key\n\n if invalidKey(deployer_private_key):\n print(\"Need valid DEPLOYER_PRIVATE_KEY\")\n sys.exit(0)\n\n # ****DEPLOY****\n deployer_wallet = Wallet(web3, private_key=deployer_private_key)\n\n print(\"****Deploy fake OCEAN: begin****\")\n # For simplicity, hijack DataTokenTemplate.\n deployer_addr = deployer_wallet.address\n OCEAN_cap = 1410 * 10 ** 6 # 1.41B\n OCEAN_cap_base = util.to_base_18(float(OCEAN_cap))\n OCEAN_token = DataToken(\n DataToken.deploy(\n web3,\n deployer_wallet,\n artifacts_path,\n \"Ocean\",\n \"OCEAN\",\n deployer_addr,\n OCEAN_cap_base,\n \"\",\n deployer_addr,\n )\n )\n addresses[\"Ocean\"] = OCEAN_token.address\n print(\"****Deploy fake OCEAN: done****\\n\")\n\n print(\"****Mint fake OCEAN: begin****\")\n OCEAN_token.mint(deployer_addr, OCEAN_cap_base, from_wallet=deployer_wallet)\n print(\"****Mint fake OCEAN: done****\\n\")\n\n print(\"****Distribute fake OCEAN: begin****\")\n amt_distribute = 1000\n amt_distribute_base = util.to_base_18(float(amt_distribute))\n for key_label in [\"TEST_PRIVATE_KEY1\", \"TEST_PRIVATE_KEY2\"]:\n key = os.environ.get(key_label)\n if not key:\n continue\n\n dst_address = privateKeyToAddress(key)\n OCEAN_token.transfer(\n dst_address, amt_distribute_base, from_wallet=deployer_wallet\n )\n print(f\"Distributed {amt_distribute} OCEAN to address {dst_address}\")\n\n print(\"****Distribute fake OCEAN: done****\\n\")\n\n print(\"****Update addresses file: begin****\\n\")\n\n print(f\"addresses file: {addresses_file}\")\n print(f\"network: {network}\")\n print(\"\")\n\n network_addresses[network].update(addresses)\n\n with open(addresses_file, \"w\") as f:\n json.dump(network_addresses, f, indent=2)\n\n _s = json.dumps(addresses, indent=4)\n\n s = \"Have deployed to, and updated the following addresses\\n\" + _s\n print(s)\n\n print(\"****Update addresses file: done****\\n\")\n\n\ndef invalidKey(private_key_str): # super basic check\n return len(private_key_str) < 10\n\n\ndef invalidAddr(addr_str): # super basic check\n return len(addr_str) < 10\n","sub_path":"ocean_lib/ocean/deploy.py","file_name":"deploy.py","file_ext":"py","file_size_in_byte":4360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"547653322","text":"#!/usr/bin/python3\n\"\"\"\nrandomquote.py - urban dictionary module\nauthor: jonorthwash \n\"\"\"\n\nimport urllib.request\nfrom urllib.error import HTTPError\nfrom tools import GrumbleError\nimport web\nimport json\nfrom modules import more\n\n#FIXME: need to implement\n#def quote(phenny, input):\n# \"\"\".quote - Get a quote from quotes.firespeaker.org.\"\"\"\n#\n# word = input.group(1)\n# if not word:\n# phenny.say(fs_quotes.__doc__.strip())\n# return\n# # create opener\n# opener = urllib.request.build_opener()\n# opener.addheaders = [\n# ('User-agent', web.Grab().version),\n# ('Referer', \"http://quotes.firespeaker.org\"),\n# ]\n#\n# try:\n# req = opener.open(\"http://api.urbandictionary.com/v0/define?term={0}\"\n# .format(web.quote(word)))\n# data = req.read().decode('utf-8')\n# data = json.loads(data)\n# except (HTTPError, IOError, ValueError) as e:\n# raise GrumbleError(\n# \"Urban Dictionary slemped out on me. Try again in a minute.\") from e\n#\n# if data['result_type'] == 'no_results':\n# phenny.say(\"No results found for {0}\".format(word))\n# return\n#\n# result = data['list'][0]\n# url = 'http://www.urbandictionary.com/define.php?term={0}'.format(web.quote(word))\n#\n# response = \"{0} - {1}\".format(result['definition'].strip()[:256], url)\n# phenny.say(response)\n\ntopics = {\"particles\": \"\\\"particle\\\" stands for \\\"defeat\\\" -spectie\",\n\t\"installing apertium\": \"try \\\"installing apertium on \\\"\",\n\t\"installing apertium on ubuntu\": \"https://wiki.apertium.org/wiki/Apertium_on_Ubuntu\",\n\t\"installing apertium on linux\": \"https://wiki.apertium.org/wiki/Apertium_on_Ubuntu\",\n\t\"installing apertium on windows\": \"https://wiki.apertium.org/wiki/Apertium_on_Windows\",\n\t\"google summer of code\": \"https://wiki.apertium.org/wiki/Google_Summer_of_Code\",\n\t\"gsoc\": \"https://wiki.apertium.org/wiki/Google_Summer_of_Code\",\n\t\"spectie\": \"https://wiki.apertium.org/wiki/User:Francis_Tyers\",\n\t\"firespeaker\": \"https://wiki.apertium.org/wiki/User:Firespeaker\",\n\t\"zfe\": \"http://quotes.firespeaker.org/?who=zfe\"\n\t}\n\ndef information(phenny, input):\n\t\"\"\".information () get information on a topic\"\"\"\n\tglobal topics\n\n\ttopic = input.group(1)\n\n\tif topic.lower() in topics:\n\t\tphenny.say(topics[topic.lower()])\n\telse:\n\t\tphenny.say(\"Sorry, no information on %s is currently available ☹\")\n\ninformation.name = 'information'\ninformation.commands = ['information']\ninformation.example = '.information (installing apertium)'\ninformation.priority = 'low'\n\n\ndef randquote_fetcher(phenny, topic, to_user):\n # create opener\n opener = urllib.request.build_opener()\n opener.addheaders = [\n ('User-agent', web.Grab().version),\n ('Referer', \"http://quotes.firespeaker.org/\"),\n ]\n\n try:\n url = \"http://quotes.firespeaker.org/random.php\"\n if topic:\n url += \"?topic=%s\" % web.quote(topic)\n req = opener.open(url)\n data = req.read().decode('utf-8')\n data = json.loads(data)\n except (HTTPError, IOError, ValueError) as e:\n raise GrumbleError(\"Firespeaker.org down? Try again later.\") from e\n\n if len(data) == 0:\n phenny.say(\"No results found\")\n return\n\n #result = data['list'][0]\n #url = 'http://www.urbandictionary.com/define.php?term={0}'.format(web.quote(word))\n #\n #response = \"{0} - {1}\".format(result['definition'].strip()[:256], url)\n\n if data['quote'] != None:\n quote = data['quote'].replace('

', '').replace('

', '').replace('', '_').replace('', '_').replace('—', '—')\n response = data['short_url'] + ' - ' + quote\n else:\n phenny.say(\"Sorry, no quotes returned!\")\n return\n\n more.add_messages(phenny, to_user, response.split('\\n'))\n\ndef randquote(phenny, input):\n \"\"\".randquote () - Get a random quote from quotes.firespeaker.org (about topic). (supports pointing)\"\"\"\n topic = input.group(1)\n to_nick = input.group(2)\n\n randquote_fetcher(phenny, topic, to_nick or input.nick)\n\nrandquote.name = 'randquote'\nrandquote.commands = ['randquote']\nrandquote.example = '.randquote (linguistics)'\nrandquote.priority = 'low'\nrandquote.point = True\n\n\ndef randquote4(phenny, input):\n nick, _, __, topic = input.groups()\n\n randquote_fetcher(phenny, topic, nick)\n\nrandquote4.rule = r'(\\S*)(:|,)\\s\\.(randquote)\\s(.*)'\nrandquote4.example = 'svineet: .randquote Linguistics'\n\n\ndef randquote5(phenny, input):\n nick, _, __ = input.groups()\n\n randquote_fetcher(phenny, \"\", nick)\n\nrandquote5.rule = r'(\\S*)(:|,)\\s\\.(randquote)$'\nrandquote5.example = 'svineet: .randquote'\n\nif __name__ == '__main__':\n print(__doc__.strip())\n","sub_path":"modules/fs_quotes.py","file_name":"fs_quotes.py","file_ext":"py","file_size_in_byte":4741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"631176552","text":"'''\n取值\n# list1 = [True,200,33,\"颜色\",[1,2,3]]\n# print(list1)\n# print(list1[2])\n# print(list1[0:2:1])\n# print(list1[-1][1])\n'''\n\n# #增加\n# list1 = [True,200,33,\"颜色\",[1,2,3]]\n# print(list1)\n# list1.append(\"红色\") #默认添加在末尾\n# print(list1)\n# list1.insert(1,\"蓝色\") #指定位置添加\n# print(list1)\n# list1.extend([555,666]) #添加多个元素---列表合并\n# print(list1)\n\n# #删除\n# list1 = [True,200,33,\"颜色\",[1,2,3]]\n# print(list1)\n# list1.pop() #默认删除最后一个\n# print(list1)\n# list1.pop(0) #删除指定位置\n# print(list1)\n# list1.remove(\"颜色\") #删除指定元素\n# print(list1)\n\n# # 修改\n# list1 = [True,200,33,\"颜色\",[1,2,3]]\n# print(list1)\n# list1[0]=False\n# print(list1)\n\n\n# #元组取值\n# tuple1=(100,200,300,\"小红花\",(1,2,3))\n# print(tuple1[1])\n# print(tuple1[1:3:1])\n# print(tuple1[-1][2])\n\n# #修改\n# tuple1=(100,200,300,\"小红花\",(1,2,3))\n# list1=list(tuple1)\n# list1[1]=\"奖励\"\n# tuple2=tuple(list1)\n# print(tuple2)\n\n\n#字典\n# dict1={\"name\":\"点点\",\"age\":18,\"gender\":\"male\"}\n# print(dict1[\"name\"])\n# print(dict1.get(\"name\"))\n# dict1[\"name\"] = \"花花\" #修改name的值\n# dict1[\"hobby\"] = \"看书\" #key不存在，则新增键值对\n# print(dict1)\n# dict1.update({\"age\":20,\"hobby\":\"运动\"})\n# print(dict1)\n# dict1.pop(\"name\")\n# print(dict1)\n\n\n# a=[1,2,'6','summer']\n# print(\"i\" in a)\n\n# dict_1={\"class_id\":45,'num':20}\n# if dict_1[\"num\"]>5:\n# print(\"班级上课人数为{}\".format(dict_1[\"num\"]))\n# else:\n# print(\"班级上课人数不大于5\")\n\n\n# list1 = ['方方土', '七木', '荷花鱼', 'kingo', 'Amiee', '焕蓝']\n# 列表当中的每一个值包含：姓名、性别、年龄、城市。以字典的形式表达。\n# 并且把字典都存在新的 list中，最后打印最终的列表。\n# 方法1： 手动扩充--字典--存放在列表里面；\n# 方法2： 自动--dict（）\n\n\n# dict1 = {\"姓名\":list1[0],\"性别\":\"男\",\"年龄\":19,\"城市\":\"北京\"}\n# dict2 = {\"姓名\":list1[1],\"性别\":\"女\",\"年龄\":18,\"城市\":\"上海\"}\n# dict3 = {\"姓名\":list1[2],\"性别\":\"女\",\"年龄\":19,\"城市\":\"杭州\"}\n# dict4 = {\"姓名\":list1[3],\"性别\":\"男\",\"年龄\":20,\"城市\":\"深圳\"}\n# dict5 = {\"姓名\":list1[4],\"性别\":\"女\",\"年龄\":22,\"城市\":\"北京\"}\n# dict6 = {\"姓名\":list1[5],\"性别\":\"男\",\"年龄\":21,\"城市\":\"广州\"}\n# list2 = list(dict1.values()),list(dict2.values()),list(dict3.values()),list(dict4.values()),list(dict5.values()),list(dict6.values())\n# for list3 in list2:\n# print(list3)\n\nlist1 = ['方方土', '七木', '荷花鱼', 'kingo', 'Amiee', '焕蓝']\nlist3 = ['男', '女', '女', '男', '女', '男']\nlist4 = ['19', '18', '19', '20', '22', '21']\nlist5 = ['北京', '上海', '杭州', '深圳', '北京', '广州']\nfor i in range(6):\n dict7 = dict(姓名=list1[i], 性别=list3[i], 年龄=list4[i], 城市=list5[i])\n print(list(dict7.values()))","sub_path":"python_class/test03.py","file_name":"test03.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"357413768","text":"import pytest\nfrom fastai.datasets import URLs\n\n\n@pytest.mark.parametrize(\"dataset\", [\n 'adult', 'mnist', 'movie_lens',\n # 'imdb', # imdb fails unless 'en' spacy language is available\n])\ndef test_get_samples(dataset, tmpdir):\n method = f'get_{dataset}'\n df = getattr(URLs, method)()\n assert df is not None\n","sub_path":"tests/test_datasets.py","file_name":"test_datasets.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"407099753","text":"# -*- coding: utf-8 -*-\nimport sys, os, time\nimport queue\nimport threading\nsys.path.append(os.path.abspath('../misc'))\n\nimport db_helper\nfrom db_model.IPPool import IPPool\nfrom db_model.IPOldPool import IPOldPool\nimport lam_tools\n\n_now_time = int(time.time())\n\nconfig = lam_tools.get_config()\nmax_fail = config['TOOL']['ip_proxy_max_fail']\ncheck_time_span = config['TOOL']['ip_proxy_check_time_span']\nselect_size = config['DB']['limit']\nthread_num = config['TOOL']['thread_num']\n\nsession = db_helper.get_ip_pool_session()\nold_session = db_helper.get_ip_old_pool_session()\n\n# step 1: 找出失败次数为 n 次的记录，删除\ndef step1():\n # (暂时不删吧)\n return # 代码没有经过测试，所以注释掉\n '''\n _query = session.query(IPPool.ip_port, IPPool.is_https).filter(IPPool.fail_num > max_fail)\n _count = _query.count()\n if _count == 0:\n return\n \n _start_select = 0\n while _start_select < _count:\n _data = _query.order_by(IPPool.ip_port).offset(_start_select).limit(select_size).all()\n\n _ip_port_list = []\n for d in _data:\n try:\n _ip_port_list.append(d.ip_port)\n old_session.add(IPOldPool(ip_port=d.ip_port, is_https=d.is_https, uptime=int(time.time())))\n except Exception as e:\n print('delete ip exception:', e)\n \n session.query(IPPool).filter(IPPool.ip_port._in(_ip_port_list)).delete()\n\n _start_select += select_size\n '''\n\n# step 2: 检测 ip\n# 多线程， thread 1 \ndef step2():\n\n class Productcer(threading.Thread):\n def run(self):\n pass\n \n class Consumer(threading.Thread):\n def run(self):\n pass\n\n def _build_queue():\n \n _check_time = _now_time - int(check_time_span)\n _query = session.query(IPPool).filter(IPPool.fail_num <= max_fail).filter(IPPool.uptime < _check_time)\n # count = _query.count()\n _queue = queue.Queue()\n\n data = _query.all()\n for d in data:\n _queue.put((d.ip_port, d.fail_num))\n \n return _queue\n \n def _run(q, db_session):\n _name = threading.currentThread().getName()\n print(_name, ' start')\n while (q.qsize() > 0):\n data = q.get()\n # ip_port = d[0]\n # fail_num = d[1]\n if data:\n _core_process(data, db_session)\n\n def _core_process(data, db_session):\n ip_port = data[0]\n fail_num = data[1]\n _ip, _port = ip_port.split(':')\n\n is_ok = lam_tools.check_ip_proxy_with_sock(_ip, int(_port), int(config['TOOL']['ip_proxy_timeout']))\n print(_ip, _port, '【bingo】' if is_ok else '*fail*')\n \n if is_ok:\n save_data = {'fail_num': 0, 'is_ok': 1, 'uptime': _now_time}\n else:\n save_data = {'fail_num': fail_num + 1, 'is_ok': 0, 'uptime': _now_time}\n \n db_session.query(IPPool).filter(IPPool.ip_port == ip_port).update(save_data)\n db_session.commit()\n \n _queue = _build_queue()\n\n # python 的线程有点不一样，join不能start之后，调用，否则其他 线程 start 不起来\n threads = []\n for i in range(int(thread_num)):\n _name = \"thread %d\" % i\n # sqlalchemy 的session 创建的线程 和 使用的线程必须一直，否则报错,所以才创建多个session，提供多个线程\n db_session = db_helper.get_ip_pool_session()\n t = threading.Thread(target=_run, name=_name, args=(_queue, db_session))\n threads.append(t)\n t.start()\n\n for t in threads:\n t.join()\n\nif __name__ == '__main__':\n step1()\n step2()","sub_path":"scripts/check_ip_deprecated.py","file_name":"check_ip_deprecated.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"146988006","text":"# Copyright 2020 Huawei Technologies Co., Ltd.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# ==============================================================================\n\"\"\"Define the NodeStruct which stores all info. of a node.\"\"\"\nfrom collections import OrderedDict\n\nfrom .scope_utils import Scope\nfrom .args_translator import ArgsTranslation\nfrom ..common.code_fragment import CodeFragment\nfrom ..third_party_graph.pytorch_graph_node import PyTorchGraphNode\nfrom ..third_party_graph.onnx_graph_node import OnnxGraphNode\nfrom ..common.global_context import GlobalContext\nfrom ..constant import InputType\nfrom ...common.exceptions import GeneratorError\n\n\nclass NodeStruct:\n \"\"\"\n Define a node struct which stores all info. to generate statement.\n\n Args:\n args (Union[PyTorchGraphNode, OnnxGraphNode, dict]): Node related obj.\n\n Note:\n You can pass as many args as possible and the Node Struct will update\n by arguments order.\n \"\"\"\n GLOBAL_CONTEXT_MGR = GlobalContext()\n\n def __init__(self, args):\n # define attributes here\n self._identifier = None\n self._fragment = None\n self._args_translator = None\n self._parent_module_struct = None\n self.topo_idx = None\n self.node_type = None\n self.onnx_name = None\n self.onnx_op = None\n self.graph_node_ref = None\n self.scope_name = None\n self.ms_var_name = None\n self.ms_op = None\n self.ready_to_generate = False\n\n # Define attributes converted from mapper\n self.ms_params = dict()\n self.ms_settings = dict()\n self.ms_weights = dict()\n self.ms_inputs = OrderedDict()\n\n # Defined Scope class\n self.scope = None\n\n # Define attributes used for code generation\n\n # key is prec_node_name, value is x; For code line use\n self.inputs_in_construct_header = OrderedDict()\n\n # key is prec_node_name, value is its closet opt_var_name\n self.inputs_in_parent_module = OrderedDict()\n\n # Matched inputs will can be directly used by code line generation\n self.matched_inputs = list()\n\n # initialize funcs.\n for arg in args:\n self.update(arg)\n\n def __repr__(self):\n return str({\n \"address\": hex(id(self)),\n \"idx\": self.topo_idx,\n \"identifier\": self.identifier\n })\n\n def ori_topo_idx(self):\n \"\"\"Get the original topological index in the onnx graph.\"\"\"\n ori_name = self.identifier.replace('$', '').split('/')[-1].replace(\"::\", '/')\n self.onnx_name = ori_name\n return self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_topo_idx.get(ori_name)\n\n def update_var_name(self, idx=None):\n \"\"\"\n Update the var_name of each node.\n\n Args:\n idx (int): The index of the node in this module.\n \"\"\"\n if idx is not None:\n self.ms_var_name = self.ms_op.replace('nn.', '').replace('P.', '').lower() + '_' + str(idx)\n elif self.topo_idx is not None:\n self.ms_var_name = self.ms_op.replace('nn.', '').replace('P.', '').lower() + '_' + str(self.topo_idx)\n else:\n raise ValueError(\"Unable to update var name when topo_idx is None.\")\n\n def _update_basics_from_gn(self, gn):\n \"\"\"Update basic info from GraphNode.\"\"\"\n self.graph_node_ref = gn\n self.scope_name = gn.scope_name\n\n def _update_from_pytorch_gn(self, gn: PyTorchGraphNode):\n \"\"\"Update basic info from PyTorchGraphNode.\"\"\"\n self.node_type = \"PyTorchGraphNode\"\n self._update_basics_from_gn(gn)\n\n def _update_from_onnx_gn(self, gn: OnnxGraphNode):\n \"\"\"Update basic info from OnnxGraphNode.\"\"\"\n self.node_type = \"OnnxGraphNode\"\n self._update_basics_from_gn(gn)\n\n def _update_from_mapper(self, d):\n \"\"\"Update info from mapper.\"\"\"\n if d.get('op_name'):\n self.ms_op = d.get('op_name')\n if d.get('params'):\n self.ms_params = d.get('params')\n if d.get('settings'):\n self.ms_settings = d.get('settings')\n if d.get('weights'):\n self.ms_weights = d.get('weights')\n\n def _update_from_fragment(self, frag: CodeFragment):\n \"\"\"Update info from CodeFragment.\"\"\"\n self._fragment = frag\n if frag.operation:\n self.ms_op = frag.operation\n idx = self.GLOBAL_CONTEXT_MGR.latest_node_struct_count\n self.update_var_name(idx=idx)\n\n def _set_scope_from_identifier(self):\n \"\"\"Set the Node scope from identifier.\"\"\"\n parsed_scope = Scope.parse_scope_from_node_identifier(self.identifier)\n self.scope = Scope(parsed_scope)\n\n @GeneratorError.check_except(\"Generator occurs an error when initializing node's args translator.\")\n def init_args_translator(self, translated_args: list):\n \"\"\"\n Initialize the ArgsTranslator for each Node.\n\n Args:\n translated_args (list): The list of args should be translated to formal args.\n \"\"\"\n if not self._fragment:\n raise ValueError(\"Initialize argument translator failed.\")\n if self._fragment.actual_args and translated_args:\n self._args_translator = ArgsTranslation(self._fragment.actual_args, self.ms_var_name, translated_args)\n\n def check_if_generate_ready(self):\n \"\"\"Check if the NodeStruct is able to generate code.\"\"\"\n # check essential params exists\n if all([self.identifier,\n self.node_type,\n self.scope_name,\n self.ms_var_name,\n self.ms_opt_var_name,\n self.ms_op]):\n self.ready_to_generate = True\n\n @GeneratorError.check_except(\"Generator occurs an error when creating node struct.\")\n def update(self, arg, force_ready=False):\n \"\"\"\n Pass Node info. to generator NodeStruct.\n\n Args:\n arg (Union[PyTorchGraphNode, OnnxGraphNode, dict]): Node related obj.\n force_ready (bool): Force this NodeStruct is ready to generate.\n \"\"\"\n if isinstance(arg, PyTorchGraphNode):\n self._update_from_pytorch_gn(arg)\n elif isinstance(arg, OnnxGraphNode):\n self._update_from_onnx_gn(arg)\n elif isinstance(arg, (dict, OrderedDict)):\n self._update_from_mapper(arg)\n elif isinstance(arg, CodeFragment):\n self._update_from_fragment(arg)\n else:\n raise TypeError(\"NodeStruct received an unsupported initializing argument.\")\n\n if force_ready:\n self.ready_to_generate = True\n else:\n self.check_if_generate_ready()\n\n @property\n def identifier(self):\n \"\"\"Return the identifier of the node.\"\"\"\n return self._identifier\n\n @identifier.setter\n def identifier(self, s):\n \"\"\"\n Set the Node identifier, and update the scope.\n\n Args:\n s (str): The node identifier string.\n \"\"\"\n self._identifier = s\n self._set_scope_from_identifier()\n self.topo_idx = self.ori_topo_idx()\n self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_node_struct_map[self.onnx_name] = self\n\n @property\n def fragment(self):\n \"\"\"Return the fragment of the node.\"\"\"\n return self._fragment\n\n @fragment.setter\n def fragment(self, frag):\n \"\"\"\n Set the Node fragment.\n\n Args:\n s (NodeFragment): The node identifier string.\n \"\"\"\n self._fragment = frag\n\n @property\n def graph_node(self):\n \"\"\"Return the GraphNode reference.\"\"\"\n return self.graph_node_ref\n\n @graph_node.setter\n def graph_node(self, graphnode):\n \"\"\"Set the GraphNode reference.\"\"\"\n self.graph_node_ref = graphnode\n\n @property\n def onnx_node(self):\n \"\"\"Return the original onnx node reference.\"\"\"\n return self.GLOBAL_CONTEXT_MGR.onnx_nodes_collection.get(self.onnx_name)\n\n @property\n def ms_opt_var_name(self):\n \"\"\"Return the output variable name of current node.\"\"\"\n return \"{}_opt\".format(self.ms_var_name).lower()\n\n\n @property\n def args_translator(self):\n \"\"\"Return the args translator of this Node.\"\"\"\n return self._args_translator\n\n @property\n def precursor_nodes_names(self) -> list:\n \"\"\"Return the names of precursor nodes.\"\"\"\n return self.graph_node_ref.precursor_nodes\n\n @property\n def precursor_nodes_structs(self) -> list:\n \"\"\"Return the node struct instances of precursor nodes.\"\"\"\n ret = []\n precursor_nodes_names = self.precursor_nodes_names\n for pre_node_name in precursor_nodes_names:\n nd_struct = self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_node_struct_map.get(pre_node_name)\n ret.append(nd_struct)\n return ret\n\n @property\n def successor_nodes_names(self) -> list:\n \"\"\"Return the names of successor nodes.\"\"\"\n return self.graph_node_ref.successor_nodes\n\n @property\n def successor_nodes_structs(self) -> list:\n \"\"\"Return the node struct instances of successor nodes.\"\"\"\n ret = []\n for pre_node_name in self.successor_nodes_names:\n nd_struct = self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_node_struct_map.get(pre_node_name)\n ret.append(nd_struct)\n return ret\n\n @property\n def parent_module_struct(self):\n \"\"\"Return the parent struct of this node.\"\"\"\n return self._parent_module_struct\n\n @parent_module_struct.setter\n def parent_module_struct(self, ref):\n self._parent_module_struct = ref\n\n # Code Generation funcs below\n\n def code_line_in_init(self):\n \"\"\"Initialization line of code in module init block.\"\"\"\n unconverted = False\n if \"onnx::\" in self.ms_var_name:\n unconverted = True\n self.ms_var_name = self.ms_var_name.replace(\"onnx::\", \"\")\n left = \"self.{}\".format(self.ms_var_name)\n\n args_list = list()\n if self._args_translator is not None:\n args_list += self._args_translator.actual_args_to_str_list\n args_list += self._args_translator.formal_args_to_str_list\n else:\n actual_args_str = ArgsTranslation.dict_data_to_args_str_list(self._fragment.actual_args)\n args_list += actual_args_str\n\n if unconverted:\n args_list.append('='.join([\"input_shape\", str(self._fragment.input_shape)]))\n args_list.append('='.join([\"output_shape\", str(self._fragment.output_shape)]))\n right = f\"{self.ms_op.replace('::', '.')}({', '.join(args_list)})\"\n else:\n right = f\"{self.ms_op}({', '.join(args_list)})\"\n return left, right\n\n def _get_correct_in_module_returns(self, prec_node, in_module_return):\n \"\"\"\n Find the correct precursor node name in return statement of its parent module.\n\n Args:\n prec_node (str): The onnx name of the precursor node given.\n in_module_return (list[tuple]): The list of outputs which contains parent module identifier\n and module opt_var_name.\n\n Return:\n str, correct opt_var_name to be passed in current node.\n \"\"\"\n found_return = False\n for ret in in_module_return:\n (md_identifier, input_name_to_use) = ret\n p_node_struct = self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_node_struct_map.get(prec_node)\n # recursive check the p node parent\n parent = p_node_struct\n while not found_return:\n parent = parent.parent_module_struct\n if parent is None:\n break\n if parent.identifier == md_identifier:\n return input_name_to_use\n return None\n\n def code_line_in_construct(self, inputs=None):\n \"\"\"Construct line of code in module construct block. \"\"\"\n left = self.ms_opt_var_name\n\n if not self.matched_inputs and inputs is None:\n raise ValueError(\"Unable to generate the code construct statement due to empty inputs.\")\n\n if self.matched_inputs:\n inputs = self.matched_inputs\n\n # Check original onnx node's input to ensure double inputs are not ignored\n original_inputs = self.GLOBAL_CONTEXT_MGR.onnx_node_inputs.get(self.onnx_name)\n new_inputs = []\n for idx, prec_node in enumerate(self.precursor_nodes_names):\n occurence = original_inputs.count(prec_node)\n for _ in range(occurence):\n new_inputs.append(inputs[idx])\n inputs = new_inputs\n\n if isinstance(inputs, str):\n inputs = [inputs]\n\n if self._fragment.code_setting and self._fragment.code_setting.op_ipt_type == InputType.LIST.value:\n inputs = [str(tuple(inputs)).replace(\"\\'\", \"\")]\n\n if self._fragment.code_setting and self._fragment.code_setting.op_extra_input:\n for _, val in self._fragment.code_setting.op_extra_input.items():\n inputs.append(str(val))\n\n if self._fragment.code_setting and self._fragment.code_setting.op_extra_tensor:\n inputs.append(f\"self.{self.ms_var_name}_w\")\n right = f\"self.{self.ms_var_name}({', '.join(inputs)})\"\n return left, right\n\n def add_extra_tensor(self):\n \"\"\" Add extra tensor.\"\"\"\n left = \"self.{}_w\".format(self.ms_var_name)\n shape = self._fragment.code_setting.op_extra_tensor.shape\n right = f\"Tensor(np.random.uniform(0, 1, {shape}), mindspore.float32)\"\n return left, right\n\n # The following functions are specified for multiple in/out support.\n # and should be called only after generator._recursive_form_modules()\n\n def set_inputs_in_construct_header(self, header_x, onnx_precursor_node_name):\n \"\"\"\n Mark the registered external inputs for code generation.\n\n Note:\n This function to be called by its parent (ModuleStruct).\n\n Args:\n header_x (str): The `x` in module construct header.\n onnx_precursor_node_name (str): The original onnx node name.\n \"\"\"\n if self.inputs_in_construct_header.get(onnx_precursor_node_name) is not None:\n raise ValueError(\"The input from {} has already registered. Check this node \\\n {} has duplicate inputs or not.\".format(onnx_precursor_node_name, self.identifier))\n self.inputs_in_construct_header[onnx_precursor_node_name] = header_x\n\n def _check_target_node_internal(self, name: str) -> bool:\n \"\"\"\n Check given node under the same scope.\n\n Args:\n name (str): Can accept both node identifier or original onnx node name.\n \"\"\"\n target_nd_struct = self.GLOBAL_CONTEXT_MGR.node_struct_collections.get(name) \\\n or self.GLOBAL_CONTEXT_MGR.onnx_node_name_to_node_struct_map.get(name)\n if target_nd_struct is None and self.topo_idx == 0: # First node always has external input\n return False\n\n if target_nd_struct is None:\n raise ValueError(\"Unable to find the NodeStruct of given target node {}.\".format(name))\n return target_nd_struct.scope.path == self.scope.path\n\n @property\n def has_successor_node_external(self) -> bool:\n \"\"\"Check if any successor_node is in external module.\"\"\"\n for name in self.successor_nodes_names:\n if not self._check_target_node_internal(name):\n return False\n\n return True\n\n @property\n def precursor_nodes_names_external(self) -> list:\n \"\"\"Return a list of external precursor nodes names.\"\"\"\n return [name for name in self.precursor_nodes_names\n if not self._check_target_node_internal(name)]\n\n @property\n def successor_nodes_names_external(self) -> list:\n \"\"\"Return a list of external successor nodes names.\"\"\"\n return [name for name in self.successor_nodes_names\n if not self._check_target_node_internal(name)]\n","sub_path":"mindinsight/mindconverter/graph_based_converter/generator/node_struct.py","file_name":"node_struct.py","file_ext":"py","file_size_in_byte":16576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"411493445","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 24 16:26:52 2019\n\n@author: Kanishk\n\"\"\"\n\nfrom environment import MountainCar\n# environment is the executable python library provided by the course intructors\nimport numpy as np\nimport sys\n\ndef weight(state, w, b): # state is the dictionary here\n sTw = np.zeros(3)\n\n for i in range(act_set):\n for j in state.keys():\n sTw[i] += w[j][i] * state[j]\n return sTw + b\n\ndef Action_select(q_vals, epsilon):\n \n prob= np.random.random(1)\n if prob<1-epsilon:\n a=np.argmax(q_vals)\n else:\n a = np.random.randint(0,3)\n return a\n \ndef Q_train(alpha, gamma, epsilon, max_iterations):\n w = np.zeros((SS,act_set)) # Initialize\n b = 0 # Initialize\n Rewards = []\n \n for noe in range(episodes):\n state = Car.reset()\n r = 0 # Initialize reward\n done = False\n \n for m in range(max_iterations):\n if done == True:\n break\n \n q_vals = weight(state, w, b)\n a = Action_select(q_vals, epsilon)\n Q = q_vals[a]\n Sprime, reward, done = Car.step(a)\n \n '''Computing q_pi (s,a)'''\n Qprime = weight(Sprime, w, b)\n Q_next = max(Qprime)\n \n '''Gradient Update''' \n grad = alpha * (Q - (reward + gamma*Q_next))\n for j in state.keys():\n w[j][a] = w[j][a] - grad * state[j]\n \n b = b - grad\n state = Sprime\n r += reward\n \n ## Rendering ##\n '''Executed to see improvements after every 1000 episodes else it slows the overall execution'''\n if noe%1000 == 0:\n MountainCar.render(Car)\n \n #env \n Rewards.append(r)\n \n MountainCar.close(Car) \n return w, b, Rewards\n \nif __name__ == \"__main__\":\n pass\n\nmode = sys.argv[1]\nweight_out = sys.argv[2]\nreturn_out = sys.argv[3]\nepisodes = int(sys.argv[4])\nmax_iter = int(sys.argv[5])\nepsilon = float(sys.argv[6])\ngamma = float(sys.argv[7])\nalpha = float(sys.argv[8])\n\nCar = MountainCar(mode)\nSS = Car.state_space\nact_set = 3 # Action space has 3 options: (Left, No Action, Right)\n\nW, B, Rewards = Q_train(alpha, gamma, epsilon, max_iter)\n\n# Weight files\n'''Writing the output of the weights of the model learned'''\nwith open(weight_out, 'w+') as wt_file:\n wt_file.write('%s' %(B) + '\\n')\n for j in range(SS):\n for i in range(act_set):\n wt_file.write('%s' %(W[j,i]) + '\\n')\n\n# Return files\n'''Writing the values obtained by implementation of Q-learning algorithm after every iteration'''\nwith open(return_out, 'w+') as ret_file:\n for j in range(episodes):\n ret_file.write('%s' %(Rewards[j]) + '\\n')\n","sub_path":"python/q_learning.py","file_name":"q_learning.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"169948235","text":"import json\n\n\nclass StateMachine:\n def __init__(self, filename):\n with open(filename, 'r') as f:\n j = json.load(f)\n self.name = j['name']\n self.states = j['states']\n self.input = j['input']\n self.output = j['output']\n self.transfer = j['transfer']\n self.initial_state = j['initial_state']\n self.state_output = j['state_output']\n\n def CCodeGenerator(self):\n states = \"\"\n i = 0\n for a in self.states:\n states += f\"#define {a} {i}\\n\"\n i += 1\n initial_state = f\"int state = {self.initial_state};\"\n outputs = \"\"\n for a in self.output:\n outputs += f\"int {a};\"\n cases = \"\"\n for a in self.states:\n transfer = \"\"\n for b in self.transfer:\n if a == b['state']:\n transfer += f\"if({b['guide']}) state = {b['toState']};\\n\"\n output = \"\"\n for b in self.state_output[a]:\n output += f\"{b};\\n\"\n cases += f\"case {a}:\\n {transfer} {output} break;\\n\"\n\n code = f'''\n#include \n#include \n\n{states}\nint main(){{\n{initial_state}\n{outputs}\nwhile(1){{\nswitch(state){{\n{cases}\ndefault:\nbreak;\n}}\n}}\nreturn 0;\n}}\n'''\n return code\n\n\nif __name__ == '__main__':\n s = StateMachine('moore.json')\n code = s.CCodeGenerator()\n with open('CSM.c', 'w') as f:\n f.write(code)\n","sub_path":"StateMachine.py","file_name":"StateMachine.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"397358823","text":"from django.shortcuts import get_object_or_404, render\n\n# Create your views here.\n\nfrom .models import Comunidad\n\ndef comunidades(request):\n comunidades = Comunidad.objects.all()\n contexto = {\n 'comunidades': comunidades\n }\n return render(request, 'comunidades.html', contexto)\n\ndef comunidad(request, url_comunidad):\n comunidad = get_object_or_404(Comunidad, url=url_comunidad)\n contexto = {\n 'comunidad': comunidad,\n }\n return render(request, 'comunidad.html', contexto)","sub_path":"buses/comunidades/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"501759359","text":"#source ~/.virtualenvs/cv/bin/activate\n\nimport cv2\nimport numpy as np\nimport time\nfrom tflite_runtime.interpreter import Interpreter\n\nfrom FunctionsS3RSS import *\n\n\ndef load_labels(path): # Read the labels from the text file as a Python list.\n with open(path, 'r') as f:\n return [line.strip() for i, line in enumerate(f.readlines())]\n\ndef set_input_tensor(interpreter, image):\n tensor_index = interpreter.get_input_details()[0]['index']\n input_tensor = interpreter.tensor(tensor_index)()[0]\n input_tensor[:, :] = image\n\ndef classify_image(interpreter, image, top_k=1):\n set_input_tensor(interpreter, image)\n\n interpreter.invoke()\n output_details = interpreter.get_output_details()[0]\n output = np.squeeze(interpreter.get_tensor(output_details['index']))\n\n scale, zero_point = output_details['quantization']\n output = scale * (output - zero_point)\n\n ordered = np.argpartition(-output, 1)\n return [(i, output[i]) for i in ordered[:top_k]][0]\n\n#/home/pi/tflite_models/inception_v4_299_quant_20181026/\ndata_folder = '/home/pi/tflite_models/inception_v4_299_quant_20181026/'\nmodel_path = data_folder + \"inception_v4_299_quant.tflite\"\n#label_path = data_folder + \"labels_mobilenet_quant_v1_224.txt\"\n\ndata_folder = '/home/pi/tflite_models/mobilenet_v1_1.0_224_quant_and_labels/'\n#model_path = data_folder + \"mobilenet_v1_1.0_224_quant.tflite\"\nlabel_path = data_folder + \"labels_mobilenet_quant_v1_224.txt\"\n\n\ninterpreter = Interpreter(model_path)\nprint(\"Model Loaded Successfully.\")\n\ninterpreter.allocate_tensors()\n_, height, width, _ = interpreter.get_input_details()[0]['shape']\nprint(\"Input Image Shape (\", width, \",\", height, \")\")\n\n#Load the target labels\nlabels = load_labels(label_path)\n\ncam = cv2.VideoCapture(0)\n\n#cv2.namedWindow(\"test\")\n\nimg_counter = 0\n\nnumPredictionsHonored = 1\nLastXTracker = [(\"dummy\",0)]*numPredictionsHonored\n\nlastTime = time.time()\nlastFrame = None\nmseMotion = None\n\nwhile True:\n\tret, frame = cam.read()\n\t\n\tif lastFrame is not None:\n\t\tmseMotion = np.square(np.subtract(frame, lastFrame)).mean()\n\tlastFrame = frame\n\t\n\tif not ret:\n\t\tprint(\"failed to grab frame\")\n\t\tbreak\n\t\n\t#Resize image to to imput tensor size.\n\timage = cv2.resize(frame,(height,width))\n\t\n\t#Classify Image\n\tlabel_id, prob = classify_image(interpreter, image)\n\tclassification_label = labels[label_id]\n\tprint(f\"Image Label is :{classification_label}, with Accuracy :{np.round(prob*100, 2)}%, Motion mse: {mseMotion}.\")\n\t\n\tLastXTracker = [(classification_label, prob)] + LastXTracker[0:-1]\n\t\n\t#Check if all labels are the same\n\tLastXLabels = [x[0] for x in LastXTracker]\n\tlabelsAllSame = LastXLabels.count(LastXLabels[0])==len(LastXLabels)\n\tif labelsAllSame:\n\t\tLastXprobs = [x[1] for x in LastXTracker]\n\t\tif min(LastXprobs) > 0.96 and classification_label == 'hummingbird':\n\t\t\tprint(f\"Image Classified as {classification_label}\")\n\t\t\t\n\t\t\timgPath = '/home/pi/Documents/idAnimal/temp.jpg'\n\t\t\tcv2.imwrite(imgPath,frame)\n\t\t\t\n\t\t\tUploadToS3AndDDB('testuser',imgPath,{\"source\": model_path, \"score\": str(prob), \"species\":classification_label},prob,'MobileNetV1')\n\t\t\t\n\t\t\tcreateRSSFeed()\n\t\t\t\n\t\t\t#Reset the tracker\n\t\t\tLastXTracker = [(\"dummy\",0)]*numPredictionsHonored\n\t\t\tprint('Wait 20 seconds after uploading image')\n\t\t\ttime.sleep(20)\n\t\t\t\n\t#cv2.imshow(\"test\", frame)\n\t\n\tk = cv2.waitKey(1)\n\tif k%256 == 27:\n\t\t# ESC pressed\n\t\tprint(f'Image size: {frame.shape}')\n\t\tprint(f'Frame type: {type(frame)}')\n\t\tprint(\"Escape hit, closing...\")\n\t\tbreak\n\t\n\t\n\tclassification_time = np.round(time.time()-lastTime, 3)\n\tprint(f\"Frame Time = {classification_time} seconds\")\n\tlastTime = time.time()\n\ncam.release()\n\ncv2.destroyAllWindows()\n","sub_path":"tfInference.py","file_name":"tfInference.py","file_ext":"py","file_size_in_byte":3612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"33595505","text":"import numpy as np\nfrom scipy.optimize import curve_fit\n\n\ndef linear(x, m, c):\n\treturn m*x+c\n\n\nclass Linear:\n\tdef __init__(self):\n\t\tself.VLARGE = 5000\n\n\tdef predict(self, data, confs, gap=0, predtill=1):\n\t\tassert predtill-1 <= gap\n\t\ttrue = data[:, -predtill:, :]\n\t\tpred = []\n\n\t\tfor i in range(data.shape[2]):\n\t\t\ttrend = data[0, :-1-gap, i]\n\t\t\tconf = confs[0, :-1-gap, i]\n\n\t\t\terrs = []\n\t\t\terrf = self.VLARGE\n\t\t\tfits = []\n\n\t\t\tweeks = list(range(trend.shape[0]))\n\t\t\ttry:\n\t\t\t\tparams, _ = curve_fit(linear, range(len(weeks)), trend, method='lm', p0=(0, 0), sigma=conf)\n\t\t\t\terr_l = np.sqrt(np.sum(np.square(np.array([linear(tmp, params[0], params[1]) for tmp in range(len(weeks))])-trend)))\n\t\t\t\terrs.append(err_l)\n\t\t\t\tfits.append(params)\n\t\t\t\tif err_l < errf:\n\t\t\t\t\terrf = err_l\n\t\t\t\t\tpred_tmp = [linear(tmp, params[0], params[1]) for tmp in range(data.shape[1]-predtill, data.shape[1])]\n\t\t\texcept:\n\t\t\t\traise\n\t\t\tif i%100 == 0:\n\t\t\t\tprint(\"Done\", i, \"/\", data.shape[2])\n\t\t\tpred.append(pred_tmp)\n\t\tpred = np.expand_dims(np.array(pred).T, axis=0)\n\t\tmae = np.mean(np.abs(pred-true))\n\t\tmape = np.mean(np.abs(pred-true)/true)*100\n\t\treturn mae, mape, pred\n","sub_path":"trends_events/models/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":1139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"60956183","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Zenno Vaccarezza\nVERSION: 1\nDESCRIPTION: Sleep tracker\nFirst Static Code Analysis:1.76\nLast Static Code Analysis: 9.24\n\"\"\"\n#import numpy as np # linear algebra\nimport pandas as pd # data processing\nimport matplotlib.pyplot as plt\nimport scipy\nfrom fpdf import FPDF\n\n\n# Arranging columns\nDF1 = pd.read_csv(r'nosnore_bright.csv')\n#DF.columns = DF.columns.str.strip()\nDF1.columns = DF1.columns.str.replace('/', '')\nDF1.columns = DF1.columns.str.replace('²', '2')\nDF1.columns = DF1.columns.str.replace('°', '0')\nDF1.columns = DF1.columns.str.replace('-', '')\nDF1.columns = DF1.columns.str.replace('_', '')\nDF1.columns = DF1.columns.str.replace('(', '')\nDF1.columns = DF1.columns.str.replace(')', '')\nDF1.columns = DF1.columns.str.replace(':', '')\nDF1.columns = DF1.columns.str.replace(' ', '')\n#%% Shape the dataframe\nDF3 = DF1.drop(['LOCATIONAltitudegooglem', 'LOCATIONSpeedKmh', 'LOCATIONORIENTATION0',\n 'GRAVITYXms2', 'GRAVITYYms2', 'GRAVITYZms2', 'GYROSCOPEX0s',\n 'GYROSCOPEY0s', 'GYROSCOPEZ0s'], axis=1)\nDF2 = DF3.drop(['MAGNETICFIELDXμT', 'MAGNETICFIELDYμT', 'MAGNETICFIELDZμT',\n 'ORIENTATIONZazimuth0', 'ORIENTATIONXpitch0', 'ORIENTATIONYroll0'], axis=1)\nDF = DF2.drop(['LOCATIONAccuracym', 'Satellitesinrange', 'PROXIMITYm',\n 'LOCATIONLatitude', 'LOCATIONLongitude', 'LOCATIONAltitudem',\n 'ACCELEROMETERXms2', 'ACCELEROMETERYms2', 'ACCELEROMETERZms2'], axis=1)\nDF_INFO = DF.describe()\n#Add ID column\nDF['ID'] = range(1, len(DF) + 1)\n\nCOLUMNTITLES = ['ID', 'LINEARACCELERATIONXms2', 'LINEARACCELERATIONYms2', 'LINEARACCELERATIONZms2',\n 'Timesincestartinms', 'YYYYMODDHHMISSSSS',\n 'SOUNDLEVELdB', 'LIGHTlux',]\nDF = DF.reindex(columns=COLUMNTITLES)\n\n#%% RMS for Linear Acceleration\nLGTH_DATA = DF.iloc[len(DF)-1]['ID']\nDATA_RMS = pd.Series([])\nfor i in range(0, LGTH_DATA):\n x_axis = DF.iloc[i]['LINEARACCELERATIONXms2']\n y_axis = DF.iloc[i]['LINEARACCELERATIONYms2']\n z_axis = DF.iloc[i]['LINEARACCELERATIONZms2']\n rms_acceleration = ((x_axis**2)+(y_axis**2)+(z_axis**2)) ** 0.5\n DATA_RMS[i] = rms_acceleration\n\n\nDF.insert(4, 'RMSLINEARACCELERATION', DATA_RMS)\n\n#%% RMS axis by time grapf\n\nX = DF['Timesincestartinms'].as_matrix() / 60000\nY = DF['RMSLINEARACCELERATION'].as_matrix()\nFIG = plt.figure()\nAXES = FIG.add_axes([0, 0, 1, 1])\nAXES.plot(X, Y, 'b')\nAXES.set_title('Graphic')\nplt.xlabel(\"Min\")\nplt.ylabel(\"Acceleration in ms2\")\nplt.show()\n\n#%% Data analysis\n\nCOUNT = 0\nCONTROL = 0\nLGTH_DATA = DF.iloc[len(DF)-1]['ID']\nMEAN_LIGHT = DF_INFO.iloc[1]['LIGHTlux']\nREM_DF = pd.DataFrame()\nREM_DF2 = pd.DataFrame()\n\n\nfor i in range(0, LGTH_DATA):\n rms_axis = DF.iloc[i]['RMSLINEARACCELERATION']\n actual_time = DF.iloc[i]['Timesincestartinms']\n RME_time = DF.iloc[0]['Timesincestartinms']\n if CONTROL >= 1 and CONTROL < 5:\n CONTROL = CONTROL+1\n else:\n CONTROL = 0\n if rms_axis > 1 and CONTROL < 1:\n REM_DF[i] = DF.loc[i, : ]\n COUNT = COUNT+1\n CONTROL = CONTROL+1\n\nREM_DF = REM_DF.transpose()\nREM_DF = REM_DF.reset_index()\n\n#%% Filter for Noise level\n\nFFT = scipy.fft(DF['SOUNDLEVELdB'])\nBP = FFT[ : ]\nfor i in range(len(BP)):\n if i >= 20:\n BP[i] = 0\nIBP = scipy.ifft(BP)\n\n#%%Plot and Report\n\nplt.plot(DF.Timesincestartinms/60000, IBP*4, color=\"blue\", label=\"Sound dB\")\nplt.legend()\nplt.xlabel(\"Time elapsed in min\")\nplt.ylabel(\"Sound in dB\")\nplt.savefig('sound.png')\nplt.show()\n\nplt.scatter(REM_DF.Timesincestartinms/60000, REM_DF.RMSLINEARACCELERATION,\n color=\"blue\", label=\"RMSLINEARACCELERATION\")\nplt.legend()\nplt.xlabel(\"Time elapsed in min\")\nplt.ylabel(\"Movement RMS variation\")\nplt.savefig('REM.png')\nplt.show()\n\n#%% Report text\n\nif COUNT == 0:\n REM_CYCLES = 'no'\nelse:\n REM_CYCLES = str(COUNT)\n\nif MEAN_LIGHT > 4:\n LIGHT_LVL = \"Ambient light is high, please try to sleep in a darker room to achieve better sleep.\"\nelse:\n LIGHT_LVL = \"Ambient light level was adequate for sleeping.\"\n\n\nTEXT = \"According with the data receive you had \" + REM_CYCLES + \" cycles of REM sleep. \" + LIGHT_LVL\nTEXT2 = \"Based on scientific research we can seee in the image below that usally the REM Cycle is together with Brief Awakening, as the image show below\"\nTEXT3 = \"Please observe the graphs below first for when the REM cycles occured and the second for snoring data:\"\nTEXT4 = \"Snoring is consider any value greater then 60dB in the graph\"\n\n#%% PDF Report generator\npdf = FPDF()\npdf.add_page()\npdf.set_font(\"Arial\", size=16)\npdf.cell(200, 10, txt=\"Sleep Report base on CSV file data!\", ln=1, align=\"C\")\npdf.set_font(\"Arial\", size=12)\npdf.cell(200, 10, txt=\"Data Analysis:\", ln=4, align=\"\")\npdf.set_font(\"Arial\", size=8)\npdf.cell(200, 10, txt=TEXT, ln=1, align=\"\")\npdf.cell(200, 10, txt=TEXT2, ln=1, align=\"\")\npdf.ln(3)\npdf.image('Hypnogram.png', x=35, y=None, w=120, h=71, type='', link='')\npdf.ln(2)\npdf.cell(200, 10, txt=TEXT3, ln=2, align=\"\")\npdf.ln(1)\npdf.image('REM.png', x=50, y=None, w=81, h=54, type='', link='')\npdf.image('sound.png', x=50, y=None, w=81, h=54, type='', link='')\npdf.ln(2)\npdf.cell(200, 10, txt=TEXT4, ln=2, align=\"\")\npdf.output('SleepReport.pdf', 'F')\n","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":5216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"45254145","text":"\n##########\n#character_input.py\n##########\nname = str( input( 'Please enter your name ' ) )\nage = input( 'Please enter your age ' )\nif( False == age.isdigit() ) :\n\tprint( 'You didn\\'t enter number' )\n\tprint( 'Your name is ' + name )\nelse :\n\tprint( 'Hi ' + name + ' you are still young and ' + age )\n##########\n#check_email_addresses_from_file.py\n##########\nimport re\nwith open( 'email_addresses.txt', 'r' ) as email_addresses :\n\tline = email_addresses.readline()\n\twhile line :\n\t\temail_address = line.strip( '\\n' )\n\t\tlstMatches = re.findall( \"[a-zA-Z0-9-_]+@[a-zA-Z0-9]+\\.[a-zA-Z0-9]+\", email_address )\n\t\tif 0 != len(lstMatches) :\n\t\t\tprint( lstMatches[0] )\n\t\tline = email_addresses.readline()\n##########\n#check_primality.py\n##########\nimport sys\nnumber = input( 'Please enter number : ' )\n\nif( False == number.isdigit() ):\n\tprint( 'not a number' )\n\tsys.exit()\nelse :\n\t\tnumber = int(number)\n\t\tfor i in range( 2, (number//2) + 1 ) :\n\t\t\tif( 0 == number% i ) :\n\t\t\t\tprint( f'{number} is not prime' )\n\t\t\t\tsys.exit()\n\t\tprint( f'{number} is prime' )\n\n\t\t#################################################### Print till n number #############################################################\n\t\tlstPrimes = []\n\t\tfor i in range( 2, number + 1) :\n\t\t\tisPrime = 1\n\t\t\tfor j in range( 2, (i//2)+1) :\n\t\t\t\tif( 0 == i % j ) :\n\t\t\t\t\tisPrime = 0\n\t\t\t\t\tbreak\n\t\t\tif( 1 == isPrime ) :\n\t\t\t\tlstPrimes.append( i )\n\t\tprint(lstPrimes)\n\n\t\t#################################################### Print first n number of primes #############################################################\n\n\t\tlstPrimes = []\n\t\ti = 0\n\t\trangedNumber = 2\n\t\twhile( i < number ) :\n\t\t\tfor j in range( 2, rangedNumber + 1 ) :\n\t\t\t\tisPrime = 1\n\t\t\t\tfor k in range( 2, (rangedNumber // 2) + 1 ) :\n\t\t\t\t\tif( 0 == j % k ) :\n\t\t\t\t\t\tisPrime = 0\n\t\t\t\t\t\tbreak\n\t\t\tif( 1 == isPrime ) :\n\t\t\t\tlstPrimes.append( j )\n\t\t\t\ti = i + 1\n\t\t\trangedNumber = rangedNumber + 1\n\t\tprint( lstPrimes )\n\n\n\n##########\n#check_tic_tac_toe.py\n##########\nlstGame = [[1, 2, 2],\n\t[2, 2, 0],\n\t[2, 1, 1]]\n\npl1 = 0\npl2 = 0\npln = 0\n\nfor i in range(len(lstGame)) :\n\tfor j in range(len(lstGame)) :\n\n\t\tif( 0 == i and 0 == j ) :\n\t\t\tif( 1 == lstGame[i][j] ) :\n\t\t\t\tpl1 = 1\n\t\t\t\tpl2 = 0\n\t\t\t\tpln = 0\n\t\t\telif( 2 == lstGame[i][j] ) :\n\t\t\t\tpl1 = 0\n\t\t\t\tpl2 = 1\n\t\t\t\tpln = 0\n\t\t\telse :\n\t\t\t\tpl1 = 0\n\t\t\t\tpl2 = 0\n\t\t\t\tpln = 1\n\t\telse :\n\t\t\tif( 1 == lstGame[i][j] and lstGame[i][j] == lstGame[i][j-1] and 1 == pl1 ) :\n\t\t\t\tpl1 = 1\n\t\t\t\tpl2 = 0\n\t\t\t\tpln = 0\n\t\t\telif( 2 == lstGame[i][j] and lstGame[i][j] == lstGame[i][j-1] and 1 == pl2) :\n\t\t\t\tpl1 = 0\n\t\t\t\tpl2 = 1\n\t\t\t\tpln = 0\n\t\t\telse :\n\t\t\t\tpl1 = 0\n\t\t\t\tpl2 = 0\n\t\t\t\tpln = 1\n\n\t\tprint ( lstGame[i][j],lstGame[i][j-1] )\n\t# print( '' )\n\n\t# print( '' )\n\n# print( pl1, pl2, pln )\n##########\n#cows_and_bulls.py\n##########\nimport sys\nimport random\n\ndef cowsnbulls() :\n\tnumber = random.randint( 1000, 9999 )\n\n\twhile(1) :\n\t\tuserinput = input( 'Guess the number or press \"q\" to quit : ' )\n\t\tif 'q' == userinput :\n\t\t\tsys.exit()\n\t\telif False == userinput.isdigit() or 4 != len( userinput ):\n\t\t\tprint( 'Not a valid number, exiting' )\n\t\t\tsys.exit()\n\t\telse :\n\t\t\tcows = 0\n\t\t\tbulls = 0\n\t\t\tnumber = str(number)\n\t\t\tfor i in range( len( userinput ) ):\n\t\t\t\tif( userinput[i] == number[i] ) :\n\t\t\t\t\tcows = cows + 1\n\t\t\t\telse :\n\t\t\t\t\tbulls = bulls + 1\n\t\t\tprint( f'cows = {cows} and bulls = {bulls}' )\n\t\t\tif( 4 == cows ) :\n\t\t\t\tprint( 'You got the 4 cows, number is correct' )\n\t\t\t\tsys.exit()\n\nif __name__ == '__main__' :\n\tcowsnbulls()\n##########\n#cut_rectangle_in_min_sq.py\n##########\n# 36 - 2,3,4,6,9,12,18\n# 30 - 2,3,5,6,10,15\n\ndef handleCalculateSquares( height, width ) :\n\n\twhile height != width and 0 not in [height, width] :\n\t\tif height == width :\n\t\t\tprint( '1 square of' + str(height) )\n\t\telif height < width :\n\t\t\tprint ( str(width // height) + ' squares of ' + str( height ) )\n\t\t\theight, width = width % height, height\n\t\telse :\n\t\t\tprint (str(height // width) + ' squares of ' + str( width ))\n\t\t\twidth, height = height % width, width\n\nhandleCalculateSquares( 30, 36 )\nprint('-'*10)\nhandleCalculateSquares( 13, 29 )\n##########\n#date_time.py\n##########\nfrom datetime import datetime\nfrom datetime import timedelta\n\nstrDateTime = datetime(2016,1,1) + timedelta(days=255)\n\nprint( str(strDateTime.day) + '/' + str(strDateTime.month) + '/' + str(strDateTime.year) )\n##########\n#decode_web_page.py\n##########\nimport requests\nimport bs4\n\nstrLink = 'https://www.nytimes.com'\n\nresponse = requests.get( strLink )\nhtml = response.text\n\nsoup = bs4.BeautifulSoup( html, 'html.parser' )\n\nstrContent = soup.find( id=\"site-content\")\n\nfor div in strContent.find('div') :\n\tif None != div:\n\t\tprint(div.get_text())\n##########\n#divisors.py\n##########\nintnumber = int( input( 'Enter number ' ) )\n\nfor i in range( 1, ( intnumber//2 ) + 1 ) :\n\tif( 0 == intnumber % i ) :\n\t\tprint( i )\n##########\n#draw_a_game_board.py\n##########\nplot_range = int( input( 'Enter range : ' ) )\nverticle = '| '\nhorizontal = ' ---'\n\nfor i in range( plot_range ) :\n\tprint( horizontal * plot_range )\n\tprint( verticle * ( plot_range + 1 ) )\nprint( horizontal * plot_range )\n##########\n#element_search.py\n##########\nlstNumbers = [1, 2, 3, 4, 5, 6, 7, 9, 12, 19, 23, 24, 43, 45, 54, 78, 87, 98, 123, 234, 457, 654, 723, 856, 942, 967, 998]\ninputnumber = int( input( 'Enter number : ' ) )\n\nwhile( 1 ) :\n\tstart = 0\n\tmiddle = len( lstNumbers ) // 2\n\tend = len( lstNumbers )\n\n\tif( 2 == len( lstNumbers ) ) :\n\t\tif( inputnumber in lstNumbers ) :\n\t\t\tprint( 'Found it' )\n\t\telse :\n\t\t\tprint( 'Nope not there' )\n\t\t# found = 1\n\t\tbreak\n\tif inputnumber > lstNumbers[middle] :\n\t\tlstNumbers = lstNumbers[middle:end]\n\t\t# print(lstNumbers)\n\telse :\n\t\tlstNumbers = lstNumbers[start:middle]\n\t\t# print(lstNumbers)\n\n##########\n#fibonacci.py\n##########\nnumber = input( 'Enter number : ' )\nif False == number.isdigit() :\n\tprint( 'not a number' )\nelse :\n\tnumber = int( number )\n\t################### Print first n fibonacci numbers ##############################\n\tlstFibbo = []\n\ta = 1\n\tb = 1\n\tfor i in range(number) :\n\t\tlstFibbo.append(a)\n\t\ta , b = b, a + b\n\tprint(lstFibbo)\n\t################### Print all fibonacci numbers less than n ##############################\n\tlstFibbo = []\n\ta = 1\n\tb = 1\n\twhile a < number :\n\t\tlstFibbo.append(a)\n\t\ta,b = b, a+b\n\tprint( lstFibbo )\n##########\n#file_names.py\n##########\n\n##########\n#file_overlap.py\n##########\nwith open( 'primenumbers.txt', 'r' ) as prime_numbers_file :\n\tprime_numbers = (prime_numbers_file.readlines())\n\nwith open ( 'happynumbers.txt', 'r' ) as happy_numbers_file :\n\thappy_numbers = happy_numbers_file.readlines()\n\nfor i in prime_numbers :\n\tfor j in happy_numbers :\n\t\tif( i == j ) :\n\t\t\tprint( i.strip( '\\n' ) )\n##########\n#guessing_game_1.py\n##########\nimport sys\nimport random\n\ntries = 0\nsecret_number = random.randint( 1, 9 )\n\nwhile( 1 ) :\n\tuser_input = input( 'Guess the number or press \"q\" to quit : ' )\n\tif( 'q' == user_input ) :\n\t\tprint( f' Total tries : {tries} and correct number is {secret_number}')\n\t\tsys.exit()\n\telse :\n\t\ttries = tries + 1\n\t\tuser_input = int( user_input )\n\t\tif( user_input == secret_number ) :\n\t\t\tprint( 'There you are!' )\n\t\t\tprint( f' Total tries : {tries}')\n\t\t\tsys.exit()\n\t\telif( user_input < secret_number ) :\n\t\t\tprint( 'Too low!' )\n\t\telse :\n\t\t\tprint( 'To high!' )\n##########\n#guessing_game_2.py\n##########\nimport sys\nimport random\n\nprint( 'Keep number in your head between 1 to 100, I will guess the nuber' )\n\nguess_attempts = 1\n\nstart = 1\nend = 100\nmiddle = end // 2\n\nwhile( 1 ) :\n\n\tmiddle = (start+end) // 2\n\n\tprint( f'{start}, {middle}, {end}' )\n\n\tprint( f'number is {middle}' )\n\n\tuserinput = int( input( 'Press \"1\" for high, \"2\" for low, 3 for correct answer and 4 to quit : ' ) )\n\tif( 4 == userinput or 3 == userinput ) :\n\t\tprint( f'Guesses = {guess_attempts}')\n\t\tsys.exit()\n\telif( 1 == userinput ) :\n\t\tguess_attempts = guess_attempts + 1\n\t\tend = middle\n\telse :\n\t\tguess_attempts = guess_attempts + 1\n\t\tstart = middle\n##########\n#hackerrank_alphabets_rangoli.py\n##########\nimport string\n\ndef print_rangoli(size):\n\tlstAlphabets = list(string.ascii_lowercase)[:size]\n\tlstAlphabets.reverse()\n\tlstPattern = []\n\ti = (size*2) - 2\n\tj = 0\n\twhile i >= 0 :\n\t\tlstMiddlePattern = []\n\t\tstrPattern = '-' * (i)\n\t\t# print(strPattern, end='')\n\t\tfor k in range(0,j+1) :\n\t\t\tlstMiddlePattern.append( lstAlphabets[k] )\n\t\tl = j - 1\n\t\twhile l >= 0 :\n\t\t\tlstMiddlePattern.append( lstAlphabets[l] )\n\t\t\tl = l - 1\n\t\tstrPattern = strPattern + '-'.join( lstMiddlePattern )\n\t\tstrPattern = strPattern + '-' * (i)\n\t\ti = i - 2\n\t\tj = j + 1\n\t\tprint(strPattern)\n\t\tlstPattern.append(strPattern)\n\tlstPattern.pop()\n\tfor i in lstPattern[::-1] :\n\t\tprint(i)\nif __name__ == '__main__':\n\tn = int(input())\n\tprint_rangoli(n)\n##########\n#hackerrank_birthday_candles.py\n##########\nfrom itertools import groupby\n\nif __name__ == '__main__' :\n\tn = int(input())\n\ttotalCandles = list(map(int,input().rstrip().split()))\n\ttotalCandles.sort(reverse = True )\n\ttotalCandles = groupby(totalCandles)\n\tfor i,j in totalCandles :\n\t\tprint(len(list(j)))\n\t\tbreak\n##########\n#hackerrank_check_valid_string.py\n##########\nn = int(input('enter number : '))\n\nfor i in range(n) :\n\n\tstrUid = input('enter string : ')\n\n\tlstUid = set(list(strUid))\n\tintCount = 0\n\tstrUpCount = 0\n\n\tif strUid.isalnum() and 10 == len(lstUid):\n\t\tfor inputchar in strUid :\n\t\t\tif inputchar.isdigit() :\n\t\t\t\tintCount = intCount + 1\n\t\t\tif inputchar.isupper() :\n\t\t\t\tstrUpCount = strUpCount + 1\n\n\tif 2 <= strUpCount and 3 <= intCount :\n\t\tprint( 'Valid' )\n\telse :\n\t\tprint( 'Invalid' )\n##########\n#hackerrank_chocolate_bar.py\n##########\ndef birthday(s, d, m):\n\n\tintTotalWays = 0\n\t# print( len(s) - m)\n\tfor i in range( len(s) ):\n\t\t# print(i)\n\t\tnextValSum = 0\n\t\tfor j in range(m) :\n\t\t\tif i+j < len(s) :\n\t\t\t\tnextValSum = nextValSum + s[i+j]\n\t\tif nextValSum == d :\n\t\t\tintTotalWays = intTotalWays + 1\n\n\treturn intTotalWays\n\n\nif __name__ == '__main__':\n\tn = int(input().strip())\n\ts = list(map(int, input().rstrip().split()))\n\tdm = input().rstrip().split()\n\td = int(dm[0])\n\tm = int(dm[1])\n\tresult = birthday(s, d, m)\n\tprint(result)\n##########\n#hackerrank_cloud_jumps.py\n##########\nif __name__ == '__main__' :\n\tn = int(input())\n\tlstNumbers = list(map(int,input().split()))\n\tintPosition = 0\n\tintJumpCount = 0\n\n\twhile intPosition < len(lstNumbers)-2 :\n\t# for intPosition in range(0,len(lstNumbers)-2) :\n\t\t# print(intPosition)\n\t\t# if 1 != lstNumbers[intPosition] :\n\t\tif 0 == lstNumbers[intPosition] and 0 == lstNumbers[intPosition+2] :\n\t\t\t# print( '--1--', end = ' ')\n\t\t\t# print(intPosition)\n\t\t\tintPosition = intPosition + 2\n\t\t\tintJumpCount = intJumpCount + 1\n\t\telif 0 == lstNumbers[intPosition] and 0 == lstNumbers[intPosition+1] and 1 == lstNumbers[intPosition+2] :\n\t\t\t# print( '--2--', end = ' ')\n\t\t\t# print(intPosition)\n\t\t\tintPosition = intPosition + 1\n\t\t\tintJumpCount = intJumpCount + 1\n\t\telse :\n\t\t\tintPosition = intPosition + 1\n\n\tif 0 == lstNumbers[len(lstNumbers)-2] :\n\t\tintJumpCount = intJumpCount + 1\n\n\tprint(intJumpCount)\n##########\n#hackerrank_compare_triplets.py\n##########\n# Complete the compareTriplets function below.\ndef compareTriplets(a, b):\n\tascore = 0\n\tbscore = 0\n\tfor i in range(len(a)) :\n\t\tif a[i] > b[i] :\n\t\t\tascore = ascore + 1\n\t\telif a[i] < b[i] :\n\t\t\tbscore = bscore + 1\n\treturn f'{ascore} {bscore}'\n\nif __name__ == '__main__':\n\ta = list(map(int, input().rstrip().split()))\n\n\tb = list(map(int, input().rstrip().split()))\n\n\tresult = compareTriplets(a, b)\n\tprint(result)\n##########\n#hackerrank_day_in_programers_life.py\n##########\nfrom datetime import datetime\nfrom datetime import timedelta\n\nif __name__ == '__main__' :\n\tintYear = int(input())\n\tdays = 255\n\n\tif intYear < 1919 and intYear % 4 == 0 and intYear % 100 == 0:\n\t\tdays = 254\n\tif intYear == 1918 :\n\t\tdays = 268\n\n\tstrDate = datetime.strftime(datetime(intYear,1,1) + timedelta(days=days), \"%d.%m.%Y\")\n\tprint(strDate)\n##########\n#hackerrank_default_argument.py\n##########\nlstNumbers = []\nclass EvenStream(object):\n\tdef __init__(self):\n\t\tself.current = 0\n\n\tdef get_next(self):\n\t\tto_return = self.current\n\t\tself.current += 2\n\t\treturn to_return\n\nclass OddStream(object):\n\tdef __init__(self):\n\t\tself.current = 1\n\n\tdef get_next(self):\n\t\tto_return = self.current\n\t\tself.current += 2\n\t\treturn to_return\n\ndef print_from_stream1(n, stream=EvenStream()):\n\tlstNumbersFromStream = []\n\tfor _ in range(n):\n\t\tyield(stream.get_next())\n\ndef print_from_stream(n, stream=EvenStream()):\n\tlstNumbers.append( print_from_stream1(n, stream) )\n\n\nqueries = int(input())\nfor _ in range(queries):\n\tstream_name, n = input().split()\n\tn = int(n)\n\tif stream_name == \"even\":\n\t\tprint_from_stream(n)\n\telse:\n\t\tprint_from_stream(n, OddStream())\n\nfor i in list(lstNumbers) :\n\tfor j in list(i) :\n\t\tprint(j)\n##########\n#hackerrank_divisible_sum_pairs.py\n##########\nif __name__ == '__main__' :\n\tnk = list(map(int, input().strip().split()))\n\tintDivisor = nk[1]\n\tlstNumbers = list(map(int, input().strip().split()))\n\n\tprint( ([(i,j) for i in range(n) for j in range(n) if i < j and (ar[i]+ar[j])%k == 0]) )\n##########\n#hackerrank_door_mat.py\n##########\nif __name__ == '__main__' :\n\tmn = list(map(int,input().strip().split()))\n\tm = mn[0]\n\tn = mn[1]\n\tj = 2\n\tstrlstLines = []\n\tfor i in range( 1, m, 2 ):\n\t\tdashes = ( n - ( i*3 ) ) // 2\n\t\tstrLine = '-' * dashes\n\t\tstrLine = strLine + '.|.' * i\n\t\tstrLine = strLine + '-' * dashes\n\t\tprint(strLine)\n\t\tstrlstLines.append(strLine)\n\tprint( '-' * ( ( n - 7 ) // 2 ) + 'WELCOME' + '-' * ( ( n - 7 ) // 2 ) )\n\tstrlstLines.reverse()\n\tfor i in strlstLines :\n\t\tprint(i)\n##########\n#hackerrank_find_avrage.py\n##########\nif __name__ == '__main__':\n\tn = int(input())\n\tstudent_marks = {}\n\tfor _ in range(n):\n\t\tname, *line = input().split()\n\t\tscores = list(map(float, line))\n\t\tstudent_marks[name] = scores\n\tquery_name = input()\n\n\tfor name, marks in student_marks.items():\n\t\ttotal = 0\n\t\tfor mark in marks :\n\t\t\ttotal = total + mark\n\t\tstudent_marks[name] = total/3\n\tprint( \"%.2f\" % student_marks[query_name] )\n##########\n#hackerrank_find_string.py\n##########\ndef count_substring(strParent, strSubString):\n\ti = 0\n\ttotalMatches = 0\n\twhile i < len(strParent) :\n\t\tintPosition = strParent.find(strSubString)\n\t\tif -1 != intPosition :\n\t\t\ttotalMatches = totalMatches + 1\n\t\t\tstrParent = strParent[intPosition+1:]\n\t\telse :\n\t\t\tbreak\n\n\treturn totalMatches\n\nif __name__ == '__main__' :\n\tstrParent = input().strip()\n\tstrSubString = input().strip()\n\tcount = count_substring(strParent, strSubString)\n\tprint(count)\n\n##########\n#hackerrank_grading_student.py\n##########\nif __name__ == '__main__':\n\tn = int(input())\n\tgrades = []\n\tfor _ in range(n):\n\t\tgrades_item = int(input())\n\t\tgrades.append(grades_item)\n\tfor grade in grades :\n\t\troundedGrade = grade + 5 - grade%5\n\t\tif ( roundedGrade - grade ) < 3 and roundedGrade >= 40 :\n\t\t\tprint( roundedGrade )\n\t\telse :\n\t\t\tprint( grade )\n##########\n#hackerrank_itertools.py\n##########\nfrom itertools import combinations_with_replacement\nstrWord = input()\nstrWord = strWord.split()\nintNumber = int(strWord[1])\nstrWord = list(strWord[0])\n\nif True == ''.join(strWord).isupper() and 0 < intNumber and intNumber <= len(strWord) :\n\tstrWord.sort()\n\tfor i in combinations_with_replacement( strWord, intNumber ) :\n\t\tprint(''.join(i))\n##########\n#hackerrank_itertools_groupby.py\n##########\nfrom itertools import groupby\nstrInput = input()\n\nif strInput.isdigit() :\n\tfor i,j in groupby(strInput) :\n\t\tprint( '(' + str( len( list( j ) ) ) + ', ' + str(i) + ')', end = ' ' )\n##########\n#hackerrank_itertools_product.py\n##########\nfrom itertools import product\n\nif __name__ == '__main__' :\n\tlist1 = map( int, input().split() )\n\tlist2 = map( int, input().split() )\n\n\tfor i in product(list1, list2) :\n\t\tprint( i, end = ' ' )\n##########\n#hackerrank_kangaroo.py\n##########\nimport sys\ndef kangaroo(x1, v1, x2, v2):\n\n\tfor i in range(10000) :\n\t\tif x1 == x2 :\n\t\t\tprint('YES')\n\t\t\tsys.exit()\n\t\tx1 = x1 + v1\n\t\tx2 = x2 + v2\n\n\tprint('NO')\n\n\nif __name__ == '__main__':\n\n\tarrNumbers = list(map(int, input().strip().split()))\n\tx1 = arrNumbers[0]\n\tv1 = arrNumbers[1]\n\tx2 = arrNumbers[2]\n\tv2 = arrNumbers[3]\n\tif x1 < x2 and v1 < v2 :\n\t\tprint('NO')\n\telse :\n\t\tresult = kangaroo(x1, v1, x2, v2)\n##########\n#hackerrank_knight_moves.py\n##########\nx = 3\ny = 3\n\nfor i in range(8) :\n\tfor j in range(8) :\n\t\tif (x-i in [2,-2] and y-j in [1,-1]) or (x-i in [1,-1] and y-j in [2,-2]) :\n\t\t\tprint(i,j)\n##########\n#hackerrank_list_comprehensions.py\n##########\nif __name__ == '__main__':\n\tx = int(input())\n\ty = int(input())\n\tz = int(input())\n\tn = int(input())\n\nprint( [ [i,j,k] for i in range(x+1) for j in range(y+1) for k in range(z+1) if (i+j+k) != n ] )\n##########\n#hackerrank_list_left_rotation.py\n##########\ndef rotLeft( oldList, intRotations ):\n\tnewList = oldList.copy()\n\tintListLength = len(oldList)\n\tintShiftPosition = len(oldList) - intRotations\n\ti = 0\n\twhile i < intRotations :\n\t\tnewList[i+intShiftPosition] = oldList[i]\n\t\ti = i + 1\n\ti = 0\n\twhile i < intShiftPosition :\n\t\tnewList[i] = oldList[intRotations+i]\n\t\ti = i + 1\n\treturn newList\n\nif __name__ == '__main__':\n\n\tnd = input().split()\n\n\tn = int(nd[0])\n\n\td = int(nd[1])\n\n\ta = list(map(int, input().rstrip().split()))\n\n\tresult = rotLeft(a, d)\n\tprint( ' '.join( str(i) for i in result ) )\n##########\n#hackerrank_list_operations.py\n##########\nif __name__ == '__main__':\n\tlstOperations = ['insert','remove','append','sort','pop','reverse','print']\n\tlstNumbers = []\n\tn = int(input())\n\tfor _ in range(n) :\n\t\tstrOperation, *lstOpNumbers = input().split()\n\t\tlstOpNumbers = list(map(int,lstOpNumbers))\n\t\tif( strOperation in lstOperations ) :\n\t\t\tif( 'insert' == strOperation ) :\n\t\t\t\tlstNumbers.insert( lstOpNumbers[0], lstOpNumbers[1] )\n\t\t\telif( 'remove' == strOperation ) :\n\t\t\t\tlstNumbers.remove(lstOpNumbers[0])\n\t\t\telif( 'append' == strOperation ) :\n\t\t\t\tlstNumbers.append(lstOpNumbers[0])\n\t\t\telif( 'sort' == strOperation ) :\n\t\t\t\tlstNumbers.sort()\n\t\t\telif( 'pop' == strOperation ) :\n\t\t\t\tlstNumbers.pop()\n\t\t\telif( 'reverse' == strOperation ) :\n\t\t\t\tlstNumbers.reverse()\n\t\t\telse :\n\t\t\t\tprint( lstNumbers )\n##########\n#hackerrank_mean_var_std.py\n##########\nimport numpy as np\n\nif __name__ == '__main__' :\n\tlstNumbers=[]\n\tn, m = map(int,input().split())\n\tfor _ in range( n ) :\n\t\t\tlstNumbers.append(list(map(int,input().split())))\n\tlstNumbers = np.array(lstNumbers)\n\tprint( np.mean(lstNumbers, axis = 1 ) )\n\tprint( np.var(lstNumbers, axis = 0 ) )\n\tprint( np.std(lstNumbers, axis = None ) )\n##########\n#hackerrank_merge_the_tools.py\n##########\nfrom collections import OrderedDict\n\ndef merge_the_tools(string, k):\n\ti = 0\n\twhile i < len(string) :\n\t\tprint( ''.join( list( OrderedDict.fromkeys( string[i:i+k] ) ) ) )\n\t\ti = i + k\n\nif __name__ == '__main__':\n\tstring, k = input(), int(input())\n\tmerge_the_tools(string, k)\n##########\n#hackerrank_migratory_birds.py\n##########\nfrom itertools import groupby\n\ndef migratoryBirds(arr):\n\tarr.sort()\n\tintCount = 0\n\tfor i, j in groupby(arr) :\n\t\tinTotalCount = len(list(j))\n\t\tif intCount < inTotalCount :\n\t\t\tintCount = inTotalCount\n\t\t\tintNumber = i\n\tprint(intNumber)\n\nif __name__ == '__main__':\n\tarr_count = int(input().strip())\n\tarr = list(map(int, input().rstrip().split()))\n\tresult = migratoryBirds(arr)\n\n##########\n#hackerrank_minions.py\n##########\ndef minion_game(string):\n\tstring = string.lower()\n\tkevin = 0\n\tstuart = 0\n\tfor i in range(len(string)) :\n\t\tif string[i] in ['a','e','i','o','u'] :\n\t\t\t# kevin = kevin + len(string[i:])\n\t\t\tkevin = kevin + (len(string)-i)\n\t\telse :\n\t\t\t# stuart = stuart + len(string[i:])\n\t\t\tstuart = stuart + (len(string)-i)\n\n\tif kevin > stuart :\n\t\tprint( f\"Kevin {kevin}\" )\n\telif kevin < stuart :\n\t\tprint( f\"Stuart {stuart}\" )\n\telse :\n\t\tprint( 'Draw' )\n\nif __name__ == '__main__':\n\ts = input().lower()\n\tminion_game(s)\n##########\n#hackerrank_mutable_Strings.py\n##########\ndef mutate_string( strInput, intIndex, strChar):\n\tstrInput = list(strInput)\n\tstrInput[intIndex] = strChar\n\treturn ''.join(strInput)\n\n\nif __name__ == '__main__':\n\ts = input()\n\ti, c = input().split()\n\ts_new = mutate_string(s, int(i), c)\n\tprint(s_new)\n##########\n#hackerrank_nested_lists.py\n##########\nfrom itertools import groupby\nfrom operator import itemgetter\n\nif __name__ == '__main__':\n\n\tlstStudentGrades= []\n\n\tfor _ in range(int(input())):\n\t\tname = input()\n\t\tscore = float(input())\n\t\tlstStudentGrades.append([name,score])\n\n\tlstStudentGrades.sort( key = lambda x : x[1] )\n\ty = groupby(lstStudentGrades, lambda x: x[1])\n\n\tintCount = 1\n\tfor i,j in y:\n\t\tif 2 == intCount :\n\t\t\tlstNames = [x[0] for x in list(j)]\n\t\t\tlstNames.sort()\n\t\t\tfor strName in lstNames :\n\t\t\t\tprint( strName )\n\t\t\tbreak\n\t\tintCount = intCount + 1\n##########\n#hackerrank_new_year_chaos.py\n##########\n#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the minimumBribes function below.\ndef minimumBribes(q):\n\tpass\n\nif __name__ == '__main__':\n\tt = int(input())\n\n\tfor t_itr in range(t):\n\t\tn = int(input())\n\n\t\tq = list(map(int, input().rstrip().split()))\n\n\t\tminimumBribes(q)\n\n##########\n#hackerrank_plus_minus.py\n##########\n#!/bin/python3\n# Complete the plusMinus function below.\ndef plusMinus(arr):\n\n\tintTotalPositive = 0\n\tintTotalNegative = 0\n\tintTotalZeros = 0\n\n\tfor i in arr:\n\t\tif i < 0 :\n\t\t\tintTotalNegative = intTotalNegative + 1\n\t\telif i > 0 :\n\t\t\tintTotalPositive = intTotalPositive + 1\n\t\telse :\n\t\t\tintTotalZeros = intTotalZeros + 1\n\n\tprint( \"{0:.6f}\".format(round(intTotalPositive/len(arr),6)) )\n\tprint(\"{0:.6f}\".format(round(intTotalNegative/len(arr),6)))\n\tprint( \"{0:.6f}\".format(round(intTotalZeros/len(arr),6)) )\n\nif __name__ == '__main__':\n\tn = int(input())\n\n\tarr = list(map(int, input().rstrip().split()))\n\n\tplusMinus(arr)\n##########\n#hackerrank_repeated_strings.py\n##########\nif __name__ == '__main__':\n\ts = input()\n\tn = int(input())\n\n\tintTotalCountOfFirstChar = s.count(\"a\")\n\tintTotalOccurances = ( n // len(s) * intTotalCountOfFirstChar )\n\tintTotalOccurances = intTotalOccurances + s[:n%len(s)].count(\"a\")\n\tprint(intTotalOccurances)\n##########\n#hackerrank_runner_up.py\n##########\nif __name__ == '__main__':\n\tn = int(input())\n\tarr = map(int, input().split())\n\tarr = list(arr)\n\tarr = list(set(arr))\n\tarr.sort()\n\tprint(arr[-2])\n##########\n#hackerrank_set.py\n##########\nif __name__ == '__main__' :\n\ts = set()\n\tn = int(input())\n\tfor _ in range(n) :\n\t\tstrCountry = input()\n\t\ts.add(strCountry)\n\tprint(len(s))\n##########\n#hackerrank_sock_merchant.py\n##########\nfrom itertools import groupby\n\ndef sockMerchant(n, ar):\n\tar = groupby( ar )\n\ttotalSocks = 0\n\tfor i,j in ar :\n\t\ttotalSocks = totalSocks + len( list( j ) ) // 2\n\treturn totalSocks\n\nif __name__ == '__main__':\n\tn = int(input())\n\tar = list(map(int, input().rstrip().split()))\n\tar.sort()\n\ttotalSocks = sockMerchant(n, ar)\n\n\tprint( totalSocks )\n##########\n#hackerrank_staircase.py\n##########\nif __name__ == '__main__':\n\tn = int(input())\n\tfor i in range(1,n+1) :\n\t\tprint( ''.rjust(n-i) + '#'*i )\n##########\n#hackerrank_string_formatting.py\n##########\nif __name__ == '__main__' :\n\tn = int(input())\n\twidth = len(bin(n)[2:])\n\tfor i in range( 1, n + 1 ) :\n\t\tprint( str(i).rjust(width, ' '), end = ' ' )\n\t\tprint(oct(i)[2:].rjust(width, ' '), end = ' ' )\n\t\tprint(str(hex(i)).upper()[2:].rjust(width, ' '), end = ' ' )\n\t\tprint(bin(i)[2:].rjust(width, ' '))\n##########\n#hackerrank_string_ops.py\n##########\nimport re\nif __name__ == '__main__':\n\ts = input()\n\tif( 0 != len(list(re.findall( '[0-9a-zA-Z]', s )) )):\n\t\tprint( True )\n\telse :\n\t\tprint( False )\n\tif( 0 != len(list(re.findall( '[a-zA-Z]', s )) )) :\n\t\tprint( True )\n\telse :\n\t\tprint( False )\n\tif( 0 != len(list(re.findall( '[0-9]', s )) ) ):\n\t\tprint( True )\n\telse :\n\t\tprint( False )\n\tif( 0 != len(list(re.findall( '[a-z]', s )) ) ):\n\t\tprint( True )\n\telse :\n\t\tprint( False )\n\tif( 0 != len(list(re.findall( '[A-Z]', s )) ) ):\n\t\tprint( True )\n\telse :\n\t\tprint( False )\n##########\n#hackerrank_textwrap.py\n##########\nimport textwrap\n\ndef wrap(string, max_width):\n\treturn textwrap.fill(string,max_width)\n\nif __name__ == '__main__':\n\tstring, max_width = input(), int(input())\n\tresult = wrap(string, max_width)\n\tprint(result)\n##########\n#hackerrank_text_alignment.py\n##########\n#Replace all ______ with rjust, ljust or center.\n\nthickness = int(input()) #This must be an odd number\nc = 'H'\n\n#Top Cone\nfor i in range(thickness):\n\tprint((c*i).______(thickness-1)+c+(c*i).______(thickness-1))\n\n#Top Pillars\nfor i in range(thickness+1):\n\tprint((c*thickness).______(thickness*2)+(c*thickness).______(thickness*6))\n\n#Middle Belt\nfor i in range((thickness+1)//2):\n\tprint((c*thickness*5).______(thickness*6))\n\n#Bottom Pillars\nfor i in range(thickness+1):\n\tprint((c*thickness).______(thickness*2)+(c*thickness).______(thickness*6))\n\n#Bottom Cone\nfor i in range(thickness):\n\tprint(((c*(thickness-i-1)).______(thickness)+c+(c*(thickness-i-1)).______(thickness)).______(thickness*6))\n##########\n#hackerrank_timeconversion.py\n##########\nfrom datetime import datetime\n\nif __name__ == '__main__' :\n\tstrTime = input()\n\tprint( datetime.strftime( datetime.strptime( strTime, \"%I:%M:%S%p\" ), \"%H:%M:%S\" ) )\n##########\n#hackerrank_title_word.py\n##########\nimport string\ndef solve(s):\n\treturn string.capwords(s, ' ')\n\t# lst = []\n\t# for i in s.split() :\n\t# \tprint(i)\n\t# \tlst.append( i.capitalize() )\n\t# return ' '.join( lst )\n\nif __name__ == '__main__':\n\t# fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n\ts = input()\n\n\tprint(solve(s))\n\n\t# fptr.write(result + '\\n')\n\n\t# fptr.close()\n##########\n#hackerrank_tuple_hash.py\n##########\nprint(hash((1,2)))\n##########\n#hackerrank_valley_count.py\n##########\nfrom itertools import groupby\nif __name__ == '__main__':\n\tn = int(input())\n\tlstSteps = list(input())\n\tvalleycount = 0\n\tprevious = ''\n\tfor i,j in groupby(lstSteps) :\n\t\tif 'D' == previous and 'U' == i and 1 < len(list(j)) :\n\t\t\tvalleycount = valleycount + 1\n\t\tprevious = i\n\tprint(valleycount)\n\n# This script has failed exam.\n##########\n#juice_stalls.py\n##########\nimport sys\n\nnumOfStalls = 4\ndistOfStalls = [5,7,8,10]\njuiceQuantity = [2,5,2,6]\ndistance = 15\ninitialEnergy = 17\n\nnumOfStalls = int(raw_input())\ndistOfStalls = map( int, (raw_input()).split() )\njuiceQuantity = map( int, (raw_input()).split() )\ndistance = int(raw_input())\ninitialEnergy = int(raw_input())\n\n\n# initialEnergy = 5\n# juiceQuantity = [2,3,1,5]\n\nstallsVisited = 0\ndisttocover = 0\ncurrentEnergy = initialEnergy\n\nif initialEnergy >= distance :\n\tprint(0)\n\tsys.exit()\n\ni = 0\nwhile i < numOfStalls :\n\n\tif( i == 0 ) :\n\t\tdisttocover = distOfStalls[i]\n\telse :\n\t\tdisttocover = distOfStalls[i] - distOfStalls[i-1]\n\n\tjuiceAtStall = juiceQuantity[i]\n\n\tcurrentEnergy = currentEnergy - disttocover\n\n\tif currentEnergy > 0 :\n\t\tj = i + 1\n\t\twhile j < numOfStalls :\n\n\t\t\tdisttocover1 = distOfStalls[j] - distOfStalls[j-1]\n\n\t\t\tif currentEnergy - disttocover1 > 0 :\n\t\t\t\tcurrentEnergy = currentEnergy - disttocover1\n\t\t\t\tj = j + 1\n\t\t\t\ti = j - 1\n\t\t\telif (currentEnergy - disttocover1) == 0:\n\t\t\t\tcurrentEnergy = (currentEnergy - disttocover1) + juiceQuantity[j]\n\t\t\t\tj = j + 1\n\t\t\t\ti = j\n\t\t\t\tstallsVisited = stallsVisited + 1\n\t\t\t\tbreak\n\t\t\telse :\n\t\t\t\tprint(-1)\n\t\t\t\tsys.exit()\n\n\telif currentEnergy == 0 :\n\t\tstallsVisited = stallsVisited + 1\n\t\tcurrentEnergy = currentEnergy + juiceAtStall\n\t\ti = i + 1\n\telse :\n\t\tprint(-1)\n\t\tsys.exit()\n\nif( currentEnergy < (distance - distOfStalls[numOfStalls-1]) ) :\n\tprint(-1)\nelse :\n\tprint(stallsVisited)\n\n\n##########\n#lamdba_functions.py\n##########\n# f = lambda x,y : return x + y\n\nf = lambda x : x*x\nlstNumbers = [1,2,3,4,5]\n\nprint( [f(i) for i in lstNumbers] )\n\nx = map( f, lstNumbers )\n\nprint(list(x))\n\n\ndef filtereven( i ):\n\tif 0 == i % 2 :\n\t\treturn True\n\treturn False\n\nx = filter( filtereven, lstNumbers)\nprint(list(x))\n\ndef filterodd() :\n\tlstNumbers = [1,2,3,4,5]\n\tfor i in lstNumbers :\n\t\tif 1 == i % 2 :\n\t\t\tyield i\n\nx = filterodd()\nfor i in x:\n\tprint( i )\n##########\n#list_all_files_n_contents.py\n##########\nimport os\n\nobjFile = open( 'new_file.py', 'w' )\n\nfor file_name in os.listdir('./') :\n\tif file_name.endswith( '.py' ) :\n\t\tobjFile.write( '#'*10 )\n\t\tobjFile.write( file_name )\n\t\tobjFile.write( '#'*10 )\n\t\twith open( file_name, 'r' ) as file_to_read :\n\t\t\tline = file_to_read.readline()\n\t\t\twhile line :\n\t\t\t\tobjFile.write( line )\n\t\t\t\tline = file_to_read.readline()\n\n\nobjFile.close()\n##########\n#list_comprehensions.py\n##########\na = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]\n\n\n# Print even numbers\nprint( [i for i in a if 0 == i % 2] )\n##########\n#list_less_than_ten.py\n##########\nlstintNumbers = [1,2,5,765,763,3456,7,5632,123,87654,4565,4]\nprint( [ i for i in lstintNumbers if i > 10 ] )\n##########\n#list_overlap.py\n##########\nfrom random import randint\n\nlstintRandom1 = range( 0, randint( 1, 100 ) )\nlstintRandom2 = range( 0, randint( 1, 100 ) )\n\nprint( lstintRandom1 )\nprint( lstintRandom2 )\n\nprint( [ i for i in lstintRandom1 if i in lstintRandom2 ] )\n##########\n#list_overlap_comprehensions.py\n##########\nimport random\n\nlistA = random.sample( range( 100 ), 15 )\nlistB = random.sample (range( 100 ), 20 )\n\nprint(listA)\nprint(listB)\n\nprint( [ i for i in set(listA) if i in set(listB)] )\n##########\n#list_remove_duplicates.py\n##########\nlstNumbers = [1,2,3,4,5,6,7,9,9,987,4,234,5,3456,2,1,234,6,2,112,34,56,2,2345,2,345,6,2,334,43,123,4,45,3]\n\nprint(len((lstNumbers)))\nprint((list(set(lstNumbers))))\n\nlstNew = []\nfor i in lstNumbers :\n\tif( i not in lstNew ):\n\t\tlstNew.append(i)\n\nprint(lstNew)\n##########\n#matrix_and_transpose.py\n##########\nnumbers = map( int, (raw_input()).split() )\nstart, rows, columns = numbers[0], numbers[1], numbers[2]\n\nx = []\nfor i in range(0,rows) :\n\trows = []\n\tfor j in range(0,columns) :\n\t\trows.append(start)\n\t\tstart = start +1\n\tx.append(rows)\n\nprint(x)\n\n\nresult = x\n\nxt =x\n\nxt = [[x[j][i] for j in range(len(x))] for i in range(len(x[0]))]\nprint(xt)\n\nresult = [[sum(a*b for a,b in zip(X_row,Y_col)) for Y_col in zip(*xt)] for X_row in x]\n\nfor r in result:\n\tprint(r)\n##########\n#odd_or_even.py\n##########\nnumber = input( 'Gimmie number ' )\nif False == number.isdigit() :\n\tprint( 'You didn\\'t give integer, bye!' )\nelse :\n\tnumber = int( number )\n\tif 0 == number % 2:\n\t\tprint( 'It is even' )\n\telse :\n\t\tprint( 'It is odd' )\n##########\n#password_generator.py\n##########\nimport random\nimport string\n\npassword = ''\npassword = password + ''.join( random.sample( string.ascii_lowercase, 5) )\npassword = password + ''.join( random.sample( string.ascii_uppercase, 5 ) )\npassword = password + ''.join( random.sample( string.digits, 6 ) )\npassword = password + ''.join( random.sample( string.punctuation, 4 ) )\n\npassword = list( password )\nrandom.shuffle( password )\n\nprint( ''.join( password ) )\n##########\n#program1.py\n##########\nfrom itertools import groupby\ndef findDifferentWays(size, allowedChanges, str):\n\n\tlstNumbers = list(map(int, list(str) ))\n\n\tlstNumbers.sort()\n\n\tlstLengths = []\n\tfor i,j in groupby(lstNumbers) :\n\t\tlstLengths.append( len(list(j)) )\n\n\t# print(lstLengths)\n\tnumofZeros = lstLengths[0]\n\tnumofOnes = lstLengths[1]\n\n\tif allowedChanges < numofZeros :\n\t\treturn (numofOnes - allowedChanges)\n\telif allowedChanges == numofZeros :\n\t\treturn numofZeros-numofOnes\n\telse :\n\t\treturn allowedChanges\n\n\nprint(findDifferentWays(7,1,'1010101'))\nprint(findDifferentWays(5,3,'01010'))\n##########\n#program2.py\n##########\n\n##########\n#read_from_file.py\n##########\nnames = {}\nwith open( 'names.txt', 'r' ) as open_file :\n\tline = open_file.readline()\n\twhile line :\n\t\tline = line.strip( '\\n' )\n\t\tif line not in names :\n\t\t\t# names.append( line )\n\t\t\tnames[line] = 1\n\t\telse :\n\t\t\tnames[line] = names[line] + 1\n\t\t# print(line)\n\t\tline = open_file.readline()\nprint(names)\n##########\n#read_from_json.py\n##########\nimport json\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\nmonths = {}\n\nwith open( 'json_file.json' ) as json_file :\n\tline = json_file.read()\n\tline = json.loads( line )\n\tfor i in line :\n\t\tjsondate = line[i].split('/')\n\t\tdatem = datetime( int(jsondate[2]), int(jsondate[0]), int(jsondate[1]) ).strftime('%b')\n\t\tif( datem not in months ) :\n\t\t\tmonths[datem] = 1\n\t\telse :\n\t\t\tmonths[datem] = months[datem] + 1\n\nplt.bar( months.keys(), months.values() )\nplt.show()\n##########\n#regex.py\n##########\nimport re\n\nstrLine = \"The rain in Spain\"\n\n# findall - prints all matches, returns \"None\" otherwise\nlstMatches = re.findall( \"ai\", strLine )\nprint( lstMatches )\n\nlstMatches = re.findall( \"[\\a-z]\", strLine )\nprint( lstMatches )\n\n# search - it searches for the match and if there are more than one match first one is returned returns \"None\" otherwise\n\nlstMatches = re.search( 'rain', strLine )\n\nprint( lstMatches.start() )\n\n# split - returns the list, where the string splits at match\nlstMatches = re.split( 'ain', strLine )\nprint( lstMatches )\n\n# sub - replaces every match with given string\nlstMatches = re.sub( \"\\s\", \"9\", strLine )\nprint( lstMatches )\n\nip = \"7.0.0.1\"\nstrMatches = re.findall( \"^\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}$\", ip )\nprint(strMatches)\n\nfor i in range(1,10) :\n\tprint(i)\n##########\n#reverse_word_order.py\n##########\nstrName = 'My name is Michael'\n\nstrName = ' ' .join(strName.split()[ : : -1])\nprint( strName )\n##########\n#rock_scissor_paper.py\n##########\nquit_game = False\nwhile False == quit_game :\n\tuser_input = input( 'Enter \"q\" to quit the game or press enter to continue game ' )\n\tif \"q\" == user_input :\n\t\tprint( 'Bye!' )\n\t\tquit_game = True\n\telse :\n\t\tplayer_1 = int( input( 'Player 1, enter your weapon no. \\n 1. Rock \\n 2. Scissor \\n 3. Paper : ' ) )\n\t\tplayer_2 = int( input ( 'Player 2, enter your weapon no. \\n 1. Rock \\n 2. Scissor \\n 3. Paper : ' ) )\n\n\t\tif( player_1 == player_2 ) :\n\t\t\tprint( 'Tied!!' )\n\t\telif( 1 == player_1 ) :\n\t\t\tif( 2 == player_2 ) :\n\t\t\t\tprint( 'Winner: Player 1' )\n\t\t\telse :\n\t\t\t\tprint( 'Winner: Player 2' )\n\t\telif( 2 == player_1 ) :\n\t\t\tif( 3 == player_2 ) :\n\t\t\t\tprint( 'Winner: Player 1' )\n\t\t\telse :\n\t\t\t\tprint( 'Winner: Player 2' )\n\t\telif( 3 == player_1 ) :\n\t\t\tif( 1 == player_2 ) :\n\t\t\t\tprint( 'Winner: Player 1' )\n\t\t\telse :\n\t\t\t\tprint( 'Winner: Player 2' )\n##########\n#string_functions.py\n##########\n# Capitalize first letter of string\nstrLine = 'this is text'\nprint( strLine.capitalize() )\n\n# title - Convert first character of the word to upper in given string\nprint( strLine.title() )\n\n# Upper - convert to uppercase\nprint( strLine.upper() )\n\n# Lower - convert to lowercase\nprint( strLine.lower() )\n\n# Casefold - convert to lowercase - simmilar to lower\nprint( strLine.casefold() )\n\n# Swapcase - Swap the case of string\nstrLine = 'ThIs Is TeXt'\nprint( strLine.swapcase() )\n\n# Count - Count the string occurances in given string\nstrLine ='I love apples, apple are my favorite fruit'\nprint( strLine.count( 'apple' ) )\nprint( strLine.count( 'e' ) )\n\n#Startswith - returns true if it starts with given letter - This is case sensitive\nprint( strLine.startswith( 'i' ) )\nprint( strLine.startswith( 'I' ) )\n\n#Endswith - returns true if it string ends with given character - Case sensitive function\nprint( strLine.endswith('t') )\nprint( strLine.endswith('T') )\n\n# Find - finds the given string in string and returns position of first occurance else -1\nprint( strLine.find( 'apple' ) )\n\n# Index - Returns the location of the searched string, in given string else raises exception\nprint( strLine.index( 'apple' ) )\n\n#isalpha - Checks if the string has all letters\nstrLine = 'Test'\nprint( strLine.isalpha() )\n\n# isalnum - check if the string is alphanumeric\nstrLine = 'Test123'\nprint( strLine.isalnum() )\n\n# isdigit - Checks if string has all digits\nstrLine = '12345'\nprint( strLine.isdigit() )\n\n# isdecimal - Checks if all characters are decimals\nstrLine = '12345'\nprint( strLine.isdecimal() )\n\n# isnumeric - Checks if all characters are numeric\nstrLine = '1234567'\nprint( strLine.isnumeric() )\n\n# islower - Checks if all character are lower\nstrLine = 'test'\nprint( strLine.islower() )\n\n# isupper - Checks if all characters are upper\nstrLine = 'TEST'\nprint( strLine.isupper() )\n\n# join - joins the list items by a seperator\nlstNames = ['one', 'two', 'three', 'four']\nprint( ' & '.join( lstNames ) )\n\n# split - Split the string by seperator and converts it to list\nstrLine = '&'.join( lstNames )\nprint( strLine.split( '&' ) )\n\n# replace - replace the substrings by given string\nstrLine ='I love apples, apples are my favorite fruit'\nstrLine = strLine.replace( 'apples', 'mangoes' )\nprint( strLine )\n\n# lstrip - strip spaces/characters from left of the string\nstrLine =' Banana Apple'\nprint( strLine.lstrip() )\n\n# rstrip - strip spaces/characters from right of the string\nstrLine ='Apple Banana '\nprint( strLine.rstrip() )\n\n# rfind - find last occurance of substring in a given string\nstrLine ='I love apples, apples are my favorite fruit'\nprint( strLine.rfind( 'apple' ) )\n\n# rsplit - will start splitting from right and max is the number how many parts to b\n# e split into, if not given it works as split()\nstrLine ='I love apples, apples are my favorite fruit'\nprint( strLine.rsplit( ' ', 2 ) )\n\n# splitline - Split the string where new line is given\nstrLine = \"Thank you for the music\\nWelcome to the jungle\"\nprint(strLine.splitlines())\n\n\n# zfill - insert the zeros at begining of the string till it reaches the length\nstrLine = 'Zero'\nprint( strLine.zfill( 10 ) )\n\n# pad with characters\nstrLine = 'One'\nprint(strLine.ljust(5,'1'))\nprint(strLine.rjust(5,'1'))\n\n\nstrLine = 1\nprint( strLine.isdigit() )\n\n##########\n#test.py\n##########\nimport os\nlstFiles = (list(os.listdir('./')))\nwith open('file_names.py', 'w') as writefile :\n\tfor filename in lstFiles:\n\t\tif filename.endswith('.py') :\n\t\t\tprint( filename )\n\t\t\twritefile.write('\\n'+'#'*10+'\\n')\n\t\t\twritefile.write('#'+filename+'\\n')\n\t\t\twritefile.write('#'*10+'\\n')\n\t\t\twith open( filename, 'r' ) as readfile:\n\t\t\t\t# writefile.writelines(readfile.readlines())\n\t\t\t\tfor line in readfile.readlines() :\n\t\t\t\t\twritefile.write(line)\n##########\n#write_to_file.py\n##########\nwith open( 'writetofile.txt', 'w' ) as open_file:\n\tfor i in range(10) :\n\t\topen_file.writelines( str(i) )\n##########\n#write_to_json.py\n##########\nimport json\n\nbirthdays = {\n\t\t'Albert Einstein': '03/14/1879',\n\t\t'Benjamin Franklin': '01/17/1706',\n\t\t'Ada Lovelace': '12/10/1815',\n\t\t'Donald Trump': '06/14/1946',\n\t\t'Rowan Atkinson': '01/6/1955'}\n\nwith open( 'json_file.json', 'a' ) as json_file :\n\tjson_file.writelines( json.dumps( birthdays ) )","sub_path":"file_names.py","file_name":"file_names.py","file_ext":"py","file_size_in_byte":37823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"298688846","text":"#!/usr/bin/env python\n\nPACKAGE = 'amr_bugs'\nNODE = 'bug2'\n\nimport roslib\nroslib.load_manifest(PACKAGE)\nimport rospy\nimport tf\nimport smach\nfrom smach_ros import SimpleActionState, ActionServerWrapper\nimport dynamic_reconfigure.client\nfrom std_srvs.srv import Empty\nfrom geometry_msgs.msg import PoseStamped\n\nfrom amr_msgs.msg import MoveToAction, MoveToActionGoal\nfrom amr_bugs.bug_brain import BugBrain\nfrom amr_bugs.bug_brain_visualizer import BugBrainVisualizer\n\n\nclass NewGoalState(smach.State):\n \"\"\"\n This state retrieves the wallfollower mode from its dynamic reconfiguration\n server and creates a new instance of BugBrain for the new goal.\n \"\"\"\n def __init__(self, ws):\n smach.State.__init__(self,\n outcomes=['succeeded'],\n input_keys=['action_goal'])\n self.ws = ws\n self.d_client = dynamic_reconfigure.client.Client('wallfollower')\n\n def execute(self, ud):\n wallfollower_config = self.d_client.get_configuration()\n self.ws.new_brain(ud.action_goal.target_pose.pose.position.x,\n ud.action_goal.target_pose.pose.position.y,\n wallfollower_config['mode'])\n return 'succeeded'\n\n\nclass WallfollowerState(smach.State):\n \"\"\"\n This state enables the wallfollower node and goes into an infinite loop\n until BugBrain decides that it is time to leave the wall, or until a\n preemption request is received. Then it disables the wallfollower node and\n returns.\n \"\"\"\n def __init__(self, freq=10):\n smach.State.__init__(self, outcomes=['leave_wall',\n 'unreachable_goal',\n 'preempted',\n 'aborted'])\n self.tf_listener = tf.TransformListener(True, rospy.Duration(5))\n self.brain = None\n self.brain_vis = None\n self.frequency = freq\n\n def execute(self, ud):\n # Try to enable wallfollower\n if not self.enable_wallfollower():\n return 'aborted'\n # Notify brain that we started to follow a wall\n self.brain.follow_wall(*self.get_current_pose())\n # Go into infinite loop until preemped or shut down\n r = rospy.Rate(self.frequency)\n while not rospy.is_shutdown() and not self.preempt_requested():\n x, y, theta = self.get_current_pose()\n # Check brain's belief about the reachability of the goal\n if self.brain.is_goal_unreachable(x, y, theta):\n self.disable_wallfollower()\n return 'unreachable_goal'\n # Check if it is the time to leave the wall\n if self.brain.is_time_to_leave_wall(x, y, theta):\n # Try to disable wallfollower\n if not self.disable_wallfollower():\n return 'aborted'\n # Notify brain that we left the wall\n self.brain.leave_wall(x, y, theta)\n return 'leave_wall'\n r.sleep()\n # In case of preemption also disable wallfollower\n self.disable_wallfollower()\n self.service_preempt()\n return 'preempted'\n\n def new_brain(self, x, y, mode):\n \"\"\"Create a new instance of BugBrain (and visualizer as well).\"\"\"\n if self.brain_vis:\n self.brain_vis.shutdown()\n self.brain = BugBrain(x, y, mode)\n self.brain_vis = BugBrainVisualizer(self.brain)\n\n def get_current_pose(self):\n \"\"\"Get current (2D) pose as a tuple (x, y, theta).\"\"\"\n time = self.tf_listener.getLatestCommonTime('odom', 'base_link')\n p, q = self.tf_listener.lookupTransform('odom', 'base_link', time)\n e = tf.transformations.euler_from_quaternion(q)\n return p[0], p[1], e[2]\n\n def enable_wallfollower(self):\n try:\n rospy.ServiceProxy('wallfollower/enable', Empty)()\n return True\n except rospy.ServiceException:\n return False\n\n def disable_wallfollower(self):\n try:\n rospy.ServiceProxy('wallfollower/disable', Empty)()\n return True\n except rospy.ServiceException:\n return False\n\nif __name__ == '__main__':\n rospy.init_node(NODE)\n\n sm = smach.StateMachine(input_keys=['action_goal'],\n outcomes=['succeeded',\n 'preempted',\n 'aborted',\n 'unreachable_goal'])\n #sm.userdata.action_feedback = None\n ws = WallfollowerState()\n with sm:\n smach.StateMachine.add('NEW_GOAL',\n NewGoalState(ws),\n transitions={'succeeded': 'MOVE_TO'})\n smach.StateMachine.add('MOVE_TO',\n SimpleActionState('/motion_controller/move_to',\n MoveToAction,\n goal_key='action_goal'),\n transitions={'preempted': 'aborted',\n 'aborted': 'WALLFOLLOWER'})\n smach.StateMachine.add('WALLFOLLOWER',\n ws,\n transitions={'leave_wall': 'MOVE_TO'})\n\n # Wrap an action server around the state machine.\n asw = ActionServerWrapper('~move_to',\n MoveToAction,\n wrapped_container=sm,\n succeeded_outcomes=['succeeded'],\n aborted_outcomes=['aborted, unreachable_goal'],\n preempted_outcomes=['preempted'])\n\n # Similarly to how it is done in MotionController, create a combination of\n # listened and re-publisher to allow the user to send a goal as a message\n # (not as an action service request).\n action_goal_pub = rospy.Publisher('~move_to/goal', MoveToActionGoal)\n\n def simple_goal_cb(target_pose):\n rospy.loginfo('Received target pose through the \"simple goal\" topic. '\n 'Wrapping it in the action message and forwarding to '\n 'the server.')\n msg = MoveToActionGoal()\n msg.header.stamp = rospy.Time.now()\n msg.goal.target_pose = target_pose\n action_goal_pub.publish(msg)\n\n simple_goal_sub = rospy.Subscriber('~move_to_simple/goal', PoseStamped,\n simple_goal_cb)\n\n # Run the server\n asw.run_server()\n rospy.spin()\n","sub_path":"amr_bugs/nodes/bug2.py","file_name":"bug2.py","file_ext":"py","file_size_in_byte":6602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"407775590","text":"import time\nimport nmap\nimport smtplib\nfrom datadata import local_ip\nfrom datadata import my_mac_address\nfrom datadata import account\nfrom datadata import pswd\nfrom datadata import f_account\nfrom datadata import server\nfrom datadata import port\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\nnm = nmap.PortScanner()\n\nnm.scan(hosts=local_ip, arguments='-sP', sudo=True)\n\nhost_list = nm.all_hosts()\n\nserver = smtplib.SMTP(server, port)\nserver.starttls()\nserver.login(account, pswd)\nmessage = MIMEMultipart()\nbody_msg = 'Este es un bot que te avisa cuando tu hijo esta en casa'\n\nn = len(host_list)\nfor host in host_list:\n if 'mac' in nm[host]['addresses']:\n print(host+' : '+nm[host]['addresses']['mac'])\n if my_mac_address == nm[host]['addresses']['mac']:\n print('Dispositivo encontrado, enviando correo:', my_mac_address)\n message['From']=account\n message['To']=f_account\n message['Subject']= \"YA LLEGUE A CASA\"\n message.attach(MIMEText(body_msg, 'plain'))\n server.send_message(message)\n #server.sendmail(account, f_account, \"YA estoy en casa\")\n server.quit()\n n = 123456\n\n\n n = n-1\n if n == 0:\n print('No estoy en casa')\n message['From']=account\n message['To']=f_account\n message['Subject']= \"NO estoy en casa\"\n message.attach(MIMEText(body_msg, 'plain'))\n server.send_message(message)\n #server.sendmail(account, f_account, \"AUN NO llego a casa\")\n server.quit()\n elif n == 123455:\n \n print('Ya estoy en casa, apagando programa...')\n time.sleep(5)\n quit()\n \n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"527867688","text":"from zoundry.appframework.ui.events.listevents import ZEVT_CHECKBOX_LIST_CHANGE\r\nfrom zoundry.appframework.ui.widgets.controls.common.panel import ZTransparentPanel\r\nfrom zoundry.appframework.ui.widgets.controls.listex import ZCheckBoxListViewWithButtons\r\nfrom zoundry.blogapp.messages import _extstr\r\nfrom zoundry.blogapp.ui.common.pubdatawidgets import ZPingListContentProvider\r\nfrom zoundry.blogapp.ui.dialogs.accountmanpages.acctsubpages.subpage import ZAccountPrefsSubPage\r\nimport wx\r\n\r\n# ------------------------------------------------------------------------------\r\n# Implements the account preferences sub-page for ping sites options.\r\n# ------------------------------------------------------------------------------\r\nclass ZPingSitesPrefSubPage(ZAccountPrefsSubPage):\r\n\r\n def __init__(self, parent, session):\r\n ZAccountPrefsSubPage.__init__(self, parent, session)\r\n # end __init__()\r\n\r\n def _createWidgets(self):\r\n self.overrideCB = wx.CheckBox(self, wx.ID_ANY, self._getOverridePingSitesLabel())\r\n self.panel = ZTransparentPanel(self, wx.ID_ANY)\r\n\r\n self.staticBox = wx.StaticBox(self.panel, wx.ID_ANY, _extstr(u\"pingsubpage.PingSites\")) #$NON-NLS-1$\r\n self.contentProvider = ZPingListContentProvider()\r\n self.pingSites = ZCheckBoxListViewWithButtons(self.contentProvider, self.panel, wx.ID_ANY)\r\n # end _createWidgets()\r\n\r\n def _getOverridePingSitesLabel(self):\r\n return _extstr(u\"pingsubpage.OverrideGlobalPingSettings\") #$NON-NLS-1$\r\n # end _getOverridePingSitesLabel()\r\n\r\n def _bindWidgetEvents(self):\r\n self.Bind(wx.EVT_CHECKBOX, self.onOverrideCB, self.overrideCB)\r\n self.Bind(ZEVT_CHECKBOX_LIST_CHANGE, self.onCheckListChange, self.pingSites)\r\n # end _bindWidgetEvents()\r\n\r\n def _populateWidgets(self):\r\n override = self._getSession().isOverridePingSites()\r\n self.overrideCB.SetValue(override)\r\n self.panel.Enable(override)\r\n self.contentProvider.setSelectedPingSites(self._getSession().getSelectedPingSites())\r\n self.pingSites.refresh()\r\n # end _populateWidgets()\r\n\r\n def _layoutWidgets(self):\r\n sbSizer = wx.StaticBoxSizer(self.staticBox, wx.VERTICAL)\r\n sbSizer.Add(self.pingSites, 1, wx.EXPAND | wx.ALL, 8)\r\n\r\n self.panel.SetSizer(sbSizer)\r\n self.panel.SetAutoLayout(True)\r\n\r\n box = wx.BoxSizer(wx.VERTICAL)\r\n box.Add(self.overrideCB, 0, wx.ALL | wx.EXPAND, 5)\r\n box.Add(self.panel, 1, wx.ALL | wx.EXPAND, 5)\r\n\r\n self.SetAutoLayout(True)\r\n self.SetSizer(box)\r\n self.Layout()\r\n # end layoutWidgets()\r\n\r\n def onOverrideCB(self, event):\r\n override = event.IsChecked()\r\n self._getSession().setOverridePingSites(override)\r\n if override:\r\n sites = self.contentProvider.getSelectedPingSites()\r\n self._getSession().setSelectedPingSites(sites)\r\n self._populateWidgets()\r\n event.Skip()\r\n # end onOverrideCB()\r\n\r\n def onCheckListChange(self, event):\r\n if self.overrideCB.IsChecked():\r\n sites = self.contentProvider.getSelectedPingSites()\r\n self._getSession().setSelectedPingSites(sites)\r\n event.Skip()\r\n # end onCheckListChange()\r\n\r\n# end ZPingSitesPrefSubPage\r\n","sub_path":"src/python/zoundry/blogapp/ui/dialogs/accountmanpages/acctsubpages/pingsubpage.py","file_name":"pingsubpage.py","file_ext":"py","file_size_in_byte":3281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"623015943","text":"# ----------\n# User Instructions:\n# \n# Define a function, search() that returns a list\n# in the form of [optimal path length, row, col]. For\n# the grid shown below, your function should output\n# [11, 4, 5].\n#\n# If there is no valid path from the start point\n# to the goal, your function should return the string\n# 'fail'\n# ----------\n\n# Grid format:\n# 0 = Navigable space\n# 1 = Occupied space\n\n\n#grid = [[0, 0, 1, 0, 0, 0],\n# [0, 0, 1, 0, 0, 0],\n# [0, 0, 0, 0, 1, 0],\n# [0, 0, 1, 1, 1, 0],\n# [0, 0, 0, 0, 1, 0]]\n\ngrid = [[0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0]]\n\nheuristic = [[9, 8, 7, 6, 5, 4],\n [8, 7, 6, 5, 4, 3],\n [7, 6, 5, 4, 3, 2],\n [6, 5, 4, 3, 2, 1],\n [5, 4, 3, 2, 1, 0]]\n\n# Initial point, goal and cost\ninit = [0, 0]\ngoal = [len(grid)-1, len(grid[0])-1]\ncost = 1\n\n# possible movements\ndelta = [[-1, 0], # go up\n [ 0,-1], # go left\n [ 1, 0], # go down\n [ 0, 1]] # go right\n\ndelta_name = ['^', '<', 'v', '>']\n\n# Handy function to display a matrix\ndef print_matrix(matrix):\n for row in range(len(matrix)):\n print(matrix[row])\n\ndef search():\n # Buffer to mark closed nodes\n \n # Init expand, closed and directions and directions_mark matrices\n node_index = 0\n expand = []\n closed = []\n directions = []\n directions_marks = []\n\n for row in range(len(grid)):\n expand_row = []\n for col in range(len(grid[0])):\n expand_row.append(0)\n expand.append(expand_row)\n \n for row in range(len(grid)):\n closed_row = []\n for col in range(len(grid[0])):\n closed_row.append(0)\n closed.append(closed_row)\n\n for row in range(len(grid)):\n directions_row = []\n for col in range(len(grid[0])):\n directions_row.append(\" \")\n directions.append(directions_row)\n\n for row in range(len(grid)):\n directions_marks_row = []\n for col in range(len(grid[0])):\n directions_marks_row.append(0)\n directions_marks.append(directions_marks_row)\n\n # Mark initial point as closed \n closed[init[0]][init[1]] = 1\n\n row = init[0]\n col= init[1]\n g = 0\n f = g + heuristic[row][col]\n\n # Init open list\n open = [[f, g, row, col]]\n\n # process flags\n found = False\n resign = False\n\n # print('Initial open list')\n # print_matrirow(open)\n\n # Main iteration\n while found is False and resign is False: \n # Empty open list means no solution\n if len(open) == 0:\n resign = True\n print('fail')\n # Else continue search\n else:\n # Remove minor g value node from list\n open.sort()\n open.reverse()\n next = open.pop()\n\n g = next[1]\n row = next[2]\n col = next[3]\n\n # Check if next node is the goal\n if row == goal[0] and col == goal[1]:\n found = True\n directions[row][col] = \"*\"\n print(next)\n # It is not the goal then evaluate movements\n else:\n # Explore winning node, try to move on all possible directions\n for i in range(len(delta)):\n row_inc = row + delta[i][0]\n col_inc = col + delta[i][1]\n # Only add nodes within the grid\n if row_inc >= 0 and col_inc >= 0 and row_inc < len(grid) and col_inc < len(grid[0]):\n # Only add nodes on available locations (open and no obstacle)\n if closed[row_inc][col_inc] == 0 and grid[row_inc][col_inc] == 0:\n # Update previous cost\n g_inc = g + cost\n f = g_inc + heuristic[row_inc][col_inc]\n next_step = [f, g_inc, row_inc, col_inc]\n open.append(next_step)\n node_index += 1\n directions[row][col] = delta_name[i]\n expand[row_inc][col_inc] = node_index\n closed[row_inc][col_inc] = 1\n\n # Set to -1 no expanded locations\n for row in range(len(expand)):\n for col in range(len(expand[0])):\n if expand[row][col] == 0 and not (row == init[0] and col == init[1]):\n expand[row][col] = -1\n\n # Analyze directions to get clean final path\n found = False\n row_cur = init[0]\n col_cur = init[1]\n\n while not found:\n if row_cur == goal[0] and col_cur == goal[1]:\n found = True\n directions_marks[row_cur][col_cur] = 1\n if directions[row_cur][col_cur] == '>':\n col_cur += 1\n elif directions[row_cur][col_cur] == '<':\n col_cur -= 1\n elif directions[row_cur][col_cur] == '^':\n row_cur -= 1\n elif directions[row_cur][col_cur] == 'v':\n row_cur += 1\n\n for row in range(len(directions)):\n for col in range(len(directions[0])):\n if directions_marks[row][col] == 0:\n directions[row][col] = ' '\n\n print(\"-------------\")\n print(\"Expand matrix\")\n print_matrix(expand)\n\n print(\"-------------\")\n print(\"Directions matrix\")\n print_matrix(directions)\n\nsearch()\n\n","sub_path":"path_planning_test1/path_planning_a_start.py","file_name":"path_planning_a_start.py","file_ext":"py","file_size_in_byte":5445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"552048641","text":"# coding=utf-8\n\"\"\"\nadd log.\nps>!!不知道怎么回事在flask-script下add log不起作用，这个问题有待解决.\n\n :copyright: (c) 2015 by fangpeng.\n :license: MIT, see LICENSE for more details.\n\"\"\"\n__date__ = '12/18/15'\n\nimport logging\nfrom flask import Flask\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n assert 0 == 1 # test\n return 'test'\n\n\nif __name__ == '__main__':\n\n file_handler = logging.FileHandler('flask.log', encoding='UTF-8')\n file_handler.setLevel(logging.DEBUG)\n # logging_format = logging.Formatter(\n # '%(asctime)s - %(levelname)s - %(filename)s - %(funcName)s - %(lineno)s - %(message)s')\n # file_handler.setFormatter(logging_format)\n app.logger.addHandler(file_handler)\n\n app.run(port=8000)","sub_path":"flask-log-app.py","file_name":"flask-log-app.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"575910159","text":"#!/usr/bin/env python\n\nimport os, sys, argparse\n\nBENCHMARK_DIR = os.path.dirname(os.path.abspath(__file__))\n\nTHEMIS_SCRIPTS_DIR = os.path.join(\n BENCHMARK_DIR, os.pardir, os.pardir, os.pardir, \"scripts\", \"themis\")\n\nCLUSTER_SCRIPTS_DIR = os.path.join(THEMIS_SCRIPTS_DIR, \"cluster\")\n\n# Prepend so we can get the other run_benchmark module\nsys.path.insert(0, THEMIS_SCRIPTS_DIR)\n\nfrom run_benchmark import run_benchmark_iterations\nimport utils\n\nsys.path.append(CLUSTER_SCRIPTS_DIR)\n\nfrom conf_utils import read_conf_file\n\ndef main():\n log_directory = read_conf_file(\"cluster.conf\", \"cluster\", \"log_directory\")\n log_directory = os.path.expanduser(log_directory)\n log_directory = os.path.join(log_directory, \"networkbench\")\n\n parser = argparse.ArgumentParser(\n description=\"Harness for network benchmark application\")\n parser.add_argument(\n \"--config\", \"-c\", help=\"config file to use for the benchmark \"\n \"(default: %(default)s)\",\n default=os.path.join(BENCHMARK_DIR, \"config.yaml\"), type=str)\n parser.add_argument(\n \"--log_directory\", \"-l\",\n help=\"directory containing logs for an experiment \"\n \"(default: %(default)s)\",\n default=log_directory)\n parser.add_argument(\n \"--profiler\", help=\"path to the binary of a profiling tool to use, for \"\n \"example valgrind or operf\")\n parser.add_argument(\n \"--profiler_options\", help=\"options surrounded by quotes to pass to \"\n \"the profiler\", type=str, default=\"\")\n parser.add_argument(\n \"--iterations\", \"-i\", help=\"run the benchmark this many times \"\n \"(default: %(default)s)\", type=int, default=1)\n parser.add_argument(\n \"--sleep\", \"-s\", help=\"sleep this many seconds between iterations \"\n \"(default: %(default)s)\", type=int, default=0)\n parser.add_argument(\n \"--per_peer_config\", help=\"use separate config files for each peer, by \"\n \"appending the peer's IP address to the config file name: .A.B.C.D\",\n action=\"store_true\", default=False)\n parser.add_argument(\n \"--dump_core_directory\", \"-d\", help=\"dump core file to this directory \"\n \"if the benchmark crashes\", default=None)\n parser.add_argument(\n \"peer_ips\", help=\"comma delimited list of host IPs to use for \"\n \"benchmarking\")\n parser.add_argument(\n \"--remote_connections_only\", \"-r\", help=\"Only send to remote peers, \"\n \"instead of sending all-to-all, which includes localhost\",\n action=\"store_true\", default=False)\n\n utils.add_interfaces_params(parser)\n\n args = parser.parse_args()\n binary = os.path.join(BENCHMARK_DIR, \"networkbench\")\n delete_output = False\n solo_mode = False\n stage_stats = \"sender,receiver\"\n\n params = \"-REMOTE_CONNECTIONS_ONLY %d\" % (args.remote_connections_only)\n\n run_benchmark_iterations(\n binary, args.log_directory, args.config, args.peer_ips, args.profiler,\n args.profiler_options, args.iterations, args.sleep, delete_output,\n args.per_peer_config, args.dump_core_directory, solo_mode,\n stage_stats, args.interfaces, params)\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"src/tritonsort/benchmarks/networkbench/run_benchmark.py","file_name":"run_benchmark.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"343315742","text":"# coding: utf-8\n\nfrom __future__ import absolute_import, division, print_function, \\\n unicode_literals\n\n\n# Defines standardized Fireworks that can be chained easily to perform various\n# sequences of QChem calculations.\n\n\nfrom fireworks import Firework\n\nfrom atomate.qchem.firetasks.parse_outputs import *\nfrom atomate.qchem.firetasks.run_calc import *\nfrom atomate.qchem.firetasks.write_inputs import *\n\n__author__ = \"Samuel Blau\"\n__copyright__ = \"Copyright 2018, The Materials Project\"\n__version__ = \"0.1\"\n__maintainer__ = \"Samuel Blau\"\n__email__ = \"samblau1@gmail.com\"\n__status__ = \"Alpha\"\n__date__ = \"5/23/18\"\n__credits__ = \"Brandon Wood, Shyam Dwaraknath\"\n\n\nclass OptimizeFW(Firework):\n def __init__(self,\n molecule=None,\n name=\"structure optimization\",\n qchem_cmd=\"qchem\",\n multimode=\"openmp\",\n input_file=\"mol.qin\",\n output_file=\"mol.qout\",\n max_cores=32,\n qchem_input_params=None,\n db_file=None,\n parents=None,\n **kwargs):\n \"\"\"\n Optimize the given structure.\n\n Args:\n molecule (Molecule): Input molecule.\n name (str): Name for the Firework.\n qchem_cmd (str): Command to run QChem. Defaults to qchem.\n multimode (str): Parallelization scheme, either openmp or mpi.\n input_file (str): Name of the QChem input file. Defaults to mol.qin.\n output_file (str): Name of the QChem output file. Defaults to mol.qout.\n max_cores (int): Maximum number of cores to parallelize over. Defaults to 32.\n qchem_input_params (dict): Specify kwargs for instantiating the input set parameters.\n For example, if you want to change the DFT_rung, you should\n provide: {\"DFT_rung\": ...}. Defaults to None.\n db_file (str): Path to file specifying db credentials to place output parsing.\n parents ([Firework]): Parents of this particular Firework.\n **kwargs: Other kwargs that are passed to Firework.__init__.\n \"\"\"\n\n qchem_input_params = qchem_input_params or {}\n t = []\n t.append(\n WriteInputFromIOSet(\n molecule=molecule,\n qchem_input_set=\"OptSet\",\n input_file=input_file,\n qchem_input_params=qchem_input_params))\n t.append(\n RunQChemCustodian(\n qchem_cmd=qchem_cmd,\n multimode=multimode,\n input_file=input_file,\n output_file=output_file,\n max_cores=max_cores,\n job_type=\"normal\"))\n t.append(\n QChemToDb(\n db_file=db_file,\n input_file=input_file,\n output_file=output_file,\n additional_fields={\"task_label\": name}))\n super(OptimizeFW, self).__init__(\n t,\n parents=parents,\n name=name,\n **kwargs)\n\n\nclass FrequencyFlatteningOptimizeFW(Firework):\n def __init__(self,\n molecule=None,\n name=\"frequency flattening structure optimization\",\n qchem_cmd=\"qchem\",\n multimode=\"openmp\",\n input_file=\"mol.qin\",\n output_file=\"mol.qout\",\n max_cores=32,\n qchem_input_params=None,\n max_iterations=10,\n max_molecule_perturb_scale=0.3,\n reversed_direction=False,\n db_file=None,\n parents=None,\n **kwargs):\n \"\"\"\n Iteratively optimize the given structure and flatten imaginary frequencies to ensure that\n the resulting structure is a true minima and not a saddle point.\n\n Args:\n molecule (Molecule): Input molecule.\n name (str): Name for the Firework.\n qchem_cmd (str): Command to run QChem. Defaults to qchem.\n multimode (str): Parallelization scheme, either openmp or mpi.\n input_file (str): Name of the QChem input file. Defaults to mol.qin.\n output_file (str): Name of the QChem output file. Defaults to mol.qout.\n max_cores (int): Maximum number of cores to parallelize over. Defaults to 32.\n qchem_input_params (dict): Specify kwargs for instantiating the input set parameters.\n For example, if you want to change the DFT_rung, you should\n provide: {\"DFT_rung\": ...}. Defaults to None.\n max_iterations (int): Number of perturbation -> optimization -> frequency\n iterations to perform. Defaults to 10.\n max_molecule_perturb_scale (float): The maximum scaled perturbation that can be\n applied to the molecule. Defaults to 0.3.\n reversed_direction (bool): Whether to reverse the direction of the vibrational\n frequency vectors. Defaults to False.\n db_file (str): Path to file specifying db credentials to place output parsing.\n parents ([Firework]): Parents of this particular Firework.\n **kwargs: Other kwargs that are passed to Firework.__init__.\n \"\"\"\n\n qchem_input_params = qchem_input_params or {}\n t = []\n t.append(\n WriteInputFromIOSet(\n molecule=molecule,\n qchem_input_set=\"OptSet\",\n input_file=input_file,\n qchem_input_params=qchem_input_params))\n t.append(\n RunQChemCustodian(\n qchem_cmd=qchem_cmd,\n multimode=multimode,\n input_file=input_file,\n output_file=output_file,\n max_cores=max_cores,\n job_type=\"opt_with_frequency_flattener\",\n max_iterations=max_iterations,\n max_molecule_perturb_scale=max_molecule_perturb_scale,\n reversed_direction=reversed_direction))\n t.append(\n QChemToDb(\n db_file=db_file,\n input_file=input_file,\n output_file=output_file,\n additional_fields={\n \"task_label\": name,\n \"special_run_type\": \"frequency_flattener\"\n }))\n super(FrequencyFlatteningOptimizeFW, self).__init__(\n t,\n parents=parents,\n name=name,\n **kwargs)\n","sub_path":"atomate/qchem/fireworks/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":6688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"6965522","text":"from pico2d import *\nimport random\n\nKPU_WIDTH, KPU_HEIGHT = 1280, 1024\nopen_canvas(KPU_WIDTH, KPU_HEIGHT)\nkpu_ground = load_image('KPU_GROUND.png')\ncharacter = load_image('animation_sheet.png')\nhand = load_image('hand_arrow.png')\n\nrunning = True\nclick = True\nx, y = KPU_WIDTH // 2, KPU_HEIGHT // 2\nframe = 0\nhide_cursor()\nhandx = random.randrange(0,1281)\nhandy = random.randrange(0,1025)\nwhile running:\n clear_canvas()\n if click == True:\n handx = random.randrange(0,1281)\n handy = random.randrange(0,1025)\n click = False\n kpu_ground.draw(KPU_WIDTH // 2, KPU_HEIGHT // 2)\n character.clip_draw(frame * 100, 100 * 1, 100, 100, x, y)\n hand.draw(handx,handy)\n if handx x:\n x += 0.5\n if handyy:\n y+= 0.5\n if handx== x and handy==y:\n click = True\n update_canvas()\n frame = (frame + 1) % 8\n\n \n\nclose_canvas()\n\n\n\n\n","sub_path":"move_character_with_mouse.py","file_name":"move_character_with_mouse.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"272691698","text":"# -*- coding: utf-8 -*-\nimport os, sys, time, datetime, json\nimport csv\nimport helpers.file_helper as file_helper\nimport helpers.log_helper as log_helper\nimport helpers.sql_helper as sql_helper\nimport helpers.send_slack as send_slack\nimport helpers.logger_helper as logger_helper\nimport config, validate_katalon_report, validate_datafeed_log\n\nnow = datetime.datetime.now()\nnow_str = now.strftime(\"%Y-%m-%d %H:%M:%S\")\nnow_id = now.strftime(\"%Y%m%d%H%M%S\")\nlogger = logger_helper.mylog('main').getlog()\n\nif __name__ == \"__main__\":\n #init data what need (format data&time, env, country)\n args = sys.argv[1:]\n if not args:\n logger.info(\"not args\")\n env='Production'\n country='HK'\n report_id='20190520_143048'\n else:\n env = args[0]\n country = args[1]\n report_id = args[2]\n logger.info(\"env:{0}, country:{1}, report_id:{2}\".format(env, country, report_id))\n\n sql_insert_data_list = []\n validate_info = []\n for category in config.autotest_category:\n if category[\"status\"] == 1:\n report_path = config.path_katalon_report.format(env, category[\"name\"], report_id)\n report_result = config.url_ui_automatic_Report.format(report_id)\n is_success_purchase_ui = validate_katalon_report.validate_purchase_ui(now_id, category[\"steps\"], country, report_path,report_result,sql_insert_data_list,True) \n validate_info.append({\n \"type\": category[\"type\"],\n \"is_skip\": False #(not is_success_purchase_ui)\n })\n #if category[\"type\"]!= \"10\":\n # validate_datafeed_log.call_datafeed(env, country)\n sql_insert_data_list = validate_datafeed_log.validate_payment_log(now_id, env, country, validate_info, sql_insert_data_list)\n logger.info(\"sql_insert_data_list: %s\" % sql_insert_data_list)\n \n my_Conn = sql_helper.MYSQL(host=config.aws_sycee_monitor_mysql[\"host\"], user=config.aws_sycee_monitor_mysql[\"user\"], pwd=config.aws_sycee_monitor_mysql[\"pwd\"], db=config.aws_sycee_monitor_mysql[\"db\"]) \n insert_sql = \"\"\"\n Insert into jobsdb_purchase(batch_id, country_code, user_journey, category, response_status, remark, monitor_time, created_time, last_updated_time) \n VALUES (%s, %s, %s, %s, %s, %s, %s, now(), now())\n \"\"\"\n my_Conn.ExecManyQuery(insert_sql, sql_insert_data_list)\n ","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"487770754","text":"#!/usr/bin/env python3\n\nimport yaml\nfrom pathlib import Path\nfrom argparse import ArgumentParser\nfrom mushroom_rl_benchmark import BenchmarkSuite\n\n\ndef get_args():\n parser = ArgumentParser()\n arg_test = parser.add_argument_group('benchmark parameters')\n arg_test.add_argument(\"-e\", \"--env\", type=str, required=True,\n help='Environment to benchmark.')\n arg_test.add_argument(\"-s\", \"--slurm\", action='store_true',\n help='Flag to use of SLURM.')\n arg_test.add_argument(\"-t\", \"--test\", action='store_true',\n help='Flag to test the script and NOT execute the benchmark.')\n arg_test.add_argument(\"-r\", \"--reduced\", action='store_true',\n help='Flag to run a reduced version of the benchmark.')\n\n args = vars(parser.parse_args())\n return args.values()\n\n\nif __name__ == '__main__':\n env_id, use_slurm, test, reduced_experiment = get_args()\n cfg_dir = Path(__file__).parent / 'cfg'\n config_file = cfg_dir / 'env' / (env_id + '.yaml')\n\n agent_data, env_data = yaml.safe_load(open(config_file, 'r')).values()\n\n agents = agent_data.keys()\n agents_params = agent_data.values()\n\n env = env_data['name']\n env_params = env_data['params']\n\n print('Environment:', env)\n print('Agents:', str(list(agents)))\n print('Using SLURM:', use_slurm)\n print('Runing FULL:', reduced_experiment)\n print()\n\n exec_type = 'slurm' if use_slurm else 'parallel'\n slurm_conf = 'params_slurm.yaml' if not reduced_experiment else 'params_slurm_reduced.yaml'\n local_conf = 'params_local.yaml' if not reduced_experiment else 'params_local_reduced.yaml'\n param_file = slurm_conf if use_slurm else local_conf\n\n run_params, suite_params = yaml.safe_load(open(cfg_dir / param_file, 'r')).values()\n\n suite = BenchmarkSuite(\n **suite_params,\n **run_params)\n\n for agent, agent_params in zip(agents, agents_params):\n suite.add_experiment(env, env_params, agent, agent_params)\n\n suite.print_experiments()\n\n if not test:\n suite.run(exec_type=exec_type)\n","sub_path":"benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"499415076","text":"import tkinter as tk\nfrom tkinter import *\nfrom tkinter.filedialog import *\nfrom PIL import ImageTk, Image\nfrom threading import Thread\nimport os\nimport sys\nclass Window ():\n def __init__(self):\n if (os.environ.get('DIPSLAY','') == ''):\n print('no display found')\n os.environ.__setitem__('DISPLAY',':0.0')\n self.numLabels = 6\n self.root = tk.Tk()\n self.root.title(\"NMIoT\")\n self.app=FullScreenApp(self.root)\n self.root.tk_setPalette(background=\"#FFFFFF\")\n self.root.config(cursor=\"none\")\n self.arriving_image= ImageTk.PhotoImage(Image.open(\"arriving.png\").resize((70,70)))\n ####Creating the background\n background_image=ImageTk.PhotoImage(Image.open(\"back.jpg\").resize((self.app.width,self.app.height)))\n background_label = tk.Label(self.root, image=background_image)\n background_label.place(x=0, y=0, relwidth=1, relheight=1)\n background_label.image = background_image\n ####Creating the frame that will hold the time list\n tlframe = Frame(self.root,borderwidth=1)\n tlframe.pack()\n tlframe.place(height=450,width=400,x=850,y=200)\n ####Creating the time and date labels\n self.localTimeLabel= Label(self.root, text=\"\",font=(\"Product Sans\", 130),fg=\"#424242\", bg=\"#e8e8e6\")\n self.localTimeLabel.place(x=50,y=50)\n self.localDateLabel= Label(self.root, text=\"\",font=(\"Product Sans\", 60),fg=\"#666666\", bg=\"#e8e8e6\")\n self.localDateLabel.place(x=70,y=260)\n ####Creating the timeList labels\n self.timeLabels=[]\n self.directionLabels=[]\n for i in range(0,self.numLabels):\n self.timeLabels.append(Label(tlframe, text=\"\",font=(\"Product Sans\", 50)))\n self.timeLabels[i].grid(row = i, column = 1)\n self.directionLabels.append(Label(tlframe, text=\"\",font=(\"Product Sans\", 20)))\n self.directionLabels[i].grid(row = i, column = 0)\n \n\n ####Creating the weatherforecast frame\n self.weatherFrame=tk.Frame(self.root, width=700, height=150, borderwidth=1)\n self.weatherFrame.pack()\n self.weatherFrame.place(x=40,y=550)\n #self.weatherFrame[\"background\"]= \"#f4f4f4\"\n ####Creating the weather cards in the weatherforecast frame\n self.weatheritems=[]\n for i in range(0,7):\n self.weatheritems.append(weatheritem(self.weatherFrame,i))\n\n def start (self):\n self.root.wm_attributes('-fullscreen','true')\n self.root.mainloop()\n\n ############ Update functions definition ############\n def updateDateTime(self,date,time):\n self.localTimeLabel.configure(text=time)\n self.localDateLabel.configure(text=date)\n def updateTimeList(self,timeList):\n for i in range(0,min(len(timeList),self.numLabels)):\n self.directionLabels[i].configure(text= timeList[i].destination+\": \")\n if(timeList[i].eta < 1):\n self.timeLabels[i].configure(text= \"\",image=self.arriving_image)\n else:\n self.timeLabels[i].configure(text=timeList[i].timeStr,fg=timeList[i].color,image = \"\")\n \n def updateWeatherData(self,data):\n for i in range(0,min(7,len(data))):\n self.weatheritems[i].updateWeatherItem(data[i])\n\n\n\n############### Weather item Class definition ###############\nclass weatheritem:\n def __init__(self,wf,index):#Intializing the card elements in their positions,color etc...\n #colors=[\"blue\",\"red\",\"green\",\"orange\",\"white\",\"black\",\"purple\"]\n self.card=Frame(wf,width=100,height=150,padx=0, pady=0)\n self.card.pack()\n self.card.place(x=100*(index), y=0)\n self.highLabel= Label(self.card, text=\"\",font=(\"Product Sans\", 20),fg=\"#5f5f5f\")\n self.highLabel.place(x=10,y=120)\n self.lowLabel= Label(self.card, text=\"\",font=(\"Product Sans\", 20),fg=\"#ababab\")\n self.lowLabel.place(x=70,y=120)\n self.dayLabel= Label(self.card, text=\"\",font=(\"Product Sans\", 15),fg=\"#666666\")\n self.dayLabel.place(x=35,y=0)\n self.bcg_img=ImageTk.PhotoImage(file=\"Weather/sun.png\")\n self.forecast_label= Label(self.card, text=\"\",font=(\"Product Sans\", 10),fg=\"#ababab\")\n self.forecast_label.place(x=25,y=25)\n self.image_label = tk.Label(self.card, image=self.bcg_img,borderwidth=0,highlightthickness=0)\n self.image_label.place(x=35, y=60)\n self.image_label.image = self.bcg_img\n\n def updateWeatherItem(self,data):\n self.highLabel.configure(text=data[\"high\"])\n self.lowLabel.configure(text=data[\"low\"])\n self.dayLabel.configure(text=data[\"day\"])\n self.forecast_label.configure(text=data[\"text\"])\n self.bcg_img=ImageTk.PhotoImage(file=weatheritem.getImageByCode(data[\"code\"]))\n self.image_label.configure(image=self.bcg_img)\n self.image_label.image = self.bcg_img#AS seen on stackoverflow, for the agrbage collector\n @staticmethod\n def getImageByCode(code):\n if( code == 26 or code == 28 or code == 30):\n return \"Weather/cloudy.png\"\n elif (code == 27 or code == 29):\n return \"Weather/cloudynight.png\"\n elif (code == 8 or code == 9 or code == 18 or code ==20 or code ==21 or code ==22 or code == 23): \n return \"Weather/fog.png\"\n elif (code == 40 or code == 35):\n return \"Weather/heavyrain.png\"\n elif (code == 47 or code == 1 or code == 1 or code == 3 or code == 4 or code == 37 or code == 38):\n return \"Weather/lightning.png\"\n elif( code == 5 or code == 6 or code == 10 or code == 10 or code == 11 or code == 12 or code == 45):\n return \"Weather/rain.png\"\n elif (code == 25):\n return \"Weather/sdf.png\"\n elif (code == 46 or code == 7 or code == 13 or code == 14 or code == 15 or code ==16 or code == 17 or code ==41 or code == 42 or code ==43 or code ==46 ):\n return \"Weather/snow.png\"\n elif (code == 31 or code == 32 or code == 33 or code == 34 or code ==36):\n return \"Weather/sun.png\"\n elif( code == 39):\n return \"Weather/sunrain.png\"\n elif (code == 0 or code == 2 or code == 19 or code == 24):\n return \"Weather/wind.png\"\n else :\n return \"Weather/cloudy.png\"\n\n\n############### FullScreenApp Class definition ###############\nclass FullScreenApp:\n padding=3\n dimensions=\"{0}x{1}+0+0\"\n def __init__(self, master, **kwargs):\n self.master=master\n self.width=master.winfo_screenwidth()-self.padding\n self.height=master.winfo_screenheight()-self.padding\n master.geometry(self.dimensions.format(self.width, self.height))\n","sub_path":"WindowManager.py","file_name":"WindowManager.py","file_ext":"py","file_size_in_byte":6729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"480786516","text":"# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n\nimport six\nimport types\nimport pkgutil\nfrom knack import CLI\nfrom knack.cli import logger\nfrom importlib import import_module\nfrom collections import OrderedDict\nfrom knack.arguments import ignore_type\nfrom knack.deprecation import Deprecated\nfrom knack import CLICommandsLoader, ArgumentsContext\nfrom knack.introspection import extract_args_from_signature, extract_full_summary_from_signature\n\nfrom msgraph.cli.core.commands._util import _load_module_command_loader, _load_extension_command_loader\nfrom msgraph.cli.core.invocation import GraphCliCommandInvoker\nfrom msgraph.cli.core.installed_extensions import installed_extensions\nfrom msgraph.cli.core.commands import GraphCommandGroup, GraphCliCommand\nfrom msgraph.cli.core.commands._util import get_arg_list\nfrom msgraph.cli.core.commands.client_factory import resolve_client_arg_name\nfrom msgraph.cli.core.commands.parameters import GraphArgumentContext\nfrom ._help import GraphCliHelp\nfrom msgraph.cli.core.constants import EXCLUDED_PARAMS\n\n__version__ = '1.0.0'\n\n\nclass MainCommandsLoader(CLICommandsLoader):\n \"\"\"\n Loads command_tables from msgraph.cli.command_modules and from installed extensions.\n \"\"\"\n def __init__(self, cli_ctx=None):\n super(MainCommandsLoader, self).__init__(cli_ctx)\n self.cmd_to_loader_map = {}\n self.loaders = []\n self.command_table = dict()\n\n # pylint: disable=too-many-statements\n def load_command_table(self, args):\n \"\"\" Load commands into the command table\n\n :params args: List of the arguments from the commandline\n :type args: list\n :return: The ordered command table\n :rtype: collections.dict\n \"\"\"\n self._update_command_table_from_modules(args)\n self._update_command_table_from_extensions(args)\n return OrderedDict(self.command_table)\n\n def load_arguments(self, command=None):\n \"\"\" Load arguments for the specified command\n\n :param command: The command to load arguments for\n :type command: str\n \"\"\"\n if command is None:\n command_loaders = set()\n for loaders in self.cmd_to_loader_map.values():\n command_loaders = command_loaders.union(set(loaders))\n logger.info('Applying %s command loaders...', len(command_loaders))\n else:\n command_loaders = self.cmd_to_loader_map.get(command, None)\n\n if command_loaders:\n for loader in command_loaders:\n\n # register global args\n with loader.argument_context('') as c:\n c.argument('cmd', ignore_type)\n\n if command is None:\n # load all arguments via reflection\n for cmd in loader.command_table.values():\n cmd.load_arguments() # this loads the arguments via reflection\n loader.skip_applicability = True\n # this adds entries to the argument registries\n loader.load_arguments('')\n else:\n loader.command_name = command\n # this loads the arguments via reflection\n self.command_table[command].load_arguments()\n # this adds entries to the argument registries\n loader.load_arguments(command)\n self.argument_registry.arguments.update(loader.argument_registry.arguments)\n self.extra_argument_registry.update(loader.extra_argument_registry)\n loader._update_command_definitions() # pylint: disable=protected-access\n\n def _update_command_table_from_modules(self, args):\n \"\"\"Loads command_table from msgraph.cli.command_modules\n\n :params args: List of the arguments from the commandline\n \"\"\"\n installed_command_modules = []\n BLACKLISTED_MODS = ['context', 'shell', 'documentdb', 'component']\n\n try:\n modules = import_module('msgraph.cli.command_modules')\n installed_command_modules = [\n modname for _, modname, _ in pkgutil.iter_modules(modules.__path__)\n if modname not in BLACKLISTED_MODS\n ]\n for module in installed_command_modules:\n command_table, group_table = _load_module_command_loader(self, args, module)\n self.command_table.update(command_table)\n self.command_group_table.update(group_table)\n except ImportError as e:\n logger.warning(e)\n\n def _update_command_table_from_extensions(self, args):\n \"\"\"Loads command_table from installed extensions\n\n :params args: List of the arguments from the commandline\n \"\"\"\n try:\n for extension in installed_extensions:\n command_table, group_table = _load_extension_command_loader(self, args, extension)\n self.command_table.update(command_table)\n self.command_group_table.update(group_table)\n except ImportError as e:\n logger.warning(e)\n\n\n# This is the entry point into the Knack CLI framework.\ndef get_default_cli():\n return CLI(\n cli_name='mg',\n commands_loader_cls=MainCommandsLoader,\n invocation_cls=GraphCliCommandInvoker,\n help_cls=GraphCliHelp,\n )\n\n\nclass GraphCommandsLoader(CLICommandsLoader):\n '''This class is used by extensions for command registration.\n '''\n def __init__(self, cli_ctx=None, command_group_cls=None, argument_context_cls=None, **kwargs):\n super(GraphCommandsLoader, self).__init__(cli_ctx=cli_ctx,\n command_cls=GraphCliCommand,\n excluded_command_handler_args=EXCLUDED_PARAMS)\n self.module_kwargs = kwargs\n self._command_group_cls = command_group_cls or GraphCommandGroup\n self._argument_context_cls = ArgumentsContext\n\n def command_group(self, group_name, command_type=None, **kwargs):\n '''Used by extensions to add commands into the command_table\n\n :param group_name: group_name of the set of commands. ie users\n :param command_type: Cli command_type\n '''\n if command_type:\n kwargs['command_type'] = command_type\n return self._command_group_cls(self, group_name, **kwargs)\n\n def _cli_command(self,\n name,\n operation=None,\n handler=None,\n argument_loader=None,\n description_loader=None,\n **kwargs):\n '''Adds a command to the command table\n :param name: command name\n '''\n kwargs['deprecate_info'] = Deprecated.ensure_new_style_deprecation(\n self.cli_ctx, kwargs, 'command')\n\n if operation and not isinstance(operation, six.string_types):\n raise TypeError(\"Operation must be a string. Got '{}'\".format(operation))\n if handler and not callable(handler):\n raise TypeError(\"Handler must be a callable. Got '{}'\".format(operation))\n if bool(operation) == bool(handler):\n raise TypeError(\"Must specify exactly one of either 'operation' or 'handler'\")\n\n name = ' '.join(name.split())\n\n client_factory = kwargs.get('client_factory', None)\n\n def default_command_handler(command_args):\n ''' Handler function for user commands.\n\n :param command_args: list of commandline arguments\n '''\n\n # Gets the handler function from the specified operation template\n op = handler or self.get_op_handler(operation,\n operation_group=kwargs.get('operation_group'))\n op_args = get_arg_list(op)\n\n # Removes cmd from list of command_args. This is because the handler function\n # doesn't expect cmd as an argument.\n cmd = command_args.get('cmd') if 'cmd' in op_args else command_args.pop('cmd')\n\n # Gets the http client. In our case, the client is a GraphSession object.\n client = client_factory(cmd.cli_ctx, command_args) if client_factory else None\n\n # If a client exists, add it to the list of arguments passed to a handler function.\n if client:\n client_arg_name = resolve_client_arg_name(operation, kwargs)\n if client_arg_name in op_args:\n command_args[client_arg_name] = client\n return op(**command_args)\n\n def default_arguments_loader():\n '''Loads handler function's arguments from operation_template\n '''\n # Get the handler function for the specified operation template\n op = handler or self.get_op_handler(operation,\n operation_group=kwargs.get('operation_group'))\n\n # Extract command args from the handler function signature\n cmd_args = list(\n extract_args_from_signature(op, excluded_params=self.excluded_command_handler_args))\n return cmd_args\n\n def default_description_loader():\n '''Loads handler function's description.\n '''\n op = handler or self.get_op_handler(operation,\n operation_group=kwargs.get('operation_group'))\n return extract_full_summary_from_signature(op)\n\n kwargs['arguments_loader'] = argument_loader or default_arguments_loader\n kwargs['description_loader'] = description_loader or default_description_loader\n\n # Adds command to command_table with it's associated command handler and loaders.\n self.command_table[name] = self.command_cls(self, name, handler or default_command_handler,\n **kwargs)\n\n def argument_context(self, scope, **kwargs):\n '''Gets an instance of the ArgumentContext class.\n '''\n return self._argument_context_cls(self, scope, **kwargs)\n\n def _update_command_definitions(self):\n '''Updates command definition with arguments.\n '''\n master_arg_registry = self.cli_ctx.invocation.commands_loader.argument_registry\n master_extra_arg_registry = self.cli_ctx.invocation.commands_loader.extra_argument_registry\n\n for command_name, command in self.command_table.items():\n # Add any arguments explicitly registered for this command\n for argument_name, argument_definition in master_extra_arg_registry[command_name].items(\n ):\n command.arguments[argument_name] = argument_definition\n\n for argument_name in command.arguments:\n overrides = master_arg_registry.get_cli_argument(command_name, argument_name)\n command.update_argument(argument_name, overrides)\n\n def get_op_handler(self, operation, operation_group=None):\n '''Import and load the operation handler\n An operation handle is the function called when a user runs a command.\n\n :param operation: operation template\n '''\n try:\n mod_to_import, attr_path = operation.split('#')\n op = import_module(mod_to_import)\n for part in attr_path.split('.'):\n op = getattr(op, part)\n if isinstance(op, types.FunctionType):\n return op\n return six.get_method_function(op)\n except (ValueError, AttributeError):\n raise ValueError(\"The operation '{}' is invalid\".format(operation))\n\n\n# Generated extensions expect the CommandLoader class to have the name AzCommandLoader\nAzCommandsLoader = GraphCommandsLoader\n","sub_path":"msgraph/cli/core/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"40872711","text":"from selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nfrom components.base_component import BaseComponent\n\n\nclass VacancyListLocators:\n def __init__(self):\n self.root = '//div[@class=\"main-list\"]'\n self.vacancy_list = '//div[@class=\"list-row\"]'\n self.vacancy_list_names = '//div[@class=\"list-row-description__name\"]'\n self.vacancy_list_location = '//div[@class=\"list-row-description__location\"]'\n self.sphere = '(//div[text()=\"Направление\"])/following-sibling::div'\n\n\nclass VacancyList(BaseComponent):\n def __init__(self, driver):\n super(VacancyList, self).__init__(driver)\n\n self.wait = WebDriverWait(self.driver, 20)\n self.locators = VacancyListLocators()\n\n def vacancies_exists_by_profession(self, profession: str) -> bool:\n try:\n elements = WebDriverWait(self.driver, 3).until(\n EC.presence_of_all_elements_located((By.XPATH, self.locators.vacancy_list_names)))\n except TimeoutException:\n return True\n\n for el in elements:\n elem = el.find_element_by_tag_name(\"a\")\n if not str.lower(elem.get_attribute('text')).__contains__(str.lower(profession)):\n return False\n\n return True\n\n def vacancies_exists_by_place(self, place: str) -> bool:\n try:\n elements = WebDriverWait(self.driver, 3).until(\n EC.presence_of_all_elements_located((By.XPATH, self.locators.vacancy_list_location)))\n except TimeoutException:\n return True\n\n for el in elements:\n if not str.lower(el.get_attribute('innerText')).__contains__(str.lower(place)):\n return False\n\n return True\n\n\n def get_sphere(self) -> str:\n return self.get_field(self.locators.sphere)\n\n def click_on_first_vacancy(self):\n element = self.wait.until(\n EC.element_to_be_clickable((By.XPATH, self.locators.vacancy_list)))\n\n element.click()\n\n\n def vacancies_exists_by_name(self, name: str) -> bool:\n try:\n elements = WebDriverWait(self.driver, 3).until(\n EC.presence_of_all_elements_located((By.XPATH, self.locators.vacancy_list_names)))\n except TimeoutException:\n return True\n\n for el in elements:\n elem = el.find_element_by_tag_name(\"a\")\n if str.lower(elem.get_attribute('text')).__contains__(str.lower(name)):\n return True\n\n return False\n\n","sub_path":"components/vacancy_list.py","file_name":"vacancy_list.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"356200143","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 18 10:50:20 2021\n\n@author: shaws\n\"\"\"\n# import pandas to read the data\nimport pandas as pd\n\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.corpus import stopwords\n\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.ensemble import RandomForestClassifier\n\ndf = pd.read_csv(r'Daily news stock prediction/Combined_News_DJIA.csv')\n\n# check for null values\ndf.isnull().sum()\n\n# remove rows with null values\ndf.dropna(inplace= True)\n\nimport seaborn as sns\nsns.countplot(df['Label'])\n\n# split into train and test dataset\ndf_train = df[df['Date'] < '20150101']\ndf_test = df[df['Date'] > '20141231']\n\n\n\ndef clean_data(dataset):\n data = dataset.iloc[:,2:27]\n data.replace(\"[^a-zA-Z]\", \" \", regex=True, inplace=True)\n return data\n\n\ndef combine_data(data):\n headlines = []\n for i in range(len(data.index)):\n headlines.append(' '.join(str(x) for x in data.iloc[i, :]))\n return headlines\n\ndef lemmatize_data(data, lemmatizer):\n cleaned_dataset = []\n for i in range(len(data)):\n clean_text = data[i].lower()\n clean_text = clean_text.split()\n clean_text = [lemmatizer.lemmatize(word) for word in clean_text if word not in stopwords.words('english')]\n cleaned_dataset.append(' '.join(clean_text))\n return cleaned_dataset\n\ndef vectorize_data(data, cv):\n vectorized_dataset = cv.fit_transform(data)\n return vectorized_dataset\n\n\n# clean train and test data\nclean_train_data = clean_data(df_train)\nclean_test_data = clean_data(df_test)\n\n# combine the headllines in single column\ncomb_train_data = combine_data(clean_train_data)\ncomb_test_data = combine_data(clean_test_data)\n\nlemmatizer = WordNetLemmatizer()\n\n# lemmatize data\ntrain_data = lemmatize_data(comb_train_data, lemmatizer)\ntest_data = lemmatize_data(comb_test_data, lemmatizer)\n\ncv = CountVectorizer(ngram_range=(2,2))\n\n# vectorize data\nvec_train_data = vectorize_data(train_data, cv)\nvec_test_data = cv.transform(test_data)\n\n# create classifier\nrf_clf = RandomForestClassifier(n_estimators=200, criterion='entropy')\nrf_clf.fit(vec_train_data, df_train['Label'])\n\n# run precictions on test data\ny_pred = rf_clf.predict(vec_test_data)\n\n# check accuracy and classification report\nfrom sklearn.metrics import classification_report, accuracy_score, confusion_matrix\nprint(classification_report(df_test['Label'], y_pred))\naccuracy_score(df_test['Label'], y_pred)\nconfusion_matrix(df_test['Label'], y_pred)\n\npd.crosstab(df_test[\"Label\"], y_pred, rownames=[\"Actual\"], colnames=[\"Predicted\"])\n\n\n\n\n","sub_path":"Daily news stock prediction/stock_price_prediction.py","file_name":"stock_price_prediction.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"193324102","text":"from flask import Flask,render_template,request\nimport joblib\nimport numpy as np\nfrom keras.models import load_model\nfrom keras import backend as K\n\nmodel=load_model('models/model-189.model')\n\nscaler_data=joblib.load('models/scaler_data.sav')\nscaler_target=joblib.load('models/scaler_target.sav')\n\napp=Flask(__name__) #application\n\n@app.route('/')\ndef index():\n\n\treturn render_template('patient_details.html')\n\n@app.route('/getresults',methods=['POST'])\ndef getresults():\n\n\tresult=request.form \n\n\tprint(result)\n\n\tname=result['name']\n\tgender=float(result['gender'])\n\tage=float(result['age'])\n\ttc=float(result['tc'])\n\thdl=float(result['hdl'])\n\tsmoke=float(result['smoke'])\n\tbpm=float(result['bpm'])\n\tdiab=float(result['diab'])\n\n\ttest_data=np.array([gender,age,tc,hdl,smoke,bpm,diab]).reshape(1,-1)\n\n\ttest_data=scaler_data.transform(test_data) #scaling the features before applying to the model\n\n\tprediction=model.predict(test_data)\n\n\tprediction=scaler_target.inverse_transform(prediction) #inverse scaling the prediction(target) before returning\n\tprint(prediction,prediction[0],prediction[0][0])\n\t\n\tresultDict={\"name\":name,\"risk\":round(prediction[0][0],2)}\n\n\treturn render_template('patient_results.html',results=resultDict)\n\napp.run(debug=True)","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"475665914","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\"\"\"\nArchitecture based on InfoGAN paper.\n\"\"\"\n\nclass Generator(nn.Module):\n def __init__(self):\n super().__init__()\n self.zsize = 74\n self.network = nn.Sequential(\n # FC. 1024 RELU. batchnorm\n nn.ConvTranspose2d(self.zsize, 1024, 1, 1, bias=False),\n nn.BatchNorm2d(1024),\n nn.ReLU(True),\n # FC. 7x7x128 RELU. batchnorm\n nn.ConvTranspose2d(1024, 128, 7, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.ReLU(True), \n # 4x4 upconv, 64 RELU, stride 2. batchnorm. 1 padding for 28x28 restoration.\n nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),\n nn.BatchNorm2d(64),\n nn.ReLU(True),\n # 4x4 upconv. 1 channel.\n nn.ConvTranspose2d(64, 1, 4, 2, 1, bias=False),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n img = self.network(x)\n return img\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super().__init__()\n self.nchannels = 1\n self.convs = nn.Sequential(\n # 4x4 conv. 64 lRELU. stride 2\n nn.Conv2d(self.nchannels, 64, 4, stride=2, padding=1, bias=False),\n nn.LeakyReLU(0.1, inplace=True),\n # 4x4 conv. 128 lRELU. stride 2. batchnorm\n nn.Conv2d(64, 128, 4, stride=2, padding=1, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.1, inplace=True),\n # FC. 1024 lRELU. batchnorm.\n nn.Conv2d(128, 1024, 7, 1, bias=False),\n nn.BatchNorm2d(1024),\n nn.LeakyReLU(0.1, inplace=True)\n )\n \n def forward(self, x):\n # Input x : 28x28 Gray image.\n x = self.convs(x)\n return x\n\nclass DHead(nn.Module):\n def __init__(self):\n super().__init__()\n # output channel would be 1\n # FC output layer for D\n\n self.layers = nn.Sequential(\n nn.Conv2d(1024, 1, 1),\n nn.Sigmoid()\n )\n\n def forward(self, x):\n # output channel would be 1\n output = self.layers(x)\n return output\n\nclass QHead(nn.Module):\n def __init__(self):\n super().__init__()\n # FC.128 - batchnorm - 1RELU - FC.output for Q\n # output channel would be 10, 2, 2 for disc, mu, var\n self.main = nn.Sequential(\n nn.Conv2d(1024, 128, 1, bias=False),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.1, inplace=True)\n )\n self.fcmu = nn.Conv2d(128, 2, 1)\n self.fcvar = nn.Conv2d(128, 2, 1)\n self.fclogits = nn.Conv2d(128, 10, 1)\n \n def forward(self, x):\n output = self.main(x)\n disc_logits, mu, var = self.fclogits(output).squeeze(), self.fcmu(output).squeeze(), self.fcvar(output).squeeze()\n # output channel would be 10, 2, 2 for disc, mu, var\n return disc_logits, mu, torch.exp(var)","sub_path":"models/mnist_model.py","file_name":"mnist_model.py","file_ext":"py","file_size_in_byte":2968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"470667854","text":"class Publish_server():\n\n def __init__(self):\n self._category = ''\n self._publisher = ''\n\n @property\n def __categorias(self):\n categories_file = open('category.txt', 'r')\n category_list = []\n\n for line in categories_file:\n category_list.append(line.strip())\n\n categories_file.close()\n\n return category_list\n\n def __add_category_and_publisher(self, category, term):\n #adiciona no arquivo category.txt\n categories_file = open('category.txt', 'a')\n input_category = category+'\\n'\n categories_file.write(input_category)\n categories_file.close() \n \n #cria um novo arquivo e adiciona novo valor\n publiser_file = open(category, 'w')\n input_term = term+'\\n'\n publiser_file.write(input_term)\n publiser_file.close()\n\n def __append_new_publisher(self, category, term):\n publiser_file = open(category, 'a')\n input_term = term+'\\n'\n publiser_file.write(input_term)\n publiser_file.close()\n\n def category(self, new_category):\n self._category = new_category.strip()\n\n def publisher(self, new_term):\n self._publisher = new_term.strip()\n categories = self.__categorias\n if (self._category in categories):\n self.__append_new_publisher(self._category, self._publisher)\n else:\n self.__add_category_and_publisher(self._category, self._publisher)\n\n\npublicacao = Publish_server()\n\npublicacao.category('carros')\npublicacao.publisher('ferrari')\n\npublicacao.category('carros')\npublicacao.publisher('palio')\n\npublicacao.category('beleza')\npublicacao.publisher('maquiagem')\n\n\n \n \n","sub_path":"sistemas distribuidos/Publish–subscribe pattern/publisher.py","file_name":"publisher.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"651945432","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def buildTree(self, preorder, inorder):\n \"\"\"\n :type preorder: List[int]\n :type inorder: List[int]\n :rtype: TreeNode\n \"\"\"\n a = preorder\n if len(a) == 0:\n return None\n b = inorder\n r = TreeNode(a[0])\n ix = b.index(a[0])\n r.left = self.buildTree(a[1:ix+1], b[:ix])\n r.right = self.buildTree(a[ix+1:], b[ix+1:])\n return r\n","sub_path":"problems/105/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"549904053","text":"import os\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport pickle\r\nfrom PA4_utils import load_image, load_image_gray\r\nimport cyvlfeat as vlfeat\r\nimport sklearn.metrics.pairwise as sklearn_pairwise\r\nfrom sklearn.svm import LinearSVC\r\nfrom sklearn.multiclass import OneVsRestClassifier\r\nfrom IPython.core.debugger import set_trace\r\nimport statistics\r\nfrom statistics import mode\r\n\r\ndef build_vocabulary(image_paths, vocab_size = 100):\r\n \"\"\"\r\n This function will sample SIFT descriptors from the training images,\r\n cluster them with kmeans, and then return the cluster centers.\r\n\r\n Useful functions:\r\n - Use load_image(path) to load RGB images and load_image_gray(path) to load\r\n grayscale images\r\n - frames, descriptors = vlfeat.sift.dsift(img)\r\n http://www.vlfeat.org/matlab/vl_dsift.html\r\n - frames is a N x 2 matrix of locations, which can be thrown away\r\n here (but possibly used for extra credit in get_bags_of_sifts if\r\n you're making a \"spatial pyramid\").\r\n - descriptors is a N x 128 matrix of SIFT features\r\n Note: there are step, bin size, and smoothing parameters you can\r\n manipulate for dsift(). We recommend debugging with the 'fast'\r\n parameter. This approximate version of SIFT is about 20 times faster to\r\n compute. Also, be sure not to use the default value of step size. It\r\n will be very slow and you'll see relatively little performance gain\r\n from extremely dense sampling. You are welcome to use your own SIFT\r\n feature code! It will probably be slower, though.\r\n - cluster_centers = vlfeat.kmeans.kmeans(X, K)\r\n http://www.vlfeat.org/matlab/vl_kmeans.html\r\n - X is a N x d numpy array of sampled SIFT features, where N is\r\n the number of features sampled. N should be pretty large!\r\n - K is the number of clusters desired (vocab_size)\r\n cluster_centers is a K x d matrix of cluster centers. This is\r\n your vocabulary.\r\n\r\n Args:\r\n - image_paths: list of image paths.\r\n - vocab_size: size of vocabulary\r\n\r\n Returns:\r\n - vocab: This is a vocab_size x d numpy array (vocabulary). Each row is a\r\n cluster center / visual word\r\n \"\"\"\r\n # Load images from the training set. To save computation time, you don't\r\n # necessarily need to sample from all images, although it would be better\r\n # to do so. You can randomly sample the descriptors from each image to save\r\n # memory and speed up the clustering. Or you can simply call vl_dsift with\r\n # a large step size here, but a smaller step size in get_bags_of_sifts.\r\n #\r\n # For each loaded image, get some SIFT features. You don't have to get as\r\n # many SIFT features as you will in get_bags_of_sift, because you're only\r\n # trying to get a representative sample here.\r\n #\r\n # Once you have tens of thousands of SIFT features from many training\r\n # images, cluster them with kmeans. The resulting centroids are now your\r\n # visual word vocabulary.\r\n\r\n dim = 128 # length of the SIFT descriptors that you are going to compute.\r\n vocab = np.zeros((vocab_size,dim))\r\n \r\n for i in image_paths:\r\n \r\n # loading the image into gray-scale\r\n image = load_image_gray(i)\r\n \r\n # Computing the SIFT vectors of the feature points\r\n locations, descriptors = vlfeat.sift.dsift(image, step = 2, size = 8, fast = True)\r\n \r\n # limiting the number of key-points by taking 50% of the detected key-points\r\n # In such a way larger images will have more key-points \r\n descriptors = descriptors[:len(descriptors)//2]\r\n \r\n try :\r\n updated_descriptor = np.concatenate((updated_descriptor, descriptors))\r\n except NameError :\r\n updated_descriptor = descriptors\r\n \r\n print(\"SIFT descriptors computed\") \r\n \r\n # Computing the K-means clustering\r\n updated_descriptor = updated_descriptor.astype(float)\r\n vocab = vlfeat.kmeans.kmeans(updated_descriptor, vocab_size)\r\n \r\n print(\"Cluster centers computed\")\r\n \r\n return vocab\r\n\r\n\r\ndef get_bags_of_sifts(image_paths, vocab_filename):\r\n \"\"\"\r\n This feature representation is described in the handout, lecture\r\n materials, and Szeliski chapter 14.\r\n You will want to construct SIFT features here in the same way you\r\n did in build_vocabulary() (except for possibly changing the sampling\r\n rate) and then assign each local feature to its nearest cluster center\r\n and build a histogram indicating how many times each cluster was used.\r\n Don't forget to normalize the histogram, or else a larger image with more\r\n SIFT features will look very different from a smaller version of the same\r\n image.\r\n\r\n Useful functions:\r\n - Use load_image(path) to load RGB images and load_image_gray(path) to load\r\n grayscale images\r\n - frames, descriptors = vlfeat.sift.dsift(img)\r\n http://www.vlfeat.org/matlab/vl_dsift.html\r\n frames is a M x 2 matrix of locations, which can be thrown away here\r\n descriptors is a M x 128 matrix of SIFT features\r\n note: there are step, bin size, and smoothing parameters you can\r\n manipulate for dsift(). We recommend debugging with the 'fast'\r\n parameter. This approximate version of SIFT is about 20 times faster\r\n to compute. Also, be sure not to use the default value of step size.\r\n It will be very slow and you'll see relatively little performance\r\n gain from extremely dense sampling. You are welcome to use your own\r\n SIFT feature code! It will probably be slower, though.\r\n - assignments = vlfeat.kmeans.kmeans_quantize(data, vocab)\r\n finds the cluster assigments for features in data\r\n - data is a M x d matrix of image features\r\n - vocab is the vocab_size x d matrix of cluster centers\r\n (vocabulary)\r\n - assignments is a Mx1 array of assignments of feature vectors to\r\n nearest cluster centers, each element is an integer in\r\n [0, vocab_size)\r\n\r\n Args:\r\n - image_paths: paths to N images\r\n - vocab_filename: Path to the precomputed vocabulary.\r\n This function assumes that vocab_filename exists and contains an\r\n vocab_size x 128 ndarray 'vocab' where each row is a kmeans centroid\r\n or visual word. This ndarray is saved to disk rather than passed in\r\n as a parameter to avoid recomputing the vocabulary every run.\r\n\r\n Returns:\r\n - image_feats: N x d matrix, where d is the dimensionality of the\r\n feature representation. In this case, d will equal the number of\r\n clusters or equivalently the number of entries in each image's\r\n histogram (vocab_size) below.\r\n \"\"\"\r\n # load vocabulary\r\n with open(vocab_filename, 'rb') as f:\r\n vocab = pickle.load(f)\r\n \r\n IDF_list = np.zeros(vocab.shape[0])\r\n DF_list = np.zeros(vocab.shape[0])\r\n \r\n \r\n for i in image_paths:\r\n \r\n # dummy features variable\r\n feats = np.zeros(vocab.shape[0])\r\n \r\n # loading the image into gray-scale\r\n image = load_image_gray(i)\r\n \r\n # Computing the SIFT vectors of the feature points\r\n locations, descriptors = vlfeat.sift.dsift(image, step = 2, size = 8, fast = True)\r\n \r\n # limiting the number of key-points by taking 75% of the detected key-points\r\n # In such a way larger images will have more key-points \r\n descriptors = descriptors[:len(descriptors)*3//4]\r\n \r\n # assigning the image features to corresponding cluster centers\r\n descriptors = descriptors.astype(float)\r\n assignments = vlfeat.kmeans.kmeans_quantize(descriptors, vocab)\r\n \r\n # creating the histogram\r\n for j in assignments:\r\n feats[j] += 1\r\n \r\n try :\r\n updated_image_feats = np.vstack((updated_image_feats, feats))\r\n except NameError :\r\n updated_image_feats = feats\r\n \r\n # TF-IDF implementation\r\n for i,j in enumerate(updated_image_feats):\r\n for k,l in enumerate(j):\r\n if l>0:\r\n DF_list[k] += 1\r\n \r\n for idx in range(0, len(IDF_list)):\r\n IDF_list[idx] = np.log(len(image_paths)/DF_list[idx])\r\n \r\n # incorporating IDF in histogram contruction\r\n tfidf_image_feats = updated_image_feats*IDF_list\r\n \r\n # Normalizing the histogram using L1 (Manhattan norm) \r\n for i,j in enumerate(tfidf_image_feats):\r\n tfidf_image_feats[i] = j/np.linalg.norm(j, ord=1)\r\n\r\n return tfidf_image_feats\r\n ''' \r\n return updated_image_feats\r\n '''\r\ndef nearest_neighbor_classify(train_image_feats, train_labels, test_image_feats,\r\n metric='euclidean', K = 1):\r\n \"\"\"\r\n This function will predict the category for every test image by finding\r\n the training image with most similar features. Instead of 1 nearest\r\n neighbor, you can vote based on k nearest neighbors which will increase\r\n performance (although you need to pick a reasonable value for k).\r\n\r\n Useful functions:\r\n - D = sklearn_pairwise.pairwise_distances(X, Y)\r\n computes the distance matrix D between all pairs of rows in X and Y.\r\n - X is a N x d numpy array of d-dimensional features arranged along\r\n N rows\r\n - Y is a M x d numpy array of d-dimensional features arranged along\r\n N rows\r\n - D is a N x M numpy array where d(i, j) is the distance between row\r\n i of X and row j of Y\r\n\r\n Args:\r\n - train_image_feats: N x d numpy array, where d is the dimensionality of\r\n the feature representation\r\n - train_labels: N element list, where each entry is a string indicating\r\n the ground truth category for each training image\r\n - test_image_feats: M x d numpy array, where d is the dimensionality of the\r\n feature representation. You can assume N = M, unless you have changed\r\n the starter code\r\n - metric: (optional) metric to be used for nearest neighbor.\r\n Can be used to select different distance functions. The default\r\n metric, 'euclidean' is fine for tiny images. 'chi2' tends to work\r\n well for histograms\r\n - K: The number of neighbours to be compared. Default is 1\r\n Returns:\r\n - test_labels: M element list, where each entry is a string indicating the\r\n predicted category for each testing image\r\n \"\"\"\r\n test_labels = []\r\n\r\n # constructing the distance matrix\r\n D = sklearn_pairwise.pairwise_distances(test_image_feats, train_image_feats, metric = metric)\r\n \r\n if K != 1:\r\n print(\"KNN neighbourhood size chosen : \", K)\r\n for i in range(0, D.shape[0]):\r\n category_list = []\r\n min_indices = sorted(range(len(D[i])), key = lambda sub: D[i][sub])[:K] \r\n for i in min_indices:\r\n category_list.append(train_labels[i])\r\n try:\r\n mode_category = mode(category_list)\r\n except statistics.StatisticsError:\r\n mode_category = category_list[0]\r\n test_labels.append(mode_category)\r\n else :\r\n for i in range(0, D.shape[0]):\r\n test_labels.append(train_labels[np.argmin(D[i])])\r\n \r\n return test_labels\r\n\r\ndef svm_classify(train_image_feats, train_labels, test_image_feats):\r\n \"\"\"\r\n This function will train a linear SVM for every category (i.e. one vs all)\r\n and then use the learned linear classifiers to predict the category of\r\n every test image. Every test feature will be evaluated with all 15 SVMs\r\n and the most confident SVM will \"win\". Confidence, or distance from the\r\n margin, is W*X + B where '*' is the inner product or dot product and W and\r\n B are the learned hyperplane parameters.\r\n\r\n Useful functions:\r\n - sklearn LinearSVC\r\n http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html\r\n - svm.fit(X, y)\r\n - set(l)\r\n\r\n Args:\r\n - train_image_feats: N x d numpy array, where d is the dimensionality of\r\n the feature representation\r\n - train_labels: N element list, where each entry is a string indicating the\r\n ground truth category for each training image\r\n - test_image_feats: M x d numpy array, where d is the dimensionality of the\r\n feature representation. You can assume N = M, unless you have changed\r\n the starter code\r\n Returns:\r\n - test_labels: M element list, where each entry is a string indicating the\r\n predicted category for each testing image\r\n \"\"\"\r\n \r\n# categories = list(set(train_labels))\r\n\r\n\r\n# svms = {cat: LinearSVC(random_state=0, tol=0.0001, loss='hinge', C=5, max_iter=5000) for cat in categories}\r\n \r\n svm = LinearSVC(random_state=0, tol=0.1, loss='hinge', C=5, max_iter=4000)\r\n \r\n # construct 1 vs all SVMs for each category ...\r\n # The Sklearn library has inbuilt implementation of OneVsRest Multiclass SVM classifier\r\n # Using the inbulit support as follows :\r\n test_labels = OneVsRestClassifier(svm).fit(train_image_feats, train_labels).predict(test_image_feats)\r\n \r\n\r\n return list(test_labels)\r\n","sub_path":"Assignment_4/CV_Programming_Assignment4/code/Bag_of_Features_code.py","file_name":"Bag_of_Features_code.py","file_ext":"py","file_size_in_byte":13032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"88604120","text":"\n\nfrom multiprocessing import Process, Queue\nfrom bottle import route, run, request, response\nfrom bottle import static_file\nimport random, argparse\n# import RPi.GPIO as GPIO\nfrom time import sleep\n# import Adafruit_DHT\nimport serial\nimport sys, select, os, globals\n\n@route('/hello')\ndef hello():\n return \"Hello, this is the jquery image test program\"\n\n@route('/jquerytest')\ndef bstest():\n return static_file('jqueryImageTest.html', root='.')\n\n@route('/pycam')\ndef bstest():\n return static_file('PyCamWebPage.html', root='.')\n\n\n@route('/')\ndef javascripts(filename):\n print(\"Now serving : \" + filename)\n return static_file(filename, root='.')\n\n@route('/bootstrapcss/')\ndef serveBootStrapCSS(filename):\n print(\"Now serving bootstrap CSS: \" + filename)\n return static_file(filename, root='bootstrap/css')\n\n@route('/static/')\ndef server_static(filepath):\n print(\"Now serving static file: \" + filepath)\n return static_file(filepath, root='.')\n\n@route('/images/')\ndef server_static(filepath):\n print(\"Now serving static file: \" + filepath)\n return static_file(filepath, root='images')\n\n@route('/icons/')\ndef server_static(filepath):\n print(\"Now serving icon: \" + filepath)\n return static_file(filepath, root='icons')\n\n\n@route('latestimage')\ndef serveLatestImage():\n if globals.latestimage != \"\":\n print(\"Now serving image file: \" + globals.latestimage)\n return static_file(globals.latestimage)\n\n@route('/sendStuffToBottle', method='POST')\ndef do_login():\n username = request.forms.get('username')\n password = request.forms.get('password')\n print (\"Bottle received username = \" + username + \" and password = \" + password)\n return '''\n \n \n \n \n '''\n\n@route('/takeAPhoto')\ndef take_photo():\n print (\"Bottle received take photo\")\n return '''\n \n \n \n \n '''\n\n\n\n\n\n\n# main() function\ndef main():\n\n # run(host='192.168.240.32', port=8080, debug=False)\n # run(host='localhost', port=8284, debug=False)\n run(host='192.168.2.71', port=8284, debug=False)\n\n print(\"Exited bottle server\")\n\n\n# call main\nif __name__ == '__main__':\n main()","sub_path":"jqueryImageTest.py","file_name":"jqueryImageTest.py","file_ext":"py","file_size_in_byte":2502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"10905671","text":"import mysql.connector\nimport pysam\nimport os\n\n__author__ = 'Max Prem'\n\n##\n## Concept:\n## Init class Assignment1 with name of gene, genome reference, a bam file and a filename for output.\n## Information to gene (transcript, location and gene region) will be shown, as well as a summary of the bam file\n## regarding coverage and reads.\n\n\nclass Assignment1:\n \n def __init__(self, gene_of_interest, genome_reference, bam_file, output_file):\n\n ## Define parameters from function call\n self.gene = gene_of_interest\n self.genome_reference = genome_reference\n self.bam_file = bam_file\n self.alignment_file = pysam.AlignmentFile(bam_file, \"rb\")\n self.output_file = output_file\n\n ## Get gene information from data base or existing file\n if os.path.exists(output_file) and os.path.isfile(output_file):\n print(\"Fetch data from file <\"+output_file+\">\")\n file = open(output_file, \"r\")\n self.transcript_info = file.readline().strip(\"\\n\").split(\"\\t\")\n else:\n self.transcript_info = self.download_gene_coordinates()\n\n print(\"\\nPreparing report of <\"+str(bam_file)+\">\")\n\n ## Set variables for gene information\n self.gene_symbol = self.transcript_info[0]\n self.transcript = self.transcript_info[1]\n self.chromosome = self.transcript_info[2]\n self.start_position = int(self.transcript_info[3])\n self.stop_position = int(self.transcript_info[4])\n self.strand = self.transcript_info[5]\n self.number_of_exons = int(self.transcript_info[6])\n self.exon_coordinates = self.get_exon_coordinates()\n\n ## Set variables retrieved from functions\n self.sam_header = self.get_sam_header()\n self.number_of_properly_paired_reads = self.get_number_of_properly_paired_reads_of_gene()\n self.number_of_gene_reads_with_indels = self.get_number_of_gene_reads_with_indels()\n self.mean_gene_coverage = self.get_average_gene_coverage()\n self.mean_total_coverage = self.get_total_average_coverage()\n self.number_of_mapped_reads = self.get_number_mapped_reads()\n\n def download_gene_coordinates(self):\n print(\"Connecting to UCSC to fetch data\")\n\n ## Open connection\n cnx = mysql.connector.connect(host='genome-mysql.cse.ucsc.edu', user='genomep', passwd='password',\n db=self.genome_reference)\n\n ## Get cursor\n cursor = cnx.cursor()\n \n ## Build query fields\n query_fields = [\"refGene.name2\",\n \"refGene.name\",\n \"refGene.chrom\",\n \"refGene.txStart\",\n \"refGene.txEnd\",\n \"refGene.strand\",\n \"refGene.exonCount\",\n \"refGene.exonStarts\",\n \"refGene.exonEnds\"]\n \n ## Build query\n query = \"SELECT DISTINCT %s from refGene\" % \",\".join(query_fields)\n \n ## Execute query\n cursor.execute(query)\n\n attributes_of_all_transcripts = []\n separator = '\\t'\n\n ## Get query entries for selected gene\n for row in cursor:\n if row[0] == self.gene:\n transcript_attributes = []\n\n for query_field in row:\n transcript_attributes.append(query_field)\n\n attributes_of_all_transcripts.append(transcript_attributes)\n\n ## Close cursor & connection\n cursor.close()\n cnx.close()\n \n print(\"Done fetching data\")\n\n transcript_attributes = attributes_of_all_transcripts[0]\n\n ## Write to file\n with open(self.output_file, \"w\") as fh:\n fh.write(separator.join(str(attribute) for attribute in transcript_attributes) + \"\\n\")\n\n return transcript_attributes\n\n def print_gene_symbol(self):\n print(\"Genome reference:\".ljust(20, \" \")+self.genome_reference)\n print(\"Gene symbol:\".ljust(20, \" \")+self.gene_symbol)\n print(\"Transcript:\".ljust(20, \" \")+self.transcript)\n\n def print_coordinates_of_gene(self):\n string_chromosome = \"Chromosome \"+self.chromosome[3:]\n string_start_position = format(self.start_position, \"08,d\")\n string_stop_position = format(self.stop_position, \"08,d\")\n\n if self.strand == \"-\":\n string_strand = \"reverse strand\"\n elif self.strand == \"+\":\n string_strand = \"forward strand\"\n else:\n string_strand = \"unknown orientation\"\n\n print((\"Location:\").ljust(20, \" \")+\n string_chromosome+\": \"+string_start_position+\"-\"+string_stop_position+\" \"+string_strand)\n\n def get_exon_coordinates(self):\n start_of_exons = str(self.transcript_info[7]).strip(\"b\\'\").strip(\",\\'\").split(\",\")\n stop_of_exons = str(self.transcript_info[8]).strip(\"b\\'\").strip(\",\\'\").split(\",\")\n list_of_exon_coordinates = []\n\n for exon in range(self.number_of_exons):\n list_of_exon_coordinates.append([int(start_of_exons[exon]), int(stop_of_exons[exon])])\n\n return list_of_exon_coordinates\n\n def print_exon_information(self):\n print(\"Number of exons:\".ljust(20, \" \")+str(self.number_of_exons))\n\n print(\"Coordinates:\".ljust(20, \" \")+\"Exon\"+\" \"*6+\"Start\".ljust(15, \" \")+\"End\")\n\n for exon in range(self.number_of_exons):\n print(\" \"*20+(str(exon+1)).ljust(10, \" \")+\n format(self.exon_coordinates[exon][0], \"08,d\").ljust(15, \" \")+\n format(self.exon_coordinates[exon][1], \"08,d\"))\n\n def get_sam_header(self):\n header = self.alignment_file.header[\"HD\"]\n\n headerline = \"\"\n\n for key in header:\n headerline += key + \": \" + header[key] + \"\\t\"\n\n return headerline\n\n def get_number_of_properly_paired_reads_of_gene(self):\n counter_properly_paired_reads = 0\n\n for read in self.alignment_file.fetch(self.chromosome, self.start_position, self.stop_position):\n if read.is_proper_pair:\n counter_properly_paired_reads += 1\n\n return counter_properly_paired_reads\n \n def get_number_of_gene_reads_with_indels(self):\n counter_reads_with_indels = 0\n\n for pileupcolumn in self.alignment_file.pileup(self.chromosome, self.start_position, self.stop_position):\n for pileupread in pileupcolumn.pileups:\n if pileupread.indel:\n counter_reads_with_indels +=1\n\n return counter_reads_with_indels\n\n def get_total_average_coverage(self):\n coverage_sum = 0\n counter_column = 0\n\n for pileupcolumn in self.alignment_file.pileup(self.chromosome):\n coverage_sum += pileupcolumn.n\n counter_column += 1\n\n average_total_coverage = round((coverage_sum / counter_column), 1)\n\n return average_total_coverage\n\n def get_average_gene_coverage(self):\n coverage_sum = 0\n counter_column = 0\n\n for pileupcolumn in self.alignment_file.pileup(self.chromosome, self.start_position, self.stop_position):\n coverage_sum += pileupcolumn.n\n counter_column += 1\n\n average_gene_coverage = round((coverage_sum / counter_column), 1)\n\n return average_gene_coverage\n\n def get_number_mapped_reads(self):\n counter_mapped_reads = 0\n\n for read in self.alignment_file.fetch(self.chromosome, self.start_position, self.stop_position):\n if not read.is_unmapped:\n counter_mapped_reads += 1\n\n return counter_mapped_reads\n\n def print_summary(self):\n separate_blocks = \"\\n\"+\"=\"*80+\"\\n\"\n print(separate_blocks)\n\n print(\"Information to selected gene\\n\")\n self.print_gene_symbol()\n self.print_coordinates_of_gene()\n print()\n self.print_exon_information()\n print(separate_blocks)\n\n print(\"Bamfile information\\n\")\n print(\"Samfile header:\".ljust(25, \" \")+self.sam_header)\n print(\"Mean total coverage:\".ljust(25, \" \")+str(self.mean_total_coverage)+\" %\")\n print()\n print(\"Information to selected gene\")\n print(\"Mapped reads:\".ljust(25, \" \")+str(self.number_of_mapped_reads))\n print(\"Properly paired reads:\".ljust(25, \" \")+str(self.number_of_properly_paired_reads))\n print(\"Reads with indels:\".ljust(25, \" \")+str(self.number_of_gene_reads_with_indels))\n print(\"Mean gene coverage:\".ljust(25, \" \")+str(self.mean_gene_coverage)+\" %\")\n print(separate_blocks)\n\n\ndef main():\n print(\"Assignment 1\\n\")\n assignment1 = Assignment1(\"FRGCA\", \"hg38\", \"chr21.bam\", \"FRGCA_transcripts.txt\")\n assignment1.print_summary()\n print(\"Done with assignment 1\")\n assignment1.alignment_file.close()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"assignment1.py","file_name":"assignment1.py","file_ext":"py","file_size_in_byte":8860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"287667866","text":"import tensorflow as tf\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport time\r\nimport os\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\n\r\n# Load the data\r\ndef loadData():\r\n with np.load(\"notMNIST.npz\") as data:\r\n Data, Target = data[\"images\"], data[\"labels\"]\r\n np.random.seed(521)\r\n randIndx = np.arange(len(Data))\r\n np.random.shuffle(randIndx)\r\n Data = Data[randIndx] / 255.0\r\n Target = Target[randIndx]\r\n trainData, trainTarget = Data[:10000], Target[:10000]\r\n validData, validTarget = Data[10000:16000], Target[10000:16000]\r\n testData, testTarget = Data[16000:], Target[16000:]\r\n return trainData, validData, testData, trainTarget, validTarget, testTarget\r\n\r\n# Implementation of a neural network using only Numpy - trained using gradient descent with momentum\r\ndef convertOneHot(trainTarget, validTarget, testTarget):\r\n newtrain = np.zeros((trainTarget.shape[0], 10))\r\n newvalid = np.zeros((validTarget.shape[0], 10))\r\n newtest = np.zeros((testTarget.shape[0], 10))\r\n\r\n for item in range(0, trainTarget.shape[0]):\r\n newtrain[item][trainTarget[item]] = 1\r\n for item in range(0, validTarget.shape[0]):\r\n newvalid[item][validTarget[item]] = 1\r\n for item in range(0, testTarget.shape[0]):\r\n newtest[item][testTarget[item]] = 1\r\n return newtrain, newvalid, newtest\r\n\r\n\r\ndef shuffle(trainData, trainTarget):\r\n np.random.seed(421)\r\n randIndx = np.arange(len(trainData))\r\n target = trainTarget\r\n np.random.shuffle(randIndx)\r\n data, target = trainData[randIndx], target[randIndx]\r\n return data, target\r\n\r\n\r\ndef relu(x):\r\n return np.maximum(0,x)\r\n\r\ndef gradReLU(x):\r\n return (x > 0)\r\n\r\ndef softmax(x):\r\n exp_x = np.exp(x-np.amax(x, axis=0)) #the negative term fixes NaN error when x gets very large\r\n exp_x_denom = np.sum(exp_x, axis=0)\r\n return exp_x/exp_x_denom\r\n\r\ndef gradSoftmax(x):\r\n e = np.ones((x.shape[1], x.shape[1])) \r\n return np.multiply(np.dot(x,e.T), (1-np.dot(e,x.T)).T)\r\n\r\ndef computeLayer(X, W, b):\r\n return np.dot(W,X)+b\r\n\r\ndef CE(target, prediction):\r\n N = target.shape[1]\r\n ce = (-1/N)*np.sum(np.multiply(target,np.log(prediction)))\r\n return np.squeeze(ce)\r\n\r\ndef gradCE(target, prediction):\r\n N = target.shape[1]\r\n return (-1/N)*np.multiply(target,(1/prediction))\r\n\r\ndef NN_forward(X, weights):\r\n ''' Forward Pass \r\n returns the output prediction and intermediate network values for backpropagation\r\n '''\r\n W1 = weights[\"W1\"]; W2 = weights[\"W2\"]\r\n b1 = weights[\"b1\"]; b2 = weights[\"b2\"]\r\n \r\n S1 = computeLayer(X, W1, b1)\r\n X1 = relu(S1)\r\n S2 = computeLayer(X1, W2, b2)\r\n X2 = softmax(S2)\r\n store = {\"S1\": S1, \"X1\": X1, \"S2\":S2, \"X2\":X2}\r\n return store\r\n\r\ndef NN_backpropagation(X, Y, weights, momentum, store, lr, gamma):\r\n ''' '''\r\n N = X.shape[1]\r\n W1 = weights[\"W1\"]; W2 = weights[\"W2\"]\r\n b1 = weights[\"b1\"]; b2 = weights[\"b2\"]\r\n v1 = momentum[\"v1\"]; v2 = momentum[\"v2\"]\r\n vb1 = momentum[\"vb1\"]; vb2 = momentum[\"vb2\"]\r\n \r\n Yhat = store[\"X2\"]\r\n X1 = store[\"X1\"]\r\n \r\n #calculate gradients of error w.r.t weights\r\n dE_dS2 = (Yhat - Y)\r\n dE_dW2 = (1/N)*np.dot(dE_dS2, X1.T)\r\n dE_db2 = (1/N)*np.sum(dE_dS2, axis=1, keepdims=True)\r\n dE_dS1 = np.dot(W2.T, dE_dS2)*gradReLU(X1)\r\n dE_dW1 = (1/N)*np.dot(dE_dS1, X.T)\r\n dE_db1 = (1/N)*np.sum(dE_dS1, axis=1, keepdims=True)\r\n \r\n #update momentum\r\n v1 = gamma*v1 + lr*dE_dW1\r\n v2 = gamma*v2 + lr*dE_dW2\r\n vb1 = gamma*vb1 + lr*dE_db1\r\n vb2 = gamma*vb2 + lr*dE_db2\r\n \r\n #update weights\r\n W1 -= v1\r\n W2 -= v2\r\n b1 -= vb1\r\n b2 -= vb2\r\n \r\n weights = {\"W1\": W1, \"b1\": b1, \"W2\": W2, \"b2\": b2}\r\n momentum = {\"v1\": v1, \"vb1\": vb1, \"v2\": v2, \"vb2\": vb2}\r\n \r\n return weights, momentum\r\n \r\ndef save_weights(weights):\r\n '''Store Weights during training'''\r\n W1 = weights[\"W1\"]; W2 = weights[\"W2\"]\r\n b1 = weights[\"b1\"]; b2 = weights[\"b2\"]\r\n \r\n np.savetxt('W1.txt', W1)\r\n np.savetxt('b1.txt', b1)\r\n np.savetxt('W2.txt', W2)\r\n np.savetxt('b2.txt', b2)\r\n print(\"Weights Saved\")\r\n\r\ndef load_weights(X_train, Y_train, n_hidden):\r\n '''initialize or load weight parameters'''\r\n if os.path.isfile(\"W1.txt\"):\r\n W1 = np.loadtxt(\"W1.txt\")\r\n else:\r\n W1 = np.random.randn(n_hidden, X_train.shape[1])*(2/(n_hidden+X_train.shape[1])) #Xavier initialization\r\n if os.path.isfile(\"b1.txt\"):\r\n b1 = np.loadtxt(\"b1.txt\")\r\n b1 = b1.reshape((b1.shape[0], 1))\r\n else:\r\n b1 = np.zeros((n_hidden, 1))\r\n if os.path.isfile(\"W2.txt\"):\r\n W2 = np.loadtxt(\"W2.txt\")\r\n else:\r\n W2 = np.random.randn(Y_train.shape[1], n_hidden)*(2/(Y_train.shape[1]+n_hidden)) #Xavier initialization\r\n if os.path.isfile(\"b2.txt\"):\r\n b2 = np.loadtxt(\"b2.txt\")\r\n b2 = b2.reshape((b2.shape[0], 1))\r\n else:\r\n b2 = np.zeros((Y_train.shape[1], 1))\r\n \r\n return {\"W1\": W1, \"b1\": b1, \"W2\": W2, \"b2\": b2}\r\n \r\n\r\ndef NN_numpy(X_train, Y_train, X_valid, Y_valid, X_test, Y_test, epochs, n_hidden, lr, gamma):\r\n '''Neural Network Model with Numpy'''\r\n \r\n #initialize parameter weights\r\n weights = load_weights(X_train, Y_train, n_hidden)\r\n \r\n #momentum parameters\r\n v1 = np.full((n_hidden, X_train.shape[1]), 1e-5)\r\n vb1 = np.full((n_hidden, 1), 1e-5)\r\n v2 = np.full((Y_train.shape[1], n_hidden), 1e-5)\r\n vb2 = np.full((Y_train.shape[1], 1), 1e-5)\r\n \r\n momentum = {\"v1\": v1, \"vb1\": vb1, \"v2\": v2, \"vb2\": vb2}\r\n \r\n Y_train = Y_train.T #being consistent with parameter dimensions\r\n X_train = X_train.T\r\n Y_valid = Y_valid.T \r\n X_valid = X_valid.T\r\n Y_test = Y_test.T \r\n X_test = X_test.T\r\n \r\n #For plotting error per iteration\r\n train_error = np.zeros((epochs,1))\r\n valid_error = np.zeros((epochs,1))\r\n test_error = np.zeros((epochs,1))\r\n train_acc = np.zeros((epochs,1))\r\n valid_acc = np.zeros((epochs,1))\r\n test_acc = np.zeros((epochs,1))\r\n \r\n #training\r\n for i in range(epochs):\r\n \r\n store = NN_forward(X_train, weights)\r\n Yhat = store[\"X2\"]\r\n train_error[i] = CE(Y_train, Yhat)\r\n train_acc[i] = calculateAccuraccy(Y_train, Yhat)\r\n print(\"Iteration : \",i, \" Training Accuracy = \", train_acc[i])\r\n \r\n #validation\r\n valid_store = NN_forward(X_valid, weights); valid_Yhat = valid_store[\"X2\"]\r\n valid_error[i] = CE(Y_valid, valid_Yhat)\r\n valid_acc[i] = calculateAccuraccy(Y_valid, valid_Yhat)\r\n \r\n #test\r\n test_store = NN_forward(X_test, weights); test_Yhat = test_store[\"X2\"]\r\n test_error[i] = CE(Y_test, test_Yhat)\r\n test_acc[i] = calculateAccuraccy(Y_test, test_Yhat)\r\n \r\n if np.isnan(train_error[i]):\r\n break\r\n if (i+1)%25 == 0:\r\n save_weights(weights)\r\n weights, momentum = NN_backpropagation(X_train, Y_train, weights, momentum, store, lr, gamma)\r\n \r\n \r\n #plot\r\n print_errorCurve(train_error, valid_error, test_error)\r\n print_accCurve(train_acc, valid_acc, test_acc)\r\n \r\n #print accuracies\r\n print(\"Final Training Accuracy = \", train_acc[epochs-1]*100, \"%\")\r\n print(\"Final Validation Accuracy = \", valid_acc[epochs-1]*100, \"%\")\r\n print(\"Final Test Accuracy = \", test_acc[epochs-1]*100, \"%\")\r\n\r\ndef calculateAccuraccy(Y, Yhat):\r\n pred = np.argmax(Yhat, axis=0)\r\n Y_class = np.argmax(Y, axis=0)\r\n acc = np.sum(pred == Y_class)/Y.shape[1]\r\n return acc\r\n \r\ndef print_errorCurve(train_error, valid_error, test_error):\r\n ''' '''\r\n plt.plot(train_error, label=\"training error\")\r\n plt.plot(valid_error, label=\"validation error\")\r\n plt.plot(test_error, label=\"testing error\")\r\n plt.legend(loc='upper right')\r\n plt.xlabel(\"Iterations\")\r\n plt.ylabel(\"Error\")\r\n plt.show()\r\n\r\ndef print_accCurve(train_acc, valid_acc, test_acc):\r\n ''' '''\r\n plt.plot(train_acc, label=\"training accuracy\")\r\n plt.plot(valid_acc, label=\"validation accuracy\")\r\n plt.plot(test_acc, label=\"testing accuracy\")\r\n plt.legend(loc='lower right')\r\n plt.xlabel(\"Iterations\")\r\n plt.ylabel(\"Accuracy\")\r\n plt.show()\r\n\r\ndef convolutional_layer(x, weights, biases, strides=1):\r\n x = tf.nn.conv2d(x, filter=weights, strides=[1, strides, strides, 1], padding='SAME')\r\n x = tf.nn.bias_add(x, biases)\r\n return tf.nn.relu(x) \r\n\r\ndef maxpooling_layer(x, k=2):#k stands for kernal => 2 means 2x2 pool matrix with stride 2\r\n return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')\r\n\r\ndef batch_normalization_layer(x):\r\n mean, variance = tf.nn.moments(x,[0])#axes = [0] this is just the mean and variance of a vector.\r\n offset = tf.Variable(tf.zeros([32]))\r\n scale = tf.Variable(tf.ones([32]))\r\n epsilon = 1e-3\r\n return tf.nn.batch_normalization(x,mean=mean,variance=variance,offset=offset,scale=scale,variance_epsilon=epsilon)\r\n\r\ndef fully_connected_layer(x,weights, biases,rate):\r\n # Step 6 : Flatten Layer : Reshape output x to fit fully connected layer input\r\n fully_connected = tf.reshape(x, [-1, weights['wd1'].get_shape().as_list()[0]])\r\n \r\n # Step 7 : Fully connected layer (with 784 output units, i.e. corresponding to each pixel)\r\n fully_connected = tf.add(tf.matmul(fully_connected, weights['wd1']), biases['bd1'])\r\n \r\n #for Section 2.3.2 dropout\r\n dropout = tf.layers.dropout(inputs=fully_connected, rate=rate)\r\n \r\n # Step 8 : ReLU activation\r\n fully_connected = tf.nn.relu(dropout)\r\n\r\n # Step 9 : Fully connected layer (with 10 output units, i.e. corresponding to each class)\r\n result = tf.add(tf.matmul(fully_connected, weights['out']), biases['out'])\r\n\r\n return result\r\n\r\ndef softmax_cross_entropy_layer(x,y):\r\n return tf.losses.softmax_cross_entropy(logits=x, onehot_labels=y)\r\n\r\ndef adam_optimizer(cost):\r\n return tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)\r\n\r\n\r\ndef NN_tf(X_train, Y_train, X_valid, Y_valid, X_test, Y_test, epochs, batchSize, lr, noOfImages, lambda_val,rate):\r\n '''Neural Network Model with tf'''\r\n print(\"Starting Part 2\")\r\n \r\n #initialize parameter weights\r\n x = tf.placeholder(tf.float32, shape=None)#input placeholder dimension of BatchSize x 784\r\n y = tf.placeholder(tf.int32, shape=None)#label of training images\r\n\r\n weights = {\r\n 'wc1': tf.get_variable('W0', shape=(3,3,1,32), initializer=tf.contrib.layers.xavier_initializer()), \r\n 'wd1': tf.get_variable('W1', shape=(14*14*32,784), initializer=tf.contrib.layers.xavier_initializer()), \r\n 'out': tf.get_variable('W2', shape=(784,noOfImages), initializer=tf.contrib.layers.xavier_initializer()), \r\n }\r\n biases = {\r\n 'bc1': tf.get_variable('B0', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),\r\n 'bd1': tf.get_variable('B1', shape=(784), initializer=tf.contrib.layers.xavier_initializer()),\r\n 'out': tf.get_variable('B3', shape=(10), initializer=tf.contrib.layers.xavier_initializer()),\r\n }\r\n \r\n # Step 1: Input Layer\r\n input_layer = tf.reshape(x, [-1,28,28,1])\r\n\r\n # Step 2. A 3 × 3 convolutional layer, with 32 filters, using vertical and horizontal strides of 1. \r\n # Step 3. ReLU activation\r\n layer = convolutional_layer(input_layer, weights['wc1'], biases['bc1'])\r\n #print(\"size of layer after convolution = \", layer.shape)\r\n\r\n # Step 4. A batch normalization layer\r\n layer = batch_normalization_layer(layer)\r\n #print(\"size of layer after normalization = \", layer.shape)\r\n\r\n # Step 5. A max 2 × 2 max pooling layer\r\n layer = maxpooling_layer(layer, k=2)\r\n #print(\"size of layer after maxpooling = \", layer.shape)\r\n\r\n # Step 6. Flatten Layer\r\n # Step 7. Fully connected layer\r\n # Step 8. ReLU activation\r\n # Step 9. Fully connected layer\r\n prediction = fully_connected_layer(layer,weights,biases,rate)\r\n #print(\"size of layer after fully connected = \", prediction.shape)\r\n\r\n # 10. Softmax output\r\n softmax = tf.nn.softmax(prediction)\r\n #print(\"size of layer after softmax = \", softmax.shape)\r\n\r\n # 11. Cross Entropy loss\r\n cost = softmax_cross_entropy_layer(softmax, y) + lambda_val*tf.reduce_sum(tf.square(weights[\"out\"])) + lambda_val*tf.reduce_sum(tf.square(weights[\"wd1\"])) + lambda_val*tf.reduce_sum(tf.square(weights[\"wc1\"])) \r\n #print(\"size of layer after softmax cross extropy = \", cost)\r\n\r\n # Part 2.2 Adam optimizer\r\n adam_optimizer_val = adam_optimizer(cost)\r\n\r\n #prediction and accurracies\r\n correct_prediction = tf.equal(tf.argmax(softmax, 1), tf.argmax(y, 1))\r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n\r\n # Initializing global variables\r\n init = tf.global_variables_initializer()\r\n \r\n #For plotting error per iteration\r\n num_iterations = epochs\r\n train_error = np.zeros((num_iterations,1))\r\n valid_error = np.zeros((num_iterations,1))\r\n test_error = np.zeros((num_iterations,1))\r\n train_acc = np.zeros((num_iterations,1))\r\n valid_acc = np.zeros((num_iterations,1))\r\n test_acc = np.zeros((num_iterations,1))\r\n \r\n with tf.Session() as sess:\r\n sess.run(init) \r\n train_acc = np.zeros((epochs,1))\r\n \r\n for i in range(epochs):\r\n opt = sess.run(adam_optimizer_val, feed_dict={x:X_train[0:batchSize], y: Y_train[0:batchSize]})\r\n \r\n #measurements\r\n train_error[i], train_acc[i] = sess.run([cost, accuracy], feed_dict={x: X_train, y: Y_train})\r\n valid_error[i], valid_acc[i] = sess.run([cost, accuracy], feed_dict={x: X_valid, y: Y_valid})\r\n test_error[i], test_acc[i] = sess.run([cost, accuracy], feed_dict={x: X_test, y: Y_test})\r\n print(\"Iteration\", i ,\" Training Accuraccy = \", train_acc[i])\r\n \r\n X_train,Y_train = shuffle(X_train,Y_train)\r\n\r\n #plot error and accuracy\r\n print_errorCurve(train_error, valid_error, test_error)\r\n print_accCurve(train_acc, valid_acc, test_acc)\r\n \r\n print(\"Final Training Accuracy = \", train_acc[epochs-1]*100, \"%\")\r\n print(\"Final Validation Accuracy = \", valid_acc[epochs-1]*100, \"%\")\r\n print(\"Final Test Accuracy = \", test_acc[epochs-1]*100, \"%\")\r\n\r\ndef ReshapeData(X_train, X_valid, X_test):\r\n X_train = X_train.reshape(10000,784)\r\n X_valid = X_valid.reshape(6000,784)\r\n X_test = X_test.reshape(2724,784)\r\n return X_train, X_valid, X_test\r\n \r\ndef main():\r\n #get data\r\n X_train, X_valid, X_test, Y_train, Y_valid, Y_test = loadData()\r\n \r\n #reshape data\r\n X_train, X_valid, X_test = ReshapeData(X_train, X_valid, X_test)\r\n Y_train, Y_valid, Y_test = convertOneHot(Y_train, Y_valid, Y_test)\r\n \r\n #check\r\n assert(X_train.shape == (10000, 784))\r\n assert(X_valid.shape == (6000, 784))\r\n assert(X_test.shape == (2724, 784))\r\n assert(Y_train.shape == (10000, 10))\r\n assert(Y_valid.shape == (6000, 10))\r\n assert(Y_test.shape == (2724, 10))\r\n \r\n '''PART1'''\r\n NN_numpy(X_train, Y_train, X_valid, Y_valid, X_test, Y_test, epochs=200, n_hidden=1000, lr=0.05, gamma=0.99)\r\n \r\n '''PART2'''\r\n NN_tf(X_train, Y_train, X_valid, Y_valid, X_test, Y_test, epochs=50, batchSize=32, lr=0.0001, noOfImages=10,lambda_val=0,rate=1)\r\n\r\n \r\nmain()\r\n","sub_path":"a2/starter.py","file_name":"starter.py","file_ext":"py","file_size_in_byte":15416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"315571010","text":"import functools\nimport itertools\nimport os\nimport pickle\nimport urllib.request\n\nfrom collections import defaultdict\nfrom threading import Thread\n\nfrom kivy.clock import mainthread\nfrom kivy.garden.androidtabs import AndroidTabsBase\nfrom kivy.graphics.texture import Texture\nfrom kivy.metrics import sp\nfrom kivy.properties import ListProperty, NumericProperty, StringProperty\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.image import Image\nfrom kivy.uix.label import Label\nfrom kivy.uix.relativelayout import RelativeLayout\n\nfrom mtgworkshop.configuration import DefaultConfiguration\n\n\nclass MultiLineLabel(Label):\n def __init__(self, **kwargs):\n super(MultiLineLabel, self).__init__(**kwargs)\n self.text_size = self.size\n self.bind(size=self.on_size)\n self.bind(text=self.on_text_changed)\n self.size_hint_y = None # Not needed here\n\n def on_size(self, widget, size):\n self.text_size = size[0], None\n self.texture_update()\n if self.size_hint_y is None and self.size_hint_x is not None:\n self.height = max(self.texture_size[1], self.line_height)\n elif self.size_hint_x is None and self.size_hint_y is not None:\n self.width = self.texture_size[0]\n\n def on_text_changed(self, widget, text):\n self.on_size(self, self.size)\n\n\nclass ManaCost(RelativeLayout):\n symbols = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11',\n '12', '13', '14', '15', '16', '17', '18', '19', '20', 'X', 'Y',\n 'Z', 'W', 'U', 'B', 'R', 'G', 'S', 'W/U', 'W/B', 'U/B', 'U/R',\n 'B/R', 'B/G', 'R/W', 'R/G', 'G/W', 'G/U', 'T', 'Q', '∞', '½',\n 'FET', '4ET', 'OW']\n mana_cost = StringProperty('', allownone=True)\n\n MANA_SIZE = sp(18)\n\n def __init__(self, **kwargs):\n self.cache_images = defaultdict(list)\n super(ManaCost, self).__init__(**kwargs)\n\n def on_mana_cost(self, instance, value):\n self.clear_widgets()\n\n if value is None:\n return\n\n mana_cost = value.replace('{', '').split('}')\n count = 0\n pre_cache_images = defaultdict(list)\n for m in mana_cost:\n m = str(m)\n if len(m) == 0:\n continue\n if len(self.cache_images[m]) > 0:\n mana_image = self.cache_images[m].pop()\n elif m in self.symbols:\n path = 'atlas://res/mana/{}'.format(m.replace('/', '-'))\n mana_image = Image(source=path, mipmap=True, allow_stretch=True,\n size_hint=(None, None), size=(self.MANA_SIZE, self.MANA_SIZE))\n else:\n mana_image = Label(text=m, color=[0, 0, 0, 1],\n size=(self.MANA_SIZE, self.MANA_SIZE))\n mana_image.pos = (self.MANA_SIZE * 1.1 * count, 0)\n self.add_widget(mana_image)\n pre_cache_images[m].append(mana_image)\n count += 1\n for m, cache in pre_cache_images.items():\n self.cache_images[m] += cache\n\n\nclass MyTab(BoxLayout, AndroidTabsBase):\n pass\n\n\ndef split_and_cut(s, txt, ind, *args):\n \"\"\"\n Split a string on a sequence of txt arguments and pull out specific indexes.\n\n Assumes at least one of find, sind is not None\n \"\"\"\n ret_list = s.split(txt)\n if isinstance(ind, tuple):\n find, sind = ind\n if find is None:\n ret_list = ret_list[:sind]\n elif sind is None:\n ret_list = ret_list[find:]\n else:\n ret_list = ret_list[find:sind]\n ret = txt.join(ret_list)\n else:\n ret = ret_list[ind]\n if len(args) > 0:\n return split_and_cut(ret, *args)\n else:\n return ret\n\n\ndef disk_cache(cache_file):\n class Decorator:\n def __init__(self, func):\n self.f = func\n self.cache = {}\n self.to_disk = True\n if os.path.exists(cache_file):\n with open(cache_file, 'rb') as inp:\n self.cache = pickle.load(inp)\n\n def save_to_disk(self):\n with open(self.cache_file, 'wb') as inp:\n pickle.dump(self.cache, inp)\n\n def __call__(self, *args, **kwargs):\n args_tuple = frozenset(kwargs.items()), *args\n res = self.cache.get(args_tuple, None)\n if res is not None:\n return res\n res = self.f(*args, **kwargs)\n self.cache[args_tuple] = res\n if self.to_disk:\n self.save_to_disk()\n return res\n Decorator.cache_file = cache_file\n return Decorator\n\n\ndef make_unique(lst, func=lambda x: x):\n res = []\n used = set()\n for x in lst:\n val = func(x)\n if val not in used:\n res.append(x)\n used.add(val)\n return res\n\n\nclass Gradient(object):\n @staticmethod\n def horizontal(*points):\n texture = Texture.create(size=(len(points), 1), colorfmt=\"rgba\")\n buf = bytes(itertools.chain(*points))\n texture.blit_buffer(buf, colorfmt='rgba', bufferfmt='ubyte')\n return texture\n\n @staticmethod\n def vertical(*points):\n texture = Texture.create(size=(1, len(points)), colorfmt=\"rgba\")\n buf = bytes(itertools.chain(*points))\n texture.blit_buffer(buf, colorfmt='rgba', bufferfmt='ubyte')\n return texture\n\n\nclass CachedImage(Image):\n image_location = StringProperty()\n\n original_height = NumericProperty(None, allownone=True)\n crop_to = ListProperty(None, allownone=True)\n\n cache_path = 'cache/images/{}'\n\n def __init__(self, image_format='.jpeg', **kwargs):\n self.cropped_image = None\n self.image_format = image_format\n if 'source' in kwargs:\n self.source = kwargs['source']\n super(CachedImage, self).__init__(**kwargs)\n\n def on_source(self, instance, value):\n if self.source.startswith('http'):\n cache_path = self.get_cached_path(value)\n if os.path.exists(cache_path):\n self.source = cache_path\n else:\n self.saved_crop_to = self.crop_to\n self.saved_original_height = self.original_height\n self.crop_to = []\n self.original_height = None\n offline = DefaultConfiguration.offline.lower()\n if not ('true'.startswith(offline) or 'yes'.startswith(offline)):\n get_thread = Thread(target=self.download_image, args=(value,))\n get_thread.start()\n self.source = 'res/loading.jpeg'\n\n def get_cached_path(self, original_value):\n res = original_value[1:][-20:]\n if 'multiverseid' in original_value:\n res = split_and_cut(original_value, 'multiverseid=', 1, '&', 0)\n if '.' in original_value[-5:]:\n res += split_and_cut(original_value, '.', -1)\n if '.' not in res[-5:]:\n res += self.image_format\n return self.cache_path.format(res)\n\n @mainthread\n def set_image_location(self, value):\n self.source = value\n\n def download_image(self, value):\n cache_path = self.get_cached_path(value)\n urllib.request.urlretrieve(value, cache_path)\n self.crop_to = self.saved_crop_to\n self.original_height = self.saved_original_height\n self.set_image_location(cache_path)\n\n def crop_image(self, *args):\n if self.image.texture is None:\n return\n\n theight = self.image.texture.height\n ratio = theight / self.original_height\n x, y, width, height = (a * ratio for a in self.crop_to)\n self.texture = \\\n self.image.texture.get_region(x, theight - y - height,\n width, height)\n\n def render_image(self, *args):\n self.texture = self.image.texture\n\n def on_texture(self, instance, value):\n if self.crop_to is None or self.original_height is None or \\\n self.original_height == 0 or len(self.crop_to) != 4 or \\\n self.cropped_image == self.texture:\n return\n\n theight = self.texture.height\n ratio = theight / self.original_height\n x, y, width, height = (a * ratio for a in self.crop_to)\n temp_texture = \\\n self.texture.get_region(x, theight - y - height,\n width, height)\n self.cropped_image = temp_texture\n self.texture = temp_texture\n\n\nclass ThreadWithReturnValue(Thread):\n def __init__(self, group=None, target=None, name=None, args=(), kwargs=None, *, daemon=None):\n Thread.__init__(self, group, target, name, args, kwargs, daemon=daemon)\n\n self._return = None\n\n def run(self):\n if self._target is not None:\n self._return = self._target(*self._args, **self._kwargs)\n\n def join(self):\n Thread.join(self)\n return self._return\n\n\ndef backgroundthread(func):\n @functools.wraps(func)\n def delayed_func(*args, **kwargs):\n res = ThreadWithReturnValue(target=func, args=args, kwargs=kwargs)\n res.start()\n return res\n return delayed_func\n","sub_path":"src/mtgworkshop/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"196567126","text":"'''\n @author Jared Scott\n Prob 37 of Project Euler \n'''\nimport math \nimport time\n\ndef isPrime(numI):\n '''\n This function will return a true or false representing whether or not the given number ('num') is prime\n '''\n num = int(numI)\n if num < 0:\n num = -1 * num\n if all(num%i!=0 for i in range(2,int(math.sqrt(num)) + 1)) and num > 1:\n return True\n else:\n return False\n \nprimes = []\nfor i in range(750000):\n if isPrime(i) and i > 10:\n primes.append(i)\n \ndef truncateLeft(num):\n numStr = str(num) \n num = numStr[1:]\n return num\ndef truncateRight(num):\n numStr = str(num) \n num = numStr[:-1]\n return num\n\nsumPrimes = []\nsumP = 0\nfor prime in primes:\n addToSum = True \n curNum = prime\n while len(str(curNum)) > 1:\n newNum = truncateLeft(curNum)\n if not isPrime(newNum):\n addToSum = False \n curNum = newNum \n curNum = prime\n while len(str(curNum)) > 1:\n newNum = truncateRight(curNum)\n if not isPrime(newNum):\n addToSum = False \n curNum = newNum \n \n if addToSum:\n sumPrimes.append(prime)\n sumP += prime\n \nprint(sumP,\" : \",sumPrimes) \n\n","sub_path":"src/python/prob37_truncatePrimes.py","file_name":"prob37_truncatePrimes.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"595660793","text":"from zing_it import views\nfrom django.urls import path\n\nurlpatterns = [\n path('', views.home, name=\"home\"),\n path('about/', views.about, name=\"about\"),\n path('signup/', views.signup, name=\"signup\"),\n path('login/', views.login, name=\"login\"),\n path('playlist/', views.playlist, name=\"playlist\"),\n path('edit/', views.edit, name=\"edit\"),\n]\n","sub_path":"zing_it/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"198964604","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# setup.py\n# Copyright (c) 2020 Hugh Coleman\n#\n# This file is part of hughcoleman/lorenz, a historically accurate simulator of\n# the Lorenz SZ40 Cipher Machine. It is released under the MIT License (see\n# LICENSE.)\nfrom pathlib import Path\n\nimport setuptools\n\nimport lorenz\n\nwith open(Path(__file__).parent / \"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"lorenz\",\n version=lorenz.__version__,\n author=\"Hugh Coleman\",\n author_email=\"33557709+hughcoleman@users.noreply.github.com\",\n url=\"https://github.com/hughcoleman/lorenz\",\n license=\"MIT\",\n description=\"A historically accurate simulator of the Lorenz SZ40 Cipher Machine.\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n packages=[\"lorenz\"],\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Intended Audience :: End Users/Desktop\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: Information Technology\",\n \"Intended Audience :: Science/Research\",\n \"Intended Audience :: Other Audience\",\n \"Intended Audience :: Education\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Natural Language :: English\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Topic :: Communications\",\n \"Topic :: Security\",\n \"Topic :: Security :: Cryptography\",\n \"Topic :: Software Development :: Libraries\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n \"Topic :: Utilities\",\n ],\n python_requires=\">=3.8\",\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"91320533","text":"filename = 'pi_million_digits.txt'\nwith open(filename) as file_object:\n lines = file_object.readlines()\n\npi_string = ''\nfor line in lines:\n pi_string += line.rstrip()\n\nprint(pi_string[:50])\nprint(len(pi_string))\n\nbirthday = input(\"What is your birthday? ddmmyyyy/n\")\nif birthday in pi_string:\n print(\"Yes\")\nelse:\n print(\"No\")\n","sub_path":"Chapter 10/pi_string.py","file_name":"pi_string.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"189692663","text":"__author__ = 'Captain_Ron'\n\nfrom fuzzywuzzy import process\n\ndef parse_csv(path):\n\n with open(path,'r') as f:\n for row in f:\n print(row)\n yield row\n\nif __name__ == \"__main__\":\n ## Create lookup dictionary by parsing the products csv\n data = dict((row[0], row[1]) for row in parse_csv(\"Prod.csv\"))\n\n ## For each row in the lookup compute the partial ratio\n for row in parse_csv(\"LookUp.csv\"):\n for found, score in process.extract(row[0], data.keys(), limit=100):\n if score >= 60:\n print('%d%% partial match: \"%s\" with \"%s\" ' % (score, row[0], found))\n\n\n","sub_path":"FuzzyMatchTake3 - Copy.py","file_name":"FuzzyMatchTake3 - Copy.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"289881049","text":"#!/usr/bin/env python\n#\n# Created by Samvel Khalatyan on Mar 15, 2014\n# Copyright (c) 2014 Samvel Khalatyan. All rights reserved\n#\n# Use of this source code is governed by a license that can be found in\n# the LICENSE file.\n#\n# The code is tested under Python 3.3.5\n# Doc: http://docs.python.org/3/library/threading.html\n# http://docs.python.org/3.3/library/queue.html\n#\n# Share data between threads. This example uses an array of tasks shared\n# among threads. The main thread maintains the list of tasks by adding more\n# tasks as needed. In the meantime thead workers extract tasks, perform job\n# on these and takes the next task if available. The main thread will wait\n# for all tasks to finish before quitting.\n#\n# queue is a multi-producer, multi-consumer FIFO.\n#\n# usage: one_queue.py [max_threads]\n# default: max_threads = 1\n\nimport queue # http://docs.python.org/3.3/library/queue.html\nimport random\nimport sys\nimport time\nimport threading\n\nclass ThreadClass(threading.Thread):\n ''' Worker is initialized with queue and uses this to get task\n \n Worker is designed to work as a Daemon, e.g. there is no exit condition.\n\n '''\n\n def __init__(self, tasks, *parg, **karg):\n ''' Initialize worker with a queue '''\n\n threading.Thread.__init__(self, *parg, **karg)\n self._queue = tasks\n self._tasks = []\n\n @property\n def tasks(self):\n return self._tasks\n\n def run(self):\n ''' Process tasks as they become available in the queue\n\n The worker does not have an exit condition. It is designed to work\n as a Daemon.\n\n '''\n\n while True:\n # retrieve task from the queue\n #\n task = self._queue.get()\n\n # perform some work on the task: emulate work by going to short\n # random sleep\n #\n self._tasks.append(task.strip())\n time.sleep(random.uniform(10**-4, 10**-2))\n\n # mark task as done in the queue. Note that each Queue.get()\n # should be balanced with Queue.task_done() for Queue.join()\n # to work.\n #\n self._queue.task_done()\n\ndef usage():\n ''' Help message on the scrip usage '''\n\n print(\"usage:\", sys.argv[0], \"[max_threads]\")\n print(\"default: max_threads = 1\")\n\nif \"__main__\" == __name__:\n max_threads = 1\n try:\n # User may specify number of threads to be run\n #\n if 1 < len(sys.argv):\n max_threads = int(sys.argv[1])\n else:\n usage()\n print('-' * 10)\n\n tasks = queue.Queue()\n\n # Create and start threads: the queue of tasks is empty at the moment\n #\n threads = []\n for i in range(max_threads):\n thread = ThreadClass(tasks)\n thread.setDaemon(True)\n thread.start()\n\n threads.append(thread)\n\n # populate queue with tasks: workers will automatically start to pick\n # tasks and work on them\n #\n with open(\"tasks_list.txt\") as istream:\n for task in istream:\n tasks.put(task)\n\n # let workers finish working on tasks\n #\n tasks.join()\n\n # workers are designed to work as Daemons. Therefore the main thread\n # assumes that worker can be killed once it finished working on task.\n #\n # print what tasks each worker worked on\n #\n for thread in threads:\n print('-' * 2, thread.getName())\n for task in thread.tasks:\n print(task)\n\n except ValueError:\n usage()\n","sub_path":"thread/one_queue.py","file_name":"one_queue.py","file_ext":"py","file_size_in_byte":3610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"391682709","text":"ServiceMgr.MessageSvc.OutputLevel = DEBUG\n\nfrom AthenaCommon.DetFlags import DetFlags\nDetFlags.ALFA_setOn()\n\nfrom AthenaCommon.AthenaCommonFlags import athenaCommonFlags\nathenaCommonFlags.PoolEvgenInput = ['evnt.ALFA.pool.root']\nathenaCommonFlags.PoolHitsOutput = \"hits.ALFA.pool.root\"\nathenaCommonFlags.EvtMax = 10\n\nfrom G4AtlasApps.SimFlags import simFlags\nsimFlags.load_atlas_flags()\nsimFlags.EventFilter.set_Off()\nsimFlags.MagneticField.set_Off()\nsimFlags.ForwardDetectors.set_On()\n\nfrom AthenaCommon.AlgSequence import AlgSequence\ntopSeq = AlgSequence()\n\ninclude(\"G4AtlasApps/G4Atlas.flat.configuration.py\")\n\nfrom AthenaCommon.CfgGetter import getAlgorithm\ntopSeq += getAlgorithm(\"G4AtlasAlg\",tryDefaultConfigurable=True)\n\ninclude(\"ForwardTransportSvc/preInclude.ForwardTransportFlags_4.0TeV_0090.00m_nominal_v01.py\")\ninclude(\"ForwardTransportSvc/ForwardTransportSvcConfig.ALFA.py\")\ninclude(\"ForwardTransportSvc/postInclude.ForwardTransportSvcConfig.FillRootTree.py\")\ninclude(\"ForwardTransportFast/ForwardTransportFast.py\")\n\ntopSeq.ForwardTransportFast.ForwardTransportSvc = forwardTransportSvc\n","sub_path":"athena/ForwardDetectors/ForwardTransportFast/share/jobOptions.ForwardTransportFast.ALFA.evnt.py","file_name":"jobOptions.ForwardTransportFast.ALFA.evnt.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"94594005","text":"#!/usr/bin/env Python3\n# -*- coding:utf-8 -*-\n\n\"\"\"\n@version: 0.1\n@author: paranoidQ\n@license: Apache Licence \n@contact: paranoid_qian@163.com\n@file: groupby_wrt_key.py\n@time: 15/12/23 23:13\n\"\"\"\n\nrows = [\n{'address': '5412 N CLARK', 'date': '07/01/2012'}, {'address': '5148 N CLARK', 'date': '07/04/2012'}, {'address': '5800 E 58TH', 'date': '07/02/2012'}, {'address': '2122 N CLARK', 'date': '07/03/2012'}, {'address': '5645 N RAVENSWOOD', 'date': '07/02/2012'}, {'address': '1060 W ADDISON', 'date': '07/02/2012'}, {'address': '4801 N BROADWAY', 'date': '07/01/2012'}, {'address': '1039 W GRANVILLE', 'date': '07/04/2012'},\n]\n\n\nfrom operator import itemgetter\nfrom itertools import groupby\n\nrst = rows.sort(key=itemgetter('date'))\n# print(rst) # None\nfor date, items in groupby(rows, key=itemgetter('date')):\n print(date)\n for i in items:\n print(' ', i)\n\n# 必须先sort!\n# sorted也不行,因为不会改变内部顺序\n\n# groupby返回的是迭代器\n\n\nfrom collections import defaultdict\nrows_by_date = defaultdict(list)\nfor row in rows:\n rows_by_date[row['date']].append(row)\n\n\n\n\n\n\n\n\n\n","sub_path":"python-cookbook/cp-1/groupby_wrt_key.py","file_name":"groupby_wrt_key.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"320393365","text":"import pandas as pd\r\nimport datetime as dt\r\nimport numpy as np\r\nimport dateutil.relativedelta as rd\r\nimport xlwings as xw\r\nfrom os import path\r\nimport sys\r\n\r\ndef calcul_pertes(filepath):\r\n\r\n # reglages des options d'affichage pour les print de pandas\r\n pd.set_option('display.width', 5000)\r\n pd.set_option('display.max_columns', None)\r\n pd.set_option('display.max_rows', None)\r\n pd.set_option('display.max_colwidth', -1)\r\n\r\n # chargement de toutes les feuilles Excel du fichier dans un dicionnaire de feuilles\r\n xls_file = pd.ExcelFile(filepath)\r\n dfs = {sheet_name: xls_file.parse(sheet_name) for sheet_name in xls_file.sheet_names}\r\n # nettoyage des donnees Excel, calcul d'un timestamp\r\n alarm_table = dfs['Alarmes_Enercon']\r\n alarm_table = alarm_table[alarm_table['Eolienne'].notna()]\r\n alarm_table= alarm_table.astype(dtype = {'Eolienne' : str, 'Date' : str, 'Time' : str})\r\n alarm_table.reset_index(inplace = True)\r\n alarm_table = alarm_table.astype(dtype = {'Main status' : int, 'Additional Status' : int})\r\n alarm_table.insert(1, 'Timestamp', int)\r\n alarm_table.insert(5, 'dispo', bool)\r\n alarm_table.Timestamp = pd.to_datetime(alarm_table.Date + \" \" + alarm_table.Time, format=r\"%Y-%m-%d %H:%M:%S\")\r\n # alarm_table.insert(6, 'plouf', int)\r\n # alarm_table['plouf'] = alarm_table['plouf'].apply(lambda x: 2)\r\n # print(alarm_table)\r\n\r\n alarm_table = alarm_table[['Eolienne', 'Timestamp', 'Main status', 'Additional Status', 'dispo']]\r\n # liste des status Enercon à utiliser pour le calcul des dispos\r\n statuts_dispo = {(0,0), (2,0), (2,1), (2,2)}\r\n statuts_heritants = {(0,1), (0,2), (0,3), (0,5)}\r\n\r\n # calcul de la dispo pour chaque statut. Les statuts dispos recoice un statut de 1 et non dispo de 0\r\n for index, row in alarm_table.iterrows():\r\n if (row['Main status'], row['Additional Status']) in statuts_dispo:\r\n alarm_table.at[index, 'dispo'] = True\r\n # Le calcul de statuts heritants est un peu particulier. Il faut faire attention au changement d'eolienne et au premier statut de la liste qui ne peut pas heriter\r\n elif (row['Main status'], row['Additional Status']) in statuts_heritants and index != 0 and alarm_table.at[index-1, 'Eolienne'] == alarm_table.at[index, 'Eolienne']:\r\n alarm_table.at[index, 'dispo'] = alarm_table.at[index-1, 'dispo']\r\n else:\r\n alarm_table.at[index, 'dispo'] = False\r\n\r\n # Les colonnes Main Status et Additional Status sont devenues inutiles\r\n alarm_table.drop(columns=['Main status', 'Additional Status'], inplace=True)\r\n\r\n #Creation d'une Dataframe vide qui recevra les statuts de dispo consolides\r\n at_cons = pd.DataFrame(data=None, columns=['Eolienne', 'Timestamp', 'Timestamp_fin','dispo'])\r\n\r\n # Initialisation de la dataframe consolidee avec les premiere valeur de alarm_table\r\n last_row = alarm_table.loc[0]\r\n at_cons = at_cons.append(last_row)\r\n\r\n # On consolide en supprimant les valeurs de dispo consecutive identiques\r\n for i, row in alarm_table.iterrows():\r\n\r\n # Si on reste sur la meme eolienne, le timestamp de fin de la derniere alarme est egal au timestamp de debut de l'alarme courante\r\n if row['dispo'] != last_row['dispo'] and row['Eolienne'] == last_row['Eolienne']:\r\n at_cons.iloc[-1, at_cons.columns.get_loc('Timestamp_fin')] = row['Timestamp']\r\n at_cons = at_cons.append(row, ignore_index = True)\r\n last_row = row\r\n # Si on est sur la derniere alarme d'une eolienne on met le time stamp de fin a minuit du jour suivant\r\n elif row['Eolienne'] != last_row['Eolienne']:\r\n new_day = at_cons.iloc[-1, at_cons.columns.get_loc('Timestamp')] + rd.relativedelta(months=+1)\r\n at_cons.iloc[-1, at_cons.columns.get_loc('Timestamp_fin')] = new_day.replace(day = 1, hour = 0, minute = 0, second = 0)\r\n at_cons = at_cons.append(row, ignore_index = True)\r\n last_row = row\r\n\r\n # On donne un timestamp de fin a la derniere alarme de la derniere eolienne\r\n new_day = at_cons.iloc[-1, at_cons.columns.get_loc('Timestamp')] + rd.relativedelta(months=+1)\r\n at_cons.iloc[-1, at_cons.columns.get_loc('Timestamp_fin')] = new_day.replace(day = 1, hour = 0, minute = 0, second = 0)\r\n\r\n\r\n # On genere la liste des jours du mois a partir de la liste des onglets Excel\r\n sheet_list = [s for s in xls_file.sheet_names if s.isdigit()]\r\n\r\n # On cree une dataframe vide que l'on va remplir avec chacun des onglets journaliers d'excel\r\n month_sheet = pd.DataFrame(columns=['Eolienne', 'Timestamp', 'Vent', 'Prod', 'Dispo', 'PC_Vent_KW'])\r\n for s in sheet_list:\r\n day_sheet = dfs[s].loc[:431, ['Eolienne','Heure', 'Vent Ø [m/s]', 'Energie prod. [kWh]']]\r\n day_sheet.rename(columns = {'Energie prod. [kWh]' : 'Prod', 'Vent Ø [m/s]' : 'Vent', 'Heure' : 'Timestamp'}, inplace = True)\r\n month_sheet = month_sheet.append(day_sheet, ignore_index = True, sort=False)\r\n\r\n\r\n month_sheet['Prod'] = pd.to_numeric(month_sheet['Prod'], downcast='integer', errors='coerce') #On force le champ prod en integer a la place de float\r\n month_sheet.sort_values(by=['Eolienne', 'Timestamp'], inplace = True) #\r\n month_sheet.reset_index(drop = True, inplace = True)\r\n month_sheet['Dispo'] = month_sheet.apply((lambda row: False if pd.isna(row['Vent']) else None), axis=1 )\r\n\r\n for index, row in month_sheet.iterrows():\r\n debut = row['Timestamp'] - dt.timedelta(minutes = 10)\r\n fin = row['Timestamp']\r\n slot = at_cons.loc[(at_cons['Timestamp'] <= debut) & (at_cons['Timestamp_fin'] >= fin) & (row['Eolienne'] == at_cons['Eolienne']), 'dispo']\r\n if not slot.empty and slot.bool() and row['Dispo'] is None:\r\n month_sheet.at[index, 'Dispo'] = True\r\n else:\r\n month_sheet.at[index, 'Dispo'] = False\r\n\r\n\r\n #month_sheet.to_csv(r'C:\\Users\\fr.chalopin.ZEPHYR-LOCAL\\Desktop\\calcul pertes Enercon\\export_temp_2018-07.csv')\r\n\r\n\r\n pc_large =pd.Series([x/10 for x in range(10,241)], [x/10 for x in range(10,241)])\r\n pc_small = dict(zip(range(1,26),[x/6 for x in[0, 3, 25, 82, 174, 321, 532, 815, 1180, 1612, 1890, 2000, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050, 2050]]))\r\n pc_large = pc_large.map(pc_small).interpolate().round(0)\r\n moins1 = pd.Series([0 for x in range(0,10)], [x/10 for x in range(0,10)])\r\n plus25 = pd.Series([0 for x in range(251,801)], [x/10 for x in range(251,801)])\r\n pc_large = moins1.append(pc_large)\r\n pc_large = pc_large.append(plus25)\r\n pc_large.at[np.NaN] = None\r\n month_sheet['PC_Vent_KW'] = (month_sheet['Vent']).map(pc_large)\r\n\r\n matrice = pd.DataFrame({'first' : ['E2', 'E3', 'E2'], 'second' : ['E3', 'E1', 'E1']}, index = ['E1', 'E2', 'E3'] )\r\n\r\n loss_table = month_sheet.loc[month_sheet['Dispo'] == False]\r\n loss_table.reset_index(drop=True, inplace=True)\r\n loss_table = loss_table.drop(columns=['Dispo'])\r\n\r\n a_inserer = [(4, 'Pertes_Prod', int), (5, 'Neighbour1_KW', int), (6, 'Neighbour2_KW', int)]\r\n for x, y, z in a_inserer:\r\n loss_table.insert(x, y, z)\r\n\r\n for index, row in loss_table.iterrows():\r\n Eolienne = row['Eolienne']\r\n Neighbour1 = matrice.at[Eolienne, 'first']\r\n dispo_Neighbour1 = month_sheet.loc[(month_sheet.loc[:, 'Timestamp'] == row['Timestamp']) & (month_sheet.loc[:, 'Eolienne'] == Neighbour1), 'Dispo'].iat[0]\r\n prod_neighbour1 = month_sheet.loc[(month_sheet.loc[:, 'Timestamp'] == row['Timestamp']) & (month_sheet.loc[:, 'Eolienne'] == Neighbour1), 'Prod'].iat[0]\r\n loss_table.at[index, 'Neighbour1_KW'] = prod_neighbour1 if dispo_Neighbour1 else None\r\n Neighbour2 = matrice.at[Eolienne, 'second']\r\n dispo_Neighbour2 = month_sheet.loc[(month_sheet.loc[:, 'Timestamp'] == row['Timestamp']) & (month_sheet.loc[:, 'Eolienne'] == Neighbour2), 'Dispo'].iat[0]\r\n prod_neighbour2 = month_sheet.loc[(month_sheet.loc[:, 'Timestamp'] == row['Timestamp']) & (month_sheet.loc[:, 'Eolienne'] == Neighbour2), 'Prod'].iat[0]\r\n loss_table.at[index, 'Neighbour2_KW'] = prod_neighbour2 if dispo_Neighbour2 else None\r\n\r\n if dispo_Neighbour1:\r\n loss_table.at[index, 'Pertes_Prod'] = ((prod_neighbour1 - loss_table.at[index, 'Prod']) + abs(prod_neighbour1 - loss_table.at[index, 'Prod']))/2\r\n elif dispo_Neighbour2:\r\n loss_table.at[index, 'Pertes_Prod'] = ((prod_neighbour2 - loss_table.at[index, 'Prod']) + abs(prod_neighbour2 - loss_table.at[index, 'Prod']))/2\r\n elif loss_table.at[index, 'PC_Vent_KW'] != None:\r\n loss_table.at[index, 'Pertes_Prod'] = ((loss_table.at[index, 'PC_Vent_KW'] - loss_table.at[index, 'Prod']) + abs(loss_table.at[index, 'PC_Vent_KW'] - loss_table.at[index, 'Prod']))/2\r\n else:\r\n loss_table.at[index, 'Pertes_Prod'] = 1000000000\r\n #loss_table.to_csv(r'C:\\Users\\fr.chalopin.ZEPHYR-LOCAL\\Desktop\\calcul pertes Enercon\\export_loss_table_2018-07.csv')\r\n\r\n return loss_table\r\n\r\ndef calcul_pertes_macro(adresse):\r\n loss_table = calcul_pertes(adresse)\r\n #loss_table = pd.read_csv(r'C:\\Users\\fr.chalopin.ZEPHYR-LOCAL\\Desktop\\calcul pertes Enercon\\export_loss_table_2018-07.csv', parse_dates=['Timestamp'], index_col=0)\r\n wb = xw.Book.caller()\r\n sht = wb.sheets[\"Pertes_Prod\"]\r\n sht.range('A1').value = loss_table\r\n #sht.range('A2').select()\r\n\r\nif __name__ == \"__main__\":\r\n print(calcul_pertes(r\"C:\\Users\\fr.chalopin.ZEPHYR-LOCAL\\Desktop\\calcul pertes Enercon\\HC - Données Scada - 2018-08.xlsm\"))\r\n\r\n\r\n","sub_path":"Enercon/calcul_pertes.py","file_name":"calcul_pertes.py","file_ext":"py","file_size_in_byte":9604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"216826572","text":"import miscellaneous\nimport time\n\n\n\n\n\n\n\n\ndef execute(data, output_location):\n \"\"\"\n This function decrypts data using a key.\n\n :param data: (string) the data to be decrypted\n :param output_location: (string) the location to save relevant info into\n :return: (string) the decrypted data\n \"\"\"\n\n\n # Obtain the decrypted text. Also write statistics and relevant info to a file\n decrypted = miscellaneous.symmetric_encrypt_or_decrypt_without_key(data, output_location,\n \"Decryption\", \"vigenere_nokey\", \"decrypt\")\n\n\n # Return encrypted text to be written in cryptography_runner\n return decrypted\n\n\n\n# Decrypt in testing mode. So add more statistics about performance. Check for correctness\ndef testing_execute(ciphertext, output_location, plaintext, encryption_time):\n \"\"\"\n Decrypt and save statistics.\n\n :param ciphertext: (string) the encrypted text to decipher\n :param output_location: (string) the file to save statistics into\n :param plaintext: (string) the original plaintext\n :param key: (string) the key used to decrypt\n :param char_set_size: (integer) the character set used\n :param encryption_time: (double) the time it took to encrypt using vigenere\n :return: None\n \"\"\"\n\n # Run the decryption algorithm on the ciphertext\n start_time = time.time()\n decrypted, char_set, key, percent_english = decrypt(ciphertext)\n decryption_time = time.time() - start_time\n\n # Open file for writing\n new_file = open(output_location, \"w\", encoding=\"utf-8\")\n\n # Set up a space for notes\n if decrypted == plaintext:\n new_file.writelines([\"Vigenere without key\\nCORRECT \\n\\n\\nNotes: \"])\n print(\"Vignere No Key: CORRECT\\n\")\n else:\n # Calculate the number of characters that differ\n count = sum(1 for a, b in zip(decrypted, plaintext) if a != b)\n new_file.writelines([\"Vigenere without key\" + \"\\nINCORRECT\"\n + \"\\tDiffering characters: \" + str(count)\n + \"\\tPercentage difference: \" + str((count / len(plaintext)) * 100) + \"\\n\\n\\nNotes: \"])\n print(\"Vigenere No Key: INCORRECT\\n\")\n\n # Encryption information\n new_file.writelines([\"\\n\\n\\nEncryptionEncryptionEncryptionEncryptionEncryptionEncryptionEncryptionEncryption\",\n \"\\nThe key is: \" + key,\n \"\\nEncrypted in: \" + str(encryption_time) + \" seconds.\",\n \"\\nThat is \" + str(encryption_time / len(decrypted)) + \" seconds per character.\",\n \"\\nThat is \" + str((encryption_time / len(decrypted) * 1000000))\n + \" microseconds per character.\"])\n\n\n # Decryption information\n new_file.writelines([\"\\n\\n\\nDecryptionDecryptionDecryptionDecryptionDecryptionDecryptionDecryptionDecryption\",\n \"\\nThe character set is : \" + char_set,\n \"\\nThe key is: \" + key,\n \"\\nThe percent of words that are English are : \" + str(percent_english),\n \"\\nDecrypted in: \" + str(decryption_time) + \" seconds.\",\n \"\\nThat is \" + str(encryption_time / len(decrypted)) + \" seconds per character.\",\n \"\\nThat is \" + str((decryption_time / len(decrypted) * 1000000))\n + \" microseconds per character.\"])\n\n # Print out the ciphertext\n new_file.writelines([\"\\n\\n\\nciphertext: \\n\" + ciphertext])\n\n # Print out the decrypted\n new_file.writelines([\"\\n\\n\\nDecrypted text: \\n\" + decrypted])\n\n # Print out the plaintext\n new_file.writelines([\"\\n\\n\\nplaintext: \\n\" + plaintext])\n\n new_file.close()\n\n\n\n\n\n\n# Contains the actual algorithm to decrypt with vigenere cipher without a key TODO\ndef decrypt(ciphertext):\n \"\"\"\n This function decrypts a vigenere cipher without a key\n\n :param ciphertext: (string) the ciphertext to decrypt\n :return: (string) the deciphered text\n \"\"\"\n\n plaintext = \"\" # Build up the decrypted text here\n key_index = 0 # The index of the current char in the key. Iterates from 0 to len() - 1 and repeats.\n\n\n\n\n return 0\n\n\n\n\n\n\n\n\n","sub_path":"Decryption/vigenere_nokey.py","file_name":"vigenere_nokey.py","file_ext":"py","file_size_in_byte":4249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"555804940","text":"#!/usr/bin/env python3\n\nimport serial\nfrom common import detect_baudrate, port, look_for_ack, interpret_messages, read_lines_ignoring_timeouts\nfrom common import baudrate as desired_baudrate\nfrom input_messages import ConfigureSerialPortMessage\n\ni_br, current_baudrate = detect_baudrate(serial_port=port, look_for_ack_limit=50, retries=2)\n\nif current_baudrate == desired_baudrate:\n print(\"Baudrate is already set to the desired value of {} bps.\".format(desired_baudrate))\n exit()\n\nprint(\"Baudrate is set to {} bps, but the desired value is {} bps.\".format(current_baudrate, desired_baudrate))\n\nmsg = ConfigureSerialPortMessage(rate=desired_baudrate, permanent=True)\nwith serial.Serial(port=port, baudrate=current_baudrate) as ser:\n print(\"Setting baudrate to {} bps...\".format(desired_baudrate))\n ser.write(bytes(msg))\n print(\"Waiting for ACK...\")\n try:\n i_ackmsg = look_for_ack(\n messages=interpret_messages(read_lines_ignoring_timeouts(ser)),\n msg_id=ConfigureSerialPortMessage.msg_id,\n limit=25,\n )\n print(\"Got ACK after {} messages.\".format(i_ackmsg))\n except TimeoutError:\n print(\"Timeout.\")\n","sub_path":"configure_uart.py","file_name":"configure_uart.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"148795402","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 25 22:21:03 2020\n\n@author: USER\n\"\"\"\nfrom bs4 import BeautifulSoup\nimport time\n\nfrom selenium import webdriver\nimport pymysql\n\nfrom selenium.webdriver.chrome.options import Options\nchrome_options = Options()\nchrome_options.add_argument('--no-sandbox')\nchrome_options.add_argument('--disable-dev-shm-usage')\nchrome_options.add_argument('--headless')\n\nwhile True:\n\tdb = pymysql.connect(host=\"\",\n\t\t\t\t\t user=\"root\", \n\t\t\t\t\t passwd=\"\", \n\t\t\t\t\t db=\"dbtest\")\n\tcursor = db.cursor()\n\n\tdriver = webdriver.Chrome(chrome_options=chrome_options)\n\tdriver.get(\"https://www.twreporter.org/i/covid-2019-keep-tracking-gcs\")\n\ttime.sleep(5)\n\tsoup = BeautifulSoup(driver.page_source, 'html.parser')\n\tdriver.close()\n\tdriver.quit()\n\n\tregion_data = soup.find_all('td',class_='table__TD-sc-1ji413w-0 idjyyX')\n\n\tall_data = []\n\tfor i in range(0,len(region_data),3):\n\t\tdata = []\n\t\tdata.append(region_data[i].text)\n\t\tdata.append((region_data[i+1].text).replace(',' , ''))\n\t\tdata.append((region_data[i+2].text).replace(',' , ''))\n\t\tall_data.append(data)\n\n\tcursor.execute(\"SELECT N_ID , N_CH_Name , N_EN_Name FROM nation \")\n\n\tdbdata=[item for item in cursor.fetchall()]\n\n\tdb_dict_CH = {}\n\tdb_dict_EN = {}\n\tfor i in range(len(dbdata)):\n\t\tdb_dict_CH[dbdata[i][1]]=dbdata[i][0]\n\t\tdb_dict_EN[dbdata[i][2]]=dbdata[i][0]\n\n\ttoday = time.strftime(\"%Y/%m/%d\", time.localtime())\n\tinsert_data = \"insert into infect (I_N_ID , I_Infect,I_Death , date) value\"\n\tupdata_data = \"update infect set\"\n\n\tdb_list =[]\n\tdb_id = []\n\tfor i in range(len(all_data)):\n\t\ttry:\n\t\t\tgetid = db_dict_CH[all_data[i][0]]\n\t\t\tcursor.execute(\"SELECT I_N_ID, I_Infect , I_Death FROM infect where I_N_ID =\" + str(getid) +\"\" )\n\t\t\tdbnationdata=[item for item in cursor.fetchall()]\n\t\t\tdb_list.append(dbnationdata)\n\t\t\tdb_id.append(getid)\n\t\texcept:\n\t\t\tdb_list.append([])\n\t\t\tdb_id.append([])\n\t\t\tcontinue\n\t \n\tfor i in range(len(db_list)):\n\t\tif db_list[i] != []:\n\t\t\tif int(db_list[i][0][1]) < int(all_data[i][1]) or int(db_list[i][0][2]) < int(all_data[i][2]):\n\t\t\t updata = \" I_Infect = '\"+str(all_data[i][1])+ \"', I_Death = '\" +str(all_data[i][2])+ \"' , date = '\" +today+ \"' where (I_N_ID ='\" +str(db_list[i][0][0])+ \"')\"\n\t\t\t cursor.execute(updata_data+updata)\n\t\t\t db.commit()\n\t\t\t print(\"updata : \"+ updata)\n\t\telif db_id[i] != [] :\n\t\t\tinsert = \"('\" + str(db_id[i]) + \"','\" + str(all_data[i][1]) + \"','\" + str(all_data[i][2]) + \"','\" + today + \"')\" \n\t\t\tcursor.execute(insert_data+insert)\n\t\t\tdb.commit()\n\t\t\tprint(\"insert data : \" + insert)\n\n\tcursor.close()\n\tdb.close()\n\tprint(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()))\n\ttime.sleep(600)","sub_path":"infect_data.py","file_name":"infect_data.py","file_ext":"py","file_size_in_byte":2621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"355406312","text":"#!usr/bin/env python \n#-*- coding:utf-8 -*-\nimport requests\nimport json,jsonpath\nimport hashlib\nimport time\nimport base64\nclass Ip():\n def __init__(self):\n self.timestamp = int(time.time())\n self.headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',\n 'Cookie':'sessionid=tb08ddgthpwy9u5jmbvrrvjqi73bohrt',\n }\n def get_base64_contetn(self,page,num):\n token = hashlib.md5((str(page) + str(num) + str(self.timestamp)).encode('utf-8')).hexdigest()\n url = 'https://nyloner.cn/proxy?page={}&num={}&token={}&t={}'.format(page,num,token,self.timestamp)\n response = requests.get(url,headers=self.headers)\n response.encoding = response.apparent_encoding\n html = json.loads(response.text)\n ip_ports_str = html['list'] if html['list'] else ''\n return ip_ports_str\n def get_true_content(self,page,num):\n \"\"\"\n base64解密\n scHZjLUh1 = Base64[\"\\x64\\x65\\x63\\x6f\\x64\\x65\"](scHZjLUh1); decode\n key = '\\x6e\\x79\\x6c\\x6f\\x6e\\x65\\x72'; nyloner\n len = key[\"\\x6c\\x65\\x6e\\x67\\x74\\x68\"]; length\n code_util = '';\n for (i = 0; i < scHZjLUh1[\"\\x6c\\x65\\x6e\\x67\\x74\\x68\"]; i++) { length\n var coeFYlqUm2 = i % len;\n code_util += window[\"\\x53\\x74\\x72\\x69\\x6e\\x67\"][\"\\x66\\x72\\x6f\\x6d\\x43\\x68\\x61\\x72\\x43\\x6f\\x64\\x65\"](scHZjLUh1[\"\\x63\\x68\\x61\\x72\\x43\\x6f\\x64\\x65\\x41\\x74\"](i) ^ key[\"\\x63\\x68\\x61\\x72\\x43\\x6f\\x64\\x65\\x41\\x74\"](coeFYlqUm2))\n String fromCharCode charCodeAt charCodeAt\n }\n return Base64[\"\\x64\\x65\\x63\\x6f\\x64\\x65\"](code_util)\n\n \"\"\"\n scHZjLUh1 = base64.decodebytes(self.get_base64_contetn(page,num).encode())\n key = 'nyloner'\n length = len(key)\n code = ''\n for i in range(len(scHZjLUh1)):\n coeFYlqUm2 = i % length\n code += chr(scHZjLUh1[i]^ ord(key[coeFYlqUm2]))\n code = base64.decodebytes(code.encode()).decode()\n ip_ports = json.loads(code) if code else ''\n for ip_port in ip_ports:\n ip = ip_port['ip']\n port = ip_port['port']\n print(ip,port)\n\nif __name__ == '__main__':\n ip = Ip()\n\n ip.get_true_content(1,15)","sub_path":"spider/ArticleSpider/spiderproject/ip_deal_js.py","file_name":"ip_deal_js.py","file_ext":"py","file_size_in_byte":2448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"346285763","text":"#7 1 12 2 8 3 11 4 9 5 13 6 10\n#from listQfile import ListQ\nfrom sortFile import Sort\nfrom cardDict import cardDict\n\ndef main():\n\tquestion = input(\"Du har 5 kort. I vilken ordning vill du laegga dessa?\")\n\tnumbers = question.split()\n\t\n\tfor n in numbers:\n\t\tif not n.isdigit():\n\t\t\ttry:\n\t\t\t\tnumbers[numbers.index(n)] = cardDict[n]\n\t\t\texcept KeyError:\n\t\t\t\tprint('Du angav ett eller flera kort som inte finns i kortleken!')\n\n\tx = Sort(numbers)\n\tprint(x)\n\nmain() ","sub_path":"lab2/lab2/listQmain.py","file_name":"listQmain.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"117287883","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 10 14:39:29 2020\n\n@author: Admin\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ng = 9.81\nm1 = 1.0\nm2 = 1.0\nL1 = 1.0\nL2 = 1.0\n\ndef dw1dt(teta1,teta2,w1,w2,T1,T2,m1,m2,L1,L2):\n dw1 = ((T2*np.sin(teta2))-(T1*np.sin(teta1)))/(m1*L1*np.cos(teta1))+(w1**2*np.tan(teta1))\n return dw1\n\ndef dw2dt(teta1,teta2,w1,w2,T1,T2,m1,m2,L1,L2):\n teta1_2puntos = dw1dt(teta1,teta2,w1,w2,T1,T2,m1,m2,L1,L2)\n dw2 = (m1*L1*(w1**2*np.sin(teta1)-teta1_2puntos*np.cos(teta1))-T2*np.sin(teta2)+m2*L2*(w2**2*np.sin(teta2)))/(m2*L2*np.cos(teta2))\n return dw2\n\ndef funciones(t,x,i): \n #x --> [teta1,teta2,w1,w2]\n teta1 = x[0]\n teta2 = x[1]\n w1 = x[2]\n w2 = x[3]\n \n T2 = m2*g*np.cos(teta2)\n T1 = T2*np.cos(teta2-teta1) + m1*g*np.cos(teta1)\n \n if(i==0):\n f = w1\n elif(i==1):\n f = w2\n elif(i==2):\n f = dw1dt(teta1,teta2,w1,w2,T1,T2,m1,m2,L1,L2)\n elif(i==3):\n f = dw2dt(teta1,teta2,w1,w2,T1,T2,m1,m2,L1,L2)\n return f\n\ndef euler(fi_in,n,dt,t):\n #fi_in = [x,y] del tiempo conocido\n #fi_out = [x,y] del tiempo futuro\n #n es el número de variables\n #t es el instante de tiempo\n fi_out = np.zeros(n)\n for i in range(n):\n fi_out[i] = fi_in[i] + funciones(t,fi_in,i)*dt\n return fi_out\n\ndt = 0.001\ntf = 20.0 #Tiempo de simulación \nit = int(tf/dt)\n\nt = np.zeros(it+1)\nx = np.zeros((it+1,4)) \n#x --> [teta1,teta2,w1,w2] en cualquier fila\n\nx[0,0] = 0.1*np.pi\n\nfor i in range(1,it+1):\n t[i] = t[i-1] + dt\n x[i,:] = euler(x[i-1,:],4,dt,t[i-1])\n\nplt.plot(t,x[:,0],\"-g\",label=\"teta1\")\nplt.plot(t,x[:,1],\"-b\",label=\"teta2\") \n\n#plt.plot(t,x_rk4[:,2],\"-g\",label=\"w1\")\n#plt.plot(t,x_rk4[:,3],\"-b\",label=\"w2\")\n\nplt.legend(loc=\"upper right\")\nplt.xlabel('Tiempo(s)')\nplt.ylabel('Ángulo (rad)')\nplt.grid()\n\n\n","sub_path":"Códigos_2/pendulo_doble(1).py","file_name":"pendulo_doble(1).py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"29590161","text":"from falcon_rest.conf import settings\n\nfrom sqlalchemy.sql import and_, or_\nfrom sqlalchemy import literal_column\nimport sqlalchemy as sa\n\nfrom falcon_rest.paginators import PageNumberPagination\nimport falcon \n\n\nclass Resource:\n\n \"\"\" This is the base resource class. \n\n Currently suports Marshmallow serilaizers only.\n\n Search params example format: ?search=subject__startswith:test3,state__eq:Initial\n\n\n\n \"\"\"\n\n table = None\n limit = settings.PAGINATION_PAGE_SIZE\n serializer_class = None\n pagination_class = PageNumberPagination\n\n search_fields = None\n filter_fields = None\n ordering_fields = ['id']\n ordering = 'id:desc' #of default fields for ordering if none specified\n\n\n def map_column_filter(self, column, action, value):\n #example input => ('name', 'contains','mosoti',)\n \n\n col = literal_column( column ) #to sqlalchemy column\n\n ops_dict = {\n 'startswith':col.startswith(value),\n 'endswith': col.endswith(value),\n 'contains': col.contains(value),\n 'eq': col.op(\"=\")(value),\n 'lt': col.op(\"<\")(value),\n 'lte': col.op(\"<=\")(value),\n 'gt': col.op(\">\")(value),\n 'gte': col.op(\">=\")(value)\n }\n \n return ops_dict[action]\n \n \n\n def get_query(self, session):\n return session.query( self.table)\n \n \n def filter_queryset(self, queryset, filter_params):\n try:\n filter_params = filter_params.split() #some params come as one string\n except AttributeError:\n pass\n\n #make filters\n filters = []\n for sp in filter_params:\n col_action, value = sp.split(':')\n column, action = col_action.split('__') #this is double underscore\n\n if column in self.filter_fields:\n filters.append( self.map_column_filter( column, action, value ) )\n \n #apply filters\n return queryset.filter(\n and_(\n *filters\n )\n )\n\n \n def search_queryset(self, queryset, search_params):\n try:\n search_params = search_params.split() #some params come as one string\n except AttributeError:\n pass\n\n #make filters\n filters = []\n for sp in search_params:\n col_action, value = sp.split(':')\n column, action = col_action.split('__') #this is double underscore\n\n if column in self.search_fields:\n filters.append( self.map_column_filter( column, action, value ) )\n \n #apply filters\n return queryset.filter(\n or_(\n *filters\n )\n )\n \n def order_queryset(self, queryset, req):\n params = req.params\n ordering_params = params.get(settings.ORDERING_QUERY_PARAM)\n try:\n ordering_params = ordering_params.split() #some come as string\n except AttributeError:\n pass\n\n if not ordering_params:\n #apply default ordering\n ordering = self.ordering\n ordering_params = ordering.split()\n \n for op in ordering_params:\n col,style = op.split(':')\n if col in self.ordering_fields:\n if style == 'desc':\n queryset = queryset.order_by( literal_column(col).desc() )\n elif style == 'asc':\n queryset = queryset.order_by( literal_column(col).asc() )\n \n return queryset\n\n\n\n \n\n \n\n\n\n \n \n def get_filtered_query(self, session, req=None):\n #both search and filter are applied to the query as per given GET params\n \n queryset = self.get_query(session)\n if not req:\n return queryset\n\n params = req.params\n\n\n search_params = params.get(settings.SEARCH_QUERY_PARAM)\n filter_params = params.get(settings.FILTER_QUERY_PARAM)\n \n\n \n if search_params and self.search_fields:\n queryset = self.search_queryset(queryset, search_params)\n \n if filter_params and self.filter_fields:\n queryset = self.filter_queryset(queryset, filter_params)\n\n #1. Apply search\n return queryset\n\n \n\n\n\n def get_object_queryset(self, session, pk , req=None):\n \n filtered_query = self.get_filtered_query(session, req)\n\n #apply pk filtering\n return filtered_query.filter( self.table.id == pk )\n\n \n def get_object(self, session, pk , req=None):\n try:\n return self.get_object_queryset(session,pk, req).one()\n except sa.orm.exc.NoResultFound:\n raise falcon.HTTPNotFound( description=\"The requested record doesnot exist\")\n\n\n \n\n\n\n\n\n","sub_path":"falcon_rest/resources/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":4820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"498554177","text":"class Solution:\n def threeSum(self, nums: List[int]) -> List[List[int]]:\n \n triplets = []\n nums.sort()\n \n for i, x in enumerate(nums):\n if i > 0 and x == nums[i - 1]:\n continue\n \n leftPtr, rightPtr = i + 1 , len(nums) - 1 #left and right pointers on input array\n while leftPtr < rightPtr:\n triplet = x + nums[leftPtr] + nums[rightPtr]\n if triplet > 0:\n rightPtr -= 1\n elif triplet < 0:\n leftPtr += 1\n else:\n triplets.append([x, nums[leftPtr], nums[rightPtr]])\n leftPtr += 1\n while nums[leftPtr] == nums[leftPtr -1] and leftPtr < rightPtr:\n leftPtr += 1\n \n return triplet\n","sub_path":"3sum.py","file_name":"3sum.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"355441627","text":"from bs4 import BeautifulSoup\nimport urllib.request as urllib2\nimport dbm\n\ndef read_page(page):\n #using request module load page to memory\n opened_page = urllib2.urlopen(page)\n read = opened_page.read()\n opened_page.close()\n soup = BeautifulSoup(read, \"html.parser\")\n return soup\n\ndef last_key(db):\n #return max value which represents last key\n temp =[]\n for key in db:\n temp.append(int(key))\n return max(temp)\n\ndef db_comparision(db_1, db_tp):\n #compare to db's, if there is new link, print it and at to local database\n list_1 = [db_1[key] for key in db_1]\n list_2 = [db_tp[key] for key in db_tp]\n\n new = [site for site in list_2 if site not in list_1]\n if not new:\n print(\"Nie ma nowych linków\")\n else:\n print(new)\n ls = last_key(db_1)\n for x, item in enumerate(new, 1):\n db_1[str(ls+x)] = item\n\n\n\n\n#get source of page\nsite = \"https://allegro.pl/kategoria/czesci-samochodowe-czesci-karoserii-4094?string=e46%20silbergrau&order=m&bmatch=ss-base-relevance-floki-5-nga-hcp-aut-1-2-1003\"\nhtml = read_page(site)\n\n\n#select div\nallegro_div = html.select(\"div\")\n\n#how many subsites at site\nh_num = []\nfor classes in allegro_div:\n my_class = classes.get('class')\n if str(my_class) == \"['pagination-bottom']\":\n pagination = classes.select('a')\n for links in pagination:\n h_num.append(links.get('data-page'))\n\n#delete NoneType at pagination-bottom, get maximum value\nfor num in h_num:\n if isinstance(num, str):\n num = int(num)\n else:\n h_num.remove(num)\nmax_num = max(h_num)\n\ndb = dbm.open('local_links', 'c')\ntemp = dbm.open('temp_links', 'n')\n\nx = 0\n#get links with DEV attribiutes from all subsites\nfor page in range(int(max_num)):\n #load subsite\n print(site+\"&p=\"+str(page+1))\n html = read_page(site+\"&p=\"+str(page+1))\n allegro_div = html.select(\"div\")\n for ids in allegro_div:\n my_id = ids.get('id')\n if str(my_id) == 'opbox-listing':\n opbox_a = ids.select('a')\n for link in opbox_a:\n if 'events' not in link.get('href'):\n if 'blotnik' in link.get('href'):\n x += 1\n temp[str(x)] = link.get('href')\n\nprint('Pobralem '+str(x)+' linkow odnoszacych sie do blotnikow do e46 w kolorze silbergrau')\n\ndb_comparision(db, temp)\n\ndb.close()\ntemp.close()\n\n","sub_path":"allegro.py","file_name":"allegro.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"111859313","text":"\"\"\"Add basic template table\n\nRevision ID: 393db40d179\nRevises: 400cee903f\nCreate Date: 2015-02-24 21:59:28.454757\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '393db40d179'\ndown_revision = '400cee903f'\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\ndef upgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.create_table('template',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.Text(), nullable=True),\n sa.Column('description_on', sa.Boolean(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_index('ix_template_description_on', 'template', ['description_on'], unique=False)\n ### end Alembic commands ###\n\n\ndef downgrade():\n ### commands auto generated by Alembic - please adjust! ###\n op.drop_index('ix_template_description_on', 'template')\n op.drop_table('template')\n ### end Alembic commands ###\n","sub_path":"migrations/versions/393db40d179_add_basic_template_table.py","file_name":"393db40d179_add_basic_template_table.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"504009867","text":"#! /Library/Frameworks/Python.framework/Versions/Current/bin/python3\n# -*- coding: utf-8 -*-\n####################\n# uniFi Plugin\n# Developed by Karl Wachs\n# karlwachs@me.com\n\nimport datetime\ntry:\n\timport json\nexcept:\n\timport simplejson as json\nimport subprocess\nimport fcntl\nimport os \nimport sys\nimport pwd\nimport time\nimport traceback\nimport platform\nimport struct\nimport codecs\n\ntry:\n\timport queue as queue\n\tfrom queue import PriorityQueue\n\tqueueOrQueue = \"queue\"\nexcept:\n\timport Queue as queue\n\tfrom Queue import PriorityQueue\n\tqueueOrQueue = \"Queue\"\n\nimport random\nimport socket\nimport getNumber as GT\nimport MAC2Vendor\nimport threading\nimport logging\nimport copy\nimport requests\nimport inspect\nfrom checkIndigoPluginName import checkIndigoPluginName \n\nrequests.packages.urllib3.disable_warnings(requests.packages.urllib3.exceptions.InsecureRequestWarning)\n\nimport cProfile\nimport pstats\n\n\ntry:\n\tunicode(\"x\")\nexcept:\n\tunicode = str\n\n######### set new pluginconfig defaults\n# this needs to be updated for each new property added to pluginProps. \n# indigo ignores the defaults of new properties after first load of the plugin \nkDefaultPluginPrefs = {\n\t\"MSG\":\t\t\t\t\t\t\t\t\t\t\"please enter values\",\n\t\"updateDescriptions\":\t\t\t\t\t\tTrue,\n\t\"expirationTime\":\t\t\t\t\t\t\t\"120\",\n\t\"fixExpirationTime\":\t\t\t\t\t\tTrue,\n\t\"expTimeMultiplier\":\t\t\t\t\t\t\"2\",\n\t\"launchWaitSeconds\":\t\t\t\t\t\t\"1.13\",\n\t\"ignoreNewClients\":\t\t\t\t\t\t\tFalse,\n\t\"ignoreNewNeighbors\":\t\t\t\t\t\tFalse,\n\t\"ignoreNeighborForFing\":\t\t\t\t\tTrue,\n\t\"enableBroadCastEvents\":\t\t\t\t\t\"0\",\n\t\"enableFINGSCAN\":\t\t\t\t\t\t\tFalse,\n\t\"enableSqlLogging\":\t\t\t\t\t\t\tTrue,\n\t\"enableMACtoVENDORlookup\":\t\t\t\t\t\"21\",\n\t\"requestOrcurl\":\t\t\t\t\t\t\t\"curl\",\n\t\"curlPath\":\t\t\t\t\t\t\t\t\t\"/usr/bin/curl\",\n\t\"folderNameCreated\":\t\t\t\t\t\t\"UNIFI_created\",\n\t\"folderNameSystem\":\t\t\t\t\t\t\t\"UNIFI_system\",\n\t\"folderNameNeighbors\":\t\t\t\t\t\t\"UNIFI_neighbors\",\n\t\"folderNameVariables\":\t\t\t\t\t\t\"UNIFI\",\n\t\"Group0\":\t\t\t\t\t\t\t\t\t\"Group0\",\n\t\"Group1\":\t\t\t\t\t\t\t\t\t\"Group1\",\n\t\"Group2\":\t\t\t\t\t\t\t\t\t\"Group2\",\n\t\"Group3\":\t\t\t\t\t\t\t\t\t\"Group3\",\n\t\"Group4\":\t\t\t\t\t\t\t\t\t\"Group4\",\n\t\"Group5\":\t\t\t\t\t\t\t\t\t\"Group5\",\n\t\"Group6\":\t\t\t\t\t\t\t\t\t\"Group6\",\n\t\"Group7\":\t\t\t\t\t\t\t\t\t\"Group7\",\n\t\"unifiCONTROLLERUserID\":\t\t\t\t\t\"\",\n\t\"unifiCONTROLLERPassWd\":\t\t\t\t\t\"\",\n\t\"unifiUserID\":\t\t\t\t\t\t\t\t\"\",\n\t\"unifiPassWd\":\t\t\t\t\t\t\t\t\"\",\n\t\"unifiUserIDUDM\":\t\t\t\t\t\t\t\"\",\n\t\"unifiPassWdUDM\":\t\t\t\t\t\t\t\"\",\n\t\"useStrictToLogin\":\t\t\t\t\t\t\tFalse,\n\t\"unifiControllerType\":\t\t\t\t\t\t\"std\",\n\t\"unifiCloudKeyMode\":\t\t\t\t\t\t\"ON\",\n\t\"enablecheckforUnifiSystemDevicesState\":\t\"off\",\n\t\"useDBInfoForWhichDevices\":\t\t\t\t\t\"all\",\n\t\"unifiCloudKeyIP\":\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"refreshCallbackMethod\":\t\t\t\t\t\"no\",\n\t\"unifiCloudKeySiteName\":\t\t\t\t\t\"\",\n\t\"overWriteControllerPort\":\t\t\t\t\t\"\",\n\t\"unifiControllerBackupON\":\t\t\t\t\tTrue,\n\t\"ControllerBackupPath\":\t\t\t\t\t\t\"/data/autobackup\",\n\t\"infoLabelbackup1\":\t\t\t\t\t\t\t\"/usr/lib/unifi/data/backup/autobackup\",\n\t\"infoLabelbackup2\":\t\t\t\t\t\t\t\"/data/unifi/data/backup/autobackup\",\n\t\"infoLabelbackup2a\":\t\t\t\t\t\t\"/data/autobackup\",\n\t\"infoLabelbackup3\":\t\t\t\t\t\t\t\"/Preferences/Plugins/com.karlwachs.uniFiAP/backup\",\n\t\"ipUDMON\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipUDM\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debUD\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"apON\":\t\t\t\t\t\t\t\t\t\tTrue,\n\t\"ipON0\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip0\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP0\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON1\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip1\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP1\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON2\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip2\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP2\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON3\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip3\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP3\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON4\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip4\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP4\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON5\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip5\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP5\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON6\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip6\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP6\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON7\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip7\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP7\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON8\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip8\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP8\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON9\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip9\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP9\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON10\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip10\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP10\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON11\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip11\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP11\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON12\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip12\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP12\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON13\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip13\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP13\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON14\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip14\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP14\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON15\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip15\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP15\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON16\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip16\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP16\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON17\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip17\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP17\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON18\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip18\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP18\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipON19\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ip19\":\t\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debAP19\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipUGAON\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"GWtailEnable\":\t\t\t\t\t\t\t\tFalse,\n\t\"ipUGA\":\t\t\t\t\t\t\t\t\t\"192.168.1.1\",\n\t\"debGW\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"count_APDL_inPortCount\":\t\t\t\t\t\"1\",\n\t\"ipSWON0\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW0\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW0\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON1\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW1\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW1\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON2\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW2\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW2\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON3\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW3\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW3\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON4\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW4\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW4\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON5\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW5\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW5\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON6\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW6\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW6\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON7\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW7\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW7\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON8\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW8\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW8\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON9\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW9\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW9\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON10\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW10\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW10\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON11\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW11\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW11\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSWON12\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"ipSW12\":\t\t\t\t\t\t\t\t\t\"192.168.1.x\",\n\t\"debSW12\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"cameraSystem\":\t\t\t\t\t\t\t\t\"off\",\n\t\"protecEventSleepTime\":\t\t\t\t\t\t2,\n\t\"refreshProtectCameras\":\t\t\t\t\t60,\n\t\"copyProtectsnapshots\":\t\t\t\t\t\t\"no\",\n\t\"changedImagePath\":\t\t\t\t\t\t\t\"/Users/YOURID/.....\",\n\t\"debugLogic\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugLog\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"debugLogDetails\":\t\t\t\t\t\t\tFalse,\n\t\"debugDict\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugDictDetails\":\t\t\t\t\t\t\tFalse,\n\t\"debugConnectionCMD\":\t\t\t\t\t\tFalse,\n\t\"debugConnectionRET\":\t\t\t\t\t\tFalse,\n\t\"debugExpect\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugExpectRET\":\t\t\t\t\t\t\tFalse,\n\t\"debugVideo\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugFing\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugBC\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"debugPing\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugUDM\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"debugIgnoreMAC\":\t\t\t\t\t\t\tFalse,\n\t\"debugDBinfo\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugProtect\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugProtDetails\":\t\t\t\t\t\t\tFalse,\n\t\"debugProtEvents\":\t\t\t\t\t\t\tFalse,\n\t\"debugSpecial\":\t\t\t\t\t\t\t\tFalse,\n\t\"debugDictFile\":\t\t\t\t\t\t\tFalse,\n\t\"debugall\":\t\t\t\t\t\t\t\t\tFalse,\n\t\"showLoginTest\":\t\t\t\t\t\t\tTrue,\n\t\"do_cProfile\":\t\t\t\t\t\t\t\t\"on/off/print\",\n\t\"rebootUnifiDeviceOnError\":\t\t\t\t\tTrue,\n\t\"restartListenerEvery\":\t\t\t\t\t\t\"999999999\",\n\t\"maxConsumedTimeQueueForWarning\":\t\t\t\"10\",\n\t\"maxConsumedTimeForWarning\":\t\t\t\t\"15\",\n\t\"hostFileCheck\":\t\t\t\t\t\t\t\"no\",\n\t\"requestTimeout\":\t\t\t\t\t\t\t\"10\",\n\t\"readBuffer\":\t\t\t\t\t\t\t\t\"16384\" \n}\n\n\"\"\"\ngood web pages for unifi API\nhttps://ubntwiki.com/products/software/unifi-controller/api\nhttps://github.com/NickWaterton/Unifi-websocket-interface/blob/master/controller.py\nhttps://github.com/Art-of-WiFi/UniFi-API-client\n\n\"\"\"\n\ndataVersion = 2.0\n\n## Static parameters, not changed in pgm\n_GlobalConst_numberOfAP\t = 20\n_GlobalConst_numberOfSW\t = 13\n\n_GlobalConst_numberOfGroups = 8\n_GlobalConst_groupList\t\t= [\"Group{}\".format(i) for i in range(_GlobalConst_numberOfGroups)]\n_GlobalConst_dTypes\t\t\t= [\"UniFi\",\"gateway\",\"DHCP\",\"SWITCH\",\"Device-AP\",\"Device-SW-4\",\"Device-SW-5\",\"Device-SW-6\",\"Device-SW-7\",\"Device-SW-8\",\"Device-SW-10\",\"Device-SW-11\",\"Device-SW-12\",\"Device-SW-14\",\"Device-SW-16\",\"Device-SW-18\",\"Device-SW-26\",\"Device-SW-52\",\"neighbor\"]\n_debugAreas\t\t\t\t\t= [\"Logic\",\"Log\",\"Dict\",\"LogDetails\",\"DictDetails\",\"ConnectionCMD\",\"ConnectionRET\",\"Expect\",\"ExpectRET\",\"Video\",\"Fing\",\"BC\",\"Ping\",\"Protect\",\"ProtDetails\",\"ProtEvents\",\"all\",\"Special\",\"UDM\",\"IgnoreMAC\",\"DBinfo\",\"DictFile\",\"UpdateStates\"]\n_numberOfPortsInSwitch\t\t= [4, 5, 7, 8, 10, 11, 12, 16, 18, 26, 52]\n################################################################################\n# noinspection PyUnresolvedReferences,PySimplifyBooleanCheck,PySimplifyBooleanCheck\nclass Plugin(indigo.PluginBase):\n\t####-----------------\t\t\t ---------\n\tdef __init__(self, pluginId, pluginDisplayName, pluginVersion, pluginPrefs):\n\t\tindigo.PluginBase.__init__(self, pluginId, pluginDisplayName, pluginVersion, pluginPrefs)\n\n\t\n\t\tself.pluginShortName \t\t\t= \"UniFi\"\n\t\tself.quitNOW\t\t\t\t\t= \"\"\n\t\tself.delayedAction\t\t\t\t={}\n\t\tself.updateConnectParams\t\t= time.time() - 100\n############### common for all plugins ############\n\t\tself.getInstallFolderPath\t\t= indigo.server.getInstallFolderPath()+\"/\"\n\t\tself.indigoPath\t\t\t\t\t= indigo.server.getInstallFolderPath()+\"/\"\n\t\tself.indigoRootPath \t\t\t= indigo.server.getInstallFolderPath().split(\"Indigo\")[0]\n\t\tself.pathToPlugin \t\t\t\t= self.completePath(os.getcwd())\n\n\t\tmajor, minor, release \t\t\t= map(int, indigo.server.version.split(\".\"))\n\t\tself.indigoVersion \t\t\t\t= float(major)+float(minor)/10.\n\t\tself.indigoRelease \t\t\t\t= release\n\n\t\tself.pluginVersion\t\t\t\t= pluginVersion\n\t\tself.pluginId\t\t\t\t\t= pluginId\n\t\tself.pluginName\t\t\t\t\t= pluginId.split(\".\")[-1]\n\t\tself.myPID\t\t\t\t\t\t= os.getpid()\n\t\tself.pluginState\t\t\t\t= \"init\"\n\n\t\tself.myPID \t\t\t\t\t\t= os.getpid()\n\t\tself.MACuserName\t\t\t\t= pwd.getpwuid(os.getuid())[0]\n\n\t\tself.MAChome\t\t\t\t\t= os.path.expanduser(\"~\")\n\t\tself.userIndigoDir\t\t\t\t= self.MAChome + \"/indigo/\"\n\t\tself.indigoPreferencesPluginDir = self.getInstallFolderPath+\"Preferences/Plugins/\"+self.pluginId+\"/\"\n\t\tself.indigoPluginDirOld\t\t\t= self.userIndigoDir + self.pluginShortName+\"/\"\n\t\tself.PluginLogFile\t\t\t\t= indigo.server.getLogsFolderPath(pluginId=self.pluginId) +\"/plugin.log\"\n\t\tself.showLoginTest \t\t\t\t= pluginPrefs.get('showLoginTest',True)\n\n\t\tformats=\t{ logging.THREADDEBUG: \"%(asctime)s %(msg)s\",\n\t\t\t\t\t\tlogging.DEBUG: \"%(asctime)s %(msg)s\",\n\t\t\t\t\t\tlogging.INFO: \"%(asctime)s %(msg)s\",\n\t\t\t\t\t\tlogging.WARNING: \"%(asctime)s %(msg)s\",\n\t\t\t\t\t\tlogging.ERROR: \"%(asctime)s.%(msecs)03d\\t%(levelname)-12s\\t%(name)s.%(funcName)-25s %(msg)s\",\n\t\t\t\t\t\tlogging.CRITICAL: \"%(asctime)s.%(msecs)03d\\t%(levelname)-12s\\t%(name)s.%(funcName)-25s %(msg)s\" }\n\n\t\tdate_Format = { logging.THREADDEBUG: \"%Y-%m-%d %H:%M:%S\",\t\t# 5\n\t\t\t\t\t\tlogging.DEBUG: \"%Y-%m-%d %H:%M:%S\",\t\t# 10\n\t\t\t\t\t\tlogging.INFO: \"%Y-%m-%d %H:%M:%S\",\t\t# 20\n\t\t\t\t\t\tlogging.WARNING: \"%Y-%m-%d %H:%M:%S\",\t\t# 30\n\t\t\t\t\t\tlogging.ERROR: \"%Y-%m-%d %H:%M:%S\",\t\t# 40\n\t\t\t\t\t\tlogging.CRITICAL: \"%Y-%m-%d %H:%M:%S\" }\t\t# 50\n\t\tformatter = LevelFormatter(fmt=\"%(msg)s\", datefmt=\"%Y-%m-%d %H:%M:%S\", level_fmts=formats, level_date=date_Format)\n\n\t\tself.plugin_file_handler.setFormatter(formatter)\n\t\tself.indiLOG = logging.getLogger(\"Plugin\") \n\t\tself.indiLOG.setLevel(logging.THREADDEBUG)\n\n\t\tself.indigo_log_handler.setLevel(logging.INFO)\n\n\t\tself.indiLOG.log(20,\"initializing ...\")\n\t\tself.indiLOG.log(20,\"path To files: =================\")\n\t\tself.indiLOG.log(10,\"indigo {}\".format(self.indigoRootPath))\n\t\tself.indiLOG.log(10,\"installFolder {}\".format(self.indigoPath))\n\t\tself.indiLOG.log(10,\"plugin.py {}\".format(self.pathToPlugin))\n\t\tself.indiLOG.log(10,\"indigo {}\".format(self.indigoRootPath))\n\t\tself.indiLOG.log(20,\"detailed logging {}\".format(self.PluginLogFile))\n\t\tif self.showLoginTest:\n\t\t\tself.indiLOG.log(20,\"testing logging levels, for info only: \")\n\t\t\tself.indiLOG.log( 0,\"logger enabled for 0 ==> TEST ONLY \")\n\t\t\tself.indiLOG.log( 5,\"logger enabled for THREADDEBUG ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(10,\"logger enabled for DEBUG ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(20,\"logger enabled for INFO ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(30,\"logger enabled for WARNING ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(40,\"logger enabled for ERROR ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(50,\"logger enabled for CRITICAL ==> TEST ONLY \")\n\t\t\tself.indiLOG.log(10,\"Plugin short Name {}\".format(self.pluginShortName))\n\t\tself.indiLOG.log(10,\"my PID {}\".format(self.myPID))\t \n\t\tself.indiLOG.log(10,\"Achitecture {}\".format(platform.platform()))\t \n\t\tself.indiLOG.log(10,\"OS {}\".format(platform.mac_ver()[0]))\t \n\t\tself.indiLOG.log(10,\"indigo V {}\".format(indigo.server.version))\t \n\t\tself.indiLOG.log(10,\"python V {}.{}.{}\".format(sys.version_info[0], sys.version_info[1] , sys.version_info[2]))\t \n\n\t\tself.pythonPath = \"\"\n\t\tif sys.version_info[0] >2:\n\t\t\tif os.path.isfile(\"/Library/Frameworks/Python.framework/Versions/Current/bin/python3\"):\n\t\t\t\tself.pythonPath\t\t\t\t= \"/Library/Frameworks/Python.framework/Versions/Current/bin/python3\"\n\t\telse:\n\t\t\tif os.path.isfile(\"/usr/local/bin/python\"):\n\t\t\t\tself.pythonPath\t\t\t\t= \"/usr/local/bin/python\"\n\t\t\telif os.path.isfile(\"/usr/bin/python2.7\"):\n\t\t\t\tself.pythonPath\t\t\t\t= \"/usr/bin/python2.7\"\n\t\tif self.pythonPath == \"\":\n\t\t\t\tself.indiLOG.log(40,\"FATAL error: none of python versions 2.7 3.x is installed ==> stopping {}\".format(self.pluginId))\n\t\t\t\tself.quitNOW = \"none of python versions 2.7 3.x is installed \"\n\t\t\t\texit()\n\t\tself.indiLOG.log(20,\"using '{}' for utily programs\".format(self.pythonPath))\n\n############### END common for all plugins ############\n\n\t\treturn\n\t\t\n####\n\n\t####-----------------\t\t\t ---------\n\tdef __del__(self):\n\t\tindigo.PluginBase.__del__(self)\n\n\t###########################\t\tINIT\t## START ########################\n\n\t####----------------- @ startup set global parameters, create directories etc ---------\n\tdef startup(self):\n\t\tif not checkIndigoPluginName(self, indigo): \n\t\t\texit() \n\n\t\ttry:\n\n\t\t\tself.checkcProfile()\n\n\t\t\tself.setDebugFromPrefs(self.pluginPrefs)\n\n\n\n\t\t\tif not os.path.isdir(self.indigoPreferencesPluginDir):\n\t\t\t\tself.indiLOG.log(20, \" creating plugin prefs directory:{}\".format(self.indigoPreferencesPluginDir))\n\t\t\t\tos.mkdir(self.indigoPreferencesPluginDir)\n\n\t\t\tself.varExcludeSQLList = [\"Unifi_New_Device\",\"Unifi_With_IPNumber_Change\",\"Unifi_With_Status_Change\",\"Unifi_Camera_with_Event\",\"Unifi_Camera_Event_PathToThumbnail\",\"Unifi_Camera_Event_DateOfThumbNail\",\"Unifi_Camera_Event_Date\"]\n\t\t\t#self.varExcludeSQLList = [\"Unifi_New_Device\",\"Unifi_With_IPNumber_Change\",\"Unifi_With_Status_Change\"]\n\n\t\t\tself.UserID\t\t\t\t\t= {}\n\t\t\tself.PassWd\t\t\t\t\t= {}\n\t\t\tself.connectParamsDefault \t= {}\n\t\t\tself.connectParamsDefault[\"expectRestart\"]\t\t= {\t\"APtail\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWctrl\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDctrl\": \"restart.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDdict\": \"restart.exp\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"expectCmdFile\"]\t\t= {\t\"APtail\": \"execLog.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \"execLog.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"execLog.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": \"execLog.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"execLogVideo.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \"dictLoop.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"dictLoop.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"dictLoop.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"dictLoop.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWctrl\": \"simplecmd.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDctrl\": \"simplecmd.exp\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDdict\": \"simplecmd.exp\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"commandOnServer\"]\t= {\t\"APtail\": \"/usr/bin/tail -F /var/log/messages\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \"/usr/bin/tail -F /var/log/messages\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"/usr/bin/tail -F /var/log/messages\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": \"/usr/bin/tail -F /var/log/messages\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"/usr/bin/tail -F /var/lib/unifi-video/logs/motion.log\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDdict\": \"not implemented\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \"mca-ctrl -t dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"mca-ctrl -t dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"mca-ctrl -t dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWctrl\": \"mca-ctrl -t dump-cfg | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDctrl\": \"mca-ctrl -t dump-cfg | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"mca-ctrl -t dump | sed -e 's/^ *//'\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"enableListener\"]\t= {\t\"APtail\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDdict\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWctrl\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDctrl\": True,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": True\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"promptOnServer\"] \t= {}\n\t\t\t\"\"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APtail\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \":~\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWctrl\": \":~\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDctrl\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"VirtualBox\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDdict\": \"VirtualBox\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \":~\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"\\# \",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"\\# \"}\n\t\t\t\"\"\"\n\t\t\tself.connectParamsDefault[\"startDictToken\"]\t= {\t\"APtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \"mca-dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"mca-dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"mca-dump | sed -e 's/^ *//'\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"mca-dump | sed -e 's/^ *//'\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"endDictToken\"]\t\t= {\t\"APtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"VDtail\": \"x\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"GWdict\": \"xxxThisIsTheEndTokenxxx\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"UDdict\": \"xxxThisIsTheEndTokenxxx\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"SWdict\": \"xxxThisIsTheEndTokenxxx\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"APdict\": \"xxxThisIsTheEndTokenxxx\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"UserID\"]\t\t\t= {\t\"unixDevs\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"unixUD\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"unixNVR\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"nvrWeb\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"webCTRL\": \"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.connectParamsDefault[\"PassWd\"]\t\t\t= {\t\"unixDevs\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"unixUD\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"unixNVR\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"nvrWeb\": \"\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"webCTRL\": \"\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t}\n\t\t\tself.tryHTTPPorts \t\t= [\"443\",\"8443\"]\n\t\t\tself.HTTPretCodes\t\t= { \"200\": {\"os\":\"unifi_os\", \"unifiApiLoginPath\":\"/api/auth/login\", \"unifiApiWebPage\":\"/proxy/network/api/s\" },\n\t\t\t\t\t\t\t\t\t\t\"302\": {\"os\":\"std\", \"unifiApiLoginPath\":\"/api/login\", \"unifiApiWebPage\":\"/api/s\" } }\n\t\t\tself.OKControllerOS = [\"std\",\"unifi_os\"]\n\n\n\t\t\tself.connectParams = copy.copy(self.connectParamsDefault)\n\n\t\t\ttry: \t\n\t\t\t\txx = json.loads(self.pluginPrefs.get(\"connectParams\",\"{}\"))\n\t\t\t\tif xx != {}:\n\t\t\t\t\tself.connectParams = copy.copy(xx)\n\t\t\t\tfor item1 in self.connectParamsDefault:\n\t\t\t\t\tif item1 not in self.connectParams:\n\t\t\t\t\t\tself.connectParams[item1] = copy.deepcopy(self.connectParamsDefault[item1])\n\t\t\t\t\telse:\n\t\t\t\t\t\tfor item2 in self.connectParamsDefault[item1]:\n\t\t\t\t\t\t\tif item2 not in self.connectParams[item1]:\n\t\t\t\t\t\t\t\tself.connectParams[item1][item2] = copy.copy(self.connectParamsDefault[item1][item2])\n\n\t\t\t\t\tif item1 in [\"startDictToken\",\"endDictToken\"]:\n\t\t\t\t\t\tself.connectParams[item1] = copy.deepcopy(self.connectParamsDefault[item1])\n\t\t\texcept:\t\n\t\t\t\tpass\n\n\t\t\tif self.connectParams[\"UserID\"][\"unixDevs\"] == \"\": \tself.connectParams[\"UserID\"][\"unixDevs\"] = self.pluginPrefs.get(\"unifiUserID\",\"\")\n\t\t\tif self.connectParams[\"UserID\"][\"unixUD\"] == \"\": \tself.connectParams[\"UserID\"][\"unixUD\"] = self.pluginPrefs.get(\"unifiUserIDUDM\",\"\")\n\t\t\tif self.connectParams[\"UserID\"][\"unixNVR\"] == \"\": \tself.connectParams[\"UserID\"][\"unixNVR\"] = self.pluginPrefs.get(\"nvrUNIXUserID\",\"\")\n\t\t\tif self.connectParams[\"UserID\"][\"nvrWeb\"] == \"\": \tself.connectParams[\"UserID\"][\"nvrWeb\"] = self.pluginPrefs.get(\"nvrWebUserID\",\"\")\n\t\t\tif self.connectParams[\"PassWd\"][\"webCTRL\"] == \"\": \tself.connectParams[\"PassWd\"][\"nvrWeb\"] = self.pluginPrefs.get(\"unifiCONTROLLERUserID\",\"\")\n\n\t\t\tif self.connectParams[\"PassWd\"][\"unixDevs\"] == \"\": \tself.connectParams[\"PassWd\"][\"unixDevs\"] = self.pluginPrefs.get(\"unifiPassWd\",\"\")\n\t\t\tif self.connectParams[\"PassWd\"][\"unixUD\"] == \"\": \tself.connectParams[\"PassWd\"][\"unixUD\"] = self.pluginPrefs.get(\"unifiPassWdUDM\",\"\")\n\t\t\tif self.connectParams[\"PassWd\"][\"unixNVR\"] == \"\": \tself.connectParams[\"PassWd\"][\"unixNVR\"] = self.pluginPrefs.get(\"nvrUNIXPassWd\",\"\")\n\t\t\tif self.connectParams[\"PassWd\"][\"nvrWeb\"] == \"\": \tself.connectParams[\"PassWd\"][\"nvrWeb\"] = self.pluginPrefs.get(\"nvrWebPassWd\",\"\")\n\t\t\tif self.connectParams[\"PassWd\"][\"webCTRL\"] == \"\": \tself.connectParams[\"PassWd\"][\"nvrWeb\"] = self.pluginPrefs.get(\"unifiCONTROLLERPassWd\",\"\")\n\t\t\t##indigo.server.log(\" connectParams:{}\".format(self.connectParams))\n\n\t\t\tself.stop \t\t\t\t\t\t\t\t\t\t\t= []\n\t\t\tself.PROTECT \t\t\t\t\t\t\t\t\t\t= {}\n\n\t\t\tself.launchWaitSeconds\t\t\t\t\t\t\t\t= float(self.pluginPrefs.get(\"launchWaitSeconds\",\"1.13\"))\n\t\t\tself.vboxPath\t\t\t\t\t\t\t\t\t\t= self.completePath(self.pluginPrefs.get(\"vboxPath\", \t\t\"/Applications/VirtualBox.app/Contents/MacOS/\"))\n\t\t\tself.changedImagePath\t\t\t\t\t\t\t\t= self.completePath(self.pluginPrefs.get(\"changedImagePath\", \tself.MAChome))\n\t\t\tself.videoPath\t\t\t\t\t\t\t\t\t\t= self.completePath(self.pluginPrefs.get(\"videoPath\", \t\t\"/Volumes/data4TB/Users/karlwachs/video/\"))\n\t\t\tself.unifiNVRSession\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.nvrVIDEOapiKey\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"nvrVIDEOapiKey\",\"\")\n\n\t\t\tself.copyProtectsnapshots\t\t\t\t\t\t\t= self.pluginPrefs.get(\"copyProtectsnapshots\",\"on\")\n\t\t\tself.refreshProtectCameras\t\t\t\t\t\t\t= float(self.pluginPrefs.get(\"refreshProtectCameras\",180.))\n\t\t\tself.protecEventSleepTime \t\t\t\t\t\t\t= float(self.pluginPrefs.get(\"protecEventSleepTime\",4.))\n\t\t\tself.vmMachine\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"vmMachine\", \"\")\n\t\t\tself.vboxPath\t\t\t\t\t\t\t\t\t\t= self.completePath(self.pluginPrefs.get(\"vboxPath\", \t\t\"/Applications/VirtualBox.app/Contents/MacOS/\"))\n\t\t\tself.vmDisk\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"vmDisk\", \t\t\t\t\t\t\t\t\"/Volumes/data4TB/Users/karlwachs/VirtualBox VMs/ubuntu/NewVirtualDisk1.vdi\")\n\t\t\tself.mountPathVM\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"mountPathVM\", \"/home/yourid/osx\")\n\t\t\tself.videoPath\t\t\t\t\t\t\t\t\t\t= self.completePath(self.pluginPrefs.get(\"videoPath\", \t\t\"/Volumes/data4TB/Users/karlwachs/video/\"))\n\n\t\t\tself.menuXML\t\t\t\t\t\t\t\t\t\t= json.loads(self.pluginPrefs.get(\"menuXML\", \"{}\"))\n\t\t\tself.pluginPrefs[\"menuXML\"]\t\t\t\t\t\t= json.dumps(self.menuXML)\n\t\t\tself.restartRequest\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.lastMessageReceivedInListener\t\t\t\t\t= {}\n\t\t\tself.waitForMAC2vendor \t\t\t\t\t\t\t\t= False\n\t\t\tself.enableMACtoVENDORlookup\t\t\t\t\t\t= int(self.pluginPrefs.get(\"enableMACtoVENDORlookup\",\"21\"))\n\t\t\tif self.enableMACtoVENDORlookup != \"0\":\n\t\t\t\tself.M2V \t\t\t\t\t\t\t\t\t\t= MAC2Vendor.MAP2Vendor(pathToMACFiles=self.indigoPreferencesPluginDir+\"mac2Vendor/\", refreshFromIeeAfterDays = self.enableMACtoVENDORlookup, myLogger = self.indiLOG.log)\n\t\t\t\tself.waitForMAC2vendor \t\t\t\t\t\t\t= self.M2V.makeFinalTable()\n\n\n\t\t\tself.enableSqlLogging\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"enableSqlLogging\",True)\n\t\t\tself.pluginPrefs[\"createUnifiDevicesCounter\"]\t\t= int(self.pluginPrefs.get(\"createUnifiDevicesCounter\",0))\n\n\t\t\tself.lastupdateDevStateswRXTXbytes\t\t\t\t\t= time.time() - 100\n\t\t\tself.updateDescriptions\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"updateDescriptions\", True)\n\t\t\tself.ignoreNeighborForFing\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ignoreNeighborForFing\", True)\n\t\t\tself.ignoreNewNeighbors\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ignoreNewNeighbors\", False)\n\t\t\tself.ignoreNewClients\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ignoreNewClients\", False)\n\t\t\tself.enableFINGSCAN\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"enableFINGSCAN\", False)\n\t\t\tself.count_APDL_inPortCount\t\t\t\t\t\t\t= self.pluginPrefs.get(\"count_APDL_inPortCount\", \"1\")\n\t\t\tself.sendUpdateToFingscanList\t\t\t\t\t\t= {}\n\t\t\tself.enableBroadCastEvents\t\t\t\t\t\t\t= self.pluginPrefs.get(\"enableBroadCastEvents\", \"0\")\n\t\t\tself.sendBroadCastEventsList\t\t\t\t\t\t= []\n\t\t\tself.unifiCloudKeyListOfSiteNames\t\t\t\t\t= json.loads(self.pluginPrefs.get(\"unifiCloudKeyListOfSiteNames\", \"[]\"))\n\t\t\tself.unifiCloudKeyIP\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeyIP\", \"\")\n\t\t\tself.csrfToken \t\t\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.numberForUDM\t\t\t\t\t\t\t\t\t= {\"AP\":4,\"SW\":12}\n\n\t\t\tself.rebootUnifiDeviceOnError\t\t\t\t\t\t= self.pluginPrefs.get(\"rebootUnifiDeviceOnError\", True)\n\n\n\t\t\tself.refreshCallbackMethodAlreadySet \t\t\t\t= \"no\" \n\n\t\t\tself.unifiControllerOS \t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.unifiApiWebPage\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.unifiApiLoginPath\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.unifControllerCheckPortNumber\t\t\t\t\t= self.pluginPrefs.get(\"unifControllerCheckPortNumber\", \"1\") \n\t\t\tself.overWriteControllerPort\t\t\t\t\t\t= self.pluginPrefs.get(\"overWriteControllerPort\", \"\")\n\t\t\tself.lastPortNumber\t\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.unifiCloudKeyPort\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.unifiControllerType\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiControllerType\", \"std\")\n\t\t\tself.unifiCloudKeyMode\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeyMode\", \"ON\")\n\t\t\tself.unifiCloudKeySiteName\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeySiteName\", \"default\")\n\t\t\tself.requestTimeout\t\t\t\t\t\t\t\t\t= max(1., float(self.pluginPrefs.get(\"requestTimeout\", 10.)))\n\t\t\tself.unifiCloudKeySiteNameGetNew \t\t\t\t\t= False\n\n\n\t\t\tif self.unifiControllerType == \"off\" or self.unifiCloudKeyMode\t== \"off\" or self.connectParams[\"UserID\"][\"webCTRL\"] == \"\":\n\t\t\t\tself.unifiCloudKeyMode = \"off\"\n\t\t\t\tself.pluginPrefs[\"unifiCloudKeyMode\"] = \"\"\n\t\t\t\tself.unifiControllerType = \"off\"\n\t\t\t\tself.pluginPrefs[\"unifiControllerType\"] = \"\"\n\t\t\t\tself.connectParams[\"UserID\"][\"nvrWeb\"] = \"\"\n\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1:\n\t\t\t\tself.unifiCloudKeyMode\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeyMode\", \"ON\")\n\t\t\t\tif self.unifiControllerType.find(\"UDM\") > -1:\n\t\t\t\t\tself.unifiCloudKeyMode = \"ON\"\n\t\t\t\t\tself.pluginPrefs[\"unifiCloudKeyMode\"] \t\t= \"ON\"\n\n\t\t\ttry:\n\t\t\t\tself.controllerWebEventReadON \t\t\t\t\t= int(self.pluginPrefs.get(\"controllerWebEventReadON\",\"-1\"))\n\t\t\texcept:\n\t\t\t\tself.controllerWebEventReadON \t\t\t\t\t= -1\n\t\t\tif self.unifiControllerType == \"UDMpro\": \n\t\t\t\tself.controllerWebEventReadON \t\t\t\t\t= -1\n\n\t\t\tself.unifiControllerBackupON\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiControllerBackupON\", False)\n\t\t\tself.ControllerBackupPath\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ControllerBackupPath\", \"\")\n\n\t\t\ttry: self.readBuffer\t\t\t\t\t\t\t\t= int(self.pluginPrefs.get(\"readBuffer\", \"16384\"))\n\t\t\texcept: self.readBuffer\t\t\t\t\t\t\t\t= 16384\n\t\t\tself.maxConsumedTimeQueueForWarning\t\t\t\t\t= float(self.pluginPrefs.get(\"maxConsumedTimeQueueForWarning\", \"5\"))\n\t\t\tself.maxConsumedTimeForWarning\t\t\t\t\t\t= float(self.pluginPrefs.get(\"maxConsumedTimeForWarning\", \"3\"))\n\n\n\t\t\tself.lastCheckForCAMERA\t\t\t\t\t\t\t\t= 0\n\t\t\tself.saveCameraEventsLastCheck\t\t\t\t\t\t= 0\n\t\t\tself.cameraEventWidth\t\t\t\t\t\t\t\t= int(self.pluginPrefs.get(\"cameraEventWidth\", \"720\"))\n\t\t\tself.imageSourceForEvent\t\t\t\t\t\t\t= self.pluginPrefs.get(\"imageSourceForEvent\", \"noImage\")\n\t\t\tself.imageSourceForSnapShot\t\t\t\t\t\t\t= self.pluginPrefs.get(\"imageSourceForSnapShot\", \"unoImage\")\n\n\t\t\tself.listenStart\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.useStrictToLogin\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"useStrictToLogin\", False)\n\t\t\tself.unifiControllerSession\t\t\t\t\t\t\t= \"\"\n\n\t\t\tself.curlPath\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"curlPath\", \"/usr/bin/curl\")\n\t\t\tif len(self.curlPath) < 4:\n\t\t\t\tself.curlPath\t\t\t\t\t\t\t\t\t= \"/usr/bin/curl\"\n\t\t\t\tself.pluginPrefs[\"curlPath\"] \t\t\t\t\t= self.curlPath\n\n\t\t\tself.requestOrcurl\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"requestOrcurl\", \"curl\")\n\n\t\t\tself.expectPath \t\t\t\t\t\t\t\t\t= \"/usr/bin/expect\"\n\n\t\t\tself.restartIfNoMessageSeconds\t\t\t\t\t\t= 130 #int(self.pluginPrefs.get(\"restartIfNoMessageSeconds\", 130))\n\t\t\tself.expirationTime\t\t\t\t\t\t\t\t\t= int(self.pluginPrefs.get(\"expirationTime\", 120) )\n\t\t\tself.expTimeMultiplier\t\t\t\t\t\t\t\t= float(self.pluginPrefs.get(\"expTimeMultiplier\", 2))\n\n\t\n\t\t\tself.loopSleep\t\t\t\t\t\t\t\t\t\t= 5 # float(self.pluginPrefs.get(\"loopSleep\", 8))\n\t\t\tself.timeoutDICT\t\t\t\t\t\t\t\t\t= \"15\"\n\t\t\t#self.timeoutDICT\t\t\t\t\t\t\t\t\t= \"{}\".format(int(self.pluginPrefs.get(\"timeoutDICT\", \"15\")))\n\t\t\tself.folderNameCreated\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"folderNameCreated\", \"UNIFI_created\")\n\t\t\tself.folderNameNeighbors\t\t\t\t\t\t\t= self.pluginPrefs.get(\"folderNameNeighbors\", \"UNIFI_neighbors\")\n\t\t\tself.folderNameVariables\t\t\t\t\t\t\t= self.pluginPrefs.get(\"folderNameVariables\", \"UNIFI\")\n\t\t\tself.folderNameSystem\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"folderNameSystem\",\t \"UNIFI_system\")\n\t\t\tself.fixExpirationTime\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"fixExpirationTime\",\tTrue)\n\t\t\tself.MACignorelist\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.MACSpecialIgnorelist\t\t\t\t\t\t\t= {}\n\t\t\tself.HANDOVER\t\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.lastUnifiCookieCurl\t\t\t\t\t\t\t= 0\n\t\t\tself.lastUnifiCookieRequests\t\t\t\t\t\t= 0\n\t\t\tself.lastNVRCookie\t\t\t\t\t\t\t\t\t= 0\n\t\t\tself.pendingCommand\t\t\t\t\t\t\t\t\t= []\n\t\t\tself.groupNames\t\t\t\t\t\t\t\t\t\t= []\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tself.groupNames.append(self.pluginPrefs.get(\"Group{}\".format(groupNo), \"Group\".format(groupNo)))\n\n\t\t\tself.groupStatusList\t\t\t\t\t\t\t\t= [{\"members\":{},\"allHome\":False,\"allAway\":False,\"oneHome\":False,\"oneAway\":False,\"nHome\":0,\"nAway\":0} for i in range(_GlobalConst_numberOfGroups )]\n\t\t\tself.groupStatusListALL\t\t\t\t\t\t\t\t= {\"nHome\":0,\"nAway\":0,\"anyChange\":False}\n\n\t\t\tself.triggerList\t\t\t\t\t\t\t\t\t= []\n\t\t\tself.statusChanged\t\t\t\t\t\t\t\t\t= 0\n\t\t\tself.msgListenerActive\t\t\t\t\t\t\t\t= {}\n\n\t\t\tself.devsEnabled\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.debugDevs\t\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.ipNumbersOf\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp\t\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.numberOfActive\t\t\t\t\t\t\t\t\t= {}\n\n\n\n\t\t\tself.createEntryInUnifiDevLogActive\t\t\t\t\t= True #self.pluginPrefs.get(\"createEntryInUnifiDevLogActive\",\tFalse)\n\t\t\tself.lastcreateEntryInUnifiDevLog \t\t\t\t\t= time.time()\n\n\t\t\tself.updateStatesList\t\t\t\t\t\t\t\t= {}\n\t\t\tself.logCount\t\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.ipNumbersOf[\"AP\"]\t\t\t\t\t\t\t\t= [\"\" for nn in range(_GlobalConst_numberOfAP)]\n\t\t\tself.devsEnabled[\"AP\"]\t\t\t\t\t\t\t\t= [False for nn in range(_GlobalConst_numberOfAP)]\n\t\t\tself.debugDevs[\"AP\"]\t\t\t\t\t\t\t\t= [False for nn in range(_GlobalConst_numberOfAP)]\n\n\t\t\tself.ipNumbersOf[\"SW\"]\t\t\t\t\t\t\t\t= [\"\" for nn in range(_GlobalConst_numberOfSW)]\n\t\t\tself.devsEnabled[\"SW\"]\t\t\t\t\t\t\t\t= [False for nn in range(_GlobalConst_numberOfSW)]\n\t\t\tself.debugDevs[\"SW\"]\t\t\t\t\t\t\t\t= [False for nn in range(_GlobalConst_numberOfSW)]\n\t\t\tself.isMiniSwitch\t\t\t\t\t\t\t\t\t= [False for nn in range(_GlobalConst_numberOfSW)]\n\n\n\t\t\tself.devNeedsUpdate\t\t\t\t\t\t\t\t\t= {}\n\n\t\t\tself.MACloglist\t\t\t\t\t\t\t\t\t\t= {}\n\n\t\t\tself.readDictEverySeconds\t\t\t\t\t\t\t= {}\n\t\t\tself.readDictEverySeconds[\"AP\"]\t\t\t\t\t\t= 65\n\t\t\tself.readDictEverySeconds[\"GW\"]\t\t\t\t\t\t= 65\n\t\t\tself.readDictEverySeconds[\"SW\"]\t\t\t\t\t\t= 65\n\t\t\tself.readDictEverySeconds[\"UD\"]\t\t\t\t\t\t= 65\n\t\t\tself.readDictEverySeconds[\"DB\"]\t\t\t\t\t\t= 45\n\t\t\tself.getcontrollerDBForClientsLast\t\t\t\t\t= 0\n\t\t\tself.lastResetUnifiDevice\t\t\t\t\t\t\t= {}\n\t\t\tself.devStateChangeList\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp[\"AP\"]\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp[\"SW\"]\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp[\"GW\"]\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp[\"VD\"]\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.deviceUp[\"UD\"]\t\t\t\t\t\t\t\t\t= {}\n\t\t\tself.version\t\t\t \t\t\t\t\t\t\t= self.getParamsFromFile(self.indigoPreferencesPluginDir+\"dataVersion\", default=0)\n\n\t\t\tself.restartListenerEvery\t\t\t\t\t\t\t= float(self.pluginPrefs.get(\"restartListenerEvery\", \"999999999\"))\n\n\t\t\t##### check AP parameters\n\t\t\tself.numberOfActive[\"AP\"] =0\n\t\t\tfor i in range(_GlobalConst_numberOfAP):\n\t\t\t\tip0 \t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ip{}\".format(i), \"\")\n\t\t\t\tac\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipON{}\".format(i), \"\")\n\t\t\t\tdeb\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"debAP{}\".format(i), \"\")\n\t\t\t\tif not self.isValidIP(ip0): ac \t\t\t\t\t= False\n\t\t\t\tself.deviceUp[\"AP\"][ip0] \t\t\t\t\t\t= time.time()\n\t\t\t\tself.ipNumbersOf[\"AP\"][i] \t\t\t\t\t\t= ip0\n\t\t\t\tself.debugDevs[\"AP\"][i] \t\t\t\t\t\t= deb\n\t\t\t\tif ac:\n\t\t\t\t\tself.devsEnabled[\"AP\"][i]\t\t\t\t\t= True\n\t\t\t\t\tself.numberOfActive[\"AP\"] \t\t\t\t\t+= 1\n \n\t\t\t##### check switch parameters\n\t\t\tself.numberOfActive[\"SW\"]\t\t\t\t\t\t\t\t\t= 0\n\t\t\tfor i in range(_GlobalConst_numberOfSW):\n\t\t\t\tself.isMiniSwitch[i]\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"isMini{}\".format(i),False)\n\t\t\t\tip0\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipSW{}\".format(i), \"\")\n\t\t\t\tac\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipSWON{}\".format(i), \"\")\n\t\t\t\tdeb\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"debSW{}\".format(i), \"\")\n\t\t\t\tif not self.isValidIP(ip0): ac \t\t\t\t\t\t\t= False\n\t\t\t\tself.deviceUp[\"SW\"][ip0] \t\t\t\t\t\t\t\t= time.time()\n\t\t\t\tself.ipNumbersOf[\"SW\"][i] \t\t\t\t\t\t\t\t= ip0\n\t\t\t\tself.debugDevs[\"SW\"][i] \t\t\t\t\t\t\t\t= deb\n\t\t\t\tif ac:\n\t\t\t\t\tself.devsEnabled[\"SW\"][i] \t\t\t\t\t\t\t= True\n\t\t\t\t\tself.numberOfActive[\"SW\"] \t\t\t\t\t\t\t+= 1\n\n\t\t\t##### check UGA parameters\n\t\t\tip0 \t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipUGA\", \"\")\n\t\t\tac\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipUGAON\",False)\n\t\t\tself.debugDevs[\"GW\"] \t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"debGW\",False)\n\n\n\t\t\tif self.isValidIP(ip0) and ac:\n\t\t\t\tself.ipNumbersOf[\"GW\"] \t\t\t\t\t\t\t\t= ip0\n\t\t\t\tself.devsEnabled[\"GW\"] \t\t\t\t\t\t\t\t= True\n\t\t\t\tself.deviceUp[\"GW\"][ip0] \t\t\t\t\t\t\t\t= time.time()\n\t\t\telse:\n\t\t\t\tself.ipNumbersOf[\"GW\"] \t\t\t\t\t\t\t\t= \"\"\n\t\t\t\tself.devsEnabled[\"GW\"]\t\t\t\t\t\t\t\t\t= False\n\n\t\t\tself.enablecheckforUnifiSystemDevicesState\t\t\t\t\t= self.pluginPrefs.get(\"enablecheckforUnifiSystemDevicesState\",\"off\")\n\n\t\t\t##### check DB parameters\n\t\t\tip0 \t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeyIP\", \"\")\n\t\t\tac\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"unifiCloudKeyMode\",\"ON\")\n\t\t\tself.useDBInfoForWhichDevices\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"useDBInfoForWhichDevices\",\"all\")\n\t\t\tif self.isValidIP(self.unifiCloudKeyIP) and (ac.find(\"ON\") > -1 or ac.find(\"UDM\") or self.useDBInfoForWhichDevices in [\"all\",\"perDevice\"]):\n\t\t\t\tself.unifiCloudKeyMode = \"ON\"\n\t\t\t\tself.pluginPrefs[\"unifiCloudKeyMode\"] = \"ON\"\n\t\t\t\tself.devsEnabled[\"DB\"] = True\n\t\t\telse:\n\t\t\t\tself.devsEnabled[\"DB\"]\t= False\n\n\t\t\t##### check UDM parameters\n\t\t\tip0 \t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipUDM\", \"\")\n\t\t\tac\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"ipUDMON\",False)\n\t\t\tself.debugDevs[\"UD\"] \t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"debUD\",False)\n\t\t\tself.ipNumbersOf[\"UD\"] \t\t\t\t\t\t\t\t\t= ip0\n\t\t\tself.deviceUp[\"UD\"]\t\t\t\t\t\t\t\t\t\t= time.time()\n\n\t\t\tif self.isValidIP(ip0) and ac:\n\t\t\t\tself.devsEnabled[\"UD\"] \t\t\t\t\t\t\t\t= True\n\t\t\t\tself.ipNumbersOf[\"SW\"][self.numberForUDM[\"SW\"]]\t\t= ip0\n\t\t\t\tself.ipNumbersOf[\"AP\"][self.numberForUDM[\"AP\"]]\t\t= ip0\n\t\t\t\tself.ipNumbersOf[\"GW\"] \t\t\t\t\t\t\t \t= ip0\n\t\t\t\tself.devsEnabled[\"SW\"][self.numberForUDM[\"SW\"]] \t\t= True\n\t\t\t\tself.devsEnabled[\"AP\"][self.numberForUDM[\"AP\"]] \t\t= True\n\t\t\t\tself.numberOfActive[\"SW\"] \t\t\t\t\t\t\t\t= max(1,self.numberOfActive[\"SW\"] )\n\t\t\t\tself.numberOfActive[\"AP\"] \t\t\t\t\t\t\t\t= max(1,self.numberOfActive[\"AP\"] )\n\t\t\t\tself.pluginPrefs[\"ipON\"] \t\t\t\t\t\t\t\t= True\n\t\t\t\tself.pluginPrefs[\"ipSWON\"] \t\t\t\t\t\t\t= True\n\t\t\t\tself.pluginPrefs[\"ip{}\".format(self.numberForUDM[\"AP\"])] = ip0\n\t\t\t\tself.pluginPrefs[\"ipSW{}\".format(self.numberForUDM[\"SW\"])] = ip0\n\t\t\telse:\n\t\t\t\tself.devsEnabled[\"UD\"] = False\n\n\n\n\t\t\t##### check video parameters\n\t\t\tself.cameraSystem\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"cameraSystem\", \"off\")\n\t\t\tif self.cameraSystem ==\"nvr\": self.cameraSystem = \"off\"\n\n\t\t\tself.cameras\t\t\t\t\t\t\t \t\t\t\t= {}\n\t\t\tself.ipNumbersOf[\"VD\"] \t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.VIDEOUP\t\t\t\t\t\t\t\t\t\t\t= 0\n\t\t\tself.unifiVIDEONumerOfEvents \t\t\t\t\t\t\t= 0\n\t\t\tif self.cameraSystem == \"nvr\":\n\t\t\t\ttry:\tself.unifiVIDEONumerOfEvents \t\t\t\t= int(self.pluginPrefs.get(\"unifiVIDEONumerOfEvents\", 1000))\n\t\t\t\texcept: self.unifiVIDEONumerOfEvents\t\t\t\t= 1000\n\t\t\t\tself.cameras\t\t\t\t\t\t \t\t\t\t= {}\n\t\t\t\tself.saveCameraEventsStatus\t\t\t \t\t\t\t= False\n\n\t\t\t\tip0 \t\t\t\t\t\t\t\t\t\t\t\t= self.pluginPrefs.get(\"nvrIP\", \"192.168.1.x\")\n\t\t\t\tself.ipNumbersOf[\"VD\"] \t\t\t\t\t\t\t= ip0\n\t\t\t\tself.VIDEOUP\t\t\t\t\t\t\t\t\t\t= 0\n\t\t\t\tif self.isValidIP(ip0) and self.connectParams[\"UserID\"][\"unixNVR\"] != \"\" and self.connectParams[\"PassWd\"][\"unixNVR\"] != \"\":\n\t\t\t\t\tself.VIDEOUP\t \t\t\t\t\t\t\t\t= time.time()\n\t\t\telif self.cameraSystem == \"protect\":\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tpass\n\n\t\t\tself.lastCheckForNVR \t\t\t\t\t\t\t\t= 0\n\n\t\t\tself.getFolderId()\n\n\t\t\tself.readSuspend()\n\n\n\t\t\tfor ll in range(len(self.ipNumbersOf[\"AP\"])):\n\t\t\t\tself.killIfRunning(self.ipNumbersOf[\"AP\"][ll],\"\")\n\t\t\tfor ll in range(len(self.ipNumbersOf[\"SW\"])):\n\t\t\t\tself.killIfRunning(self.ipNumbersOf[\"SW\"][ll],\"\")\n\t\t\tself.killIfRunning(self.ipNumbersOf[\"GW\"], \"\")\n\n\n\t\t\tself.readDataStats() # must come before other dev/states updates ...\n\n\t\t\tself.groupStatusINIT()\n\t\t\tself.setGroupStatus(init=True)\n\t\t\tself.readCamerasStats()\n\t\t\tself.readMACdata()\n\t\t\tself.checkDisplayStatus()\n\t\t\tself.getMACloglist()\n\n\t\t\tself.pluginStartTime \t\t\t\t\t\t\t\t= time.time()+150\n\n\n\t\t\tself.checkforUnifiSystemDevicesState \t\t\t\t= \"start\"\n\n\t\t\tself.killIfRunning(\"\", \"\")\n\n\t\t\ttry: \tos.mkdir(self.indigoPreferencesPluginDir+\"backup\")\n\t\t\texcept: pass\n\n\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\texit(0)\n\n\n\t\treturn\n\n\t\n\t####-----------------\t ---------\n\tdef getMACloglist(self):\n\t\ttry:\n\t\t\tself.MACloglist= self.getParamsFromFile(self.indigoPreferencesPluginDir+\"MACloglist\", default ={})\n\t\t\tif self.MACloglist !={}:\n\t\t\t\tself.indiLOG.log(10,\"start track-logging for MAC#s {}\".format(self.MACloglist) )\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef checkDisplayStatus(self):\n\t\ttry:\n\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\t\tif \"displayStatus\" not in dev.states: continue\n\n\t\t\t\tif \"MAC\" in dev.states and dev.deviceTypeId == \"UniFi\" and self.testIgnoreMAC(dev.states[\"MAC\"], fromSystem=\"checkDisp\"):\n\t\t\t\t\tif dev.states[\"displayStatus\"].find(\"ignored\") ==-1:\n\t\t\t\t\t\tdev.updateStateOnServer(\"displayStatus\",self.padDisplay(\"ignored\")+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\t\t\t\t\tif \"{}\".format(dev.displayStateImageSel) !=\"PowerOff\":\n\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.PowerOff)\n\t\t\t\telse:\n\t\t\t\t\tself.exeDisplayStatus(dev, dev.states[\"status\"], force =False)\n\n\n\t\t\t\told = dev.states[\"displayStatus\"].split(\" \")\n\t\t\t\tif len(old) ==3:\n\t\t\t\t\tnew = self.padDisplay(old[0].strip())+dev.states[\"lastStatusChange\"]\n\t\t\t\t\tif dev.states[\"displayStatus\"] != new:\n\t\t\t\t\t\tdev.updateStateOnServer(\"displayStatus\",new)\n\t\t\t\telse:\n\t\t\t\t\tdev.updateStateOnServer(\"displayStatus\",self.padDisplay(old[0].strip())+dev.states[\"lastStatusChange\"])\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef setDebugFromPrefs(self, theDict, writeToLog=True):\n\n\t\tself.debugLevel = []\n\t\tfor d in _debugAreas:\n\t\t\tif theDict.get(\"debug\"+d, False): self.debugLevel.append(d)\n\t\tself.showLoginTest = self.pluginPrefs.get(\"showLoginTest\", True)\n\t\tif writeToLog: self.indiLOG.log(20,\"debug settings :{} \".format(self.debugLevel))\n\n\n\n\t####-----------------\t ---------\n\tdef isValidIP(self, ip0):\n\t\tif ip0 == \"localhost\": \t\t\t\t\t\treturn True\n\n\t\tipx = ip0.split(\".\")\n\t\tif len(ipx) != 4:\t\t\t\t\t\t\t\treturn False\n\n\t\telse:\n\t\t\tfor ip in ipx:\n\t\t\t\ttry:\n\t\t\t\t\tif int(ip) < 0 or int(ip) > 255: \treturn False\n\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn False\n\t\treturn True\n\n\t####-----------------\t ---------\n\tdef fixIP(self, ip): # make last number always 3 digits for sorting\n\t\tif len(ip) < 7: return ip\n\t\tipx = ip.split(\"/\")[0].split(\".\")\n\t\tipx[3] = \"{:03d}\".format(int(ipx[3]))\n\t\treturn \".\".join(ipx)\n\n\n\t####-----------------\t ---------\n\tdef isValidMAC(self, mac):\n\t\txxx = mac.split(\":\")\n\t\tif len(xxx) != 6:\t\t\treturn False\n\t\telse:\n\t\t\tfor xx in xxx:\n\t\t\t\tif len(xx) != 2: \treturn False\n\t\t\t\ttry: \tint(xx, 16)\n\t\t\t\texcept: \t\t\treturn False\n\t\treturn True\n\n\t####-----------------\t ---------\n\tdef checkMAC(self, MAC):\n\t\tif self.isValidMAC(MAC): return MAC\n\t\tmacs = MAC.split(\":\")\n\t\tfor nn in range(len(macs)):\n\t\t\tmac = macs[nn]\n\t\t\tif len(mac) < 2: macs[nn] = \"0\" + mac\n\t\treturn \":\".join(macs)\n\n####-------------------------------------------------------------------------####\n\tdef getParamsFromFile(self,newName, oldName=\"\", default ={}): # called from read config for various input files\n\t\t\tout = copy.copy(default)\n\t\t\tif os.path.isfile(newName):\n\t\t\t\ttry:\n\t\t\t\t\tf = self.openEncoding(newName, \"r\")\n\t\t\t\t\tout\t = json.loads(f.read())\n\t\t\t\t\tf.close()\n\t\t\t\t\tif oldName !=\"\" and os.path.isfile(oldName):\n\t\t\t\t\t\tos.system(\"rm \"+oldName)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tout = copy.copy(default)\n\t\t\telse:\n\t\t\t\tout = copy.copy(default)\n\t\t\tif oldName !=\"\" and os.path.isfile(oldName):\n\t\t\t\ttry:\n\t\t\t\t\tf = self.openEncoding(oldName, \"r\")\n\t\t\t\t\tout\t = json.loads(f.read())\n\t\t\t\t\tf.close()\n\t\t\t\t\tos.system(\"rm \"+oldName)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tout = copy.copy(default)\n\t\t\treturn out\n\n\n####-------------------------------------------------------------------------####\n\tdef writeJson(self, data, fName=\"\", sort = True, doFormat=False ):\n\t\ttry:\n\n\t\t\tif format:\n\t\t\t\tout = json.dumps(data, sort_keys=sort, indent=2)\n\t\t\telse:\n\t\t\t\tout = json.dumps(data, sort_keys=sort)\n\n\t\t\tif fName !=\"\":\n\t\t\t\tf = self.openEncoding(fName,\"w\")\n\t\t\t\tf.write(out)\n\t\t\t\tf.close()\n\t\t\treturn out\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \"\"\n\n\n\n\t####----------------- update state lists ---------\n\tdef deviceStartComm(self, dev):\n\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"starting device: {} {} {}\".format(dev.name, dev.id, dev.states[\"MAC\"]))\n\n\t\tif\tself.pluginState == \"init\":\n\t\t\tdev.stateListOrDisplayStateIdChanged()\n\n\t\t\tif self.version < 2.0:\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tself.indiLOG.log(10,\"Checking for deviceType Update: \"+ dev.name )\n\t\t\t\tif \"SupportsOnState\" not in props:\n\t\t\t\t\tself.indiLOG.log(10,\" processing: \"+ dev.name)\n\t\t\t\t\tdev = indigo.device.changeDeviceTypeId(dev, dev.deviceTypeId)\n\t\t\t\t\tdev.replaceOnServer()\n\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\tprops[\"SupportsSensorValue\"] \t\t= False\n\t\t\t\t\tprops[\"SupportsOnState\"] \t\t\t= True\n\t\t\t\t\tprops[\"AllowSensorValueChange\"] \t= False\n\t\t\t\t\tprops[\"AllowOnStateChange\"] \t\t= False\n\t\t\t\t\tprops[\"SupportsStatusRequest\"] \t\t= False\n\t\t\t\t\tself.indiLOG.log(10, \"{}\".format(dev.pluginProps))\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\t\tdev= indigo.devices[dev.id]\n\n\t\t\t\t\tif (dev.states[\"status\"].lower()).lower() in [\"up\",\"rec\",\"ON\"]:\n\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\t\telif (dev.states[\"status\"].lower()).find(\"down\")==0:\n\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOff)\n\t\t\t\t\telse:\n\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorTripped)\n\t\t\t\t\tdev.replaceOnServer()\n\t\t\t\t\t#dev= indigo.devices[dev.id]\n\t\t\t\t\tdev.updateStateOnServer(\"onOffState\",value=(dev.states[\"status\"].lower()) in[\"up\",\"rec\",\"ON\"], uiValue=dev.states[\"displayStatus\"] )\n\t\t\t\t\tself.indiLOG.log(10,\"SupportsOnState after replacePluginPropsOnServer\")\n\n\t\t\tisType={\"UniFi\":\"isUniFi\",\"camera\":\"isCamera\",\"gateway\":\"isGateway\",\"Device-SW\":\"isSwitch\",\"Device-AP\":\"isAP\",\"neighbor\":\"isNeighbor\",\"NVR\":\"isNVR\"}\n\t\t\tprops = dev.pluginProps\n\t\t\tdevTid = dev.deviceTypeId\n\t\t\tfor iT in isType:\n\t\t\t\ttestId = devTid[0:min( len(iT),len(devTid) ) ]\n\t\t\t\tif iT == testId:\n\t\t\t\t\tisT = isType[iT]\n\t\t\t\t\tif isT not in props or props[isT] != True:\n\t\t\t\t\t\tprops[isT] = True\n\t\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\t\tbreak\n\n\t\t\tif \"enableBroadCastEvents\" not in props:\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"enableBroadCastEvents\"] = \"0\"\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\n\n\t\telif self.pluginState == \"run\":\n\t\t\tself.devNeedsUpdate[dev.id] = True\n\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef deviceStopComm(self, dev):\n\t\tif\tself.pluginState != \"stop\":\n\t\t\tself.devNeedsUpdate[dev.id] = True\n\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"stopping device: {} {}\".format(dev.name, dev.id) )\n\n\t####-----------------\t ---------\n\tdef didDeviceCommPropertyChange(self, origDev, newDev):\n\t\t#if origDev.pluginProps['xxx'] != newDev.pluginProps['address']:\n\t\t#\t return True\n\t\treturn False\n\t###########################\t\tINIT\t## END\t ########################\n\n\n\t####-----------------\t ---------\n\tdef getFolderId(self):\n\n\t\t\tself.folderNameIDCreated\t\t= 0\n\t\t\tself.folderNameIDSystemID\t = 0\n\t\t\ttry:\n\t\t\t\tself.folderNameIDCreated = indigo.devices.folders.getId(self.folderNameCreated)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tif self.folderNameIDCreated ==0:\n\t\t\t\ttry:\n\t\t\t\t\tff = indigo.devices.folder.create(self.folderNameCreated)\n\t\t\t\t\tself.folderNameIDCreated = ff.id\n\t\t\t\texcept:\n\t\t\t\t\tself.folderNameIDCreated = 0\n\n\t\t\ttry:\n\t\t\t\tself.folderNameIDSystemID = indigo.devices.folders.getId(self.folderNameSystem)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tif self.folderNameIDSystemID ==0:\n\t\t\t\ttry:\n\t\t\t\t\tff = indigo.devices.folder.create(self.folderNameSystem)\n\t\t\t\t\tself.folderNameIDSystemID = ff.id\n\t\t\t\texcept:\n\t\t\t\t\tself.folderNameIDSystemID = 0\n\n\t\t\ttry:\n\t\t\t\tself.folderNameIDNeighbors = indigo.devices.folders.getId(self.folderNameNeighbors)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tif self.folderNameIDNeighbors ==0:\n\t\t\t\ttry:\n\t\t\t\t\tff = indigo.devices.folder.create(self.folderNameNeighbors)\n\t\t\t\t\tself.folderNameIDNeighbors = ff.id\n\t\t\t\texcept:\n\t\t\t\t\tself.folderNameIDNeighbors = 0\n\n\t\t\ttry:\n\t\t\t\tself.folderNameIDVariables = indigo.variables.folders.getId(self.folderNameVariables)\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tif self.folderNameIDVariables ==0:\n\t\t\t\ttry:\n\t\t\t\t\tff = indigo.variables.folder.create(self.folderNameVariables)\n\t\t\t\t\tself.folderNameIDVariables = ff.id\n\t\t\t\texcept:\n\t\t\t\t\tself.folderNameIDVariables = 0\n\n\n\t\t\treturn\n\n\n\n\t###########################\t\tDEVICE\t#################################\n####-------------------------------------------------------------------------####\n\tdef getDeviceConfigUiValues(self, pluginProps, typeId, devId):\n\t\ttry:\n\t\t\ttheDictList = super(Plugin, self).getDeviceConfigUiValues(pluginProps, typeId, devId)\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\ttheDictList[0][\"Gtext{}\".format(groupNo)] = self.groupNames[groupNo]\n\t\t\treturn theDictList\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn super(Plugin, self).getDeviceConfigUiValues(pluginProps, typeId, devId)\n\n\n\t####-----------------\t ---------\n\tdef validateDeviceConfigUi(self, valuesDict=None, typeId=\"\", devId=0):\n\t\ttry:\n\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"Validate Device dict:, devId:{} vdict:{}\".format(devId,valuesDict) )\n\t\t\tself.devNeedsUpdate[int(devId)] = True\n\n\t\t\tdev = indigo.devices[int(devId)]\n\t\t\tif \"groupMember\" in dev.states:\n\t\t\t\tgMembers =\"\"\n\t\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\t\tif \"Group{}\".format(groupNo) in valuesDict:\n\t\t\t\t\t\tif valuesDict[\"Group{}\".format(groupNo)]:\n\t\t\t\t\t\t\tgMembers += self.groupNames[groupNo]+\",\"\n\t\t\t\t\t\t\tself.groupStatusList[groupNo][\"members\"][\"{}\".format(devId)] = True\n\n\t\t\t\t\t\telif \"{}\".format(devId) in\tself.groupStatusList[groupNo][\"members\"]: \n\t\t\t\t\t\t\tdel self.groupStatusList[groupNo][\"members\"][\"{}\".format(devId)]\n\n\t\t\t\t\telif \"{}\".format(devId) in\tself.groupStatusList[groupNo][\"members\"]: \n\t\t\t\t\t\tdel self.groupStatusList[groupNo][\"members\"][\"{}\".format(devId)]\n\t\t\t\tif gMembers != \"\":\n\t\t\t\t\tif devId not in self.delayedAction:\n\t\t\t\t\t\tself.delayedAction[devId] = []\n\t\t\t\t\tself.delayedAction[devId].append({\"action\":\"updateState\", \"state\":\"groupMember\", \"value\":gMembers})\n\t\t\treturn (True, valuesDict)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\terrorDict = valuesDict\n\t\treturn (False, valuesDict, errorDict)\n\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the XML for the PluginConfig.xml by default; you probably don't\n\t# want to use this unless you have a need to customize the XML (again, uncommon)\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef xxgetPrefsConfigUiXml(self):\n\t\treturn super(Plugin, self).getPrefsConfigUiXml()\n\n\n\n\t####----------------- setconfig default values\t---------\n\tdef setfilterunifiCloudKeyListOfSiteNames(self, valuesDict):\n\n\t\t# not set yet, for future use\n\t\tif self.refreshCallbackMethodAlreadySet == \"yes\": return valuesDict \n\n\t\tif valuesDict[\"unifiControllerType\"] == \"hosted\":\n\t\t\tvaluesDict[\"unifiCloudKeyMode\"] \t\t\t\t\t = \"ON\"\n\t\t\tvaluesDict[\"overWriteControllerPort\"] \t\t\t\t = \"8443\"\n\t\t\tvaluesDict[\"unifiCloudKeySiteName\"] \t\t\t\t = \"default\"\n\n\t\tcontrollerType = valuesDict[\"unifiControllerType\"]\n\t\tif controllerType == \"UDM\":\n\t\t\tvaluesDict[\"unifiCloudKeyMode\"] \t= \"ON\"\n\t\t\tvaluesDict[\"ControllerBackupPath\"]\t= kDefaultPluginPrefs.get(\"infoLabelbackup1\",\"/usr/lib/unifi/data/backup/autobackup\")\n\t\t\tvaluesDict[\"ipUDMON\"]\t \t\t\t= True\n\n\t\telif controllerType == \"UDMPro\":\n\t\t\tvaluesDict[\"unifiCloudKeyMode\"] \t= \"ON\"\n\t\t\tvaluesDict[\"ControllerBackupPath\"]\t= kDefaultPluginPrefs.get(\"infoLabelbackup1\",\"/usr/lib/unifi/data/backup/autobackup\")\n\t\t\tvaluesDict[\"ipUDMON\"]\t \t\t\t= True\n\n\t\telif controllerType == \"std\":\n\t\t\tvaluesDict[\"unifiCloudKeyMode\"] \t= \"ON\"\n\t\t\tvaluesDict[\"ControllerBackupPath\"]\t= kDefaultPluginPrefs.get(\"infoLabelbackup2a\",\"\")\n\t\t\tvaluesDict[\"ipUDMON\"]\t \t\t\t= False\n\n\t\telif controllerType == \"off\":\n\t\t\tvaluesDict[\"unifiCloudKeyMode\"] \t= \"off\"\n\t\t\tvaluesDict[\"ControllerBackupPath\"]\t= kDefaultPluginPrefs.get(\"infoLabelbackup2\",\"/data/unifi/data/backup/autobackup\")\n\t\t\tvaluesDict[\"ipUDMON\"]\t \t\t\t= False\n\n\t\telse:\n\t\t\tpass\n\n\t\tvaluesDict[\"unifiCloudKeySiteName\"]\t= self.unifiCloudKeySiteName\n\n\t\t#self.refreshCallbackMethodAlreadySet = \"yes\" # only do it once after called \n\t\treturn valuesDict\n\n\t####----------------- set unifi controller site ID anmes in dynamic list ---------\n\tdef filterunifiCloudKeyListOfSiteNames(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = [[\"x\",\"set to empty = re-read list from controller\"]]\n\t\tfor xx in self.unifiCloudKeyListOfSiteNames:\n\t\t\txList.append([xx,xx])\n\t\txList.append([\"set\",\"overwrite in next field\"])\n\t\treturn xList\n\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the UI values for the configuration dialog; the default is to\n\t# simply return the self.pluginPrefs dictionary. It can be used to dynamically set\n\t# defaults at run time\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getPrefsConfigUiValues(self):\n\t\ttry:\n\t\t\t(valuesDict, errorsDict) = super(Plugin, self).getPrefsConfigUiValues()\n\n\t\t\tvaluesDict[\"unifiUserID\"]\t\t\t\t= self.connectParams[\"UserID\"][\"unixDevs\"]\n\t\t\tvaluesDict[\"unifiUserIDUDM\"]\t\t\t= self.connectParams[\"UserID\"][\"unixUD\"]\n\t\t\tvaluesDict[\"nvrUNIXUserID\"]\t\t\t= self.connectParams[\"UserID\"][\"unixNVR\"]\n\t\t\tvaluesDict[\"nvrWebUserID\"]\t\t\t\t= self.connectParams[\"UserID\"][\"nvrWeb\"]\n\t\t\tvaluesDict[\"unifiCONTROLLERUserID\"]\t= self.connectParams[\"UserID\"][\"webCTRL\"]\n\n\t\t\tvaluesDict[\"unifiPassWd\"]\t\t\t\t= self.connectParams[\"PassWd\"][\"unixDevs\"]\n\t\t\tvaluesDict[\"unifiPassWdUDM\"]\t\t\t= self.connectParams[\"PassWd\"][\"unixUD\"]\n\t\t\tvaluesDict[\"nvrUNIXPassWd\"]\t\t\t= self.connectParams[\"PassWd\"][\"unixNVR\"]\n\t\t\tvaluesDict[\"unifiCONTROLLERPassWd\"]\t= self.connectParams[\"PassWd\"][\"webCTRL\"]\n\n\n\t\t\tvaluesDict[\"GWtailEnable\"]\t\t\t\t= self.connectParams[\"enableListener\"][\"GWtail\"]\n\t\t\tvaluesDict[\"refreshCallbackMethod\"]\t= \"setfilterunifiCloudKeyListOfSiteNames\"\n\t\t\tvaluesDict[\"unifiCloudKeySiteName\"]\t= self.unifiCloudKeySiteName\n\t\t\tvaluesDict[\"unifControllerCheckPortNumber\"] = self.unifControllerCheckPortNumber\n\t\t\t#self.refreshCallbackMethodAlreadySet\t= \"yes\"\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn (valuesDict, errorsDict)\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine is called once the user has exited the preferences dialog\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef closedPrefsConfigUi(self, valuesDict , userCancelled):\n\t\t# if the user saved his/her preferences, update our member variables now\n\t\tif userCancelled == False:\n\t\t\tpass\n\t\treturn\n\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine is called once the user has exited the preferences dialog\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t####----------------- set the geneeral config parameters---------\n\tdef validatePrefsConfigUi(self, valuesDict):\n\n\t\ttry:\n\t\t\tvaluesDict[\"MSG\"]\t\t\t\t\t\t\t\t= \"ok\"\n\t\t\trebootRequired\t\t\t\t\t\t\t\t\t= \"\"\n\t\t\tself.lastUnifiCookieCurl\t\t\t\t\t\t= 0\n\t\t\tself.lastUnifiCookieRequests\t\t\t\t\t= 0\n\n\t\t\tself.lastNVRCookie\t\t\t\t\t\t\t\t= 0\n\t\t\tself.checkforUnifiSystemDevicesState\t\t\t= \"validateConfig\"\n\t\t\tself.enableSqlLogging\t\t\t\t\t\t\t= valuesDict[\"enableSqlLogging\"]\n\t\t\tself.enableFINGSCAN\t\t\t\t\t\t\t\t= valuesDict[\"enableFINGSCAN\"]\n\t\t\tself.count_APDL_inPortCount\t\t\t\t\t\t= valuesDict[\"count_APDL_inPortCount\"]\n\n\t\t\tself.enableBroadCastEvents\t\t\t\t\t\t= valuesDict[\"enableBroadCastEvents\"]\n\t\t\tself.sendBroadCastEventsList\t\t\t\t\t= []\n\t\t\tself.ignoreNewNeighbors\t\t\t\t\t\t\t= valuesDict[\"ignoreNewNeighbors\"]\n\t\t\tself.ignoreNewClients\t\t\t\t\t\t\t= valuesDict[\"ignoreNewClients\"]\n\t\t\t#self.loopSleep\t\t\t\t\t\t\t\t\t= float(valuesDict[\"loopSleep\"])\n\t\t\tself.unifiControllerBackupON\t\t\t\t\t= valuesDict[\"unifiControllerBackupON\"]\n\t\t\tself.ControllerBackupPath\t\t\t\t\t\t= valuesDict[\"ControllerBackupPath\"]\n\n\t\t\tself.unifiControllerOS\t\t\t\t\t\t\t= \"\" # force initialization of connection\n\t\t\tself.copyProtectsnapshots\t\t\t\t\t\t= valuesDict[\"copyProtectsnapshots\"]\n\t\t\tself.refreshProtectCameras\t\t\t\t\t\t= float(valuesDict[\"refreshProtectCameras\"])\n\t\t\tself.protecEventSleepTime\t\t\t\t\t\t= float(valuesDict[\"protecEventSleepTime\"])\n\t\t\tself.unifControllerCheckPortNumber\t\t\t\t= valuesDict[\"unifControllerCheckPortNumber\"] \n\t\t\tself.requestTimeout\t\t\t\t\t\t\t\t= max(1., float(valuesDict[\"requestTimeout\"]))\n\n\t\t\tself.cameraEventWidth\t\t\t\t\t\t\t= int(valuesDict[\"cameraEventWidth\"])\n\n\t\t\tself.imageSourceForEvent\t\t\t\t\t\t= valuesDict[\"imageSourceForEvent\"]\n\n\t\t\tself.imageSourceForSnapShot\t\t\t\t\t\t= valuesDict[\"imageSourceForSnapShot\"]\n\n\t\t\ttry: self.readBuffer\t\t\t\t\t\t\t= int(valuesDict[\"readBuffer\"])\n\t\t\texcept: self.readBuffer\t\t\t\t\t\t\t= 32767\n\n\t\t\ttry:\tself.maxConsumedTimeQueueForWarning\t\t= float(valuesDict[\"maxConsumedTimeQueueForWarning\"])\n\t\t\texcept:\tself.maxConsumedTimeQueueForWarning\t\t= 5.\n\t\t\tvaluesDict[\"maxConsumedTimeQueueForWarning\"]\t= self.maxConsumedTimeQueueForWarning\n\n\t\t\ttry:\tself.maxConsumedTimeForWarning\t\t\t= float(valuesDict[\"maxConsumedTimeForWarning\"])\n\t\t\texcept:\tself.maxConsumedTimeForWarning\t\t\t= 3.\n\t\t\tvaluesDict[\"maxConsumedTimeForWarning\"]\t\t= self.maxConsumedTimeForWarning\n\n\t\t\tif self.launchWaitSeconds != float(valuesDict[\"launchWaitSeconds\"]):\n\t\t\t\trebootRequired +=\"launchWaitSeconds changed \"\n\t\t\tself.launchWaitSeconds\t\t\t\t\t\t\t= float(valuesDict[\"launchWaitSeconds\"])\n\n\t\t\tself.rebootUnifiDeviceOnError\t\t\t\t\t= valuesDict[\"rebootUnifiDeviceOnError\"]\n\n\t\t\tif self.connectParams[\"UserID\"][\"unixDevs\"]\t!= valuesDict[\"unifiUserID\"]:\t\t\t\trebootRequired += \" unifiUserID changed;\"\n\t\t\tif self.connectParams[\"PassWd\"][\"unixDevs\"]\t!= valuesDict[\"unifiPassWd\"]:\t\t\t\trebootRequired += \" unifiPassWd changed;\"\n\t\t\tif self.connectParams[\"UserID\"][\"unixUD\"] \t!= valuesDict[\"unifiUserIDUDM\"]:\t\t\trebootRequired += \" unifiUserIDUDM changed;\"\n\t\t\tif self.connectParams[\"PassWd\"][\"unixUD\"] \t!= valuesDict[\"unifiPassWdUDM\"]:\t\t\trebootRequired += \" unifiPassWdUDM changed;\"\n\n\t\t\tself.connectParams[\"UserID\"][\"unixUD\"]\t\t= valuesDict[\"unifiUserIDUDM\"]\n\t\t\tself.connectParams[\"PassWd\"][\"unixUD\"]\t\t= valuesDict[\"unifiPassWdUDM\"]\n\t\t\tself.useStrictToLogin\t\t\t\t\t\t\t= valuesDict[\"useStrictToLogin\"]\n\n\t\t\tself.connectParams[\"UserID\"][\"webCTRL\"]\t\t= valuesDict[\"unifiCONTROLLERUserID\"]\n\t\t\tself.connectParams[\"PassWd\"][\"webCTRL\"]\t\t= valuesDict[\"unifiCONTROLLERPassWd\"]\n\n\t\t\tself.connectParams[\"UserID\"][\"unixDevs\"]\t\t= valuesDict[\"unifiUserID\"]\n\t\t\tself.connectParams[\"PassWd\"][\"unixDevs\"]\t\t= valuesDict[\"unifiPassWd\"]\n\n\t\t\tself.restartListenerEvery = float(valuesDict[\"restartListenerEvery\"])\n\n\t\t\tself.curlPath\t\t\t\t\t\t\t\t\t= valuesDict[\"curlPath\"]\n\t\t\tself.requestOrcurl\t\t\t\t\t\t\t\t= valuesDict[\"requestOrcurl\"]\n\n\n\t\t\tself.unifiCloudKeyIP\t\t\t\t\t\t\t= valuesDict[\"unifiCloudKeyIP\"]\n\t\t\tif self.overWriteControllerPort != valuesDict[\"overWriteControllerPort\"]:\n\t\t\t\trebootRequired\t\t\t\t\t\t\t\t+= \"controller port overwrite changed\"\n\t\t\tself.overWriteControllerPort\t\t\t\t\t= valuesDict[\"overWriteControllerPort\"]\n\n\n\t\t\t#self.indiLOG.log(10,\"unifiCloudKeySiteName old:>{}< new:>{}<, types:{} {}\".format(self.unifiCloudKeySiteName, valuesDict[\"unifiCloudKeySiteName\"], type(\" \") , type(valuesDict[\"unifiCloudKeySiteName\"]) ) )\n\t\t\tif type(\" \") != type(valuesDict[\"unifiCloudKeySiteName\"]): valuesDict[\"unifiCloudKeySiteName\"] = \"\"\n\t\t\t#self.indiLOG.log(10,\"unifiCloudKeySiteName old:>{}< new:>{}<\".format(self.unifiCloudKeySiteName, valuesDict[\"unifiCloudKeySiteName\"] ) )\n\t\t\tif len(valuesDict[\"unifiCloudKeySiteName\"]) < 3: \n\t\t\t\tvaluesDict[\"unifiCloudKeySiteName\"] = \"\"\n\t\t\tif self.unifiCloudKeySiteName != valuesDict[\"unifiCloudKeySiteName\"]:\n\t\t\t\tself.indiLOG.log(20,\"setting unifiCloudKeySiteName from:>{}< to:>{}<\".format(self.unifiCloudKeySiteName, valuesDict[\"unifiCloudKeySiteName\"] ) )\n\t\t\t\tself.executeCMDOnControllerReset()\n\t\t\tself.unifiCloudKeySiteName = valuesDict[\"unifiCloudKeySiteName\"] \n\n\t\t\tvaluesDict[\"unifiCloudKeySiteNameFreeText\"] = \"\"\n\n\t\t\tif valuesDict[\"unifiControllerType\"] == \"off\" or valuesDict[\"unifiCloudKeyMode\"] == \"off\" or self.connectParams[\"UserID\"][\"webCTRL\"] == \"\":\n\t\t\t\tself.unifiControllerType \t\t\t\t\t= \"off\"\n\t\t\t\tself.unifiCloudKeySiteName\t\t\t\t\t= \"off\"\n\t\t\t\tself.connectParams[\"UserID\"][\"webCTRL\"]\t= \"\"\n\t\t\t\tvaluesDict[\"unifiControllerType\"]\t\t\t= \"off\"\n\t\t\t\tvaluesDict[\"unifiCloudKeyMode\"]\t\t\t= \"off\"\n\t\t\t\tvaluesDict[\"unifiCONTROLLERUserID\"]\t\t= \"\"\n\n\t\t\tself.unifiControllerType\t\t\t\t\t\t= valuesDict[\"unifiControllerType\"]\n\t\t\tself.unifiCloudKeyMode\t\t\t\t\t\t\t= valuesDict[\"unifiCloudKeyMode\"]\n\n\n\t\t\tself.ignoreNeighborForFing\t\t\t\t\t\t= valuesDict[\"ignoreNeighborForFing\"]\n\t\t\tself.updateDescriptions\t\t\t\t\t\t\t= valuesDict[\"updateDescriptions\"]\n\t\t\tself.folderNameCreated\t\t\t\t\t\t\t= valuesDict[\"folderNameCreated\"]\n\t\t\tself.folderNameVariables\t\t\t\t\t\t= valuesDict[\"folderNameVariables\"]\n\t\t\tself.folderNameNeighbors\t\t\t\t\t\t= valuesDict[\"folderNameNeighbors\"]\n\t\t\tself.folderNameSystem\t\t\t\t\t\t\t= valuesDict[\"folderNameSystem\"]\n\t\t\tself.getFolderId()\n\t\t\tif self.enableMACtoVENDORlookup != valuesDict[\"enableMACtoVENDORlookup\"] and self.enableMACtoVENDORlookup == \"0\":\n\t\t\t\trebootRequired\t\t\t\t\t\t\t+= \" MACVendor lookup changed; \"\n\t\t\tself.enableMACtoVENDORlookup\t\t\t\t= valuesDict[\"enableMACtoVENDORlookup\"]\n\n\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tself.groupNames[groupNo] = valuesDict[\"Group{}\".format(groupNo)]\n\n\n\t#new for UDM (pro)\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1 and valuesDict[\"unifiControllerType\"].find(\"UDM\") == -1:\n\t\t\t\t# make sure the devices are disabled when going from UDM to std. will require to edit config again\n\t\t\t\tvaluesDict[\"ipsw{}\".format(self.numberForUDM[\"SW\"])]\t= \"\"\n\t\t\t\tvaluesDict[\"ipSWON{}\".format(self.numberForUDM[\"SW\"])]\t= False\n\t\t\t\tvaluesDict[\"ip{}\".format(self.numberForUDM[\"SW\"])]\t\t= \"\"\n\t\t\t\tvaluesDict[\"ipON{}\".format(self.numberForUDM[\"SW\"])] \t= False\n\t\t\t\t\n\t\t\t\t\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1:\n\t\t\t\tself.unifiCloudKeyMode = \"ON\"\n\t\t\t\tvaluesDict[\"unifiCloudKeyMode\"] = \"ON\"\n\t\t\ttry: \tself.controllerWebEventReadON\t\t= int(valuesDict[\"controllerWebEventReadON\"])\n\t\t\texcept: self.controllerWebEventReadON\t\t= -1\n\t\t\tif self.unifiControllerType == \"UDMpro\":\n\t\t\t\t\tself.controllerWebEventReadON\t\t= -1 \n\n\t\t\t\"\"\"\n\t\t\txx\t\t\t\t\t\t\t\t\t\t\t= \"{}\".format(int(valuesDict[\"timeoutDICT\"]))\n\t\t\tif xx != self.timeoutDICT:\n\t\t\t\trebootRequired\t+= \" timeoutDICT changed; \"\n\t\t\t\tself.timeoutDICT\t\t\t\t\t\t= xx\n\t\t\t\"\"\"\n\n\t\t\t##\n\t\t\tself.setDebugFromPrefs(valuesDict)\n\n\t\t\tif False:\n\t\t\t\tfor TT in[\"AP\",\"GW\",\"SW\"]:\n\t\t\t\t\ttry:\txx\t\t\t = \"{}\".format(int(valuesDict[\"readDictEverySeconds\"+TT]))\n\t\t\t\t\texcept: xx\t\t\t = \"120\"\n\t\t\t\t\tif xx != self.readDictEverySeconds[TT]:\n\t\t\t\t\t\tself.readDictEverySeconds[TT]\t\t\t\t = xx\n\t\t\t\t\t\tvaluesDict[\"readDictEverySeconds\"+TT]\t\t = xx\n\t\t\t\t\t\trebootRequired\t+= \" readDictEverySeconds changed; \"\n\n\n\t\t\tif False:\n\t\t\t\ttry:\txx\t\t\t = int(valuesDict[\"restartIfNoMessageSeconds\"])\n\t\t\t\texcept: xx\t\t\t = 500\n\t\t\t\tif xx != self.restartIfNoMessageSeconds:\n\t\t\t\t\tself.restartIfNoMessageSeconds\t\t\t\t\t = xx\n\t\t\t\t\tvaluesDict[\"restartIfNoMessageSeconds\"]\t\t = xx\n\n\t\t\ttry:\tself.expirationTime\t\t\t\t\t= int(valuesDict[\"expirationTime\"])\n\t\t\texcept: self.expirationTime\t\t\t\t\t= 120\n\t\t\tvaluesDict[\"expirationTime\"]\t\t\t\t= self.expirationTime\n\t\t\ttry:\tself.expTimeMultiplier\t\t\t\t= int(valuesDict[\"expTimeMultiplier\"])\n\t\t\texcept: self.expTimeMultiplier\t\t\t\t= 2.\n\t\t\tvaluesDict[\"expTimeMultiplier\"]\t\t\t= self.expTimeMultiplier\n\n\t\t\tself.fixExpirationTime\t\t\t\t\t\t= valuesDict[\"fixExpirationTime\"]\n\n\t\t\t## AP parameters\n\t\t\tacNew = [False for i in range(_GlobalConst_numberOfAP)]\n\t\t\tipNew = [\"\" for i in range(_GlobalConst_numberOfAP)]\n\t\t\tself.numberOfActive[\"AP\"] = 0\n\t\t\tfor i in range(_GlobalConst_numberOfAP):\n\t\t\t\tip0 = valuesDict[\"ip{}\".format(i)]\n\t\t\t\tac\t= valuesDict[\"ipON{}\".format(i)]\n\t\t\t\tself.debugDevs[\"AP\"][i] = valuesDict[\"debAP{}\".format(i)]\n\t\t\t\tif not self.isValidIP(ip0): ac = False\n\t\t\t\tacNew[i]\t\t\t = ac\n\t\t\t\tipNew[i]\t\t\t = ip0\n\t\t\t\tif ac: \n\t\t\t\t\tacNew[i] = True\n\t\t\t\t\tself.numberOfActive[\"AP\"] \t+= 1\n\t\t\t\tif acNew[i] != self.devsEnabled[\"AP\"][i]:\n\t\t\t\t\trebootRequired\t+= \" enable/disable AP changed; \"\n\t\t\t\tif ipNew[i] != self.ipNumbersOf[\"AP\"][i]:\n\t\t\t\t\trebootRequired\t+= \" Ap ipNumber changed; \"\n\t\t\t\t\tself.deviceUp[\"AP\"][ipNew[i]] = time.time()\n\t\t\tself.ipNumbersOf[\"AP\"] = copy.copy(ipNew)\n\t\t\tself.devsEnabled[\"AP\"] = copy.copy(acNew)\n\n\t\t\t## Switch parameters\n\t\t\tacNew = [False for i in range(_GlobalConst_numberOfSW)]\n\t\t\tipNew = [\"\" for i in range(_GlobalConst_numberOfSW)]\n\t\t\tself.numberOfActive[\"SW\"] = 0\n\t\t\tfor i in range(_GlobalConst_numberOfSW):\n\t\t\t\tif self.isMiniSwitch[i] != valuesDict[\"isMini{}\".format(i)]: rebootRequired\t+= \" SW type changed: {} vs {}\".format(self.isMiniSwitch[i], valuesDict[\"isMini{}\".format(i)])\n\t\t\t\tself.isMiniSwitch[i] = valuesDict[\"isMini{}\".format(i)]\n\t\t\t\tip0 = valuesDict[\"ipSW{}\".format(i)]\n\t\t\t\tac\t= valuesDict[\"ipSWON{}\".format(i)]\n\t\t\t\tself.debugDevs[\"SW\"][i] = valuesDict[\"debSW{}\".format(i)]\n\t\t\t\tif not self.isValidIP(ip0): ac = False\n\t\t\t\tacNew[i] = ac\n\t\t\t\tipNew[i] = ip0\n\t\t\t\tif ac: \n\t\t\t\t\tacNew[i] = True\n\t\t\t\t\tself.numberOfActive[\"SW\"] \t+= 1\n\n\t\t\t\tif acNew[i] != self.devsEnabled[\"SW\"][i]:\n\t\t\t\t\trebootRequired\t+= \" enable/disable SW changed; \"\n\t\t\t\tif ipNew[i] != self.ipNumbersOf[\"SW\"][i]:\n\t\t\t\t\trebootRequired\t+= \" SW ipNumber changed; \"\n\t\t\t\t\tself.deviceUp[\"SW\"][ipNew[i]] = time.time()\n\t\t\tself.ipNumbersOf[\"SW\"] = copy.copy(ipNew)\n\t\t\tself.devsEnabled[\"SW\"] = copy.copy(acNew)\n\n\n\n\t\t\t## UGA parameters\n\t\t\tip0\t\t\t= valuesDict[\"ipUGA\"]\n\t\t\tif self.ipNumbersOf[\"GW\"] != ip0:\n\t\t\t\trebootRequired\t+= \" GW ipNumber changed; \"\n\n\t\t\tac\t\t\t= valuesDict[\"ipUGAON\"]\n\t\t\tif not self.isValidIP(ip0): ac = False\n\t\t\tif self.devsEnabled[\"GW\"] != ac:\n\t\t\t\trebootRequired\t+= \" enable/disable GW changed; \"\n\n\t\t\tself.devsEnabled[\"GW\"]\t \t= ac\n\t\t\tself.ipNumbersOf[\"GW\"] \t= ip0\n\t\t\tself.debugDevs[\"GW\"] \t\t= valuesDict[\"debGW\"]\n\n\t\t\tif \tself.connectParams[\"enableListener\"][\"GWtail\"] != valuesDict[\"GWtailEnable\"]:\n\t\t\t\trebootRequired\t+= \" enable/disable GW changed; \"\n\n\t\t\tself.connectParams[\"enableListener\"][\"GWtail\"] = valuesDict[\"GWtailEnable\"]\t\n\n\n\t\t\t## UDM parameters\n\t\t\tip0\t\t\t= valuesDict[\"ipUDM\"]\n\t\t\tif self.ipNumbersOf[\"UD\"] != ip0:\n\t\t\t\trebootRequired\t+= \" UDM ipNumber changed; \"\n\n\t\t\tac\t\t\t= valuesDict[\"ipUDMON\"]\n\t\t\tif not self.isValidIP(ip0): ac = False\n\t\t\tif self.devsEnabled[\"UD\"] != ac:\n\t\t\t\trebootRequired\t+= \" enable/disable UDM changed; \"\n\n\t\t\tself.devsEnabled[\"UD\"]\t\t= ac\n\t\t\tself.ipNumbersOf[\"UD\"]\t\t= ip0\n\t\t\tself.debugDevs[\"UD\"]\t\t= valuesDict[\"debUD\"]\n\t\t\tif self.devsEnabled[\"UD\"]:\n\t\t\t\tself.ipNumbersOf[\"SW\"][self.numberForUDM[\"SW\"]]\t\t= ip0\n\t\t\t\tvaluesDict[\"ipsw{}\".format(self.numberForUDM[\"SW\"])]\t= ip0\n\t\t\t\tvaluesDict[\"ipSWON{}\".format(self.numberForUDM[\"SW\"])] = True\n\t\t\t\tself.devsEnabled[\"SW\"][self.numberForUDM[\"SW\"]]\t\t= True\n\t\t\t\tself.numberOfActive[\"SW\"] \t\t\t\t\t\t\t= max(1,self.numberOfActive[\"SW\"] )\n\n\t\t\t\tself.ipNumbersOf[\"GW\"] \t\t\t\t\t\t \t\t= ip0\n\t\t\t\tvaluesDict[\"ipNumbersOfGW\"]\t\t\t\t\t\t= ip0\n\t\t\t\tvaluesDict[\"ipUGAON\"]\t\t\t\t\t\t\t\t= True\n\n\t\t\t\tif valuesDict[\"unifiControllerType\"] == \"UDM\": ## only for UDM not for UDM-pro,has no AP\n\t\t\t\t\tself.ipNumbersOf[\"AP\"][self.numberForUDM[\"AP\"]]\t\t= ip0\n\t\t\t\t\tvaluesDict[\"ip{}\".format(self.numberForUDM[\"AP\"])]\t\t= ip0\n\t\t\t\t\tvaluesDict[\"ipON{}\".format(self.numberForUDM[\"AP\"])]\t= True\n\t\t\t\t\tself.devsEnabled[\"AP\"][self.numberForUDM[\"AP\"]] \t\t= True\n\t\t\t\t\tself.numberOfActive[\"AP\"] \t\t\t\t\t\t\t\t= max(1,self.numberOfActive[\"AP\"] )\n\n\t\t\t\tvaluesDict[\"ipNumbersOfGW\"]\t\t\t\t\t\t\t\t= ip0\n\n\t\t\t\"\"\"\n\t\t\t## video parameters\n\t\t\tif self.connectParams[\"UserID\"][\"unixNVR\"]\t!= valuesDict[\"nvrUNIXUserID\"]:\t rebootRequired += \" nvrUNIXUserID changed;\"\n\t\t\tif self.connectParams[\"PassWd\"][\"unixNVR\"]\t!= valuesDict[\"nvrUNIXPassWd\"]:\t rebootRequired += \" nvrUNIXPassWd changed;\"\n\n\t\t\tself.unifiVIDEONumerOfEvents\t= int(valuesDict[\"unifiVIDEONumerOfEvents\"])\n\t\t\tself.connectParams[\"UserID\"][\"unixNVR\"]\t= valuesDict[\"nvrUNIXUserID\"]\n\t\t\tself.connectParams[\"PassWd\"][\"unixNVR\"]\t= valuesDict[\"nvrUNIXPassWd\"]\n\t\t\tself.connectParams[\"UserID\"][\"nvrWeb\"]\t= valuesDict[\"nvrWebUserID\"]\n\t\t\tself.connectParams[\"PassWd\"][\"nvrWeb\"]\t= valuesDict[\"nvrWebPassWd\"]\n\t\t\tself.vmMachine\t\t\t\t\t\t\t\t= valuesDict[\"vmMachine\"]\n\t\t\tself.mountPathVM\t\t\t\t\t\t\t= valuesDict[\"mountPathVM\"]\n\t\t\tself.videoPath\t\t\t\t\t\t\t\t= self.completePath(valuesDict[\"videoPath\"])\n\t\t\tself.vboxPath\t\t\t\t\t\t\t\t= self.completePath(valuesDict[\"vboxPath\"])\n\t\t\tself.changedImagePath\t\t\t\t\t\t= self.completePath(valuesDict[\"changedImagePath\"])\n\t\t\tself.vmDisk\t\t\t\t\t\t\t\t\t= valuesDict[\"vmDisk\"]\n\t\t\tenableVideoSwitch\t\t\t\t\t\t\t= valuesDict[\"cameraSystem\"]\n\t\t\tip0\t\t\t\t\t\t\t\t\t\t\t= valuesDict[\"nvrIP\"]\n\n\n\t\t\tself.cameraSystem = enableVideoSwitch\n\t\t\tif self.cameraSystem ==\"nvr\":\n\t\t\t\tself.ipNumbersOf[\"VD\"]\t= ip0\n\t\t\t\tif self.ipNumbersOf[\"VD\"] != ip0 :\n\t\t\t\t\trebootRequired\t+= \" VIDEO ipNumber changed;\"\n\t\t\t\t\tself.indiLOG.log(10,\"IP# old:{}, new:{}\".format(self.ipNumbersOf[\"VD\"], ip0) )\n\n\t\t\t\"\"\"\n\n\n\t\t\tif self.cameraSystem != valuesDict[\"cameraSystem\"]:\n\t\t\t\trebootRequired\t+= \" video enabled/disabled;\"\n\t\t\t\tself.cameraSystem = valuesDict[\"cameraSystem\"]\n\n\t\t\tself.enablecheckforUnifiSystemDevicesState = valuesDict[\"enablecheckforUnifiSystemDevicesState\"]\n\n\t\t\tself.useDBInfoForWhichDevices = valuesDict[\"useDBInfoForWhichDevices\"]\n\t\t\tif self.isValidIP(self.unifiCloudKeyIP) and (self.unifiCloudKeyMode.find(\"ON\") >-1 or self.unifiCloudKeyMode.find(\"UDM\") > -1 or self.useDBInfoForWhichDevices in [\"all\",\"perDevice\"]):\n\t\t\t\tif self.unifiCloudKeyMode != \"ON\":\n\t\t\t\t\tself.unifiCloudKeyMode = \"ON\"\n\t\t\t\tself.devsEnabled[\"DB\"] = True\n\t\t\telse:\n\t\t\t\tself.devsEnabled[\"DB\"]\t= False\n\n\n\n\t\t\tif rebootRequired != \"\":\n\t\t\t\tself.indiLOG.log(30,\"restart \" + rebootRequired)\n\t\t\t\tself.quitNOW = \"config changed\"\n\t\t\tself.updateConnectParams = time.time() - 100\n\t\t\tvaluesDict[\"connectParams\"] = json.dumps(self.connectParams)\n\n\t\t\tself.groupStatusINIT()\n\n\t\t\treturn True, valuesDict\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\terrorDict = indigo.Dict()\n\t\t\terrorDict[\"MSG\"] = \"error please check indigo eventlog\"\n\t\t\treturn (False, errorDict, valuesDict)\n\n\n\n\n\n\t####-----------------\t ---------\n\tdef completePath(self,inPath):\n\t\tif len(inPath) == 0: return \"\"\n\t\tif inPath == \" \":\t return \"\"\n\t\tif inPath[-1] !=\"/\": inPath +=\"/\"\n\t\treturn inPath\n\n\t####-----------------\t ---------\n\tdef getNewValusDictField(self,item,\t valuesDict, old, rebootRequired):\n\t\txxx\t = valuesDict[item]\n\t\tif xxx != old:\n\t\t\trebootRequired += \" \"+item+\" changed\"\n\t\t\t#indigo.server.log(\" changed: \"+item+ \" new: >\"+ xxx +\"< old:>\"+old+\"<\") \n\t\treturn\t xxx, rebootRequired\n\n\t####----------------- config setting ---- END ----------#########\n\n\t####-----------------\t ---------\n\tdef getCPU(self,pid):\n\t\tret, err = self.readPopen(\"ps -ef | grep {}\".format(pid) + \" | grep -v grep\")\n\t\tlines = ret.strip(\"\\n\").split(\"\\n\")\n\t\tfor line in lines:\n\t\t\tif len(line) < 100: continue\n\t\t\titems = line.split()\n\t\t\tif items[1] != \"{}\".format(pid): continue\n\t\t\tif len(items) < 6: continue\n\t\t\treturn (items[6])\n\t\treturn \"\"\n\n\tdef replaceTrueFalse(self,inString):\n\t\toutString = \"{}\".format(inString)\n\t\ttry:\n\t\t\toutString = outString.replace(\"True\",\"T\").replace(\"False\",\"F\").replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\").replace(\"{\",\"\").replace(\"}\",\"\").replace(\"(\",\"\").replace(\")\",\"\")\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn outString\n\t####-----------------\t ---------\n\tdef printConfigMenu(self, valuesDict=None, typeId=\"\"):\n\t\ttry:\n\t\t\tout = \"\\n\"\n\t\t\tout += \"\\n \"\n\t\t\tout += \"\\nUniFi =============plugin config Parameters========\"\n\n\t\t\tout += \"\\ndebugAreas\".ljust(40)\t\t\t\t\t\t\t\t+\t\"{}\".format(self.debugLevel)\n\t\t\tout += \"\\nlogFile\".ljust(40)\t\t\t\t\t\t\t\t+\tself.PluginLogFile\n\t\t\tout += \"\\nenableFINGSCAN\".ljust(40)\t\t\t\t\t\t\t+\t\"{}\".format(self.enableFINGSCAN)[0]\n\t\t\tout += \"\\ncount_APDL_inPortCount\".ljust(40)\t\t\t\t\t+\t\"{}\".format(self.count_APDL_inPortCount == \"1\")[0]\n\t\t\tout += \"\\nenableBroadCastEvents\".ljust(40)\t\t\t\t\t+\t\"{}\".format(self.enableBroadCastEvents == \"1\")[0]\n\t\t\tout += \"\\nignoreNeighborForFing\".ljust(40)\t\t\t\t\t+\t\"{}\".format(self.ignoreNeighborForFing)[0]\n\t\t\tout += \"\\nexpirationTime - default\".ljust(40)\t\t\t\t+\t\"{:.0f} [sec]\".format(self.expirationTime)\n\t\t\tout += \"\\nsleep in main loop \".ljust(40)\t\t\t\t\t+\t\"{:.0f} [sec]\".format(self.loopSleep)\n\t\t\tout += \"\\nuse curl or request\".ljust(40)\t\t\t\t\t+\tself.requestOrcurl\n\t\t\tout += \"\\ncurl path\".ljust(40)\t\t\t\t\t\t\t\t+\tself.curlPath\n\t\t\tout += \"\\ncurl/requests timeout\".ljust(40)\t\t\t\t\t+\t\"{:.0f} [sec]\".format(self.requestTimeout)\n\t\t\tout += \"\\ncpu used since restart: \".ljust(40) \t\t\t\t+\tself.getCPU(self.myPID)\n\t\t\tout += \"\\n\" \n\t\t\tout += \"\\n====== used in ssh userid@switch-IP, AP-IP, USG-IP to get DB dump and listen to events\"\n\t\t\tout += \"\\nUserID-ssh\".ljust(40)\t\t\t\t\t\t\t\t+\tself.connectParams[\"UserID\"][\"unixDevs\"]\n\t\t\tout += \"\\nPassWd-ssh\".ljust(40)\t\t\t\t\t\t\t\t+\tself.connectParams[\"PassWd\"][\"unixDevs\"]\n\t\t\tout += \"\\nUserID-ssh-UDM\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"UserID\"][\"unixUD\"]\n\t\t\tout += \"\\nPassWd-ssh-UDM\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"PassWd\"][\"unixUD\"]\n\t\t\tout += \"\\nread buffer size \".ljust(40)\t\t\t\t\t\t+\t\"{:.0f}\".format(self.readBuffer)\n\t\t\tfor ipN in self.connectParams[\"promptOnServer\"]:\n\t\t\t\tout += (\"\\npromptOnServer \"+ipN).ljust(40)\t\t\t\t+\t\"'\"+self.connectParams[\"promptOnServer\"][ipN]+\"'\"\n\n\t\t\tout += \"\\nGW tailCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"GWtail\"]\n\t\t\tout += \"\\nGW dictCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"GWdict\"]\n\t\t\tout += \"\\nSW tailCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"SWtail\"]\n\t\t\tout += \"\\nSW dictCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"SWdict\"]\n\t\t\tout += \"\\nAP tailCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"APtail\"]\n\t\t\tout += \"\\nAP dictCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"APdict\"]\n\t\t\tout += \"\\nUD dictCommand\".ljust(40)\t\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"UDdict\"]\n\t\t\tout += \"\\nAP enabled:\".ljust(40)\t\t\t\t\t\t\t+\tself.replaceTrueFalse(self.devsEnabled[\"AP\"])\n\t\t\tout += \"\\nSW enabled:\".ljust(40)\t\t\t\t\t\t\t+\tself.replaceTrueFalse(self.devsEnabled[\"SW\"])\n\t\t\tout += \"\\nGW enabled:\".ljust(40)\t\t\t\t\t\t\t+\tself.replaceTrueFalse(self.devsEnabled[\"GW\"])\n\t\t\tout += \"\\ncontrolelr DB read enabled\".ljust(40)\t\t\t\t+\tself.replaceTrueFalse(self.devsEnabled[\"DB\"])\n\t\t\tout += \"\\nUDM enabled\".ljust(40)\t\t\t\t\t\t\t+\tself.replaceTrueFalse(self.devsEnabled[\"UD\"])\n\t\t\tout += \"\\nread DB Dict every\".ljust(40)\t\t\t\t\t\t+\t\"{}\".format(self.readDictEverySeconds).replace(\"'\",\"\").replace(\"u\",\"\").replace(\" \",\"\")+\" [sec]\"\n\t\t\tout += \"\\nrestart listeners if NoMessage for\".ljust(40)\t\t+\t\"{:.0f} [sec]\".format(self.restartIfNoMessageSeconds)\n\t\t\tout += \"\\nforce restart of listeners \".ljust(40)\t\t\t+\t\"{:.0f} [sec]\".format(self.restartListenerEvery)\n\t\t\tout += \"\\nmax Consumed Time For Warning\".ljust(40)\t\t\t+\t\"{:.0f} [sec]\".format(self.maxConsumedTimeForWarning)\n\t\t\tout += \"\\nmax Consumed Time Queue For Warning\".ljust(40)\t+\t\"{:.0f} [sec]\".format(self.maxConsumedTimeQueueForWarning)\n\t\t\tout += \"\\nwait time betwen lauch of listeners\".ljust(40)\t+\t\"{:.2f} [sec]\".format(self.launchWaitSeconds)\n\n\t\t\tout += \"\\n\"\n\t\t\tout += \"\\n====== CONTROLLER/UDM WEB ACCESS , set parameters and reporting\"\n\t\t\tout += \"\\n curl data={WEB-UserID:..,WEB-PassWd:..} https://controllerIP: ..--------------\"\n\t\t\tout += \"\\nMode: off, ON, UDM, reports only\".ljust(40)\t\t+\tself.unifiCloudKeyMode \n\t\t\tout += \"\\nWEB-UserID\".ljust(40)\t\t\t\t\t\t\t\t+\tself.connectParams[\"UserID\"][\"webCTRL\"]\n\t\t\tout += \"\\nWEB-PassWd\".ljust(40)\t\t\t\t\t\t\t\t+\tself.connectParams[\"PassWd\"][\"webCTRL\"]\n\t\t\tout += \"\\nController Type (UDM,..,std)\".ljust(40)\t\t\t+\tself.unifiControllerType \n\t\t\tout += \"\\nuse strict:true for web login\".ljust(40)\t\t\t+\t\"{}\".format(self.useStrictToLogin)[0] \n\t\t\tout += \"\\nController port#\".ljust(40)\t\t\t\t\t\t+\tself.unifiCloudKeyPort \n\t\t\tout += \"\\noverWriteControllerPort\".ljust(40)\t\t\t\t+\tself.overWriteControllerPort \n\t\t\tout += \"\\nController site Name\".ljust(40)\t\t\t\t\t+\tself.unifiCloudKeySiteName \n\t\t\tout += \"\\nController site NameList \".ljust(40)\t\t\t\t+\t\"{}\".format(self.unifiCloudKeyListOfSiteNames)\n\n\t\t\tout += \"\\nController API WebPage\".ljust(40)\t\t\t\t\t+\tself.unifiApiWebPage \n\t\t\tout += \"\\nController API login WebPage\".ljust(40)\t\t\t+\tself.unifiApiLoginPath\n\t\t\tout += \"\\n\"\n\t\t\tif self.cameraSystem == \"nvr\":\n\t\t\t\tout += \"\\n====== camera NVR stuff ---------------------------\"\n\t\t\t\tout += \"\\nCamera enabled\".ljust(40)\t\t\t\t\t\t+\tself.cameraSystem \n\t\t\t\tout += \"\\n= get camera DB config and listen to recording event logs\"\n\t\t\t\tout += \"\\n ssh NVR-UNIXUserID@NVR-IP \"\n\t\t\t\tout += \"\\nNVR-UNIXUserID\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"UserID\"][\"unixNVR\"]\n\t\t\t\tout += \"\\nNVR-UNIXpasswd\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"PassWd\"][\"unixNVR\"] \n\t\t\t\tout += \"\\nVD tailCommand\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"VDtail\"]\n\t\t\t\tout += \"\\nVD dictCommand\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"commandOnServer\"][\"VDdict\"] \n\t\t\t\tout += \"\\n= getting snapshots and reading and changing parameters\"\n\t\t\t\tout += \"\\n curl data={WEB-UserID:..,WEB-PassWd:..} https://NVR-IP#: .... for commands and read parameters \"\n\t\t\t\tout += \"\\n requests(http://IP-NVR:7080/api/2.0/snapshot/camera/**camApiKey**?force=true&width=1024&apiKey=nvrAPIkey,stream=True) for snap shots\"\n\t\t\t\tout += \"\\nimageSourceForSnapShot\".ljust(40)\t\t\t\t+\tself.imageSourceForSnapShot\n\t\t\t\tout += \"\\nimageSourceForEvent\".ljust(40)\t\t\t\t+\tself.imageSourceForEvent \n\t\t\t\tout += \"\\nNVR-WEB-UserID\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"UserID\"][\"nvrWeb\"]\n\t\t\t\tout += \"\\nNVR-WEB-passWd\".ljust(40)\t\t\t\t\t\t+\tself.connectParams[\"PassWd\"][\"nvrWeb\"]\n\t\t\t\tout += \"\\nNVR-API Key\".ljust(40)\t\t\t\t\t\t+\tself.nvrVIDEOapiKey\n\t\t\t\tout += \"\\nVideo NVR-IP#\".ljust(40)\t\t\t\t\t\t+\tself.ipNumbersOf[\"VD\"]\n\t\t\telif self.cameraSystem == \"protect\":\n\t\t\t\tpass\n\t\t\tout += \"\\n\"\n\t\t\tout += \"\\n\" + \"# AP ip#\".ljust(20) \t\t\t\t\t\t+\t\"enabled / disabled / is mini switch \"\n\t\t\tfor ll in range(len(self.ipNumbersOf[\"AP\"])):\n\t\t\t\tout += \"\\n{:2} \".format(ll) + self.ipNumbersOf[\"AP\"][ll].ljust(20) \t+\t\t\"{:1}\".format(self.devsEnabled[\"AP\"][ll])\n\n\t\t\tout += \"\\n\" + \"# SW ip#\"\n\t\t\tfor ll in range(len(self.ipNumbersOf[\"SW\"])):\n\t\t\t\tout += \"\\n{:2} \".format(ll) + self.ipNumbersOf[\"SW\"][ll].ljust(20) \t+\t\t\"{:1}, {:1}\".format(self.devsEnabled[\"SW\"][ll], self.isMiniSwitch[ll])\n\n\t\t\tout += \"\\n\" + \" USG/UGA gateway/router\"\n\t\t\tout += \"\\n \" + self.ipNumbersOf[\"GW\"].ljust(20) \t\t\t+\t\t\"{}\".format(self.devsEnabled[\"GW\"])[0]\n\n\t\t\tout += \"\\n\" + \" Controller / cloud Key IP#\"\n\t\t\tout += \"\\n \" + self.unifiCloudKeyIP.ljust(20) \t\t\t\t\n\t\t\tout += \"\\n\" + \"--------------------------\"\n\t\t\tout += \"\\n\"\n\n\t\t\tout += \"\\nUniFi =============plugin config Parameters======== END \"\n\t\t\tout += \"\\n \"\n\n\t\t\tself.indiLOG.log(20,out)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef printMACs(self,MAC=\"\"):\n\t\ttry:\n\n\t\t\tout = \"\\n ===== UNIFI device info =========\"\n\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\t\tif dev.deviceTypeId == \"client\":\t\t continue\n\t\t\t\tif MAC !=\"\" and dev.states[\"MAC\"] != MAC: continue\n\t\t\t\tout += \"\\ndevice info {} id: {:<12d}; type:{:}\".format(dev.name, dev.id, dev.deviceTypeId)\n\t\t\t\tout += \"\\n props:\"\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tfor p in props:\n\t\t\t\t\tout += \"\\n {}: {}\".format(p, props[p])\n\n\t\t\t\tout += \"\\n states:\"\n\t\t\t\tfor p in dev.states:\n\t\t\t\t\tout += \"\\n {}: {}\".format(p, dev.states[p])\n\n\t\t\tout += \"\\n counters, timers etc:\"\n\t\t\tif MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\t\tout += \"\\nUniFi {}\".format(self.MAC2INDIGO[\"UN\"][MAC])\n\n\t\t\tif MAC in self.MAC2INDIGO[\"AP\"]:\n\t\t\t\tout += \"\\nAP {}\".format(self.MAC2INDIGO[\"AP\"][MAC])\n\n\t\t\tif MAC in self.MAC2INDIGO[\"SW\"]:\n\t\t\t\tout += \"\\nSWITCH {}\".format(self.MAC2INDIGO[\"SW\"][MAC])\n\n\t\t\tif MAC in self.MAC2INDIGO[\"GW\"]:\n\t\t\t\tout += \"\\nGATEWAY {}\".format(self.MAC2INDIGO[\"GW\"][MAC])\n\n\t\t\tif MAC in self.MAC2INDIGO[\"NB\"]:\n\t\t\t\tout += \"\\nNEIGHBOR {}\".format(self.MAC2INDIGO[\"NB\"][MAC])\n\n\n\t\t\tout += \"\\ndevice info ===== UNIFI device info ========= END \"\n\n\t\t\tself.indiLOG.log(20,out)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t####-----------------\t ---------\n\tdef printALLMACs(self):\n\t\ttry:\n\t\t\tout = \"\\n ===== UNIFI device info =========\"\n\n\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\t\tif dev.deviceTypeId == \"client\": continue\n\t\t\t\tout += \"\\n{:20s} id:{:12d}; type:{:s}\".format(dev.name, dev.id, dev.deviceTypeId)\n\t\t\t\tline=\"props: \"\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tfor p in props:\n\t\t\t\t\tline+= \"{}:{}; \".format(p, props[p])\n\t\t\t\tout += \"\\n {}\".format(line)\n\t\t\t\tline=\"states: \"\n\t\t\t\tfor p in dev.states:\n\t\t\t\t\tline += \"{}:{}; \".format(p, dev.states[p])\n\t\t\t\tout += \"\\n {}\".format(line)\n\n\t\t\t\tout += \"\\n======Temp data, counters, timer etc=======\"\n\n\t\t\tfor dd in self.MAC2INDIGO[\"UN\"]:\n\t\t\t\tout += \"\\nUNIFI {} {}\".format(dd, self.MAC2INDIGO[\"UN\"][dd])\n\t\t\tfor dd in self.MAC2INDIGO[\"AP\"]:\n\t\t\t\tout += \"\\AP {} {}\".format(dd, self.MAC2INDIGO[\"AP\"][dd])\n\t\t\tfor dd in self.MAC2INDIGO[\"SW\"]:\n\t\t\t\tout += \"\\SWITCH {} {}\".format(dd, self.MAC2INDIGO[\"SW\"][dd])\n\t\t\tfor dd in self.MAC2INDIGO[\"GW\"]:\n\t\t\t\tout += \"\\GATEWAY {} {}\".format(dd, self.MAC2INDIGO[\"GW\"][dd])\n\t\t\tfor dd in self.MAC2INDIGO[\"NB\"]:\n\t\t\t\tout += \"\\nNB {} {}\".format(dd, self.MAC2INDIGO[\"NB\"][dd])\n\n\t\t\tout += \"\\n ===== UNIFI device info ========= END \"\n\n\t\t\tself.indiLOG.log(20,out)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\t####----------------- ---------\n\tdef printALLUNIFIsreduced(self):\n\t\ttry:\n\n\t\t\tlineI = []\n\t\t\tlineE = []\n\t\t\tlineD = []\n\t\t\tout = \"\\n\"\n\t\t\tdType =\"UniFi\"\n\t\t\tout +=\"\\n ===== UniFi device info ========= curr.; exp; use ping ; use WOL; use what 4; WiFi;WiFi-max; DHCP; SW-UPtm; lastStatusChge; reason; member of;\"\n\t\t\tout +=\"\\ndev Name id: MAC# ; status; time; up; down; [sec]; Status; Status; idle-T; max-AGE; chged; ; for change; groups;\"\n\t\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\t\tline = \"\"\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tmac = dev.states[\"MAC\"]\n\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"] == \"WiFi\": wf = True\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t wf = False\n\n\t\t\t\tif True:\t\t\t\t\t\t\t\t\t\t\tline = \"{}\".format(dev.id).ljust(12)+mac+\"; \"\n\n\t\t\t\tif mac in self.MACignorelist and self.MACignorelist[mac]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tline += (\"IGNORED\").rjust(7)+\"; \"\n\t\t\t\telif \"status\" in dev.states:\t\t\t\t\t\tline += (dev.states[\"status\"]).rjust(7)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += (\"-------\").rjust(7)+\"; \"\n\n\t\t\t\tif \"expirationTime\" in props :\t\t\t\t\t\tline += (\"{}\".format(props[\"expirationTime\"]).split(\".\")[0]).rjust(4)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(4)+\"; \"\n\n\t\t\t\tif \"usePingUP\" in props :\t\t\t\t\t\t\tline += (\"{}\".format(props[\"usePingUP\"])).rjust(5)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(5)+\"; \"\n\n\t\t\t\tif \"usePingDOWN\" in props :\t\t\t\t\t\tline += (\"{}\".format(props[\"usePingDOWN\"])).rjust(5)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(5)+\"; \"\n\n\t\t\t\tif \"useWOL\" in props :\n\t\t\t\t\tif props[\"useWOL\"] ==\"0\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tline += (\"no\").ljust(7)+\"; \"\n\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tline += (\"{}\".format(props[\"useWOL\"])).rjust(7)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \"no\".ljust(7)+\"; \"\n\n\t\t\t\tif \"useWhatForStatus\" in props :\t\t\t\t\tline += (\"{}\".format(props[\"useWhatForStatus\"])).rjust(14)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(14)+\"; \"\n\n\t\t\t\tif \"useWhatForStatusWiFi\" in props and wf:\t\t\tline += (\"{}\".format(props[\"useWhatForStatusWiFi\"])).rjust(10)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(10)+\"; \"\n\n\t\t\t\tif \"idleTimeMaxSecs\" in props and wf:\t\t\t\tline += (\"{}\".format(props[\"idleTimeMaxSecs\"])).rjust(7)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(7)+\"; \"\n\n\t\t\t\tif \"useAgeforStatusDHCP\" in props and not wf:\t\tline += (\"{}\".format(props[\"useAgeforStatusDHCP\"])).rjust(7)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(7)+\"; \"\n\n\t\t\t\tif \"useupTimeforStatusSWITCH\" in props and not wf: line += (\"{}\".format(props[\"useupTimeforStatusSWITCH\"])).rjust(8)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(8)+\"; \"\n\n\t\t\t\tif \"lastStatusChange\" in dev.states:\t\t\t\tline += (\"{}\".format(dev.states[\"lastStatusChange\"])[5:]).rjust(14)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(14)+\"; \"\n\t\t\t\tif \"lastStatusChangeReason\" in dev.states:\t\t\tline += (\"{}\".format(dev.states[\"lastStatusChangeReason\"])[0:35]).rjust(35)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(35)+\"; \"\n\n\t\t\t\tif \"groupMember\" in dev.states:\t\t\t\t\tline += ( (\"{}\".format(dev.states[\"groupMember\"]).replace(\"Group\",\"\")).strip(\",\")\t).rjust(13)+\"; \"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tline += \" \".ljust(13)+\"; \"\n\n\t\t\t\tdevName = dev.name\n\t\t\t\tif len(devName) > 28: devName = devName[:28]+\"..\" # max len of 30indicate cutoff if > 28 with ..\n\t\t\t\tif line.find(\"IGNORED;\") >-1:\n\t\t\t\t\tlineI.append([line,devName])\n\t\t\t\telif line.find(\"expired;\") >-1:\n\t\t\t\t\tlineE.append([line,devName])\n\t\t\t\telif line.find(\"down;\") >-1:\n\t\t\t\t\tlineD.append([line,devName])\n\t\t\t\telse:\n\t\t\t\t\tout+= \"\\n{:30s} {}\".format(devName, line)\n\n\t\t\tif lineD !=[]:\n\t\t\t\tfor xx in lineD:\n\t\t\t\t\tout+= \"\\n{:30s} {}\".format(xx[1],xx[0])\n\t\t\tif lineE !=[]:\n\t\t\t\tfor xx in lineE:\n\t\t\t\t\tout+= \"\\n{:30s} {}\".format(xx[1],xx[0])\n\t\t\tif lineI !=[]:\n\t\t\t\tfor xx in lineI:\n\t\t\t\t\tout+= \"\\n{:30s} {}\".format(xx[1],xx[0])\n\n\t\t\tout+= \"\\n ===== UniFi device info ========= END \"\n\n\t\t\tself.indiLOG.log(20,out)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t####----------------- printGroups\t ---------\n\tdef printGroups(self):\n\t\ttry:\n\t\t\tout = \"\\nGROUPS----- -------MEMBERS ( status = /Up/ Down/ Expired/ Ignored) ----\"\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tindent = \"\\n \"\n\t\t\t\txList = \"\"\n\t\t\t\tmemberNames = []\n\t\t\t\tfor member in self.groupStatusList[groupNo][\"members\"]:\n\t\t\t\t\tif len(member) <2: continue\n\t\t\t\t\ttry:\n\t\t\t\t\t\tID = int(member)\n\t\t\t\t\t\tdev = indigo.devices[ID]\n\t\t\t\t\texcept: continue\n\t\t\t\t\tmemberNames.append(dev.name)\n\n\t\t\t\tnewLine = indent\n\t\t\t\tfor member in sorted(memberNames):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdev = indigo.devices[member]\n\t\t\t\t\t\tnewLine += \"{:30s}/{}; \".format(member, dev.states[\"status\"][0].upper())\n\t\t\t\t\t\tif len(newLine) > 180:\n\t\t\t\t\t\t\txList += newLine\n\t\t\t\t\t\t\tnewLine = indent\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tif\tnewLine != indent:\n\t\t\t\t\txList += newLine\n\n\t\t\t\tif\txList != indent:\n\t\t\t\t\tgName = self.groupNames[groupNo]\n\t\t\t\t\thomeaway = \"\"\n\t\t\t\t\ttry:\n\t\t\t\t\t\thomeaway += \" Home: \" + indigo.variables[\"Unifi_Count_\"+gName+\"_Home\"].value\n\t\t\t\t\t\thomeaway += \"; away: \" + indigo.variables[\"Unifi_Count_\"+gName+\"_Away\"].value\n\t\t\t\t\texcept: pass\n\t\t\t\t\tout += \"\\n {:15s} {} \".format(gName, homeaway+xList.strip(\",\"))\n\t\t\tout += \"\\nGROUPS----- -------MEMBERS --------------- END\"\n\n\t\t\tout += \"\\n\"\n\n\n\t\t\txList = \"\\n-------MEMBERS ---------------- MAC# \\n \"\n\t\t\tlineNumber =0\n\t\t\tfor member in sorted(self.MACignorelist):\n\t\t\t\txList += member+\", \"\n\t\t\t\tif len(xList)/180 > lineNumber:\n\t\t\t\t\tlineNumber +=1\n\t\t\t\t\txList +=\"\\n \"\n\t\t\tout += \"\\nIGNORED ----- {}\".format(xList.strip(\",\"))\n\t\t\tout += \"\\nIGNORED ----- -------MEMBERS ----------------- END\\n\"\n\t\t\tself.indiLOG.log(20, out )\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\n####-------------------------------------------------------------------------####\n\tdef resetHostsFileCALLBACKmenu(self, valuesDict=None, typeId=\"\", devId=0):\n\t\tif valuesDict is None: valuesDict = {}\n\t\tfn = \"{}/.ssh/known_hosts\".format(self.MAChome)\n\n\t\tif os.path.isfile(fn):\n\t\t\tos.remove(fn)\n\n\t\tif not os.path.isfile(fn):\n\t\t\tvaluesDict[\"MSG\"] = \"{} file deleted\".format(fn)\n\t\t\tself.indiLOG.log(30,\"ssh known hosts file deleted:{}\".format(fn))\n\n\t\telse:\n\t\t\tvaluesDict[\"MSG\"] = \"ERROR {} file NOT deleted\".format(fn)\n\t\t\tself.indiLOG.log(30,\"Error ssh known hosts file NOT deleted:{}\".format(fn))\n\n\t\treturn valuesDict\n\n\n####-------------------------------------------------------------------------####\n\tdef resetHostsFileOnlyUnifiCALLBACKmenu(self, valuesDict=None, typeId=\"\", devId=0):\n\t\tif valuesDict is None: valuesDict = {}\n\t\tfn = \"{}/.ssh/known_hosts\".format(self.MAChome)\n\t\tremoved = \"\"\n\n\t\tipList = self.ipNumbersOf[\"AP\"] + self.ipNumbersOf[\"SW\"] + [self.ipNumbersOf[\"GW\"] ] +[self.ipNumbersOf[\"UD\"]]\n\t\tif os.path.isfile(fn):\n\t\t\ttry:\n\t\t\t\tfor ipN in ipList:\n\t\t\t\t\tif self.isValidIP(ipN):\n\t\t\t\t\t\tf = open(fn, \"r\")\n\t\t\t\t\t\tlines = f.readlines()\n\t\t\t\t\t\tf.close()\n\n\t\t\t\t\t\tf = open(fn, \"w\")\n\t\t\t\t\t\tfor line in lines:\n\t\t\t\t\t\t\tif len(line) < 10: continue\n\t\t\t\t\t\t\tif line.find(ipN) >-1:\n\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"ssh known_hosts: removed line:{}\".format(line.strip(\"\\n\")))\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tf.write(line.strip(\"\\n\")+\"\\n\")\n\t\t\t\t\t\tf.close()\n\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\tvaluesDict[\"MSG\"] = \"rmved IPNs entries, see logfile\"\n\n\t\treturn valuesDict\n\n\n\n\t####----------------- data stats menu items\t---------\n\tdef buttonRestartVDListenerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.restartRequest[\"VDtail\"] = \"VD\"\n\t\treturn valuesDict\n\n\tdef buttonRestartGWListenerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.restartRequest[\"GWtail\"] = \"GW\"\n\t\tself.restartRequest[\"GWdict\"] = \"GW\"\n\t\tself.indiLOG.log(10,\"menu RestartGWListener:{}\".format(self.restartRequest))\n\t\treturn valuesDict\n\n\n\tdef buttonRestartAPListenerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tif valuesDict[\"pickAP\"] != \"-1\":\n\t\t\tself.restartRequest[\"APtail\"] = valuesDict[\"pickAP\"]\n\t\t\tself.restartRequest[\"APdict\"] = valuesDict[\"pickAP\"]\n\t\tself.indiLOG.log(10,\"menu RestartAPListener:{}\".format(self.restartRequest))\n\t\treturn valuesDict\n\n\n\tdef buttonRestartAPProcessCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tif valuesDict[\"pickAP\"] != \"-1\":\n\t\t\tself.restartRequest[\"APtail\"] = valuesDict[\"pickAP\"] + \"-restart\"\n\t\t\tself.restartRequest[\"APdict\"] = valuesDict[\"pickAP\"] + \"-restart\"\n\t\tself.indiLOG.log(10,\"menu Restart AP :{}\".format(self.restartRequest))\n\t\treturn valuesDict\n\n\tdef buttonRestartSWListenerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tif valuesDict[\"pickSW\"] != \"-1\":\n\t\t\tself.restartRequest[\"SWdict\"] = valuesDict[\"pickSW\"]\n\t\tself.indiLOG.log(10,\"menu Restart SW Listener:{}\".format(self.restartRequest))\n\t\treturn valuesDict\n\n\tdef buttonRestartSWProcessCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tif valuesDict[\"pickAP\"] != \"-1\":\n\t\t\tself.restartRequest[\"APtail\"] = valuesDict[\"pickAP\"] + \"-restart\"\n\t\t\tself.restartRequest[\"APdict\"] = valuesDict[\"pickAP\"] + \"-restart\"\n\t\tself.indiLOG.log(10,\"menu Restart SW :{}\".format(self.restartRequest))\n\t\treturn valuesDict\n\n\tdef buttonResetPromptsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.connectParams[\"promptOnServer\"] = {}\n\t\tself.pluginPrefs[\"connectParams\"] = json.dumps(self.connectParams)\n\t\tindigo.server.savePluginPrefs()\t\n\t\tself.quitNOW = \"restart due to prompt settings reset\"\n\t\tself.indiLOG.log(30,\" reset prompts, initating restart\")\n\t\treturn valuesDict\n\n\tdef buttonstopVideoServiceCALLBACKaction(self, valuesDict):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"service unifi-video stop\\\"\")\n\t\treturn valuesDict\n\n\tdef buttonstopVideoServiceCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"service unifi-video stop\\\"\")\n\t\treturn valuesDict\n\n\tdef buttonstartVideoServiceCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"service unifi-video start\\\"\")\n\t\treturn valuesDict\n\tdef buttonstartVideoServiceCALLBACKaction(self, valuesDict):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"service unifi-video start\\\"\")\n\t\treturn valuesDict\n\n\tdef buttonMountOSXDriveOnVboxCALLBACKaction(self, valuesDict):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"sudo mount -t vboxsf -o uid=unifi-video,gid=unifi-video video \"+self.mountPathVM+\"\\\"\")\n\t\treturn valuesDict\n\n\tdef buttonMountOSXDriveOnVboxCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.execVideoAction(\" \\\"sudo mount -t vboxsf -o uid=unifi-video,gid=unifi-video video \"+self.mountPathVM+\"\\\"\", returnCmd=returnCmd)\n\t\treturn valuesDict\n\n\tdef execVideoAction(self,cmdIN,returnCmd=False):\n\t\treturn\n\t\ttry:\n\t\t\tuType = \"VDdict\"\n\t\t\tuserid, passwd = self.getUidPasswd(uType,self.ipNumbersOf[\"VD\"])\n\t\t\tif userid == \"\":\n\t\t\t\tself.indiLOG.log(10,\"CameraInfo Video Action : userid not set\")\n\t\t\t\treturn \"\"\n\n\t\t\tif self.ipNumbersOf[\"VD\"] not in self.connectParams[\"promptOnServer\"]:\n\t\t\t\tself.testServerIfOK(self.ipNumbersOf[\"VD\"],uType)\n\n\t\t\tcmd = self.expectPath +\" \"\n\t\t\tcmd +=\t\"'\" + self.pathToPlugin + \"videoServerAction.exp' \"\n\t\t\tcmd +=\t\" '\" +userid + \"' '\" + passwd + \"' \" \n\t\t\tcmd +=\tself.ipNumbersOf[\"VD\"] + \" \" \n\t\t\tcmd +=\t\"'\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][self.ipNumbersOf[\"VD\"]])+\"' \" \n\t\t\tcmd += cmdIN\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CameraInfo \"+ cmd)\n\t\t\tcmd += self.getHostFileCheck()\n\n\t\t\tif returnCmd: return cmd\n\n\t\t\tret, err = self.readPopen(cmd)\n\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tself.indiLOG.log(40,\"promptOnServer={}\".format(self.connectParams[\"promptOnServer\"]))\n\t\t\t\tself.indiLOG.log(40,\"ipNumbersOf={}\".format(self.ipNumbersOf[\"VD\"]))\n\t\t\t\tself.indiLOG.log(40,\"userid:{}, passwd:{}\".format(userid, passwd ))\n\n\t\treturn \"\"\n\n\t####-----------------\t ---------\n\t####-----send commd parameters to cameras through VNR ------\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRLEDCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRLEDCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRLEDCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"enableStatusLed\":valuesDict[\"camLED\"] == \"1\"})\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRSoundsCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRSoundsCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRSoundsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"systemSoundsEnabled\":valuesDict[\"camSounds\"] == \"1\"} )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRenableSpeakerCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRenableSpeakerCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRenableSpeakerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"enableSpeaker\":valuesDict[\"enableSpeaker\"] == \"1\", \"speakerVolume\":int(valuesDict[\"enableSpeaker\"])} )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRmicVolumeCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRmicVolumeCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRmicVolumeCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tvaluesDict[\"MSG\"] = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"micVolume\":int(valuesDict[\"micVolume\"])} )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRRecordCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToNVRRecordCALLBACK(valuesDict= action1.props)\n\n\tdef buttonSendCommandToNVRRecordCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tif valuesDict[\"postPaddingSecs\"] ==\"-1\" and valuesDict[\"prePaddingSecs\"] ==\"-1\":\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],\n\t\t\t\t\t{\"recordingSettings\":{\"motionRecordEnabled\": valuesDict[\"motionRecordEnabled\"] == \"1\",\"fullTimeRecordEnabled\": valuesDict[\"fullTimeRecordEnabled\"] == \"1\", 'channel': valuesDict[\"channel\"]}\n\t\t\t\t\t} )\n\t\telif valuesDict[\"postPaddingSecs\"] !=\"-1\" and valuesDict[\"prePaddingSecs\"] !=\"-1\":\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],\n\t\t\t\t\t{\"recordingSettings\":{\"motionRecordEnabled\": valuesDict[\"motionRecordEnabled\"] == \"1\",\"fullTimeRecordEnabled\": valuesDict[\"fullTimeRecordEnabled\"] == \"1\", 'channel': valuesDict[\"channel\"],\n\t\t\t\t\t\"postPaddingSecs\": int(valuesDict[\"postPaddingSecs\"]),\n\t\t\t\t\t\"prePaddingSecs\": int(valuesDict[\"prePaddingSecs\"]) }\n\t\t\t\t\t} )\n\t\telif valuesDict[\"postPaddingSecs\"] !=\"-1\":\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],\n\t\t\t\t\t{\"recordingSettings\":{\"motionRecordEnabled\": valuesDict[\"motionRecordEnabled\"] == \"1\",\"fullTimeRecordEnabled\": valuesDict[\"fullTimeRecordEnabled\"] == \"1\", 'channel': valuesDict[\"channel\"],\n\t\t\t\t\t\"postPaddingSecs\": int(valuesDict[\"postPaddingSecs\"]) }\n\t\t\t\t\t} )\n\t\telif valuesDict[\"prePaddingSecs\"] !=\"-1\":\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],\n\t\t\t\t\t{\"recordingSettings\":{\"motionRecordEnabled\": valuesDict[\"motionRecordEnabled\"] == \"1\",\"fullTimeRecordEnabled\": valuesDict[\"fullTimeRecordEnabled\"] == \"1\", 'channel': valuesDict[\"channel\"],\n\t\t\t\t\t\"prePaddingSecs\": int(valuesDict[\"prePaddingSecs\"]) }\n\t\t\t\t\t} )\n\t\telse:\n\t\t\tvaluesDict[\"MSG\"]=\"bad selection for recording\"\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRIRCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRIRCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRIRCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\txxx = valuesDict[\"irLedMode\"]\n\t\tif xxx.find(\"auto\") >-1:\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"ispSettings\":{\"enableExternalIr\": int(valuesDict[\"enableExternalIr\"]),\"irLedMode\":\"auto\" }} )\n\t\telse:# for manual 0/100/255 level\n\t\t\txxx = xxx.split(\"-\")\n\t\t\tvaluesDict[\"MSG\"],x = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],{\"ispSettings\":{\"enableExternalIr\": int(valuesDict[\"enableExternalIr\"]),\"irLedMode\":xxx[0], \"irLedLevel\": int(xxx[1])} } )\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRvideostreamingCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRIRCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRvideostreamingCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\n\t\t# first we need to get the current values\n\t\terror, ret = self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"],\"\", cmdType=\"get\")\n\t\tif \"channels\" not in ret[0] or len(ret[0][\"channels\"]) !=3 : # something went wrong\n\t\t\tself.indiLOG.log(40,\"videostreaming error: {} \\n>>{}<<\".format(error, ret))\n\t\t\tvaluesDict[\"MSG\"] = error\n\t\t\treturn valuesDict\n\n\t\t# modify the required ones\n\t\tchannels = ret[0][\"channels\"]\n\t\tchannel\t = int(valuesDict[\"channelS\"])\n\t\tchannels[channel][\"isRtmpEnabled\"]\t = valuesDict[\"isRtmpEnabled\"] == \"1\"\n\t\tchannels[channel][\"isRtmpsEnabled\"] = valuesDict[\"isRtmpsEnabled\"] == \"1\"\n\t\tchannels[channel][\"isRtspEnabled\"] = valuesDict[\"isRtspEnabled\"] == \"1\"\n\t\t# send back\n\t\terror, data= self.setupNVRcmd(valuesDict[\"cameraDeviceSelected\"], {\"channels\":channels}, cmdType=\"put\")\n\t\tvaluesDict[\"MSG\"]=error\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToNVRgetSnapshotCALLBACKaction (self, action1=None):\n\t\treturn\n\t\treturn self.buttonSendCommandToNVRgetSnapshotCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToNVRgetSnapshotCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\treturn\n\t\tself.addToMenuXML(valuesDict)\n\t\tif self.imageSourceForSnapShot == \"imageFromNVR\": \tvaluesDict[\"MSG\"] = self.getSnapshotfromNVR(valuesDict[\"cameraDeviceSelected\"], valuesDict[\"widthOfImage\"], valuesDict[\"fileNameOfImage\"] )\n\t\telif self.imageSourceForSnapShot == \"imageFromCamera\":\tvaluesDict[\"MSG\"] = self.getSnapshotfromCamera(valuesDict[\"cameraDeviceSelected\"], valuesDict[\"fileNameOfImage\"] )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef setupNVRcmd(self, devId, payload,cmdType=\"put\"):\n\t\treturn\n\n\t\tdev = indigo.devices[int(devId)]\n\t\ttry:\n\t\t\tif not self.isValidIP(self.ipNumbersOf[\"VD\"]): return \"error IP\",\"\"\n\t\t\tif self.cameraSystem != \"nvr\":\t\t\t\t\t \treturn \"error enabled\",\"\"\n\t\t\tif len(self.nvrVIDEOapiKey) < 5:\t\t\t\treturn \"error apikey\",\"\"\n\n\t\t\tif payload != \"\": payload['name']= dev.states[\"nameOnNVR\"]\n\t\t\tret = self.executeCMDonNVR(payload, dev.states[\"apiKey\"], cmdType=cmdType)\n\t\t\tself.fillCamerasIntoIndigo(ret, calledFrom=\"setupNVRcmd\")\n\t\t\treturn \"ok\",ret\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\t####-----------------\t ---------\n\tdef executeCMDonNVR(self, data, cameraKey,\tcmdType=\"put\"):\n\t\treturn\n\n\t\ttry:\n\t\t\tif cameraKey !=\"\":\n\t\t\t\turl = \"https://\"+self.ipNumbersOf[\"VD\"]+ \":7443/api/2.0/camera/\"+ cameraKey+ \"?apiKey=\" + self.nvrVIDEOapiKey\n\n\t\t\telse:\n\t\t\t\turl = \"https://\"+self.ipNumbersOf[\"VD\"]+ \":7443/api/2.0/camera/\"+\"?apiKey=\" + self.nvrVIDEOapiKey\n\n\t\t\tif self.requestOrcurl.find(\"curl\") > -1:\n\t\t\t\tcmdL = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -c /tmp/nvrCookie --data '\"+json.dumps({\"username\":self.connectParams[\"UserID\"][\"nvrWeb\"],\"password\":self.connectParams[\"PassWd\"][\"nvrWeb\"]})+\"' 'https://\"+self.ipNumbersOf[\"VD\"]+\":7443/api/login'\"\n\t\t\t\tif data =={} or data ==\"\": dataDict = \"\"\n\t\t\t\telse:\t\t\t\t\t dataDict = \" --data '\"+json.dumps(data)+\"' \"\n\t\t\t\tif\t cmdType == \"put\":\t cmdTypeUse= \" -X PUT \"\n\t\t\t\telif cmdType == \"post\": cmdTypeUse= \" -X post \"\n\t\t\t\telif cmdType == \"get\":\t cmdTypeUse= \" \"\n\t\t\t\telse:\t\t\t\t\t cmdTypeUse= \" \"\n\t\t\t\tcmdR = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -b /tmp/nvrCookie --header \\\"Content-Type: application/json\\\" \"+cmdTypeUse + dataDict + \"'\" +url+ \"'\"\n\n\t\t\t\ttry:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif time.time() - self.lastNVRCookie > 100: # re-login every 90 secs\n\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video cmd \"+ cmdL )\n\t\t\t\t\t\t\tret, err = self.readPopen(cmdL)\n\n\t\t\t\t\t\t\tif err.find(\"error\") >-1:\n\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"error: (wrong UID/passwd, ip number?) ...>>{}<<\\n{}<< Video error\".format(ret, err))\n\t\t\t\t\t\t\t\treturn {}\n\t\t\t\t\t\t\tself.lastNVRCookie =time.time()\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video {}\".format(cmdR) )\n\t\t\t\t\t\tret, err = self.readPopen(cmdR)\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tjj = json.loads(ret)\n\t\t\t\t\t\texcept :\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"UNIFI Video error executeCMDonNVR has error, no json object returned: {} \\n{}\".format(ret, err) )\n\t\t\t\t\t\t\treturn []\n\t\t\t\t\t\tif \"rc\" in jj[\"meta\"] and \"{}\".format(jj[\"meta\"][\"rc\"]).find(\"error\")>-1:\n\t\t\t\t\t\t\tself.indiLOG.log(40,\"error: data:>>{}<<\\n>>{}<<\\n\" +\" UNIFI Video error\".format(ret, err))\n\t\t\t\t\t\t\treturn []\n\t\t\t\t\t\telif self.decideMyLog(\"Video\"):\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"UNIFI Video executeCMDonNV- camera Data:\\n\" +json.dumps(jj[\"data\"], sort_keys=True, indent=2) )\n\n\t\t\t\t\t\treturn jj[\"data\"]\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\t\t#############does not work on OSX el capitan ssl lib too old ##########\n\t\t\telif self.requestOrcurl ==\"requests\":\n\t\t\t\tif self.unifiNVRSession ==\"\" or (time.time() - self.lastNVRCookie) > 300:\n\t\t\t\t\tself.unifiNVRSession = requests.Session()\n\t\t\t\t\turlLogin = \"https://\"+self.ipNumbersOf[\"VD\"]+\":7443/api/login\"\n\t\t\t\t\tdataLogin = json.dumps({\"username\":self.connectParams[\"UserID\"][\"nvrWeb\"],\"password\":self.connectParams[\"PassWd\"][\"nvrWeb\"]})\n\t\t\t\t\tresp = self.unifiNVRSession.post(urlLogin, data = dataLogin, verify=False)\n\t\t\t\t\tself.lastNVRCookie =time.time()\n\n\n\t\t\t\tif data == {}: dataDict = \"\"\n\t\t\t\telse:\t\t dataDict = json.dumps(data)\n\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video executeCMDonNVR cmdType: \"+cmdType+\"; url: \"+url +\"; dataDict: \"+ dataDict+\"<<\")\n\t\t\t\ttry:\n\t\t\t\t\t\tif\t cmdType == \"put\":\t resp = self.unifiNVRSession.put(url,data = dataDict, headers={'content-type': 'application/json'})\n\t\t\t\t\t\telif cmdType == \"post\": resp = self.unifiNVRSession.post(url,data = dataDict, headers={'content-type': 'application/json'})\n\t\t\t\t\t\telif cmdType == \"get\":\t resp = self.unifiNVRSession.get(url,data = dataDict)\n\t\t\t\t\t\telse:\t\t\t\t\t resp = self.unifiNVRSession.get(url,data = dataDict)\n\t\t\t\t\t\tresponse = resp.text.decode(\"utf8\")\n\t\t\t\t\t\tjj = json.loads(response)\n\t\t\t\t\t\tif \"rc\" in jj[\"meta\"] and \"{}\".format(jj[\"meta\"][\"rc\"]).find(\"error\") >-1:\n\t\t\t\t\t\t\tself.indiLOG.log(40,\"executeCMDonNVR requests error: >>{}<<>>{}\".format(resp.status_code, response) )\n\t\t\t\t\t\t\treturn []\n\t\t\t\t\t\telif self.decideMyLog(\"Video\"):\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"Video executeCMDonNVR requests {}\".format(response) )\n\t\t\t\t\t\treturn jj[\"data\"]\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn []\n\n\n\n\t####----------------- VBOX ACTIONS\t ---------\n\tdef execVboxAction(self,action,action2=\"\"):\n\t\treturn\n\t\ttestCMD = \"ps -ef | grep '/vboxAction.py ' | grep -v grep\"\n\t\tret, err = self.readPopen(testCMD)\n\n\t\tif len(ret) > 10:\n\t\t\ttry: self.indiLOG.log(10,\"CameraInfo VBOXAction: still runing, not executing: {} {}\".format(action, action2) )\n\t\t\texcept:self.indiLOG.log(10,\"CameraInfo VBOXAction: still runing, not executing: \")\n\t\t\treturn False\n\t\tcmd = self.pythonPath + \" \\\"\" + self.pathToPlugin + \"vboxAction.py\\\" '\"+action+\"'\"\n\t\tif action2 !=\"\":\n\t\t\tcmd += \" '\"+action2+\"'\"\n\t\tcmd +=\" &\"\n\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"CameraInfo VBOXAction: \"+cmd )\n\t\tret, err = self.readPopen(cmd)\n\t\treturn\n\n\t####----------------- Stop ---------\n\tdef buttonVboxActionStopCALLBACKaction(self, action1=None):\n\t\treturn\n\t\tself.buttonVboxActionStopCALLBACK(valuesDict= action1.props)\n\tdef buttonVboxActionStopCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tcmd = json.dumps({\"action\":[\"stop\"], \"vmMachine\":self.vmMachine, \"vboxPath\":self.vboxPath, \"logfile\":self.PluginLogFile})\n\t\tif not self.execVboxAction(cmd): return\n\t\tip = self.ipNumbersOf[\"VD\"]\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif ip == dev.states[\"ipNumber\"]:\n\t\t\t\tself.setSuspend(ip,time.time()+1000000000)\n\t\t\t\tbreak\n\t\treturn valuesDict\n\n\n\t####----------------- Start\t---------\n\tdef buttonVboxActionStartCALLBACKaction(self, action1=None):\n\t\treturn\n\t\tself.buttonVboxActionStartCALLBACK(valuesDict= action1.props)\n\tdef buttonVboxActionStartCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tcmd = {\"action\":[\"start\",\"mountDisk\"], \"vmMachine\":self.vmMachine, \"vboxPath\":self.vboxPath, \"logfile\":self.PluginLogFile,\"vmDisk\":self.vmDisk }\n\t\tmountCmd = self.buttonMountOSXDriveOnVboxCALLBACK(returnCmd=True)\n\t\tself.execVboxAction(json.dumps(cmd),action2=json.dumps(mountCmd))\n\t\treturn valuesDict\n\n\t####----------------- compress ---------\n\tdef buttonVboxActionCompressCALLBACKaction(self, action1=None):\n\t\treturn\n\t\tself.buttonVboxActionCompressCALLBACK(valuesDict= action1.props)\n\tdef buttonVboxActionCompressCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tcmd = {\"action\":[\"stop\",\"compress\",\"start\",\"mountDisk\"], \"vmMachine\":self.vmMachine, \"vboxPath\":self.vboxPath, \"logfile\":self.PluginLogFile,\"vmDisk\":self.vmDisk }\n\t\tmountCmd = self.buttonMountOSXDriveOnVboxCALLBACK(returnCmd=True)\n\t\tif not self.execVboxAction(json.dumps(cmd),action2=json.dumps(mountCmd)): return\n\t\tip = self.ipNumbersOf[\"VD\"]\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif ip == dev.states[\"ipNumber\"]:\n\t\t\t\tself.setSuspend(ip, time.time()+300.)\n\t\t\t\tbreak\n\t\treturn valuesDict\n\n\t####----------------- backup\t ---------\n\tdef buttonVboxActionBackupCALLBACKaction(self, action1=None):\n\t\treturn\n\t\tself.buttonVboxActionBackupCALLBACK(valuesDict= action1.props)\n\tdef buttonVboxActionBackupCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn\n\t\tcmd = {\"action\":[\"stop\",\"backup\",\"start\",\"mountDisk\"], \"vmMachine\":self.vmMachine, \"vboxPath\":self.vboxPath, \"logfile\":self.PluginLogFile,\"vmDisk\":self.vmDisk }\n\t\tmountCmd = self.buttonMountOSXDriveOnVboxCALLBACK(returnCmd=True)\n\t\tif not self.execVboxAction(json.dumps(cmd),action2=json.dumps(mountCmd)): return\n\t\tip = self.ipNumbersOf[\"VD\"]\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif ip == dev.states[\"ipNumber\"]:\n\t\t\t\tself.setSuspend(ip, time.time()+220.)\n\t\t\t\tbreak\n\t\treturn valuesDict\n\n\n\t####----------------- \t---------\n\tdef checkIfPrintProcessingTime(self):\n\t\tif \"today\" \t\tnot in self.waitTimes: return \n\t\tif \"lastPrint\"\tnot in self.waitTimes[\"today\"]: return \n\n\t\tif time.time() - self.waitTimes[\"today\"][\"lastPrint\"] > 6*60*60:\n\t\t\tself.buttonPrintWaitStatsCALLBACK(hourlyReport=True)\n\t\t\tself.waitTimes[\"today\"][\"lastPrint\"] = time.time() \n\n\t\t#reset at midnight\n\t\tif datetime.datetime.now().hour == 0 and datetime.datetime.now().minute == 0:\n\t\t\tif time.time() - self.waitTimes[\"today\"][\"startTime\"] > 65:\n\t\t\t\tself.buttonZeroWaitStatsCALLBACK()\n\n\n\t####----------------- ---------\n\tdef buttonZeroWaitStatsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.waitTimes[\"yesterday\"] = copy.copy(self.waitTimes[\"today\"] ) \n\t\tself.waitTimes[\"today\"] = {}\n\t\tself.saveDataStats(force=True)\n\t\treturn valuesDict\n\n\n\t####----------------- data stats menu items\t---------\n\tdef buttonPrintWaitStatsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",hourlyReport= False):\n\t\ttry:\n\t\t\tif \"yesterday\" not in self.waitTimes: return \n\t\t\ttrailer = \"================================================ END ================================================\"\n\t\t\tout = \"\\n\"\n\t\t\tfor dd in [\"today\", \"yesterday\"]:\n\t\t\t\tif dd not in self.waitTimes:\t\t\t\tcontinue\n\t\t\t\tif \"startDate\" not in self.waitTimes[dd]:\tcontinue\n\t\t\t\tif self.waitTimes[dd][\"startDate\"] == \"\":\tcontinue\n\t\t\t\tout += \"Wait times[secs] during processing of incoming data, from {} to {} - {}\\n\".format(self.waitTimes[dd][\"startDate\"], self.waitTimes[dd][\"endDate\"], dd)\n\t\t\t\tfor waitORbusy in [\"Waiting\", \"Blocking\"]:\n\t\t\t\t\tytag = waitORbusy+\"Pgm\"\n\t\t\t\t\tout += \"Pgm Module {:8}--- nMeasuremts Tot{:4}Time Ave{:4} >.1 >.5 >1 >3 >6 >12 >20 maxWait\\n\".format(waitORbusy,waitORbusy[:4],waitORbusy[:4])\n\t\t\t\t\tfor tag in sorted(self.waitTimes[dd][ytag]):\n\t\t\t\t\t\tif tag == \"---TOTAL----\": continue\n\t\t\t\t\t\txx = self.waitTimes[dd][ytag][tag]\n\t\t\t\t\t\tavWait = xx[\"tot\"] / max(1, xx[\"n\"])\n\t\t\t\t\t\tout += \"{:21s} {:11}{:13.3f} {:7.3f} {:5}{:5}{:5}{:5}{:5}{:5}{:5} {:8.1f}\\n\".format(tag, xx[\"n\"], xx[\"tot\"], avWait, xx[\".1\"], xx[\".5\"], xx[\"1\"], xx[\"3\"], xx[\"6\"], xx[\"12\"],xx[\"20\"], xx[\"max\"] )\n\n\t\t\t\t\ttag = \"---TOTAL----\"\n\t\t\t\t\txx = self.waitTimes[dd][ytag][tag]\n\t\t\t\t\tavWait = xx[\"tot\"] / max(1, xx[\"n\"])\n\t\t\t\t\tout += \"{:21s} {:11}{:13.3f} {:7.3f} {:5}{:5}{:5}{:5}{:5}{:5}{:5} {:8.1f}\\n\".format(tag, xx[\"n\"], xx[\"tot\"], avWait, xx[\".1\"], xx[\".5\"], xx[\"1\"], xx[\"3\"], xx[\"6\"], xx[\"12\"],xx[\"20\"], xx[\"max\"] )\n\t\t\t\tout += \"N-pgms Blocking > 1={}, max blocking pgms={} (several pgms waiting in sequence)\\n\\n\".format(self.waitTimes[dd][\"QlenGT1\"], self.waitTimes[dd][\"QlenMax\"] )\n\n\t\t\tif hourlyReport:\n\t\t\t\tself.indiLOG.log(10, out+trailer)\n\t\t\telse:\n\t\t\t\tself.indiLOG.log(20, out+trailer)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn valuesDict\n\n\n\t####----------------- data stats menu items\t---------\n\tdef buttonPrintStatsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.buttonPrintTcpipStats()\n\t\tself.printUpdateStats()\n\t\tvaluesDict[\"MSG\"] = \"check logfile for output\"\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonPrintTcpipStats(self):\n\n\t\ttry:\n\t\t\tif len(self.dataStats[\"tcpip\"]) == 0: return\n\t\t\tnMin\t= 0\n\t\t\tnSecs\t= 0\n\t\t\ttotByte = 0\n\t\t\ttotMsg\t= 0\n\t\t\ttotErr\t= 0\n\t\t\ttotRes\t= 0\n\t\t\ttotAli\t= 0\n\t\t\tout\t\t= \"\"\n\t\t\tout = \"\\n\"\n\t\t\tfor uType in sorted(self.dataStats[\"tcpip\"].keys()):\n\t\t\t\tfor ipNumber in sorted(self.dataStats[\"tcpip\"][uType].keys()):\n\t\t\t\t\tif nSecs ==0:\n\t\t\t\t\t\tout += \"\\n=== data stats for received messages ==== collection started at {}\".format( time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(self.dataStats[\"tcpip\"][uType][ipNumber][\"startTime\"])) )\n\t\t\t\t\t\tout += \"\\ndev type ipNumber msgcount; msgBytes; errCount; restarts;aliveCount; msg/min; bytes/min; err/min; aliveC/min\"\n\t\t\t\t\tnSecs = time.time() - self.dataStats[\"tcpip\"][uType][ipNumber][\"startTime\"]\n\t\t\t\t\tnMin = nSecs/60.\n\t\t\t\t\toutx = ipNumber.ljust(18)\n\t\t\t\t\toutx += \"{:10d};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageCount\"])\n\t\t\t\t\toutx += \"{:13d};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageBytes\"])\n\t\t\t\t\toutx += \"{:10d};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorCount\"])\n\t\t\t\t\toutx += \"{:10d};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"restarts\"])\n\t\t\t\t\toutx += \"{:10d};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"aliveTestCount\"]) \n\t\t\t\t\toutx += \"{:10.3f};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageCount\"]/nMin) \n\t\t\t\t\toutx += \"{:10.1f};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageBytes\"]/nMin)\n\t\t\t\t\toutx += \"{:10.7f};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorCount\"]/nMin)\n\t\t\t\t\toutx += \"{:10.3f};\".format(self.dataStats[\"tcpip\"][uType][ipNumber][\"aliveTestCount\"]/nMin) \n\t\t\t\t\ttotByte += self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageBytes\"]\n\t\t\t\t\ttotMsg\t+= self.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageCount\"]\n\t\t\t\t\ttotErr\t+= self.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorCount\"]\n\t\t\t\t\ttotRes\t+= self.dataStats[\"tcpip\"][uType][ipNumber][\"restarts\"]\n\t\t\t\t\ttotAli\t+= self.dataStats[\"tcpip\"][uType][ipNumber][\"aliveTestCount\"]\n\n\t\t\t\t\tout += \"\\n {}-{:4s} {}\".format(uType, self.dataStats[\"tcpip\"][uType][ipNumber][\"APN\"], outx)\n\t\t\tout += \"\\nT O T A L S total {:10d};{:13d};{:10d};{:10d};{:10d};{:10.3f};{:10.1f};{:10.7f};{:10.3f}\".format(totMsg, totByte, totErr, totRes, totAli, totMsg/nMin, totByte/nMin, totErr/nMin, totAli/nMin)\n\t\t\tout += \"\\ndata stats === END=== total time measured: {:10.0f}[s] = {:s} \".format( nSecs/(24*60*60) ,time.strftime(\"%H:%M:%S\", time.gmtime(nSecs)) ) \n\t\t\tself.indiLOG.log(20, out )\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef printUpdateStats(self):\n\t\ttry:\n\t\t\tif len(self.dataStats[\"updates\"]) == 0: return\n\t\t\tnSecs = max(1,(time.time()-\t self.dataStats[\"updates\"][\"startTime\"]))\n\t\t\tnMin = nSecs/60.\n\t\t\tout = \"\\n\"\n\t\t\tout +=\"\\nindigo update stats === === measuring started at: {}\" .format( time.strftime(\"%H:%M:%S\",time.localtime(self.dataStats[\"updates\"][\"startTime\"])) )\n\t\t\tout +=\"\\n device updates: {:10d}; updates/sec: {:10.2f}; updates/minute: {:10.2f}\".format( self.dataStats[\"updates\"][\"devs\"], self.dataStats[\"updates\"][\"devs\"] /nMin, self.dataStats[\"updates\"][\"devs\"] /nSecs )\n\t\t\tout +=\"\\n device updates: {:10d}; updates/sec: {:10.2f}; updates/minute: {:10.2f}\".format( self.dataStats[\"updates\"][\"states\"], self.dataStats[\"updates\"][\"states\"]/nMin, self.dataStats[\"updates\"][\"states\"]/nSecs )\n\t\t\tout +=\"\\nindigo update stats === END=== total time measured: {:10.0f}[s] = {:s}\".format( nSecs/(24*60*60), time.strftime(\"%H:%M:%S\", time.gmtime(nSecs)) )\n\t\t\tself.indiLOG.log(20,out)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef addToMenuXML(self, valuesDict):\n\t\tif valuesDict:\n\t\t\tfor item in valuesDict:\n\t\t\t\tself.menuXML[item] = copy.copy(valuesDict[item])\n\t\t\tself.pluginPrefs[\"menuXML\"]\t = json.dumps(self.menuXML)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef buttonprintNVRCameraEventsCALLBACK(self,valuesDict, typeId=\"\", devId=\"\"):\n\t\tmaxEvents= int(valuesDict[\"maxEvents\"])\n\t\ttotEvents= 0\n\t\tfor MAC in self.cameras:\n\t\t\ttotEvents += len(self.cameras[MAC][\"events\"])\n\n\t\tself.addToMenuXML(valuesDict)\n\n\t\tout = \"\\n======= Camera Events ======\"\n\t\tout += \"\\nDev MAC dev.id Name \"\n\t\tout += \"\\n Ev# start end dur[secs]\\n\"\n\t\tfor MAC in self.cameras:\n\t\t\tout += MAC+\" {:11d} {} # events total: {}\\n\".format(self.cameras[MAC][\"devid\"], self.cameras[MAC][\"cameraName\"].ljust(25), len(self.cameras[MAC][\"events\"]))\n\t\t\tevList=[]\n\t\t\tfor evNo in self.cameras[MAC][\"events\"]:\n\t\t\t\tdateStart = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(self.cameras[MAC][\"events\"][evNo][\"start\"]))\n\t\t\t\tdateStop = time.strftime(\" .. %H:%M:%S\",time.localtime(self.cameras[MAC][\"events\"][evNo][\"stop\"]))\n\t\t\t\tdelta = self.cameras[MAC][\"events\"][evNo][\"stop\"]\n\t\t\t\tdelta -= self.cameras[MAC][\"events\"][evNo][\"start\"]\n\t\t\t\tevList.append(\" {}\".format(evNo).rjust(10)+\" {:} {:8.1f}\\n\".format(dateStart+dateStop, delta))\n\t\t\tevList= sorted(evList, reverse=True)\n\t\t\tcount =0\n\t\t\tfor o in evList:\n\t\t\t\tcount+=1\n\t\t\t\tif count > maxEvents: break\n\t\t\t\tout += o\n\t\tout += \"====== Camera Events ======; all # events total: \" +\"{}\".format(totEvents) +\"\\n\"\n\n\t\tself.indiLOG.log(20,out )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonresetNVRCameraEventsCALLBACK(self,valuesDict, typeId=\"\", devId=\"\"):\n\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\tdev.updateStateOnServer(\"eventNumber\",0)\n\t\t\tself.indiLOG.log(10,\"reset event number for {}\".format(dev.name) )\n\t\tself.resetCamerasStats()\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\n\n\t####-----------------\t ---------\n\tdef buttonPrintNVRCameraSystemCALLBACK(self,valuesDict, typeId=\"\", devId=\"\"):\n\t\tif self.cameraSystem == \"nvr\":\n\t\t\tself.pendingCommand.append(\"getNVRCamerasFromMongoDB-print\")\n\t\telif self.cameraSystem == \"protect\":\n\t\t\tself.pendingCommand.append(\"getProtectamerasInfo-print\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonrefreshNVRCameraSystemCALLBACK(self,valuesDict, typeId=\"\", devId=\"\"):\n\t\tif self.cameraSystem == \"nvr\":\n\t\t\tself.pendingCommand.append(\"getConfigFromNVR\")\n\t\telif self.cameraSystem == \"protect\":\n\t\t\tself.pendingCommand.append(\"getConfigFromProtect\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef getMongoData(self, cmdstr, uType=\"VDdict\"):\n\t\tret =[\"\",\"\"]\n\t\ttry:\n\t\t\tuserid, passwd = self.getUidPasswd(uType,self.ipNumbersOf[\"VD\"])\n\t\t\tif userid == \"\": return {}\n\n\t\t\tcmd = self.expectPath +\" \"\n\t\t\tcmd += \"'\" + self.pathToPlugin + self.connectParams[\"expectCmdFile\"][uType] + \"' \" \n\t\t\tcmd += \"'\" + userid + \"' '\"+passwd + \"' \" \n\t\t\tcmd += self.ipNumbersOf[\"VD\"] + \" \" \n\t\t\tcmd += \"'\" + self.escapeExpect(self.connectParams[\"promptOnServer\"][self.ipNumbersOf[\"VD\"]]) + \"' \" \n\t\t\tcmd += \" XXXXsepXXXXX \" \n\t\t\tcmd += cmdstr\n\t\t\tcmd += self.getHostFileCheck()\n\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"UNIFI getMongoData cmd \" +cmd )\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\tdbJson, error= self.makeJson(ret, \"XXXXsepXXXXX\")\n\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"UNIFI getMongoData return {}\\n{}\".format(ret, err) )\n\t\t\tif error !=\"\":\n\t\t\t\tself.indiLOG.log(40,\"getMongoData camera system (dump, no json conversion)\tinfo:\\n>>{} {}<<\\n>>{}\".format(error, cmd, ret) )\n\t\t\t\treturn []\n\t\t\treturn\tdbJson\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(40,\" getMongoData error: {}\\n{}\".format(ret[0], ret[1]))\n\t\treturn []\n\n\t####-----------------\t ---------\n\tdef makeJson(self, dumpIN, sep): ## {} separated by \\n\n\t\ttry:\n\t\t\tout =[]\n\t\t\ttemp = \"empty\"\n\t\t\ttemp2 = \"empty\"\n\t\t\tbegStr,endStr =\"{\",\"}\"\n\t\t\tdump\t\t = dumpIN.split(sep)\n\t\t\tlDump = len(dump)\n\t\t\tif lDump <3: return \"\",\"error len(split):{}\".format(lDump)\n\t\t\tif lDump >3:\n\t\t\t\tdump = dump[lDump-3:]\n\t\t\tdump = dump[1].strip(\"\\n\").strip(\"\\r\")\n\t\t\ts1 = dump.find(begStr)\n\t\t\tdump = dump[s1:]\n\t\t\ts2 = dump.rfind(endStr)\n\t\t\tdump = dump[:s2+1].strip(\"\\n\").strip(\"\\r\")\n\t\t\tdumpSplit = dump.split(\"\\n\")\n\t\t\tfor line in dumpSplit:\n\t\t\t\tif len(line) < 5: continue\n\t\t\t\tnnn1 = line.find(begStr)\n\t\t\t\ttemp = line[nnn1:]\n\t\t\t\tnnn2 = temp.rfind(endStr)\n\t\t\t\ttemp = temp[0:nnn2+1]\n\t\t\t\ttemp2\t= self.replaceFunc(temp).strip()\n\t\t\t\tif len(temp2) >2:\n\t\t\t\t\ttry:\n\t\t\t\t\t\to =json.loads(temp2)\n\t\t\t\t\t\tout.append(o)\n\t\t\t\t\texcept:\n\t\t\t\t\t\tself.indiLOG.log(40,\"makeJson error , trying to fix:\\ntemp2>>>>>{}\".format(temp2)+\"<<<<<\\n>>>>{}\".format(dumpIN)+\"<<<<<\" )\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\to=json.loads(temp2+\"}\")\n\t\t\t\t\t\t\tout.append(o)\n\t\t\t\t\t\t\tself.indiLOG.log(40,\"makeJson error fixed \" )\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\treturn out, \"\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tself.indiLOG.log(40,\"makeJson error :\\ndump>>>>{}\".format(dumpIN)+\"<<<<<\" )\n\t\treturn dump, \"error\"\n\t####-----------------\t ---------\n\tdef makeJson2(self, dump, sep):\n\t\ttry:\n\t\t\tout={}\n\t\t\tbegStr,endStr =\"{\",\"}\"\n\t\t\tdump\t\t = dump.split(sep)\n\t\t\tif len(dump) !=3: return \"\"\n\t\t\tdump = dump[1].replace(\"\\n\",\"\").replace(\"\\r\",\"\")\n\t\t\ts1 = dump.find(begStr)\n\t\t\tdump = dump[s1:]\n\t\t\ts2 = dump.rfind(endStr)\n\t\t\tout=json.loads(dump[:s2+1])\n\t\t\treturn out, \"\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t####-----------------\t ---------\n\tdef replaceFunc(self, dump):\n\t\ttry:\n\t\t\tfor ii in range(500): # remove binData(xxxxx)\n\t\t\t\tnn = dump.find(\"BinData(\")\n\t\t\t\tif nn ==-1: break\n\t\t\t\tendst = dump[nn:].find(\")\")\n\t\t\t\tdump = dump[0:nn-1]+'\"xxx\"'+ dump[nn+endst+1:]\n\n\t\t\tfor kk in range(1000):\t# loop over func Names, max 30\n\t\t\t\tss = 0\n\t\t\t\tfor ll in range(100): # remove \" (xxx) from targest only abc(xx)\n\t\t\t\t\tnn = dump[ss:].find(\"(\")\n\t\t\t\t\tif nn ==-1: break\n\t\t\t\t\tif dump[ss+nn-1] != \" \":\n\t\t\t\t\t\tnn+=ss\n\t\t\t\t\t\tbreak\n\t\t\t\t\tss = nn+1\n\n\n\t\t\t\tif nn ==-1: break\n\t\t\t\tstartSt= dump[0:nn].rfind(\" \")\n\t\t\t\treplString= dump[startSt+1:nn+1]\n\t\t\t\tlenrepString = len(replString)\n\t\t\t\tfor ii in range(100): # loop of all occurance of func replacements\n\t\t\t\t\tnn = dump.find(replString)\n\t\t\t\t\tif nn == -1: break\n\t\t\t\t\tpp = dump[nn:].find(\")\")\n\t\t\t\t\tdump = dump[0:nn] + dump[nn+lenrepString:nn+pp] + dump[nn+pp+1:]\n\t\t\treturn dump\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \"\"\n\n\t####-----------------\t ---------\n\tdef buttonZeroStatsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.zeroDataStats()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonResetStatsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.resetDataStats(calledFrom=\"buttonResetStatsCALLBACK\")\n\t\treturn valuesDict\n\n\t####----------------- reboot unifi device\t ---------\n\n\t####-----------------\t ---------\n\tdef filterUnifiDevices(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor ll in range(_GlobalConst_numberOfAP):\n\t\t\tif self.devsEnabled[\"AP\"][ll]:\n\t\t\t\txlist.append((self.ipNumbersOf[\"AP\"][ll]+\"-APdict\",\"AP -\"+self.ipNumbersOf[\"AP\"][ll]))\n\t\tfor ll in range(_GlobalConst_numberOfSW):\n\t\t\tif self.devsEnabled[\"SW\"][ll]:\n\t\t\t\txlist.append((self.ipNumbersOf[\"SW\"][ll]+\"-SWtail\",\"SW -\"+self.ipNumbersOf[\"SW\"][ll]))\n\t\tif self.devsEnabled[\"GW\"]:\n\t\t\t\txlist.append((self.ipNumbersOf[\"GW\"]+\"-GWtail\",\"GW -\"+self.ipNumbersOf[\"GW\"]))\n\t\treturn xlist\n\n\t####-----------------\t ---------\n\tdef buttonConfirmrebootCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmrebootCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmrebootCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tip_type\t =\tvaluesDict[\"rebootUNIFIdeviceSelected\"].split(\"-\")\n\t\tipNumber = ip_type[0]\n\t\tdtype\t = ip_type[1] # not used\n\t\tuType \t = \"unixDevs\"\n\t\tcmd = self.expectPath +\" \"\n\t\tcmd+= \"'\"+self.pathToPlugin + \"rebootUNIFIdeviceAP.exp\" + \"' \"\n\t\tcmd+= \"'\"+self.connectParams[\"UserID\"][uType] + \"' '\"+self.connectParams[\"PassWd\"][uType] + \"' \"\n\t\tcmd+= ipNumber + \" \"\n\t\tcmd+= \"'\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber]) + \"' \"\n\t\tcmd += self.getHostFileCheck()\n\t\tcmd += \" &\"\n\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"REBOOT: \"+cmd )\n\t\tret, err = self.readPopen(cmd)\n\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"REBOOT returned: {}-{}\".format(ret, err) )\n\t\tself.addToMenuXML(valuesDict)\n\n\t\treturn valuesDict\n\n\n\t####----------------- set properties for all devices\t---------\n\tdef buttonConfirmSetWifiOptCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tself.indiLOG.log(10,\"doing {}\".format(dev.name) )\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\t\t\t\t\tprops[\"useWhatForStatusWiFi\"]\t= \"Optimized\"\n\t\t\t\t\tprops[\"idleTimeMaxSecs\"]\t\t= \"30\"\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\n\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\tself.indiLOG.log(10,\"done {} {} \".format(dev.name, \"{}\".format(props)) )\n\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonConfirmSetWifiIdleCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\t\t\t\t\tprops[\"useWhatForStatusWiFi\"]\t= \"IdleTime\"\n\t\t\t\t\tprops[\"idleTimeMaxSecs\"]\t\t= \"30\"\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonConfirmSetWifiUptimeCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\t\t\t\t\tprops[\"useWhatForStatusWiFi\"]\t= \"UpTime\"\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonConfirmSetNonWifiOptCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") == -1:\n\t\t\t\t\tprops[\"useWhatForStatus\"]\t\t\t= \"OptDhcpSwitch\"\n\t\t\t\t\tprops[\"useAgeforStatusDHCP\"]\t\t= \"60\"\n\t\t\t\t\tprops[\"useupTimeforStatusSWITCH\"]\t= True\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonConfirmSetNonWifiToSwitchCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") == -1:\n\t\t\t\t\tprops[\"useWhatForStatus\"]\t\t\t= \"SWITCH\"\n\t\t\t\t\tprops[\"useupTimeforStatusSWITCH\"]\t= True\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetNonWifiToDHCPCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") == -1:\n\t\t\t\t\tprops[\"useWhatForStatus\"]\t\t\t= \"DHCP\"\n\t\t\t\t\tprops[\"useAgeforStatusDHCP\"]\t\t= \"60\"\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetUsePingUPonCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"usePingUP\"]\t\t\t = True\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetUsePingUPoffCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"usePingUP\"]\t\t\t = False\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetUsePingDOWNonCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"usePingDOWN\"]\t\t = True\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetUsePingDOWNoffCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"usePingDOWN\"]\t\t = False\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetExpTimeCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tprops[\"expirationTime\"]\t\t\t =int(valuesDict[\"expirationTime\"])\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmSetExpTimeMinCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tfor MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\ttry:\n\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\ttry:\n\t\t\t\t\tif int(props[\"expirationTime\"]) < int(valuesDict[\"expirationTime\"]):\n\t\t\t\t\t\tprops[\"expirationTime\"]\t\t= int(valuesDict[\"expirationTime\"])\n\t\t\t\texcept:\n\t\t\t\t\tprops[\"expirationTime\"]\t\t\t= int(valuesDict[\"expirationTime\"])\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\texcept:\n\t\t\t\tpass\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef inpDummy(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\treturn valuesDict\n\n\t####----------------- filter\t---------\n\n\n\t####-----------------\t ---------\n\tdef filterWiFiDevice(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif \"AP\" not\t in dev.states:\t\t continue\n\t\t\tif len(dev.states[\"AP\"]) < 5:\t continue\n\t\t\txList.append([dev.states[\"MAC\"].lower(),dev.name+\"--\"+ dev.states[\"MAC\"] +\"-- AP:\"+dev.states[\"AP\"]])\n\t\treturn sorted(xList, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterUNIFIsystemDevice(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isSwitch,props.isGateway,props.isAP\"):\n\t\t\txList.append([dev.states[\"MAC\"].lower(),dev.name+\"--\"+ dev.states[\"MAC\"] ])\n\t\treturn sorted(xList, key=lambda x: x[1])\n\t####-----------------\t ---------\n\tdef filterCameraDevice(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tif self.cameraSystem == \"nvr\":\t\n\t\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\t\txList.append([dev.id,dev.name])\n\t\tif self.cameraSystem == \"protect\":\t\n\t\t\tfor dev in indigo.devices.iter(\"props.isProtectCamera\"):\n\t\t\t\tfor camId in self.PROTECT:\n\t\t\t\t\tif dev.id == self.PROTECT[camId][\"devId\"]:\n\t\t\t\t\t\txList.append([dev.id,dev.name])\n\t\t\t\t\t\tbreak\n\t\treturn sorted(xList, key=lambda x: x[1])\n\n\n\t####-----------------\t ---------\n\tdef filterUNIFIsystemDeviceSuspend(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isSwitch,props.isGateway,props.isAP\"):\n\t\t\txList.append([dev.id,dev.name])\n\t\treturn sorted(xList, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterUNIFIsystemDeviceSuspended(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isSwitch,props.isGateway,props.isAP\"):\n\t\t\txList.append([dev.id,dev.name])\n\t\treturn sorted(xList, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterAPdevices(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isAP\"):\n\t\t\txList.append([dev.id,dev.name])\n\t\treturn sorted(xList, key=lambda x: x[1])\n\n\n\n\t####-----------------\t ---------\n\tdef filterMACNoIgnored(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tif \"displayStatus\" in dev.states and dev.states[\"displayStatus\"].find(\"ignored\") >-1: continue\n\t\t\t\tmac = dev.states[\"MAC\"]\n\t\t\t\tif self.isValidMAC(mac):\n\t\t\t\t\txlist.append([mac,dev.states[\"MAC\"] + \" - \"+dev.name])\n\t\t\t\telse:\n\t\t\t\t\txlist.append([\"bad mac\",\"badMAC#-\"+dev.states[\"MAC\"] + \" - \"+dev.name])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterMAC(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tmac = dev.states[\"MAC\"]\n\t\t\t\tif self.isValidMAC(mac):\n\t\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\" - \"+dev.states[\"MAC\"]])\n\t\t\t\telse:\n\t\t\t\t\txlist.append([\"bad mac\",\"badMAC#-\"+dev.name+\" - \"+dev.states[\"MAC\"]])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterMACunifiOnly(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\"--\"+dev.states[\"MAC\"] ])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterMACunifiAndCameraOnly(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tmaclist =[]\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tif dev.deviceTypeId not in [\"UniFi\"] : continue\n\t\t\t\tif \"status\" in dev.states and dev.states[\"status\"].find(\"up\") >-1:\n\t\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\"--\"+dev.states[\"MAC\"] ])\n\t\t\t\t\tmaclist.append(dev.states[\"MAC\"])\n\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tif dev.deviceTypeId not in [\"camera\"] : continue\n\t\t\t\tif dev.states[\"MAC\"] in maclist: continue\n\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\"--\"+dev.states[\"MAC\"] ])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterMACunifiOnlyUP(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tif \"status\" in dev.states and dev.states[\"status\"].find(\"up\") >-1:\n\t\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\"--\"+dev.states[\"MAC\"] ])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####-----------------\t ---------\n\tdef filterMAConlyAP(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor dev in indigo.devices.iter(\"props.isAP\"):\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tif \"status\" in dev.states and dev.states[\"status\"].find(\"up\") >-1:\n\t\t\t\t\txlist.append([dev.states[\"MAC\"],dev.name+\"--\"+dev.states[\"MAC\"] ])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\n\t####-----------------\t ---------\n\tdef filterMACunifiIgnored(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor MAC in self.MACignorelist:\n\t\t\t\ttextMAC = MAC\n\t\t\t\tfor dev in indigo.devices.iter(\"props.isUniFi,props.isCamera\"):\n\t\t\t\t\tif \"MAC\" in dev.states and MAC == dev.states[\"MAC\"]:\n\t\t\t\t\t\ttextMAC = dev.name+\" - \"+MAC\n\t\t\t\t\t\tbreak\n\t\t\t\txlist.append([MAC,textMAC])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####----------------- logging for specific MAC number\t ---------\n\t####-----------------\t ---------\n\tdef filterMACspecialUNIgnore(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txlist = []\n\t\tfor MAC in self.MACSpecialIgnorelist:\n\t\t\txlist.append([MAC,MAC])\n\t\treturn sorted(xlist, key=lambda x: x[1])\n\n\t####----------------- logging for specific MAC number\t ---------\n\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmStartLoggingCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.MACloglist[valuesDict[\"MACdeviceSelected\"]]=True\n\t\tself.indiLOG.log(10,\"start track-logging for MAC# {}\".format(valuesDict[\"MACdeviceSelected\"]) )\n\t\tif valuesDict[\"keepMAClogList\"] == \"1\":\n\t\t\tself.writeJson(self.MACloglist, fName=self.indigoPreferencesPluginDir+\"MACloglist\")\n\t\telse:\n\t\t\tself.writeJson({}, fName=self.indigoPreferencesPluginDir+\"MACloglist\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmStopLoggingCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.MACloglist = {}\n\t\tself.writeJson({}, fName=self.indigoPreferencesPluginDir+\"MACloglist\")\n\t\tself.indiLOG.log(10,\" stop logging of MAC #s\")\n\t\treturn valuesDict\n\n\t####----------------- device info\t ---------\n\tdef buttonConfirmPrintMACCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.printMACs(MAC=valuesDict[\"MACdeviceSelected\"])\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef buttonprintALLMACsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.printALLMACs()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\tdef printALLUNIFIsreducedMenue(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.printALLUNIFIsreduced()\n\t\treturn valuesDict\n\t####-----------------\t ---------\n\n\n\n\n\t####----------------- GROUPS START\t ---------\n\t####-----------------\t ---------\n\n\tdef printGroupsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.printGroups()\n\t\treturn valuesDict\n\n\n\t####----------------- add devices to groups menu\t ---------\n\tdef buttonConfirmAddDevGroupCALLBACK(self, valuesDict=None, typeId=\"\", devId=0):\n\t\ttry:\n\t\t\tnewGroup =\tvaluesDict[\"addRemoveGroupsWhichGroup\"]\n\t\t\tdevtypes =\tvaluesDict[\"addRemoveGroupsWhichDevice\"]\n\t\t\ttypes\t =\"\"; lanWifi = \"\"\n\t\t\tif\t devtypes == \"system\":\t types =\"props.isGateway,props.isSwitch,props.isAP\"\n\t\t\telif devtypes == \"neighbor\": types =\"props.isNeighbor\"\n\t\t\telif devtypes == \"LAN\":\t\t types =\"props.isUniFi\" ; lanWifi =\"LAN\"\n\t\t\telif devtypes == \"wifi\":\t types =\"props.isUniFi\" ; lanWifi =\"wifi\"\n\t\t\tif types !=\"\":\n\t\t\t\tfor dev in indigo.devices.iter(types):\n\t\t\t\t\tif lanWifi == \"wifi\" and \"AP\" in dev.states:\n\t\t\t\t\t\tif ( dev.states[\"AP\"] == \"\" or\n\t\t\t\t\t\t\t dev.states[\"signalWiFi\"]\t\t== \"\" ): continue\n\t\t\t\t\tif lanWifi == \"LAN\" and \"AP\" in dev.states:\n\t\t\t\t\t\tif not\t( dev.states[\"AP\"] ==\"\" or\n\t\t\t\t\t\t\t\t dev.states[\"signalWiFi\"]\t== \"\" ): continue\n\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\tprops[newGroup] = True\n\t\t\t\t\tgMembers = self.makeGroupMemberstring(props)\n\t\t\t\t\tself.updateDevStateGroupMembers(dev, gMembers)\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\tself.statusChanged = 1\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef updateDevStateGroupMembers(self, dev, gMembers, delay=False):\n\t\tif dev.states[\"groupMember\"] != gMembers:\n\t\t\tif delay:\n\t\t\t\tself.addToStatesUpdateList(dev.id,\"groupMember\", gMembers)\n\t\t\telse:\n\t\t\t\tdev.updateStateOnServer(\"groupMember\", gMembers)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef makeGroupMemberstring(self, inputDict):\n\t\tgMembers = \"\"\n\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\tgroup = \"Group{}\".format(groupNo)\n\t\t\tif group in inputDict and inputDict[group]:\n\t\t\t\tgMembers += self.groupNames[groupNo]+\",\"\n\t\treturn gMembers.strip(\",\")\n\n\n\n\t####----------------- remove devices to groups\t menu\t---------\n\tdef buttonConfirmRemDevGroupCALLBACK(self, valuesDict=None, typeId=\"\", devId=0):\n\t\ttry:\n\t\t\tnewGroup =\tvaluesDict[\"addRemoveGroupsWhichGroup\"]\n\t\t\tdevtypes =\tvaluesDict[\"addRemoveGroupsWhichDevice\"]\n\t\t\ttypes\t = \"\"; lanWifi=\"\"\n\t\t\tif\t devtypes == \"system\":\t types =\",props.isGateway,props.isSwitch,props.isAP\"\n\t\t\telif devtypes == \"neighbor\": types =\",props.isNeighbor\"\n\t\t\telif devtypes == \"LAN\":\t\t types =\",props.isUniFi\" ; lanWifi = \"LAN\"\n\t\t\telif devtypes == \"wifi\":\t types =\",props.isUniFi\" ; lanWifi = \"wifi\"\n\t\t\tfor dev in indigo.devices.iter(self.pluginId+types):\n\t\t\t\tif lanWifi == \"wifi\" and \"AP\" in dev.states:\n\t\t\t\t\tif ( dev.states[\"AP\"] ==\"\" or\n\t\t\t\t\t\t dev.states[\"signalWiFi\"]\t ==\"\" ): continue\n\t\t\t\tif lanWifi == \"LAN\" and \"AP\" in dev.states:\n\t\t\t\t\tif not\t( dev.states[\"AP\"] == \"\" or\n\t\t\t\t\t\t\t dev.states[\"signalWiFi\"]\t ==\"\" ): continue\n\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif newGroup in props:\n\t\t\t\t\tdel props[newGroup]\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\tgMembers = self.makeGroupMemberstring(props)\n\t\t\t\tself.updateDevStateGroupMembers(dev, gMembers)\n\n\t\t\tself.statusChanged = 1\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef filterGroupNoName(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\ttry:\n\t\t\txList=[]\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tmembers = self.groupStatusList[groupNo][\"members\"]\n\t\t\t\tgName = self.groupNames[groupNo] \n\t\t\t\txList.append([\"Group{}\".format(groupNo), gName])\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn xList\n\t####-----------------\t ---------\n\tdef filterGroups(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\ttry:\n\t\t\txList=[]\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tmembers = self.groupStatusList[groupNo][\"members\"]\n\t\t\t\tgName = self.groupNames[groupNo] \n\t\t\t\t#try:\n\t\t\t\t#except: pass\n\t\t\t\tmemberMAC = \"\"\n\t\t\t\tnn = 0\n\t\t\t\tfor id in members:\n\t\t\t\t\tnn +=1\n\t\t\t\t\tif nn < 6:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tmemberMAC = indigo.devices[int(id)].states[\"MAC\"]\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tmemberMAC += memberMAC+\";\"\n\t\t\t\t\telif nn == 6:\n\t\t\t\t\t\tmemberMAC +=\"...\"\n\t\t\t\txList.append([\"{}\".format(groupNo), \"{}= {}\".format(gName, memberMAC.strip(\"; \"))])\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn xList\n\n\t####-----------------\t ---------\n\tdef buttonConfirmgroupCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.selectedGroup\t\t = int(valuesDict[\"selectedGroup\"])\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef filterGroupMembers(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\ttry:\n\t\t\txList=[]\n\t\t\ttry: groupNo = int(self.selectedGroup)\n\t\t\texcept: return xList\n\t\t\tfor memberDevID in self.groupStatusList[groupNo][\"members\"]:\n\t\t\t\ttry:\n\t\t\t\t\tdev = indigo.devices[int(memberDevID)]\n\t\t\t\texcept: continue\n\t\t\t\txList.append([memberDevID,dev.name + \"- \"+ dev.states[\"MAC\"]])\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn xList\n\n\t####-----------------\t ---------\n\tdef buttonConfirmremoveGroupMemberCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdevIdOfGroupMember\t= valuesDict[\"selectedGroupMemberIndigoIdremove\"]\n\t\ttry: groupNo = int(self.selectedGroup)\n\t\texcept: return valuesDict\n\t\tgroupPropsName = \"Group{}\".format(groupNo)\n\t\ttry:\n\t\t\tdev = indigo.devices[int(devIdOfGroupMember)]\n\t\texcept:\n\t\t\tself.indiLOG.log(30,\" bad dev id: {}\".format(devIdOfGroupMember) )\n\t\t\treturn\n\t\tprops = dev.pluginProps\n\t\tif devIdOfGroupMember in self.groupStatusList[groupNo][\"members\"]:\n\t\t\tdel self.groupStatusList[groupNo][\"members\"][devIdOfGroupMember]\n\t\tif groupPropsName in props and props[groupPropsName]:\n\t\t\tprops[groupPropsName] = False\n\t\t\tgMembers = self.makeGroupMemberstring(props)\n\t\t\tself.updateDevStateGroupMembers(dev, gMembers)\n\t\t\tdev.replacePluginPropsOnServer(props)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmremoveALLGroupMembersCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttry: groupNo = int(self.selectedGroup)\n\t\texcept: return valuesDict\n\t\tgroupPropsName = \"Group\".format(groupNo)\n\t\tself.indiLOG.log(20,\" groupStatusList:{} removing all members\".format(self.groupStatusList) )\n\t\tself.groupStatusList[groupNo][\"members\"] = {}\n\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\tprops=dev.pluginProps\n\t\t\tif groupPropsName in props and props[groupPropsName]:\n\t\t\t\tprops[groupPropsName] = False\n\t\t\t\tgMembers = self.makeGroupMemberstring(props)\n\t\t\t\tgMembers = self.makeGroupMemberstring(props)\n\t\t\t\tself.updateDevStateGroupMembers(dev, gMembers)\n\t\t\t\tdev.replacePluginPropsOnServer(props)\n\n\t\treturn valuesDict\n\n\n\n\t####-----------------\t ---------\n\tdef filterDevicesToAddToGroup(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\ttry:\n\t\t\txList=[]\n\t\t\ttry: groupNo = int(self.selectedGroup)\n\t\t\texcept: return xList\n\t\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif \"{}\".format(dev.id) in self.groupStatusList[groupNo][\"members\"]: continue\n\t\t\t\txList.append([\"{}\".format(dev.id), dev.name + \"- \"+ dev.states[\"MAC\"]])\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn xList\n\n\t####-----------------\t ---------\n\tdef buttonConfirmADDGroupMemberCALLBACK(self, valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\tdevIdOfGroupMember\t= valuesDict[\"selectedGroupMemberIndigoIdadd\"]\n\t\ttry: groupNo = int(self.selectedGroup)\n\t\texcept: return valuesDict\n\t\ttry:\n\t\t\tdev = indigo.devices[int(devIdOfGroupMember)]\n\t\texcept:\n\t\t\tself.indiLOG.log(30,\" bad dev id: {}\".format(devIdOfGroupMember) )\n\t\t\treturn\n\t\tprops = dev.pluginProps\n\t\tif devIdOfGroupMember not in self.groupStatusList[groupNo][\"members\"]:\n\t\t\tself.groupStatusList[groupNo][\"members\"][devIdOfGroupMember] = True\n\t\tprops[\"Group{}\".format(groupNo)] = True\n\t\tgMembers = self.makeGroupMemberstring(props)\n\t\tself.updateDevStateGroupMembers(dev, gMembers)\n\t\tdev.replacePluginPropsOnServer(props)\n\t\treturn valuesDict\n\n\n\n\t####----------------- GROUPS END\t ---------\n\t####-----------------\t ---------\n\n\n\t####----------------- Ignore special MAC info\t ---------\n\tdef buttonConfirmStartIgnoringSpecialMACCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tMAC = valuesDict[\"MACspecialIgnore\"]\n\t\tif not self.isValidMAC(MAC):\n\t\t\tvaluesDict[\"MSG\"] = \"bad MAC.. must be 12:xx:23:xx:45:aa\"\n\t\t\treturn valuesDict\n\t\tself.MACSpecialIgnorelist[valuesDict[\"MACspecialIgnore\"]]=1\n\t\tself.indiLOG.log(10,\"start ignoring MAC# \"+valuesDict[\"MACspecialIgnore\"])\n\t\tself.saveMACdata(force=True)\n\t\tvaluesDict[\"MSG\"] = \"ok\"\n\t\treturn valuesDict\n\t####----------------- UN- Ignore special MAC info\t ---------\n\t####----------------- ---------\n\tdef buttonConfirmStopIgnoringSpecialMACCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\ttry: del self.MACSpecialIgnorelist[valuesDict[\"MACspecialUNIgnored\"]]\n\t\texcept: pass\n\t\tself.indiLOG.log(10,\" stop ignoring MAC# \" +valuesDict[\"MACspecialUNIgnored\"])\n\t\tself.saveMACdata(force=True)\n\t\tvaluesDict[\"MSG\"] = \"ok\"\n\t\treturn valuesDict\n\n\t####----------------- Ignore MAC info\t ---------\n\tdef buttonConfirmStartIgnoringCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tself.MACignorelist[valuesDict[\"MACdeviceSelected\"]]=1\n\t\tself.indiLOG.log(10,\"start ignoring MAC# \"+valuesDict[\"MACdeviceSelected\"])\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi,props.isCamera\"):\n\t\t\tif \"MAC\" in dev.states\t and dev.states[\"MAC\"] == valuesDict[\"MACdeviceSelected\"]:\n\t\t\t\tif \"displayStatus\" in dev.states:\n\t\t\t\t\tdev.updateStateOnServer(\"displayStatus\",self.padDisplay(\"ignored\")+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.PowerOff)\n\t\t\t\tdev.updateStateOnServer(\"status\",value= \"ignored\", uiValue=self.padDisplay(\"ignored\")+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\tvaluesDict[\"MSG\"] = \"ok\"\n\t\tself.saveMACdata(force=True)\n\t\treturn valuesDict\n\t####----------------- UN- Ignore MAC info ---------\n\t####-----------------\t ---------\n\tdef buttonConfirmStopIgnoringCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\tfor dev in indigo.devices.iter(\"props.isUniFi,props.isCamera\"):\n\t\t\tif \"MAC\" in dev.states\t and dev.states[\"MAC\"] == valuesDict[\"MACdeviceIgnored\"]:\n\t\t\t\tif \"displayStatus\" in dev.states:\n\t\t\t\t\tdev.updateStateOnServer(\"displayStatus\",self.padDisplay(\"\")+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.PowerOff)\n\t\t\t\tdev.updateStateOnServer(\"status\",\"\")\n\t\t\t\tdev.updateStateOnServer(\"onOffState\", value=False, uiValue=self.padDisplay(\"\")+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\ttry: del self.MACignorelist[valuesDict[\"MACdeviceIgnored\"]]\n\t\texcept: pass\n\t\tvaluesDict[\"MSG\"] = \"ok\"\n\t\tself.saveMACdata(force=True)\n\t\tself.indiLOG.log(10,\" stop ignoring MAC# \" +valuesDict[\"MACdeviceIgnored\"])\n\t\treturn valuesDict\n\n\n\n\t####----------------- powercycle switch port\t---------\n\t####-----------------\t ---------\n\tdef filterUnifiSwitchACTION(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\treturn self.filterUnifiSwitch(valuesDict)\n\n\t####-----------------\t ---------\n\tdef filterUnifiSwitch(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\txList = []\n\t\tfor dev in indigo.devices.iter(\"props.isSwitch\"):\n\t\t\tswNo = int(dev.states[\"switchNo\"])\n\t\t\tif self.devsEnabled[\"SW\"][swNo]:\n\t\t\t\txList.append((\"{}\".format(swNo)+\"-SWtail-{}\".format(dev.id), \"{}\".format(swNo)+\"-\"+self.ipNumbersOf[\"SW\"][swNo]+\"-\"+dev.name))\n\t\treturn xList\n\n\tdef buttonConfirmSWCALLBACK(self, valuesDict=None, typeId=\"\", targetId=\"\"):\n\t\tself.unifiSwitchSelectedID = valuesDict[\"selectedUnifiSwitch\"].split(\"-\")[2]\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef filterUnifiSwitchPort(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\ttry:\tint(self.unifiSwitchSelectedID)\n\t\texcept: return xList\n\n\t\tdev = indigo.devices[int(self.unifiSwitchSelectedID)]\n\t\tsnNo = \"{}\".format(dev.states[\"switchNo\"] )\n\t\tfor port in range(99):\n\t\t\tppp = \"port_{:02d}\".format(port) \n\t\t\tif ppp not in dev.states: continue\n\t\t\tif\tdev.states[ppp].find(\"poe\") >-1:\n\t\t\t\tname = \"\"\n\t\t\t\tif\tdev.states[ppp].find(\"poeX\") >-1:\n\t\t\t\t\tname = \" - empty\"\n\t\t\t\telse:\n\t\t\t\t\tname = \"\"\n\t\t\t\t\tfor dev2 in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\t\t\t\tif \"SW_Port\" in dev2.states and len(dev2.states[\"SW_Port\"]) > 2:\n\t\t\t\t\t\t\tif not dev2.enabled: continue\n\t\t\t\t\t\t\tsw\t = dev2.states[\"SW_Port\"].split(\":\")\n\t\t\t\t\t\t\tif sw[0] == snNo:\n\t\t\t\t\t\t\t\tif sw[1].find(\"poe\") > -1 and dev.states[\"status\"] != \"expired\":\n\t\t\t\t\t\t\t\t\tif \"{}\".format(port) == sw[1].split(\"-\")[0]:\n\t\t\t\t\t\t\t\t\t\tname = \" - {}\".format(dev2.name)\n\t\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\telif dev.states[\"status\"] != \"expired\":\n\t\t\t\t\t\t\t\t\tif \"{}\".format(port) == sw[1].split(\"-\")[0]:\n\t\t\t\t\t\t\t\t\t\tname = \" - {}\".format(dev2.name)\n\t\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tif \"{}\".format(port) == sw[1].split(\"-\")[0]:\n\t\t\t\t\t\t\t\t\t\tname = \" - {}\".format(dev2.name)\n\t\t\t\txList.append([port,\"port#{}{}\".format(port, name)])\n\t\treturn xList\n\n\t####-----------------\t ---------\n\tdef filterUnifiClient(self, filter=\"\", valuesDict=None, typeId=\"\", targetId=\"\"):\n\n\t\txList = []\n\t\tfor dev2 in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\tif \"SW_Port\" in dev2.states and len(dev2.states[\"SW_Port\"]) > 2:\n\t\t\t\tsw\t = dev2.states[\"SW_Port\"].split(\":\")\n\t\t\t\tif sw[1].find(\"poe\") >-1:\n\t\t\t\t\tport = sw[1].split(\"-\")[0]\n\t\t\t\t\txList.append([sw[0]+\"-\"+port,\"sw{}-port#{} - {}\".format(sw[0], port, dev2.name)])\n\t\txList.sort(key = lambda x: x[1]) \n\t\treturn xList\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmpowerCycleCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmpowerCycleCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmpowerCycleClientsCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmpowerCycleClientsCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmpowerCycleCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tonOffCycle\t= valuesDict[\"onOffCycle\"]\n\t\tip_type\t\t= valuesDict[\"selectedUnifiSwitch\"].split(\"-\")\n\t\tipNumber\t= self.ipNumbersOf[\"SW\"][int(ip_type[0])]\n\t\tdtype\t\t= ip_type[1]\n\t\tport\t\t= \"{}\".format(valuesDict[\"selectedUnifiSwitchPort\"])\n\t\tcmd \t\t= self.expectPath +\" \"\n\t\tif onOffCycle == \"CYCLE\":\n\t\t\tcmd += \"'\"+self.pathToPlugin + \"cyclePort.exp\" + \"' \"\n\t\telif onOffCycle ==\"ON\":\n\t\t\tcmd += \"'\"+self.pathToPlugin + \"onPort.exp\" + \"' \"\n\t\telif onOffCycle ==\"OFF\":\n\t\t\tcmd += \"'\"+self.pathToPlugin + \"offPort.exp\" + \"' \"\n\t\tcmd += \"'\"+self.connectParams[\"UserID\"][\"unixDevs\"] + \"' '\"+self.connectParams[\"PassWd\"][\"unixDevs\"] + \"' \"\n\t\tcmd += ipNumber + \" \"\n\t\tcmd += port + \" \"\n\t\tcmd += \"'\" + self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber]) +\"' \"\n\t\tcmd += self.getHostFileCheck()\n\t\tcmd += \" &\"\n\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"RECYCLE: \"+cmd )\n\t\tret, err = self.readPopen(cmd)\n\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"RECYCLE returned: {}-{}\".format(ret, err))\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmpowerCycleClientsCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tip_type\t =\tvaluesDict[\"selectedUnifiClientSwitchPort\"].split(\"-\")\n\t\tif len(ip_type) != 2: return valuesDict\n\t\tvaluesDict[\"selectedUnifiSwitch\"]\t\t= ip_type[0]+\"-SWtail\"\n\t\tvaluesDict[\"selectedUnifiSwitchPort\"]\t= ip_type[1]\n\t\tself.buttonConfirmpowerCycleCALLBACK(valuesDict)\n\t\treturn valuesDict\n\n\n\t####----------------- suspend / activate unifi devices\t ---------\n\tdef buttonConfirmsuspendCALLBACKaction(self, action1=None):\n\t\tself.buttonConfirmsuspendCALLBACKbutton(valuesDict=action1.props)\n\n\t####----------------- suspend / activate unifi devices\t ---------\n\tdef buttonConfirmactivateCALLBACKaction(self, action1=None):\n\t\tself.buttonConfirmactivateCALLBACKbutton(valuesDict=action1.props)\n\n\n\t####-----------------\tsuspend / activate unifi devices\t---------\n\tdef buttonConfirmsuspendCALLBACKbutton(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttry:\n\t\t\tID = int(valuesDict[\"selectedDevice\"])\n\t\t\tdev= indigo.devices[ID]\n\t\t\tip = dev.states[\"ipNumber\"]\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\treturn\n\t\tself.indiLOG.log(10,\"suspending Unifi system device {} {} - only in plugin\".format(dev.name, ip) )\n\t\tself.setSuspend(ip, time.time()+9999999)\n\t\tself.exeDisplayStatus(dev,\"susp\")\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\tdef buttonConfirmactivateCALLBACKbutton(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttry:\n\t\t\tID = int(valuesDict[\"selectedDevice\"])\n\t\t\tdev= indigo.devices[ID]\n\t\t\tip = dev.states[\"ipNumber\"]\n\t\t\ttry:\n\t\t\t\tself.delSuspend(ip)\n\t\t\t\tself.exeDisplayStatus(dev,\"up\")\n\t\t\t\tself.indiLOG.log(10,\"reactivating Unifi system device {} {} - only in plugin\".format(dev.name, ip) )\n\t\t\texcept: pass\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\n\n\t####----------------- Unifi controller backup ---------\n\tdef getBackupFilesFromController(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tif not self.unifiControllerBackupON: return \n\n\t\tcmd = \"cd '\"+self.indigoPreferencesPluginDir+\"backup';\"\n\t\tcmd += self.expectPath \n\t\tcmd += \" '\"+self.pathToPlugin + \"controllerbackup.exp' \"\n\t\tcmd += \" '\"+self.connectParams[\"UserID\"][\"unixDevs\"]+\"' \"\n\t\tcmd += \" '\"+self.connectParams[\"PassWd\"][\"unixDevs\"]+\"' \"\n\t\tcmd += self.unifiCloudKeyIP\n\t\tcmd += \" '\"+self.ControllerBackupPath.rstrip(\"/\")+\"'\"\n\t\tcmd += self.getHostFileCheck()\n\n\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"backup cmd: {}\".format(cmd) )\n\n\t\tret, err = self.readPopen(cmd)\n\n\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"backup cmd returned: {}-{}\".format(ret, err))\n\n\t\treturn \n\n\n\t####----------------- Unifi api calls\t ---------\n\n\n######## block / unblock reconnect\n\t####-----------------\t ---------\n\tdef buttonConfirmReconnectCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmReconnectCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmReconnectCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"cmd\":\"kick-sta\",\"mac\":valuesDict[\"selectedDevice\"]},pageString=\"/cmd/stamgr\",cmdType=\"post\")\n\t\tself.indiLOG.log(10,\"reconnect cmd: return {}\".format(ret) )\n\t\tvaluesDict[\"MSG\"] = \"command send\"\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmBlockCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmBlockCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmBlockCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"cmd\":\"block-sta\",\"mac\":valuesDict[\"selectedDevice\"]},pageString=\"/cmd/stamgr\",cmdType=\"post\")\n\t\tself.getcontrollerDBForClientsLast = time.time() - self.readDictEverySeconds[\"DB\"]\n\t\tvaluesDict[\"MSG\"] = \"error\"\n\t\tfor rr in ret:\n\t\t\tif \"blocked\" in rr: \n\t\t\t\tself.indiLOG.log(20,\"MAC#: {} {}\".format(valuesDict[\"selectedDevice\"], \"blocked\" if rr[\"blocked\"] else \"not executed\") )\n\t\t\t\tvaluesDict[\"MSG\"] = \"{} {}\".format(valuesDict[\"selectedDevice\"], \"blocked\" if rr[\"blocked\"] else \"not executed\")\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmUnBlockCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmUnBlockCALLBACK(valuesDict=action1.props)\n\n\t####-----------------\t ---------\n\tdef buttonConfirmUnBlockCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"cmd\":\"unblock-sta\",\"mac\":valuesDict[\"selectedDevice\"]}, pageString=\"/cmd/stamgr\",cmdType=\"post\")\n\t\tself.getcontrollerDBForClientsLast = time.time() - self.readDictEverySeconds[\"DB\"]\n\t\tvaluesDict[\"MSG\"] = \"error\"\n\t\tfor rr in ret:\n\t\t\tif len(rr) ==0: continue\n\t\t\tif \"blocked\" in rr: \n\t\t\t\tself.indiLOG.log(20,\"MAC#:{} {}\".format(valuesDict[\"selectedDevice\"],\"un-blocked\" if not rr[\"blocked\"] else \"not executed\") )\n\t\t\t\tvaluesDict[\"MSG\"] = \"{} {}\".format(valuesDict[\"selectedDevice\"], \"un-blocked\" if not rr[\"blocked\"] else \"not executed\")\n\t\treturn valuesDict\n\n######## block / unblock reconnec end\n\n######## reports for specific stations / devices\n\tdef buttonConfirmGetAPDevInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tvaluesDict[\"MSG\"] = \"\"\n\t\tfor dev in indigo.devices.iter(\"props.isAP\"):\n\t\t\tMAC = dev.states[\"MAC\"]\n\t\t\tif \"MAClan\" in dev.states: \n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif \"useWhichMAC\" in props and props[\"useWhichMAC\"] == \"MAClan\":\n\t\t\t\t\tMAC = dev.states[\"MAClan\"]\n\t\t\tself.indiLOG.log(10,\"unifi-Report getting _id for AP {} /stat/device/{}\".format(dev.name, MAC) )\n\t\t\tjData = self.executeCMDOnController(dataSEND={}, pageString=\"/stat/device/\"+MAC, jsonAction=\"returnData\", cmdType=\"get\")\n\n\t\t\tif len(jData) == 0 and self.unifiCloudKeyPort == \"\": \n\t\t\t\tself.indiLOG.log(10,\"unifi-Report: controller not setup, skipping other querries\" )\n\t\t\t\tbreak\n\n\t\t\tfor dd in jData:\n\t\t\t\tif \"_id\" not in dd:\n\t\t\t\t\tself.indiLOG.log(10,\"unifi-Report _id not in data\")\n\t\t\t\t\tcontinue\n\t\t\t\tself.indiLOG.log(10,\"unifi-Report _id in data:{}\".format(dd[\"_id\"]) )\n\t\t\t\tdev.updateStateOnServer(\"ap_id\", dd[\"_id\"])\n\t\t\t\tbreak\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintDevInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tMAC = valuesDict[\"MACdeviceSelectedsys\"]\n\t\tfor dev in indigo.devices.iter(\"props.isAP,props.isSwitch,props.isGateway\"):\n\t\t\tif \"MAC\" in dev.states and dev.states[\"MAC\"] != MAC: continue\n\t\t\tif \"MAClan\" in dev.states and dev.states[\"MAClan\"] != MAC:\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif \"useWhichMAC\" in props and props[\"useWhichMAC\"] == \"MAClan\":\n\t\t\t\t\tMAC = dev.states[\"MAClan\"]\n\t\t\tbreak\t\n\t\tself.executeCMDOnController(dataSEND={}, pageString=\"/stat/device/\"+MAC, jsonAction=\"print\",startText=\"== Unifi Device print: /stat/device/\"+MAC+\" ==\", cmdType=\"get\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintClientInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tMAC = valuesDict[\"MACdeviceSelectedclient\"]\n\t\tself.executeCMDOnController(dataSEND={}, pageString=\"/stat/sta/\"+MAC, jsonAction=\"print\",startText=\"== Client print: /stat/sta/\"+MAC+\" ==\", cmdType=\"get\")\n\t\treturn valuesDict\n\n######## reports all devcies\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintalluserInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/stat/alluser/\", jsonAction=\"returnData\", cmdType=\"get\")\n#\t\tdata = self.executeCMDOnController(dataSEND={\"type\":\"all\",\"conn\":\"all\"}, pageString=\"/stat/alluser/\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tself.unifsystemReport3(data, \"== ALL USER report ==\")\n\t\tself.fillcontrollerDBForClients(data)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintuserInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\", cmdType=\"get\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/list/user/\", jsonAction=\"returnData\", cmdType=cmdType)\n\t\tself.unifsystemReport3(data, \"== USER report ==\")\n\t\treturn valuesDict\n\n\n\n\t####-----------------print DPI info ---------\n\tdef buttonConfirmPrintListOfBackupsFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttry:\n\t\t\tdata = self.executeCMDOnController(dataSEND={'cmd': 'list-backups'}, pageString=\"cmd/backup\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\t\tif len(data) == 0: \n\t\t\t\tself.indiLOG.log(20,\"no data returned from backup list\")\n\t\t\t\treturn valuesDict\n\t\t\t##[{\"controller_name\":\"192.168.1.2\",\"filename\":\"autobackup_6.0.43_20210208_0600_1612764000017.unf\",\"type\":\"primary\",\"version\":\"6.0.43\",\"time\":1612764000017,\"datetime\":\"2021-02-08T06:00:00Z\",\"format\":\"bson\",\"days\":30,\"size\":25885584},\n\t\t\tout = \"\"\n\t\t\tfor rec in data:\n\t\t\t\tif out == \"\": \n\t\t\t\t\tout = \"\\n== UniFi list of backups on system {}\\n\".format(rec[\"controller_name\"])\n\t\t\t\t\tout += \"fileName ---------------------------------------- size days type version date\\n\"\n\t\t\t\tout += \"{:50}\".format(rec[\"filename\"]) \n\t\t\t\tout += \"{:>17,d} \".format(rec[\"size\"]) \n\t\t\t\tout += \"{:>5} \".format(rec[\"days\"]) \n\t\t\t\tout += \"{:11}\".format(rec[\"type\"]) \n\t\t\t\tout += \"{:9}\".format(rec[\"version\"]) \n\t\t\t\tout += time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(rec[\"time\"]/1000.))\n\t\t\t\tout += \"\\n\"\n\t\t\tself.indiLOG.log(20,out)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\t####-----------------print DPI info ---------\n\tdef buttonConfirmPrintDPIFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttry:\n\t\t\tdata = {}\n\t\t\tdata[\"app\"] = self.executeCMDOnController(dataSEND={'type': 'by_app'}, pageString=\"stat/sitedpi\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\t\tdata[\"cat\"] = self.executeCMDOnController(dataSEND={'type': 'by_cat'}, pageString=\"stat/sitedpi\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\t\tf = self.openEncoding(self.pathToPlugin+\"unifi_dpi.json\",\"r\")\n\t\t\tcatappInfo = json.loads(f.read())\n\t\t\tf.close()\n\t\t\t#self.indiLOG.log(20,\"{}\".format(catappInfo))\n\t\t\t#self.indiLOG.log(20,\"{}\".format(data[\"app\"]))\n\t\t\t#self.indiLOG.log(20,\"{}\".format(data[\"cat\"]))\n\n\t\t\tout1 = []\n\t\t\tout2 = []\n\t\t\tlastUpdated = \"\"\n\t\t\tfor rr in data[\"cat\"]: \n\t\t\t\tif 'last_updated' in rr:\n\t\t\t\t\tlastUpdated = rr[\"last_updated\"]\n\t\t\t\t\tlastUpdated = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(lastUpdated))\n\t\t\t\tif \"by_cat\" in rr:\n\t\t\t\t\tfor rec in rr[\"by_cat\"]:\n\t\t\t\t\t\to = \"{:>7d} \".format(rec[\"cat\"]) \n\t\t\t\t\t\tnn = \"{}\".format(rec[\"cat\"])\n\t\t\t\t\t\tif nn in catappInfo[\"categories\"]:\n\t\t\t\t\t\t\to += catappInfo[\"categories\"][nn][\"name\"].ljust(37)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\to += (\"??\").ljust(37)\n\t\t\t\t\t\to += \"{:>17,d}\".format(rec[\"rx_bytes\"]) \n\t\t\t\t\t\to += \"{:>17,d}\".format(rec[\"tx_bytes\"]) \n\t\t\t\t\t\tout1.append(o)\n\t\t\tfor rr in data[\"app\"]: \n\t\t\t\tif 'last_updated' in rr:\n\t\t\t\t\tlastUpdated = rr[\"last_updated\"]\n\t\t\t\t\tlastUpdated = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(lastUpdated))\n\t\t\t\tif \"by_app\" in rr:\n\t\t\t\t\tfor rec in rr[\"by_app\"]:\n\t\t\t\t\t\to = \"{:>7d} \".format(rec[\"app\"]) \n\t\t\t\t\t\tnn = \"{}\".format(rec[\"app\"])\n\t\t\t\t\t\tif nn in catappInfo[\"applications\"]:\n\t\t\t\t\t\t\to += catappInfo[\"applications\"][nn][\"name\"].ljust(32)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\to += (\"??\").ljust(32)\n\t\t\t\t\t\to += \"{:>5d}\".format(rec[\"cat\"]) \n\t\t\t\t\t\to += \"{:>17,d}\".format(rec[\"rx_bytes\"]) \n\t\t\t\t\t\to += \"{:>17,d}\".format(rec[\"rx_bytes\"]) \n\t\t\t\t\t\tif \"known_clients\" in rec:\n\t\t\t\t\t\t\to += \"{:>9,d}\".format(rec[\"known_clients\"]) \n\t\t\t\t\t\tout2.append(o)\n\n\t\t\tif out1 !=[] or out2 !=[]:\n\t\t\t\tout = \"\\n== UniFi Deep Packet Info report, lastUpdated: {} == \\n\".format(lastUpdated)\n\t\t\t\tout = \"\\n== ** cat and app #s from https://ubntwiki.com/products/software/unifi-controller/api/cat_app_json ** \\n\".format(lastUpdated)\n\t\t\t\tout += \" cat# cat Name----------------------- rx_bytes tx_Bytes\\n\"\n\t\t\t\tout += \"\\n\".join(sorted(out1))\n\t\t\t\tout += \"\\n\"\n\t\t\t\tout += \" app# app Name----------------------- cat# rx_bytes tx_Bytes #clients\\n\"\n\t\t\t\tout += \"\\n\".join(sorted(out2))\n\t\t\t\tself.indiLOG.log(20,out)\n\t\t\telse:\n\t\t\t\tself.indiLOG.log(20,\"== DPI report empty, no data returned ==\")\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n#### general reports\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintHealthInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/stat/health/\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"== HEALTH report ==\\n\"\n\t\tii=0\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tll = \"{}\".format(ii).ljust(3)\n\t\t\tll+=(item[\"subsystem\"]+\":\").ljust(10)\n\t\t\tll+=(item[\"status\"]+\";\").ljust(10)\n\t\t\tif \"rx_bytes-r\" in item:\n\t\t\t\tll+=\"rx_B:\"+(\"{}\".format(item[\"rx_bytes-r\"])+\";\").ljust(9)\n\t\t\tif \"tx_bytes-r\" in item:\n\t\t\t\tll+=\"tx_B:\"+(\"{}\".format(item[\"tx_bytes-r\"])+\";\").ljust(9)\n\n\t\t\tfor item2 in item:\n\t\t\t\tif item2 ==\"subsystem\": continue\n\t\t\t\tif item2 ==\"status\":\t continue\n\t\t\t\tif item2 ==\"tx_bytes-r\": continue\n\t\t\t\tif item2 ==\"rx_bytes-r\": continue\n\t\t\t\tll+= \"{}\".format(item2)+\":{}\".format(item[item2])+\"; \"\n\t\t\tout+=ll+(\"\\n\")\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintPortForWardInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata =self.executeCMDOnController(dataSEND={}, pageString=\"/stat/portforward/\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"== PortForward report ==\\n\"\n\t\tout += \"##\".ljust(4) + \"name\".ljust(20) + \"protocol\".ljust(10) + \"source\".ljust(16)\t+ \"fwd_port\".rjust(9)+ \"dst_port\".rjust(9)+ \" fwd_ip\".ljust(17)+ \"rx_bytes\".rjust(12)+ \"rx_packets\".rjust(12)+\"\\n\"\n\t\tii = 0\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tll = \"{}\".format(ii).ljust(4)\n\t\t\tll+= item[\"name\"].ljust(20)\n\t\t\tll+= item[\"proto\"].ljust(10)\n\t\t\tll+= item[\"src\"].ljust(16)\n\t\t\tll+= item[\"fwd_port\"].rjust(9)\n\t\t\tll+= item[\"dst_port\"].rjust(9)\n\t\t\tll+= (\" \"+item[\"fwd\"]).ljust(17)\n\t\t\tll+= \"{}\".format(item[\"rx_bytes\"]).rjust(12)\n\t\t\tll+= \"{}\".format(item[\"rx_packets\"]).rjust(12)\n\t\t\tout+=ll+(\"\\n\")\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintSessionInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ttoT\t\t= int(time.time()+100)\n\t\tfromT \t= toT - 30000\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/stat/session?type=all&start={:d}&end={:d}\".format(fromT,toT), jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"\\n\"\n\t\tii = 0\n\t\tfor xxx in data:\n\t\t\tii += 1\n\t\t\tout += \"== Session report == #{}, client: mac={} - {}\\n\".format(ii, xxx[\"mac\"], xxx[\"hostname\"])\n\t\t\tfor item in [\"ip\",\"is_wired\",\"is_guest\",\"rx_bytes\",\"tx_bytes\",\"ap_mac\"]:\n\t\t\t\tif item in xxx:\n\t\t\t\t\tout += \"{:35s}:{}\\n\".format(item, xxx[item])\n\t\t\t\telse:\n\t\t\t\t\tout += \"{:35s}: empty\\n\".format(item)\n\n\t\t\tout += (\"Accociated:\").ljust(35)+\"{} minutes ago\\n\".format(int(time.time()-xxx[\"assoc_time\"])/60)\n\t\t\tout += (\"Duration:\").ljust(35)+\"{} [secs]\\n\".format(xxx[\"duration\"])\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintAlarmInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/list/alarm/\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tself.unifsystemReport1(data, True, \" ==AlarmReport==\", limit=99999)\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintWifiConfigInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"/rest/wlanconf\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"\\n\"\n\t\tii = 0\n\t\tfor xxx in data:\n\t\t\tii += 1\n\t\t\tout += \"== Wifi Config report == # {}; SSID= {}\\n\".format(ii, xxx[\"name\"])\n\t\t\tfor item in xxx:\n\t\t\t\tif item not in [\"name\",\"site_id\",\"x_iapp_key\",\"_id\",\"wlangroup_id\"]:\n\t\t\t\t\tout += (\"{}\".format(item)+\":\").ljust(35)+\"{}\".format(xxx[item])+\"\\n\"\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintWifiChannelInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata =self.executeCMDOnController(dataSEND={}, pageString=\"/stat/current-channel\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"== Wifi Channel report ==\\n\"\n\t\tfor xxx in data:\n\t\t\tfor item in [\"code\",\"key\",\"name\"]:\n\t\t\t\t\tout += (\"{}\".format(item)+\":\").ljust(25)+\"{}\".format(xxx[item])+\"\\n\"\n\t\t\tfor item in xxx:\n\t\t\t\tif item not in [\"code\",\"key\",\"name\"]:\n\t\t\t\t\tout += (\"{}\".format(item)+\":\").ljust(25)+\"{}\".format(xxx[item])+\"\\n\"\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintEventInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\tlimit = 100\n\t\tif \"PrintEventInfoMaxEvents\" in valuesDict:\n\t\t\ttry:\tlimit = int(valuesDict[\"PrintEventInfoMaxEvents\"])\n\t\t\texcept: pass\n\n\t\tPrintEventInfoLoginEvents = False\n\t\tif \"PrintEventInfoLoginEvents\" in valuesDict:\n\t\t\ttry:\tPrintEventInfoLoginEvents = valuesDict[\"PrintEventInfoLoginEvents\"]\n\t\t\texcept: pass\n\n\n\t\tif PrintEventInfoLoginEvents:\n\t\t\tltype = \"WITH\"\n\t\t\tuseLimit = limit\n\t\telse:\n\t\t\tltype = \"Skipping\"\n\t\t\tuseLimit = 5*limit\n\t\tdata = self.executeCMDOnController(dataSEND={\"_sort\":\"+time\", \"_limit\":useLimit}, pageString=\"/stat/event/\", jsonAction=\"returnData\", cmdType=\"put\")\n\t\tself.unifsystemReport1(data, False, \" ==EVENTs ..; last {} events ; -- {} login events ==\".format(limit, ltype), limit, PrintEventInfoLoginEvents=PrintEventInfoLoginEvents)\n\t\tself.addToMenuXML(valuesDict)\n\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef updateDevStateswRXTXbytes(self):\n\t\ttry:\n\t\t\tif time.time() - self.lastupdateDevStateswRXTXbytes < 200: return \n\t\t\tself.lastupdateDevStateswRXTXbytes = time.time()\n\t\t\ten = int( time.time() ) * 1000\n\t\t\tst = en - 300*1000\n\t\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"tx_bytes\", \"rx_bytes\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/5minutes.user\", jsonAction=\"returnData\", cmdType=\"post\")\n\n\t\t\tif len(data) == 0: return\n\t\t\tMACbytes \t= {}\n\t\t\ttNow \t\t= int(time.time()*1000)\n\t\t\tanyUpdate \t= False\n\t\t\tmaxDT \t\t= 0\n\t\t\toneBad \t\t= False\n\t\t\tfor rec in data:\n\t\t\t\t#{\"rx_bytes\":1090.4761904761904,\"tx_bytes\":1428.904761904762,\"time\":1613157900000,\"user\":\"b8:27:eb:c8:c7:ab\",\"o\":\"user\",\"oid\":\"b8:27:eb:c8:c7:ab\"},\n\t\t\t\tif \"user\" not in rec or \"time\" not in rec or \"tx_bytes\" not in rec: \n\t\t\t\t\t#if not oneBad: self.indiLOG.log(10,\"updateDevStateswRXTXbytes user/time/rx tx_bytes not in rec:{}\".format(rec))\n\t\t\t\t\toneBad = True\n\t\t\t\t\tcontinue\n\n\t\t\t\tmaxDT = max( maxDT, tNow - rec[\"time\"] )\n\t\t\t\tif tNow - rec[\"time\"] > 605*1000: \n\t\t\t\t\tif not oneBad: self.indiLOG.log(10,\"updateDevStateswRXTXbytes bad time tNow:{}; recT:{}; dt:{}; rec:{}\".format(tNow, rec[\"time\"], tNow - rec[\"time\"], rec))\n\t\t\t\t\toneBad = True\n\t\t\t\t\tcontinue \n\t\t\t\ttry: \t\n\t\t\t\t\t## rx and tx are flipped in response \n\t\t\t\t\tMACbytes[rec[\"user\"]] = {\"txBytes\":int(rec[\"rx_bytes\"]),\"rxBytes\":int(rec[\"tx_bytes\"])}\n\t\t\t\texcept: \n\t\t\t\t\tif not oneBad: self.indiLOG.log(10,\"updateDevStateswRXTXbytes bad data rec:{}\".format(rec))\n\t\t\t\t\toneBad = True\n\t\t\t\t\tcontinue\n\n\t\t\tif oneBad and self.decideMyLog(\"Special\"): \n\t\t\t\tself.indiLOG.log(10,\"updateDevStateswRXTXbytes, data:{}\".format(data))\n\t\t\t\tself.indiLOG.log(10,\"updateDevStateswRXTXbytes, maxDT:{} MACBYTES:{}\".format(maxDT/1000, MACbytes))\n\n\t\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\t\tif not dev.enabled: continue\n\t\t\t\tmac = dev.address\n\t\t\t\tif \"rx_Bytes_Last5Minutes\" in dev.states:\n\t\t\t\t\tif mac in MACbytes:\n\t\t\t\t\t\ttx = MACbytes[mac][\"txBytes\"]\n\t\t\t\t\t\trx = MACbytes[mac][\"rxBytes\"]\n\t\t\t\t\telse:\n\t\t\t\t\t\ttx = 0\n\t\t\t\t\t\trx = 0\n\n\t\t\t\t\tif dev.states[\"rx_Bytes_Last5Minutes\"] != rx:\n\t\t\t\t\t\tanyUpdate = True\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"rx_Bytes_Last5Minutes\", rx)\n\t\t\t\t\tif dev.states[\"tx_Bytes_Last5Minutes\"] != tx:\n\t\t\t\t\t\tanyUpdate = True\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"tx_Bytes_Last5Minutes\", tx)\n\n\t\t\tif anyUpdate:\n\t\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint7DaysWiFiInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\ten = int( time.time() - (time.time() % 3600 ) ) * 1000\n\t\tst = en - 3600*1000*12*7 # 7 days\n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"rx_bytes\", \"tx_bytes\", \"num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/daily.ap\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.printWifiStatReport(data, \"== days WiFi-AP stat report ==\")\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint48HoursWiFiInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\ten = int( time.time() - (time.time() % 3600) ) * 1000\n\t\tst = en - 3600*1000*48 # \n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"rx_bytes\", \"tx_bytes\", \"num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/hourly.ap\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.printWifiStatReport(data, \"== hours WiFi-AP stat report ==\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint5MinutesWiFiInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\n\t\ten = int( time.time() ) * 1000\n\t\tst = en - 3600*1000*4 # 4 hours\n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"rx_bytes\", \"tx_bytes\", \"num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/5minutes.ap\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.printWifiStatReport(data, \"== minutes WiFi-AP stat report ==\")\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef printWifiStatReport(self, data, headLine):\n\t\tout = headLine+\"\\n\"\n\t\tout+= \"##\".ljust(4)\n\t\tout+= \"timeStamp\".ljust(21)\n\t\tout+= \"num_sta\".rjust(8)\n\t\tout+= \"rxBytes\".rjust(17)\n\t\tout+= \"txBytes\".rjust(17)\n\t\tout+= \"\\n\"\n\t\tii=0\n\t\tlastap = \"\"\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tif lastap != item[\"ap\"]:\n\t\t\t\tout+= \"AP MAC #:\"+item[\"ap\"]+\"\\n\"\n\t\t\tlastap = item[\"ap\"]\n\n\t\t\tll = \"{}\".format(ii).ljust(4)\n\t\t\tif \"time\" in item:\n\t\t\t\tll+= time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(item[\"time\"]/1000)).ljust(21)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(21)\n\n\t\t\tif \"num_sta\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"num_sta\"]).rjust(8)\n\t\t\telse:\t\t\t\t ll+= (\" \").rjust(8)\n\n\t\t\tif \"rx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"rx_bytes\"]))).rjust(17)\n\t\t\telse:\t\t\t\t ll+= (\" \").rjust(17)\n\t\t\tif \"tx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"tx_bytes\"]))).rjust(17)\n\t\t\telse:\t\t\t\t ll+= (\" \").rjust(17)\n\n\t\t\tout+=ll+(\"\\n\")\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint5MinutesWanInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ten = int( time.time() ) * 1000\n\t\tst = en - 3600 *1000*4 # 4 hours \n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"bytes\",\"wan-tx_bytes\",\"wan-rx_bytes\",\"wan-tx_bytes\", \"num_sta\", \"wlan-num_sta\", \"lan-num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/5minutes.site\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.unifsystemReport2(data,\"== 5 minutes WAN report ==\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint48HoursWanInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ten = int( time.time() - (time.time() % 3600) ) * 1000\n\t\tst = en - 2*86400*1000 # 2 days\n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"bytes\",\"wan-tx_bytes\",\"wan-rx_bytes\",\"wan-tx_bytes\", \"num_sta\", \"wlan-num_sta\", \"lan-num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/hourly.site\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.unifsystemReport2(data,\"== HOUR WAN report ==\")\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrint7DaysWanInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\ten = int( time.time() - (time.time() % 3600) ) * 1000\n\t\tst = en - 7*86400 *1000 # 7 days\n\t\tdata = self.executeCMDOnController(dataSEND={\"attrs\": [\"bytes\",\"wan-tx_bytes\",\"wan-rx_bytes\",\"wan-tx_bytes\", \"num_sta\", \"wlan-num_sta\", \"lan-num_sta\", \"time\"], \"start\": st, \"end\": en}, pageString=\"/stat/report/daily.site\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\tself.unifsystemReport2(data,\"== DAY WAN report ==\")\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonConfirmPrintWlanConfInfoFromControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tdata = self.executeCMDOnController(dataSEND={}, pageString=\"list/wlanconf\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\tout = \"==WLan Report ==\"+\"\\n\"\n\t\tout+= \" \".ljust(4+20+6+20)+\"bc_filter...\".ljust(6+15) +\"dtim .......\".ljust(8+3+3)+\"MAC_filter ........\".ljust(6+20+8)+\" \".ljust(15+8)+\"wpa......\".ljust(6+6)+\"\\n\"\n\t\tout+= \"##\".ljust(4)\n\t\tout+= \"name\".ljust(20)\n\t\tout+= \"passphrase\".ljust(20)\n\t\tout+= \"enble\".ljust(6)\n\t\tout+= \"enble\".ljust(6)\n\t\tout+= \"list\".ljust(15)\n\t\tout+= \"mode\".ljust(8)\n\t\tout+= \"na\".ljust(3)\n\t\tout+= \"ng\".ljust(3)\n\t\tout+= \"enble\".ljust(6)\n\t\tout+= \"list\".ljust(20)\n\t\tout+= \"policy\".ljust(8)\n\t\tout+= \"schedule\".ljust(15)\n\t\tout+= \"secrty\".ljust(8)\n\t\tout+= \"enc\".ljust(6)\n\t\tout+= \"mode\".ljust(6)\n\t\tout+= \"\\n\"\n\t\tii=0\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tll = \"{}\".format(ii).ljust(4)\n\t\t\tif \"name\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"name\"]).ljust(20)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(20)\n\n\t\t\tif \"x_passphrase\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"x_passphrase\"]).ljust(20)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(16)\n\n\t\t\tif \"enabled\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"enabled\"]).ljust(6)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(6)\n\n\t\t\tif \"bc_filter_enabled\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"bc_filter_enabled\"]).ljust(6)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(6)\n\n\t\t\tif \"bc_filter_list\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"bc_filter_list\"]).ljust(15)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(15)\n\n\t\t\tif \"dtim_mode\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"dtim_mode\"]).ljust(8)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(8)\n\n\t\t\tif \"dtim_na\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"dtim_na\"]).ljust(3)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(3)\n\n\t\t\tif \"dtim_ng\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"dtim_ng\"]).ljust(3)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(3)\n\n\t\t\tif \"mac_filter_enabled\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"mac_filter_enabled\"]).ljust(6)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(6)\n\n\t\t\tif \"mac_filter_list\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"mac_filter_list\"]).ljust(20)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(20)\n\n\t\t\tif \"mac_filter_policy\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"mac_filter_policy\"]).ljust(8)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(8)\n\n\t\t\tif \"schedule\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"schedule\"]).ljust(15)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(15)\n\n\t\t\tif \"security\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"security\"]).ljust(8)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(8)\n\n\t\t\tif \"wpa_enc\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"wpa_enc\"]).ljust(6)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(6)\n\n\t\t\tif \"wpa_mode\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"wpa_mode\"]).ljust(6)\n\t\t\telse:\t\t\t\t ll+= (\" \").ljust(6)\n\n\n\t\t\tout+=ll+(\"\\n\")\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef unifsystemReport1(self, data, useName, start, limit, PrintEventInfoLoginEvents=False):\n\t\tout =start+\"\\n\"\n\t\tif useName:\n\t\t\tout+= \"##### datetime------\".ljust(21+6) + \"name---\".ljust(30) + \"subsystem--\".ljust(12) + \"key--------\".ljust(30) + \"msg-----\".ljust(50)+\"\\n\"\n\t\telse:\n\t\t\tout+= \"##### datetime------\".ljust(21+6) + \"subsystem--\".ljust(12) + \"key--------\".ljust(30) + \"msg-----\".ljust(50)+\"\\n\"\n\t\tnn = 0\n\t\tfor item in data:\n\t\t\tif not PrintEventInfoLoginEvents and item[\"msg\"].find(\"log in from \")> -1: continue\n\t\t\tnn+=1\n\t\t\tif nn > limit: break\n\t\t\t## convert from UTC to local datetime string\n\t\t\tdd = datetime.datetime.strptime(item[\"datetime\"],\"%Y-%m-%dT%H:%M:%SZ\")\n\t\t\txx = (dd - datetime.datetime(1970,1,1)).total_seconds()\n\t\t\tll = \"{}\".format(nn).ljust(6)\n\t\t\tll += time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(xx)).ljust(21)\n\t\t\tif useName:\n\t\t\t\tfound = False\n\t\t\t\tfor\t xx in [\"ap_name\",\"gw_name\",\"sw_name\",\"ap_name\"]:\n\t\t\t\t\tif xx in item:\n\t\t\t\t\t\tll+= item[xx].ljust(30)\n\t\t\t\t\t\tfound = True\n\t\t\t\t\t\tbreak\n\t\t\t\tif not found:\n\t\t\t\t\t\tll+= \" \".ljust(30)\n\t\t\tll +=item[\"subsystem\"].ljust(12) + item[\"key\"].ljust(30) + item[\"msg\"].ljust(50)\n\t\t\tout+= ll.replace(\"\\n\",\"\")+\"\\n\"\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn \n\n\t####-----------------\t ---------\n\tdef unifsystemReport2(self,data, start):\n\t\tout = start+\"\\n\"\n\t\tout+= \"##\".ljust(4)\n\t\tout+= \"timeStamp\".ljust(21)\n\t\tout+= \"lanNumSta\".ljust(11)\n\t\tout+= \"num_sta\".ljust(11)\n\t\tout+= \"wlanNumSta\".ljust(11)\n\t\tout+= \"rx-WanBytes\".rjust(20)\n\t\tout+= \"tx-WanBytes\".rjust(20)\n\t\tout+= \"\\n\"\n\t\tii=0\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tll = \"{}\".format(ii).ljust(4)\n\t\t\tif \"time\" in item:\n\t\t\t\tll+= time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(item[\"time\"]/1000)).ljust(21)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(21)\n\n\t\t\tif \"lan-num_sta\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"lan-num_sta\"]).ljust(11)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(10)\n\n\t\t\tif \"num_sta\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"num_sta\"]).ljust(11)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(11)\n\n\t\t\tif \"wlan-num_sta\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"wlan-num_sta\"]).ljust(11)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(11)\n\n\t\t\tif \"wan-rx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"wan-rx_bytes\"]))).rjust(20)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(20)\n\n\t\t\tif \"wan-tx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"wan-tx_bytes\"]))).rjust(20)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(20)\n\n\t\t\tfor item2 in item:\n\t\t\t\tif item2 == \"lan-num_sta\":\t\tcontinue\n\t\t\t\tif item2 == \"wlan-num_sta\":\tcontinue\n\t\t\t\tif item2 == \"num_sta\":\t\t\tcontinue\n\t\t\t\tif item2 == \"wan-rx_bytes\":\tcontinue\n\t\t\t\tif item2 == \"wan-tx_bytes\":\tcontinue\n\t\t\t\tif item2 == \"time\":\t\t\tcontinue\n\t\t\t\tif item2 == \"oid\":\t\t\t\tcontinue\n\t\t\t\tif item2 == \"site\":\t\t\tcontinue\n\t\t\t\tif item2 == \"o\":\t\t\t\tcontinue\n\t\t\t\tll+= \" {}\".format(item2)+\":{}\".format(item[item2])+\";....\"\n\n\t\t\tout+=ll+(\"\\n\")\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef unifsystemReport3(self,data, start):\n\t\tout =start+\"\\n\"\n\t\tout+= \"##\".ljust(4) + \"mac\".ljust(18)\n\t\tout+= \"hostname\".ljust(21) + \"name\".ljust(21)\n\t\tout+= \"first_seen\".ljust(21)+ \"ulast_seen\".ljust(21)\n\t\tout+= \"vendor\".ljust(10)\n\t\tout+= \"fixedIP\".ljust(16)\n\t\tout+= \"us_f-ip\".ljust(8)\n\t\tout+= \"wired\".ljust(6)\n\t\tout+= \"blckd\".ljust(6)\n\t\tout+= \"guest\".ljust(6)\n\t\tout+= \"durationMin\".rjust(12)\n\t\tout+= \"rx_KBytes\".rjust(16)\n\t\tout+= \"rx_Packets\".rjust(15)\n\t\tout+= \"rx_KBytes\".rjust(16)\n\t\tout+= \"tx_Packets\".rjust(15)\n\t\tout+=\"\\n\"\n\t\tii=0\n\t\tfor item in data:\n\t\t\tii+=1\n\t\t\tll = \"{}\".format(ii).ljust(4)\n\t\t\tif \"mac\" in item:\n\t\t\t\tll+= item[\"mac\"].ljust(18)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(18)\n\n\t\t\tif \"hostname\" in item:\n\t\t\t\tll+= (item[\"hostname\"][0:20]).ljust(21)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(21)\n\n\t\t\tif \"name\" in item:\n\t\t\t\tll+= (item[\"name\"][0:20]).ljust(21)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(21)\n\n\t\t\tif \"first_seen\" in item:\n\t\t\t\tll+= time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(item[\"first_seen\"])).ljust(21)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(21)\n\n\t\t\tif \"last_seen\" in item:\n\t\t\t\tll+= time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(item[\"last_seen\"])).ljust(21)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(21)\n\n\t\t\tif \"oui\" in item:\n\t\t\t\tll+= (item[\"oui\"][0:20]).ljust(10)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(10)\n\n\t\t\tif \"fixed_ip\" in item:\n\t\t\t\tll+= (item[\"fixed_ip\"]).ljust(16)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(16)\n\n\t\t\tif \"use_fixedip\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"use_fixedip\"]).ljust(8)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(8)\n\n\t\t\tif \"is_wired\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"is_wired\"]).ljust(6)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(6)\n\n\t\t\tif \"blocked\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"blocked\"]).ljust(6)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(6)\n\n\t\t\tif \"is_guest\" in item:\n\t\t\t\tll+= \"{}\".format(item[\"is_guest\"]).ljust(6)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(6)\n\n\t\t\tif \"duration\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"duration\"])/60)).rjust(12)\n\t\t\telse:\n\t\t\t\tll+= (\" \").rjust(12)\n\n\t\t\tif \"rx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"rx_bytes\"]/1024.))).rjust(16)\n\t\t\telse:\n\t\t\t\tll+= (\" \").rjust(16)\n\n\t\t\tif \"rx_packets\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"rx_packets\"]))).rjust(15)\n\t\t\telse:\n\t\t\t\tll+= (\" \").rjust(15)\n\n\t\t\tif \"tx_bytes\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"tx_bytes\"]/1024.))).rjust(16)\n\t\t\telse:\n\t\t\t\tll+= (\" \").rjust(16)\n\n\t\t\tif \"tx_packets\" in item:\n\t\t\t\tll+= (\"{0:,d}\".format(int(item[\"tx_packets\"]))).rjust(15)\n\t\t\telse:\n\t\t\t\tll+= (\" \").ljust(15)\n\n\t\t\tfor item2 in item:\n\t\t\t\tif item2 ==\"hostname\":\t continue\n\t\t\t\tif item2 ==\"mac\":\t\t\tcontinue\n\t\t\t\tif item2 ==\"first_seen\":\tcontinue\n\t\t\t\tif item2 ==\"last_seen\":\tcontinue\n\t\t\t\tif item2 ==\"site_id\":\t \tcontinue\n\t\t\t\tif item2 ==\"_id\":\t\t \tcontinue\n\t\t\t\tif item2 ==\"network_id\": continue\n\t\t\t\tif item2 ==\"usergroup_id\": continue\n\t\t\t\tif item2 ==\"noted\":\t\tcontinue\n\t\t\t\tif item2 ==\"name\":\t\t\tcontinue\n\t\t\t\tif item2 ==\"is_wired\":\t\tcontinue\n\t\t\t\tif item2 ==\"oui\":\t\t\tcontinue\n\t\t\t\tif item2 ==\"blocked\":\t\tcontinue\n\t\t\t\tif item2 ==\"fixed_ip\":\t\tcontinue\n\t\t\t\tif item2 ==\"use_fixedip\":\tcontinue\n\t\t\t\tif item2 ==\"is_guest\":\t\tcontinue\n\t\t\t\tif item2 ==\"duration\":\t\tcontinue\n\t\t\t\tif item2 ==\"rx_bytes\":\t\tcontinue\n\t\t\t\tif item2 ==\"tx_bytes\":\t\tcontinue\n\t\t\t\tif item2 ==\"tx_packets\":\tcontinue\n\t\t\t\tif item2 ==\"rx_packets\": continue\n\t\t\t\tll+= \"{}\".format(item2)+\":{}\".format(item[item2])+\";....\"\n\t\t\tout+=ll+(\"\\n\")\n\n\n\t\tself.indiLOG.log(20,\"unifi-Report \")\n\t\tself.indiLOG.log(20,\"unifi-Report \"+out)\n\t\treturn\n\n\n######## reports end\n\n\n\n######## actions and menu set leds on /off\n\t####-----------------\t ---------\n\tdef buttonConfirmAPledONControllerCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmAPledONControllerCALLBACK(valuesDict=action1.props)\n\t####-----------------\t ---------\n\tdef buttonConfirmAPledONControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"led_enabled\":True}, pageString=\"/set/setting/mgmt\", cmdType=\"post\")\n\t\tfor rr in ret:\n\t\t\tif len(rr) ==0: continue\n\t\t\tif \"led_enabled\" in rr: \n\t\t\t\tself.indiLOG.log(10, \"LED cmd ret:{}\".format(rr) )\n\t\t\t\tvaluesDict[\"MSG\"] = \"LED cmd enabled:{}\".format(rr[\"led_enabled\"])\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmAPledOFFControllerCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmAPledOFFControllerCALLBACK(valuesDict=action1.props)\n\t####-----------------\t ---------\n\tdef buttonConfirmAPledOFFControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"led_enabled\":False}, pageString=\"/set/setting/mgmt\", cmdType=\"post\")\n\t\tfor rr in ret:\n\t\t\tif len(rr) ==0: continue\n\t\t\tif \"led_enabled\" in rr: \n\t\t\t\tself.indiLOG.log(10, \"LED cmd ret:{}\".format(rr) )\n\t\t\t\tvaluesDict[\"MSG\"] = \"LED cmd enabled:{}\".format(rr[\"led_enabled\"])\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmAPxledONControllerCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmAPxledONControllerCALLBACK(valuesDict=action1.props)\n\t####-----------------\t ---------\n\tdef buttonConfirmAPxledONControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"cmd\":\"set-locate\",\"mac\":valuesDict[\"selectedAPDevice\"]}, pageString=\"/cmd/devmgr\", cmdType=\"post\")\n\t\tvaluesDict[\"MSG\"] = \"command send\"\n\t\tself.indiLOG.log(10,\"set-locate cmd: return {}\".format(ret) )\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmAPxledOFFControllerCALLBACKaction(self, action1=None):\n\t\treturn self.buttonConfirmAPxledOFFControllerCALLBACK(valuesDict=action1.props)\n\t####-----------------\t ---------\n\tdef buttonConfirmAPxledOFFControllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.executeCMDOnController(dataSEND={\"cmd\":\"unset-locate\",\"mac\":valuesDict[\"selectedAPDevice\"]}, pageString=\"/cmd/devmgr\", cmdType=\"post\")\n\t\tvaluesDict[\"MSG\"] = \"command send\"\n\t\tself.indiLOG.log(10,\"unset-locate cmd: return {}\".format(ret) )\n\t\treturn valuesDict\n\n######## actions and menu set dev on /off\n\t####-----------------\t ---------\n\tdef buttonConfirmEnableAPConllerCALLBACKaction(self, action1=None):\n\t\tself.execDisableAP(action1.props,False)\n\tdef buttonConfirmEnableAPConllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.execDisableAP(valuesDict, False)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonConfirmDisableAPConllerCALLBACKaction(self, action1=None):\n\t\tself.execDisableAP(action1.props, True)\n\tdef buttonConfirmDisableAPConllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tret = self.execDisableAP(valuesDict, True)\n\t\treturn valuesDict\n\n\tdef execDisableAP(self, valuesDict , disable): #( true if disable )\n\t\tdev = indigo.devices[int(valuesDict[\"apDeviceSelected\"])]\n\t\tID = dev.states[\"ap_id\"]\n\t\tip = dev.states[\"ipNumber\"]\n\t\tif disable: self.setSuspend(ip, time.time() + 99999999)\n\t\telse\t : self.delSuspend(ip)\n\t\tvaluesDict[\"MSG\"] = \"command send\"\n\t\tret = self.executeCMDOnController(dataSEND={\"disabled\":disable}, pageString=\"/rest/device/\"+ID, cmdType=\"put\", cmdTypeForce=True)\n\t\tfor rr in ret:\n\t\t\tif len(rr) ==0: continue\n\t\t\tif \"disabled\" in rr: \n\t\t\t\tself.indiLOG.log(10, \"enable ret:{}\".format(rr) )\n\t\t\t\tvaluesDict[\"MSG\"] = \"enabled:{}\".format(not rr[\"disabled\"])\n\t\tself.indiLOG.log(10,\"execDisableAP cmd: return {}\".format(ret) )\n\t\treturn ret\n\n######## actions and menu restart unifi devices\n\t####-----------------\t ---------\n\tdef buttonConfirmRestartUnifiDeviceConllerCALLBACKaction(self, action1=None):\n\t\tself.buttonConfirmRestartUnifiDeviceConllerCALLBACK(action1.props)\n\n\tdef buttonConfirmRestartUnifiDeviceConllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tmac = valuesDict[\"selectedUnifiDevice\"]\n\t\tif not self.isValidMAC(mac): \n\t\t\tvaluesDict[\"MSG\"] = \"no valid mac given:{}\".format(mac)\n\t\t\treturn valuesDict\n\t\tvaluesDict[\"MSG\"] = \"restart command send to:{}\".format(mac)\n\t\tret = self.executeCMDOnController(dataSEND={'cmd':'restart','mac':mac}, pageString=\"/cmd/devmgr\", cmdType=\"post\", cmdTypeForce=True)\n\t\tself.indiLOG.log(10,\"restart cmd: return {}\".format(ret) )\n\t\treturn valuesDict\n\n\n######## actions and menu provision unifi devices\n\t####-----------------\t ---------\n\tdef buttonConfirmProvisionUnifiDeviceConllerCALLBACKaction(self, action1=None):\n\t\tself.buttonConfirmProvisionUnifiDeviceConllerCALLBACK(action1.props)\n\n\tdef buttonConfirmProvisionUnifiDeviceConllerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\"):\n\t\tmac = valuesDict[\"selectedUnifiDeviceProvision\"]\n\t\tif not self.isValidMAC(mac): \n\t\t\tvaluesDict[\"MSG\"] = \"no valid mac given:{}\".format(mac)\n\t\t\treturn valuesDict\n\t\tvaluesDict[\"MSG\"] = \"Provision command send to:{}\".format(mac)\n\t\tdataDict = {'cmd':'force-provision','mac':mac}\n\t\tpage = \"/cmd/devmgr\"\n\t\tret = self.executeCMDOnController(dataSEND=dataDict, pageString=page, cmdType=\"post\", cmdTypeForce=True, repeatIfFailed=False)\n\t\tself.indiLOG.log(20,\"provision cmd: return {}\".format(ret) )\n\t\treturn valuesDict\n\n\n\n\n\n\n######## set defaults for action and menu screens\n\t#/////////////////////////////////////////////////////////////////////////////////////\n\t# Actions Configuration\n\t#/////////////////////////////////////////////////////////////////////////////////////\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the actions for the plugin; you normally don't need to\n\t# override this as the base class returns the actions from the Actions.xml file\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getActionsDict(self):\n\t\treturn super(Plugin, self).getActionsDict()\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine obtains the callback method to execute when the action executes; it\n\t# normally just returns the action callback specified in the Actions.xml file\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getActionCallbackMethod(self, typeId):\n\t\treturn super(Plugin, self).getActionCallbackMethod(typeId)\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the configuration XML for the given action; normally this is\n\t# pulled from the Actions.xml file definition and you need not override it\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getActionConfigUiXml(self, typeId, devId):\n\t\treturn super(Plugin, self).getActionConfigUiXml(typeId, devId)\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the UI values for the action configuration screen prior to it\n\t# being shown to the user\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t####-----------------\t ---------\n\tdef getActionConfigUiValues(self, pluginProps, typeId, devId):\n\t\tif \"fileNameOfImage\" in pluginProps:\n\t\t\tif len(self.changedImagePath) > 5:\n\t\t\t\tpluginProps[\"fileNameOfImage\"] = self.changedImagePath+\"nameofCamera.jpeg\"\n\t\t\telse:\n\t\t\t\tpluginProps[\"fileNameOfImage\"] = self.indigoPreferencesPluginDir+\"nameofCamera.jpeg\"\n\t\treturn super(Plugin, self).getActionConfigUiValues(pluginProps, typeId, devId)\n\n\n\t#/////////////////////////////////////////////////////////////////////////////////////\n\t# Menu Item Configuration\n\t#/////////////////////////////////////////////////////////////////////////////////////\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the menu items for the plugin; you normally don't need to\n\t# override this as the base class returns the menu items from the MenuItems.xml file\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getMenuItemsList(self):\n\t\treturn super(Plugin, self).getMenuItemsList()\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the configuration XML for the given menu item; normally this is\n\t# pulled from the MenuItems.xml file definition and you need not override it\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\tdef getMenuActionConfigUiXml(self, menuId):\n\t\treturn super(Plugin, self).getMenuActionConfigUiXml(menuId)\n\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t# This routine returns the initial values for the menu action config dialog, if you\n\t# need to set them prior to the GUI showing\n\t#-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\t####-----------------\t ---------\n\tdef getMenuActionConfigUiValues(self, menuId):\n\t\tvaluesDict = indigo.Dict()\n\t\tself.menuXML ={}\n\t\tif menuId == \"CameraActions\" and (\"fileNameOfImage\" not in self.menuXML or len(self.menuXML[\"fileNameOfImage\"]) <10 ):\n\t\t\tif len(self.changedImagePath) > 5:\n\t\t\t\tself.menuXML[\"fileNameOfImage\"] = self.changedImagePath+\"nameofCamera.jpeg\"\n\t\t\telse:\n\t\t\t\tself.menuXML[\"fileNameOfImage\"] = \"/tmp/nameofCamera.jpeg\"\n\t\tself.menuXML[\"snapShotTextMethod\"] = self.imageSourceForSnapShot\n\t\tself.menuXML[\"fileNameOfImage\"] = self.completePath(self.changedImagePath)+\"snapshot.jpeg\"\n\t\tself.menuXML[\"MSG\"] = \"\"\n\t\t\n\n\t\tfor item in self.menuXML:\n\t\t\tvaluesDict[item] = self.menuXML[item]\n\t\terrorsDict = indigo.Dict()\n\t\t#self.indiLOG.log(20,\"getMenuActionConfigUiValues - menuId:{}\".format(menuId))\n\t\treturn (valuesDict, errorsDict)\n\n\n\n######## check if we have new unifi system devces, if yes: litt basic variables and request a reboot\n\t####-----------------\t ---------\n\tdef checkForNewUnifiSystemDevices(self):\n\t\ttry:\n\t\t\tif not self.enablecheckforUnifiSystemDevicesState : return\n\t\t\tif self.checkforUnifiSystemDevicesState == \"\": \t\treturn\n\t\t\tself.checkforUnifiSystemDevicesState = \"\"\n\t\t\tif self.unifiCloudKeyMode != \"ON\"\t\t\t: \t\treturn\n\t\t\tnewDeviceFound = []\n\n\t\t\tdeviceDict =\t\tself.executeCMDOnController( pageString=\"/stat/device/\", jsonAction=\"returnData\", cmdType=\"get\")\n\t\t\tif self.decideMyLog(\"DictFile\"): \n\t\t\t\tself.writeJson( deviceDict, fName=\"{}dict-Controller.json\".format(self.indigoPreferencesPluginDir), sort=False, doFormat=True )\n\n\t\t\tif deviceDict == []: return\n\n\t\t\tdevType =\"\"\n\t\t\tcounter = 0\n\t\t\tfor device in deviceDict:\n\t\t\t\tcounter +=1\n\t\t\t\tipNumber = \"\"\n\t\t\t\tMAC\t\t = \"\"\n\t\t\t\tif \"type\" not in device: continue\n\t\t\t\tuType\t = device[\"type\"]\n\n\t\t\t\tif uType == \"ugw\":\n\t\t\t\t\tif \"network_table\" in device:\n\t\t\t\t\t\tfor nwt in device[\"network_table\"]:\n\t\t\t\t\t\t\tif \"mac\" in nwt and \"ip\" in nwt and \"name\" in nwt and nwt[\"name\"].lower() == \"lan\":\n\t\t\t\t\t\t\t\tipNumber = nwt[\"ip\"]\n\t\t\t\t\t\t\t\tMAC\t\t = nwt[\"mac\"]\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t#### do nto handle UDM type devices (yet)\n\t\t\t\telif uType.find(\"udm\") > -1:\n\t\t\t\t\tcontinue\n\n\t\t\t\telse:\n\t\t\t\t\tif \"mac\" in device and \"ip\" in device:\n\t\t\t\t\t\tipNumber = device[\"ip\"]\n\t\t\t\t\t\tMAC\t\t = device[\"mac\"]\n\n\t\t\t\tif MAC == \"\" or ipNumber == \"\":\n\t\t\t\t\tcontinue\n\n\t\t\t\tfound = False\n\t\t\t\tname = \"--\"\n\n\t\t\t\tfor dev in indigo.devices.iter(\"props.isAP,props.isSwitch,props.isGateway\"):\n\t\t\t\t\tif \"MAClan\" in dev.states and dev.states[\"MAClan\"] == MAC:\n\t\t\t\t\t\tfound = True\n\t\t\t\t\t\tname = dev.name\n\t\t\t\t\t\tbreak\n\t\t\t\t\tif \"MAC\" in dev.states and dev.states[\"MAC\"] == MAC:\n\t\t\t\t\t\tfound = True\n\t\t\t\t\t\tname = dev.name\n\t\t\t\t\t\tbreak\n\t\t\t\t\t\t## found devce no action\n\n\n\n\t\t\t\tif uType.find(\"usw\") >-1: # check for miniswitches, they can not be ssh-ed, only info is in controller db\n\t\t\t\t\tfor i in range(len(self.ipNumbersOf[\"SW\"])):\n\t\t\t\t\t\tif not self.isMiniSwitch[i]: \t\t\t\t\t\tcontinue \n\t\t\t\t\t\tif ipNumber != self.ipNumbersOf[\"SW\"][i]: \t\t\tcontinue\n\t\t\t\t\t\tif not self.isValidIP(self.ipNumbersOf[\"SW\"][i]): \tcontinue\n\t\t\t\t\t\tself.doMimiTypeSwitchesWithControllerData(device, i, found)\n\t\t\t\t\t\tbreak\n\n\n\t\t\t\tif not found:\n\n\t\t\t\t\tif uType.find(\"uap\") >-1:\n\t\t\t\t\t\tfor i in range(len(self.ipNumbersOf[\"AP\"])):\n\t\t\t\t\t\t\tif\tnot self.isValidIP(self.ipNumbersOf[\"AP\"][i]):\n\t\t\t\t\t\t\t\tnewDeviceFound.append(\"uap:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"AP\"][i]) )\n\t\t\t\t\t\t\t\tself.ipNumbersOf[\"AP\"][i]\t\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ip{}\".format(i)]\t\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipON{}\".format(i)]\t\t= True\n\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t\t= \"reboot\"\n\t\t\t\t\t\t\t\tnewDeviceFound.append(\"uap: {}\".format(i)+\", \"+ipNumber)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif self.ipNumbersOf[\"AP\"][i]\t == ipNumber:\n\t\t\t\t\t\t\t\t\tif not self.devsEnabled[\"AP\"][i]: break # we know this one but it is disabled on purpose\n\t\t\t\t\t\t\t\t\tnewDeviceFound.append(\"uap:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"AP\"][i] ) )\n\t\t\t\t\t\t\t\t\tself.ipNumbersOf[\"AP\"][i]\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\t\t#self.devsEnabled[\"AP\"][i]\t\t\t\t\t\t= True # will be enabled after restart\n\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipON{}\".format(i)]\t= True\n\t\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t= \"reboot\"\n\t\t\t\t\t\t\t\t\tnewDeviceFound.append(\"uap: {}\".format(i)+\", \"+ipNumber)\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\telif uType.find(\"usw\") >-1:\n\t\t\t\t\t\tfor i in range(len(self.ipNumbersOf[\"SW\"])):\n\t\t\t\t\t\t\tif\tnot self.isValidIP(self.ipNumbersOf[\"SW\"][i] ):\n\t\t\t\t\t\t\t\tnewDeviceFound.append(\"usw:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"SW\"][i]) )\n\t\t\t\t\t\t\t\tself.ipNumbersOf[\"SW\"][i]\t\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipSW{}\".format(i)]\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipSWON{}\".format(i)]\t\t= True\n\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t\t= \"reboot\"\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif self.ipNumbersOf[\"SW\"][i] == ipNumber:\n\t\t\t\t\t\t\t\t\tif not self.devsEnabled[\"SW\"][i]: break # we know this one but it is disabled on purpose\n\t\t\t\t\t\t\t\t\tnewDeviceFound.append(\"usw:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"SW\"][i]) )\n\t\t\t\t\t\t\t\t\tself.ipNumbersOf[\"SW\"][i]\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\t\t#self.devsEnabled[\"SW\"][i]\t\t\t\t\t\t= True # will be enabled after restart\n\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipSWON{}\".format(i)]\t= True\n\t\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t= \"reboot\"\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\telif uType.find(\"ugw\") >-1:\n\t\t\t\t\t\t\t#### \"ip\" in the dict is the ip number of the wan connection NOT the inernal IP for the gateway !!\n\t\t\t\t\t\t\t#### using 2 other places instead to get the LAN IP\n\t\t\t\t\t\t\tif\tnot self.isValidIP(self.ipNumbersOf[\"GW\"]):\n\t\t\t\t\t\t\t\tnewDeviceFound.append(\"ugw:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"GW\"]) )\n\t\t\t\t\t\t\t\tself.ipNumbersOf[\"GW\"]\t\t\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipUGA\"]\t\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipUGAON\"]\t\t\t\t\t= True\n\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t\t= \"reboot\"\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif not self.devsEnabled[\"GW\"]: break # we know this one but it is disabled on purpose\n\t\t\t\t\t\t\t\tif self.ipNumbersOf[\"GW\"] != ipNumber:\n\t\t\t\t\t\t\t\t\tnewDeviceFound.append(\"ugw:\t , new {} existing: {}\".format(ipNumber, self.ipNumbersOf[\"GW\"]) )\n\t\t\t\t\t\t\t\t\tself.ipNumbersOf[\"GW\"]\t\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipUGA\"]\t\t\t\t= ipNumber\n\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipUGAON\"]\t\t\t= True\n\t\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t= \"reboot\"\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tnewDeviceFound.append(\"ugw:\t , new {} existing: {}\".format(ipNumber, self.devsEnabled[\"GW\"]) )\n\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"ipUGAON\"]\t\t\t= True\n\t\t\t\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t= \"reboot\"\n\n\t\t\tif self.checkforUnifiSystemDevicesState ==\"reboot\":\n\t\t\t\ttry:\n\t\t\t\t\tself.pluginPrefs[\"createUnifiDevicesCounter\"] = int(self.pluginPrefs[\"createUnifiDevicesCounter\"] ) +1\n\t\t\t\t\tif int(self.pluginPrefs[\"createUnifiDevicesCounter\"] ) > 1: # allow only 1 unsucessful try, then wait 10 minutes\n\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t\t = \"\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.indiLOG.log(10,\"Connection reboot required due to new UNIFI system device found:{}\".format(newDeviceFound))\n\t\t\t\texcept:\n\t\t\t\t\t\tself.checkforUnifiSystemDevicesState\t\t = \"\"\n\t\t\ttry:\tindigo.server.savePluginPrefs()\n\t\t\texcept: pass\n\n\t\t\tif self.checkforUnifiSystemDevicesState ==\"\":\n\t\t\t\tself.pluginPrefs[\"createUnifiDevicesCounter\"] = 0\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n\n\n\n\t####-----------------\t --------- This one is not working .. disabled in menu\n\tdef executeMCAconfigDumpOnGW(self, valuesDict=None,typeId=\"\" ):\n\t\tkeepList=[\"vpn\",\"port-forward\",\"service:radius-server\",\"service:dhcp-server\"]\n\t\tjsonAction=\"print\"\n\t\tret =[]\n\t\tif self.connectParams[\"commandOnServer\"][\"GWctrl\"].find(\"off\") == 0: return valuesDict\n\t\ttry:\n\t\t\tcmd = self.expectPath +\" \"\n\t\t\tcmd += \"'\"+self.pathToPlugin + self.connectParams[\"expectCmdFile\"][\"GWctrl\"] + \"' \" \n\t\t\tcmd += \"'\"+self.connectParams[\"UserID\"][\"unixDevs\"]+ \"' '\"+self.connectParams[\"PassWd\"][\"unixDevs\"]+ \"' \" \n\t\t\tcmd += self.ipNumbersOf[\"GW\"] + \" \" \n\t\t\tcmd += \"'\"+self.connectParams[\"promptOnServer\"][self.ipNumbersOf[\"GW\"]] + \"' \" \n\t\t\tcmd += \" XXXXsepXXXXX \" + \" \" \n\t\t\tcmd += \"\\\"\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][self.ipNumbersOf[\"GW\"]])+\"\\\"\"\n\t\t\tcmd += self.getHostFileCheck()\n\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\" UGA EXPECT CMD: {}\".format(cmd))\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"returned from expect-command: {}\".format(ret))\n\t\t\tdbJson, error= self.makeJson2(ret, \"XXXXsepXXXXX\")\n\t\t\toutLine = \"\"\n\t\t\tif jsonAction == \"print\":\n\t\t\t\tfor xx in keepList:\n\t\t\t\t\tif xx.find(\":\") >-1:\n\t\t\t\t\t\tyy = xx.split(\":\")\n\t\t\t\t\t\tif yy[0] in dbJson and yy[1] in dbJson[yy[0]]:\n\t\t\t\t\t\t\tshort = dbJson[yy[0]][yy[1]]\n\t\t\t\t\t\t\tif yy[1] ==\"dhcp-server\":\n\t\t\t\t\t\t\t\tfor z in short:\n\t\t\t\t\t\t\t\t\tif z ==\"shared-network-name\":\n\t\t\t\t\t\t\t\t\t\tfor zz in short[z]:\n\t\t\t\t\t\t\t\t\t\t\tfor zzz in short[z][zz]: # net_LAN_192.168.1.0-24\"\n\t\t\t\t\t\t\t\t\t\t\t\tif zzz ==\"subnet\":\n\t\t\t\t\t\t\t\t\t\t\t\t\tfor zzzz in short[z][zz][zzz]:\t# \"192.168.1.0/24\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor zzzzz in short[z][zz][zzz][zzzz]:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tif zzzzz ==\"static-mapping\":\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ts0 = short[z][zz][zzz][zzzz][zzzzz]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t## need to sort\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tu =[]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tv =[]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor t in s0:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tu.append((s0[t][\"mac-address\"],s0[t][\"ip-address\"]))\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tv.append((self.fixIP(s0[t][\"ip-address\"]),s0[t][\"ip-address\"],s0[t][\"mac-address\"]))\n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsortMacKey = sorted(u)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsortIPKey = sorted(v)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tout = \" static DHCP mappings:\\n\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfor m in range(len(sortMacKey)):\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tout += sortMacKey[m][0]+\" --> \"+ sortMacKey[m][1].ljust(20)+\" \" +sortIPKey[m][1].ljust(18)+\"--> \"+ sortIPKey[m][2]+\"\\n\"\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\toutLine += \"\\n==== UGA-setup ==== {}\".format(out)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\toutLine += \"\\n==== UGA-setup ==== {}:\\n{}\".format(xx,json.dumps(short,sort_keys=True,indent=2))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\toutLine += \"\\n==== UGA-setup ==== {} not in json returned from UGA\".format(xx)\n\t\t\t\t\telse:\n\t\t\t\t\t\tif xx in dbJson:\n\t\t\t\t\t\t\toutLine += \"\\n==== UGA-setup ==== {}:\\n{}\".format(xx,json.dumps(dbJson[xx],sort_keys=True,indent=2))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\toutLine += \"\\n==== UGA-setup ==== not in json returned from UGA\"\n\t\t\t\tself.indiLOG.log(20,outLine)\n\t\t\telse:\n\t\t\t\treturn valuesDict\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n\n\t####-----------------\t ---------\n\tdef getunifiOSAndPort(self):\n\t\ttry:\n\t\t\tif self.overWriteControllerPort != \"\":\n\t\t\t\tif self.unifControllerCheckPortNumber == \"0\": \n\t\t\t\t\trespCode = \"200\"\n\t\t\t\t\tself.unifiControllerOS = self.HTTPretCodes[respCode][\"os\"]\n\t\t\t\t\tself.unifiApiLoginPath = self.HTTPretCodes[respCode][\"unifiApiLoginPath\"]\n\t\t\t\t\tself.unifiApiWebPage = self.HTTPretCodes[respCode][\"unifiApiWebPage\"]\n\t\t\t\t\tself.unifiCloudKeyPort = self.overWriteControllerPort\n\t\t\t\t\tself.lastPortNumber\t = self.overWriteControllerPort\n\t\t\t\t\treturn True\n\n\t\t\t\telse:\n\t\t\t\t\tif self.unifiControllerOS != \"\" and self.lastPortNumber\t!= \"\": \n\t\t\t\t\t\treturn True\n\n\t\t\t\t\tcmd = \"https://{}:{}\".format(self.unifiCloudKeyIP, self.overWriteControllerPort)\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(20,\"getunifiOSAndPort cmd:{}\".format(cmd) )\n\t\t\t\t\tresp = requests.head(cmd, verify=False, timeout=self.requestTimeout)\n\n\t\t\t\trespCode = str(resp.status_code)\n\t\t\t\tif respCode in [\"200\", \"302\"]:\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(20,\"getunifiOSAndPort sucess: {}:{} ==> osCode:{}, OS:{}\".format(self.unifiCloudKeyIP,self.overWriteControllerPort, respCode, self.HTTPretCodes[respCode][\"os\"]))\n\t\t\t\t\tself.unifiControllerOS = self.HTTPretCodes[respCode][\"os\"]\n\t\t\t\t\tself.unifiApiLoginPath = self.HTTPretCodes[respCode][\"unifiApiLoginPath\"]\n\t\t\t\t\tself.unifiApiWebPage = self.HTTPretCodes[respCode][\"unifiApiWebPage\"]\n\t\t\t\t\tself.unifiCloudKeyPort = self.overWriteControllerPort\n\t\t\t\t\tself.lastPortNumber\t = self.overWriteControllerPort\n\t\t\t\t\treturn True\n\n\t\t\t\tself.indiLOG.log(40,\"getunifiOSAndPort {}: no contact to controller using overWriteControllerPort\".format(self.unifiCloudKeyIP, self.overWriteControllerPort))\n\t\t\t\treturn False\n\n\n\t\t\tif self.unifiControllerType == \"hosted\":\n\t\t\t\tret = \"302\"\n\t\t\t\tif self.unifiApiLoginPath == \"\" or self.unifiApiWebPage == \"\" or self.unifiControllerOS == \"\": logmsg = True\n\t\t\t\telse: logmsg = False\n\t\t\t\tself.unifiCloudKeyPort = self.overWriteControllerPort\n\t\t\t\tself.unifiControllerOS = self.HTTPretCodes[ret][\"os\"]\n\t\t\t\tself.unifiApiLoginPath = self.HTTPretCodes[ret][\"unifiApiLoginPath\"]\n\t\t\t\tself.unifiApiWebPage = self.HTTPretCodes[ret][\"unifiApiWebPage\"]\n\t\t\t\tif logmsg:\n\t\t\t\t\tself.indiLOG.log(10,\"getunifiOSAndPort setting OS:{}, port#:{} using ip#:{}, loginpath:{}, wepAPipAge:{}\".format(self.unifiControllerOS, self.unifiCloudKeyPort, self.unifiCloudKeyIP, self.unifiApiLoginPath, self.unifiApiWebPage) )\n\t\t\t\treturn True\t\t\t\t\n\n\t\t\tret \t\t\t= \"\"\n\t\t\tfor ii in range(3):\n\t\t\t\t# get port and which unifi os:\n\t\t\t\tif self.unifiControllerOS != \"\" and (\n\t\t\t\t\tself.unifiCloudKeyPort in self.tryHTTPPorts or (\n\t\t\t\t\t\tself.unifiCloudKeyPort == self.overWriteControllerPort and self.overWriteControllerPort !=\"\"\n\t\t\t\t\t) \n\t\t\t\t) : return True\n\t\t\t\tif self.overWriteControllerPort != \"\":\n\t\t\t\t\ttryport = [self.overWriteControllerPort]\n\t\t\t\telse:\n\t\t\t\t\tif self.lastPortNumber != \"\":\n\t\t\t\t\t\ttryport = [self.lastPortNumber] + self.tryHTTPPorts\n\t\t\t\t\telse:\n\t\t\t\t\t\ttryport = self.tryHTTPPorts\n\t\t\t\tself.indiLOG.log(10,\"getunifiOSAndPort existing os>{}< .. ip#>{}< .. trying ports>{}<\".format( self.unifiControllerOS, self.unifiCloudKeyIP, tryport ) )\n\t\t\t\tself.executeCMDOnControllerReset(calledFrom=\"getunifiOSAndPort\")\n\n\t\t\t\tfor port in tryport:\n\t\t\t\t\t# this cmd will return http code only (I= header only, -s = silent -o send std to null, -w print http reply code)\n\t\t\t\t\t# curl --insecure -I -s -o /dev/null -w \"%{http_code}\" 'https://192.168.1.2:8443'\n\t\t\t\t\tcmdOS = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -I -s -o /dev/null -w \\\"%{http_code}\\\" 'https://\"+self.unifiCloudKeyIP+\":\"+port+\"'\"\n\t\t\t\t\tret, err = self.readPopen(cmdOS)\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(10,\"getunifiOSAndPort trying port#:>{}< gives ret code:{}\".format(cmdOS, ret) )\n\t\t\t\t\tif ret in self.HTTPretCodes: \n\t\t\t\t\t\tself.unifiCloudKeyPort = port\n\t\t\t\t\t\tself.lastPortNumber\t = port\n\t\t\t\t\t\tself.unifiControllerOS = self.HTTPretCodes[ret][\"os\"]\n\t\t\t\t\t\tself.unifiApiLoginPath = self.HTTPretCodes[ret][\"unifiApiLoginPath\"]\n\t\t\t\t\t\tself.unifiApiWebPage = self.HTTPretCodes[ret][\"unifiApiWebPage\"]\n\t\t\t\t\t\tself.indiLOG.log(10,\"getunifiOSAndPort found OS:{}, port#:{} using ip#:{}\".format(self.unifiControllerOS, port, self.unifiCloudKeyIP) )\n\t\t\t\t\t\treturn True\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.indiLOG.log(10,\"getunifiOSAndPort trying port:{}, wrong ret code from curl test>{}< expecting {}, for contoller os >= 6.5.55 set port to 443 and checkcontroller port to OFF\".format(port, ret, self.HTTPretCodes) )\n\n\t\t\t\tself.sleep(1)\n\t\t\t\t\n\t\t\tif self.unifiControllerOS == \"\": \n\t\t\t\tself.indiLOG.log(30,\"getunifiOSAndPort bad return from unifi controller {}, no os and / port found, tried ports:{}\".format(self.unifiCloudKeyIP, tryport) )\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.indiLOG.log(40,\"getunifiOSAndPort ret:\\n>>{}<<\".format(\"{}\".format(ret)[0:100]) )\n\t\treturn False\n\n\n\t####-----------------\t ---------\n\tdef executeCMDOnControllerReset(self, wait=False, calledFrom=\"\"):\n\t\ttry:\n\t\t\tif calledFrom != \"\":\n\t\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"executeCMDOnControllerReset called from:{}\".format(calledFrom) )\n\t\t\tif self.unifiControllerSession != \"\":\n\t\t\t\ttry: self.unifiControllerSession.close()\n\t\t\t\texcept: pass\n\t\t\tself.unifiControllerSession = \"\"\n\t\t\tself.unifiControllerOS = \"\"\n\t\t\tself.lastUnifiCookieRequests = 0.\n\t\t\tself.lastUnifiCookieCurl = 0\t\n\t\t\tself.unifiCloudKeySiteNameGetNew = True\n\t\t\tif wait: self.sleep(1.0)\t\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t####-----------------\t ---------\n\tdef setunifiCloudKeySiteName(self, method=\"response\", cookies=\"\", headers=\"\" ):\n\t\ttry:\n\t\t\tif self.unifiControllerType == \"hosted\":\n\t\t\t\tself.indiLOG.log(10,\"setunifiCloudKeySiteName set site name to default\")\n\t\t\t\tself.unifiCloudKeySiteName = \"default\"\n\t\t\t\treturn True\n\n\t\t\telif method == \"response\":\n\t\t\t\turlSite\t= \"https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+\"/proxy/network/api/self/sites\"\n\t\t\t\tret\t= self.unifiControllerSession.get(urlSite, cookies=cookies, headers=headers, timeout=self.requestTimeout, verify=False).text\n\t\t\t\t# should get: {\"meta\":{\"rc\":\"ok\"},\"data\":[{\"_id\":\"5750f2ade4b04dab3d3d0d4f\",\"name\":\"default\",\"desc\":\"stanford\",\"attr_hidden_id\":\"default\",\"attr_no_delete\":true,\"role\":\"admin\",\"role_hotspot\":false}]}\n\n\t\t\telif method == \"curl\":\n\t\t\t\tcmdSite = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure 'https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+\"/api/self/sites'\"\n\t\t\t\t#cmdSite = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" 'https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+\"/api/self/sites'\"\n\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"):self.indiLOG.log(10,\"setunifiCloudKeySiteName cmd:{}\".format(cmdSite))\n\t\t\t\tret, err = self.readPopen(cmdSite)\n\t\t\t\t# should get: {\"meta\":{\"rc\":\"ok\"},\"data\":[{\"_id\":\"5750f2ade4b04dab3d3d0d4f\",\"name\":\"default\",\"desc\":\"stanford\",\"attr_hidden_id\":\"default\",\"attr_no_delete\":true,\"role\":\"admin\",\"role_hotspot\":false}]}\n\n\t\t\telse:\n\t\t\t\treturn False\n\n\t\t\tif self.decideMyLog(\"ConnectionRET\"):self.indiLOG.log(10,\"setunifiCloudKeySiteName ret text:{}\".format(\"{}\".format(ret)))\n\n\t\t\ttry:\n\t\t\t\tdictRET = json.loads(ret)\n\t\t\texcept :\n\t\t\t\tself.indiLOG.log(30,\"setunifiCloudKeySiteName for {} has error, getting site ID, no json object returned: >>{}<<\".format(self.unifiCloudKeyIP, \"{}\".format(ret)))\n\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"setunifiCloudKeySiteName1\")\n\t\t\t\treturn False\n\t\t\n\t\t\t\t\n\t\t\toneFound = False\n\t\t\tif \"meta\" in dictRET and \"rc\" in dictRET[\"meta\"] and dictRET[\"meta\"][\"rc\"] == \"ok\" and \"data\" in dictRET:\n\t\t\t\tif len(dictRET[\"data\"]) >0:\n\t\t\t\t\tfor site in dictRET[\"data\"]:\n\t\t\t\t\t\tif \"name\" in site:\n\t\t\t\t\t\t\tif not oneFound: \n\t\t\t\t\t\t\t\tself.unifiCloudKeyListOfSiteNames = []\n\t\t\t\t\t\t\t\toneFound = True\n\t\t\t\t\t\t\tif site[\"name\"] not in self.unifiCloudKeyListOfSiteNames:\n\t\t\t\t\t\t\t\tself.unifiCloudKeyListOfSiteNames.append(site[\"name\"])\n\n\t\t\t\t\tif self.unifiCloudKeyListOfSiteNames != []:\n\t\t\t\t\t\tif self.unifiCloudKeySiteName not in self.unifiCloudKeyListOfSiteNames:\n\t\t\t\t\t\t\tif self.unifiCloudKeySiteName ==\"\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"setunifiCloudKeySiteName setting site id name to >>{}<<, was empty, available list:{}\".format(self.unifiCloudKeyListOfSiteNames[0], self.unifiCloudKeyListOfSiteNames))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"setunifiCloudKeySiteName overwriting site id name with >>{}<<, was {}, available list:{}\".format(self.unifiCloudKeyListOfSiteNames[0], self.unifiCloudKeySiteName, self.unifiCloudKeyListOfSiteNames))\n\t\t\t\t\t\t\tself.unifiCloudKeySiteName = self.unifiCloudKeyListOfSiteNames[0]\n\t\t\t\t\t\tself.pluginPrefs[\"unifiCloudKeySiteName\"] = self.unifiCloudKeySiteName \n\t\t\t\t\t\tself.pluginPrefs[\"unifiCloudKeyListOfSiteNames\"] = json.dumps(self.unifiCloudKeyListOfSiteNames)\n\t\t\t\t\t\treturn True\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.indiLOG.log(20,\"setunifiCloudKeySiteName setting site id name returned empty\")\n\t\t\t\t\t\treturn False\n\n\n\t\t\tself.indiLOG.log(20,\"setunifiCloudKeySiteName id not found ret:>>{}<<\".format(ret))\n\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"setunifiCloudKeySiteName2\")\n\t\t\treturn False\n\n\t\texcept\tException as e:\n\t\t\tself.indiLOG.log(40,\"setunifiCloudKeySiteName: \" )\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"setunifiCloudKeySiteName3\")\n\t\treturn False\n\n\n\n\t####-----------------\t ---------\n\tdef executeCMDOnController(self, dataSEND={}, pageString=\"\",jsonAction=\"returnData\", startText=\"\", cmdType=\"put\", cmdTypeForce = False, repeatIfFailed=True, raw=False, protect=False, ignore40x=False):\n\n\t\ttry:\n\t\t\tif self.unifiControllerType == \"off\": \t\t\t\t\treturn []\n\t\t\tif self.unifiCloudKeyMode == \"off\":\t\t\t\t\treturn []\n\t\t\tif not self.isValidIP(self.unifiCloudKeyIP): \t\t\treturn []\n\t\t\tif len(self.connectParams[\"UserID\"][\"webCTRL\"]) < 2: \treturn []\n\t\t\tif self.unifiCloudKeyMode.find(\"ON\") == -1 and self.unifiCloudKeyMode != \"UDM\": return []\n\n\t\t\tfor iii in range(2):\n\t\t\t\tif not repeatIfFailed and iii > 0: return []\n\t\t\t\tif iii == 1: self.sleep(0.2)\n\n\t\t\t\t# get port and which unifi os:\n\t\t\t\tif not self.getunifiOSAndPort(): \n\t\t\t\t\tself.executeCMDOnControllerReset(wait=False)\n\t\t\t\t\treturn []\n\t\t\t\tif self.unifiControllerOS not in self.OKControllerOS:\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(10,\"unifiControllerOS not set : {}\".format(self.unifiControllerOS) )\n\t\t\t\t\treturn []\n\n\t\t\t\t# now execute commands\n\t\t\t\t#### use curl if ...\n\t\t\t\tuseCurl = self.requestOrcurl.find(\"curl\") > -1 and self.unifiControllerOS == \"std\"\n\n\t\t\t\tif useCurl:\n\t\t\t\t\t#cmdL = curl --insecure -c /tmp/unifiCookie -H \"Content-Type: application/json\" --data '{\"username\":\"karlwachs\",\"password\":\"457654aA.unifi\"}' https://192.168.1.2:8443/api/login\n\t\t\t\t\t#cmdL = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -c /tmp/unifiCookie --data '\" +json.dumps({\"username\":self.connectParams[\"UserID\"][\"webCTRL\"],\"password\":self.connectParams[\"PassWd\"][\"webCTRL\"]})+\"' 'https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+\"/api/login'\"\n\t\t\t\t\tcmdLogin = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -c /tmp/unifiCookie -H \\\"Content-Type: application/json\\\" --data '\"+json.dumps({\"username\":self.connectParams[\"UserID\"][\"webCTRL\"],\"password\":self.connectParams[\"PassWd\"][\"webCTRL\"],\"strict\":self.useStrictToLogin})+\"' 'https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+self.unifiApiLoginPath+\"'\"\n\t\t\t\t\tif dataSEND =={}: \tdataSendSTR = \"\"\n\t\t\t\t\telse:\t\t \t\tdataSendSTR = \" --data '\"+json.dumps(dataSEND)+\"' \"\n\t\t\t\t\tif\t cmdType == \"put\":\t \t\t\t\t\t\tcmdTypeUse= \" -X PUT \"\n\t\t\t\t\telif cmdType == \"post\":\t \t\t\t\t\tcmdTypeUse= \" -X POST \"\n\t\t\t\t\telif cmdType == \"get\":\t \t\t\t\t\t\tcmdTypeUse= \" \" # \n\t\t\t\t\telse:\t\t\t\t\t \t\t\t\t\t\tcmdTypeUse= \" \";\tcmdType = \"get\"\n\t\t\t\t\tif not cmdTypeForce: cmdTypeUse = \" \"\n\t\t\t\t\tif self.unifiControllerType.find(\"UDM\") >-1 and cmdType == \"get\":\tcmdTypeUse = \" \" \n\n\n\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(10,\"executeCMDOnController: {}\".format(cmdLogin) )\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif time.time() - self.lastUnifiCookieCurl > 100: # re-login every 90 secs\n\t\t\t\t\t\t\trespText, errText = self.readPopen(cmdLogin)\n\t\t\t\t\t\t\ttry: loginDict = json.loads(respText)\n\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\tif iii == 0:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"UNIFI executeCMDOnController error no json object: (wrong UID/passwd, ip number?{}) ...>>{}<<\\n{}\".format(self.unifiCloudKeyIP,respText,errText))\n\t\t\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True)\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif ( \"meta\" not in loginDict or loginDict[\"meta\"][\"rc\"] != \"ok\"):\n\t\t\t\t\t\t\t\tif iii == 0:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"UNIFI executeCMDOnController login cmd:{}\\ngives error: {}\\n {}\".format(cmdLogin, respText,errText) )\n\t\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True)\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif self.decideMyLog(\"ConnectionRET\"):\t self.indiLOG.log(10,\"Connection-{}: {}\".format(self.unifiCloudKeyIP,respText) )\n\t\t\t\t\t\t\tself.lastUnifiCookieCurl = time.time()\n\n\n\n\t\t\t\t\t\tif self.unifiCloudKeySiteName == \"\" or self.unifiCloudKeySiteNameGetNew:\n\t\t\t\t\t\t\tif not self.setunifiCloudKeySiteName(method=\"curl\"): continue\n\n\t\t\t\t\t\tif self.unifiCloudKeySiteName == \"\": continue\n\t\t\t\t\t\tself.unifiCloudKeySiteNameGetNew = False\n\n\n\t\t\t\t\t\tif self.unifiCloudKeySiteName == \"\":\n\t\t\t\t\t\t\tif not self.setunifiCloudKeySiteName(method = \"curl\"): continue\n\n\t\t\t\t\t\t#cmdDATA = curl --insecure -b /tmp/unifiCookie' --data '{\"within\":999,\"_limit\":1000}' https://192.168.1.2:8443/api/s/default/stat/event\n\t\t\t\t\t\tcmdDATA = self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" --insecure -b /tmp/unifiCookie \" +dataSendSTR+cmdTypeUse+ \" 'https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+self.unifiApiWebPage+\"/\"+self.unifiCloudKeySiteName+\"/\"+pageString.strip(\"/\")+\"'\"\n\n\t\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"):\tself.indiLOG.log(10,\"Connection: {}\".format(cmdDATA) )\n\t\t\t\t\t\tif startText != \"\":\t\t\t\t\t \tself.indiLOG.log(10,\"Connection: {}\".format(startText) )\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tret = subprocess.Popen(cmdDATA, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True).communicate()\n\t\t\t\t\t\t\trespText = ret[0]#.decode(\"utf8\")\n\t\t\t\t\t\t\terrText = ret[1]#.decode(\"utf8\")\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tdictRET = json.loads(respText)\n\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\tif iii > 0:\n\t\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: \n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"\", exc_info=True)\n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"UNIFI executeCMDOnController to {} curl errortext:{}\".format(self.unifiCloudKeyIP, errText))\n\t\t\t\t\t\t\t\t\t\tself.printHttpError(\"{}\".format(e), respText, ind=iii)\n\t\t\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-curl json\")\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tif dictRET[\"meta\"][\"rc\"] != \"ok\":\n\t\t\t\t\t\t\t\tif iii == 0:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\" Connection error: >>{}<<\\n{}\".format(self.unifiCloudKeyIP, respText, errText))\n\t\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-curl dict not ok\")\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tif self.decideMyLog(\"ConnectionRET\"):\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"Connection to {}: returns >>{}<<\".format(self.unifiCloudKeyIP, respText) )\n\n\t\t\t\t\t\t\tif jsonAction == \"print\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\" Connection to:{} info\\n{}\".format(self.unifiCloudKeyIP, json.dumps(dictRET[\"data\"],sort_keys=True, indent=2)))\n\t\t\t\t\t\t\t\treturn []\n\n\t\t\t\t\t\t\tif jsonAction == \"returnData\":\n\t\t\t\t\t\t\t\t#self.writeJson(dictRET[\"data\"], fName=self.indigoPreferencesPluginDir+\"dict-Controller-\"+pageString.replace(\"/\",\"_\").replace(\" \",\"-\").replace(\":\",\"=\").strip(\"_\")+\".txt\", sort=True, doFormat=True )\n\t\t\t\t\t\t\t\treturn dictRET[\"data\"]\n\t\t\t\t\t\t\tif jsonAction == \"protect\":\n\t\t\t\t\t\t\t\t#self.writeJson(dictRET[\"data\"], fName=self.indigoPreferencesPluginDir+\"dict-Controller-\"+pageString.replace(\"/\",\"_\").replace(\" \",\"-\").replace(\":\",\"=\").strip(\"_\")+\".txt\", sort=True, doFormat=True )\n\t\t\t\t\t\t\t\treturn dictRET\n\n\t\t\t\t\t\t\treturn []\n\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\t\t\t############# does not work on OSX\tel capitan ssl lib too old\t##########\n\t\t\t\tif not useCurl:\n\n\t\t\t\t\tif self.unifiControllerSession == \"\" or (time.time() - self.lastUnifiCookieRequests) > 99: # every 99 secs token cert\n\t\t\t\t\t\tif self.unifiControllerSession != \"\":\n\t\t\t\t\t\t\ttry: \tself.unifiControllerSession.close()\n\t\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\t\tself.unifiControllerSession = \"\"\n\n\t\t\t\t\t\tif self.unifiControllerSession == \"\":\n\t\t\t\t\t\t\tself.unifiControllerSession\t = requests.Session()\n\n\t\t\t\t\t\turl = \"https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+self.unifiApiLoginPath\n\t\t\t\t\t\tloginHeaders = {\"Accept\": \"application/json\", \"Content-Type\": \"application/json\", \"referer\": \"/login\"}\n\t\t\t\t\t\tdataLogin = json.dumps({\"username\":self.connectParams[\"UserID\"][\"webCTRL\"],\"password\":self.connectParams[\"PassWd\"][\"webCTRL\"]}) # , \"strict\":self.useStrictToLogin})\n\t\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"): self.indiLOG.log(10,\"Connection: requests login url:{};\\ndataLogin:{};\\nloginHeaders:{};\".format(url, dataLogin, loginHeaders) )\n\n\t\t\t\t\t\tresp = self.unifiControllerSession.post(url, headers=loginHeaders, data = dataLogin, timeout=self.requestTimeout, verify=False)\n\t\t\t\t\t\tif self.decideMyLog(\"ConnectionRET\"): self.indiLOG.log(10,\"Connection: requests login code:{}; ret-Text:\\n {} ...\".format(resp.status_code, resp.text) )\n\n\t\t\t\t\t\ttry: loginDict = json.loads(resp.text)\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tself.indiLOG.log(30,\"UNIFI executeCMDOnController error no json object: (wrong UID/passwd, ip number?{}) ...>>{}<<\".format(self.unifiCloudKeyIP, resp.text))\n\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-login json\")\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tif resp.status_code != requests.codes.ok:\n\t\t\t\t\t\t\tself.indiLOG.log(30,\"UNIFI executeCMDOnController login url:{}\\ngives, ok not found or status_code:{} not in [{}]\\n error: {}\\n\".format(url,resp.status_code, requests.codes.ok, resp.text[0:300]) )\n\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-login ret code not ok\")\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tif 'X-CSRF-Token' in resp.headers:\n\t\t\t\t\t\t\tself.csrfToken = resp.headers['X-CSRF-Token']\n\t\t\t\t\n\n\t\t\t\t\t\tself.lastUnifiCookieRequests = time.time()\n\t\t\n\t\t\t\t\tif self.unifiControllerSession == \"\": \n\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=False, calledFrom=\"executeCMDOnController-unifiControllerSession = blank\")\n\t\t\t\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"Connection: session =blank, continue \")\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\theaders = {\"Accept\": \"application/json\", \"Content-Type\": \"application/json\"}\n\t\t\t\t\tif self.csrfToken != \"\":\n\t\t\t\t\t\theaders['X-CSRF-Token'] = self.csrfToken\n\n\n\t\t\t\t\tcookies_dict = requests.utils.dict_from_cookiejar(self.unifiControllerSession.cookies)\n\t\t\t\t\tif self.unifiControllerOS == \"unifi_os\":\n\t\t\t\t\t\tcookies = {\"TOKEN\": cookies_dict.get('TOKEN')}\n\t\t\t\t\telse:\n\t\t\t\t\t\tcookies = {\"unifises\": cookies_dict.get('unifises'), \"csrf_token\": cookies_dict.get('csrf_token')}\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"):\tself.indiLOG.log(10,\"Connection: unifiControllerOS:{}, unifiControllerSession>>>{}<<<\".format(self.unifiControllerOS, str(self.unifiControllerSession)) )\n\n\n\t\t\t\t\tif self.unifiCloudKeySiteName == \"\" or self.unifiCloudKeySiteNameGetNew:\n\t\t\t\t\t\tif not self.setunifiCloudKeySiteName(method=\"response\", cookies=cookies, headers=headers ): continue\n\n\t\t\t\t\tif self.unifiCloudKeySiteName == \"\": continue\n\t\t\t\t\tself.unifiCloudKeySiteNameGetNew = False\n\t\t\t\t\n\t\t\t\t\tif protect:\n\t\t\t\t\t\turl = \"https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+\"/proxy/protect/\"+pageString.strip(\"/\")\n\t\t\t\t\telse:\n\t\t\t\t\t\turl = \"https://\"+self.unifiCloudKeyIP+\":\"+self.unifiCloudKeyPort+self.unifiApiWebPage+\"/\"+self.unifiCloudKeySiteName+\"/\"+pageString.strip(\"/\")\n\n\t\t\t\t\tif self.decideMyLog(\"ConnectionCMD\"):\tself.indiLOG.log(10,\"Connection: requests:{};\\nheader:{};\\ndataSEND:{};\\ncookies:{};\\ncmdType:{}\".format(url, headers, dataSEND, cookies,cmdType) )\n\t\t\t\t\tif startText !=\"\":\t\t\t\t\t\tself.indiLOG.log(10,\"Connection: requests: startText{},\".format(startText) )\n\t\t\t\t\ttry:\n\t\t\t\t\t\t\tretCode\t\t\t= \"\"\n\t\t\t\t\t\t\trespText \t\t= \"\"\n\t\t\t\t\t\t\tdictRET\t\t\t= \"\"\n\t\t\t\t\t\t\trawData\t\t\t= \"\"\n\t\t\t\t\t\t\tif raw:\t\n\t\t\t\t\t\t\t\tsetStream\t= True\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tsetStream\t= False\n\t\t\t\t\t\t\ttimeused\t\t= 0\n\n\t\t\t\t\t\t\tif\t cmdType == \"put\":\tresp = self.unifiControllerSession.put(url, \tjson=dataSEND,\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\telif cmdType == \"post\":\tresp = self.unifiControllerSession.post(url, \tjson=dataSEND,\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\telif cmdType == \"get\":\t\n\t\t\t\t\t\t\t\tif dataSEND == {}:\n\t\t\t\t\t\t\t\t\t\t\t\t\tresp =\tself.unifiControllerSession.get(url,\t\t\t\t\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tif protect: # get protect needs params= not json=\n\t\t\t\t\t\t\t\t\t\t\t\t\tresp =\tself.unifiControllerSession.get(url, \tparams=dataSEND,\tcookies=cookies, headers=headers, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\t\t\t\t\t\t\tif setStream: \n\t\t\t\t\t\t\t\t\t\t\t\t\t\trawData = resp.raw.read()\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t#self.indiLOG.log(10,\"executeCMDOnController protect url:{} params:{}; stream:{}, len(resp.raw.read):{}\".format(url, dataSEND, setStream, len(rawData) ))\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\tresp =\tself.unifiControllerSession.get(url, \tjson=dataSEND,\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\n\t\t\t\t\t\t\telif cmdType == \"patch\":resp = self.unifiControllerSession.patch(url,\tjson=dataSEND,\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\telse:\t\t\t\t\tresp = self.unifiControllerSession.put(url, \tjson=dataSEND,\t\tcookies=cookies, headers=headers, allow_redirects=False, verify=False, timeout=self.requestTimeout, stream=setStream)\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tretCode\t\t= copy.copy(resp.status_code )\n\t\t\t\t\t\t\t\trespText \t= copy.copy(resp.text)\n\t\t\t\t\t\t\t\tretStatus\t= resp.status_code\n\t\t\t\t\t\t\t\t##respText \t= respText.decode(\"utf8\")\n\t\t\t\t\t\t\t\ttimeused \t= resp.elapsed.total_seconds()\t\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"ConnectionRET\"):\t\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"executeCMDOnController retCode:{}, time used:{}; cont length:{} os:{}; cmdType:{}, url:{}\\n>>>{}<<<\".format(retCode, timeused, len(respText), self.unifiControllerOS, cmdType, url, respText))\n\t\t\t\t\t\t\t\theaders \t= copy.copy(resp.headers)\n\n\t\t\t\t\t\t\t\tif not raw:\n\t\t\t\t\t\t\t\t\tdictRET\t= json.loads(respText)\n\n\t\t\t\t\t\t\t\tresp.close()\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\tif iii > 0:\n\t\t\t\t\t\t\t\t\terrText = \"{}\".format(e)\n\t\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: \n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"\", exc_info=True)\n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"executeCMDOnController has error, retCode:{}, time used:{}; cont length:{} os:{}; cmdType:{}, url:{}\".format(retCode, timeused, len(respText), self.unifiControllerOS, cmdType, url))\n\t\t\t\t\t\t\t\t\t\tself.printHttpError(errText, respText)\n\t\t\t\t\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-exception after json/decode ..\")\n\t\t\t\t\t\t\t\ttry: resp.close()\n\t\t\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\t\t\tcontinue\n \n\t\t\t\t\t\t\tif protect:\n\t\t\t\t\t\t\t\tif retCode != requests.codes.ok:\n\t\t\t\t\t\t\t\t\tif iii == 1 and (not ignore40x or \"{}\".format(retCode).find(\"40\") !=0):\n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"error:>> url:{}, resp code:{}\".format(url, retCode))\n\t\t\t\t\t\t\t\t\tif (not ignore40x or \"{}\".format(retCode).find(\"40\") !=0): self.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-retcode not ok\")\n\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif dictRET[\"meta\"][\"rc\"] != \"ok\":\n\t\t\t\t\t\t\t\t\tif iii == 1 and (not ignore40x or \"{}\".format(retCode).find(\"40\") !=0):\n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"error:>> url:{}, resp:{}\".format(url, respText[0:100]))\n\t\t\t\t\t\t\t\t\tif (not ignore40x or \"{}\".format(retCode).find(\"40\") !=0): self.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-dict ret not ok\")\n\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tself.lastUnifiCookieRequests = time.time()\n\n\t\t\t\t\t\t\tif 'X-CSRF-Token' in headers:\n\t\t\t\t\t\t\t\tself.csrfToken = headers['X-CSRF-Token']\n\n\t\t\t\t\t\t\tif jsonAction == \"print\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"Reconnect: executeCMDOnController info\\n{}\".format(json.dumps(dictRET[\"data\"], sort_keys=True, indent=2)) )\n\t\t\t\t\t\t\t\treturn dictRET[\"data\"]\n\n\t\t\t\t\t\t\tif raw:\t\t\t\t\t\t\t\treturn rawData\n\t\t\t\t\t\t\telif jsonAction == \"returnData\":\treturn dictRET[\"data\"]\n\t\t\t\t\t\t\telif jsonAction == \"protect\":\t\treturn dictRET\n\t\t\t\t\t\t\telse:\t\t\t\t\t\t\t\treturn []\n\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\t\t## we get here when not successful\n\t\t\t\tself.executeCMDOnControllerReset(wait=True, calledFrom=\"executeCMDOnController-end-error\")\n\n\t\t\treturn []\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.executeCMDOnControllerReset(wait=False, calledFrom=\"executeCMDOnController-exception\")\n\t\treturn []\n\n\n\n\t####-----------------\t ---------\n\tdef printHttpError(self, errtext, respText, ind=0):\n\t\ttry:\n\t\t\tdetected = False\n\t\t\ttest =[[\"error=Expecting object\",\"( char \",\")\"],[\"ordinal not in range\",\" in position \",\":\"]]\n\t\t\tfor tt in test:\n\t\t\t\tif errtext.find(tt[0]) > -1: # eg Expecting object: line 1 column 65733 (char 65732)\n\t\t\t\t\ttry: \n\t\t\t\t\t\tcpos = errtext.find(tt[1])\n\t\t\t\t\t\tif cpos > 2: \n\t\t\t\t\t\t\tcharpos = errtext[cpos+len(tt[1]):]\n\t\t\t\t\t\t\tcharpos = int(charpos.split(tt[2])[0])\n\t\t\t\t\t\t\tcp = max(0,charpos-10)\n\t\t\t\t\t\t\tself.indiLOG.log(20,\"executeCMDOnController Ind:{} bad char >>{}<<; @{} in\\n {}...{}\".format(ind, respText[cp:cp+20], charpos, respText[0:200], respText[-200:]))\n\t\t\t\t\t\t\tdetected = True\n\t\t\t\t\texcept: pass\n\n\t\t\tif not detected:\n\t\t\t\tself.indiLOG.log(20,\"executeCMDOnController resp:>>{} ... {}<<<\".format(respText[0:200], respText[-200:]) )\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef getSnapshotfromCamera(self, indigoCameraId, fileName):\n\t\ttry:\n\t\t\tdev\t\t= indigo.devices[int(indigoCameraId)]\n\t\t\tcmdR\t= self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout) +\" 'http://\"+dev.states[\"ip\"] +\"/snap.jpeg' > \"+ fileName\n\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video: getSnapshotfromNVR with: {}\".format(cmdR) )\n\t\t\trespText, errText = self.readPopen(cmdR)\n\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video: getSnapshotfromCamera response: {}\".format(respText))\n\t\t\treturn \"ok\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\treturn \"error:{}\".format(e)\n\t\treturn \" error\"\n\n\n\t####-----------------\t ---------\n\tdef getSnapshotfromNVR(self, indigoCameraId, width, fileName):\n\n\t\ttry:\n\t\t\tcamApiKey = indigo.devices[int(indigoCameraId)].states[\"apiKey\"]\n\t\t\turl\t\t\t= \"http://\"+self.ipNumbersOf[\"VD\"] +\":7080/api/2.0/snapshot/camera/\"+camApiKey+\"?force=true&width={}\".format(width)+\"&apiKey=\"+self.nvrVIDEOapiKey\n\t\t\tif self.requestOrcurl.find(\"curl\") > -1:\n\t\t\t\tcmdR\t= self.curlPath+\" --max-time {:.0f}\".format(self.requestTimeout)+\" -o '\" + fileName +\"' '\"+ url+\"'\"\n\t\t\t\ttry:\n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video: {}\".format(cmdR) )\n\t\t\t\t\tret = subprocess.Popen(cmdR, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True).communicate()[1]\n\t\t\t\t\ttry:\n\t\t\t\t\t\tfs1\t = \"\"\n\t\t\t\t\t\tfs\t = 0\n\t\t\t\t\t\tfs0\t = \"\"\n\t\t\t\t\t\tunit = \"\"\n\t\t\t\t\t\tif ret.find(\"\\r\")> -1: ret = ret.split(\"\\r\")\n\t\t\t\t\t\telse: ret = ret.split(\"\\n\")\n\t\t\t\t\t\tfs0 = ret[-1] # last line\n\t\t\t\t\t\tfs1 = fs0.split()[-1] # last number\n\t\t\t\t\t\tunit = fs1[-1] # last char\n\t\t\t\t\t\tfs = int(fs1.strip(\"k\").strip(\"m\").strip(\"M\"))\n\t\t\t\t\texcept: fs = 0\n\t\t\t\t\tif fs == 0:\n\t\t\t\t\t\tself.indiLOG.log(40,\"getSnapshotfromNVR has error, no file returned, Video error: \\n{}\\n{}\".format(ret[1], cmdR))\n\t\t\t\t\t\treturn \"error, no file returned\"\n\t\t\t\t\treturn \"ok, bytes transfered: {}{}\".format(fs, unit)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\treturn \"error:{}\".format(e)\n\n\t\t\telse:\n\t\t\t\tsession = requests.Session()\n\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video: getSnapshotfromNVR login with: {}\".format(url) )\n\n\t\t\t\tresp\t= session.get(url, stream=True)\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"Video: getSnapshotfromNVR response {}[kB]: {}; \".format(len(resp.content)/1024, resp.status_code) )\n\t\t\t\tif \"{}\".format(resp.status_code) == \"200\":\n\t\t\t\t\tf = self.openEncoding(fileName,\"wb\")\n\t\t\t\t\tf.write(resp.content)\n\t\t\t\t\tf.close()\n\t\t\t\t\tunit=\"\"\n\t\t\t\t\ttry:\n\t\t\t\t\t\tll = int(len(resp.content))\n\t\t\t\t\t\tif ll > 1024:\n\t\t\t\t\t\t\tll /=1024\n\t\t\t\t\t\t\tunit=\"k\"\n\t\t\t\t\t\t\tif ll > 1024:\n\t\t\t\t\t\t\t\tll /=1024\n\t\t\t\t\t\t\t\tunit=\"M\"\n\t\t\t\t\texcept: ll = \"\"\n\t\t\t\t\treturn \"ok, bytes transfered: {} {}\".format(ll, unit)\n\t\t\t\treturn \"error {}\".format(resp.status_code)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \"error:{}\".format(e)\n\n\n\n\t####-----------------\t ---------\n\tdef groupStatusINIT(self):\n\t\tfor gNumber in range(_GlobalConst_numberOfGroups):\n\t\t\tvarN = \"Unifi_Count_{}_\".format(self.groupNames[gNumber])\n\t\t\tfor tType in [\"Home\",\"Away\",\"lastChange\"]:\n\t\t\t\tvarName = varN+tType\n\t\t\t\tif varName not in self.varExcludeSQLList: self.varExcludeSQLList.append(varName)\n\t\t\t\ttry:\n\t\t\t\t\tvar = indigo.variables[varName]\n\t\t\t\texcept:\n\t\t\t\t\tindigo.variable.create(varName,\"0\",folder=self.folderNameIDVariables)\n\n\t\tfor tType in [\"Home\",\"Away\",\"lastChange\"]:\n\t\t\tvarName=\"Unifi_Count_ALL_\"+tType\n\t\t\tif self.enableSqlLogging:\n\t\t\t\tif varName not in self.varExcludeSQLList: self.varExcludeSQLList.append(varName)\n\t\t\ttry:\n\t\t\t\tvar = indigo.variables[varName]\n\t\t\texcept:\n\t\t\t\tindigo.variable.create(varName,\"0\",folder=self.folderNameIDVariables)\n\n\t\ttry:\tindigo.variable.create(\"Unifi_With_Status_Change\",value=\"\", folder=self.folderNameIDVariables)\n\t\texcept: pass\n\t\ttry:\tindigo.variable.create(\"Unifi_With_IPNumber_Change\",value=\"\", folder=self.folderNameIDVariables)\n\t\texcept: pass\n\t\ttry:\tindigo.variable.create(\"Unifi_New_Device\",value=\"\", folder=self.folderNameIDVariables)\n\t\texcept: pass\n\n\t####-----------------\t ---------\n\tdef setGroupStatus(self, init=False):\n\t\tself.statusChanged = 0\n\t\ttry:\n\n\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tself.groupStatusList[groupNo][\"nAway\"] = 0\n\t\t\t\tself.groupStatusList[groupNo][\"nHome\"] = 0\n\t\t\tself.groupStatusListALL[\"nHome\"] = 0\n\t\t\tself.groupStatusListALL[\"nAway\"] = 0\n\n\t\t\tokList = {}\n\n\n\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\t\tif \"groupMember\" not in dev.states: continue\n\n\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\tself.groupStatusListALL[\"nHome\"]\t +=1\n\t\t\t\telse:\n\t\t\t\t\tself.groupStatusListALL[\"nAway\"]\t +=1\n\n\t\t\t\tif dev.states[\"groupMember\"] == \"\": continue\n\t\t\t\tif not dev.enabled:\t continue\n\t\t\t\tokList[\"{}\".format(dev.id)] = True\n\t\t\t\tprops= dev.pluginProps\n\t\t\t\tgNames = (dev.states[\"groupMember\"].strip(\",\")).split(\",\")\n\t\t\t\tfor groupName in gNames:\n\t\t\t\t\tif groupName == \"\": continue\n\t\t\t\t\tif groupName in self.groupNames:\n\t\t\t\t\t\tgroupNo = self.groupNames.index(groupName)\n\t\t\t\t\t\tself.groupStatusList[groupNo][\"members\"][\"{}\".format(dev.id)] = True\n\t\t\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\tif self.groupStatusList[groupNo][\"oneHome\"] == \"0\":\n\t\t\t\t\t\t\t\tself.groupStatusList[groupNo][\"oneHome\"]\t= \"1\"\n\t\t\t\t\t\t\tself.groupStatusList[groupNo][\"nHome\"]\t \t+=1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif self.groupStatusList[groupNo][\"oneAway\"] == \"0\":\n\t\t\t\t\t\t\t\tself.groupStatusList[groupNo][\"oneAway\"]\t= \"1\"\n\t\t\t\t\t\t\tself.groupStatusList[groupNo][\"nAway\"]\t \t+=1\n\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tremoveList=[]\n\t\t\t\tfor member in self.groupStatusList[groupNo][\"members\"]:\n\t\t\t\t\tif member not in okList:\n\t\t\t\t\t\tremoveList.append(member)\n\t\t\t\tfor member in removeList:\n\t\t\t\t\tself.indiLOG.log(20,\"removing from group#:{} memberId:{}\".format(groupNo, member))\n\t\t\t\t\tdel self.groupStatusList[groupNo][\"members\"][member]\n\n\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tif self.groupStatusList[groupNo][\"nAway\"] == len(self.groupStatusList[groupNo][\"members\"]):\n\t\t\t\t\tif self.groupStatusList[groupNo][\"allAway\"] == \"0\":\n\t\t\t\t\t\tself.groupStatusList[groupNo][\"allAway\"]\t = \"1\"\n\t\t\t\t\tself.groupStatusList[groupNo][\"oneHome\"]\t = \"0\"\n\t\t\t\telse:\n\t\t\t\t\tself.groupStatusList[groupNo][\"allAway\"]\t = \"0\"\n\n\t\t\t\tif self.groupStatusList[groupNo][\"nHome\"] == len(self.groupStatusList[groupNo][\"members\"]):\n\t\t\t\t\tif self.groupStatusList[groupNo][\"allHome\"] == \"0\":\n\t\t\t\t\t\tself.groupStatusList[groupNo][\"allHome\"]\t = \"1\"\n\t\t\t\t\tself.groupStatusList[groupNo][\"oneAway\"]\t = \"0\"\n\t\t\t\telse:\n\t\t\t\t\tself.groupStatusList[groupNo][\"allHome\"]\t = \"0\"\n\n\n\t\t\t# now variables\n\t\t\tfor groupNo in range(_GlobalConst_numberOfGroups):\n\t\t\t\tvarN = \"Unifi_Count_{}_\".format(self.groupNames[groupNo])\n\t\t\t\tif\tnot init:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tvarName = varN \n\t\t\t\t\t\tvarHomeV = indigo.variables[varName+\"Home\"].value\n\t\t\t\t\t\tvarAwayV = indigo.variables[varName+\"Away\"].value\n\t\t\t\t\t\tif\tvarHomeV != \"{}\".format(self.groupStatusList[groupNo][\"nHome\"]) or varAwayV != \"{}\".format(self.groupStatusList[groupNo][\"nAway\"]) or len(indigo.variables[varName+\"lastChange\"].value) == 0 :\n\t\t\t\t\t\t\t\tindigo.variable.updateValue(varName+\"lastChange\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\t\t\t\t\texcept:\n\t\t\t\t\t\tself.groupStatusINIT()\n\n\t\t\t\tfor tType in [\"Home\",\"Away\"]:\n\t\t\t\t\tvarName = varN + tType\n\t\t\t\t\tgName = \"n\"+tType\n\t\t\t\t\ttry:\n\t\t\t\t\t\tvar = indigo.variables[varName]\n\t\t\t\t\texcept:\n\t\t\t\t\t\tindigo.variable.create(varName,\"0\",folder=self.folderNameIDVariables)\n\t\t\t\t\t\tvar = indigo.variables[varName]\n\t\t\t\t\tif var.value !=\t \"{}\".format(self.groupStatusList[groupNo][gName]):\n\t\t\t\t\t\tindigo.variable.updateValue(varName, \"{}\".format(self.groupStatusList[groupNo][gName]))\n\n\n\t\t\tif\tnot init:\n\t\t\t\tvarName = \"Unifi_Count_ALL_\"\n\t\t\t\ttry:\n\t\t\t\t\tvarHomeV = indigo.variables[varName+\"Home\"].value\n\t\t\t\t\tvarAwayV = indigo.variables[varName+\"Away\"].value\n\t\t\t\t\tif varHomeV != \"{}\".format(self.groupStatusListALL[\"nHome\"]) or varAwayV != \"{}\".format(self.groupStatusListALL[\"nAway\"]) or len(indigo.variables[\"Unifi_Count_ALL_lastChange\"].value) == 0:\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Count_ALL_lastChange\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\t\t\t\texcept:\n\t\t\t\t\tself.groupStatusINIT()\n\n\t\t\tfor tType in [\"Home\",\"Away\"]:\n\t\t\t\tvarName = \"Unifi_Count_ALL_\"+tType\n\t\t\t\tgName = \"n\"+tType\n\t\t\t\ttry:\n\t\t\t\t\tvar = indigo.variables[varName]\n\t\t\t\texcept:\n\t\t\t\t\tindigo.variable.create(varName,\"0\",folder=self.folderNameIDVariables)\n\t\t\t\t\tvar = indigo.variables[varName]\n\t\t\t\tif var.value != \"{}\".format(self.groupStatusListALL[gName]):\n\t\t\t\t\tindigo.variable.updateValue(varName, \"{}\".format(self.groupStatusListALL[gName]))\n\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n######################################################################################\n\t# Indigo Trigger Start/Stop\n######################################################################################\n\n\t####-----------------\t ---------\n\tdef triggerStartProcessing(self, trigger):\n\t\tself.triggerList.append(trigger.id)\n\n\t####-----------------\t ---------\n\tdef triggerStopProcessing(self, trigger):\n\t\tif trigger.id in self.triggerList:\n\t\t\tself.triggerList.remove(trigger.id)\n\n\t#def triggerUpdated(self, origDev, newDev):\n\t#\tself.triggerStopProcessing(origDev)\n\t#\tself.triggerStartProcessing(newDev)\n\n\n######################################################################################\n\t# Indigo Trigger Firing\n######################################################################################\n\n\t####-----------------\t ---------\n\tdef triggerEvent(self, eventId):\n\t\tfor trigId in self.triggerList:\n\t\t\ttrigger = indigo.triggers[trigId]\n\t\t\tif trigger.pluginTypeId == eventId:\n\t\t\t\tindigo.trigger.execute(trigger)\n\t\treturn\n\n\n\n\n\t####-----------------setup empty dicts for pointers\t type, mac --> indigop and indigo --> mac,\ttype ---------\n\tdef setUpDownStateValue(self, dev):\n\t\tupdate=False\n\t\ttry:\n\t\t\tupDown = \"\"\n\t\t\tprops=dev.pluginProps\n\t\t\tif \"expirationTime\" not in props:\n\t\t\t\tprops[\"expirationTime\"] = self.expirationTime\n\t\t\t\tupdate=True\n\t\t\tif \"useWhatForStatus\" in props:\n\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\t\t\t\t\tif \"useWhatForStatusWiFi\" in props:\n\t\t\t\t\t\tif props[\"useWhatForStatusWiFi\"] != \"\" and props[\"useWhatForStatusWiFi\"] != \"Expiration\":\n\t\t\t\t\t\t\tif props[\"useWhatForStatusWiFi\"]in [\"IdleTime\",\"Optimized\",\"FastDown\"]:\n\t\t\t\t\t\t\t\tif \"idleTimeMaxSecs\" not in props:\n\t\t\t\t\t\t\t\t\tprops[\"idleTimeMaxSecs\"]= \"10\"\n\t\t\t\t\t\t\t\t\tupdate=True\n\t\t\t\t\t\t\t\tupDown = \"WiFi\" + \"/\" + props[\"useWhatForStatusWiFi\"]+\"-idle:\"+props[\"idleTimeMaxSecs\"]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tupDown = \"WiFi\" + \"/\" + props[\"useWhatForStatusWiFi\"]\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tupDown = \"Wifi\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tupDown = \"Wifi\"\n\n\t\t\t\telif props[\"useWhatForStatus\"].find(\"DHCP\") > -1:\n\t\t\t\t\tif \"useAgeforStatusDHCP\" in props and\tprops[\"useAgeforStatusDHCP\"] != \"\" and props[\"useAgeforStatusDHCP\"] != \"-1\":\n\t\t\t\t\t\tupDown = \"DHCP\" + \"-age:\" + props[\"useAgeforStatusDHCP\"]\n\t\t\t\t\telse:\n\t\t\t\t\t\tupDown = \"DHCP\"\n\n\t\t\t\telif props[\"useWhatForStatus\"] == \"OptDhcpSwitch\":\n\t\t\t\t\tupDown =\"OPT: \"\n\t\t\t\t\tif \"useAgeforStatusDHCP\" in props and\tprops[\"useAgeforStatusDHCP\"] != \"\" and props[\"useAgeforStatusDHCP\"] != \"-1\":\n\t\t\t\t\t\tupDown += \"DHCP\" + \"-age:\" + props[\"useAgeforStatusDHCP\"]+\" \"\n\t\t\t\t\telse:\n\t\t\t\t\t\tupDown += \"DHCP \"\n\n\t\t\t\t\tif \"useupTimesforStatusSWITCH\" in props and props[\"useupTimeforStatusSWITCH\"]:\n\t\t\t\t\t\t\tupDown += \"SWITCH\" + \"/upTime-notchgd\"\n\t\t\t\t\telse:\n\t\t\t\t\t\t\tupDown += \"SWITCH\"\n\n\t\t\t\telif props[\"useWhatForStatus\"] == \"SWITCH\":\n\t\t\t\t\tif \"useupTimesforStatusSWITCH\" in props and props[\"useupTimeforStatusSWITCH\"]:\n\t\t\t\t\t\t\tupDown += \"SWITCH\" + \"/upTime-notchgd\"\n\t\t\t\t\telse:\n\t\t\t\t\t\t\tupDown += \"SWITCH\"\n\n\t\t\t\tupDown += \"-exp:{}\".format(self.getexpT(props)).split(\".\")[0]\n\t\t\t\tself.addToStatesUpdateList(dev.id,\"upDownSetting\", upDown)\n\n\t\t\tif \"expirationTime\" not in props:\n\t\t\t\tprops[\"expirationTime\"] = self.expirationTime\n\t\t\t\tupdate=True\n\n\t\t\tif \"AP\" in dev.states:\n\t\t\t\tif dev.states[\"AP\"].find(\"-\") == -1 :\n\t\t\t\t\tnewAP= dev.states[\"AP\"]+\"-\"\n\t\t\t\t\tdev.updateStateOnServer(\"AP\",newAP)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tif update:\n\t\t\tdev.replacePluginPropsOnServer(props)\n\t\treturn\n\n\n\t####-----------------setup empty dicts for pointers\t type, mac --> indigop and indigo --> mac,\ttype ---------\n\tdef setupStructures(self, xType, dev, MAC, init=False):\n\t\tdevIds =\"\"\n\t\ttry:\n\n\t\t\tdevIds = \"{}\".format(dev.id)\n\t\t\tif devIds not in self.xTypeMac:\n\t\t\t\tself.xTypeMac[devIds] = {\"xType\":\"\", \"MAC\":\"\"}\n\t\t\tself.xTypeMac[devIds][\"xType\"]\t= xType\n\t\t\tself.xTypeMac[devIds][\"MAC\"]\t= MAC\n\n\t\t\tif xType not in self.MAC2INDIGO:\n\t\t\t\tself.MAC2INDIGO[xType]={}\n\n\t\t\tif MAC not in self.MAC2INDIGO[xType]:\n\t\t\t\tself.MAC2INDIGO[xType][MAC] = {}\n\n\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\tif \"ipNumber\" in dev.states:\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = dev.states[\"ipNumber\"]\n\n\t\t\ttry:\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] == float(self.MAC2INDIGO[xType][MAC][\"lastUp\"])\n\t\t\texcept: self.MAC2INDIGO[xType][MAC][\"lastUp\"] = 0.\n\n\n\t\t\tif \"last_seen\" \tnot in self.MAC2INDIGO[xType][MAC]:\tself.MAC2INDIGO[xType][MAC][\"last_seen\"] \t\t= -1\n\t\t\tif \"use_fixedip\" \tnot in self.MAC2INDIGO[xType][MAC]:\tself.MAC2INDIGO[xType][MAC][\"use_fixedip\"] \t= False\n\t\t\tif \"blocked\" \t\tnot in self.MAC2INDIGO[xType][MAC]:\tself.MAC2INDIGO[xType][MAC][\"blocked\"] \t\t= False\n\t\t\tif \"lastAPMessage\" not in self.MAC2INDIGO[xType][MAC]:\tself.MAC2INDIGO[xType][MAC][\"lastAPMessage\"] \t= 0\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.indiLOG.log(40,\" {} {} {} {}\".format( \"{}\".format(xType), devIds, \"{}\".format(MAC), \"{}\".format(self.MAC2INDIGO)) )\n\t\t\ttime.sleep(300)\n\n\t\tif xType ==\"UN\":\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"AP\"]\t\t\t = dev.states[\"AP\"].split(\"-\")[0]\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastWOL\"]\t\t = 0.\n\n\t\t\t\tfor item in [\"inListWiFi\",\"inListDHCP\",]:\n\t\t\t\t\tif item not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][item] = False\n\t\t\t\tfor item in [\"GHz\",\"idleTimeWiFi\",\"upTimeWifi\",\"upTimeDHCP\"]:\n\t\t\t\t\tif item not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][item] = \"\"\n\n\t\t\t\tfor ii in range(_GlobalConst_numberOfSW):\n\t\t\t\t\tfor item in [\"inListSWITCH_\"]:\n\t\t\t\t\t\tif item+\"{}\".format(ii) not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][item+\"{}\".format(ii)] = -1\n\t\t\t\t\tfor item in [\"ageSWITCH_\",\"upTimeSWITCH_\"]:\n\t\t\t\t\t\tif item+\"{}\".format(ii) not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][item+\"{}\".format(ii)] = \"\"\n\n\n\t\tif xType ==\"SW\":\n\t\t\tif \"ports\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ports\"] = {}\n\t\t\tif \"upTime\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"] = \"\"\n\n\t\telif xType ==\"AP\":\n\t\t\tself.MAC2INDIGO[xType][MAC][\"apNo\"] = dev.states[\"apNo\"]\n\n\t\telif xType ==\"GW\":\n\t\t\tpass\n\n\t\telif xType ==\"NB\":\n\t\t\tif \"age\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"] = \"\"\n\n\n\n\t####-----------------init main loop ---------\n\tdef setupCameraVariables(self):\n\t\ttry:\n\t\t\tif self.cameraSystem == \"protect\":\n\t\t\t\tif False and not self.enableSqlLogging:\n\t\t\t\t\ttry: \tindigo.variable.delete(\"Unifi_Camera_with_Event\")\n\t\t\t\t\texcept: pass\n\t\t\t\t\ttry: \tindigo.variable.delete(\"Unifi_Camera_Event_PathToThumbnail\")\n\t\t\t\t\texcept: pass\n\t\t\t\t\ttry: \tindigo.variable.delete(\"Unifi_Camera_Event_DateOfThumbNail\")\n\t\t\t\t\texcept: pass\n\t\t\t\t\ttry: \tindigo.variable.delete(\"Unifi_Camera_Event_Date\")\n\t\t\t\t\texcept: pass\n\t\t\n\t\t\t\ttry: \tindigo.variable.create(\"Unifi_Camera_Event_Date\", value =\"\", folder=self.folderNameIDVariables)\n\t\t\t\texcept: pass\n\t\t\t\ttry: \tindigo.variable.create(\"Unifi_Camera_with_Event\", value =\"\", folder=self.folderNameIDVariables)\n\t\t\t\texcept: pass\n\t\t\t\ttry: \tindigo.variable.create(\"Unifi_Camera_Event_PathToThumbnail\", value =\"\", folder=self.folderNameIDVariables)\n\t\t\t\texcept: pass\n\t\t\t\ttry: \tindigo.variable.create(\"Unifi_Camera_Event_DateOfThumbNail\", value =\"\", folder=self.folderNameIDVariables)\n\t\t\t\texcept: pass\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t###########################\t MAIN LOOP ############################\n\t####-----------------init main loop ---------\n\tdef fixBeforeRunConcurrentThread(self):\n\n\t\tnowDT = datetime.datetime.now()\n\t\tself.lastMinute\t\t= nowDT.minute\n\t\tself.lastHour\t\t= nowDT.hour\n\t\tself.logQueue\t\t= queue.Queue()\n\t\tself.logQueueDict\t= queue.Queue()\n\t\tself.blockWaitQueue\t= PriorityQueue()\n\t\tself.apDict\t\t\t= {}\n\t\tself.countLoop\t\t= 0\n\t\tself.upDownTimers\t= {}\n\t\tself.xTypeMac\t\t= {}\n\t\tself.broadcastIP\t= \"9.9.9.255\"\n\n\t\tself.writeJson(dataVersion, fName=self.indigoPreferencesPluginDir + \"dataVersion\")\n\n\n\t\tself.buttonConfirmGetAPDevInfoFromControllerCALLBACK({})\n\n\t\t## if video enabled\n\t\tif self.cameraSystem == \"nvr\" and self.vmMachine !=\"\":\n\t\t\tself.buttonVboxActionStartCALLBACK()\n\n######## this for fixing the change from mac to MAC in states\n\t\tself.indiLOG.log(10,\"..getting vendor names for MAC#s\")\n\t\tself.MacToNamesOK = True\n\t\tif self.enableMACtoVENDORlookup:\n\t\t\tself.indiLOG.log(10,\"..getting missing vendor names for MAC #\")\n\t\tself.MAC2INDIGO = {}\n\t\tself.readMACdata()\n\n\t\tself.indiLOG.log(10,\"..setup dicts ..\")\n\t\tdelDEV = {}\n\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\tif dev.deviceTypeId in[\"client\",\"camera\",\"NVR-video\",\"NVR\"]: continue\n\t\t\tif \"status\" not in dev.states:\n\t\t\t\tself.indiLOG.log(10,\"{} has no status\".format(dev.name))\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tif \"onOffState\" in dev.states and ( (dev.states[\"status\"] in [\"up\",\"rec\",\"ON\"]) != dev.states[\"onOffState\"] ):\n\t\t\t\t\t\t\tdev.updateStateOnServer(\"onOffState\", value= dev.states[\"status\"] in [\"up\",\"rec\",\"ON\"], uiValue=dev.states[\"displayStatus\"])\n\n\t\t\tprops= dev.pluginProps\n\t\t\tgoodDevice = True\n\t\t\tdevId = \"{}\".format(dev.id)\n\n\t\t\tif \"MAC\" in dev.states:\n\t\t\t\tMAC = dev.states[\"MAC\"]\n\t\t\t\tif dev.states[\"MAC\"] == \"\":\n\t\t\t\t\tif dev.address != \"\":\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", dev.address)\n\t\t\t\t\t\t\tMAC = dev.address\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tgoodDevice = False\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"{} no MAC # deleting\".format(dev.name))\n\t\t\t\t\t\t\tdelDEV[devId]=True\n\t\t\t\t\t\t\tcontinue\n\t\t\t\tif dev.address != MAC:\n\t\t\t\t\tprops[\"address\"] = MAC\n\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\n\t\t\tif self.MacToNamesOK and \"vendor\" in dev.states:\n\t\t\t\tif (dev.states[\"vendor\"] == \"\" or dev.states[\"vendor\"].find(\"\") >-1 ) and goodDevice:\n\t\t\t\t\tvendor = self.getVendortName(MAC)\n\t\t\t\t\tif vendor != \"\":\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"vendor\", vendor)\n\t\t\t\t\tif\tdev.states[\"vendor\"].find(\"\") >-1 and\t vendor ==\"\" :\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"vendor\", \"\")\n\n\n\t\t\tif dev.deviceTypeId == \"UniFi\":\n\t\t\t\tself.setupStructures(\"UN\", dev, MAC)\n\n\n\t\t\tif dev.deviceTypeId == \"Device-AP\":\n\t\t\t\tself.setupStructures(\"AP\", dev, MAC)\n\n\t\t\tif dev.deviceTypeId.find(\"Device-SW\")>-1:\n\t\t\t\tself.setupStructures(\"SW\", dev, MAC)\n\n\t\t\tif dev.deviceTypeId == \"neighbor\":\n\t\t\t\tself.setupStructures(\"NB\", dev, MAC)\n\n\t\t\tif dev.deviceTypeId == \"gateway\":\n\t\t\t\tself.setupStructures(\"GW\", dev, MAC)\n\n\t\t\tif \"isProtectCamera\" not in props:\n\t\t\t\tself.setImageAndStatus(dev, dev.states[\"status\"], force=True)\n\n\t\t\tif \"created\" in dev.states and dev.states[\"created\"] == \"\":\n\t\t\t\tself.addToStatesUpdateList(dev.id,\"created\", nowDT.strftime(\"%Y-%m-%d %H:%M:%S\"))\n\n\n\t\t\tself.setUpDownStateValue(dev)\n\n\t\t\tself.executeUpdateStatesList()\n\n\t\tfor devid in delDEV:\n\t\t\tself.indiLOG.log(10,\" deleting , bad mac \"+ devid )\n\t\t\tindigo.device.delete(int(devid))\n\n\n\n\t\t## remove old non exiting MAC / indigo devices\n\t\tfor xType in self.MAC2INDIGO:\n\t\t\tif \"\" in self.MAC2INDIGO[xType]:\n\t\t\t\tdel self.MAC2INDIGO[xType][\"\"]\n\t\t\tdelXXX = {}\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tif len(MAC) < 16:\n\t\t\t\t\tdelXXX[MAC] = True\n\t\t\t\t\tcontinue\n\t\t\t\ttry: indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tself.indiLOG.log(10,\"removing indigo dev.id: {} from internal list, does not exist as an indigo device\".format(self.MAC2INDIGO[xType][MAC][\"devId\"]))\n\t\t\t\t\ttime.sleep(1)\n\t\t\t\t\tdelXXX[MAC] = True\n\t\t\tfor MAC in delXXX:\n\t\t\t\tdel self.MAC2INDIGO[xType][MAC]\n\t\t\t# some cleanup it is now upTime xxx not uptime xxx\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tdelXXX={}\n\t\t\t\tfor yy in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\tif yy.find(\"uptime\") >-1:\n\t\t\t\t\t\tdelXXX[yy]=True\n\t\t\t\tfor yy in delXXX:\n\t\t\t\t\tdel self.MAC2INDIGO[xType][MAC][yy]\n\t\tdelXXX = {}\n\n\t\tfor devId in self.xTypeMac:\n\t\t\ttry:\t dev = indigo.devices[int(devId)]\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tif \"{}\".format(e).find(\"timeout\") >-1:\n\t\t\t\t\tself.sleep(20)\n\t\t\t\t\treturn False\n\t\t\t\tdelXXX[devId]=True\n\t\t\tMAC = self.xTypeMac[devId][\"MAC\"]\n\n\n\t\t\tif self.xTypeMac[devId][\"xType\"]==\"SW\":\n\t\t\t\tipN = dev.states[\"ipNumber\"]\n\t\t\t\tsw\t= dev.states[\"switchNo\"]\n\t\t\t\ttry:\n\t\t\t\t\tsw = int(sw)\n\t\t\t\t\tif ipN != self.ipNumbersOf[\"SW\"][sw]:\n\t\t\t\t\t\tdev.updateStateOnServer(\"ipNumber\",self.ipNumbersOf[\"SW\"][sw])\n\t\t\t\texcept:\n\t\t\t\t\tif self.isValidIP(ipN):\n\t\t\t\t\t\tfor ii in range(len(self.ipNumbersOf[\"SW\"])):\n\t\t\t\t\t\t\tif self.ipNumbersOf[\"SW\"][ii] == ipN:\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"apNo\",ii)\n\t\t\t\t\t\t\t\tbreak\n\n\n\t\t\tif self.xTypeMac[devId][\"xType\"]==\"AP\":\n\t\t\t\tipN = dev.states[\"ipNumber\"]\n\t\t\t\tsw\t= dev.states[\"apNo\"]\n\t\t\t\ttry:\n\t\t\t\t\tsw = int(sw)\n\t\t\t\t\tif ipN != self.ipNumbersOf[\"AP\"][sw]:\n\t\t\t\t\t\tdev.updateStateOnServer(\"ipNumber\",self.ipNumbersOf[\"AP\"][sw])\n\t\t\t\texcept:\n\t\t\t\t\tif self.isValidIP(ipN):\n\t\t\t\t\t\tfor ii in range(len(self.ipNumbersOf[\"AP\"])):\n\t\t\t\t\t\t\tif self.ipNumbersOf[\"AP\"][ii] == ipN:\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"apNo\",ii)\n\t\t\t\t\t\t\t\tbreak\n\n\n\n\t\tfor devId in delXXX:\n\t\t\tdel self.xTypeMac[devId]\n\t\tdelXXX = {}\n\n\t\tself.saveMACdata()\n\n\t\tself.lastSecCheck\t= time.time()\n\n\t\tself.readupDownTimers()\n\t\tself.saveupDownTimers()\n\n\t\t##start accepting staus = DOWN in 30secs\n\t\tself.pluginStartTime = time.time() +30\n\n\t\tself.pluginState = \"run\"\n\n\t\twaitBeforeStart = 1\n\t\taddtoWait = self.launchWaitSeconds # make sure not all listeners start at the same time \n\n\t\tself.consumeDataThread = {\"log\":{},\"dict\":{}}\n\t\tself.consumeDataThread[\"log\"][\"status\"] = \"run\"\n\t\tself.consumeDataThread[\"log\"][\"thread\"] = threading.Thread(name='comsumeLogData', target=self.comsumeLogData)\n\t\tself.consumeDataThread[\"log\"][\"thread\"].start()\n\t\tself.consumeDataThread[\"dict\"][\"status\"] = \"run\"\n\t\tself.consumeDataThread[\"dict\"][\"thread\"] = threading.Thread(name='comsumeDictData', target=self.comsumeDictData)\n\t\tself.consumeDataThread[\"dict\"][\"thread\"].start()\n\n\n\n\t\tif self.cameraSystem == \"nvr\":\n\n\t\t\tself.indiLOG.log(10,\"..setup NVR -1 getNVRIntoIndigo\")\n\t\t\tself.testServerIfOK(self.ipNumbersOf[\"VD\"], \"VDdict\")\n\t\t\tself.getNVRIntoIndigo(force= True)\n\t\t\tself.indiLOG.log(10,\"..setup NVR -2 getNVRCamerastoIndigo\")\n\t\t\tself.getNVRCamerastoIndigo(force=True)\n\t\t\tself.indiLOG.log(10,\"..setup NVR -3 saveCameraEventsStatus\")\n\t\t\tself.saveCameraEventsStatus=True; self.saveCamerasStats(force=False)\n\n\t\t\t\t### start video logfile listening\n\t\t\twaitBeforeStart += addtoWait\n\t\t\tself.trVDLog = \"\"\n\t\t\tself.indiLOG.log(10,\"..starting threads for VIDEO NVR log event capture\")\n\t\t\tself.trVDLog = threading.Thread(name='getMessages-VD-log', target=self.getMessages, args=(self.ipNumbersOf[\"VD\"],0,\"VDtail\",waitBeforeStart,))\n\t\t\tself.trVDLog.start()\n\t\t\tself.sleep(addtoWait)\n\n\t\tself.lastRefreshProtect = 0\n\n\t\tself.setupCameraVariables()\n\t\tself.getProtectIntoIndigo()\n\t\tself.protectThread = {\"thread\":\"\", \"status\":\"\"}\n\n\t\tif self.cameraSystem == \"protect\":\n\t\t\tself.protectThread[\"status\"] = \"run\"\n\t\t\tself.protectThread[\"thread\"] = threading.Thread(name='get-protectevents', target=self.getProtectEvents)\n\t\t\tself.protectThread[\"thread\"].start()\n\t\t\tself.sleep(addtoWait)\n\n\n\n\t\tself.getcontrollerDBForClients()\n\n\t\ttry:\n\t\t\tself.trAPLog = {}\n\t\t\tself.trAPDict = {}\n\t\t\tif self.numberOfActive[\"AP\"] > 0:\n\t\t\t\tself.indiLOG.log(10,\"..starting threads for {} APs, (MSG-log and db-DICT)\".format(self.numberOfActive[\"AP\"]) )\n\t\t\t\tfor ll in range(_GlobalConst_numberOfAP):\n\t\t\t\t\tif self.devsEnabled[\"AP\"][ll]:\n\t\t\t\t\t\tif (self.unifiControllerType == \"UDM\" or self.controllerWebEventReadON > 0) and ll == self.numberForUDM[\"AP\"]: continue\n\t\t\t\t\t\tipn = self.ipNumbersOf[\"AP\"][ll]\n\t\t\t\t\t\tself.broadcastIP = ipn\n\t\t\t\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"START: AP Thread # {} {}\".format(ll, ipn) )\n\t\t\t\t\t\tif self.connectParams[\"commandOnServer\"][\"APtail\"].find(\"off\") ==-1: \n\t\t\t\t\t\t\twaitBeforeStart +=addtoWait\n\t\t\t\t\t\t\tself.trAPLog[\"{}\".format(ll)] = threading.Thread(name='getMessages-AP-log-'+\"{}\".format(ll), target=self.getMessages, args=(ipn,ll,\"APtail\",waitBeforeStart,))\n\t\t\t\t\t\t\tself.trAPLog[\"{}\".format(ll)].start()\n\t\t\t\t\t\twaitBeforeStart +=addtoWait\n\t\t\t\t\t\tself.trAPDict[\"{}\".format(ll)] = threading.Thread(name='getMessages-AP-dict-'+\"{}\".format(ll), target=self.getMessages, args=(ipn,ll,\"APdict\",waitBeforeStart,))\n\t\t\t\t\t\tself.trAPDict[\"{}\".format(ll)].start()\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.quitNOW = \"stop\"\n\t\t\tself.stop = copy.copy(self.ipNumbersOf[\"AP\"])\n\t\t\treturn False\n\n\n\n\t\tif self.devsEnabled[\"GW\"] and not self.devsEnabled[\"UD\"]:\n\t\t\tself.indiLOG.log(10,\"..starting threads for GW (MSG-log and db-DICT)\")\n\t\t\tself.broadcastIP = self.ipNumbersOf[\"GW\"]\n\t\t\tif self.connectParams[\"enableListener\"][\"GWtail\"]: \n\t\t\t\twaitBeforeStart +=addtoWait\n\t\t\t\tself.trGWLog = threading.Thread(name='getMessages-UGA-log', target=self.getMessages, args=(self.ipNumbersOf[\"GW\"],0,\"GWtail\",waitBeforeStart,))\n\t\t\t\tself.trGWLog.start()\n\t\t\twaitBeforeStart +=addtoWait\n\t\t\tself.trGWDict = threading.Thread(name='getMessages-UGA-dict', target=self.getMessages, args=(self.ipNumbersOf[\"GW\"],0,\"GWdict\",waitBeforeStart,))\n\t\t\tself.trGWDict.start()\n\n\n\t\t### for UDM devices..\n\t\t#1. get mca dump dict \n\t\tif self.devsEnabled[\"UD\"]:\n\t\t\tself.indiLOG.log(10,\"..starting threads for UDM (db-DICT)\")\n\t\t\tself.broadcastIP = self.ipNumbersOf[\"UD\"]\n\t\t\twaitBeforeStart +=addtoWait\n\t\t\tself.trUDDict = threading.Thread(name='getMessages-UDM-dict', target=self.getMessages, args=(self.ipNumbersOf[\"GW\"],0,\"UDdict\",waitBeforeStart,))\n\t\t\tself.trUDDict.start()\n\t\t\t# 2. this runs every xx secs http get data \n\t\t\ttry:\n\t\t\t\tself.trWebApiEventlog = \"\"\n\t\t\t\tif self.controllerWebEventReadON > 0:\n\t\t\t\t\twaitBeforeStart +=addtoWait\n\t\t\t\t\tself.trWebApiEventlog = threading.Thread(name='controllerWebApilogForUDM', target=self.controllerWebApilogForUDM, args=(waitBeforeStart, ))\n\t\t\t\t\tself.trWebApiEventlog.start()\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tself.quitNOW = \"stop\"\n\t\t\t\tself.stop = copy.copy(self.ipNumbersOf[\"SW\"])\n\t\t\t\treturn False\n\n\n\t\ttry:\n\t\t\tself.trSWLog = {}\n\t\t\tself.trSWDict = {}\n\t\t\tif self.numberOfActive[\"SW\"] > 0:\n\t\t\t\tself.indiLOG.log(10,\"..starting threads for {} SWs (db-DICT only)\".format(self.numberOfActive[\"SW\"]) )\n\t\t\t\tfor ll in range(_GlobalConst_numberOfSW):\n\t\t\t\t\tif self.devsEnabled[\"SW\"][ll]:\n\t\t\t\t\t\tif self.isMiniSwitch[ll]: continue\n\t\t\t\t\t\tif self.unifiControllerType.find(\"UDM\") > -1 and ll == self.numberForUDM[\"SW\"]: continue\n\t\t\t\t\t\tipn = self.ipNumbersOf[\"SW\"][ll]\n\t\t\t\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"START SW Thread tr # {} uDM#:{} {}\".format(ll, self.numberForUDM[\"SW\"], ipn, self.unifiControllerType))\n\t \t\t\t\t\t#\t\t\t\t\t self.trSWLog[\"{}\".format(ll)] = threading.Thread(name='self.getMessages', target=self.getMessages, args=(ipn, ll, \"SWtail\",float(self.readDictEverySeconds[\"SW\"]*2,))\n\t \t\t\t\t\t#\t\t\t\t\t self.trSWLog[\"{}\".format(ll)].start()\n\t\t\t\t\t\twaitBeforeStart += addtoWait\n\t\t\t\t\t\tself.trSWDict[\"{}\".format(ll)] = threading.Thread(name='getMessages-SW-Dict', target=self.getMessages, args=(ipn, ll, \"SWdict\",waitBeforeStart,))\n\t\t\t\t\t\tself.trSWDict[\"{}\".format(ll)].start()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.quitNOW = \"stop\"\n\t\t\treturn False\n\n\n\t\ttry:\n\t\t\tip = self.broadcastIP.split(\".\")\n\t\t\tself.broadcastIP = ip[0]+\".\"+ip[1]+\".\"+ip[2]+\".255\"\n\t\texcept:\n\t\t\tpass\n\t\t\n\t\tself.indiLOG.log(10,\"..starting threads finished\" )\n\n\n\t\treturn True\n\n\n\n\n\t###########################\t cProfile stuff ############################ START\n\t####----------------- ---------\n\tdef getcProfileVariable(self):\n\n\t\ttry:\n\t\t\tif self.timeTrVarName in indigo.variables:\n\t\t\t\txx = (indigo.variables[self.timeTrVarName].value).strip().lower().split(\"-\")\n\t\t\t\tif len(xx) ==1:\n\t\t\t\t\tcmd = xx[0]\n\t\t\t\t\tpri = \"\"\n\t\t\t\telif len(xx) == 2:\n\t\t\t\t\tcmd = xx[0]\n\t\t\t\t\tpri = xx[1]\n\t\t\t\telse:\n\t\t\t\t\tcmd = \"off\"\n\t\t\t\t\tpri = \"\"\n\t\t\t\tself.timeTrackWaitTime = 20\n\t\t\t\treturn cmd, pri\n\t\texcept\tException as e:\n\t\t\tpass\n\n\t\tself.timeTrackWaitTime = 60\n\t\treturn \"off\",\"\"\n\n\t####----------------- ---------\n\tdef printcProfileStats(self,pri=\"\"):\n\t\ttry:\n\t\t\tif pri !=\"\": pick = pri\n\t\t\telse:\t\t pick = 'cumtime'\n\t\t\toutFile\t\t= self.indigoPreferencesPluginDir+\"timeStats\"\n\t\t\tself.indiLOG.log(10,\" print time track stats to: {}.dump / txt with option:{} \".format(outFile, pick) )\n\t\t\tself.pr.dump_stats(outFile+\".dump\")\n\t\t\tsys.stdout \t= self.openEncoding(outFile+\".txt\", \"w\")\n\t\t\tstats \t\t= pstats.Stats(outFile+\".dump\")\n\t\t\tstats.strip_dirs()\n\t\t\tstats.sort_stats(pick)\n\t\t\tstats.print_stats()\n\t\t\tsys.stdout = sys.__stdout__\n\t\texcept: pass\n\t\t\"\"\"\n\t\t'calls'\t\t\tcall count\n\t\t'cumtime'\t\tcumulative time\n\t\t'file'\t\t\tfile name\n\t\t'filename'\t\tfile name\n\t\t'module'\t\tfile name\n\t\t'pcalls'\t\tprimitive call count\n\t\t'line'\t\t\tline number\n\t\t'name'\t\t\tfunction name\n\t\t'nfl'\t\t\tname/file/line\n\t\t'stdname'\t\tstandard name\n\t\t'time'\t\t\tinternal time\n\t\t\"\"\"\n\n\t####----------------- ---------\n\tdef checkcProfile(self):\n\t\ttry:\n\t\t\tif time.time() - self.lastTimegetcProfileVariable < self.timeTrackWaitTime:\n\t\t\t\treturn\n\t\texcept:\n\t\t\tself.cProfileVariableLoaded = 0\n\t\t\tself.do_cProfile \t\t\t= \"x\"\n\t\t\tself.timeTrVarName \t\t\t= \"enableTimeTracking_\"+self.pluginShortName\n\t\t\tself.indiLOG.log(10,\"testing if variable {} is == on/off/print-option to enable/end/print time tracking of all functions and methods (option:'',calls,cumtime,pcalls,time)\".format(self.timeTrVarName))\n\n\t\tself.lastTimegetcProfileVariable = time.time()\n\n\t\tcmd, pri = self.getcProfileVariable()\n\t\tif self.do_cProfile != cmd:\n\t\t\tif cmd == \"on\":\n\t\t\t\tif self.cProfileVariableLoaded ==0:\n\t\t\t\t\tself.indiLOG.log(10,\"======>>>> loading cProfile & pstats libs for time tracking; starting w cProfile \")\n\t\t\t\t\tself.pr = cProfile.Profile()\n\t\t\t\t\tself.pr.enable()\n\t\t\t\t\tself.cProfileVariableLoaded = 2\n\t\t\t\telif self.cProfileVariableLoaded >1:\n\t\t\t\t\tself.quitNOW = \" restart due to change ON requested for print cProfile timers\"\n\t\t\telif cmd == \"off\" and self.cProfileVariableLoaded >0:\n\t\t\t\t\tself.pr.disable()\n\t\t\t\t\tself.quitNOW = \" restart due to OFF request for print cProfile timers \"\n\t\tif cmd == \"print\" and self.cProfileVariableLoaded >0:\n\t\t\t\tself.pr.disable()\n\t\t\t\tself.printcProfileStats(pri=pri)\n\t\t\t\tself.pr.enable()\n\t\t\t\tindigo.variable.updateValue(self.timeTrVarName,\"done\")\n\n\t\tself.do_cProfile = cmd\n\t\treturn\n\n\t####----------------- ---------\n\tdef checkcProfileEND(self):\n\t\tif self.do_cProfile in[\"on\",\"print\"] and self.cProfileVariableLoaded >0:\n\t\t\tself.printcProfileStats(pri=\"\")\n\t\treturn\n\t###########################\t cProfile stuff ############################ END\n\n\t####-----------------\t ---------\n\tdef setSqlLoggerIgnoreStatesAndVariables(self):\n\t\ttry:\n\t\t\tif self.indigoVersion < 7.4: return \n\t\t\tif self.indigoVersion == 7.4 and self.indigoRelease == 0: return \n\t\t\t#tt = [\"beacon\", \"rPI\",\"rPI-Sensor\",\"BLEconnect\",\"sensor\"]\n\t\t\tif not self.enableSqlLogging: \n\t\t\t\tfor v in self.varExcludeSQLList:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif v not in indigo.variables: continue\n\t\t\t\t\t\tvar = indigo.variables[v]\n\t\t\t\t\t\tsp = var.sharedProps\n\t\t\t\t\t\tif \"sqlLoggerIgnoreChanges\" not in sp or sp[\"sqlLoggerIgnoreChanges\"] != \"true\":\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\toutONV += var.name+\"; \"\n\t\t\t\t\t\tsp[\"sqlLoggerIgnoreChanges\"] = \"\"\n\t\t\t\t\t\tvar.replaceSharedPropsOnServer(sp)\n\t\t\t\t\texcept: pass\n\t\t\t\treturn \n\n\n\t\t\toutOffV = \"\"\n\t\t\tfor v in self.varExcludeSQLList:\n\t\t\t\tif v in indigo.variables:\n\t\t\t\t\tvar = indigo.variables[v]\n\t\t\t\t\tsp = var.sharedProps\n\t\t\t\t\t#self.indiLOG.log(30,\"setting /testing off: Var: {} sharedProps:{}\".format(var.name, sp) )\n\t\t\t\t\tif \"sqlLoggerIgnoreChanges\" in sp and sp[\"sqlLoggerIgnoreChanges\"] == \"true\": \n\t\t\t\t\t\tcontinue\n\t\t\t\t\t#self.indiLOG.log(30,\"====set to off \")\n\t\t\t\t\toutOffV += var.name+\"; \"\n\t\t\t\t\tsp[\"sqlLoggerIgnoreChanges\"] = \"true\"\n\t\t\t\t\tvar.replaceSharedPropsOnServer(sp)\n\n\t\t\tif len(outOffV) > 0: \n\t\t\t\tself.indiLOG.log(10,\" \\n\")\n\t\t\t\tself.indiLOG.log(10,\"switching off SQL logging for variables\\n :{}\".format(outOffV) )\n\t\t\t\tself.indiLOG.log(10,\"switching off SQL logging for variables END\\n\")\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn \n\n\n\n####----------------- main loop ---------\n\tdef runConcurrentThread(self):\n\t\t### self.indiLOG.log(50,\"CLASS: Plugin\")\n\n\n\t\tif not self.fixBeforeRunConcurrentThread():\n\t\t\tself.indiLOG.log(40,\"..error in startup\")\n\t\t\tself.sleep(10)\n\t\t\treturn\n\n\t\tself.setSqlLoggerIgnoreStatesAndVariables()\n\n\t\tself.indiLOG.log(10,\"runConcurrentThread.....\")\n\n\t\tself.dorunConcurrentThread()\n\t\tself.checkcProfileEND()\n\n\t\tself.sleep(1)\n\t\tif self.quitNOW !=\"\":\n\t\t\tself.indiLOG.log(20, \"runConcurrentThread stopping plugin due to: ::::: {} :::::\".format(self.quitNOW))\n\t\t\tserverPlugin = indigo.server.getPlugin(self.pluginId)\n\t\t\tserverPlugin.restart(waitUntilDone=False)\n\t\treturn\n\n####----------------- main loop ---------\n\tdef dorunConcurrentThread(self):\n\n\t\tself.indiLOG.log(10,\" start runConcurrentThread, initializing loop settings and threads ..\")\n\n\n\t\tindigo.server.savePluginPrefs()\n\t\tself.lastDayCheck\t\t= -1\n\t\tself.lastHourCheck\t\t= datetime.datetime.now().hour\n\t\tself.lastMinuteCheck\t= datetime.datetime.now().minute\n\t\tself.lastMinute10Check\t= datetime.datetime.now().minute/10\n\t\tself.pluginStartTime \t= time.time()\n\t\tself.indiLOG.log(20,\"initialized ... looping\")\n\t\tindigo.server.savePluginPrefs()\t\n\t\tself.lastcreateEntryInUnifiDevLog = time.time() \n\n\t\ttry:\n\t\t\twhile True:\n\t\t\t\tsl = max(1., self.loopSleep / 10. )\n\t\t\t\tsli = int(self.loopSleep / sl)\n\t\t\t\tfor ii in range(sli):\n\t\t\t\t\tif self.quitNOW != \"\": \n\t\t\t\t\t\tbreak\n\t\t\t\t\tself.sleep(sl)\n\n\t\t\t\tif self.quitNOW != \"\": \n\t\t\t\t\tbreak\n\n\n\t\t\t\tif time.time() - self.updateConnectParams > 0 :\n\t\t\t\t\tself.updateConnectParams = time.time() + 100\n\t\t\t\t\t#self.indiLOG.log(10,\"saving updated connect parameters from config\")\n\t\t\t\t\tself.pluginPrefs[\"connectParams\"] = json.dumps(self.connectParams)\n\t\t\t\t\tindigo.server.savePluginPrefs()\t\n\t \n\t\t\t\tself.countLoop += 1\n\t\t\t\tret = self.doTheLoop()\n\t\t\t\tif ret != \"ok\":\n\t\t\t\t\tself.indiLOG.log(10,\"LOOP return break: >>{}<<\".format(ret) )\n\t\t\t\t\tbreak\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.indiLOG.log(20,\"after loop , quitNow= >>{}<<\".format(self.quitNOW ) )\n\n\t\tself.postLoop()\n\n\t\treturn\n\n\n\t###########################\t exec the loop ############################\n\t####-----------------\t ---------\n\tdef doTheLoop(self):\n\n\t\tif self.checkforUnifiSystemDevicesState == \"validateConfig\" or \\\n\t\t (self.checkforUnifiSystemDevicesState == \"start\" and (time.time() - self.pluginStartTime) > 30):\n\t\t\tself.checkForNewUnifiSystemDevices()\n\t\t\tif self.checkforUnifiSystemDevicesState == \"reboot\":\n\t\t\t\tself.quitNOW =\"new devices\"\n\t\t\t\tself.checkforUnifiSystemDevicesState =\"\"\n\t\t\t\treturn \"new Devices\"\n\n\t\tif self.pendingCommand != []:\n\t\t\tif \"getNVRCamerasFromMongoDB-print\" in self.pendingCommand: self.getNVRCamerasFromMongoDB(doPrint = True, action=[\"system\",\"cameras\"])\n\t\t\tif \"getNVRCamerastoIndigo\"\t in self.pendingCommand: self.getNVRCamerastoIndigo(force = True)\n\t\t\tif \"getConfigFromNVR\"\t\t in self.pendingCommand: self.getNVRIntoIndigo(force = True); self.getNVRCamerastoIndigo(force = True)\n\t\t\tif \"saveCamerasStats\"\t\t in self.pendingCommand: self.saveCameraEventsStatus = True; self.saveCamerasStats(force = True)\n\t\t\tself.pendingCommand =[]\n\n\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\tself.getNVRCamerastoIndigo(periodCheck = True)\n\t\tself.saveCamerasStats()\n\t\tself.saveDataStats()\n\t\tself.saveMACdata()\n\n\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\tself.setBlockAccess(\"main\")\n\n\t\t## check for expirations etc\n\n\t\tself.checkOnChanges()\n\t\tself.checkOnDelayedActions()\n\t\tself.executeUpdateStatesList()\n\n\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\tself.periodCheck()\n\t\tself.executeUpdateStatesList()\n\t\tself.sendUpdatetoFingscanNOW()\n\t\tif\t self.statusChanged == 1: self.setGroupStatus()\n\t\telif self.statusChanged == 2: self.setGroupStatus(init=True)\n\n\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\tself.checkOnDevNeedsUpdate()\n\n\t\tself.executeUpdateStatesList()\n\t\tif len(self.sendBroadCastEventsList) >0: self.sendBroadCastNOW()\n\n\t\tself.unsetBlockAccess(\"main\")\n\n\t\tif self.lastMinuteCheck != datetime.datetime.now().minute:\n\t\t\tself.lastMinuteCheck = datetime.datetime.now().minute\n\t\t\tself.statusChanged = max(1,self.statusChanged)\n\n\t\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\t\tself.getUDMpro_sensors()\n\n\t\t\tif datetime.datetime.now().minute%5 == 0: \n\t\t\t\tself.updateDevStateswRXTXbytes()\n\n\t\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\t\tif self.cameraSystem == \"nvr\" and self.vmMachine !=\"\":\n\t\t\t\tif \"VDtail\" in self.msgListenerActive and time.time() - self.msgListenerActive[\"VDtail\"] > 600: # no recordings etc for 10 minutes, reissue mount command\n\t\t\t\t\tself.msgListenerActive[\"VDtail\"] = time.time()\n\t\t\t\t\tself.buttonVboxActionStartCALLBACK()\n\n\t\t\tif self.lastMinute10Check != (datetime.datetime.now().minute)/10:\n\t\t\t\tself.lastMinute10Check = datetime.datetime.now().minute/10\n\t\t\t\tself.checkforUnifiSystemDevicesState = \"10minutes\"\n\t\t\t\tself.checkForNewUnifiSystemDevices()\n\t\t\t\tself.checkInListSwitch()\n\n\t\t\t\tif self.checkforUnifiSystemDevicesState == \"reboot\":\n\t\t\t\t\tself.quitNOW =\"new devices\"\n\t\t\t\t\tself.checkforUnifiSystemDevicesState = \"\"\n\t\t\t\t\treturn \"new devices\"\n\n\n\t\t\t\tif self.lastHourCheck != datetime.datetime.now().hour:\n\t\t\t\t\tself.lastHourCheck = datetime.datetime.now().hour\n\n\t\t\t\t\tif self.quitNOW != \"\": return \"break\"\n\n\t\t\t\t\tself.saveupDownTimers()\n\t\t\t\t\tif self.lastHourCheck ==1: # recycle at midnight\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\ttry:\tindigo.variable.create(\"Unifi_With_Status_Change\",value=\"\", folder=self.folderNameIDVariables)\n\t\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\texcept:\t\tpass\n\n\t\t\t\t\tif self.lastDayCheck != datetime.datetime.now().day:\n\t\t\t\t\t\tself.lastDayCheck = datetime.datetime.now().day\n\t\t\t\t\t\tself.getBackupFilesFromController()\n\n\t\treturn \"ok\"\n\n\n\n\t###########################\t after the loop ############################\n\t####-----------------\t ---------\n\tdef postLoop(self):\n\n\t\tself.pluginState = \"stop\"\n\t\tindigo.server.savePluginPrefs()\t\n\n\t\tif self.quitNOW == \"config changed\":\n\t\t\tself.resetDataStats(calledFrom=\"postLoop\")\n\t\tif self.quitNOW == \"\": self.quitNOW = \" restart / stop requested \"\n\t\tself.pluginPrefs[\"connectParams\"] = json.dumps(self.connectParams)\n\n\t\tself.consumeDataThread[\"log\"][\"status\"] = \"stop\"\n\t\tself.consumeDataThread[\"dict\"][\"status\"] = \"stop\"\n\n\t\tif True:\n\t\t\tfor ll in range(len(self.devsEnabled[\"SW\"])):\n\t\t\t\ttry: \tself.trSWLog[\"{}\".format(ll)].join()\n\t\t\t\texcept: pass\n\t\t\t\ttry: \tself.trSWDict[\"{}\".format(ll)].join()\n\t\t\t\texcept: pass\n\t\t\tfor ll in range(len(self.devsEnabled[\"AP\"])):\n\t\t\t\ttry: \tself.trAPLog[\"{}\".format(ll)].join()\n\t\t\t\texcept: pass\n\t\t\t\ttry: \tself.trAPDict[\"{}\".format(ll)].join()\n\t\t\t\texcept: pass\n\n\t\ttry: \tself.trGWLog.join()\n\t\texcept: pass\n\t\ttry: \tself.trGWDict.join()\n\t\texcept: pass\n\t\ttry: \tself.trVDLog.join()\n\t\texcept: pass\n\n\t\t## kill all expect \"uniFi\" programs\n\t\tself.killIfRunning(\"\", \"\")\n\n\t\tself.saveupDownTimers()\n\t\treturn \n\n\t####-----------------\t ---------\n\tdef checkOnDelayedActions(self):\n\t\ttry:\n\t\t\tif self.delayedAction == {}: return \n\t\t\tfor devId in self.delayedAction:\n\t\t\t\tfor actionDict in self.delayedAction[devId]:\n\t\t\t\t\tif actionDict[\"action\"] == \"updateState\":\n\t\t\t\t\t\tself.addToStatesUpdateList(devId,actionDict[\"state\"], actionDict[\"value\"] )\n\t\t\tself.delayedAction = {}\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\n\t####-----------------\t ---------\n\tdef checkOnDevNeedsUpdate(self):\n\t\ttry:\n\t\t\tif len(self.devNeedsUpdate) == 0: return \n\n\t\t\tfor devId in self.devNeedsUpdate:\n\t\t\t\tif devId not in indigo.devices: \n\t\t\t\t\tself.indiLOG.log(30,\"checkOnDevNeedsUpdate: device id not in indigo :{}, skipping, please restart plugin\".format(devId))\n\t\t\t\t\tcontinue\n\t\t\t\tself.setUpDownStateValue(indigo.devices[devId])\n\n\t\t\tself.devNeedsUpdate = {}\n\t\t\tself.saveupDownTimers()\n\t\t\tself.setGroupStatus(init=True)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\t####-----------------\t ---------\n\tdef saveupDownTimers(self):\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"upDownTimers\",\"w\")\n\t\t\tf.write(json.dumps(self.upDownTimers))\n\t\t\tf.close()\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t####-----------------\t ---------\n\tdef readupDownTimers(self):\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"upDownTimers\",\"r\")\n\t\t\tself.upDownTimers = json.loads(f.read())\n\t\t\tf.close()\n\t\texcept:\n\t\t\tself.upDownTimers ={}\n\t\t\ttry:\n\t\t\t\tf.close()\n\t\t\texcept:\n\t\t\t\tpass\n\n\t####-----------------\t ---------\n\tdef checkOnChanges(self):\n\t\txType\t= \"UN\"\n\t\ttry:\n\t\t\tif self.upDownTimers =={}: return\n\t\t\tdeldev={}\n\n\t\t\tfor devid in self.upDownTimers:\n\t\t\t\ttry:\n\t\t\t\t\tdev= indigo.devices[int(devid)]\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"timeout waiting\") > -1:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\treturn\n\t\t\t\t\tif \"{}\".format(e).find(\"not found in database\") >-1:\n\t\t\t\t\t\tdeldev[devid] =[-1,\"dev w devID:{} does not exist\".format(devid)]\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\treturn\n\n\t\t\t\tprops=dev.pluginProps\n\t\t\t\texpT = self.getexpT(props)\n\t\t\t\tdt\t= time.time() - expT\n\t\t\t\tdtDOWN = time.time() -\tself.upDownTimers[devid][\"down\"]\n\t\t\t\tdtUP = time.time() -\tself.upDownTimers[devid][\"up\"]\n\n\t\t\t\tif dev.states[\"status\"] != \"up\": newStat = \"down\"\n\t\t\t\telse:\t\t\t\t\t\t\t newStat = \"up\"\n\t\t\t\tMAC = dev.states[\"MAC\"]\n\t\t\t\tif self.upDownTimers[devid][\"down\"] > 10.:\n\t\t\t\t\tif dtDOWN < 2: continue # ignore and up-> in the last 2 secs to avoid constant up-down-up\n\t\t\t\t\tif self.doubleCheckWithPing(newStat,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"checkOnChanges\", \"CHAN-WiF-Pg\",\"UN\") ==0:\n\t\t\t\t\t\t\tdeldev[devid] = [MAC,\"[down]>10 ping check\"]\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\tif \"useWhatForStatusWiFi\" in props and props[\"useWhatForStatusWiFi\"] in [\"FastDown\",\"Optimized\"]:\n\t\t\t\t\t\tif dtDOWN > 10. and dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"down\", ts=dt - 0.1, fing=True, level=1, text1= \"{:30s} status was up changed period WiFi, expT={:4.1f}; dt={:4.1f}\".format(dev.name, expT, dt), iType=\"CHAN-WiFi\",reason=\"FastDown\")\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][dev.states[\"MAC\"]][\"lastUp\"] = time.time() - expT\n\t\t\t\t\t\t\tdeldev[devid] = [MAC,\"[down]>10 and fastD or optimized\"]\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\tif dtDOWN >4:\n\t\t\t\t\t\tdeldev[devid] = [MAC,\"[down]>10 and dtDown>4\"]\n\t\t\t\tif self.upDownTimers[devid][\"up\"] > 10.:\n\t\t\t\t\tif dtUP < 2: continue # ingnore and up-> in the last 2 secs to avoid constant up-down-up\n\t\t\t\t\tdeldev[devid] = [MAC,\"[up]>10 and tt-[up]>2\"]\n\t\t\t\tif self.upDownTimers[devid][\"down\"] == 0. and self.upDownTimers[devid][\"up\"] == 0.:\n\t\t\t\t\tdeldev[devid] = [MAC,\"[down]==0 and [up]==0\"]\n\n\t\t\tfor devId in deldev:\n\t\t\t\tdd = deldev[devId]\n\t\t\t\tif self.decideMyLog(\"Logic\", MAC=dd[0]): self.indiLOG.log(10,\"ChkOnChang del upDownTimers[{}],reason:{}\".format(dd[0], dd[1]) )\n\t\t\t\tdel self.upDownTimers[devId]\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef getexpT(self, props):\n\t\ttry:\n\t\t\texpT = self.expirationTime\n\t\t\tif \"expirationTime\" in props and props[\"expirationTime\"] != \"-1\":\n\t\t\t\ttry:\n\t\t\t\t\texpT = float(props[\"expirationTime\"])\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tself.indiLOG.log(40,\"props /expirationTime={}\".format(props[\"expirationTime\"]))\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn expT\n\n\t\t####----------------- check things every minute / xx minute / hour once a day .. ---------\n\n\n\n\t####-----------------\t ---------\n\tdef getUDMpro_sensors(self):\n\t\ttry:\n\t\t\tif True or self.unifiControllerType.find(\"UDM\") == -1: return \n\n\t\t\tcmd = self.expectPath \n\t\t\tcmd += \" '\"+self.pathToPlugin + \"UDM-pro-sensors.exp' \"\n\t\t\tcmd += \" '\"+self.connectParams[\"UserID\"][\"unixUD\"]+\"' \"\n\t\t\tcmd += \" '\"+self.connectParams[\"PassWd\"][\"unixUD\"]+\"' \"\n\t\t\tcmd += self.unifiCloudKeyIP\n\t\t\tcmd += \" '\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][self.unifiCloudKeyIP])+\"' \"\n\t\t\tcmd += self.getHostFileCheck()\n\n\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"getUDMpro_sensors: get sensorValues from UDMpro w cmd: {}\".format(cmd) )\n\n\t\t\tret, err = self.readPopen(cmd)\n\n\t\t\tdata0 = ret.split(\"\\n\")\n\t\t\tnextItem = \"\"\n\t\t\ttemperature = \"\"\n\t\t\ttemperature_Board_CPU = \"\"\n\t\t\ttemperature_Board_PHY = \"\"\n\t\t\tif self.decideMyLog(\"UDM\") or self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"getUDMpro_sensors returned list: {}\".format(data0) )\n\t\t\tfor dd in data0:\n\t\t\t\tif dd.find(\":\") == -1: continue\n\t\t\t\tnn = dd.strip().split(\":\")\n\t\t\t\tif nn[0] == \"temp2_input\":\n\t\t\t\t\tt2 \t= round(float(nn[1]),1)\n\t\t\t\telif nn[0] == \"temp1_input\":\n\t\t\t\t\tt1\t\t\t= round(float(nn[1]),1)\n\t\t\t\telif nn[0] == \"temp3_input\":\n\t\t\t\t\tt3 \t= round(float(nn[1]),1)\n \n\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"getUDMpro_sensors: temp values found: 1:{}, 2:{}, 3:{}\".format(t1, t2, t3) )\n\t\t\tfound = False\t\t\t\n\t\t\tfor dev in indigo.devices.iter(\"props.isGateway\"):\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"getUDMpro_sensors: adding temperature states to device: {}-{}\".format(dev.id, dev.name) )\n\t\t\t\tif dev.states[\"temperature\"] \t\t\t!= t1 and t1 != \"\": \t\t self.addToStatesUpdateList(dev.id,\"temperature\", t1)\n\t\t\t\tif dev.states[\"temperature_Board_CPU\"] != t2 and t2 != \"\": self.addToStatesUpdateList(dev.id,\"temperature_Board_CPU\", t2)\n\t\t\t\tif dev.states[\"temperature_Board_PHY\"] != t3 and t3 != \"\": self.addToStatesUpdateList(dev.id,\"temperature_Board_PHY\", t3)\n\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\tfound = True\t\t\t\n\t\t\t\tbreak\n\t\t\tif not found:\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"getUDMpro_sensors: not UDM-GW device setup in indigo\" )\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef periodCheck(self):\n\t\ttry:\n\n\t\t\tif\tself.countLoop < 10:\t\t\t\t\treturn\n\t\t\tif time.time() - self.pluginStartTime < 70: return\n\t\t\tchanged = False\n\n\t\t\tif self.countLoop%2000 == 0: self.setSqlLoggerIgnoreStatesAndVariables()\n\n\t\t\tself.checkcProfile()\n\n\t\t\tself.getNVRIntoIndigo()\n\t\t\tself.getNVRCamerastoIndigo(periodCheck = True)\n\n\t\t\tself.getProtectIntoIndigo()\n\n\t\t\tself.saveCamerasStats()\n\t\t\tself.saveDataStats()\n\t\t\tself.saveMACdata()\n\t\t\tself.createEntryInUnifiDevLog()\n\t\t\tself.getcontrollerDBForClients()\n\n\n\t\t\tself.checkIfPrintProcessingTime()\n\n\n\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\n\t\t\t\ttry:\n\t\t\t\t\tif dev.deviceTypeId == \"camera_protect\": continue\n\t\t\t\t\tif dev.deviceTypeId == \"camera\": continue\n\t\t\t\t\tif dev.deviceTypeId == \"NVR\": continue\n\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\n\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\tdevid = \"{}\".format(dev.id)\n\n\t\t\t\t\tMAC\t\t= dev.states[\"MAC\"]\n\t\t\t\t\tif dev.deviceTypeId == \"UniFi\" and self.testIgnoreMAC(MAC, fromSystem=\"periodCheck\") : continue\n\n\t\t\t\t\tif \"{}\".format(devid) not in self.xTypeMac:\n\t\t\t\t\t\tif dev.deviceTypeId == \"UniFi\":\n\t\t\t\t\t\t\tself.setupStructures(\"UN\", dev, MAC)\n\t\t\t\t\t\tif dev.deviceTypeId == \"Device-AP\":\n\t\t\t\t\t\t\tself.setupStructures(\"AP\", dev, MAC)\n\t\t\t\t\t\tif dev.deviceTypeId.find(\"Device-SW\")>-1:\n\t\t\t\t\t\t\tself.setupStructures(\"SW\", dev, MAC)\n\t\t\t\t\t\tif dev.deviceTypeId == \"neighbor\":\n\t\t\t\t\t\t\tself.setupStructures(\"NB\", dev, MAC)\n\t\t\t\t\t\tif dev.deviceTypeId == \"gateway\":\n\t\t\t\t\t\t\tself.setupStructures(\"GW\", dev, MAC)\n\t\t\t\t\txType\t= self.xTypeMac[devid][\"xType\"]\n\n\t\t\t\t\texpT= self.getexpT(props)\n\t\t\t\t\ttry:\n\t\t\t\t\t\tlastUpTT = self.MAC2INDIGO[xType][MAC][\"lastUp\"]\n\t\t\t\t\texcept:\n\t\t\t\t\t\tlastUpTT = time.time()\n\t\t\t\t\tlastUpTTFastDown = lastUpTT\n\n\t\t\t\t\tif dev.deviceTypeId == \"UniFi\":\n\t\t\t\t\t\tipN = dev.states[\"ipNumber\"]\n\n\t\t\t\t\t\tif MAC not in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"{} xType:{} MAC:{} not in self.MAC2INDIGO dict, try to restart, delete device and re-create\".format(dev.Name, xType, MAC) )\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t# check for supended status, if sup : set, if back reset susp status\n\t\t\t\t\t\tif ipN in self.suspendedUnifiSystemDevicesIP:\n\t\t\t\t\t\t\t## check if we need to reset suspend after 300 secs\n\t\t\t\t\t\t\tif (time.time() - self.suspendedUnifiSystemDevicesIP[ipN] >10 and self.checkPing(ipN,nPings=2,countPings =2, waitForPing=0.5, calledFrom=\"PeriodCheck\") == 0) :\n\t\t\t\t\t\t\t\t\tself.delSuspend(ipN)\n\t\t\t\t\t\t\t\t\tlastUpTT = time.time()\n\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"{} is back from suspended status\".format(dev.name))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != \"susp\":\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"susp\", oldStatus=dev.states[\"status\"],ts=time.time(), fing=False, level=1, text1= \"{:30s} status :{:10s}; set to suspend\".format(dev.name, status), iType=\"PER-susp\",reason=\"Period Check susp \"+status)\n\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tlastUpTT = self.checkIfControllerDBInfoActive(xType, MAC, props, lastUpTT, expT, dev)\n\n\t\t\t\t\t\tdt = time.time() - lastUpTT\n\n\t\t\t\t\t\tif \"useWhatForStatus\" in props:\n\t\t\t\t\t\t\tif props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\t\t\t\t\t\t\t\tsuffixN = \"WiFi\"\n\n\t\t\t\t\t\t\t\t######### do WOL / ping\t START ########################\n\t\t\t\t\t\t\t\tif \"useWOL\" in props and props[\"useWOL\"] !=\"0\":\n\t\t\t\t\t\t\t\t\tif \"lastWOL\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastWOL\"]\t= 0.\n\t\t\t\t\t\t\t\t\tif time.time() - self.MAC2INDIGO[xType][MAC][\"lastWOL\"] > float(props[\"useWOL\"]):\n\t\t\t\t\t\t\t\t\t\tif dt < expT:\t# if UP do minimal broadcast\n\t\t\t\t\t\t\t\t\t\t\twaitBeforePing = 0 # do a quick ping\n\t\t\t\t\t\t\t\t\t\t\twaitForPing\t = 1 # mSecs = do not wait\n\t\t\t\t\t\t\t\t\t\t\tnBC\t\t\t = 1 # # of broadcasts\n\t\t\t\t\t\t\t\t\t\t\tnPings\t\t = 0\n\t\t\t\t\t\t\t\t\t\t\twaitAfterPing = 0.0\n\t\t\t\t\t\t\t\t\t\telif dt < 2*expT:\t\t\t# if down wait between BC and ping,\t wait for ping to answer and do 2 BC\n\t\t\t\t\t\t\t\t\t\t\twaitBeforePing = 0.3 # secs\n\t\t\t\t\t\t\t\t\t\t\twaitForPing\t = 500 # msecs\n\t\t\t\t\t\t\t\t\t\t\twaitAfterPing = 0.3\n\t\t\t\t\t\t\t\t\t\t\tnBC\t\t\t = 2\n\t\t\t\t\t\t\t\t\t\t\tnPings\t\t = 2\n\t\t\t\t\t\t\t\t\t\telse:\t\t\t\t\t # expired, do a quick bc\n\t\t\t\t\t\t\t\t\t\t\twaitBeforePing = 0.0 # secs\n\t\t\t\t\t\t\t\t\t\t\twaitForPing\t = 10 # msecs\n\t\t\t\t\t\t\t\t\t\t\tnBC\t\t\t = 1\n\t\t\t\t\t\t\t\t\t\t\tnPings\t\t = 0\n\t\t\t\t\t\t\t\t\t\t\twaitAfterPing = 0.0\n\t\t\t\t\t\t\t\t\t\tif self.sendWakewOnLanAndPing( MAC, ipN, nBC=nBC, waitForPing=waitForPing, countPings=1, waitAfterPing=waitAfterPing, waitBeforePing=waitBeforePing, nPings=nPings, calledFrom=\"periodCheck\") ==0:\n\t\t\t\t\t\t\t\t\t\t\tlastUpTT = time.time()\n\t\t\t\t\t\t\t\t\t\t\tlastUpTTFastDown = time.time()\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = lastUpTT\n\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastWOL\"]\t= time.time()\n\t\t\t\t\t\t\t\t######### do WOL / ping\t END ########################\n\t\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\n\t\t\t\t\t\t\t\tif \"useWhatForStatusWiFi\" not in props or\t(\"useWhatForStatusWiFi\" in props and props[\"useWhatForStatusWiFi\"] != \"FastDown\"):\n\n\t\t\t\t\t\t\t\t\tif (devid in self.upDownTimers\tand time.time() - self.upDownTimers[devid][\"down\"] > expT ) or (dt > 1 * expT) :\n\t\t\t\t\t\t\t\t\t\tif\t dt <\t\t\t\t\t\t 1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\t\t\telif dt <\tself.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\t\t\t\telse :\t\t\t\t status = \"expired\"\n\t\t\t\t\t\t\t\t\t\tif not self.expTimerSettingsOK(\"AP\",MAC, dev): continue\n\n\t\t\t\t\t\t\t\t\t\tif status != \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\t\t\t\t\t\tif self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-WiFi\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\t\t\t\t\tstatus\t= \"up\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\", oldStatus=dev.states[\"status\"],ts=time.time(), fing=False, level=1, text1= \"{:30s} status {:10s}; set to UP, reset by ping \".format(dev.name, status), iType=\"PER-AP-Wi-0\",reason=\"Period Check Wifi \"+status)\n\t\t\t\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status, oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period WiFi, expT={:4.1f} dt={:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-AP-Wi-1\",reason=\"Period Check Wifi \"+status)\n\t\t\t\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] == \"down\" and status !=\"down\": # to expired\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status, oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period WiFi, expT={:4.1f} dt={:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-AP-Wi-1\",reason=\"Period Check Wifi \"+status)\n\t\t\t\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\t\t\t\tif self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-WiFi\", \"chk-Time\",xType) !=0:\n\t\t\t\t\t\t\t\t\t\t\t\t\tpass\n\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status, oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period WiFi, expT={:4.1f} dt={:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-AP-Wi-1\",reason=\"Period Check Wifi \"+status)\n\t\t\t\t\t\t\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t\t\t\t\telif (\"useWhatForStatusWiFi\" in props and props[\"useWhatForStatusWiFi\"] == \"FastDown\") and dev.states[\"status\"] == \"down\" and (time.time() - lastUpTTFastDown > self.expTimeMultiplier * expT):\n\t\t\t\t\t\t\t\t\t\tif not self.expTimerSettingsOK(\"AP\",MAC, dev): continue\n\t\t\t\t\t\t\t\t\t\tstatus = \"expired\"\n\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status, oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1=\"{:30s} status {:10s}; changed period WiFi, expT={:4.1f} dt={:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-AP-Wi-2\",reason=\"Period Check Wifi \"+status)\n\n\n\t\t\t\t\t\t\telif props[\"useWhatForStatus\"] ==\"SWITCH\":\n\t\t\t\t\t\t\t\tsuffixN = \"SWITCH\"\n\t\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\t\t\t\t\tif\t dt < 1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\telif dt < 2 * expT: status = \"down\"\n\t\t\t\t\t\t\t\telse :\t\t\t\t status = \"expired\"\n\t\t\t\t\t\t\t\tif not self.expTimerSettingsOK(\"SW\",MAC, dev): continue\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tif status ==\"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-SWITCH\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status,oldStatus=dev.states[\"status\"], ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period SWITCH, expT={:4.1f} dt={:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-SW-0\",reason=\"Period Check SWITCH \"+status)\n\n\n\n\t\t\t\t\t\t\telif props[\"useWhatForStatus\"].find(\"DHCP\") > -1:\n\t\t\t\t\t\t\t\tsuffixN = \"DHCP\"\n\t\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\t\t\t\t\tif\t dt < \t\t\t\t\t\t1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\telif dt < self.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\t\telse :\t\t\t\t status = \"expired\"\n\t\t\t\t\t\t\t\tif not self.expTimerSettingsOK(\"GW\",MAC, dev): continue\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tif status == \"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-DHCP\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status,oldStatus=dev.states[\"status\"], ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period DHCP, expT={:4.1f} dt= {:4.1f}\".format(dev.name, status, expT, dt), iType=\"PER-DHCP-0\",reason=\"Period Check DHCP \"+status)\n\n\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\t\t\t\t\tif\t dt < \t\t\t\t\t\t1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\telif dt < self.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\t\telse\t\t\t : status = \"expired\"\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tif status ==\"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-default\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, status,oldStatus=dev.states[\"status\"], ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period regular expiration, expT{:4.1f} dt={:4.1f} useWhatForStatus else{}\".format(dev.name, status, expT, dt,props[\"useWhatForStatus\"]) , iType=\"PER-expire\",reason=\"Period Check\")\n\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t\telif dev.deviceTypeId == \"Device-AP\":\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tipN = dev.states[\"ipNumber\"]\n\t\t\t\t\t\t\tif ipN not in self.deviceUp[\"AP\"]:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t\t#ipN = self.ipNumbersOf[\"AP\"][int(dev.states[\"apNo\"])]\n\t\t\t\t\t\t\t\t#dev.updateStateOnServer(\"ipNumber\", ipN )\n\t\t\t\t\t\t\tif ipN in self.suspendedUnifiSystemDevicesIP:\n\t\t\t\t\t\t\t\tstatus = \"susp\"\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tdt\t=99\n\t\t\t\t\t\t\t\texpT=999\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tdt = time.time() - self.deviceUp[\"AP\"][dev.states[\"ipNumber\"]]\n\t\t\t\t\t\t\t\tif\t dt < \t\t\t\t\t\t1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\telif dt < self.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\t\telse :\t\t\t\t status = \"expired\"\n\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\tif status ==\"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-dev-AP\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev,status,oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1= \"{:30s} status {:10s}; changed period, expT={:4.1f} dt= {:4.1f}\".format(dev.name, status, expT, dt), reason=\"Period Check\", iType=\"PER-DEV-AP\")\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\telif dev.deviceTypeId.find(\"Device-SW\") >-1:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tipN = dev.states[\"ipNumber\"]\n\t\t\t\t\t\t\tif ipN not in self.deviceUp[\"SW\"]:\n\t\t\t\t\t\t\t\tipN = self.ipNumbersOf[\"SW\"][int(dev.states[\"switchNo\"])]\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"ipNumber\", ipN )\n\t\t\t\t\t\t\tif ipN in self.suspendedUnifiSystemDevicesIP:\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tstatus = \"susp\"\n\t\t\t\t\t\t\t\tdt =99\n\t\t\t\t\t\t\t\texpT=999\n\t\t\t\t\t\t\telse:\n\n\t\t\t\t\t\t\t\tdt = time.time() - self.deviceUp[\"SW\"][ipN]\n\t\t\t\t\t\t\t\tif\t dt < \t\t\t\t\t\t1 * expT: status = \"up\"\n\t\t\t\t\t\t\t\telif dt < self.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\t\telse:\t\t\t\t\t\t\t\t\t status = \"expired\"\n\n\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\tif status ==\"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-dev-SW\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev,status,oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1=\"{:30s} status {:10s}; changed period, expT={:4.1f} dt= {:4.1f}\".format(dev.name, status, expT, dt),reason=\"Period Check\", iType=\"PER-DEV-SW\")\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t\telif dev.deviceTypeId == \"neighbor\":\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\t\t\t\tif\t dt < \t\t\t\t\t\t1 * expT: status = \"up\"\n\t\t\t\t\t\t\telif dt < self.expTimeMultiplier * expT: status = \"down\"\n\t\t\t\t\t\t\telse:\t\t\t\tstatus = \"expired\"\n\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tchanged=True\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev,status,oldStatus=dev.states[\"status\"],ts=time.time(), fing=self.ignoreNeighborForFing, level=1, text1=\"{:30s} status {:10s}; changed period, expT={:4.1f} dt= {:4.1f}\".format(dev.name, status, expT, dt),reason=\"Period Check other\", iType=\"PER-DEV-NB\")\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\telse:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\t\t\t\tif dt < 1 * expT:\tstatus = \"up\"\n\t\t\t\t\t\t\telif dt < 2 * expT: status = \"down\"\n\t\t\t\t\t\t\telse:\t\t\t\tstatus = \"expired\"\n\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\tif status ==\"down\" and self.doubleCheckWithPing(status,dev.states[\"ipNumber\"], props,dev.states[\"MAC\"],\"Logic\", \"Period check-def\", \"chk-Time\",xType) ==0:\n\t\t\t\t\t\t\t\t\tstatus = \"up\"\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != status:\n\t\t\t\t\t\t\t\t\tchanged=True\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev,status, oldStatus=dev.states[\"status\"],ts=time.time(), fing=True, level=1, text1=\"{:30s} status {:10s}; changed period, expT={:4.1f} dt= {:4.1f} devtype else:{}\".format(dev.name, status, expT, dt,dev.deviceTypeId),reason=\"Period Check other\", iType=\"PER-DEV-exp\")\n\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tself.lastSecCheck = time.time()\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\tchanged\n\n\n\n\n\t########################### #################\n\tdef checkIfControllerDBInfoActive(self, xType, MAC, props, lastUpTT, expT, dev):\n\t\ttry:\n\t\t\tif self.useDBInfoForWhichDevices == \"all\" or (self.useDBInfoForWhichDevices == \"perDevice\" and \"useDBInfoForDownCheck\" in props and props[\"useDBInfoForDownCheck\"] == \"useDBInfo\"):\n\t\t\t\tif time.time() - self.MAC2INDIGO[xType][MAC][\"last_seen\"] < max(99., expT):\n\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"last_seen\"] > lastUpTT:\n\t\t\t\t\t\tif self.decideMyLog(\"DBinfo\", MAC=MAC): self.indiLOG.log(10,\"overwriting lastUP w info from controllerdb {} {:28s} lastTT:{:.0f}, new lastTT:{:.0f}\".format(MAC, dev.name, time.time() - lastUpTT, time.time() - self.MAC2INDIGO[xType][MAC][\"last_seen\"] ))\n\t\t\t\t\t\tlastUpTT = self.MAC2INDIGO[xType][MAC][\"last_seen\"]\n\t\t\tif self.decideMyLog(\"DBinfo\", MAC=MAC): \n\t\t\t\tself.indiLOG.log(10,\"checking lastUP w info from controllerdb {} {:28s} lastTT:{:.0f}, lastTT-db:{:.0f}\".format(MAC, dev.name, time.time() - lastUpTT, time.time() - self.MAC2INDIGO[xType][MAC][\"last_seen\"] ))\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn lastUpTT\n\n\n\t###########################\t UTILITIES #### START #################\n\n\t### reset exp timer if it is shorter than the device exp time\n\t####-----------------\t ---------\n\tdef expTimerSettingsOK(self, xType, MAC,\tdev):\n\t\ttry:\n\t\t\tif not self.fixExpirationTime: \t\treturn True\n\t\t\tprops = dev.pluginProps\n\t\t\tif \"expirationTime\" not in props:\treturn True\n\n\t\t\tif float(self.readDictEverySeconds[xType]) < float(props[\"expirationTime\"]): return True\n\t\t\tnewExptime\t= float(self.readDictEverySeconds[xType])+10\n\t\t\tself.indiLOG.log(10,\"checking expiration timer settings {} updating exptime for {} to {} as it is shorter than reading dicts: {}+10\".format(MAC, dev.name, newExptime, self.readDictEverySeconds[xType]))\n\t\t\tprops[\"expirationTime\"] = newExptime\n\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\treturn False\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn True\n\n\t###\t kill expect pids if running\n\t####-----------------\t ---------\n\tdef killIfRunning(self,ipNumber,expectPGM):\n\t\tcmd = \"ps -ef | grep '/uniFiAP.' | grep \"+self.expectPath+\" | grep -v grep\"\n\t\tif expectPGM !=\"\":\t\tcmd += \" | grep '\" + expectPGM + \"' \"\n\t\tif ipNumber != \"\":\t\tcmd += \" | grep '\" + ipNumber + \" ' \" # add space at end of ip# for search string\n\n\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"killing request, get list with: \"+cmd)\n\t\tret, err = self.readPopen(cmd)\n\n\t\tif len(ret) < 5:\n\t\t\treturn\n\n\t\tlines = ret.split(\"\\n\")\n\t\tfor line in lines:\n\t\t\tif len(line) < 5:\n\t\t\t\tcontinue\n\n\t\t\titems = line.split()\n\t\t\tif len(items) < 5:\n\t\t\t\tcontinue\n\n\t\t\tpid = items[1]\n\t\t\ttry:\n\t\t\t\tif int(pid) < 100: continue # don't mess with any system processes\n\t\t\t\tret, err = self.readPopen(\"/bin/kill -9 \" + pid)\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"killing expect \"+expectPGM+\" w ip# \" +ipNumber +\" \" +pid+\":\\n\"+line )\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef killPidIfRunning(self,pid):\n\t\tcmd = \"ps -ef | grep '/uniFiAP.' | grep \"+self.expectPath+\" | grep {}\".format(pid)+\" | grep -v grep\"\n\n\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"killing request, for pid: {}\".format(pid))\n\t\tret, err = self.readPopen(cmd)\n\n\t\tif len(ret) < 5:\n\t\t\treturn\n\n\t\tlines = ret.split(\"\\n\")\n\t\tfor line in lines:\n\t\t\tif len(line) < 5:\n\t\t\t\tcontinue\n\n\t\t\titems = line.split()\n\t\t\tif len(items) < 5:\n\t\t\t\tcontinue\n\n\t\t\tpidInLine = items[1]\n\t\t\ttry:\n\t\t\t\tif int(pidInLine) != int(pid): continue # don't mess with any system processes\n\t\t\t\tret, err = self.readPopen(\"/bin/kill -9 \" + pidInLine)\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"killing expect \" +pidInLine+\":\\n\"+line )\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\tbreak\n\n\t\treturn\n\n\t### test if AP are up, first ping then check if expect is running\n\t####-----------------\t ---------\n\tdef testAPandPing(self,ipNumber, cType):\n\t\ttry:\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest testing if {} {} {} is running \".format(ipNumber, self.expectPath,self.connectParams[\"expectCmdFile\"][cType]))\n\t\t\tif os.path.isfile(self.pathToPlugin +self.connectParams[\"expectCmdFile\"][cType]):\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest {} exists, now doing ping\" .format(self.connectParams[\"expectCmdFile\"][cType]))\n\t\t\tif self.checkPing(ipNumber, nPings=2, waitForPing=1000, calledFrom=\"testAPandPing\", verbose=True) !=0:\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest ping not returned\" )\n\t\t\t\treturn False\n\n\t\t\tcmd = \"ps -ef | grep \" +self.connectParams[\"expectCmdFile\"][cType]+ \"| grep \" + ipNumber + \" | grep \"+self.expectPath+\" | grep -v grep\"\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest check if pgm is running {}\".format(cmd) )\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"returned from expect-command: {}\".format(ret[0]))\n\t\t\tif len(ret) < 5: return False\n\t\t\tlines = ret.split(\"\\n\")\n\t\t\tfor line in lines:\n\t\t\t\tif len(line) < 5:\n\t\t\t\t\tcontinue\n\n\t\t\t\titems = line.split()\n\t\t\t\tif len(items) < 5:\n\t\t\t\t\tcontinue\n\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest expect is running\" )\n\t\t\t\treturn True\n\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"CONNtest {} {}is NOT running\".format(cType, ipNumber) )\n\t\t\treturn False\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t### test if AP are up, first ping then check if expect is running\n\t####-----------------\t ---------\n\tdef resetUnifiDevice(self,ipNumber, uType):\n\t\ttry:\n\t\t\tuserid, passwd = self.getUidPasswd(uType,ipNumber)\n\t\t\tif userid == \"\": return\n\t\t\tself.indiLOG.log(10,\"resetUnifiDevice {}-{}; requested\".format(uType, ipNumber) )\n\t\t\tif ipNumber in self.lastResetUnifiDevice:\n\t\t\t\tif time.time() - self.lastResetUnifiDevice[ipNumber] < 180: return # only reset devices every 50 secs not more often..\n\t\t\tself.lastResetUnifiDevice[ipNumber] = time.time()\n\t\t\tcmd = self.expectPath + \" '\" \n\t\t\tcmd += self.pathToPlugin + self.connectParams[\"expectRestart\"][uType] + \"' \"\n\t\t\tcmd += \"'\"+userid + \"' '\"+passwd + \"' \" \n\t\t\tcmd += ipNumber + \" \" \n\t\t\tcmd += \"'\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber]) + \"' \" \n\t\t\tcmd += self.getHostFileCheck()\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\tself.indiLOG.log(10,\"resetUnifiDevice {}-{}; cmd:{} return:{}\".format(uType, ipNumber, cmd, ret) )\n\t\t\treturn False\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\n\t####-----------------\t --------- \n\t### init,save,write data stats for receiving messages\n\tdef addTypeToDataStats(self,ipNumber, apN, uType):\n\t\ttry:\n\t\t\tif uType not in self.dataStats[\"tcpip\"]:\n\t\t\t\tself.dataStats[\"tcpip\"][uType]={}\n\t\t\tif ipNumber not in self.dataStats[\"tcpip\"][uType]:\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber]={\"inMessageCount\":0,\"inMessageBytes\":0,\"inErrorCount\":0,\"restarts\":0,\"inErrorTime\":0,\"startTime\":time.time(),\"APN\":\"{}\".format(apN), \"aliveTestCount\":0}\n\t\t\tif \"inErrorTime\" not in self.dataStats[\"tcpip\"][uType][ipNumber]:\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorTime\"] = 0\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t####-----------------\t ---------\n\tdef zeroDataStats(self):\n\t\tfor uType in self.dataStats[\"tcpip\"]:\n\t\t\tfor ipNumber in self.dataStats[\"tcpip\"][uType]:\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageCount\"]\t= 0\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageBytes\"]\t= 0\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"aliveTestCount\"]\t= 0\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorCount\"]\t\t= 0\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"restarts\"]\t\t\t= 0\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"startTime\"]\t\t\t= time.time()\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorTime\"]\t\t= 0\n\t\tself.dataStats[\"updates\"]={\"devs\":0,\"states\":0,\"startTime\":time.time()}\n\t####-----------------\t ---------\n\tdef resetDataStats(self, calledFrom=\"\"):\n\t\tindigo.server.log(\" resetDataStats called from {}\".format(calledFrom) )\n\t\tself.dataStats={\"tcpip\":{},\"updates\":{\"devs\":0,\"states\":0,\"startTime\":time.time()}}\n\t\tself.saveDataStats()\n\n\t####-----------------\t ---------\n\tdef saveDataStats(self, force = False):\n\t\tif time.time() - 60\t < self.lastSaveDataStats and not force: return\n\t\tself.lastSaveDataStats = time.time()\n\t\tself.writeJson(self.dataStats, fName=self.indigoPreferencesPluginDir+\"dataStats\", sort=False, doFormat=True )\n\t\tself.writeJson(self.waitTimes, fName=self.indigoPreferencesPluginDir+\"waitTimes\", sort=True, doFormat=True )\n\n\n\t####-----------------\t ---------\n\tdef readDataStats(self):\n\t\tself.lastSaveDataStats\t= time.time() - 60\n\t\tself.waitTimes = {}\n\t\tself.dataStats = {}\n\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"waitTimes\",\"r\")\n\t\t\tself.waitTimes = json.loads(f.read())\n\t\t\tf.close()\n\t\texcept: pass\n\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"dataStats\",\"r\")\n\t\t\tself.dataStats = json.loads(f.read())\n\t\t\tf.close()\n\t\t\tif \"tcpip\" not in self.dataStats:\n\t\t\t\tself.resetDataStats( calledFrom=\"readDataStats 1\")\n\t\t\treturn \n\t\texcept: \n\t\t\tself.resetDataStats( calledFrom=\"readDataStats 2\")\n\n\t\treturn\n\t### init,save,write data stats for receiving messages\n\t####-----------------\t --------- END\n\n\n\t####------ camera ---\t-------START\n\tdef resetCamerasStats(self):\n\t\treturn\n\t\tself.cameras={}\n\t\tself.saveCameraEventsStatus = True\n\t\tself.saveCameraEventsLastCheck = 0\n\t\tself.saveCamerasStats()\n\n\t####-----------------\t ---------\n\tdef saveCamerasStats(self,force=False):\n\t\treturn\n\t\tif\tnot self.saveCameraEventsStatus: return\n\n\t\tif self.saveCameraEventsStatus == True:\n\t\t\tself.saveCameraEventsLastCheck = 0\n\n\t\t# check if we have deleted devices in cameras\n\t\tif time.time() - self.saveCameraEventsLastCheck > 500 or force:\n\n\t\t\tcameraMacList ={}\n\t\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\t\tcameraMacList[dev.states[\"MAC\"]] = dev.id\n\n\t\t\tdelList ={}\n\t\t\tfor MAC in self.cameras:\n\t\t\t\tif MAC not in cameraMacList:\n\t\t\t\t\tdelList[MAC]=True\n\t\t\tfor MAC in delList:\n\t\t\t\tself.cameras[MAC][\"devid\"]=-1\n\n\t\t\tself.saveCameraEventsLastCheck = time.time()\n\n\t\t# save cameras to disk\n\t\tself.writeJson( self.cameras, fName=self.indigoPreferencesPluginDir+\"CamerasStats\", sort=True, doFormat=True )\n\t\tself.saveCameraEventsStatus = False\n\n\t####-----------------\t ---------\n\tdef readCamerasStats(self):\n\t\treturn\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"CamerasStats\",\"r\")\n\t\t\tself.cameras = json.loads(f.read())\n\t\t\tf.close()\n\t\t\tself.saveCameraEventsStatus = True\n\t\t\tself.saveCamerasStats()\n\t\t\treturn\n\t\texcept: pass\n\n\t\tself.resetCamerasStats()\n\t\treturn\n\n\t####------ camera PROTEC ---\t-------END\n\n\t####------ camera NVR ---\t-------START\n\n\t####-----------------\t ---------\n\tdef getNVRCamerastoIndigo(self, force = False, periodCheck = False):\n\t\treturn\n\t\ttry:\n\t\t\tif periodCheck: test = 300\n\t\t\telse:\t\t\ttest = 30\n\t\t\tif time.time() - self.lastCheckForCAMERA < test and not force: return\n\t\t\tself.lastCheckForCAMERA = time.time()\n\t\t\ttimeElapsed = time.time()\n\t\t\tif self.cameraSystem != \"nvr\":\t\t\t\t\treturn\n\t\t\tif not self.isValidIP(self.ipNumbersOf[\"VD\"]): return\n\t\t\tinfo = self.getNVRCamerasFromMongoDB(action=[\"cameras\"])\n\t\t\tif len(info) < 1:\n\t\t\t\tself.sleep(1)\n\t\t\t\tinfo = self.getNVRCamerasFromMongoDB(action=[\"cameras\"])\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t####-----------------\t ---------\n\tdef getNVRIntoIndigo(self,force= False):\n\t\treturn\n\t\ttry:\n\t\t\tif time.time() - self.lastCheckForNVR < 451 and not force: return\n\t\t\tself.lastCheckForNVR = time.time()\n\t\t\tif not self.isValidIP(self.ipNumbersOf[\"VD\"]): return\n\t\t\tif self.cameraSystem != \"nvr\":\t\t\t \t\treturn\n\n\n\t\t\tinfo =self.getNVRCamerasFromMongoDB( action=[\"system\"])\n\n\t\t\tif len(info[\"NVR\"]) < 2:\n\t\t\t\tfor dev in indigo.devices.iter(\"props.isNVR\"):\n\t\t\t\t\tself.updateStatewCheck(dev,\"status\", \"down\")\n\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\tbreak\n\t\t\t\treturn\n\n\t\t\tNVR = info[\"NVR\"]\n\t\t\tipNumber =\"\"\n\t\t\tUnifiDevice = \"\"\n\t\t\tUnifiMAC\t= \"\"\n\t\t\tUnifiName\t= \"\"\n\t\t\tmemoryUsed\t= \"\"\n\t\t\tdirName\t\t= \"\"\n\t\t\tdiskUsed\t= \"\"\n\t\t\tdiskFree\t= \"\"\n\t\t\trtmpsPort\t= \"off\"\n\t\t\trtspPort\t= \"off\"\n\t\t\trtmpPort\t= \"off\"\n\t\t\tdiskUsed\t= \"\"\n\t\t\tdiskAvail\t= \"\"\n\t\t\tdiskUsed\t= \"\"\n\t\t\tcpuLoad\t\t= \"\"\n\t\t\tapiKey\t\t= \"\"\n\t\t\tapiAccess\t= False\n\t\t\tupSince\t\t= \"\"\n\t\t\tMAC\t\t\t= \"\"\n\n\n\t\t\tif \"host\"\t\t\t\tin NVR:\t\t\t\t\t\t\t\t ipNumber\t\t\t= NVR[\"host\"]\n\t\t\tif \"uptime\"\t\t\tin NVR:\t\t\t\t\t\t\t\t upSince\t\t\t= time.strftime( '%Y-%m-%d %H:%M:%S', time.localtime(float(NVR[\"uptime\"])/1000) )\n\n\t\t\tif \"systemInfo\"\t in NVR:\n\t\t\t\tif \"nics\"\t\t in NVR[\"systemInfo\"]:\n\t\t\t\t\tfor nic\t\t in NVR[\"systemInfo\"][\"nics\"]:\n\t\t\t\t\t\tif \"ip\"\t in nic:\t\t\t\t\t\t\t ipNumber\t\t\t= nic[\"ip\"]\n\t\t\t\t\t\tif \"mac\" in nic:\t\t\t\t\t\t\t\t MAC\t\t\t\t= nic[\"mac\"].lower()\n\t\t\t\tif \"memory\"\t in NVR[\"systemInfo\"]:\n\t\t\t\t\t\tif \"total\" in NVR[\"systemInfo\"][\"memory\"]:\t\t memoryUsed\t\t= \"%d/%d\"%( float(NVR[\"systemInfo\"][\"memory\"][\"used\"])/(1024*1024), float(NVR[\"systemInfo\"][\"memory\"][\"total\"])/(1024*1024) )+\"[GB]\"\n\t\t\t\tif \"cpuLoad\"\t in NVR[\"systemInfo\"]:\t\t\t\t\t cpuLoad\t\t\t= \"%.1f\"%( float(NVR[\"systemInfo\"][\"cpuLoad\"]))+\"[%]\"\n\t\t\t\tif \"disk\"\t\t in NVR[\"systemInfo\"]:\n\t\t\t\t\t\tif \"dirName\"\t in NVR[\"systemInfo\"][\"disk\"]:\t dirName\t\t\t= NVR[\"systemInfo\"][\"disk\"][\"dirName\"]\n\t\t\t\t\t\tif \"availKb\"\t in NVR[\"systemInfo\"][\"disk\"]:\t diskAvail\t\t\t= \"%d\"%( float(NVR[\"systemInfo\"][\"disk\"][\"availKb\"])/(1024*1024) )+\"[GB]\"\n\t\t\t\t\t\tif \"usedKb\"\t\t in NVR[\"systemInfo\"][\"disk\"]:diskUsed\t\t\t= \"%d/%d\"%( float(NVR[\"systemInfo\"][\"disk\"][\"usedKb\"])/(1024*1024) , float(NVR[\"systemInfo\"][\"disk\"][\"totalKb\"])/(1024*1024) )\t+\"[GB]\"\n\n\t\t\tif\"livePortSettings\"\t\t in NVR:\n\t\t\t\tif \"rtmpEnabled\"\t\t in NVR[\"livePortSettings\"]:\n\t\t\t\t\t\tif NVR[\"livePortSettings\"][\"rtmpEnabled\"]:\t\t rtmpPort\t\t\t= \"{}\".format( NVR[\"livePortSettings\"][\"rtmpPort\"] )\n\t\t\t\tif \"rtmpsEnabled\"\t\t in NVR[\"livePortSettings\"]:\n\t\t\t\t\t\tif NVR[\"livePortSettings\"][\"rtmpsEnabled\"]:\t\t rtmpsPort\t\t\t= \"{}\".format( NVR[\"livePortSettings\"][\"rtmpsPort\"] )\n\t\t\t\tif \"rtspEnabled\"\t\t in NVR[\"livePortSettings\"]:\n\t\t\t\t\t\tif NVR[\"livePortSettings\"][\"rtspEnabled\"]:\t\t rtspPort\t\t\t= \"{}\".format( NVR[\"livePortSettings\"][\"rtspPort\"] )\n\n\t\t\tusers = info[\"users\"]\n\n\t\t\tfor _id in users:\n\t\t\t\tif users[_id][\"userName\"] == self.connectParams[\"UserID\"][\"nvrWeb\"]:\n\t\t\t\t\tif \"apiKey\" in users[_id] and \"enableApiAccess\" in users[_id]:\n\t\t\t\t\t\tif users[_id][\"enableApiAccess\"] :\n\t\t\t\t\t\t\tapiKey\t\t= users[_id][\"apiKey\"]\n\t\t\t\t\t\t\tapiAccess \t= users[_id][\"enableApiAccess\"]\n\n\n\t\t\tUnifiName\t= ipNumber\n\t\t\tfor dev in indigo.devices.iter(\"props.isUniFi\"):\n\t\t\t\tif dev.states[\"ipNumber\"] == ipNumber and MAC == dev.states[\"MAC\"]:\n\t\t\t\t\tUnifiName\t= dev.name\n\t\t\t\t\tbreak\n\n\n\t\t\tdev = \"\"\n\t\t\tfor dd in indigo.devices.iter(\"props.isNVR\"):\n\t\t\t\tdev = dd\n\t\t\t\tbreak\n\n\t\t\tif dev ==\"\":\n\t\t\t\tif UnifiName != \"\": useName= UnifiName\n\t\t\t\telif UnifiMAC !=\"\": useName= UnifiMAC\n\t\t\t\telse:\t\t\t\tuseName= ipNumber+\"{}\".format(int(time.time()))\n\n\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\tprotocol =\t\t indigo.kProtocol.Plugin,\n\t\t\t\t\taddress =\t\t UnifiMAC,\n\t\t\t\t\tname =\t\t\t \"NVR_\" + useName,\n\t\t\t\t\tdescription =\t self.fixIP(ipNumber),\n\t\t\t\t\tpluginId =\t\t self.pluginId,\n\t\t\t\t\tdeviceTypeId =\t \"NVR\",\n\t\t\t\t\tfolder =\t\t self.folderNameIDSystemID,\n\t\t\t\t\tprops =\t\t\t {\"isNVR\":True})\n\n\t\t\tself.updateStatewCheck(dev,\"status\",\t\t\"up\")\n\t\t\tself.updateStatewCheck(dev,\"ipNumber\",\t\tipNumber)\n\t\t\tself.updateStatewCheck(dev,\"memoryUsed\",\tmemoryUsed)\n\t\t\tself.updateStatewCheck(dev,\"dirName\",\t\tdirName)\n\t\t\tself.updateStatewCheck(dev,\"diskUsed\",\t\tdiskUsed)\n\t\t\tself.updateStatewCheck(dev,\"diskAvail\",\t\tdiskAvail)\n\t\t\tself.updateStatewCheck(dev,\"rtmpPort\",\t\trtmpPort)\n\t\t\tself.updateStatewCheck(dev,\"rtmpsPort\",\t\trtmpsPort)\n\t\t\tself.updateStatewCheck(dev,\"rtspPort\",\t\trtspPort)\n\t\t\tself.updateStatewCheck(dev,\"cpuLoad\",\t\tcpuLoad)\n\t\t\tself.updateStatewCheck(dev,\"apiKey\",\t\tapiKey)\n\t\t\tself.updateStatewCheck(dev,\"apiAccess\",\t\tapiAccess)\n\t\t\tself.updateStatewCheck(dev,\"upSince\",\t\tupSince)\n\n\t\t\tself.pluginPrefs[\"nvrVIDEOapiKey\"]\t\t \t= apiKey\n\t\t\tself.nvrVIDEOapiKey\t\t\t\t\t\t \t= apiKey\n\n\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t####-----------------\t ---------\n\tdef fillCamerasIntoIndigo(self,camJson, calledFrom=\"\"):\n\t\treturn\n\t\ttry:\n\t\t\tif len(camJson) < 1: return\n\t\t\tsaveCam= False\n\t\t\tfor cam2 in camJson:\n\t\t\t\tif \"mac\" in cam2:\n\t\t\t\t\tc = cam2[\"mac\"]\n\t\t\t\t\tMAC = c[0:2]+\":\"+c[2:4]+\":\"+c[4:6]+\":\"+c[6:8]+\":\"+c[8:10]+\":\"+c[10:12]\n\t\t\t\t\tMAC = MAC.lower()\n\n\t\t\t\t\tskip = \"\"\n\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"fillCam\"):\n\t\t\t\t\t\tskip = \"MAC in ignored List\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tif \"authStatus\" in cam2 and cam2[\"authStatus\"] != \"AUTHENTICATED\":\n\t\t\t\t\t\t\tskip += \"authStatus: !=AUTHENTICATED;\"\n\t\t\t\t\t\tif \"managed\" in cam2 and not cam2[\"managed\"]:\n\t\t\t\t\t\t\tskip += \" .. != managed;\"\n\t\t\t\t\t\tif \"deleted\" in cam2 and cam2[\"deleted\"]:\n\t\t\t\t\t\t\tskip += \" deleted\"\n\t\t\t\t\t\tif skip !=\"\":\n\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"skipping camera with MAC # \"+MAC +\"; because : \"+ skip)\n\t\t\t\t\tif skip !=\"\":\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tfound = False\n\t\t\t\t\tfor cam in self.cameras:\n\t\t\t\t\t\tif MAC == cam:\n\t\t\t\t\t\t\tself.cameras[MAC][\"uuid\"]\t\t= cam2[\"uuid\"]\n\t\t\t\t\t\t\tself.cameras[MAC][\"ip\"]\t\t\t= cam2[\"host\"]\n\t\t\t\t\t\t\tself.cameras[MAC][\"apiKey\"]\t\t= cam2[\"_id\"]\n\t\t\t\t\t\t\tself.cameras[MAC][\"nameOnNVR\"]\t= cam2[\"name\"]\n\t\t\t\t\t\t\tfound = True\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif not found:\n\t\t\t\t\t\tsaveCam = True\n\t\t\t\t\t\tself.cameras[MAC]= {\"nameOnNVR\":cam2[\"name\"], \"events\":{}, \"eventsLast\":{\"start\":0,\"stop\":0},\"devid\":-1, \"uuid\":cam2[\"uuid\"], \"ip\":cam2[\"host\"], \"apiKey\":cam2[\"_id\"]}\n\n\t\t\t\t\tdevFound = False\n\t\t\t\t\tif \"devid\" in self.cameras[MAC]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.cameras[MAC][\"devid\"]]\n\t\t\t\t\t\t\tdevFound = True\n\t\t\t\t\t\texcept: pass\n\n\t\t\t\t\tif\tnot devFound:\n\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\t\t\t\t\tif \"MAC\" not in dev.states:\t continue\n\t\t\t\t\t\t\tif dev.states[\"MAC\"] == MAC:\n\t\t\t\t\t\t\t\tdevFound = True\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif not devFound:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname \t\t\t= \"Camera_\"+self.cameras[MAC][\"nameOnNVR\"]+\"_\"+MAC ,\n\t\t\t\t\t\t\t\tdescription\t\t=\"\",\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=\"camera\",\n\t\t\t\t\t\t\t\tprops\t\t\t={\"isCamera\":True},\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDSystemID\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}/{}/{}\".format(dev.name, MAC, cam2[\"host\"]) )\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"NameNotUniqueError\") >-1:\n\t\t\t\t\t\t\t\tdev \t\t\t\t= indigo.devices[\"Camera_\"+self.cameras[MAC][\"nameOnNVR\"]+\"_\"+MAC]\n\t\t\t\t\t\t\t\tprops \t\t\t\t= dev.pluginProps\n\t\t\t\t\t\t\t\tprops[\"isCamera\"] \t= True\n\t\t\t\t\t\t\t\tdev.replacePluginPropsOnServer()\n\t\t\t\t\t\t\t\tdev \t\t\t\t= indigo.devices[dev.id]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"MAC\",\t\t\tMAC)\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"apiKey\",\t\tself.cameras[MAC][\"apiKey\"])\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"uuid\",\t\t \tself.cameras[MAC][\"uuid\"])\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"ip\",\t\t\tself.cameras[MAC][\"ip\"])\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"nameOnNVR\",\t \tself.cameras[MAC][\"nameOnNVR\"])\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"eventNumber\",\t -1,\t\t\t\t\tcheck=\"\", NotEq=True)\n\t\t\t\t\tsaveCam or self.updateStatewCheck(dev,\"status\",\t\t\"ON\",\t\t\t\t\tcheck=\"\", NotEq=True)\n\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\tif not devFound:\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\n\t\t\tif saveCam:\n\t\t\t\tself.pendingCommand.append(\"saveCamerasStats\")\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\t####-----------------\t ---------\n\tdef getNVRCamerasFromMongoDB(self, doPrint = False, action=[]):\n\t\treturn\n\t\ttry:\n\t\t\ttimeElapsed = time.time()\n\t\t\tinfo\t= {\"users\":{}, \"cameras\":[], \"NVR\":{}}\n\t\t\tUSERs\t= []\n\t\t\tACCOUNTs= []\n\t\t\tcmdstr\t= [\"\\\"mongo 127.0.0.1:7441/av --quiet --eval 'db.\", \".find().forEach(printjsononeline)' | sed 's/^\\s*//' \\\"\" ]\n\n\t\t\t#self.indiLOG.log(10,\" into getNVRCamerasFromMongoDB action :{}\".format(action))\n\t\t\tif \"system\" in action:\n\t\t\t\tUSERs\t\t\t= self.getMongoData(cmdstr[0]+\"user\" +cmdstr[1])\n\t\t\t\tACCOUNTs\t\t= self.getMongoData(cmdstr[0]+\"account\"+cmdstr[1])\n\n\t\t\t\tif len(USERs)>0 and len(ACCOUNTs) >0:\n\t\t\t\t\tfor account in ACCOUNTs:\n\t\t\t\t\t\tif \"_id\" in account and \"username\" in account and \"name\" in account:\n\t\t\t\t\t\t\tID = account[\"_id\"]\n\t\t\t\t\t\t\tinfo[\"users\"][ID] ={\"userName\":account[\"username\"], \"name\":account[\"name\"]}\n\t\t\t\t\t\t\tfor user in USERs:\n\t\t\t\t\t\t\t\tif \"accountId\" in user and ID == user[\"accountId\"]:\n\t\t\t\t\t\t\t\t\tif \"apiKey\" in user and \"enableApiAccess\" in user:\n\t\t\t\t\t\t\t\t\t\tinfo[\"users\"][ID][\"apiKey\"]\t\t\t= user[\"apiKey\"]\n\t\t\t\t\t\t\t\t\t\tinfo[\"users\"][ID][\"enableApiAccess\"]\t= user[\"enableApiAccess\"]\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tif \"enableApiAccess\" in user and user[\"enableApiAccess\"]: # its enabled, but no api key\n\t\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"getNVRCamerasFromMongoDB camera users bad enableApiAccess / apiKey info for id:{}\\n{} UNIFI error\".format(ID, USERs))\n\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"UNIFI error getNVRCamerasFromMongoDB camera users enableApiAccess disabled info for id:{}\\n{}\".format(ID, USERs))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"getNVRCamerasFromMongoDB camera ACCOUNT bad _id / username / name info:\\n{}\".format(ACCOUNTs))\n\n\t\t\t\tserver = self.getMongoData(cmdstr[0]+\"server\" +cmdstr[1])\n\t\t\t\tif len(server) >0:\n\t\t\t\t\tinfo[\"NVR\"]\t\t= server[0]\n\n\t\t\tif \"cameras\" in action:\n\t\t\t\tinfo[\"cameras\"]\t = self.executeCMDonNVR( {}, \"\", cmdType=\"get\")\n\t\t\t\tif len(info[\"cameras\"]) <1:\n\t\t\t\t\tinfo[\"cameras\"] = self.getMongoData(cmdstr[0]+\"camera\" +cmdstr[1])\n\t\t\t\tif len(info[\"cameras\"]) >0: self.fillCamerasIntoIndigo(info[\"cameras\"], calledFrom=\"getNVRCamerasFromMongoDB\")\n\n\n\n\t\t\tif doPrint:\n\t\t\t\tself.printNVRCameras(info)\n\t\t\treturn info\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn {}\n\n\t####-----------------\t ---------\n\tdef printNVRCameras(self, info):\n\t\treturn\n\t\tkeepList = [\"name\",\"uuid\",\"host\",\"model\",\"_id\",\"firmwareVersion\",\"systemInfo\",\"mac\",\"controllerHostAddress\",\"controllerHostPort\",\"deviceSettings\",\"networkStatus\",\"status\",\"analyticsSettings\",\"channels\",\"ispSettings\" ]\n\t\tout = \"\"\n\t\ttry:\n\t\t\toutLine = \"\"\n\t\t\tif \"NVR\" in info:\n\t\t\t\toutLine += \"\\nSystem info-NVR: --====================++++++++++++++++++++++++++++++++++++++++====================--\"\n\t\t\t\tfor key in info[\"NVR\"]:\n\t\t\t\t\toutLine += \"\\n {:19s} {:}\".format(key, info[\"NVR\"][key])\n\n\t\t\t\toutLine += \"\\n== System info- users: --====================++++++++++++++++++++++++++++++++++++++++====================--\"\n\t\t\tif \"users\" in info:\n\t\t\t\tnn = 0\n\t\t\t\tfor user in info[\"users\"]:\n\t\t\t\t\tout = \"\"\n\t\t\t\t\tfor item in [\"name\",\"apiKey\",\"enableApiAccess\"] :\n\t\t\t\t\t\tout+=(item+\":{}\".format(info[\"users\"][user][item])+\"; \").ljust(30)\n\t\t\t\t\toutLine += \"\\n{} {}\".format( (info[\"users\"][user][\"userName\"]).ljust(18)+\" # {}\".format(nn), out.strip(\";\"))\n\t\t\t\t\tnn+=1\n\n\n\t\t\tif \"cameras\" in info:\n\t\t\t\toutLine += \"\\nSystem info- cameras: --====================++++++++++++++++++++++++++++++++++++++++====================--\"\n\t\t\t\tfor camera in info[\"cameras\"]:\n\t\t\t\t\toutLine += \"\\n{:19s}--===============--\".format(camera[\"name\"])\n\t\t\t\t\tfor item in camera:\n\t\t\t\t\t\tif item ==\"name\": continue\n\t\t\t\t\t\tif item in keepList or keepList == [\"*\"]:\n\t\t\t\t\t\t\tif item == \"channels\":\n\t\t\t\t\t\t\t\tnn = 0\n\t\t\t\t\t\t\t\tfor channel in camera[item]:\n\t\t\t\t\t\t\t\t\tout = (\"bitrates: {}\".format(channel[\"minBitrate\"])+\"..{}\".format(channel[\"maxBitrate\"])) .ljust(30)\n\t\t\t\t\t\t\t\t\tfor\t prop in [\"enabled\",\"isRtmpsEnabled\",\"isRtspEnabled\"]:\n\t\t\t\t\t\t\t\t\t\tif prop in channel:\n\t\t\t\t\t\t\t\t\t\t\tout+= prop+\": {}\".format(channel[prop])+\"; \"\n\t\t\t\t\t\t\t\t\tout = out.strip(\";....\")\n\t\t\t\t\t\t\t\t\toutLine += \"\\n{:22s} {:}\".format(\" channel#{}\".format(nn), out)\n\t\t\t\t\t\t\t\t\tnn+=1\n\t\t\t\t\t\t\telif item == \"status\":\n\t\t\t\t\t\t\t\tstatus = camera[item]\n\t\t\t\t\t\t\t\tout = \"\"\n\t\t\t\t\t\t\t\tfor\t prop in [\"remotePort\",\"remoteHost\"]:\n\t\t\t\t\t\t\t\t\tif prop in status:\n\t\t\t\t\t\t\t\t\t\tout+= prop+\":{}\".format(status[prop])+\"; \"\n\t\t\t\t\t\t\t\tout = out.strip(\"; \")\n\t\t\t\t\t\t\t\toutLine += \"\\n{:22s} {:}\".format(\" status{}\".format(nn), out)\n\t\t\t\t\t\t\t\tfor nn in range(len(status[\"recordingStatus\"])):\n\t\t\t\t\t\t\t\t\tout\t =\t(\"motionRecordingEnabled: {}\".format(status[\"recordingStatus\"][\"{}\".format(nn)][\"motionRecordingEnabled\"])).ljust(30)\n\t\t\t\t\t\t\t\t\tout += \"; fullTimeRecordingEnabled: {}\".format(status[\"recordingStatus\"][\"{}\".format(nn)][\"fullTimeRecordingEnabled\"])\n\t\t\t\t\t\t\t\t\toutLine += \"\\n{:22s} {:}\".format(\" recordingSt:#{}\".format(nn), out)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\toutLine += \"\\n{:22s} {:}\".format(\" \", (item+\":\").ljust(25)+json.dumps(camera[item]))\n\t\t\tself.indiLOG.log(20,outLine)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\n\t####------ camera NVR ----------END\n\n\n\n\t####-----------------\t ---------\n\tdef updateStatewCheck(self,dev, state , value, check = \"\", NotEq = False):\n\t\tif state not in dev.states:\t\t return False\n\t\tif NotEq:\n\t\t\tif dev.states[state] != check: return False\n\t\telse:\n\t\t\tif state == check:\t\t\t return False\n\t\tif dev.states[state] == value: return False\n\t\tself.addToStatesUpdateList(dev.id,state, value )\n\t\treturn True\n\n\n\n\n\t####-----------------\t -----------\n\t### ----------- save read MAC2INDIGO\n\tdef saveMACdata(self, force=False):\n\t\tif not force and (time.time() - 20 < self.lastSaveMAC2INDIGO): return\n\t\tself.lastSaveMAC2INDIGO = time.time()\n\t\tself.writeJson(self.MAC2INDIGO, fName=self.indigoPreferencesPluginDir+\"MAC2INDIGO\", doFormat=True )\n\t\tself.writeJson(self.MACignorelist, fName=self.indigoPreferencesPluginDir+\"MACignorelist\", doFormat=True )\n\t\tself.writeJson(self.MACSpecialIgnorelist, fName=self.indigoPreferencesPluginDir+\"MACSpecialIgnorelist\", doFormat=True )\n\n\t####-----------------\t ---------\n\tdef readMACdata(self):\n\t\tself.lastSaveMAC2INDIGO\t = time.time() -21\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"MAC2INDIGO\",\"r\")\n\t\t\tself.MAC2INDIGO= json.loads(f.read())\n\t\t\tf.close()\n\t\texcept:\n\t\t\tself.MAC2INDIGO= {\"UN\":{},\"GW\":{},\"SW\":{},\"AP\":{},\"NB\":{}}\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"MACignorelist\",\"r\")\n\t\t\tself.MACignorelist= json.loads(f.read())\n\t\t\tf.close()\n\t\texcept:\n\t\t\tself.MACignorelist ={}\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"MACSpecialIgnorelist\",\"r\")\n\t\t\tself.MACSpecialIgnorelist= json.loads(f.read())\n\t\t\tf.close()\n\t\texcept:\n\t\t\tself.MACSpecialIgnorelist ={}\n\t\treturn\n\t### ----------- save read MAC2INDIGO\n\t####-----------------\t ----------- END\n\n\n\t####-----------------\t ----------- START\n\t### ----------- manage suspend status\n\tdef setSuspend(self,ip,tt):\n\t\tself.suspendedUnifiSystemDevicesIP[ip] = tt\n\t\tself.writeSuspend()\n\t####-----------------\t ---------\n\tdef delSuspend(self,ip):\n\t\tif ip in self.suspendedUnifiSystemDevicesIP:\n\t\t\tdel self.suspendedUnifiSystemDevicesIP[ip]\n\t\tself.writeSuspend()\n\t####-----------------\t ---------\n\tdef writeSuspend(self):\n\t\ttry:\n\t\t\tself.writeJson(self.suspendedUnifiSystemDevicesIP, fName=self.indigoPreferencesPluginDir+\"suspended\", sort=False, doFormat=False)\n\t\texcept: pass\n\t####-----------------\t ---------\n\tdef readSuspend(self):\n\t\tself.suspendedUnifiSystemDevicesIP = {}\n\t\ttry:\n\t\t\tf = self.openEncoding(self.indigoPreferencesPluginDir+\"suspended\", \"r\", showError=False)\n\t\t\tself.suspendedUnifiSystemDevicesIP = json.loads(f.read())\n\t\t\tf.close()\n\t\texcept: pass\n\t\tself.writeSuspend()\n\t### ----------- manage suspend status\n\t####-----------------\t ----------- END\n\n\n\n\t### here we do the work, setup the logfiles listening and read the logfiles and check if everything is running\n\n\t### UDM log tracking\n\t####-----------------\t ---------\n\tdef controllerWebApilogForUDM(self, waitBeforeStart):\n\n\t\ttry:\n\t\t\tlastRecordTime\t= 0\n\t\t\tlastRead \t\t= 0\n\t\t\ttime.sleep(min(1,waitBeforeStart))\n\t\t\tself.indiLOG.log(10,\"ctlWebUDM: launching web log get for runs every {} secs\".format(self.controllerWebEventReadON) )\n\t\t\tnRecordsToRetriveDefault \t= 25\n\t\t\tlastRecIds\t\t\t\t\t= []\n\t\t\tthisRecIds\t\t\t\t\t= []\n\t\t\tlastRecIdFound\t\t\t\t= False\n\t\t\tlastTimeStamp\t\t\t\t= time.time() - 500\n\t\t\twhile self.pluginState != \"stop\":\n\t\t\t\ttime.sleep(0.5)\n\t\t\t\ttry:\n\t\t\t\t\tnrec = nRecordsToRetriveDefault\n\t\t\t\t\tif len(thisRecIds) > 0: \n\t\t\t\t\t\tnrec = int( float(nRecordsToRetriveDefault * self.controllerWebEventReadON) / 30.)\n\t\t\t\t\teventLogList \t\t= self.executeCMDOnController(dataSEND={\"_sort\":\"+time\", \"_limit\":min(500,max(10,nrec))}, pageString=\"/stat/event/\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\t\t\t\t#eventLogList \t\t= self.executeCMDOnController(dataSEND={}, pageString=\"/stat/event/\", jsonAction=\"returnData\", cmdType=\"get\") \n\t\t\t\t\tthisRecIds\t\t\t= []\n\t\t\t\t\t# test if we have overlap. if not read 3 times the data \n\t\t\t\t\tfor logEntry in eventLogList:\n\t\t\t\t\t\tthisRecIds.append(logEntry[\"_id\"])\n\t\t\t\t\t\tif lastRecIds !=[] and logEntry[\"_id\"] in lastRecIds: \n\t\t\t\t\t\t\tlastRecIdFound = True\n\n\t\t\t\t\tif not lastRecIdFound and lastRecIds !=[]:\n\t\t\t\t\t\teventLogList \t\t= self.executeCMDOnController(dataSEND={\"_sort\":\"+time\", \"_limit\":min(500,max(10,nrec*3))}, pageString=\"/stat/event/\", jsonAction=\"returnData\", cmdType=\"post\")\n\t\t\t\t\t\tthisRecIds\t\t\t= []\n\t\t\t\t\t\tfor logEntry in eventLogList:\n\t\t\t\t\t\t\tthisRecIds.append(logEntry[\"_id\"])\n\n\t\t\t\t\tlastRead \t\t\t= time.time()\n\t\t\t\t\tii = 0\n\t\t\t\t\tfor logEntry in eventLogList[::-1]:\n\t\t\t\t\t\tii +=1\n\t\t\t\t\t\trecId = logEntry[\"_id\"]\n\t\t\t\t\t\t# do not reprocess old records\n\t\t\t\t\t\tif recId in lastRecIds: \t\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t## filter out non AP info entries\n\t\t\t\t\t\tif \"time\" not in logEntry: \t\t\t\t\t\t\t\t\tcontinue \n\t\t\t\t\t\t# the time stamp from UFNI is in msecs, convert to float secs\n\t\t\t\t\t\tlogEntry[\"time\"] /= 1000. \n\t\t\t\t\t\t# skip already processed records\n\t\t\t\t\t\tif logEntry[\"time\"] < lastTimeStamp:\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tlastTimeStamp = logEntry[\"time\"] \n\t\t\t\t\t\t# remove junk\n\t\t\t\t\t\tif \"key\" not in logEntry: \t\t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tif logEntry[\"key\"].lower().find(\"login\") >-1:\t\t\t\t\tcontinue\n\t\t\t\t\t\tif \"user\" not in logEntry: \t\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t#\n\t\t\t\t\t\tMAC = logEntry[\"user\"]\n\t\t\t\t\t\tif self.decideMyLog(\"UDM\", MAC=MAC): self.indiLOG.log(10,\"ctlWebUDM {}, rec#{} of {} recs; logEntry:{}\".format(MAC, ii, len(thisRecIds), logEntry))\n\n\t\t\t\t\t\t# now we should have an ok event record\n\t\t\t\t\t\tapN \t\t\t= self.numberForUDM[\"AP\"]\n\t\t\t\t\t\tipNumberAP\t\t= self.ipNumbersOf[\"AP\"][apN]\n\t\t\t\t\t\tMAC_AP_Active \t= \"\"\n\t\t\t\t\t\tMAC_AP_from \t= \"\"\n\n\t\t\t\t\t\t# check if we have AP info, if not skip record\n\t\t\t\t\t\tif \"ap\" in logEntry: \n\t\t\t\t\t\t\tfromTo = \"\"\n\t\t\t\t\t\t\tMAC_AP_Active = logEntry[\"ap\"]\n\t\t\t\t\t\t\tself.createAPdeviceIfNeededForUDM(MAC_AP_Active, logEntry, fromTo)\n\t\t\t\t\t\t\tif self.MAC2INDIGO[\"AP\"][MAC_AP_Active][\"ipNumber\"] == \"\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"ctlWebUDM ap-mac:{} MAC2INDIGO: has empty ipNumber, logEntry:{}\".format(MAC_AP_Active, logEntry))\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\t#if self.MAC2INDIGO[\"AP\"][MAC_AP_Active][\"ipNumber\"] != self.ipNumbersOf[\"AP\"][self.numberForUDM[\"AP\"]]: continue # ignore non UDM log entries \n\t\t\t\t\t\tif \"ap_from\" in logEntry: \n\t\t\t\t\t\t\tfromTo = \"_from\"\n\t\t\t\t\t\t\tMAC_AP_from\t= logEntry[\"ap\"+fromTo]\n\t\t\t\t\t\t\tself.createAPdeviceIfNeededForUDM(MAC_AP_from, logEntry, fromTo)\n\t\t\t\t\t\t\tif self.MAC2INDIGO[\"AP\"][MAC_AP_from][\"ipNumber\"] == \"\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"ctlWebUDM ap-mac:{} MAC2INDIGO: has empty ipNumber, logEntry:{}\".format(MAC_AP_from, logEntry))\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tlogEntry[\"IP_from\"]\t= self.MAC2INDIGO[\"AP\"][MAC_AP_from][\"ipNumber\"]\n\t\t\t\t\t\tif \"ap_to\" in logEntry: \n\t\t\t\t\t\t\tfromTo = \"_to\"\n\t\t\t\t\t\t\tMAC_AP_Active = logEntry[\"ap\"+fromTo]\n\t\t\t\t\t\t\tself.createAPdeviceIfNeededForUDM(MAC_AP_Active, logEntry, fromTo)\n\t\t\t\t\t\t\tif self.MAC2INDIGO[\"AP\"][MAC_AP_Active][\"ipNumber\"] == \"\":\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"ctlWebUDM ap-mac:{} MAC2INDIGO: has empty ipNumber, logEntry:{}\".format(MAC_AP_Active, logEntry))\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tlogEntry[\"IP_to\"] \t= self.MAC2INDIGO[\"AP\"][MAC_AP_Active][\"ipNumber\"]\n\n\t\t\t\t\t\t# for no ap log entry check if it about an existing devices, if yes: assign to UDM\n\t\t\t\t\t\tif MAC_AP_Active == \"\":\n\t\t\t\t\t\t\tif MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\t\t\t\t\t\tfor MACap in self.MAC2INDIGO[\"AP\"]:\n\t\t\t\t\t\t\t\t\tif int(self.MAC2INDIGO[\"AP\"][MACap][\"apNo\"]) == int(self.numberForUDM[\"AP\"]):\n\t\t\t\t\t\t\t\t\t\tMAC_AP_Active = MACap\n\t\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\t\t\t# skip this event, not about existing wifi devices ...\n\t\t\t\t\t\tlogEntry[\"MAC_AP_Active\"] = MAC_AP_Active\n\n\t\t\t\t\t\tif MAC_AP_Active == \"\":\n\t\t\t\t\t\t\tif self.decideMyLog(\"UDM\", MAC=MAC): self.indiLOG.log(10,\"ctlWebUDM {} ignoring: no 'ap': .. given, logEntry:{}\".format(MAC, logEntry))\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tipNumberAP = self.MAC2INDIGO[\"AP\"][MAC_AP_Active][\"ipNumber\"]\n\t\t\t\t\t\t\tfor nn in range(_GlobalConst_numberOfAP):\n\t\t\t\t\t\t\t\tif ipNumberAP == self.ipNumbersOf[\"AP\"][nn]: \n\t\t\t\t\t\t\t\t\tapN\t= nn\n\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\tself.doAPmessages([logEntry], ipNumberAP, apN, webApiLog=True)\n\t\t\t\t\tlastRecIds = copy.copy(thisRecIds)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tfor ii in range(100):\n\t\t\t\t\tif not lastRecIdFound: break\n\t\t\t\t\tif self.pluginState == \"stop\": break\n\t\t\t\t\ttime.sleep(1)\n\t\t\t\t\tif time.time() - lastRead > self.controllerWebEventReadON: break\n\t\t\tself.indiLOG.log(10,\"ctlWebUDM: exiting plugin state = stop\" )\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\n\t####----------------- thsi is for UDM devices only\t ---------\n\tdef createAPdeviceIfNeededForUDM(self, MAC, line, fromTo):\n\t\tif MAC == \"\": \t\t\t\t\t\t\t\t\treturn False\n\t\tif MAC in self.MAC2INDIGO[\"AP\"]:\t\t\t\treturn True\n\t\tif self.unifiControllerType.find(\"UDM\") == -1: \treturn False\n\n\t\tself.indiLOG.log(30,\"==> new UDM device to be created, mac :{} not in self.MAC2INDIGO[AP]{} \".format(MAC, self.MAC2INDIGO[\"AP\"]))\n\n\t\thostname\t= \"-UDM-AP\"\n\t\tmodel\t\t= \"UDM-AP\"\n\t\ttx_power\t= \"-99\"\n\t\tGHz\t\t\t= \"2\"\n\t\tessid\t\t= \"\"\n\t\tchannel\t\t= \"\"\n\t\tradio \t\t= \"\"\n\t\tnClients\t= \"\"\n\t\tdevName\t\t= \"UDM-AP\"\n\t\txType\t\t= \"AP\"\n\t\tisType \t\t= \"isAP\"\n\t\tif \"radio\"+fromTo in line: radio = line[\"radio\"+fromTo]\n\t\tif \"essid\"+fromTo in line: essid = line[\"ssid\"+fromTo]\n\t\tif \"channel\"+fromTo in line: \n\t\t\tif int(line[\"channel\"+fromTo]) > 11: GHz = \"5\"\n\t\t\telse: \t\t\t\t\t\t\t\t GHz = \"2\"\n\t\t\tchannel = line[\"channel\"+fromTo]\n\t\ttry:\n\t\t\tipNDevice = self.ipNumbersOf[\"AP\"][self.numberForUDM[\"AP\"]]\n\t\t\tdev = indigo.device.create(\n\t\t\t\tprotocol\t\t= indigo.kProtocol.Plugin,\n\t\t\t\taddress \t\t= MAC,\n\t\t\t\tname \t\t\t= devName+\"_\" + MAC,\n\t\t\t\tdescription\t\t= self.fixIP(ipNDevice) + hostname,\n\t\t\t\tpluginId \t\t= self.pluginId,\n\t\t\t\tdeviceTypeId\t= \"Device-AP\",\n\t\t\t\tfolder\t\t\t= self.folderNameIDCreated,\n\t\t\t\tprops\t\t\t= {\"useWhatForStatus\":\"\",isType:True})\n\t\t\tself.setupStructures(\"AP\", dev, MAC)\n\t\t\tself.setupBasicDeviceStates(dev, MAC, \"AP\", ipNDevice,\"\", \"\", \" status up AP WEB new AP\", \"STATUS-AP\")\n\t\t\tself.addToStatesUpdateList(dev.id,\"essid_\" + GHz, essid)\n\t\t\tself.addToStatesUpdateList(dev.id,\"channel_\" + GHz, channel)\n\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\tself.addToStatesUpdateList(dev.id,\"hostname\", hostname)\n\t\t\tself.addToStatesUpdateList(dev.id,\"nClients_\" + GHz, nClients)\n\t\t\tself.addToStatesUpdateList(dev.id,\"radio_\" + GHz, radio)\n\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\tself.addToStatesUpdateList(dev.id,\"tx_power_\" + GHz, tx_power)\n\t\t\tself.executeUpdateStatesList()\n\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}/{}\".format(dev.name, MAC, ipNDevice) )\n\t\t\tdev = indigo.devices[dev.id]\n\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\treturn True\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n\n\n\t####-----------------\t ---------\n\tdef getcontrollerDBForClients(self):\n\t\ttry:\n\t\t\tif not self.devsEnabled[\"DB\"]:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\treturn \n\t\t\tif time.time() - self.getcontrollerDBForClientsLast < float(self.readDictEverySeconds[\"DB\"]):\treturn \n\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"getcontrollerDBForClients: start, read every:{}, dt:{}\".format(self.readDictEverySeconds[\"DB\"], time.time() - self.getcontrollerDBForClientsLast))\n\n\t\t\tif self.decideMyLog(\"DBinfo\"): self.indiLOG.log(10,\"getcontrollerDBForClients: start, read every:{}\".format(self.readDictEverySeconds[\"DB\"]))\n\t\t\tdataDict = self.executeCMDOnController(pageString=\"/stat/sta/\", cmdType=\"get\")\n\t\t\tif self.decideMyLog(\"DBinfo\"): self.indiLOG.log(10,\"getcontrollerDBForClients: \\n{} ...\".format(\"{}\".format(dataDict)[0:500]) )\n\n\t\t\tself.fillcontrollerDBForClients(dataDict)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t####-----------------\t ---------\n\tdef fillcontrollerDBForClients(self, dataDict):\n\t\ttry:\n\t\t\tself.getcontrollerDBForClientsLast = time.time() \n\t\t\tif len(dataDict) == 0: \n\t\t\t\treturn \n\n\t\t\txType = \"UN\"\n\t\t\tmacsFound = []\n\t\t\tanyChange = 0 \n\t\t\tsecChange = 0 \n\t\t\tnClients = 0.\n\t\t\tfor client in dataDict:\n\t\t\t\tif len(client) == 0: \t\t\t\t\tcontinue\n\t\t\t\tif \"mac\" not in client: \t\t\t\tcontinue\n\t\t\t\tMAC = client[\"mac\"]\n\t\t\t\tif MAC not in self.MAC2INDIGO[xType]: \tcontinue\n\t\t\t\tmacsFound.append(MAC)\n\t\t\t\tnClients +=1.\n\t\t\t\tif \"first_seen\" in client:\n\t\t\t\t\ttry: \tself.MAC2INDIGO[xType][MAC][\"first_seen\"]\t= datetime.datetime.fromtimestamp(client[\"first_seen\"]).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\texcept: pass\n\n\t\t\t\tif \"use_fixedip\" in client:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"use_fixedip\"]\t= client[\"use_fixedip\"]\n\t\t\t\telse:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"use_fixedip\"] = False\n\n\t\t\t\tif \"blocked\" in client:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"blocked\"] = client[\"blocked\"]\n\t\t\t\telse:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"blocked\"] = False\n\n\n\t\t\t\tpreviousSeen = self.MAC2INDIGO[xType][MAC][\"last_seen\"]\n\t\t\t\tif \"last_seen\" in client: \n\t\t\t\t\tlastSeen = float(client[\"last_seen\"])\n\t\t\t\t\tif previousSeen != lastSeen: \n\t\t\t\t\t\tanyChange += 1.\n\t\t\t\t\t\tsecChange += lastSeen - previousSeen\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"last_seen\"] = lastSeen\n\t\t\t\telse:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"last_seen\"] = -1\n\t\t\t\t\tlastSeen = -1\n\n\t\t\t\t#if self.decideMyLog(\"DBinfo\", MAC=MAC): self.indiLOG.log(10,\"controlDB {:15s} client:{}\".format(MAC, client) )\n\t\t\t\tif self.decideMyLog(\"DBinfo\", MAC=MAC): self.indiLOG.log(10,\"controlDB {:15s} delta delta(now-previous):{:9.0f}, dt lastseen{:9.0f} lastSeen:{:9.0f}\".format(MAC, lastSeen - previousSeen, time.time()-lastSeen, lastSeen) )\n\n\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tif MAC not in macsFound: \n\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"last_seen\"] > 0:\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"last_seen\"] = -1\n\t\t\t\t\tcontinue\n\n\t\t\t\ttry: \n\t\t\t\t\tchanged = False\n\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]]\n\t\t\t\t\tif self.decideMyLog(\"DBinfo\", MAC=MAC): \n\t\t\t\t\t\t\tself.indiLOG.log(10,\"controlDB {:15s} {:15s} {:32s}; delta lastUp:{:9.0f}, lastSeen-DB:{:9.0f}*{:9.0f}*{:9.0f}\".format(\n\t\t\t\t\t\t\tMAC, dev.states[\"ipNumber\"], dev.name, \n\t\t\t\t\t\t\ttime.time() - self.MAC2INDIGO[xType][MAC][\"lastUp\"],\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"last_seen\"] - self.MAC2INDIGO[xType][MAC][\"lastUp\"],\n\t\t\t\t\t\t\ttime.time() - self.MAC2INDIGO[xType][MAC][\"last_seen\"],\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"last_seen\"]\t\n\t\t\t\t\t\t) )\n\n\t\t\t\t\tif \"first_seen\" in self.MAC2INDIGO[\"UN\"][MAC]:\n\t\t\t\t\t\tif \"firstSeen\" in dev.states and dev.states[\"firstSeen\"] != self.MAC2INDIGO[\"UN\"][MAC][\"first_seen\"]:\n\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"firstSeen\", self.MAC2INDIGO[\"UN\"][MAC][\"first_seen\"])\n\n\t\t\t\t\tif \"use_fixedip\" in self.MAC2INDIGO[\"UN\"][MAC]:\n\t\t\t\t\t\tif \"useFixedIP\" in dev.states and dev.states[\"useFixedIP\"] != self.MAC2INDIGO[\"UN\"][MAC][\"use_fixedip\"]:\n\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"useFixedIP\", self.MAC2INDIGO[\"UN\"][MAC][\"use_fixedip\"])\n\n\t\t\t\t\tif \"blocked\" in dev.states:\n\t\t\t\t\t\tif dev.states[\"blocked\"] != self.MAC2INDIGO[\"UN\"][MAC][\"blocked\"]:\n\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"blocked\", self.MAC2INDIGO[\"UN\"][MAC][\"blocked\"])\n\t\t\t\t\tif changed:\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tif \"{}\".format(e).find(\"timeout waiting for response\")>-1:\n\t\t\t\t\t\tself.getcontrollerDBForClientsLast = time.time()\n\t\t\t\t\t\treturn \n\n\t\t\tself.getcontrollerDBForClientsLast = time.time()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\n\t### here we do the work, setup the logfiles listening and read the logfiles and check if everything is running, if not restart\n\t####-----------------\t ---------\n\tdef getMessages(self, ipNumber, apN, uType, waitAtStart):\n\n\t\tapnS = \"{}\".format(apN)\n\t\tself.addTypeToDataStats(ipNumber, apnS, uType)\n\t\tself.msgListenerActive[uType] = time.time() - 200\n\t\ttry:\n\t\t\tself.sleep(max(1.,min(60, waitAtStart )))\n\t\t\tstartErrorCount \t\t\t= 0\n\t\t\tunifiDeviceType \t\t\t= uType[0:2]\n\t\t\tcombinedLines\t\t\t\t= \"\"\n\t\t\tlastTestServer\t\t\t\t= time.time()\n\t\t\tmsgSleep\t\t\t\t\t= 1\n\t\t\trestartCount\t\t\t\t= -1\n\t\t\tlastMSG\t\t\t\t\t\t= \"\"\n\t\t\taliveReceivedTime\t\t\t= -1\n\t\t\tgoodDataReceivedTime\t\t= -1\n\t\t\tListenProcessFileHandle\t\t= \"\"\n\t\t\tlastRestartCheck\t\t\t= time.time()\n\t\t\tnewDataStartTime\t\t\t= time.time()\n\t\t\tnewlinesFromServer\t\t\t= \"\"\n\t\t\tminWaitbeforeRestart\t\t= 135. #max(float(self.restartIfNoMessageSeconds), float(repeatRead) )\n\t\t\tlastOkRestart\t\t\t\t= time.time()\n\t\t\trestartCode\t\t\t\t\t= 0\n\t\t\tapNint \t\t\t\t\t\t= int(apN)\n\t\t\tself.testServerIfOK(ipNumber,uType)\n\t\t\tif uType.find(\"tail\") > -1:\n\t\t\t\tself.lastMessageReceivedInListener[ipNumber] = time.time()\n\n\t\t\tself.lastResetUnifiDevice[ipNumber] = time.time()\n\n\t\t\tconsumeDataTime = 0\n\t\t\twhile True:\n\n\t\t\t\tif self.pluginState == \"stop\" or not self.connectParams[\"enableListener\"][uType]: \n\t\t\t\t\ttry:\tself.killPidIfRunning(ListenProcessFileHandle.pid)\n\t\t\t\t\texcept:\tpass\n\t\t\t\t\tbreak\n\n\t\t\t\t\t## should we stop?, is our IP number listed?\n\t\t\t\tif ipNumber in self.stop:\n\t\t\t\t\tself.indiLOG.log(10,uType+ \"getMessage: stop = True for ip# {}\".format(ipNumber) )\n\t\t\t\t\tself.stop.remove(ipNumber)\n\t\t\t\t\treturn\n\n\t\t\t\tif ipNumber in self.suspendedUnifiSystemDevicesIP:\n\t\t\t\t\tself.sleep(max(1, self.suspendedUnifiSystemDevicesIP[ipNumber]-time.time()))\n\t\t\t\t\tgoodDataReceivedTime = 1\n\t\t\t\t\tcontinue\n\n\t\t\t\tself.sleep(min(15, msgSleep))\n\n\t\t\t\tretCode, startErrorCount, ListenProcessFileHandle, goodDataReceivedTime, aliveReceivedTime, combinedLines, lastRestartCheck, lastOkRestart = \\\n\t\t\t\t\tself.checkIfRestartNeeded( \n\t\t\t\t\t\trestartCode, goodDataReceivedTime, aliveReceivedTime, startErrorCount, combinedLines, minWaitbeforeRestart, msgSleep, lastRestartCheck, restartCount, uType, ipNumber, apnS, lastMSG, ListenProcessFileHandle, lastOkRestart\n\t\t\t\t\t)\n\t\t\t\tif retCode == 2: continue\n\t\t\t\telse: \t\t\t restartCode = 0\n\n\n\t\t\t\t## here we actually read the stuff\n\t\t\t\tgoodDataReceivedTime, aliveReceivedTime, newlinesFromServer, msgSleep, newDataStartTime = self.readFromUnifiDevice( goodDataReceivedTime, aliveReceivedTime, ListenProcessFileHandle, uType, ipNumber, msgSleep, newlinesFromServer, newDataStartTime)\n\t\t\t\tif newlinesFromServer == \"\": continue\n\n\t\t\t\tif self.pluginState == \"stop\": \n\t\t\t\t\ttry:\tself.killPidIfRunning(ListenProcessFileHandle.pid)\n\t\t\t\t\texcept:\tpass\n\t\t\t\t\treturn\n\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageCount\"] += 1\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inMessageBytes\"] += len(newlinesFromServer)\n\n\n\t\t\t\tif self.debugThisDevices(uType, apNint):\n\t\t\t\t\tif len(newlinesFromServer) > 300:\n\t\t\t\t\t\tself.indiLOG.log(10,\"getMessages-Data: {}-{} line:{} ... {}\".format(ipNumber, uType, newlinesFromServer[0:200].replace(\"\\n\",\"\").replace(\"\\r\",\"\"), newlinesFromServer[-200:].replace(\"\\n\",\"\").replace(\"\\r\",\"\")))\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.indiLOG.log(10,\"getMessages-Data: {}-{} line:{} \".format(ipNumber, uType, newlinesFromServer.replace(\"\\n\",\"\").replace(\"\\r\",\"\")))\n\n\t\t\t\trestartCode = self.checkIfErrorReceived(newlinesFromServer, ipNumber)\n\t\t\t\tif restartCode > 0: \n\t\t\t\t\tif self.retcodeNotOk(restartCode, goodDataReceivedTime, uType,ipNumber, newlinesFromServer):\n\t\t\t\t\t\tgoodDataReceivedTime = 1\n\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t######### for tail logfile\n\t\t\t\tconsumeDataTime = time.time()\n\t\t\t\tif uType.find(\"tail\") > -1:\n\t\t\t\t\trestartCode = self.checkIfErrorReceivedTail(newlinesFromServer, ipNumber)\n\t\t\t\t\tif self.retcodeNotOk(restartCode, goodDataReceivedTime, uType,ipNumber, newlinesFromServer):\n\t\t\t\t\t\tgoodDataReceivedTime = 1\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tgoodDataReceivedTime, lastMSG = self.checkAndPrepTail(newlinesFromServer, goodDataReceivedTime, ipNumber, uType, unifiDeviceType, apN)\n\n\t\t\t\t######### for Dicts\n\t\t\t\telif uType.find(\"dict\") > -1:\n\t\t\t\t\trestartCode = self.checkIfErrorReceivedDict(newlinesFromServer, combinedLines, ipNumber)\n\t\t\t\t\tif self.retcodeNotOk(restartCode, goodDataReceivedTime, uType,ipNumber, newlinesFromServer):\n\t\t\t\t\t\tgoodDataReceivedTime = 1\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tgoodDataReceivedTime, combinedLines, lastMSG = self.checkAndPrepDict( newlinesFromServer, goodDataReceivedTime, combinedLines, ipNumber, uType, unifiDeviceType, minWaitbeforeRestart, apN, newDataStartTime)\n\n\t\t\t\t## bad setup is wrong, extit\n\t\t\t\telse:\n\t\t\t\t\tself.indiLOG.log(40,\"bad parameters for: {} {}\".format(ipNumber, uType))\n\t\t\t\t\treturn \n\n\t\t\t\tconsumeDataTime -= time.time()\n\t\t\t\tif consumeDataTime < -1:\n\t\t\t\t\tmsgSleep = 0\n\n\t\t\t\tif self.statusChanged > 0:\n\t\t\t\t\tself.setGroupStatus()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.indiLOG.log(30,\"getMessages: stopping listener process for :{} - {}\".format(uType, ipNumber ) )\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef debugThisDevices(self, uType, Nint):\n\t\ttry:\n\t\t\tut = uType[0:2]\n\t\t\tif ( (ut == \"SW\" and Nint >= 0 and Nint < len(self.debugDevs[\"SW\"]) and self.debugDevs[\"SW\"][Nint]) or\n\t\t\t\t (ut == \"AP\" and Nint >= 0 and Nint < len(self.debugDevs[\"AP\"]) and self.debugDevs[\"AP\"][Nint]) or \n\t\t\t\t (ut == \"GW\" and self.debugDevs[\"GW\"]) ): \n\t\t\t\treturn True\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n\t\t\n\t####-----------------\t ---------\n\tdef retcodeNotOk(self, restartCode, goodDataReceivedTime, uType, ipNumber, newlinesFromServer):\n\t\ttry:\n\t\t\tif restartCode > 0: \n\t\t\t\tif self.rebootUnifiDeviceOnError:\n\t\t\t\t\tself.indiLOG.log(20,\"getMessages: code:{:} need restart of listener and send reboot if code>10- lastDataRceived:{:.1f}; from {:}-{:} lines:{:}\".format(restartCode, time.time()-goodDataReceivedTime, ipNumber, uType, newlinesFromServer.strip(\"\\n\")))\n\t\t\t\t\tself.suspendedUnifiSystemDevicesIP[ipNumber] = time.time() + 60.\n\t\t\t\t\tif restartCode > 10: self.resetUnifiDevice(ipNumber, uType)\n\t\t\t\t\tself.sleep(60.)\n\t\t\t\t\ttry: del self.suspendedUnifiSystemDevicesIP[ipNumber]\n\t\t\t\t\texcept: pass\n\t\t\t\t\treturn True\n\n\t\t\t\tself.indiLOG.log(20,\"getMessages: code:{:} - no reboot issued, blocked by:rebootUnifiDeviceOnError=False, lastDataRceived:{:.1f}; from {:}-{:} lines:{:}\".format(restartCode, time.time()-goodDataReceivedTime, ipNumber, uType, newlinesFromServer.strip(\"\\n\")))\n\t\t\treturn False\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n\n\t####-----------------\t ---------\n\tdef checkIfRestartNeeded(self, restartCode, goodDataReceivedTime, aliveReceivedTime, startErrorCount, combinedLines, minWaitbeforeRestart, msgSleep, lastRestartCheck, restartCount, uType, ipNumber, apnS, lastMSG, ListenProcessFileHandle, lastOkRestart):\n\t\ttry:\n\t\t\tretCode = 0\n\t\t\tlastRestartCheck = time.time()\n\t\t\tif len(self.restartRequest) > 0:\n\t\t\t\t#self.indiLOG.log(10,\"getMessages: {}-{}-{} #req:{}; restart requested dict:{}\".format(ipNumber, uType,apnS, self.restartRequest[uType].split(\"-\")[0], self.restartRequest) )\n\t\t\t\tif uType in self.restartRequest:\n\t\t\t\t\tif self.restartRequest[uType].split(\"-\")[0] == apnS:\n\t\t\t\t\t\tif self.restartRequest[uType].find(\"reset\") > -1:\n\t\t\t\t\t\t\tif self.rebootUnifiDeviceOnError:\n\t\t\t\t\t\t\t\tself.resetUnifiDevice(ipNumber, uType)\n\t\t\t\t\t\t\t\tself.sleep(25) \n\t\t\t\t\t\tself.indiLOG.log(10,\"getMessages: {} restart requested by menu \".format(self.restartRequest) )\n\t\t\t\t\t\tgoodDataReceivedTime = -1\n\t\t\t\t\t\tself.restartRequest = {}\n\n\t\t\tforcedRestart = time.time() - lastOkRestart \n\t\t\trestartTimeout = time.time() - goodDataReceivedTime\n\n\t\t\tif restartTimeout < minWaitbeforeRestart and goodDataReceivedTime > 0 and forcedRestart < self.restartListenerEvery and restartCode == 0: \n\t\t\t\t# nothing to do\n\t\t\t\treturn retCode, startErrorCount, ListenProcessFileHandle, goodDataReceivedTime, aliveReceivedTime, combinedLines, lastRestartCheck, lastOkRestart\n\n\t\t\t## ned to restart, either new or launch command, or no messages for xx secs\n\t\t\tif goodDataReceivedTime < 0:# at startup\n\t\t\t\tself.indiLOG.log(10,\"getMessages: launching listener for: {} / {}\".format(uType, ipNumber) )\n\n\t\t\t## no messages for xx secs:\n\t\t\telse:\n\t\t\t\tif forcedRestart > self.restartListenerEvery:\n\t\t\t\t\tlogLevel = 10\n\t\t\t\t\trestartCount = 0\n\t\t\t\telse:\n\t\t\t\t\tif restartCount > 6:\tlogLevel = 30; restartCount = 0\n\t\t\t\t\telif restartCount > 2:\tlogLevel = 20\n\t\t\t\t\telse:\t\t\t\t \tlogLevel = 10\n\t\t\t\t\trestartCount += 1\n\n\t\t\t\tlsm = lastMSG.replace(\"\\n\",\"\")\n\t\t\t\tself.indiLOG.log(logLevel,\"getMessages: relaunching {} / {} / {}: code:{}; timeSinceLastRestart {:.0f} > forcedRestart:{:.0f} [sec] ; without message:{:.1f}[sec], limitforRestart:{:.1f}[sec], restartCount:{:}, len(msg):{:}; lastMSG:{:}<<\".format(self.connectParams[\"expectCmdFile\"][uType], uType, ipNumber, restartCode, forcedRestart, self.restartListenerEvery, restartTimeout, minWaitbeforeRestart, restartCount, len(lsm), lsm[-100:].replace(\"\\r\",\"\") ) )\n\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"restarts\"] += 1\n\n\t\t\t\tif restartCount in [3,5,7]:\n\t\t\t\t\tself.connectParams[\"promptOnServer\"][ipNumber] = \"\"\n\n\t\t\t\t\n\t\t\tif ListenProcessFileHandle != \"\": \n\t\t\t\tself.killPidIfRunning(ListenProcessFileHandle.pid)\n\n\n\t\t\tif not self.testServerIfOK(ipNumber,uType):\n\t\t\t\tif ipNumber in self.connectParams[\"promptOnServer\"]:\n\t\t\t\t\tprompt = self.connectParams[\"promptOnServer\"][ipNumber]\n\t\t\t\telse: \n\t\t\t\t\tprompt = \"not defined\"\n\n\t\t\t\tself.indiLOG.log(40,\"getMessages: (1 - test connect) error for {}, ip#: {}, prompt:'{}'; wrong ip/ password or system down or ssh timed out or ..? \".format(uType, ipNumber, prompt) )\n\t\t\t\n\t\t\t\tself.msgListenerActive[uType] = 0\n\t\t\t\tretCode = 2\n\t\t\t\tcombinedLines = \"\"\n\t\t\t\ttime.sleep(15)\n\t\t\t\treturn retCode, startErrorCount, ListenProcessFileHandle, goodDataReceivedTime, aliveReceivedTime, combinedLines, lastRestartCheck, lastOkRestart\n\n\t\t\tif uType==\"VDtail\":\n\t\t\t\tself.setAccessToLog(ipNumber,uType)\n\n\t\t\tListenProcessFileHandle, startError = self.startConnect(ipNumber,uType)\n\t\t\tcombinedLines = \"\"\n\t\t\tif self.decideMyLog(\"Expect\"):\n\t\t\t\ttry: \tpid = ListenProcessFileHandle.pid\n\t\t\t\texcept:\tpid = \"not defined\"\n\t\t\t\tself.indiLOG.log(10,\"getMessages: ListenProcess started for uType: {}; ip: {}, prompt:'{}', pid:{}\".format(uType, ipNumber, self.connectParams[\"promptOnServer\"][ipNumber], pid) )\n\n\n\t\t\tif startError != \"\":\n\t\t\t\tstartErrorCount +=1\n\t\t\t\tif startErrorCount%3== 0:\n\t\t\t\t\tself.indiLOG.log(40,\"getMessages: connect start connect error in listener {}, to @ {} ::::{}::::\".format(uType, ipNumber, startError) )\n\t\t\t\tretCode = 2\n\t\t\t\tself.sleep(15)\n\t\t\t\treturn retCode, startErrorCount, ListenProcessFileHandle, goodDataReceivedTime, aliveReceivedTime, combinedLines, lastRestartCheck, lastOkRestart\n\n\t\t\tself.msgListenerActive[uType]\t= time.time()\n\t\t\tgoodDataReceivedTime \t\t\t= time.time()\n\t\t\taliveReceivedTime \t \t\t\t= time.time()\n\t\t\tlastOkRestart\t\t\t\t\t= time.time()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tcombinedLines \t= \"\"\n\t\t\tretCode \t\t= 2\n\t\t\tself.sleep(15)\n\n\t\treturn retCode, startErrorCount, ListenProcessFileHandle, goodDataReceivedTime, aliveReceivedTime, combinedLines, lastRestartCheck, lastOkRestart\n\n\n\t####-----------------\t ---------\n\tdef readFromUnifiDevice(self, goodDataReceivedTime, aliveReceivedTime, ListenProcessFileHandle, uType, ipNumber, msgSleep, lastLine, newDataStartTime):\n\t\ttry:\n\t\t\ttry:\n\t\t\t\tif ListenProcessFileHandle == \"\": \n\t\t\t\t\tself.indiLOG.log(20,\"readFromUnifiDevice: read handle not defined for {}-{}, sleeping 15 secs \".format(uType, ipNumber))\n\t\t\t\t\tnewlinesFromServer\t\t= \"\"\n\t\t\t\t\tgoodDataReceivedTime\t= 1 # this forces a restart of the listener\n\t\t\t\t\tmsgSleep\t\t\t\t= 15\n\t\t\t\t\tself.sleep(10)\n\t\t\t\t\treturn goodDataReceivedTime, aliveReceivedTime, newlinesFromServer, msgSleep, newDataStartTime\n\n\t\t\t\tnewlinesFromServer = \"\"\n\t\t\t\tlfs = \"\"\n\t\t\t\tlfs = os.read(ListenProcessFileHandle.stdout.fileno(),self.readBuffer).decode(\"utf8\") \n\t\t\t\tnewlinesFromServer = \"{}\".format(lfs) \n\t\t\t\tif newlinesFromServer != \"\":\n\t\t\t\t\taliveReceivedTime = time.time()\n\t\t\t\tmsgSleep = 0.2 # fast read to follow, if data \n\t\t\t\tif lastLine == \"\" and newlinesFromServer != \"\": newDataStartTime = time.time()\n\t\t\texcept\tException as e:\n\t\t\t\tif uType.find(\"dict\") >-1:\tmsgSleep = 2 # nothing new, can wait, dicts come every 60 secs \n\t\t\t\telse:\t\t\t\t\t\tmsgSleep = 0.4 # this is for tail \n\t\t\t\tmsgSleep = min(msgSleep,4)\n\t\t\t\tif \"{}\".format(e).find(\"[Errno 35]\") == -1:\t # \"Errno 35\" is the normal response if no data, if other error stop and restart\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1:\n\t\t\t\t\t\tout = \"os.read(ListenProcessFileHandle.stdout.fileno()) in Line {} has error={}\\n ip:{} type: {}\".format(sys.exc_info()[2].tb_lineno, e, ipNumber,uType )\n\t\t\t\t\t\ttry: out+= \"fileNo: {}\".format(ListenProcessFileHandle.stdout.fileno() )\n\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\tif \"{}\".format(e).find(\"[Errno 22]\") > -1: # \"Errno 22\" is general read error \"wrong parameter\"\n\t\t\t\t\t\t\tout+= \"\\n .. try lowering/increasing read buffer parameter in config\" \n\t\t\t\t\t\t\tself.indiLOG.log(30,out)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.indiLOG.log(40,out)\n\t\t\t\t\t\t\tself.indiLOG.log(40,lfs)\n\t\t\t\t\tgoodDataReceivedTime = 1 # this forces a restart of the listener\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn goodDataReceivedTime, aliveReceivedTime, newlinesFromServer, msgSleep, newDataStartTime\n\n\t####-----------------\t ---------\n\tdef checkIfErrorReceived(self, newlinesFromServer, ipNumber):\n\t\ttry:\n\t\t\tretCode = 0\n\t\t\t## any error messages from OSX \n\t\t\tif newlinesFromServer.find(\"closed by remote host\") > -1:\t\t\t\t\t\t\t\t\t\t\tretCode = 1\n\t\t\telif newlinesFromServer.find(\"Killed by signal\") > -1:\t\t\t\t\t\t\t\t\t\t\t\tretCode = 2\n\t\t\telif newlinesFromServer.find(\"Killed -9\") > -1:\t\t\t\t\t\t\t\t\t\t\t\t\t\tretCode = 3\n\t\t\t#elif newlinesFromServer.find(\"Broken pipe\") > -1:\t\t\t\t\t\t\t\t\t\t\t\t\tretCode = 4 # only shows AFTER reboot finished\n\t\t\telif newlinesFromServer.find(\"[reboot] reboot\") > -1:\t\t\t\t\t\t\t\t\t\t\t\tretCode = 5\n\t\t\treturn retCode\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\")== -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn retCode\n\n\n\t####-----------------\t ---------\n\tdef checkIfErrorReceivedDict(self, newlinesFromServer, combinedLines, ipNumber):\n\t\ttry:\n\t\t\tretCode = 0\n\t\t\t## any error messages from UNIFI device\n\t\t\tif newlinesFromServer.find(\"mca-ctrl: error\")> -1:\t\t\t\t\t\t\t\t\t\t\t\t\tretCode = 11 #mca-ctrl: error while loading shared libraries: libubus.so: cannot ope...\n\t\t\telif len(newlinesFromServer) < 200 and len(combinedLines) < 500:\n\t\t\t\tpos6 = newlinesFromServer.find(\"xxxThisIsTheEndTokenxxx255\")\n\t\t\t\tif pos6 > -1 and pos6 < 100: \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tretCode = 12 \n\n\t\t\treturn retCode\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn retCode\n\n\t####-----------------\t ---------\n\tdef checkIfErrorReceivedTail(self, newlinesFromServer, ipNumber):\n\t\ttry:\n\t\t\tretCode = 0\n\t\t\t## any error messages from UNIFI device\n\t\t\tif newlinesFromServer.find(\"user.notice syswrapper: [state is locked] waiting for lock\") > -1:\t\tretCode = 21\n\t\t\treturn retCode\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn retCode\n\n\t####-----------------\t ---------\n\tdef checkAndPrepTail(self, newlinesFromServer, goodDataReceivedTime, ipNumber, uType, unifiDeviceType, apN):\n\t\ttry:\n\t\t\tlastMSG = newlinesFromServer\n\t\t\t## fill the queue and send to the method that uses it\n\t\t\tif\t\tunifiDeviceType == \"AP\":\n\t\t\t\tself.deviceUp[\"AP\"][ipNumber] = time.time()\n\t\t\telif\tunifiDeviceType == \"GW\":\n\t\t\t\tself.deviceUp[\"GW\"][ipNumber] = time.time()\n\t\t\telif\tunifiDeviceType == \"VD\":\n\t\t\t\tself.deviceUp[\"VD\"][ipNumber] = time.time()\n\t\t\tself.msgListenerActive[uType] = time.time()\n\n\t\t\tif time.time() > self.lastMessageReceivedInListener[ipNumber]: \n\t\t\t\tself.lastMessageReceivedInListener[ipNumber] = time.time()\n\n\t\t\t# we accept any message as good data \n\t\t\tgoodDataReceivedTime = time.time()\n\n\t\t\tif newlinesFromServer.find(\"ThisIsTheAliveTestFromUnifiToPlugin\") > -1:\n\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"aliveTestCount\"] += 1\n\t\t\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"getMessage: {} {} ThisIsTheAliveTestFromUnifiToPlugin received \".format(uType, ipNumber))\n\t\t\telse:\n\t\t\t\tself.logQueue.put((newlinesFromServer,ipNumber,apN, uType,unifiDeviceType))\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.indiLOG.log(30,\"checkAndPrepTail: error for {} - {}\".format(uType, ipNumber ) )\n\t\treturn goodDataReceivedTime, lastMSG\n\n\n\t####-----------------\t ---------\n\tdef checkAndPrepDict(self, newlinesFromServer, goodDataReceivedTime, combinedLines, ipNumber, uType, unifiDeviceType, minWaitbeforeRestart, apN, newDataStartTime):\n\t\ttry:\n\t\t\tcombinedLines += newlinesFromServer\n\t\t\tlastMSG = combinedLines\n\t\t\tppp = combinedLines.split(self.connectParams[\"startDictToken\"][uType])\n\n\t\t\tif len(ppp) == 2:\n\t\t\t\tendTokenPos = ppp[1].find(self.connectParams[\"endDictToken\"][uType])\n\t\t\t\tif endTokenPos > -1:\n\t\t\t\t\tdictData = ppp[1].lstrip(\"\\r\\n\")\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdictData = dictData[0:endTokenPos]\n\t\t\t\t\t\t## remove last line\n\t\t\t\t\t\tif dictData[-1] !=\"}\":\n\t\t\t\t\t\t\tppp = dictData.rfind(\"}\")\n\t\t\t\t\t\t\tdictData = dictData[0:ppp+1]\n\t\t\t\t\t\ttheDict= json.loads(dictData)\n\t\t\t\t\t\tif\t unifiDeviceType == \"AP\":\n\t\t\t\t\t\t\tself.deviceUp[\"AP\"][ipNumber]\t= time.time()\n\t\t\t\t\t\telif unifiDeviceType == \"SW\":\n\t\t\t\t\t\t\tself.deviceUp[\"SW\"][ipNumber]\t= time.time()\n\t\t\t\t\t\telif unifiDeviceType == \"GW\":\n\t\t\t\t\t\t\tself.deviceUp[\"GW\"][ipNumber]\t= time.time()\n\t\t\t\t\t\telif unifiDeviceType == \"UD\":\n\t\t\t\t\t\t\tself.deviceUp[\"SW\"][ipNumber]\t= time.time()\n\t\t\t\t\t\t\tself.deviceUp[\"UD\"]\t\t\t\t= time.time()\n\t\t\t\t\t\t\tself.deviceUp[\"GW\"][ipNumber]\t= time.time()\n\n\t\t\t\t\t\tcombinedLines = \"\"\n\t\t\t\t\t\tself.logQueueDict.put((theDict, ipNumber, apN, uType, unifiDeviceType))\n\t\t\t\t\t\tgoodDataReceivedTime = time.time()\n\t\t\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorTime\"] -= 30\n\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(30,\"{}; checkAndPrepDict: bad/incomplete data receivced from {}/{} @ line#,Module,Statement:{}, \\nraw data:{}\".format(e, uType, ipNumber, traceback.extract_tb(sys.exc_info()[2])[-1][1:], ppp))\n\t\t\t\t\t\t\tpingTest = self.testAPandPing(ipNumber,uType) \n\t\t\t\t\t\t\tokTest = self.testServerIfOK(ipNumber,uType) \n\t\t\t\t\t\t\tretryPeriod = float(self.readDictEverySeconds[uType[0:2]]) + 10.\n\t\t\t\t\t\t\tif time.time() - self.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorTime\"] < retryPeriod or not pingTest or not okTest:\n\t\t\t\t\t\t\t\tmsgF = combinedLines.replace(\"\\r\",\"\").replace(\"\\n\",\"\")\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"checkAndPrepDict JSON len:{}; {}...\\n... {}\".format(len(combinedLines),msgF[0:100], msgF[-40:]) )\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\".... in receiving DICTs for {}-{}; for details check unifi logfile at: {} \".format(uType, ipNumber, self.PluginLogFile ))\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\".... ping test: {}\".format(\" ok \" if pingTest else \" bad\") )\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\".... ssh test: {}\".format(\" ok \" if okTest else \" bad\") )\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\".... uid/passwd:>{}<\".format(self.getUidPasswd(uType, ipNumber)) )\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"getMessage, error reading dict >{}-{}<, not complete? len(data){}, endTokenPos:{}; error:{} ---- retrying\".format(uType, ipNumber, len(dictData), endTokenPos, e) )\n\n\t\t\t\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorCount\"]+=1\n\t\t\t\t\t\t\tself.dataStats[\"tcpip\"][uType][ipNumber][\"inErrorTime\"] = time.time()\n\t\t\t\t\t\t\tgoodDataReceivedTime = time.time() - minWaitbeforeRestart*0.95\n\t\t\t\t\tcombinedLines = \"\"\n\t\t\telse:\n\t\t\t\tcombinedLines = \"\"\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\tself.indiLOG.log(30,\"checkAndPrepDict: error for {} - {}\".format(uType, ipNumber ) )\n\t\t\tgoodDataReceivedTime = 1\n\t\t\tcombinedLines = \"\"\n\t\treturn goodDataReceivedTime, combinedLines, lastMSG\n\n\n\t### start the expect command to get the logfile\n\t####-----------------\t ---------\n\tdef startConnect(self, ipNumber, uType):\n\t\ttry:\n\t\t\tuserid, passwd = self.getUidPasswd(uType,ipNumber)\n\t\t\tif userid ==\"\": return\n\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"startConnect: with IP: {:<15}; uType: {}; UID/PWD: {}/{}\".format(ipNumber, uType, userid, passwd) )\n\n\t\t\tif ipNumber not in self.listenStart:\n\t\t\t\tself.listenStart[ipNumber] = {}\n\t\t\tself.listenStart[ipNumber][uType] = time.time()\n\t\t\tif self.connectParams[\"commandOnServer\"][uType].find(\"off\") == 0: return \"\",\"\"\n\n\t\t\tTT= uType[0:2]\n\t\t\tfor ii in range(20):\n\t\t\t\tif uType.find(\"dict\") > -1:\n\t\t\t\t\tcmd = self.expectPath + \" '\" \n\t\t\t\t\tcmd += self.pathToPlugin + self.connectParams[\"expectCmdFile\"][uType] + \"' \"\n\t\t\t\t\tcmd += \"'\"+userid + \"' '\"+passwd + \"' \" \n\t\t\t\t\tcmd += ipNumber + \" \" \n\t\t\t\t\tcmd += \"'\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber]) + \"' \" \n\t\t\t\t\tcmd += self.connectParams[\"endDictToken\"][uType]+ \" \" \n\t\t\t\t\tcmd += \"{}\".format(self.readDictEverySeconds[TT])+ \" \" \n\t\t\t\t\tcmd += \"{}\".format(self.timeoutDICT)\n\t\t\t\t\tcmd += \" \\\"\"+self.connectParams[\"commandOnServer\"][uType]+\"\\\" \"\n\t\t\t\t\tif uType.find(\"AP\") > -1:\n\t\t\t\t\t\tcmd += \" /var/log/messages\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tcmd += \" doNotSendAliveMessage\"\n\n\t\t\t\telse:\n\t\t\t\t\tcmd = self.expectPath + \" '\" \n\t\t\t\t\tcmd += self.pathToPlugin +self.connectParams[\"expectCmdFile\"][uType] + \"' \"\n\t\t\t\t\tcmd += \"'\"+userid + \"' '\"+passwd + \"' \"\n\t\t\t\t\tcmd += ipNumber + \" \"\n\t\t\t\t\tcmd += \"'\"+self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber])+\"' \" \n\t\t\t\t\tcmd += \" \\\"\"+self.connectParams[\"commandOnServer\"][uType]+\"\\\" \"\n\n\t\t\t\tcmd += self.getHostFileCheck()\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"startConnect: cmd {}\".format(cmd) )\n\t\t\t\tListenProcessFileHandle = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\t\t\t##pid = ListenProcessFileHandle.pid\n\t\t\t\tmsg = \"{}\".format(ListenProcessFileHandle.stderr)\n\t\t\t\tif msg.find(\"open file\") == -1 and msg.find(\"io.BufferedReader\") == -1:\t# try this again\n\t\t\t\t\tself.indiLOG.log(40,\"startConnect {}; IP#: {}; error connecting {}\".format(uType, ipNumber, msg) )\n\t\t\t\t\tself.sleep(20)\n\t\t\t\t\tcontinue\n\n\t\t\t\t# set the O_NONBLOCK flag of ListenProcessFileHandle.stdout file descriptor:\n\t\t\t\tflags = fcntl.fcntl(ListenProcessFileHandle.stdout, fcntl.F_GETFL) # get current p.stdout flags\n\t\t\t\tfcntl.fcntl(ListenProcessFileHandle.stdout, fcntl.F_SETFL, flags | os.O_NONBLOCK)\n\n\t\t\t\treturn ListenProcessFileHandle, \"\"\n\t\t\tself.sleep(0.1)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\treturn \"\", \"error {}\".format(e)\n\t\tself.indiLOG.log(40,\"startConnect timeout, not able to connect after 20 tries \")\n\t\treturn \"\",\"error connecting\"\n\n\n\n\t####-----------------\t ---------\n\tdef createEntryInUnifiDevLog(self):\n\t\ttry:\n\t\t\tif not self.createEntryInUnifiDevLogActive: return \n\t\t\tif time.time() - self.lastcreateEntryInUnifiDevLog < 12: return \n\t\t\tself.lastcreateEntryInUnifiDevLog = time.time()\n\t\t\tdoTestIflastMsg = 80 # do a test if last msg from listener is > xx sec ago \n\t\t\t#if self.decideMyLog(\"Special\"):self.indiLOG.log(10,\"createEntryInUnifiDevLog: testing if we should do test ok, now:{}; lastmsgs:\\n{}\".format(time.time(), self.lastMessageReceivedInListener ))\n\n\t\t\tif self.devsEnabled[\"GW\"] and not self.devsEnabled[\"UD\"]:\n\t\t\t\tipN = self.ipNumbersOf[\"GW\"]\n\t\t\t\tif ipN in self.lastMessageReceivedInListener and time.time() - self.lastMessageReceivedInListener[ipN] > doTestIflastMsg: \n\t\t\t\t\tself.testServerIfOK( ipN, \"GW\", batch=True)\n\n\t\t\tfor aa in [\"AP\",\"SW\"]:\n\t\t\t\tif self.numberOfActive[aa] > 0:\n\t\t\t\t\tfor ll in range(len(self.devsEnabled[aa])):\n\t\t\t\t\t\tif self.devsEnabled[aa][ll]:\n\t\t\t\t\t\t\tif (self.unifiControllerType == \"UDM\" or self.controllerWebEventReadON > 0) and ll == self.numberForUDM[aa]: continue\n\t\t\t\t\t\t\tipN = self.ipNumbersOf[aa][ll]\n\t\t\t\t\t\t\tif ipN in self.lastMessageReceivedInListener and time.time() - self.lastMessageReceivedInListener[ipN] > doTestIflastMsg: \n\t\t\t\t\t\t\t\tself.testServerIfOK( ipN, aa, batch=True)\n\t\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\n\n\t####-----------------\t ---------\n\tdef testServerIfOK(self, ipNumber, uType, batch=False):\n\t\ttry:\n\t\t\tuserid, passwd = self.getUidPasswd(uType,ipNumber)\n\t\t\tif userid == \"\": \n\t\t\t\tself.indiLOG.log(40,\"testServerIf ssh connection OK: userid>>{}<<, passwd>>{}<< wrong for {}-{}\".format(userid, passwd, uType, ipNumber) )\n\t\t\t\treturn False\n\n\t\t\tcmd = self.expectPath+ \" '\" + self.pathToPlugin +\"test.exp' '\" + userid + \"' '\" + passwd + \"' \" + ipNumber \n\t\t\tcmd+= self.getHostFileCheck()\n\n\n\t\t\tif ipNumber in self.lastMessageReceivedInListener: self.lastMessageReceivedInListener[ipNumber] = time.time()\n\n\t\t\tif batch:\n\t\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"testServer ssh to {}-{} to create log entry using:{}\".format(uType, ipNumber, cmd) )\n\t\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"testServerIfOK: batch {}\".format(cmd) )\n\t\t\t\tsubprocess.Popen(cmd+\" &\", stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n\t\t\t\treturn \n\n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,\"testServerIfOK: {}\".format(cmd) )\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\txx = ret.replace(\"\\r\",\"\")\n\t\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"returned from expect-command: {}\".format(xx))\n\n\t\t\t## check if we need to fix unknown host in .ssh/known_hosts\n\t\t\tif len(err) > 0:\n\t\t\t\tself.indiLOG.log(40,\"testServerIf ssh connection to server failed, cmd: {}\".format(cmd) )\n\t\t\t\tretx, ok = self.fixHostsFile(ret,ipNumber)\n\t\t\t\tif not ok: \n\t\t\t\t\tself.indiLOG.log(40,\"testServerIfOK failed, will retry \")\n\t\t\t\t\tret, err = self.readPopen(cmd)\n\t\t\t\t\txx = ret.replace(\"\\r\",\"\")\n\n\t\t\ttest = xx.lower()\n\t\t\ttags = [\"welcome\",\"unifi\",\"debian\",\"edge\",\"busybox\",\"ubiquiti\",\"ubnt\",\"login\"]\n\t\t\tloggedIn = False\n\t\t\tfor tag in tags:\n\t\t\t\tif tag in test: \n\t\t\t\t\tloggedIn = True\n\t\t\t\t\tbreak\n\t\t\tif loggedIn:\n\t\t\t\tnPrompt = 3\n\t\t\t\tif ipNumber in self.connectParams[\"promptOnServer\"]:\n\t\t\t\t\tif self.connectParams[\"promptOnServer\"][ipNumber] == xx[-nPrompt:]: \n\t\t\t\t\t\treturn True\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.indiLOG.log(10,\"testServerIfOK: =========== {}; prompt not found or reset by restart; old:'{}', new candidate:'{}'\".format(ipNumber, self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber]), xx[-nPrompt:]) )\n\t\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tself.connectParams[\"promptOnServer\"][ipNumber] = \"\"\n\t\t\t\t\tself.indiLOG.log(10,\"testServerIfOK: =========== ipNumber:{} not in connectParams\".format(ipNumber) )\n\n\t\t\t\tprompt= xx[-nPrompt:]\n\t\t\t\t# remove new line from prompts would screw up expect, does not like newline in variables ...\n\t\t\t\tnewL = prompt.find(\"\\n\")\n\t\t\t\tif newL == -1: \n\t\t\t\t\tnewL = prompt.find(\"\\r\")\n\n\t\t\t\tif newL\t== 0: \t\t\t\tprompt = prompt[1:]\n\t\t\t\telif newL\t== len(prompt)-1:\tprompt = prompt[:-1]\n\t\t\t\tnPrompt = len(prompt)\n\t\t\t\tself.indiLOG.log(10,\"testServerIfOK: =========== for {} ssh response, setting promp to:'{}' using last {} chars in ...{}<<<< \".format(ipNumber, prompt, nPrompt, xx[-20:]) )\n\n\n\t\t\t\tself.connectParams[\"promptOnServer\"][ipNumber] = prompt\n\t\t\t\t\n\t\t\t\tself.pluginPrefs[\"connectParams\"] = json.dumps(self.connectParams)\n\n\t\t\t\tself.indiLOG.log(10,\"testServerIfOK: =========== known prompts: \\n{}\".format(self.connectParams[\"promptOnServer\"]))\n\t\t\t\treturn True\n\n\t\t\tself.indiLOG.log(10,\"testServerIfOK: ==========={} ssh response, tags {} not found : ==> \\n{}\".format(ipNumber, tags, xx) )\n\t\t\treturn False\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n\n####-------------------------------------------------------------------------####\n\tdef fixHostsFile(self, ret, ipNumber):\n\t\ttry:\n\t\t\tif ret.find(\".ssh/known_hosts:\") > -1:\n\t\t\t\tret, err = self.readPopen(\"/usr/bin/csrutil status\" )\n\t\t\t\tif ret.find(\"enabled\") > -1:\n\t\t\t\t\tself.indiLOG.log(40,'ERROR can not update hosts known_hosts file, \"/usr/bin/csrutil status\" shows system enabled SIP; please edit manually with \\n\"nano {}/.ssh/known_hosts\"\\n and delete line starting with {}'.format(self.MAChome, ipNumber) )\n\t\t\t\t\tself.indiLOG.log(40,\"trying to fix from within plugin, if it happens again you need to do it manually\")\n\t\t\t\t\ttry:\n\t\t\t\t\t\tf = self.openEncoding(self.MAChome+'/.ssh/known_hosts',\"r\")\n\t\t\t\t\t\tlines = f.readlines()\n\t\t\t\t\t\tf.close()\n\t\t\t\t\t\tf = self.openEncoding(self.MAChome+'/.ssh/known_hosts',\"w\")\n\t\t\t\t\t\tfor line in lines:\n\t\t\t\t\t\t\tif line.find(ipNumber) >-1: continue\n\t\t\t\t\t\t\tif len(line) < 10: continue\n\t\t\t\t\t\t\tf.write(line+\"\\n\")\n\t\t\t\t\t\tf.close()\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\t\t\treturn [\"\",\"\"], False\n\n\t\t\t\tfix1 = ret.split(\"Offending RSA key in \")\n\t\t\t\tif len(fix1) > 1:\n\t\t\t\t\tfix2 = fix1[1].split(\"\\n\")[0].strip(\"\\n\").strip(\"\\n\")\n\t\t\t\t\tfix3 = fix2.split(\":\")\n\t\t\t\t\tif len(fix3) > 1:\n\t\t\t\t\t\tfixcode = \"/usr/bin/perl -pi -e 's/\\Q$_// if ($. == \" + fix3[1] + \");' \" + fix3[0]\n\t\t\t\t\t\tself.indiLOG.log(40, \"wrong RSA key, trying to fix with: {}\".format(fixcode) )\n\t\t\t\t\t\tret, err = self.readPopen(fixcode )\n \n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn ret, True\n\n\n\t####-----------------\t ---------\n\tdef setAccessToLog(self, ipNumber, uType):\n\t\ttry:\n\t\t\tuserid, passwd = self.getUidPasswd(uType,ipNumber)\n\t\t\tif userid ==\"\": return False\n\n\t\t\tcmd = self.expectPath +\" '\" + self.pathToPlugin +\"setaccessToLog.exp' '\" + userid + \"' '\" + passwd + \"' \" + ipNumber + \" '\" +self.escapeExpect(self.connectParams[\"promptOnServer\"][ipNumber])+\"' \"\n\t\t\tcmd += self.getHostFileCheck()\n\t\t\t#if self.decideMyLog(\"Expect\"): \n\t\t\tif self.decideMyLog(\"Expect\"): self.indiLOG.log(10,cmd)\n\t\t\tret, err = self.readPopen(cmd)\n\t\t\tif self.decideMyLog(\"ExpectRET\"): self.indiLOG.log(10,\"returned from expect-command: {}\".format(ret))\n\t\t\ttest = ret.lower()\n\t\t\ttags = [\"welcome\",\"unifi\",\"debian\",\"edge\",\"busybox\",\"ubiquiti\",\"ubnt\",\"login\"]\n\t\t\tfor tag in tags:\n\t\t\t\tif tag in test:\t return True\n\t\t\tself.indiLOG.log(10,\"\\n==========={} ssh response, tags {} not found : ==> {}\".format(ipNumber, tags, test) )\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n\n\t####-----------------\t ---------\n\tdef getUidPasswd(self, uType, ipNumber):\n\n\t\ttry:\n\t\t\tif uType.find(\"VD\") > -1:\n\t\t\t\tuserid = self.connectParams[\"UserID\"][\"unixNVR\"]\n\t\t\t\tpasswd = self.connectParams[\"PassWd\"][\"unixNVR\"]\n\n\t\t\telse:\n\t\t\t\tif self.unifiControllerType.find(\"UDM\") > -1 and (\n\t\t\t\t\t( uType.find(\"AP\") > -1 and ipNumber == self.ipNumbersOf[\"AP\"][self.numberForUDM[\"AP\"]]) or\n\t\t\t\t\t( uType.find(\"SW\") > -1 and ipNumber == self.ipNumbersOf[\"SW\"][self.numberForUDM[\"SW\"]]) or\n\t\t\t\t\t( uType.find(\"UD\") > -1 ) or\n\t\t\t\t\t( uType.find(\"GW\") > -1 and ipNumber == self.ipNumbersOf[\"GW\"]) ):\n\t\t\t\t\tuserid = self.connectParams[\"UserID\"][\"unixUD\"]\n\t\t\t\t\tpasswd = self.connectParams[\"PassWd\"][\"unixUD\"]\n\t\t\t\telse:\t\n\t\t\t\t\tuserid = self.connectParams[\"UserID\"][\"unixDevs\"]\n\t\t\t\t\tpasswd = self.connectParams[\"PassWd\"][\"unixDevs\"]\n\n\t\t\tif userid == \"\" or passwd == \"\":\n\t\t\t\tself.indiLOG.log(10,\"Connection: {} login disabled, userid is empty\".format(uType) )\n\t\t\treturn userid, passwd\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \"\",\"\"\n\n\n\n\t####-----------------\t ---------\n\tdef comsumeLogData(self):# , startTime):\n\t\tself.sleep(1)\n\t\tself.indiLOG.log(10,\"comsumeLogData: process starting\")\n\t\tnextItem = \"\"\n\t\tlines\t = \"\"\n\t\tipNumber = \"\"\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tif self.pluginState == \"stop\" or self.consumeDataThread[\"log\"][\"status\"] == \"stop\": \n\t\t\t\t\tself.indiLOG.log(30,\"comsumeLogData: stopping process due to stop request\")\n\t\t\t\t\treturn \n\t\t\t\tself.sleep(0.1)\n\t\t\t\tconsumedTimeQueue = time.time()\n\t\t\t\tqueueItemCount = 0\n\t\t\t\twhile not self.logQueue.empty():\n\t\t\t\t\tif self.pluginState == \"stop\" or self.consumeDataThread[\"log\"][\"status\"] == \"stop\": \n\t\t\t\t\t\tself.indiLOG.log(30,\"comsumeLogData: stopping process due to stop request\")\n\t\t\t\t\t\treturn \n\t\t\t\t\tqueueItemCount += 1\n\n\t\t\t\t\tnextItem = self.logQueue.get()\n\n\t\t\t\t\tlines\t\t\t= nextItem[0].split(\"\\r\\n\")\n\t\t\t\t\tipNumber\t\t= nextItem[1]\n\t\t\t\t\tapN\t\t\t\t= nextItem[2]\n\t\t\t\t\ttry: \tapNint\t= int(nextItem[2])\n\t\t\t\t\texcept: apNint\t= -1\n\t\t\t\t\tuType\t\t\t= nextItem[3]\n\t\t\t\t\txType\t\t\t= nextItem[4]\n\n\t\t\t\t\t## update device-ap with new timestamp, it is up\n\t\t\t\t\tif self.decideMyLog(\"Log\"): self.indiLOG.log(10,\"MS------- {:13s}#{} {} {} .. {}\".format(ipNumber, apN, uType, xType, \"{}\".format(nextItem[0])[0:100]) )\n\n\t\t\t\t\tif self.debugThisDevices(uType, apNint):\n\t\t\t\t\t\tself.indiLOG.log(10,\"DEVdebug {} dev #:{:2d} uType:{}, xType{}, logmessage:\\n{}\".format(ipNumber, apNint, uType, xType, \"\\n\".join(lines)) )\n\n\t\t\t\t\t### update lastup for unifi devices\n\t\t\t\t\tif xType in self.MAC2INDIGO:\n\t\t\t\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\t\tif xType == \"UN\" and self.testIgnoreMAC(MAC, fromSystem=\"log\"): continue\n\t\t\t\t\t\t\tif ipNumber == self.MAC2INDIGO[xType][MAC][\"ipNumber\"]:\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tconsumedTime = time.time()\n\n\t\t\t\t\tif\t uType == \"APtail\":\n\t\t\t\t\t\tself.doAPmessages(lines, ipNumber, apN)\n\t\t\t\t\telif uType == \"GWtail\":\n\t\t\t\t\t\tself.doGWmessages(lines, ipNumber, apN)\n\t\t\t\t\telif uType == \"SWtail\":\n\t\t\t\t\t\tself.doSWmessages(lines, ipNumber, apN)\n\t\t\t\t\telif uType == \"VDtail\":\n\t\t\t\t\t\tpass# self.doVDmessages()\n\t\t\t\t\tconsumedTime -= time.time()\n\t\t\t\t\tif consumedTime < -self.maxConsumedTimeForWarning:\tlogLevel = 20\n\t\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\tlogLevel = 10\n\t\t\t\t\tif logLevel == 20:\n\t\t\t\t\t\tself.indiLOG.log(logLevel,\"comsumeLogData excessive time consumed:{:5.1f}[secs]; {:16}; len:{:}, lines:{:}\".format(-consumedTime, ipNumber, len(lines), \"{}\".format(lines)[0:100]) )\n\n\t\t\t\t\tself.logQueue.task_done()\n\n\t\t\t\t#self.logQueue.task_done()\n\t\t\t\t\tif len(self.sendUpdateToFingscanList) > 0: self.sendUpdatetoFingscanNOW()\n\t\t\t\t\tif len(self.sendBroadCastEventsList) > 0: self.sendBroadCastNOW()\n\n\t\t\t\tconsumedTimeQueue -= time.time()\n\t\t\t\tif consumedTimeQueue < -self.maxConsumedTimeQueueForWarning:\tlogLevel = 20\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogLevel = 10\n\t\t\t\tif logLevel == 20:\n\t\t\t\t\t\tself.indiLOG.log(logLevel,\"comsumeLogData Total excessive time consumed:{:5.1f}[secs]; {:16}; items:{:2} len:{:}, lines:{:}\".format(-consumedTimeQueue, ipNumber, queueItemCount, len(lines), \"{}\".format(lines)[0:100]) )\n\n\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.indiLOG.log(30,\"comsumeLogData: stopping process (3)\")\n\t\treturn \n\n\n\n\n\t###########################################\n\t####------ camera PROTEC ---\t-------START\n\t###########################################\n\tdef getProtectIntoIndigo(self):\n\t\ttry:\n\t\t\tif self.cameraSystem != \"protect\":\t\t\t\t\t\t\t\t\t\treturn\n\t\t\tif time.time() - self.lastRefreshProtect < self.refreshProtectCameras: \treturn\n\t\t\telapsedTime \t= time.time()\n\t\t\tsystemInfoProtect = self.executeCMDOnController(dataSEND={}, pageString=\"api/bootstrap/\", jsonAction=\"protect\", cmdType=\"get\", protect=True)\n\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getProtectIntoIndigo: ********* elapsed time (1):{:.1f}, len:{}, cameraInfo:{}\".format(time.time() - elapsedTime, len(systemInfoProtect), \"cameras\" in systemInfoProtect ))\n\n\t\t\tif len(systemInfoProtect) == 0: \n\t\t\t\tself.lastRefreshProtect = time.time() - self.refreshProtectCameras +2\n\t\t\t\treturn\n\t\t\tif \"cameras\" not in systemInfoProtect:\n\t\t\t\tself.lastRefreshProtect = time.time() - self.refreshProtectCameras +2\n\t\t\t\treturn\n\n\n\t\t\tdevName = \"\"\n\t\t\tmapSensToLevel ={\"\":\"\", 0:\"low\", 1:\"med\", 2:\"high\"}\n\t\t\tlD = len(self.PROTECT)\n\t\t\t# clean up device listed in PROTECT, but not in indigo, only check at beginning and every 5 minutes\n\t\t\tif lD == 0 or time.time() - self.lastRefreshProtect > 300 or self.lastRefreshProtect ==0:\n\t\t\t\tdevList = {}\n\t\t\t\tMAClist = {}\n\t\t\t\tfor dev in indigo.devices.iter(\"props.isProtectCamera\"):\n\t\t\t\t\tcameraId = dev.states[\"id\"]\n\t\t\t\t\tif dev.states[\"MAC\"] in MAClist:\n\t\t\t\t\t\tself.indiLOG.log(30,\"getProtectIntoIndigo: duplicated MAC number:{} in indigo devices, please delete one : {}, currently ignoring: [{},{}] \".format(dev.states[\"MAC\"], MAClist[dev.states[\"MAC\"]], dev.id, dev.name ))\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tMAClist[dev.states[\"MAC\"]] = [dev.id, dev.name]\n\t\t\t\t\tif dev.states[\"id\"] not in self.PROTECT:\n\t\t\t\t\t\tself.PROTECT[cameraId] = {\"events\":{}, \"devId\":dev.id, \"devName\":dev.name, \"MAC\":dev.states[\"MAC\"], \"lastUpdate\":time.time()}\n\n\t\t\t\t\tdevList[cameraId] = 1\n\t\t\t\t\t# clean up wrong status afetr strtup\n\t\t\t\t\tif lD == 0:\n\t\t\t\t\t\tif dev.states[\"status\"] in [\"event\",\"motion\",\"ring\",\"person\",\"vehicle\"]:\n\t\t\t\t\t\t\t#self.indiLOG.log(30,\"getProtectIntoIndigo: fixing status for:{}\".format(dev.name ))\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"status\", \"CONNECTED\")\n\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\t\t\t\tself.executeUpdateStatesList()\n\n\t\t\t\tdelList = {}\n\t\t\t\tfor cameraId in self.PROTECT:\n\t\t\t\t\tif cameraId not in devList:\n\t\t\t\t\t\tdelList[cameraId] = 1\n\n\t\t\t\tfor cameraId in delList:\n\t\t\t\t\tdel self.PROTECT[cameraId]\n\t\t\t\n\t\t\t\tif lD == 0:\n\t\t\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"getProtectIntoIndigo: starting with dev list: {}\".format(self.PROTECT))\n\n\n\t\t\tfor camera in systemInfoProtect[\"cameras\"]:\n\t\t\t\ttry:\n\t\t\t\t\tstates = {}\n\t\t\t\t\tMAC \t\t\t\t\t\t\t\t\t\t= camera.get(\"mac\",\"00:00:00:00:00:00\")\n\t\t\t\t\tstates[\"MAC\"] \t\t\t\t\t\t\t\t= MAC[0:2]+\":\"+MAC[2:4]+\":\"+MAC[4:6]+\":\"+MAC[6:8]+\":\"+MAC[8:10]+\":\"+MAC[10:12]\n\t\t\t\t\tstates[\"id\"] \t\t\t\t\t\t\t\t= camera.get(\"id\",\"0\")\n\t\t\t\t\tstates[\"name\"] \t\t\t\t\t\t\t\t= camera.get(\"name\")\n\t\t\t\t\tstates[\"ip\"]\t\t \t\t\t\t\t\t= camera.get(\"host\")\n\t\t\t\t\tstates[\"status\"] \t\t\t\t\t\t\t= camera.get(\"state\")\n\t\t\t\t\tstates[\"type\"] \t\t\t\t\t\t\t\t= camera.get(\"type\")\n\t\t\t\t\tstates[\"firmwareVersion\"] \t\t\t\t\t= camera.get(\"firmwareVersion\")\n\t\t\t\t\tstates[\"isAdopted\"] \t\t\t\t\t\t= camera.get(\"isAdopted\",False)\n\t\t\t\t\tstates[\"isConnected\"] \t\t\t\t\t\t= camera.get(\"isConnected\",False)\n\t\t\t\t\tstates[\"isManaged\"] \t\t\t\t\t\t= camera.get(\"isManaged\",False)\n\t\t\t\t\tstates[\"isDark\"] \t\t\t\t\t\t\t= camera.get(\"isDark\",False)\n\t\t\t\t\tstates[\"hasSpeaker\"] \t\t\t\t\t\t= camera.get(\"hasSpeaker\",False)\n\t\t\t\t\tstates[\"modelKey\"] \t\t\t\t\t\t\t= camera.get(\"modelKey\")\n\t\t\t\t\tstates[\"lcdMessage\"] \t\t\t\t\t\t= camera[\"lcdMessage\"].get(\"text\")\n\t\t\t\t\tstates[\"isSpeakerEnabled\"] \t\t\t\t\t= camera[\"speakerSettings\"].get(\"isEnabled\",False)\n\t\t\t\t\tstates[\"isExternalIrEnabled\"] \t\t\t\t= camera[\"ispSettings\"].get(\"isExternalIrEnabled\",False)\n\t\t\t\t\tstates[\"irLedMode\"] \t\t\t\t\t\t= camera[\"ispSettings\"].get(\"irLedMode\")\n\t\t\t\t\tstates[\"irLedLevel\"] \t\t\t\t\t\t= camera[\"ispSettings\"].get(\"irLedLevel\")\n\t\t\t\t\tstates[\"isLedEnabled\"] \t\t\t\t\t\t= camera[\"ledSettings\"].get(\"isEnabled\")\n\t\t\t\t\tstates[\"motionRecordingMode\"] \t\t\t\t= camera[\"recordingSettings\"].get(\"mode\")\n\t\t\t\t\tstates[\"motionMinEventTrigger\"] \t\t\t= camera[\"recordingSettings\"].get(\"minMotionEventTrigger\")\n\t\t\t\t\tstates[\"motionSuppressIlluminationSurge\"] \t= camera[\"recordingSettings\"].get(\"suppressIlluminationSurge\")\n\t\t\t\t\tstates[\"motionUseNewAlgorithm\"] \t\t\t= camera[\"recordingSettings\"].get(\"useNewMotionAlgorithm\")\n\t\t\t\t\tstates[\"motionAlgorithm\"] \t\t\t\t\t= camera[\"recordingSettings\"].get(\"motionAlgorithm\",\"-\")\n\t\t\t\t\tstates[\"areSystemSoundsEnabled\"] \t\t\t= camera[\"speakerSettings\"].get(\"areSystemSoundsEnabled\",False)\n\t\t\t\t\tstates[\"speakerVolume\"] \t\t\t\t\t= int(camera[\"speakerSettings\"].get(\"volume\",100))\n\t\t\t\t\tstates[\"micVolume\"] \t\t\t\t\t\t= int(camera.get(\"micVolume\",100))\n\t\t\t\t\tstates[\"icrSensitivity\"] \t\t\t\t\t= mapSensToLevel[camera[\"ispSettings\"].get(\"icrSensitivity\")]\n\t\t\t\t\tstates[\"motionEndEventDelay\"] \t\t\t\t= float(camera[\"recordingSettings\"].get(\"endMotionEventDelay\"))/1000.\n\t\t\t\t\tstates[\"motionPostPaddingSecs\"] \t\t\t= float(camera[\"recordingSettings\"].get( \"postPaddingSecs\"))\n\t\t\t\t\tstates[\"motionPrePaddingSecs\"] \t\t\t\t= float(camera[\"recordingSettings\"].get( \"prePaddingSecs\"))\n\t\t\t\t\t# ret might be \"None\"\n\t\t\t\t\ttry:\tstates[\"lastSeen\"] \t\t\t\t\t= datetime.datetime.fromtimestamp(camera.get(\"lastSeen\",0)/1000.,0).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\texcept:\tstates[\"lastSeen\"] \t\t\t\t\t= \"\"\n\t\t\t\t\ttry:\tstates[\"connectedSince\"] \t\t\t= datetime.datetime.fromtimestamp(camera.get(\"connectedSince\",0)/1000.).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\texcept:\tstates[\"connectedSince\"] \t\t\t= \"\"\n\t\t\t\t\ttry:\tstates[\"lastRing\"] \t\t\t\t\t= datetime.datetime.fromtimestamp(camera.get(\"lastRing\",0)/1000.).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\texcept:\tstates[\"lastRing\"] \t\t\t\t\t= \"\"\n\t\t\t\t\tdevId = -1\n\t\t\t\t\tdev = \"\"\n\t\t\t\t\tif states[\"id\"] not in self.PROTECT:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdevName = \"Camera_Protect_\"+states[\"name\"] +\"_\"+MAC \n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t= states[\"MAC\"],\n\t\t\t\t\t\t\t\tname \t\t\t= devName,\n\t\t\t\t\t\t\t\tdescription\t\t=\"\",\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=\"camera_protect\",\n\t\t\t\t\t\t\t\tprops\t\t\t={\"isProtectCamera\":True, \"eventThumbnailOn\":True, \"eventHeatmapOn\":False, \"thumbnailwh\":\"640/480\", \"heatmapwh\":\"320/240\"\n\t\t\t\t\t\t\t\t\t\t\t\t, \"SupportsOnState\":True, \"SupportsSensorValue\":False , \"SupportsStatusRequest\":False, \"AllowOnStateChange\":False, \"AllowSensorValueChange\":False\n\t\t\t\t\t\t\t\t\t\t\t\t},\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tdevId = dev.id\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"adding {} to PROTEC list ip:{}\".format( dev.name, states[\"ip\"]))\n\t\t\t\t\t\t\tself.PROTECT[states[\"id\"]] = {\"events\":{}, \"devId\":devId, \"devName\":dev.name, \"MAC\":states[\"MAC\"] , \"lastUpdate\":time.time()}\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\terrtext = \"{}\".format(e)\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tif \"NameNotUniqueError\" in errtext:\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"error with : {} will try to update the camera id in indigo device and continue, if the error percist, please delete device, will be re-created\".format( devName ))\n\t\t\t\t\t\t\t\tdev = indigo.devices[devName]\n\t\t\t\t\t\t\t\tdevId = dev.id\n\t\t\t\t\t\t\t\tself.PROTECT[states[\"id\"]][\"devId\"] = devId\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.indiLOG.log(20,\"unknown error- please restart plugin, dev : {} / {} internal list:{}\".format( devName , states[\"id\"], self.PROTECT))\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\telse:\n\t\t\t\t\t\tdevId = self.PROTECT[states[\"id\"]][\"devId\"]\n\t\t\t\t\t\tdev = indigo.devices[devId]\n\n\t\t\t\t\tif devId ==-1:\n\t\t\t\t\t\tself.indiLOG.log(40,\"dev not found \")\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tself.PROTECT[states[\"id\"]][\"lastUpdate\"] = time.time()\n\n\t\t\t\t\tif dev != \"\":\n\t\t\t\t\t\tfor state in states:\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"checking dev {} state:{} := {}\".format(dev.name, state, states[state]))\n\t\t\t\t\t\t\tif dev.states[state] != states[state]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(devId, state, states[state])\n\n\t\t\t\t\telse:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[devId]\n\t\t\t\t\t\t\tdevId = dev.id\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"timeout waiting\") == -1: \n\t\t\t\t\t\t\t\tif states[\"id\"] in self.PROTECT:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\" due to error removing cameraId: {} from internal list:{}\".format(states[\"id\"], self.PROTECT[states[\"id\"]]))\n\t\t\t\t\t\t\t\t\tdel self.PROTECT[states[\"id\"]]\n\t\t\t\t\t\t\tcontinue\n\n\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\t# cleanup old devices not used\n\t\t\tif time.time() - self.lastRefreshProtect < 300:\n\t\t\t\tdelList = {}\n\t\t\t\tfor cameraId in self.PROTECT:\n\t\t\t\t\tdelEvents = {}\n\t\t\t\t\tfor eventID in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\t\tif ( time.time() - self.PROTECT[cameraId][\"events\"][eventID][\"eventStart\"] > 100 and \n\t\t\t\t\t\t\t self.PROTECT[cameraId][\"events\"][eventID][\"eventEnd\"] == 0 ): delEvents[eventID] = 1\n\n\t\t\t\t\tfor eventID in delEvents:\n\t\t\t\t\t\tdel self.PROTECT[cameraId][\"events\"][eventID]\n\n\t\t\t\t\tif self.PROTECT[cameraId][\"lastUpdate\"] > 24*3600: # we have received no update in > 24 hour \n\t\t\t\t\t\ttry: \tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"timeout waiting\") == -1: \n\t\t\t\t\t\t\t\tdelList[cameraId] =1\n\n\t\t\t\tfor cameraId in delList:\n\t\t\t\t\tself.indiLOG.log(30,\"removing cameraId: {} after > 24 hours w not activity and indigo dev does not exists either\".format(cameraId))\n\t\t\t\t\tif cameraId in self.PROTECT: \n\t\t\t\t\t\tself.indiLOG.log(30,\"... internal list:{}\".format(self.PROTECT[cameraId]))\n\t\t\t\t\t\tdel self.PROTECT[cameraId]\n\n\t\t\tself.executeUpdateStatesList()\n\t\t\tself.lastRefreshProtect = time.time()\n\t\t\t#self.indiLOG.log(10,\"getProtectIntoIndigo: ********* elapsed time (2):{:.1f}\".format(time.time() - elapsedTime))\n\t\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\n\t####-----------------\t ---------\n\t####----- thread to get new events every x secs ------\n\t####-----------------\t ---------\n\tdef getProtectEvents(self):# , startTime):\n\t\tself.indiLOG.log(10,\"getProtectEvents: process starting\")\n\t\tlastGetEvent = time.time()\n\t\tlastId = \"\"\n\t\tself.lastEvCheck = time.time()\n\t\tlastEvent = {}\n\t\tself.lastThumbnailTime = 0\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\trefreshCameras = False\n\n\t\t\t\tif self.cameraSystem != \"protect\":\n\t\t\t\t\tself.indiLOG.log(30,\"getProtectEvents: stopping process due to camera off\")\n\t\t\t\t\treturn \n\t\t\t\tif self.pluginState == \"stop\" or self.protectThread[\"status\"] == \"stop\": \n\t\t\t\t\tself.indiLOG.log(30,\"getProtectEvents: stopping process due to stop request\")\n\t\t\t\t\treturn \n\n\t\t\t\tself.sleep(0.2)\n\n\t\t\t\tif self.PROTECT == {}: \t\t\t\t\t\t\t\t\t\tcontinue # no camera defined\n\t\t\t\tif time.time() - lastGetEvent < self.protecEventSleepTime: \tcontinue # now yet\n\t\t\t\tlastGetEvent\t= time.time()\n\n\t\t\t\t# get new events from controller server\n\t\t\t\tendTime \t\t= int(time.time() * 1000)\n\t\t\t\tdataDict \t\t= {\"end\": str(endTime+20), \"start\": str( endTime - int(max(1,self.protecEventSleepTime)) *1000)}\n\t\t\t\tevents = self.executeCMDOnController(dataSEND=dataDict, pageString=\"api/events/\", jsonAction=\"protect\", cmdType=\"get\", protect=True)\n\t\t\t\tif False and self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getProtectEvents: ********* get events elapsed time (1):{:.2f}, len(events):{} \".format(time.time() - elapsedTime, len(events) ))\n\t\t\t\t\n\n\t\t\t\t# digest new events\n\t\t\t\tif not self.checkIfEmptyEventCleanup(events): \n\t\t\t\t\tcheckIds = self.loopThroughEventsAndFilterCameraEvents(events)\n\t\t\t\telse:\n\t\t\t\t\tcheckIds = {}\n\n\t\t\t\tself.goThroughNewEventDataGetThumbNailsAndUpdateIndigoDevicesAndVariables(checkIds)\n\n\t\t\t\tself.executeUpdateStatesList()\n\n\t\t\t\tif False and self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getProtectEvents: elapsed time (2):{:.1f}\".format(time.time() - lastGetEvent))\n\t\t\t\t\t\n\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\tself.sleep(10)\n\t\tself.indiLOG.log(30,\"comsumeLogData: stopping process (3)\")\n\t\treturn \n\n\t####-----------------\t ---------\n\t####-----loop through new evenst and check if any new, changes ------\n\t####-----------------\t ---------\n\n\tdef loopThroughEventsAndFilterCameraEvents(self, events):\n\t\ttry:\n\t\t\tcheckIds = {}\n\t\t\tif events == []: return checkIds \n\t\t\tfor event in events:\n\t\t\t\tdev = \"\"\n\t\t\t\t# first check if everything is here \n\t\t\t\tif \"modelKey\"\t\t\t\tnot in event: continue\n\t\t\t\tif event[\"modelKey\"] != \"event\": \t\t continue\n\t\t\t\tif \"camera\"\t\t\t\tnot in event: continue\n\t\t\t\tif \"id\"\t\t\t\t\tnot in event: continue\n\t\t\t\tif \"start\"\t\t\t\t\tnot in event: continue\n\t\t\t\tif \"end\"\t\t\t\t\tnot in event: continue\n\t\t\t\tif \"thumbnail\"\t\t\t\tnot in event: continue\n\t\t\t\tif \"type\"\t\t\t\t\tnot in event: continue\n\t\t\t\tif \"smartDetectEvents\"\t\tnot in event: continue\n\t\t\t\tif \"smartDetectTypes\"\t\tnot in event: continue\n\t\t\t\t# ignore old events !\n\t\t\t\tif time.time() - event[\"start\"]/1000. > 60: continue # ignore old events \n\n\t\t\t\t## we have a complete event \n\n\t\t\t\tnewId = event[\"id\"]\n\n\t\t\t\tupdateDev = False\n\t\t\t\tcameraId = event[\"camera\"]\n\t\t\t\tif cameraId not in self.PROTECT:\n\t\t\t\t\tself.lastRefreshProtect = time.time() - self.refreshProtectCameras + 2 \n\t\t\t\t\tcontinue\n\n\n\t\t\t\t#### ignore repeat event info ### start\n\t\t\t\tif self.PROTECT[cameraId][\"events\"] != {}:\n\t\t\t\t\tif newId in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\t\tdouble = True\n\t\t\t\t\t\tfor xx in event:\n\t\t\t\t\t\t\tif xx not in self.PROTECT[cameraId][\"events\"][newId][\"rawEvent\"]:\n\t\t\t\t\t\t\t\tdouble = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\tif event[xx] != self.PROTECT[cameraId][\"events\"][newId][\"rawEvent\"][xx]:\n\t\t\t\t\t\t\t\tdouble = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif not double:\n\t\t\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"rawEvent\"] = copy.deepcopy(event)\n\t\t\t\t\telse:\n\t\t\t\t\t\tdouble = False\n\n\t\t\t\t\tif double: \n\t\t\t\t\t\tif self.decideMyLog(\"Protect\"): \n\t\t\t\t\t\t\t#self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; skipping = repeat event\".format(cameraId, newId) )\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t#### ignore repeat event info ### END\n\n\t\t\t\tif self.decideMyLog(\"ProtEvents\"):\n\t\t\t\t\txxx = copy.deepcopy(event)\n\t\t\t\t\tdel xxx[\"camera\"]\n\t\t\t\t\tdel xxx[\"id\"]\n\t\t\t\t\tself.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; event {}\".format(cameraId, newId, xxx))\n\n\t\t\t\tsmartDetect = \"\"\n\n\t\t\t\t## for the time being ignore list of smart detect events. this is a list of events to follow in the next event listings, we willl deal with them then\n\t\t\t\tif event[\"smartDetectEvents\"] != []:\n\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; skipping type:{}; smart:{}\".format(cameraId, newId, event[\"type\"], event[\"smartDetectEvents\"]))\n\t\t\t\t\tcontinue\n\n\n\t\t\t\t## new event?\n\t\t\t\tif newId not in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId] = {\"eventStart\":0, \"eventEnd\":0, \"ringTime\":0, \"eventType\":\"\", \"thumbnailLastCopyTime\": time.time() + 50, \"thumbnailCopied\": False, \"status\": \"\",\"rawEvent\":copy.deepcopy(event)}\n\t\t\t\t\tif dev == \"\":\n\t\t\t\t\t\tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\n\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; {}: new event; type:{}\".format(cameraId, newId, self.PROTECT[cameraId][\"devName\"], event[\"type\"]))\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventStart\"] \t\t\t= event[\"start\"]/1000.\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] \t\t\t= 0\n\n\t\t\t\t\tif self.copyProtectsnapshots == \"on\" or (self.copyProtectsnapshots == \"selectedByDevice\" and \"eventThumbnailOn\" in props and props[\"eventThumbnailOn\"] ):\n\t\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"thumbnailLastCopyTime\"] \t= time.time() + 15 # try to get thumbnail in the next 15 secs\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"thumbnailLastCopyTime\"] \t= time.time() # no thumbnails to be copied\n\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventType\"]\t\t\t\t= event[\"type\"]\n\n\t\t\t\t\tif event[\"type\"] == \"ring\": \n\t\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"ringTime\"] \t\t\t= event[\"start\"]/1000.\n\n\t\t\t\t\tupdateDev = True\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_with_Event\", self.PROTECT[cameraId][\"devName\"])\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_Event_Date\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\t\t\t\t\tcheckIds[newId] = cameraId\n\t\t\t\t\n\n\t\t\t\tif event[\"smartDetectEvents\"] !=[]:\n\t\t\t\t\tfor evID in event[\"smartDetectEvents\"]:\n\t\t\t\t\t\tif evID in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\t\t\tcheckIds[evID] = cameraId\n\t\t\t\t\t\t\tself.PROTECT[cameraId][\"events\"][evID][\"eventEnd\"] = time.time()\n\t\t\t\t\tif self.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] != 0: self.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] = time.time()\n\t\t\t\t\tcheckIds[newId] = cameraId\n\n\t\t\t\t# event ended, can be the same event as the start event ie rings?\n\t\t\t\tif self.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] < self.PROTECT[cameraId][\"events\"][newId][\"eventStart\"] and event[\"end\"] is not None:\n\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; event ended devid:{}\".format(cameraId, newId, self.PROTECT[cameraId][\"devId\"]))\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] = event[\"end\"]/1000.\n\t\t\t\t\tcheckIds[newId] = cameraId\n\n\t\t\t\t# other vent types?\n\t\t\t\tif event[\"type\"] == \"disconnected\":\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventStart\"] = event[\"start\"]/1000.\n\t\t\t\t\tself.PROTECT[cameraId][\"events\"][newId][\"eventEnd\"] = event[\"start\"]/1000.+1\n\t\t\t\t\tcheckIds[newId] = cameraId\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn checkIds\n\n\n\t####-----------------\t ---------\n\t####-----called to check if old events need to be expired / deleted ------\n\t####-----------------\t ---------\n\tdef checkIfEmptyEventCleanup(self, events):\n\t\ttry:\n\t\t\tif events != []: return False\n\t\t\tif time.time() - self.lastEvCheck < 10: return True\n\t\t\tself.lastEvCheck = time.time()\n\n\t\t\t# close old not updated status\n\t\t\tfor cameraId in self.PROTECT:\n\t\t\t\trmEvent ={}\n\t\t\t\tif self.PROTECT[cameraId][\"devId\"] < 1: continue\n\n\t\t\t\tfor evId in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\ttestEV = self.PROTECT[cameraId][\"events\"][evId]\n\t\t\t\t\tif testEV[\"eventStart\"] == 0: \t\t\t\tcontinue\n\t\t\t\t\tif time.time() - testEV[\"eventStart\"] < 40: \tcontinue # look only at older events \n\t\t\t\t\tif testEV[\"eventType\"] == \"ring\" and self.PROTECT[cameraId][\"events\"][evId][\"status\"] == \"ring\":\n\t\t\t\t\t\tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\t\t\t\t\t\tif dev.states[\"status\"] == \"ring\":\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: setting status to CONNECTED for expired ring event {}\".format(self.PROTECT[cameraId][\"devName\"], testEV[\"thumbnailCopied\"]) )\n\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"status\", \"CONNECTED\")\n\t\t\t\t\t\t\trmEvent[evId] = 1\n\n\t\t\t\t\telif testEV[\"eventEnd\"] == 0:\n\t\t\t\t\t\tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\t\t\t\t\t\tif dev.states[\"status\"] == testEV[\"status\"]:\n\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtDeProtEventstails\"): self.indiLOG.log(10,\"getProtectEvents: setting status to CONNECTED for expired not ended event {}, Thumbnailcopied:{}\".format(self.PROTECT[cameraId][\"devName\"], testEV[\"thumbnailCopied\"]) )\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"status\", \"CONNECTED\")\n\t\t\t\t\t\t\trmEvent[evId] = 1\n\n\t\t\t\t\tif time.time() - testEV[\"eventStart\"] > 60: # remove rest of events from list after 1 minutes\n\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: removing old {}- event:{}, Thumbnailcopied:{}\".format(self.PROTECT[cameraId][\"devName\"], evId, testEV[\"thumbnailCopied\"]) )\n\t\t\t\t\t\trmEvent[evId] = 1\n\n\t\t\t\tfor evId in rmEvent:\n\t\t\t\t\tdel self.PROTECT[cameraId][\"events\"][evId]\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn True\n\n\n\t####-----------------\t ---------\n\t####-----get thumbnails and update dev states ------\n\t####-----------------\t ---------\n\tdef goThroughNewEventDataGetThumbNailsAndUpdateIndigoDevicesAndVariables(self, checkIds):\n\t\ttry:\n\t\t\tif time.time() - self.lastThumbnailTime < 2 and checkIds == {}: return\n\t\t\tself.lastThumbnailTime = time.time() \n\t\t\tdebug = True\n\t\t\t# have to wait until end of event to get the thumbnail\n\t\t\tfor cameraId in self.PROTECT:\n\t\t\t\tfor evID in self.PROTECT[cameraId][\"events\"]:\n\t\t\t\t\t#if self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; evids check :{} eventEnd:{}; copied:{}\".format(cameraId, newId, evID, self.PROTECT[cameraId][\"events\"][evID][\"eventEnd\"], self.PROTECT[cameraId][\"events\"][evID][\"thumbnailLastCopyTime\"]))\n\t\t\t\t\tif self.PROTECT[cameraId][\"events\"][evID][\"eventEnd\"] >0 or time.time() - self.PROTECT[cameraId][\"events\"][evID][\"eventStart\"] > 1.5:\n\t\t\t\t\t\tif time.time() - self.PROTECT[cameraId][\"events\"][evID][\"thumbnailLastCopyTime\"] < 0:\n\t\t\t\t\t\t\tcheckIds[evID] = cameraId\n\n\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getProtectEvents: check :{}\".format(checkIds))\n\t\t\tfor evID in checkIds:\n\t\t\t\tcameraId \t= checkIds[evID]\n\t\t\t\tprotectEV \t= self.PROTECT[cameraId][\"events\"][evID]\n\t\t\t\tsmartDetect\t= \"\"\n\t\t\t\tdev \t\t= \"\"\n\t\t\t\teventJpeg\t= \"\"\n\t\t\t\tstatus\t\t= protectEV[\"eventType\"]\n\t\t\t\tif protectEV[\"rawEvent\"][\"smartDetectTypes\"] != []:\n\t\t\t\t\tsmartDetect = \",\".join(protectEV[\"rawEvent\"][\"smartDetectTypes\"]).strip(\",\")\n\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; smartDetect-{} --> {}\".format(cameraId, evID, protectEV[\"rawEvent\"][\"smartDetectTypes\"], smartDetect) )\n\t\t\t\t\tstatus = smartDetect\n\n\t\t\t\tif not protectEV[\"thumbnailCopied\"] and ( time.time()-protectEV[\"thumbnailLastCopyTime\"] < 0 and protectEV[\"rawEvent\"][\"thumbnail\"] is not None and (time.time() - protectEV[\"eventStart\"] > 5 or protectEV[\"eventEnd\"] >0)):\n\n\t\t\t\t\t### copy thumbnail to local indigo disk -----\n\t\t\t\t\tif self.PROTECT[cameraId][\"devId\"] > 0:\n\t\t\t\t\t\tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\teventJpeg \t\t= self.changedImagePath.rstrip(\"/\")+\"/\"+dev.name+\"_\"+status+\"_thumbnail.jpeg\"\n\t\t\t\t\t\tsnapshotJpeg \t= self.changedImagePath.rstrip(\"/\")+\"/\"+dev.name+\"_\"+status+\"_snapshot.jpeg\"\n\t\t\t\t\t\twh = props.get(\"thumbnailwh\")\n\t\t\t\t\t\ttheDict = {\"cameraDeviceSelected\":dev.id}\n\t\t\t\t\t\ttheDict[\"whofImage\"] = wh\n\t\t\t\t\t\ttheDict[\"fileNameOfImage\"] = snapshotJpeg\n\t\t\t\t\t\twh = wh.split(\"/\")\n\t\t\t\t\t\tparams = {\"accessKey\": \"\", \"h\": wh[1], \"w\": wh[0]}\n\n\n\t\t\t\t\t\tif \"eventThumbnailOn\" in props and props[\"eventThumbnailOn\"] and \"thumbnailwh\" in props:\n\t\t\t\t\t\t\t#self.lastUnifiCookieRequests = 0\n\t\t\t\t\t\t\tevNumber = protectEV[\"rawEvent\"][\"thumbnail\"]\n\t\t\t\t\t\t\t#evNumber = \"e-643ec0bc01cd8f03e4000d54\"\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: waiting for 5=5 secs for ev#:{}\".format(evNumber))\n\n\n\t\t\t\t\t\t\t#as thumbnail might take some time, do a snapshot first\n\t\t\t\t\t\t\tself.buttonSendCommandToProtectgetSnapshotCALLBACK(theDict)\n\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_Event_PathToThumbnail\", snapshotJpeg)\n\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_Event_DateOfThumbNail\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\") )\n\t\t\t\t\t\t\tdev.updateStateOnServer(\"eventJpeg\",snapshotJpeg)\n\n\t\t\t\t\t\t\t# added wait for thumbnail data to be ready, might be .. 10 secs , if first read not successful, wait 3 secs before next read, then retry every 1 secs\n\t\t\t\t\t\t\tdata = \"\"\n\t\t\t\t\t\t\tfor ii in range(20):\n\t\t\t\t\t\t\t\tif len(data) < 100: \n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): \n\t\t\t\t\t\t\t\t\t\tdeb1 = self.pluginPrefs[\"debugConnectionCMD\"]; self.pluginPrefs[\"debugConnectionCMD\"] = True\n\t\t\t\t\t\t\t\t\t\tdeb2 = self.pluginPrefs[\"debugConnectionRET\"]; self.pluginPrefs[\"debugConnectionRET\"] = True\n\t\t\t\t\t\t\t\t\t\tself.setDebugFromPrefs(self.pluginPrefs, writeToLog=False)\n\t\t\t\t\t\t\t\t\tdata = self.executeCMDOnController(dataSEND=params, pageString=\"api/thumbnails/{}\".format(evNumber), jsonAction=\"protect\", cmdType=\"get\", protect=True, raw=True, ignore40x=True)\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): \n\t\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"debugConnectionCMD\"] = deb1\n\t\t\t\t\t\t\t\t\t\tself.pluginPrefs[\"debugConnectionRET\"] = deb2\n\t\t\t\t\t\t\t\t\t\tself.setDebugFromPrefs(self.pluginPrefs, writeToLog=False)\n\t\t\t\t\t\t\t\t\tif len(data) > 100: break\n\t\t\t\t\t\t\t\t\tif ii < 1: self.sleep(5) # if not successsfull imedeately, wait 5 secs\n\t\t\t\t\t\t\t\t\tself.sleep(1)\n\n\n\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; getting thumbnail, datalen:{}; thumbnail: {}; devId:{}\".format(cameraId, evID, len(data), protectEV[\"rawEvent\"][\"thumbnail\"], self.PROTECT[cameraId][\"devId\"]))\n\t\t\t\t\t\t\tif len(data) > 0:\n\t\t\t\t\t\t\t\tf = self.openEncoding(eventJpeg,\"wb\")\n\t\t\t\t\t\t\t\tf.write(data)\n\t\t\t\t\t\t\t\tf.close()\n\t\t\t\t\t\t\t\tprotectEV[\"thumbnailLastCopyTime\"] = time.time()\n\t\t\t\t\t\t\t\tprotectEV[\"thumbnailCopied\"] = True\n\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_Event_PathToThumbnail\", eventJpeg)\n\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_Camera_Event_DateOfThumbNail\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\") )\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"eventJpeg\",eventJpeg)\n\n\t\t\t\t\t\t\t\t## only do heatmaps when thumbnail is enabled too\n\t\t\t\t\t\t\t\tif \"eventHeatmapOn\" in props and props[\"eventHeatmapOn\"]:\n\t\t\t\t\t\t\t\t\twh = props[\"heatmapwh\"].split(\"/\")\n\t\t\t\t\t\t\t\t\tparams = {\"accessKey\": \"\", \"h\": wh[1], \"w\": wh[0],}\n\t\t\t\t\t\t\t\t\tdata = self.executeCMDOnController(dataSEND=params, pageString=\"api/heatmaps/{}\".format(protectEV[\"rawEvent\"][\"heatmap\"]), jsonAction=\"protect\", cmdType=\"get\", protect=True, raw=True, ignore40x=True)\n\t\t\t\t\t\t\t\t\tif len(data) > 0:\n\t\t\t\t\t\t\t\t\t\tf = self.openEncoding(self.changedImagePath.rstrip(\"/\")+\"/\"+dev.name+\"_\"+status+\"_heatmap.jpeg\",\"wb\")\n\t\t\t\t\t\t\t\t\t\tf.write(data)\n\t\t\t\t\t\t\t\t\t\tf.close()\n\n\n\n\t\t\t\t\t\t\tif protectEV[\"eventEnd\"] == 0: protectEV[\"eventEnd\"] = time.time()\n\n\n\n\t\t\t\tstatus = protectEV[\"eventType\"]\n\t\t\t\tif smartDetect != \"\":\n\t\t\t\t\tstatus = smartDetect\n\n\t\t\t\tif True:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif dev == \"\":\n\t\t\t\t\t\t\tdev = indigo.devices[self.PROTECT[cameraId][\"devId\"]]\n\n\t\t\t\t\t\tif protectEV[\"eventStart\"] != 0: \n\t\t\t\t\t\t\tevStart = datetime.datetime.fromtimestamp(protectEV[\"eventStart\"]).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\t\t\tif dev.states[\"eventStart\"] != evStart:\n\t\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorTripped)\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventStart\", evStart)\n\t\t\t\t\t\t\t\tprotectEV[\"status\"] = status\n\t\t\t\t\t\t\t\trgStart = datetime.datetime.fromtimestamp(protectEV[\"ringTime\"]).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\t\t\t\tif protectEV[\"ringTime\"] != 0 and rgStart != dev.states[\"lastRing\"]:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"lastRing\", rgStart)\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; setting status to ring, \".format(cameraId, evID))\n\n\t\t\t\t\t\t\t\tif status != dev.states[\"status\"]: self.addToStatesUpdateList(dev.id, \"status\", status)\n\t\t\t\t\t\t\t\tprotectEV[\"status\"] = status\n\n\t\t\t\t\t\t\t\ttry: evN = int(dev.states[\"eventNumber\"])\n\t\t\t\t\t\t\t\texcept: evN = 0\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventNumber\", evN+1 )\n\n\t\t\t\t\t\n\t\t\t\t\t\tif protectEV[\"eventEnd\"] != 0 and status != \"ring\": \n\t\t\t\t\t\t\tevEnd = datetime.datetime.fromtimestamp(protectEV[\"eventEnd\"]).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\t\t\tif dev.states[\"eventEnd\"] != evEnd:\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"ProtEvents\"): self.indiLOG.log(10,\"getProtectEvents: camID:{}, evId:{}; setting status to CONNECT, \".format(cameraId, evID))\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"eventEnd\", evEnd)\n\t\t\t\t\t\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"status\", \"CONNECTED\")\n\t\t\t\t\t\t\t\tprotectEV[\"status\"] = \"CONNECTED\"\n\n\t\t\t\t\t\tdt = int(max(-1,protectEV[\"eventEnd\"] - protectEV[\"eventStart\"]))\n\t\t\t\t\t\tif dev.states[\"eventLength\"] != dt:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"eventLength\", dt )\n\n\t\t\t\t\t\tif eventJpeg != \"\" and eventJpeg != dev.states[\"eventJpeg\"]:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"eventJpeg\", eventJpeg )\n\n\t\t\t\t\t\tif protectEV[\"eventType\"] not in [\"\", dev.states[\"eventType\"]]:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"eventType\", protectEV[\"eventType\"] )\n\n\t\t\t\t\t\tif smartDetect != dev.states[\"smartDetect\"]:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"smartDetect\", smartDetect )\n\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \n\n\n\t####-----------------\t ---------\n\t####-----send commd parameters to cameras through protect ------\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectLcdMessageCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectLcdMessageCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectLcdMessageCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\tarea = \"lcdMessage\"\n\t\t\tvaluesDict[\"MSG\"] = \"\"\n\t\t\tpayload ={area:{}}\n\t\t\tif valuesDict[\"lcdMessage\"]\t\t\t!= \"do not change\": payload[area][\"text\"] \t\t= valuesDict[\"lcdMessage\"]\n\t\t\tdata = self.setupProtectcmd( valuesDict[\"cameraDeviceSelected\"], payload)\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\telse:\n\t\t\t\tfor xx in data[area]:\n\t\t\t\t\tif data[area][xx] != payload[area][xx]: \n\t\t\t\t\t\tok = False\n\t\t\t\t\t\tbreak\n\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\t\t\tself.addToMenuXML(valuesDict)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectLEDCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectLEDCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectLEDCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\tvaluesDict[\"MSG\"] = \"\"\n\t\t\tarea = \"ledSettings\"\n\t\t\tpayload ={area:{}}\n\t\t\tif valuesDict[\"blinkRate\"]\t\t\t!= \"-1\": payload[area][\"blinkRate\"] \t\t= int(valuesDict[\"blinkRate\"])\n\t\t\tif valuesDict[\"camLEDenabled\"]\t\t!= \"-1\": payload[area][\"isEnabled\"] \t\t= valuesDict[\"camLEDenabled\"] == \"1\"\n\t\t\tdata = self.setupProtectcmd( valuesDict[\"cameraDeviceSelected\"], payload)\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\telse:\n\t\t\t\tfor xx in data[area]:\n\t\t\t\t\tif data[area][xx] != payload[area][xx]: \n\t\t\t\t\t\tok = False\n\t\t\t\t\t\tbreak\n\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\t\t\tself.addToMenuXML(valuesDict)\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectenableSpeakerCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectenableSpeakerCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectenableSpeakerCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\tself.addToMenuXML(valuesDict)\n\t\ttry:\n\t\t\t\"\"\"\n\t\t\t\"speakerSettings\": {\n\t\t\t\t\"areSystemSoundsEnabled\": true, \n\t\t\t\t\"isEnabled\": true, \n\t\t\t\t\"volume\": 100\n\t\t\t}\n\t\t\t\"\"\"\n\t\t\tarea = \"speakerSettings\"\n\t\t\tpayload ={area:{}}\n\t\t\tif valuesDict[\"areSystemSoundsEnabled\"]\t!= \"-1\": payload[area][\"areSystemSoundsEnabled\"] \t= valuesDict[\"areSystemSoundsEnabled\"] == \"1\"\n\t\t\tif valuesDict[\"isEnabled\"] \t\t\t\t!= \"-1\": payload[area][\"isEnabled\"] \t\t\t\t= valuesDict[\"isEnabled\"] == \"1\"\n\t\t\tif valuesDict[\"volume\"] \t\t\t\t\t!= \"-1\": payload[area][\"volume\"] \t\t\t\t\t= int(valuesDict[\"volume\"])\n\t\t\tdata = self.setupProtectcmd( valuesDict[\"cameraDeviceSelected\"], payload)\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\telse:\n\t\t\t\tfor xx in payload[area]:\n\t\t\t\t\tif data[area][xx] != payload[area][xx]: \n\t\t\t\t\t\tok = False\n\t\t\t\t\t\tbreak\n\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectmicVolumeCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectmicVolumeCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectmicVolumeCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\tvaluesDict[\"MSG\"] = \"\"\n\t\t\tself.addToMenuXML(valuesDict)\n\t\t\tif valuesDict[\"micVolume\"] == \"-1\":\treturn valuesDict\n\t\t\tarea = \"micVolume\"\n\t\t\tpayload ={area:int(valuesDict[area])}\n\t\t\tdata = self.setupProtectcmd(valuesDict[\"cameraDeviceSelected\"],payload )\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectRecordCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectRecordCALLBACK(valuesDict= action1.props)\n\n\tdef buttonSendCommandToProtectRecordCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\t\"\"\"\n\t\t \"recordingSettings\": {\n\t\t\t\"enablePirTimelapse\": false, \n\t\t\t\"endMotionEventDelay\": 3000, \n\t\t\t\"geofencing\": \"off\", \n\t\t\t\"minMotionEventTrigger\": 2000, \n\t\t\t\"mode\": \"motion\", \t\t\t\tnever, motion, always, smartDetect\n\t\t\t\"motionAlgorithm\": \"stable\", \n\t\t\t\"postPaddingSecs\": 10, \n\t\t\t\"prePaddingSecs\": 5, \n\t\t\t\"suppressIlluminationSurge\": false, \n\t\t\t\"useNewMotionAlgorithm\": false\n\t\t }, \n\t\t\t {'suppressIlluminationSurge': False, 'postPaddingSecs': 10, 'geofencing': 'off', 'motionAlgorithm': 'stable', 'prePaddingSecs': 1, 'enablePirTimelapse': False, 'minMotionEventTrigger': 0, 'mode': 'motion', 'useNewMotionAlgorithm': False, 'endMotionEventDelay': 3000} \n\t\t\t\"\"\"\n\t\t\tarea = \"recordingSettings\"\n\t\t\tpayload ={area:{}}\n\t\t\tif valuesDict[\"prePaddingSecs\"] \t\t\t\t!= \"-1\":\tpayload[area][\"prePaddingSecs\"] \t\t\t= int(valuesDict[\"prePaddingSecs\"])\n\t\t\tif valuesDict[\"postPaddingSecs\"] \t\t\t\t!= \"-1\":\tpayload[area][\"postPaddingSecs\"] \t\t\t= int(valuesDict[\"postPaddingSecs\"])\n\t\t\tif valuesDict[\"minMotionEventTrigger\"] \t\t\t!= \"-1\":\tpayload[area][\"minMotionEventTrigger\"] \t\t= int(valuesDict[\"minMotionEventTrigger\"])\n\t\t\tif valuesDict[\"motionRecordEnabledProtect\"] \t!= \"-1\":\tpayload[area][\"mode\"] \t\t\t\t\t\t= valuesDict[\"motionRecordEnabledProtect\"]\n\t\t\tif valuesDict[\"suppressIlluminationSurge\"] \t\t!= \"-1\":\tpayload[area][\"suppressIlluminationSurge\"]\t= valuesDict[\"suppressIlluminationSurge\"]\n\t\t\tif valuesDict[\"useNewMotionAlgorithm\"] \t\t\t!= \"-1\":\tpayload[area][\"useNewMotionAlgorithm\"] \t\t= valuesDict[\"useNewMotionAlgorithm\"]\n\t\t\tif valuesDict[\"motionAlgorithm\"] \t\t\t\t!= \"-1\":\tpayload[area][\"motionAlgorithm\"] \t\t\t= valuesDict[\"motionAlgorithm\"]\n\t\t\tif valuesDict[\"endMotionEventDelay\"] \t\t\t!= \"-1\":\tpayload[area][\"endMotionEventDelay\"] \t\t= valuesDict[\"endMotionEventDelay\"]\n\t\t\tdata = self.setupProtectcmd( valuesDict[\"cameraDeviceSelected\"], payload)\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\telse:\n\t\t\t\tfor xx in payload[area]:\n\t\t\t\t\tif data[area][xx] != payload[area][xx]: \n\t\t\t\t\t\tok = False\n\t\t\t\t\t\tbreak\n\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.addToMenuXML(valuesDict)\n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectIRCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectIRCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectIRCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\t\"\"\"\n\t\t \"ispSettings\": {\n\t\t\t\"aeMode\": \"auto\", \n\t\t\t\"brightness\": 50, \n\t\t\t\"contrast\": 50, \n\t\t\t\"dZoomCenterX\": 50, \n\t\t\t\"dZoomCenterY\": 50, \n\t\t\t\"dZoomScale\": 0, \n\t\t\t\"dZoomStreamId\": 4, \n\t\t\t\"denoise\": 50, \n\t\t\t\"focusMode\": \"ztrig\", \n\t\t\t\"focusPosition\": 0, \n\t\t\t\"hue\": 50, \n\t\t\t\"icrSensitivity\": 0, \n\t\t\t\"irLedLevel\": 255, \n\t\t\t\"irLedMode\": \"auto\", autoFilterOnly, on off\n\t\t\t\"is3dnrEnabled\": true, \n\t\t\t\"isAggressiveAntiFlickerEnabled\": false, \n\t\t\t\"isAutoRotateEnabled\": false, \n\t\t\t\"isExternalIrEnabled\": false, \n\t\t\t\"isFlippedHorizontal\": false, \n\t\t\t\"isFlippedVertical\": false, \n\t\t\t\"isLdcEnabled\": true, \n\t\t\t\"isPauseMotionEnabled\": false, \n\t\t\t\"saturation\": 50, \n\t\t\t\"sharpness\": 50, \n\t\t\t\"touchFocusX\": 0, \n\t\t\t\"touchFocusY\": 0, \n\t\t\t\"wdr\": 1, \n\t\t\t\"zoomPosition\": 0\n\t\t }, \n\n\n\t09 11:37:20 setupProtectcmd {'ispSettings': {'icrSensitivity': 1, 'irLedMode': 'autoFilterOnly', 'irLedLevel': 100}} , devid:1965914261, name:Camera_Protect_Reserve-UVC G3 Flex_7483C23FD3E5; id:603fe05602f2a503e70003f4\n\t09 11:37:20 setupProtectcmd returned data: {'icrSensitivity': 1, 'sharpness': 50, 'isPauseMotionEnabled': False, 'isLdcEnabled': True, 'zoomPosition': 0, 'touchFocusX': 0, 'touchFocusY': 0, 'isAggressiveAntiFlickerEnabled': False, 'is3dnrEnabled': True, 'isExternalIrEnabled': False, 'denoise': 50, 'dZoomStreamId': 4, 'irLedLevel': 100, 'aeMode': 'auto', 'contrast': 50, 'dZoomScale': 0, 'hue': 50, 'saturation': 50, 'isFlippedHorizontal': False, 'focusPosition': 0, 'isAutoRotateEnabled': True, 'irLedMode': 'autoFilterOnly', 'focusMode': 'ztrig', 'isFlippedVertical': False, 'brightness': 50, 'wdr': 1, 'dZoomCenterX': 50, 'dZoomCenterY': 50} \n\t\t\t\"\"\"\n\n\t\t\tarea = \"ispSettings\"\n\t\t\tpayload ={area:{}}\n\t\t\tif valuesDict[\"irLedMode\"] \t\t\t!= \"-1\":\tpayload[area][\"irLedMode\"] \t\t\t= valuesDict[\"irLedMode\"]\n\t\t\tif valuesDict[\"icrSensitivity\"] \t!= \"-1\":\tpayload[area][\"icrSensitivity\"] \t= int(valuesDict[\"icrSensitivity\"])\n\t\t\tif valuesDict[\"irLedLevel\"] \t\t!= \"-1\":\tpayload[area][\"irLedLevel\"] \t\t= int(valuesDict[\"irLedLevel\"])\n\t\t\tdata = self.setupProtectcmd( valuesDict[\"cameraDeviceSelected\"], payload)\n\t\t\tok = True\n\t\t\tif area not in data: ok = False\n\t\t\telse:\n\t\t\t\tfor xx in payload[area]:\n\t\t\t\t\tif data[area][xx] != payload[area][xx]: \n\t\t\t\t\t\tok = False\n\t\t\t\t\t\tbreak\n\n\t\t\tvaluesDict[\"MSG\"] = \"ok\" if ok else \"error\"\n\t\t\tif self.decideMyLog(\"ProtDetails\"): self.indiLOG.log(10,\"setupProtectcmd returned data: {} \".format(data[area]))\n\n\t\t\tself.addToMenuXML(valuesDict)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n####-----------------\t ---------\n\tdef buttonrefreshProtectCameraSystemCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\tself.addToMenuXML(valuesDict)\n\t\tself.refreshProtectCameras = 0\n\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"get protect camera setup initiated \")\n\t\tvaluesDict[\"MSG\"] = \"request Send\" \n\t\treturn valuesDict\n\n\t####-----------------\t ---------\n\tdef buttonSendCommandToProtectgetSnapshotCALLBACKaction (self, action1=None):\n\t\treturn self.buttonSendCommandToProtectgetSnapshotCALLBACK(valuesDict= action1.props)\n\tdef buttonSendCommandToProtectgetSnapshotCALLBACK(self, valuesDict=None, typeId=\"\", devId=\"\",returnCmd=False):\n\t\ttry:\n\t\t\tcamId = valuesDict[\"cameraDeviceSelected\"]\n\t\t\twh = valuesDict[\"whofImage\"].split(\"/\")\n\t\t\tfName = valuesDict[\"fileNameOfImage\"] \n\t\t\tdev = indigo.devices[int(camId)]\n\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getSnapshot dev {}; vd:{} \".format(dev.name, valuesDict))\n\t\t\tvaluesDict[\"MSG\"] = \"error\"\n\t\t\tparams = {\n\t\t\t\t\t\"accessKey\": \"\",\n\t\t\t\t\t\"h\": wh[1],\n\t\t\t\t\t\"ts\": str(int(time.time())*1000),\n\t\t\t\t\t\"force\": \"true\",\n\t\t\t\t\t\"w\": wh[0],\n\t\t\t}\n\t\t\tif self.decideMyLog(\"ProtEvents\"): \n\t\t\t\tdeb1 = self.pluginPrefs[\"debugConnectionCMD\"]; self.pluginPrefs[\"debugConnectionCMD\"] = True\n\t\t\t\tdeb2 = self.pluginPrefs[\"debugConnectionRET\"]; self.pluginPrefs[\"debugConnectionRET\"] = True\n\t\t\t\tself.setDebugFromPrefs(self.pluginPrefs, writeToLog=False)\n\n\t\t\tdata = self.executeCMDOnController(dataSEND=params, pageString=\"api/cameras/{}/snapshot\".format(dev.states[\"id\"]), jsonAction=\"protect\", protect=True, cmdType=\"get\", raw=True)\n\n\t\t\tif self.decideMyLog(\"ProtEvents\"): \n\t\t\t\tself.pluginPrefs[\"debugConnectionCMD\"] = deb1\n\t\t\t\tself.pluginPrefs[\"debugConnectionRET\"] = deb2\n\t\t\t\tself.setDebugFromPrefs(self.pluginPrefs, writeToLog=False)\n\n\t\t\tself.addToMenuXML(valuesDict)\n\n\t\t\tif len(data) < 10:\n\t\t\t\tvaluesDict[\"MSG\"] = \"no data returned\"\n\t\t\t\tself.indiLOG.log(10,\"getSnapshot no data returned data length {} \".format(len(data)))\n\t\t\t\treturn valuesDict\n\n\t\t\tf = open(fName,\"wb\")\n\t\t\tf.write(data)\n\t\t\tf.close()\n\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"getSnapshot writing data to {}; length {} \".format(fName, len(data)))\n\t\t\tvaluesDict[\"MSG\"] = \"shapshot done\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\n\t####-----------------\t ---------\n\tdef setupProtectcmd(self, devId, payload, cmdType=\"patch\"):\n\n\t\tdev = indigo.devices[int(devId)]\n\t\ttry:\n\t\t\tif self.cameraSystem != \"protect\":\t\t\t\treturn \"error protect not enabled\"\n\t\t\tif self.decideMyLog(\"Protect\"): self.indiLOG.log(10,\"setupProtectcmd {} , devid:{}, name:{}; id:{}\".format(payload, dev.id, dev.name, dev.states[\"id\"]))\n\t\t\t\t\t\n\t\t\tdata = self.executeCMDOnController(dataSEND=payload, pageString=\"cameras/{}\".format(dev.states[\"id\"]), jsonAction=\"protect\", protect=True, cmdType=cmdType)\n\t\t\tself.lastRefreshProtect = time.time() - self.refreshProtectCameras +1\n\t\t\treturn data\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\t####----------------- print\t ---------\n\tdef buttonConfirmPrintProtectDeviceInfoCALLBACK(self, valuesDict=None, typeId=\"\"):\n\t\ttry:\n\t\t\tvaluesDict[\"MSG\"] = \"\"\n\t\t\tself.lastRefreshProtect = 0\n\t\t\tself.getProtectIntoIndigo()\n\t\t\t# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16\n\t\t\t# 1234567891123456789212345678931234567894123456789512345678961234567897123456789812345678991234567890123456789012345678901234567890123456789012345678901234567890\n\t\t\tout =\"Protect Camera devices START ============================================================================================================================= \\n\"\n\t\t\tout +=\" ThumbNail HeatMap Device Events----------------------------------- is Volume- ir-LED----------------- stat \\n\"\n\t\t\tout +=\"DevName---------------------- MAC#------------- ip#----------- DevType--- FWV----- On-resolutn On-resolutn lastSeen----- last-motion-- lastRing----- ---#of Mode dark mic spk En Sens Mode Lvl LED \\n\"\n\t\t\tmapTFtoenDis \t= {\"\":\"?\", True:\"ena\", False:\"dis\"}\n\t\t\tmapTFtoNight \t= {\"\":\"?\", True:\"Nite\", False:\"Day\"}\n\t\t\tmapirMode \t\t= {\"\":\"?\", \"auto\":\"auto\", \"on\":\"on\", \"off\":\"off\", \"autoFilterOnly\":\"a-Filt-Onl\"}\n\t\t\tfor dev in indigo.devices.iter(\"props.isProtectCamera\"):\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tcameraId = dev.states[\"id\"] \n\t\t\t\tout+= \"{:30s}\".format(dev.name[:30])\n\t\t\t\tout+= \"{:18s}\".format(dev.states[\"MAC\"])\n\t\t\t\tout+= \"{:15s}\".format(dev.states[\"ip\"])\n\t\t\t\tout+= \"{:11s}\".format(dev.states[\"type\"][4:])\n\t\t\t\tout+= \"{:9s}\".format(dev.states[\"firmwareVersion\"])\n\t\t\t\tout+= \"{:3s}\".format(mapTFtoenDis[props[\"eventThumbnailOn\"]])\n\t\t\t\tout+= \"-{:10s}\".format(props[\"thumbnailwh\"])\n\t\t\t\tout+= \"{:3s}\".format(mapTFtoenDis[props[\"eventHeatmapOn\"]])\n\t\t\t\tout+= \"-{:10s}\".format(props[\"heatmapwh\"])\n\t\t\t\tout+= \"{:14s}\".format(dev.states[\"lastSeen\"][6:])\n\t\t\t\tout+= \"{:14s}\".format(dev.states[\"eventStart\"][6:])\n\t\t\t\tout+= \"{:13s}\".format(dev.states[\"lastRing\"][6:])\n\t\t\t\tout+= \"{:7d} \".format(dev.states[\"eventNumber\"])\n\t\t\t\tout+= \"{:11s}\".format(dev.states[\"motionRecordingMode\"])\n\t\t\t\tout+= \"{:4s}\".format(mapTFtoNight[dev.states[\"isDark\"]])\n\t\t\t\tout+= \"{:4d}\".format(dev.states[\"micVolume\"])\n\t\t\t\tout+= \"{:4d}\".format(dev.states[\"speakerVolume\"])\n\t\t\t\tout+= \" {:4s}\".format(mapTFtoenDis[dev.states[\"isExternalIrEnabled\"]])\n\t\t\t\tout+= \"{:5}\".format(dev.states[\"icrSensitivity\"])\n\t\t\t\tout+= \"{:11s}\".format(mapirMode[dev.states[\"irLedMode\"]])\n\t\t\t\tout+= \"{:3d}\".format(dev.states[\"irLedLevel\"])\n\t\t\t\tout+= \" {:3}\".format(mapTFtoenDis[dev.states[\"isLedEnabled\"]])\n\t\t\t\tout +=\" \\n\"\n\t\t\tout +=\" ============================================================================================================================= \\n\" \n\t\t\tout +=\" \\n\"\n\t\t\tout+= \"================= INSTALL HELP ===================================== \\n\"\n\t\t\tout+= \"To setup: select the querry every time parametes etc \\n\"\n\t\t\tout+= \"Currently the protect system must be on the same hardware as the controller eg cloudkey 2, UMDpro. \\n\"\n\t\t\tout+= \"Once started the plugin will query(http) protect and will get all cameras installed and create the appropritate indigo devices \\n\"\n\t\t\tout+= \"It then will query(http) the protect system for new events every x secs \\n\"\n\t\t\tout+= \"The events can be of type Motion/Person/Vehicle/Ring. One physical ring can create several events \\n\"\n\t\t\tout+= \"eg motion+person+ring in differnt sequences depending on how a person approaches the doorbell \\n\"\n\t\t\tout+= \"Then for each event the variables \\n\"\n\t\t\tout+= \"- Unifi_Camera_with_Event \\n\"\n\t\t\tout+= \"- Unifi_Camera_Event_PathToThumbnail \\n\"\n\t\t\tout+= \"- Unifi_Camera_Event_DateOfThumbNail \\n\"\n\t\t\tout+= \"- Unifi_Camera_Event_Date \\n\"\n\t\t\tout+= \"are updated as they come in. The event date is the first variable to be updated, some of the other images can take several seconds to be produced. \\n\"\n\t\t\tout+= \"You can trigger on any of these variables or on the device states: lastRing or eventStart. eventEnd is set when the event is over. In most cases the thumbnail should be ready. \\n\"\n\t\t\tout+= \" \\n\"\n\t\t\tout+= \"In menu / CAMERA - protect Info ... \\n\"\n\t\t\tout+= \"you can print camera info to the logfile and get a snap shot and set several parameters on the caameras \\n\"\n\t\t\tout+= \"in actions you can setup most of the config as as well as get snapshots \\n\"\n\t\t\tout +=\" \\n\"\n\t\t\tout +=\" uniFiAP Protect Camera devices END ============================================================================================================================= \\n\" \n\n\t\t\tself.indiLOG.log(20,out)\n\t\t\tvaluesDict[\"MSG\"] = \"printed\"\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn valuesDict\n\n\t###########################################\n\t####------ camera PROTEC ---\t-------END\n\t###########################################\n\n\n\t####-----------------\t ---------\n\tdef doVDmessages(self, lines, ipNumber,apN ):\n\n\t\tself.setBlockAccess(\"doVDmessages\")\n\n\t\tdateUTC = datetime.datetime.utcnow().strftime(\"%Y%m%d\")\n\t\tuType = \"VDtail\"\n\n\t\ttry:\n\t\t\tfor line in lines:\n\t\t\t\tif len(line) < 10: continue\n\t\t\t\t## this is an event tring:\n\t\t\t\t# logversion 1:\n\t\t\t\t###1524837857.747 2018-04-27 09:04:17.747/CDT: INFO Camera[F09FC2C1967B] type:start event:105 clock:58199223 (UVC G3 Micro) in ApplicationEvtBus-15\n\t\t\t\t###1524837862.647 2018-04-27 09:04:22.647/CDT: INFO Camera[F09FC2C1967B] type:stop event:105 clock:58204145 (UVC G3 Micro) in ApplicationEvtBus-18\n\t\t\t\t## new format logVersion 2:\n\t\t\t\t#1561518324.741 2019-06-25 22:05:24.741/CDT: INFO [uv.analytics.motion] [AnalyticsService] [FCECDA1F1532|LivingRoom-Window-Flex] MotionEvent type:start event:1049 clock:111842854 in AnalyticsEvtBus-0\n\n\t\t\t\titemsRaw = (line.strip()).split(\" INFO \")\n\t\t\t\tif len(itemsRaw) < 2:\n\t\t\t\t\tcontinue\n\n\n\t\t\t\ttry: timeSt= float(itemsRaw[0].split()[0])\n\t\t\t\texcept:\n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- bad float\")\n\t\t\t\t\tcontinue\n\n\t\t\t\titems= itemsRaw[1].strip().split()\n\t\t\t\tif len(items) < 5:\n\t\t\t\t\tself.indiLOG.log(10,\"MS-VD---- less than 5 items, line: \"+line)\n\t\t\t\t\tcontinue\n\n\t\t\t\tlogVersion = 0\n\t\t\t\tif items[0].find(\"Camera[\") >-1: \t\t\tlogVersion = 1\n\t\t\t\telif itemsRaw[1].find(\"MotionEvent\") >-1:\tlogVersion = 2\n\t\t\t\telse:\n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- no Camera, line: {}\".format(line) )\n\t\t\t\t\tcontinue\n\n\t\t\t\tif logVersion == 1:\n\t\t\t\t\t#Camera[F09FC2C1967B]\n\t\t\t\t\tc = items[0].split(\"[\")[1].strip(\"]\").lower()\n\t\t\t\t\t# clock:58199223 (UVC G3 Micro) in \n\t\t\t\t\tcameraName\t = \" \".join(items[4:]).split(\"(\")[1].split(\")\")[0].strip()\n\t\t\t\tif logVersion == 2:\n\t\t\t\t\t# [mac|name]\n\t\t\t\t\t# [FCECDA1F1532|LivingRoom-Window-Flex] \n\t\t\t\t\txx = items[2].split(\"|\")\n\t\t\t\t\tcameraName = xx[1].strip(\"]\")\n\t\t\t\t\tc = xx[0].strip(\"[\").lower()\n\n\t\t\t\tif len(c) < 12:\n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- bad data, line: {}\".format(line) )\n\t\t\t\t\tcontinue\n\n\t\t\t\tMAC = c[0:2]+\":\"+c[2:4]+\":\"+c[4:6]+\":\"+c[6:8]+\":\"+c[8:10]+\":\"+c[10:12]\n\n\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"doVDmsg\"): continue\n\n\t\t\t\tevType = itemsRaw[1].split(\"type:\")\n\t\t\t\tif len(evType) !=2: \n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- no type, line: {}\".format(line) )\n\t\t\t\t\tcontinue\n\t\t\t\tevType = evType[1].split()[0]\n\n\t\t\t\tif evType not in [\"start\",\"stop\"]:\n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- bad eventType {}\".format(evType) )\n\t\t\t\t\tcontinue\n\n\n\t\t\t\tevent = itemsRaw[1].split(\"event:\")\n\t\t\t\tif len(event) !=2: \n\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- no event, line: {}\".format(line) )\n\t\t\t\t\tcontinue\n\t\t\t\tevNo = int(event[1].split()[0])\n\n\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- parsed items: #{:5d} {} {:13.1f} {} {}\".format(evNo, evType, timeSt, MAC, cameraName) )\n\n\n\t\t\t\tif MAC not in self.cameras:\n\t\t\t\t\tself.cameras[MAC] = {\"cameraName\":cameraName,\"events\":{},\"eventsLast\":{\"start\":0,\"stop\":0},\"devid\":-1,\"uuid\":\"\", \"ip\":\"\", \"apiKey\":\"\"}\n\n\t\t\t\tif evNo not in\tself.cameras[MAC][\"events\"]:\n\t\t\t\t\tself.cameras[MAC][\"events\"][evNo] = {\"start\":0,\"stop\":0}\n\n\n\t\t\t\tif len(self.cameras[MAC][\"events\"]) > self.unifiVIDEONumerOfEvents:\n\t\t\t\t\tdelEvents={}\n\t\t\t\t\tfor ev in self.cameras[MAC][\"events\"]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tif int(evNo) - int(ev) > self.unifiVIDEONumerOfEvents:\n\t\t\t\t\t\t\t\tdelEvents[ev]=True\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tself.indiLOG.log(40,\"doVDmessages error in ev# {};\t evNo {};\t maxNumberOfEvents: {}\\n to fix: try to rest event count \".format(ev, evNo, self.unifiVIDEONumerOfEvents) )\n\n\n\n\t\t\t\t\tif len(delEvents) >0:\n\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- {} number of events > {}; deleting {} events\".format(cameraName, self.unifiVIDEONumerOfEvents, len(delEvents)) )\n\t\t\t\t\t\tfor ev in delEvents:\n\t\t\t\t\t\t\tdel\t self.cameras[MAC][\"events\"][ev]\n\n\t\t\t\tself.cameras[MAC][\"events\"][evNo][evType] = timeSt\n\n\n\t\t\t\tdevFound = False\n\t\t\t\tif \"devid\" in self.cameras[MAC]:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdev = indigo.devices[self.cameras[MAC][\"devid\"]]\n\t\t\t\t\t\tdevFound = True\n\t\t\t\t\texcept: pass\n\t\t\t\tif\tnot devFound:\n\t\t\t\t\tfor dev in indigo.devices.iter(\"props.isCamera\"):\n\t\t\t\t\t\tif \"MAC\" not in dev.states:\t continue\n\t\t\t\t\t\tif dev.states[\"MAC\"] == MAC:\n\t\t\t\t\t\t\tdevFound = True\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\tif not devFound:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\tname \t\t\t= \"Camera_\"+cameraName+\"_\"+MAC ,\n\t\t\t\t\t\t\tdescription\t\t=\"\",\n\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\tdeviceTypeId\t=\"camera\",\n\t\t\t\t\t\t\tprops\t\t\t={\"isCamera\":True},\n\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\tdev.updateStateOnServer(\"MAC\", MAC)\n\t\t\t\t\t\tdev.updateStateOnServer(\"eventNumber\", -1)\n\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\tprops[\"isCamera\"] = True\n\t\t\t\t\t\tdev.replacePluginPropsOnServer()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.saveCameraEventsStatus = True\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tif \"NameNotUniqueError\" in \"{}\".format(e):\n\t\t\t\t\t\t\t\tdev = indigo.devices[\"Camera_\"+cameraName+\"_\"+MAC]\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"states {}\".format(dev.states))\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"MAC\", MAC)\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(\"eventNumber\", -1)\n\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\t\tprops[\"isCamera\"] = True\n\t\t\t\t\t\t\t\tdev.replacePluginPropsOnServer()\n\t\t\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}\".format(dev.name, MAC) )\n\t\t\t\t\tself.pendingCommand.append(\"getConfigFromNVR\")\n\n\t\t\t\tself.cameras[MAC][\"devid\"] = dev.id\n\n\t\t\t\tif dev.states[\"eventNumber\"] > evNo or ( self.cameras[MAC][\"events\"][evNo][evType] <= self.cameras[MAC][\"eventsLast\"][evType]) :\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif time.time() - self.listenStart[ipNumber][uType] > 30:\n\t\t\t\t\t\t\tself.indiLOG.log(10,\"MS-VD---- \"+\"rejected event number {}\".format(evNo)+\" resetting event No ; time after listener lauch: %5.1f\"%(time.time() - self.listenStart[ipNumber][uType]))\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventNumber\", evNo)\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tself.indiLOG.log(40,\"rejected event dump \"+ipNumber+\" {}\".format(self.listenStart))\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventNumber\", evNo)\n\n\n\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD---- \"+\"event # {}\".format(evNo)+\" accepted ; delta T from listener lauch: %5.1f\"%(time.time() - self.listenStart[ipNumber][uType]))\n\t\t\t\tdateStr = time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime(timeSt))\n\t\t\t\tif evType == \"start\":\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"lastEventStart\", dateStr )\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"status\", \"REC\")\n\t\t\t\t\tif self.imageSourceForEvent == \"imageFromNVR\":\n\t\t\t\t\t\tif dev.states[\"eventJpeg\"] != self.changedImagePath+dev.name+\".jpg\": # update only if new\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventJpeg\",self.changedImagePath+dev.name+\"_event.jpg\")\n\t\t\t\t\t\tself.getSnapshotfromNVR(dev.id, self.cameraEventWidth, self.changedImagePath+dev.name+\"_event.jpg\")\n\t\t\t\t\tif self.imageSourceForEvent == \"imageFromCamera\":\n\t\t\t\t\t\tif dev.states[\"eventJpeg\"] != self.changedImagePath+dev.name+\".jpg\": # update only if new\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventJpeg\",self.changedImagePath+dev.name+\"_event.jpg\")\n\t\t\t\t\t\tself.getSnapshotfromCamera(dev.id, self.changedImagePath+dev.name+\"_event.jpg\")\n\n\t\t\t\telif evType == \"stop\":\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"lastEventStop\", dateStr )\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"status\", \"off\" )\n\t\t\t\t\tevLength = float(self.cameras[MAC][\"events\"][evNo][\"stop\"]) - float(self.cameras[MAC][\"events\"][evNo][\"start\"])\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"lastEventLength\", int(evLength))\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif self.imageSourceForEvent == \"imageFromDirectory\":\n\t\t\t\t\t\t\tif dev.states[\"uuid\"] !=\"\":\n\t\t\t\t\t\t\t\tyear = dateUTC[0:4]\n\t\t\t\t\t\t\t\tmm\t = dateUTC[4:6]\n\t\t\t\t\t\t\t\tdd\t = dateUTC[6:8]\n\n\t\t\t\t\t\t\t\tfromDir\t = self.videoPath+dev.states[\"uuid\"]+\"/\"+year+\"/\"+mm+\"/\"+dd+\"/meta/\"\n\t\t\t\t\t\t\t\ttoDir\t = self.changedImagePath\n\t\t\t\t\t\t\t\tlast\t = 0.\n\t\t\t\t\t\t\t\tnewestFile = \"\"\n\t\t\t\t\t\t\t\tfilesInDir = \"\"\n\n\t\t\t\t\t\t\t\tif not os.path.isdir(fromDir):\n\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(self.videoPath+dev.states[\"uuid\"]):\t\t\t\t\t\tos.mkdir(self.videoPath+dev.states[\"uuid\"])\n\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year):\t\t\t\tos.mkdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year)\n\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year+\"/\"+mm):\t\tos.mkdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year+\"/\"+mm)\n\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year+\"/\"+mm+\"/\"+dd): os.mkdir(self.videoPath+dev.states[\"uuid\"]+\"/\"+year+\"/\"+mm+\"/\"+dd)\n\t\t\t\t\t\t\t\t\t\tif not os.path.isdir(fromDir):\t\t\t\t\t\t\t\t\t\t\t\t\tos.mkdir(fromDir)\n\n\t\t\t\t\t\t\t\tfor testFile in os.listdir(fromDir):\n\t\t\t\t\t\t\t\t\tif testFile.find(\".jpg\") == -1: continue\n\t\t\t\t\t\t\t\t\ttimeStampOfFile = os.path.getmtime(os.path.join(fromDir, testFile))\n\t\t\t\t\t\t\t\t\tif\ttimeStampOfFile > last:\n\t\t\t\t\t\t\t\t\t\tlast = timeStampOfFile\n\t\t\t\t\t\t\t\t\t\tnewestFile = testFile\n\t\t\t\t\t\t\t\tif newestFile ==\"\":\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD-EV- {} no file found\".format(dev.name))\n\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\tif dev.states[\"eventJpeg\"] != fromDir+newestFile: # update only if new\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventJpeg\",fromDir+newestFile)\n\t\t\t\t\t\t\t\t\tif os.path.isdir(toDir): # copy to destination directory\n\t\t\t\t\t\t\t\t\t\tif os.path.isfile(fromDir+newestFile):\n\t\t\t\t\t\t\t\t\t\t\tcmd = \"cp '\"+fromDir+newestFile+\"' '\"+toDir+dev.name+\"_event.jpg' &\"\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD-EV- copy event file: {}\".format(cmd))\n\t\t\t\t\t\t\t\t\t\t\tsubprocess.Popen(cmd,shell=True)\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Video\"): self.indiLOG.log(10,\"MS-VD-EV- \"+\"path \"+ self.changedImagePath+\" does not exist.. no event files copied\")\n\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\t\tself.cameras[MAC][\"eventsLast\"] = copy.copy(self.cameras[MAC][\"events\"][evNo])\n\t\t\t\tself.addToStatesUpdateList(dev.id,\"eventNumber\", int(evNo) )\n\t\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doVDmessages\")\n\t\t\n\t\treturn\n\n\n\n\t####-----------------\t ---------\n\tdef doGWmessages(self, lines,ipNumber,apN):\n\t\ttry:\n\t\t\tdevType\t = \"UniFi\"\n\t\t\tisType\t = \"isUniFi\"\n\t\t\tdevName\t = \"UniFi\"\n\t\t\tsuffixN\t = \"DHCP\"\n\t\t\txType\t = \"UN\"\n\n\t\t\tself.setBlockAccess(\"doGWmessages\")\n\n# looking for dhcp refresh requests\n# Oct 26 22:20:00 GW sudo:\t\troot : TTY=unknown ; PWD=/ ; USER=root ; COMMAND=/bin/sh -c echo -e '192.168.1.180\\t iPhone.localdomain\\t #on-dhcp-event 18:65:90:6a:b9:c' >> /etc/hosts\n\n\t\t\ttag = \"TTY=unknown ; PWD=/ ; USER=root ; COMMAND=/bin/sh -c echo -e '\"\n\t\t\tfor line in lines:\n\t\t\t\tif len(line) < 10: continue\n\t\t\t\tif line.find(tag) ==-1: continue\n\t\t\t\tif self.decideMyLog(\"LogDetails\"): self.indiLOG.log(10,\"MS-GW--- \"+line )\n\t\t\t\titems\t= line .split(tag)\n\t\t\t\tif len(items) !=2: continue\n\t\t\t\titems\t= items[1].split(\"' >> /etc/hosts\")\n\t\t\t\tif len(items) != 2: continue\n\t\t\t\titems\t= items[0].split(\"\\\\t\")\n\t\t\t\tif len(items) != 3: continue\n\t\t\t\tip\t\t= items[0]\n\t\t\t\tname\t= items[1]\n\t\t\t\titems\t= items[2].split()\n\t\t\t\tif len(items) != 2: continue\n\n\t\t\t\tMAC = self.checkMAC(items[1])# fix a bug in hosts file\n\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"GW-msg\"): continue\n\n\t\t\t\tnew = True\n\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\tif dev.deviceTypeId != devType: 1/0\n\t\t\t\t\t\tnew = False\n\t\t\t\t\texcept:\n\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,MAC + \" {}\".format(self.MAC2INDIGO[xType][MAC][\"devId\"]) + \" wrong \" + devType)\n\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC]={}\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tdel self.MAC2INDIGO[xType][MAC]\n\t\t\t\tif not new:\n\t\t\t\t\tprops=dev.pluginProps\n\t\t\t\t\tnew = False\n\t\t\t\t\tif dev.states[\"ipNumber\"] != ip:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\t\t\t\t\t## if a device asks for dhcp extension, it must be alive, good for everyone..\n\t\t\t\t\tif True: # Always true, if active request to renew DHCP, must be present \"useWhatForStatus\" in props and props[\"useWhatForStatus\"].find(\"DHCP\") >-1:\n\t\t\t\t\t\tif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus= dev.states[\"status\"],ts=time.time(), level=1, text1= dev.name.ljust(30) +\" status up GW-DHCP renew request\", iType=\"STATUS-DHCP\",reason=\"MS-DHCP \"+\"up\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-GW-DHCP {} restarting expTimer due to DHCP renew request from device\".format(MAC) )\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\t\t\t\t\t#break\n\n\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\tname\t\t\t=devName+\"_\" + MAC,\n\t\t\t\t\t\t\tdescription\t\t=self.fixIP(ip),\n\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"DHCP\",\"useAgeforStatusDHCP\":\"-1\",isType:True})\n\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, \"UN\", \"\", \"\", \"\", \" status up GW msg new device\", \"STATUS-DHCP\")\n\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}/{}/{}\".format(dev.name, MAC, ip) )\n\n\t\t\tself.executeUpdateStatesList()\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.executeUpdateStatesList()\n\n\t\tself.unsetBlockAccess(\"doGWmessages\")\n\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef doSWmessages(self, lines, ipNumber,apN ):\n\t\treturn\n\n\t\tself.setBlockAccess(\"doSWmessages\")\n\n\t\ttry:\n\t\t\tfor line in lines:\n\t\t\t\tif len(line) < 2: continue\n\t\t\t\tif self.decideMyLog(\"Log\"): self.indiLOG.log(10,\"MS-SW--- \"+ipNumber+\" \" + line)\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doSWmessages\")\n\t\t\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef doAPmessages(self, lines, ipNumberAP, apN, webApiLog=False):\n\n\t\ttry:\n\t\t\tself.setBlockAccess(\"doAPmessages\")\n\n\t\t\tdevType = \"UniFi\"\n\t\t\tisType\t= \"isUniFi\"\n\t\t\tdevName = \"UniFi\"\n\t\t\tsuffixN\t = \"WiFi\"\n\t\t\txType\t= \"UN\"\n\n\n\t\t\tfor line in lines:\n\t\t\t\tMAC = \"\"\n\t\t\t\tGHz = \"\"\n\t\t\t\tup = False\n\t\t\t\ttoken = \"\"\n\t\t\t\tif webApiLog: # message from UDM re-packaged as AP message\n\t\t\t\t\tMAC = line[\"user\"]\n\t\t\t\t\tup = True\n\t\t\t\t\ttoken = \"steady\"\n\t\t\t\t\tif line[\"key\"].lower().find(\"disconnected\") >-1:\n\t\t\t\t\t\ttoken = \"DISCONNECTED\"\n\t\t\t\t\t\tup = False\n\t\t\t\t\tif line[\"key\"].lower().find(\"disassociated\") >-1:\n\t\t\t\t\t\ttoken = \"DISCONNECTED\"\n\t\t\t\t\t\tup = False\n\n\t\t\t\t\t#### roaming:::::\n\t\t\t\t\telif line[\"key\"].lower().find(\"roam\") >-1:\n\t\t\t\t\t\tif \"IP_to\" in line and \"IP_from\" in line:\n\t\t\t\t\t\t\tif line[\"IP_to\"] !=\"\" and line[\"IP_from\"] !=\"\":\n\t\t\t\t\t\t\t\tself.HANDOVER[MAC] = {\"tt\":line[\"time\"],\"ipNumberNew\": line[\"IP_to\"], \"ipNumberOld\": line[\"IP_from\"]}\n\t\t\t\t\t\t\t\ttoken = \"roam\"\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"UDM\", MAC=MAC):self.indiLOG.log(10,\"MS-AP-WB-E {} roam data wrong (IP_from/to empty) event:{}; \".format(MAC, line))\n\t\t\t\t\t\telif \"channel_from\" in line or \"channel_to\" in line: # this is just change of channel, no real roaming to other AP\n\t\t\t\t\t\t\tpass \n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif self.decideMyLog(\"UDM\", MAC=MAC):self.indiLOG.log(10,\"MS-AP-WB-E {} roam data wrong (IP_from/to missing) event:{}; \".format(MAC, line))\n\n\t\t\t\t\telse:\n\t\t\t\t\t\tpass\n\n\t\t\t\t\tGHz = \"2\"\n\t\t\t\t\tif \"channel\" in line and int(line[\"channel\"]) >= 12:\tGHz = \"5\"\n\t\t\t\t\tif \"channel_to\" in line and int(line[\"channel_to\"]) >= 12:\tGHz = \"5\"\n\t\t\t\t\ttimeOfMSG = line[\"time\"]\n\t\t\t\t\tif self.decideMyLog(\"UDM\", MAC=MAC):self.indiLOG.log(10,\"MS-AP-WB-0 {}; ipNumberAP:{}; GHz:{}; up:{} token:{}, dTime:{:.1f}; api-event:{}; \".format( MAC, ipNumberAP, GHz, up, token, time.time()-timeOfMSG, line))\n\n\t\t\t\telse: # regular ap message\n\t\t\t\t\tif len(line) < 2: continue\n\t\t\t\t\ttags = line.split()\n\t\t\t\t\tMAC = \"\"\n\n\t\t\t\t\tll = line.find(\"[HANDOVER]\") + 10 +1 ## len of [HANDOVER] + one space\n\t\t\t\t\tif ll > 30:\n\t\t\t\t\t\tif ll+17 >= len(line):\t \t\t\t\t\t continue # 17 = len of MAC address\n\t\t\t\t\t\tlin2 = line.split(\"[HANDOVER]\")[1]\n\t\t\t\t\t\ttags = lin2.split()\n\t\t\t\t\t\tif len(tags) !=5: \t\t\t\t\t\t\t\t continue\n\t\t\t\t\t\tMAC = tags[0]\n\t\t\t\t\t\tif not self.isValidMAC(MAC):\t\t\t\t\t continue\n\t\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"AP-msg\"): continue\n\n\t\t\t\t\t\tipNumber = tags[4]\t# new IP number of target AP\n\t\t\t\t\t\tself.HANDOVER[MAC] = {\"tt\":time.time(),\"ipNumberNew\":ipNumber, \"ipNumberOld\":tags[2]}\n\t\t\t\t\t\t\t### handle this: [HANDOVER]\n\t\t\t\t\t\t\t#13:40:42 AP----\t -192.168.1.4\tApr 16 13:40:41 4-kons daemon.notice hostapd: ath0: IEEE 802.11 UBNT-ROAM.get-sta-data for 18:65:90:6a:b9:0c\n\t\t\t\t\t\t\t#13:40:42 AP----\t -192.168.1.4\tApr 16 13:40:41 4-kons user.info kernel: [92232.074000] ubnt_roam [BASIC]:[HANDOVER] 18:65:90:6a:b9:0c from 192.168.1.4 to 192.168.1.5\n\t\t\t\t\t\t\t#13:40:42 AP----\t -192.168.1.4\tApr 16 13:40:41 4-kons daemon.notice hostapd: ath0: IEEE 802.11 UBNT-ROAM.sta-leave: 18:65:90:6a:b9:0c\n\t\t\t\t\t\t\t#13:40:42 AP----\t -192.168.1.4\tApr 16 13:40:41 4-kons daemon.info hostapd: ath0: STA 18:65:90:6a:b9:0c IEEE 802.11: disassociated\n\t\t\t\t\t\t\t#13:40:42 MS-AP-WiFi -\tAP message received 18:65:90:6a:b9:0c UniFi-iphone7-karl;\told/new associated 192.168.1.4/192.168.1.4\n\t\t\t\t\t\t\t#13:40:42 MS-AP-WiFi - 18:65:90:6a:b9:0c UniFi-iphone7-karl\t\t\t\tcheck timer, down token: disassociated time.time() -upt 1492368042.1\n\n\t\t\t\t\t###\t add test for :\n\t\t\t\t\t# 13:22:58 AP----\t -192.168.1.4\t Apr 15 13:22:57 4-kons user.info kernel: [ 4766.438000] ubnt_roam [BASIC]:Presence at AP 192.168.1.5 verified for 18:65:90:6a:b9:0c\n\t\t\t\t\telif line.find(\"Presence at AP\") > -1 and line.find(\"verified for\") > -1:\n\t\t\t\t\t\tMAC = tags[-1]\n\t\t\t\t\t\tif not self.isValidMAC(MAC):\t\t\t\t\t continue\n\t\t\t\t\t\tipNumberAP = tags[-4]\n\n\t\t\t\t\telif line.find(\"EVENT_STA_JOIN \") > -1 and line.find(\"verified for\") > -1:\n\t\t\t\t\t\t\tipNumberAP = tags[-4]\n\n\t\t\t\t\telse:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tll = tags.index(\"STA\")\n\t\t\t\t\t\t\tif ll+1 >=\tlen(tags):\t\t\t\t \t\tcontinue\n\t\t\t\t\t\t\tMAC = tags[ll + 1]\n\t\t\t\t\t\t\tif not self.isValidMAC(MAC):\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif\tline.find(\"Authenticating\") > 10:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif\tline.find(\"STA Leave!!\") != -1 :\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif\tline.find(\"STA enter\") != -1:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"not in list\") >-1: \t\tcontinue\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tup = True\n\t\t\t\t\ttoken = \"\"\n\t\t\t\t\tif line.lower().find(\"disassociated\") > -1:\n\t\t\t\t\t\ttoken = \"disassociated\"\n\t\t\t\t\t\tup = False\n\t\t\t\t\telif line.lower().find(\"disconnected\") > -1:\n\t\t\t\t\t\ttoken = \"DISCONNECTED\"\n\t\t\t\t\t\tup = False\n\t\t\t\t\telif line.find(\" sta_stats\") > -1:\n\t\t\t\t\t\ttoken = \"sta_stats\"\n\t\t\t\t\t\tup = False\n\t\t\t\t\tif line.find(\"ath0:\") > -1: GHz = \"5\"\n\t\t\t\t\tif line.find(\"ath1:\") > -1: GHz = \"2\"\n\t\t\t\t\ttimeOfMSG = time.time()\n\n\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC):self.indiLOG.log(10,\"MS-AP-WF-0 {:13s}#{}; {}; GHz:{}; up:{} token:{}, log-event:{}\".format( ipNumberAP,apN, MAC , GHz, up, token, line))\n\n\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"AP-msg\"): continue\n\n\n\t\t\t\tif MAC != \"\":\n\n\t\t\t\t\tif MAC in self.HANDOVER:\n\t\t\t\t\t\tif time.time() - self.HANDOVER[MAC][\"tt\"] <1.3: # protect for 1+ secs when in handover mode\n\t\t\t\t\t\t\tipNumber = self.HANDOVER[MAC][\"ipNumberNew\"]\n\t\t\t\t\t\t\tup = True\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tdel self.HANDOVER[MAC]\n\n\t\t\t\t\tnew = True\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"\"): self.indiLOG.log(10,\"{} {} wrong {}\".format(MAC, self.MAC2INDIGO[xType][MAC][\"devId\"], devType) )\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states:\t\t continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC:\t continue\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC]={}\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastAPMessage\"] = timeOfMSG\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif not new:\n\t\t\t\t\t\tprops =\t dev.pluginProps\n\t\t\t\t\t\tdevId = \"{}\".format(dev.id)\n\t\t\t\t\t\tif devId not in self.upDownTimers:\n\t\t\t\t\t\t\tself.upDownTimers[devId] = {\"down\": 0, \"up\": 0}\n\n\t\t\t\t\t\tif \"lastAPMessage\" not in self.MAC2INDIGO[xType][MAC]: self.MAC2INDIGO[xType][MAC][\"lastAPMessage\"] = 0\n\t\t\t\t\t\tif timeOfMSG - self.MAC2INDIGO[xType][MAC][\"lastAPMessage\"] < -2: \n\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-1 ..ignore msg, older than last AP message; lastMSG:{:.1f}, thisMSG:{:.1f}\".format(self.MAC2INDIGO[xType][MAC][\"lastAPMessage\"],timeOfMSG ) )\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\toldIP = dev.states[\"AP\"]\n\t\t\t\t\t\tif ipNumberAP != \"\" and ipNumberAP != oldIP.split(\"-\")[0]:\n\t\t\t\t\t\t\tif up:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", ipNumberAP+\"-#{}\".format(apN))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-2 .. old->new associated AP {}->{}-{} not setting to down, as associated to old AP\".format( oldIP, ipNumberAP, apN))\n\t\t\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\n\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-3 .. old->new associated {}->{}#{}\".format( oldIP, ipNumberAP, apN) )\n\n\t\t\t\t\t\t\tif up: # is up now\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"idleTime\" + suffixN] = 0\n\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"down\"] = 0\n\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"up\"] = time.time()\n\t\t\t\t\t\t\t\tif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-4 .. ipNumberAP:{} 'setting state to UP' from:{}\".format( ipNumberAP, dev.states[\"status\"]))\n\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus= dev.states[\"status\"], ts=time.time(), level=1, text1= \"{:30s} status up AP message received >{}<\".format(dev.name,ipNumberAP), iType=\"MS-AP-WF-4 \",reason=\"MSG WiFi \"+\"up\")\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-R ==> restart exptimer use AP log-msg, exp-time left:{:5.1f}\".format(self.getexpT(props) -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"]) ))\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\t\t\t\t\t\t\telse: # is down now\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif devId not in self.upDownTimers:\n\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId] = {\"down\": 0, \"up\": 0}\n\n\t\t\t\t\t\t\t\t\tif ipNumberAP == \"\" or ipNumberAP == oldIP.split(\"-\")[0]: # only if its on the same current AP\n\t\t\t\t\t\t\t\t\t\tdt = (time.time() - self.upDownTimers[devId][\"up\"])\n\n\t\t\t\t\t\t\t\t\t\tif \"useWhatForStatusWiFi\" in props and props[\"useWhatForStatusWiFi\"] in [\"FastDown\",\"Optimized\"]:\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-5 .. checking timer, token:down; tt-uptDelay:{:4.1f}\".format(dt) )\n\t\t\t\t\t\t\t\t\t\t\tif dt > 5.0 and (props[\"useWhatForStatusWiFi\"] == \"FastDown\" or (time.time() - self.MAC2INDIGO[xType][MAC][\"lastUp\"]) > self.getexpT(props) ):\n\t\t\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\t\t\t\t\t\t\tif props[\"useWhatForStatusWiFi\"] == \"FastDown\": # in fast down set it down right now\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"down\",oldStatus=\"up\", ts=time.time(), level=1, text1=MAC +\", \"+ dev.name.ljust(30)+\" status down AP message received fast down-\", iType=\"MS-AP-WF-5 \",reason=\"MSG FAST down\")\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"down\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\t\telse: # in optimized give it 3 more secs\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time() - self.getexpT(props) + 3\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"down\"] = time.time() + 3\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"up\"]\t = 0.\n\n\t\t\t\t\t\t\t\t\t\telif dt > 2.:\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"LogDetails\", MAC=MAC): self.indiLOG.log(10,\"MS-AP-WF-6 .. ipNumberAP:{} 'delay settings updown timer < 2; set uptimer =0, downtimer =tt'\".format( ipNumberAP))\n\t\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"down\"] =\t time.time() # this is a down message\n\t\t\t\t\t\t\t\t\t\t\tself.upDownTimers[devId][\"up\"]\t = 0.\n\t\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\t\tif dev.description.find(\"=WiFi\")==-1 and len(dev.description) >2:\n\t\t\t\t\t\t\t\tdev.description = dev.description+\"=WiFi\"\n\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\n\t\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\t\ttry:\n\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName+\"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=\"\",\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"WiFi\",\"useWhatForStatusWiFi\":\"Expiration\",isType:True})\n\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", ipNumberAP+\"-#{}\".format(apN))\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"idleTime\" + suffixN] = 0\n\t\t\t\t\t\tif \"{}\".format(dev.id) in self.upDownTimers:\n\t\t\t\t\t\t\tdel self.upDownTimers[\"{}\".format(dev.id)]\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, \"UN\", \"\", \"\", \"\", \" \" +MAC+\" status up AP msg new device\", \"MS-AP-WF-6 \")\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}{}\".format(dev.name, MAC) )\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.executeUpdateStatesList()\n\n\t\tself.unsetBlockAccess(\"doAPmessages\")\n\n\t\treturn\n\n\t####-----------------\t ---------\n\t### double check up/down with ping\n\t####-----------------\t ---------\n\tdef doubleCheckWithPing(self,newStatus, ipNumber, props,MAC,debLevel, section, theType,xType):\n\n\t\tif (\"usePingUP\" in props and props[\"usePingUP\"] and newStatus ==\"up\" ) or ( \"usePingDOWN\" in props and props[\"usePingDOWN\"] and newStatus !=\"up\") :\n\t\t\tif self.checkPing(ipNumber, nPings=1, waitForPing=500, calledFrom=\"doubleCheckWithPing\") !=0:\n\t\t\t\tif self.decideMyLog(debLevel, MAC=MAC): self.indiLOG.log(10,theType+\" \"+\" \"+MAC+\" \"+section+\" , status changed - not up , ping test failed\" )\n\t\t\t\treturn 1\n\t\t\telse:\n\t\t\t\tif self.decideMyLog(debLevel, MAC=MAC): self.indiLOG.log(10,theType+\" \"+\" \"+MAC+\" \"+section+\" , status changed - not up , ping test OK\" )\n\t\t\t\tif xType in self.MAC2INDIGO:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\treturn 0\n\t\treturn -1\n\n\n\t####-----------------\t ---------\n\t### for the dict,\n\t####-----------------\t ---------\n\tdef comsumeDictData(self):#, startTime):\n\t\tself.sleep(1)\n\t\tself.indiLOG.log(10,\"comsumeDictData: process starting\")\n\t\tnextItem = \" \"\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tif self.pluginState == \"stop\" or self.consumeDataThread[\"dict\"][\"status\"] == \"stop\": \n\t\t\t\t\tself.indiLOG.log(30,\"comsumeDictData: stopping process due to stop request\")\n\t\t\t\t\treturn \n\t\t\t\tself.sleep(0.1)\n\t\t\t\tconsumedTimeQueue = time.time()\n\t\t\t\tqueueItemCount = 0\n\t\t\t\twhile not self.logQueueDict.empty():\n\t\t\t\t\tif self.pluginState == \"stop\" or self.consumeDataThread[\"dict\"][\"status\"] == \"stop\": \n\t\t\t\t\t\tself.indiLOG.log(30,\"comsumeDictData: stopping process due to stop request\")\n\t\t\t\t\t\treturn \n\n\t\t\t\t\tqueueItemCount += 1\n\t\t\t\t\tnextItem = self.logQueueDict.get()\n\t\t\t\t\tconsumedTime = time.time()\n\t\t\t\t\tself.updateIndigoWithDictData( nextItem[0], nextItem[1], nextItem[2], nextItem[3], nextItem[4] )\n\t\t\t\t\tconsumedTime -= time.time()\n\n\t\t\t\t\tif consumedTime < -self.maxConsumedTimeQueueForWarning:\tlogLevel = 20\n\t\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t\tlogLevel = 10\n\t\t\t\t\tif logLevel == 20:\n\t\t\t\t\t\tself.indiLOG.log(logLevel,\"comsumeDictData excessive time consumed:{:5.1f}[secs]; {:16}-{:2}-{:6} len:{:}, item:{:}\".format(-consumedTime, nextItem[1], nextItem[2], nextItem[3], len(nextItem[0]), \"{}\".format(nextItem[0])[0:100] ) )\n\n\t\t\t\t\tself.logQueueDict.task_done()\n\n\t\t\t\t\tif len(self.sendUpdateToFingscanList) > 0: self.sendUpdatetoFingscanNOW()\n\t\t\t\t\tif len(self.sendBroadCastEventsList) > 0: self.sendBroadCastNOW()\n\n\t\t\t\tconsumedTimeQueue -= time.time()\n\t\t\t\tif consumedTimeQueue < -self.maxConsumedTimeQueueForWarning:\tlogLevel = 20\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tlogLevel = 10\n\t\t\t\tif logLevel == 20:\n\t\t\t\t\t\tself.indiLOG.log(logLevel,\"comsumeDictData Total excessive time consumed:{:5.1f}[secs]; {:16}-{:2}-{:6}; items:{:2} len:{:}, item:{:}\".format(-consumedTimeQueue, nextItem[1], nextItem[2], nextItem[3], queueItemCount, len(nextItem[0]), \"{}\".format(nextItem[0])[0:100]) )\n\t\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.indiLOG.log(30,\"comsumeDictData: stopping process (3)\")\n\t\treturn \n\n\n\n\t####-----------------\t ---------\n\tdef updateIndigoWithDictData(self, apDict, ipNumber, apNumb, uType, unifiDeviceType):\n\n\t\ttry:\n\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"updateIndigoWithDictData apDict[0:100]:{}, ipNumber:{}, apNumb:{}, uType:{}, unifiDeviceType:{}\".format(\"{}\".format(apDict)[0:100], ipNumber, apNumb, uType, unifiDeviceType ) )\n\n\t\t\tif len(apDict) < 1: return\n\t\t\tself.manageLogfile(apDict, apNumb, unifiDeviceType)\n\n\t\t\tapNumbSW = apNumb\n\t\t\tapNumbAP = apNumb\n\t\t\ttry:\tapNint\t= int(apNumb)\n\t\t\texcept: apNint\t= -1\n\t\t\tdoSW \t = False\n\t\t\tdoAP \t = False\n\t\t\tdoGW \t = False\n\t\t\t####### if this is a UDM device set AP, SW GW to tru\n\t\t\tif unifiDeviceType == \"UD\" and self.unifiControllerType.find(\"UDM\") > -1:\n\t\t\t\tdoSW \t = True\n\t\t\t\tdoGW \t = True\n\t\t\t\tdoAP \t = True\n\t\t\t\tapNumbSW = self.numberForUDM[\"SW\"]\n\t\t\t\tapNumbAP = self.numberForUDM[\"AP\"]\n\n\t\t\tif self.debugThisDevices(uType, apNumb) or self.decideMyLog(\"Dict\"): \n\t\t\t\tdd = \"{}\".format(apDict)\n\t\t\t\tself.indiLOG.log(10,\"DEVdebug {} dev #sw:{},ap:{}, uType:{}, unifiDeviceType:{}; dictmessage:\\n{} ..\\n{}\".format(ipNumber, apNumbSW, apNumbAP, uType, unifiDeviceType, dd[:50], dd[-50:] ) )\n\n\n\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"updDict ipNumber:{}; apNumb:{}; uType:{}; unifiDeviceType:{}; doGW:{}; \".format(ipNumber, apNumb, uType, unifiDeviceType, doGW) )\n\t\t\tif unifiDeviceType == \"GW\" or doGW:\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"updDict dict:\\n{}\".format(apDict) )\n\t\t\t\tself.doGatewaydictSELF(apDict, ipNumber)\n\t\t\t\tif self.unifiControllerType.find(\"UDM\") >-1: \n\t\t\t\t\tself.doGWDvi_stats(apDict, ipNumber)\n\t\t\t\telse:\n\t\t\t\t\tself.doGWHost_table(apDict, ipNumber)\n\n\n\n\t\t\tif unifiDeviceType == \"SW\" or doSW:\n\t\t\t\tif(\t\"mac\"\t\t in apDict and \n\t\t\t\t \t\"port_table\" in apDict and\n\t\t\t\t \t\"hostname\"\t in apDict and\n\t\t\t\t \t\"ip\"\t\t in apDict ):\n\n\t\t\t\t\tMACSW = apDict[\"mac\"]\n\t\t\t\t\thostname = apDict[\"hostname\"].strip()\n\t\t\t\t\tipNDevice = apDict[\"ip\"]\n\n\t\t\t\t\t################# update SWs themselves\n\t\t\t\t\tself.doSWdictSELF(apDict, apNumbSW, ipNDevice, MACSW, hostname, ipNumber)\n\n\t\t\t\t\t################# now update the devices on switch\n\t\t\t\t\tself.doSWITCHdictClients(apDict, apNumbSW, ipNDevice, MACSW, hostname, ipNumber)\n\t\t\t\telse:\n\t\t\t\t\tpass\n##\t\t\t\t\tself.indiLOG.log(10,\"DICTDATA rejected .. mac, port_table, hostname ip not in dict ..{}\".format(apDict))\n\n\n\t\t\tif unifiDeviceType == \"AP\" or doAP:\n\t\t\t\tif(\t\"mac\"\t\t in apDict and\n\t\t\t\t\t\"vap_table\" in apDict and\n\t\t\t\t\t\"ip\"\t\t in apDict):\n\n\t\t\t\t\tMACAP\t\t = apDict[\"mac\"]\n\t\t\t\t\thostname = apDict[\"hostname\"].strip()\n\t\t\t\t\tipNDevice= apDict[\"ip\"]\n\n\t\t\t\t\tclientHostnames = {\"2\":\"\",\"5\":\"\"}\n\t\t\t\t\tfor jj in range(len(apDict[\"vap_table\"])):\n\t\t\t\t\t\tif \"usage\" in apDict[\"vap_table\"][jj]: #skip if not wireless\n\t\t\t\t\t\t\tif apDict[\"vap_table\"][jj][\"usage\"] == \"downlink\": continue\n\t\t\t\t\t\t\tif apDict[\"vap_table\"][jj][\"usage\"] == \"uplink\":\tcontinue\n\n\t\t\t\t\t\tchannel = \"{}\".format(apDict[\"vap_table\"][jj][\"channel\"])\n\t\t\t\t\t\tif int(channel) >= 12:\n\t\t\t\t\t\t\tGHz = \"5\"\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tGHz = \"2\"\n\t\t\t\t\t\tif \"sta_table\" in apDict[\"vap_table\"][jj] and apDict[\"vap_table\"][jj][\"sta_table\"] !=[]:\n\t\t\t\t\t\t\tclientHostnames[GHz] = self.doWiFiCLIENTSdict(apDict[\"vap_table\"][jj][\"sta_table\"], GHz, ipNDevice, apNumbAP, ipNumber)\n\n\t\t\t\t\t\t################# update APs themselves\n\t\t\t\t\tself.doAPdictsSELF(apDict, apNumbAP, ipNDevice, MACAP, hostname, ipNumber, clientHostnames)\n\n\n\t\t\t\t\t############ update neighbors\n\t\t\t\t\tif \"radio_table\" in\t apDict:\n\t\t\t\t\t\tself.doNeighborsdict(apDict[\"radio_table\"], apNumbAP, ipNumber)\n\t\t\t\telse:\n\t\t\t\t\tpass\n###\t\t\t\t\tself.indiLOG.log(10,\"DICTDATA rejected .. mac, vap_table, ip not in dict ..{}\".format(apDict))\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\t\treturn\n\n\n\n\n\n\n\t################# update APs\n\t####-----------------\t ---------\n\tdef checkInListSwitch(self):\n\n\t\txType = \"UN\"\n\t\tignore ={}\n\t\ttry:\n\t\t\tfor dev in indigo.devices.iter(\"props.isSwitch\"):\n\t\t\t\tnn = int(dev.states[\"switchNo\"])\n\t\t\t\tif not self.devsEnabled[\"SW\"][nn]:\n\t\t\t\t\tignore[\"inListSwitch_{}\".format(nn)] = -1\n\t\t\t\tif not self.isValidIP(self.ipNumbersOf[\"SW\"][nn]):\n\t\t\t\t\tignore[\"inListSwitch_{}\".format(nn)] = -1\n\n\t\t\tfor nn in range(_GlobalConst_numberOfSW):\n\t\t\t\tif not self.devsEnabled[\"SW\"][nn]:\n\t\t\t\t\tignore[\"inListSwitch_{}\".format(nn)] = -1\n\t\t\t\tif not self.isValidIP(self.ipNumbersOf[\"SW\"][nn]):\n\t\t\t\t\tignore[\"inListSwitch_{}\".format(nn)] = -1\n\n\t\t\tif not self.devsEnabled[\"GW\"]:\n\t\t\t\tignore[\"inListDHCP\"] = 0\n\t\t\t\tignore[\"upTimeDHCP\"] = \"\"\n\t\t\tif not self.isValidIP(self.ipNumbersOf[\"GW\"]):\n\t\t\t\tignore[\"inListDHCP\"] = 0\n\t\t\t\tignore[\"upTimeDHCP\"] = \"\"\n\n\t\t\tsave = False\n\t\t\tif len(ignore) > 0:\n\t\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\tfor xx in ignore:\n\t\t\t\t\t\tif xx in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][xx] != ignore[xx]:\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][xx] = ignore[xx]\n\t\t\t\t\t\t\t\tsave = True\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][xx] = ignore[xx]\n\t\t\t\t\t\t\t\tsave = True\n\t\t\t\tif save:\n\t\t\t\t\tself.saveMACdata()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\t################# update APs\n\t####-----------------\t ---------\n\tdef doInList(self,suffixN,\twifiIPAP=\"\"):\n\n\n\t\tsuffix = suffixN.split(\"_\")[0]\n\t\ttry:\n\t\t\t## now check if device is not in dict, if not ==> initiate status --> down\n\t\t\txType = \"UN\"\n\t\t\tdelMAC={}\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] == -1: continue\t# do not test\n\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] == 1: continue\t# is here\n\t\t\t\ttry:\n\t\t\t\t\tdevId = self.MAC2INDIGO[xType][MAC][\"devId\"]\n\t\t\t\t\tdev\t = indigo.devices[devId]\n\t\t\t\t\taW\t = dev.states[\"AP\"]\n\t\t\t\t\tif wifiIPAP ==\"\" or aW == wifiIPAP:\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 0\n\t\t\t\t\tif wifiIPAP !=\"\" and aW != wifiIPAP:\t\t\t\t\t\t\t\t\t\t\t continue\n\t\t\t\t\tif dev.states[\"status\"] != \"up\":\t\t\t\t\t\t\t\t\t\t\t continue\n\n\t\t\t\t\tprops= dev.pluginProps\n\t\t\t\t\tif \"useWhatForStatus\" not in props or props[\"useWhatForStatus\"].find(suffix) == -1:\t continue\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"timeout waiting\") > -1:\n\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\tself.indiLOG.log(40,\"communication to indigo is interrupted\")\n\t\t\t\t\t\treturn\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tself.indiLOG.log(40,\"deleting device from internal lists -- MAC:\"+ MAC+\"; devId:{}\".format(devId))\n\t\t\t\t\tdelMAC[MAC]=1\n\t\t\t\t\tcontinue\n\n\t\t\t\ttry:\n\t\t\t\t\tlastUpTT = self.MAC2INDIGO[xType][MAC][\"lastUp\"]\n\t\t\t\texcept:\n\t\t\t\t\tlastUpTT = time.time() - 1000\n\n\n\t\t\t\texpT = self.getexpT(props)# this should be much faster than normal expiration\n\t\t\t\tif wifiIPAP !=\"\" : expTUse = max(expT/2.,10) # only for non wifi devices\n\t\t\t\telse:\t\t\t expTUse = expT\n\t\t\t\tdt = time.time() - lastUpTT\n\t\t\t\tif dt < \t\t\t\t\t\t1 * expT:\n\t\t\t\t\tstatus = \"up\"\n\t\t\t\telif dt < self.expTimeMultiplier * expT:\n\t\t\t\t\tstatus = \"down\"\n\t\t\t\telse:\n\t\t\t\t\tstatus = \"expired\"\n\n\n\t\t\t\tif dev.states[\"status\"] != status and status !=\"up\":\n\t\t\t\t\tif \"usePingUP\" in props and props[\"usePingUP\"]\tand status !=\"up\" and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, nPings=1, calledFrom=\"inList\") == 0:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"List-\"+suffix+\" \" +dev.states[\"MAC\"]+\" check, status changed - not up , ping test ok resetting to up\" )\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\tself.setImageAndStatus(dev, status,oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1= dev.name.ljust(30) + \" in list status \" + status.ljust(10) + \" \"+suffixN+\" dt= %5.1f\" % dt + \"; expT= %5.1f\" % expT+ \" wifi:\" +wifiIPAP, iType=\"STATUS-\"+suffix,reason=\"NotInList \"+suffixN+\" \"+wifiIPAP+\" \"+status)\n\n\t\t\tfor MAC in delMAC:\n\t\t\t\tdel\t self.MAC2INDIGO[xType][MAC]\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\n\n\t####-----------------\t ---------\n\t#### this does the unifswitch attached devices\n\t####-----------------\t ---------\n\tdef doSWITCHdictClients(self, apDict, swNumb, ipNDevice, MACSW, hostnameSW, ipNumber):\n\n\n\t\ttry:\n\n\t\t\tdevType = \"UniFi\"\n\t\t\tisType\t= \"isUniFi\"\n\t\t\tdevName = \"UniFi\"\n\t\t\tsuffix\t= \"SWITCH\"\n\t\t\txType\t= \"UN\"\n\n\t\t\tportTable = apDict[\"port_table\"]\n\n\n\t\t\tUDMswitch = False\n\t\t\tuseIP = ipNumber\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1 and swNumb == self.numberForUDM[\"SW\"]:\n\t\t\t\tUDMswitch = True\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-SW-UDM using UDM mode for IP#Dict:{} ip#proc#{} \".format(ipNDevice, ipNumber) )\n\n\n\t\t\tif useIP not in self.deviceUp[\"SW\"]:\n\t\t\t\treturn\n\n\t\t\tswitchNumber = -1\n\t\t\tfor ii in range(_GlobalConst_numberOfSW):\n\t\t\t\tif not self.devsEnabled[\"SW\"][ii]:\t\t\t\tcontinue\n\t\t\t\tif useIP != self.ipNumbersOf[\"SW\"][ii]: \tcontinue\n\t\t\t\tswitchNumber = ii\n\t\t\t\tbreak\n\n\t\t\tif switchNumber < 0:\n\t\t\t\treturn\n\n\t\t\tself.setBlockAccess(\"doSWITCHdict\")\n\n\t\t\tswN\t\t= \"{}\".format(switchNumber)\n\t\t\tsuffixN = suffix+\"_\"+swN\n\n\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tif len(MAC) < 16:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = -1\t # was not here\n\t\t\t\t\tcontinue\n\t\t\t\ttry:\n\t\t\t\t\tif \"inList\"+suffixN not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\tself.indiLOG.log(40,\"error in doSWITCHdictClients: mac:{} inList{} not in NMAC2INDIGO:{}\".format(MAC,suffix, self.MAC2INDIGO[xType][MAC]))\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] > 0:\t # was here was here , need to test\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 0\n\t\t\t\texcept:\n\t\t\t\t\t\tself.indiLOG.log(40,\"error in doSWITCHdictClients: mac:{} MAC2INDIGO:{}\".format(MAC, self.MAC2INDIGO[xType][MAC]))\n\t\t\t\t\t\treturn\n\t\t\t\telse:\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = -1\t # was not here\n\n\n\t\t\tfor port in portTable:\n\n\t\t\t\tportN = \"{}\".format(port[\"port_idx\"])\n\t\t\t\tif \"mac_table\" not in port: continue\n\t\t\t\tmacTable =\tport[\"mac_table\"]\n\t\t\t\tif macTable == []:\tcontinue\n\t\t\t\tif \"port_idx\" in port:\n\t\t\t\t\tportN = \"{}\".format(port[\"port_idx\"])\n\t\t\t\telse:\n\t\t\t\t\tportN = \"\"\n\t\t\t\tisUpLink = False\n\t\t\t\tisDownLink = False\n\n\t\t\t\tif \"is_uplink\"\t in port and port[\"is_uplink\"]:\t\t\tisUpLink = True\n\t\t\t\telif \"lldp_table\" in port and len(port[\"lldp_table\"]) > 0:\tisDownLink = True\n\n\t\t\t\t#if isUpLink:\t\t continue\n\t\t\t\t#if isDownLink:\t\t continue\n\n\t\t\t\tfor switchDevices in macTable:\n\t\t\t\t\tMAC = switchDevices[\"mac\"]\n\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"SW-Dict\"): continue\n\n\t\t\t\t\tif \"vlan\" in switchDevices:\t\tvlan\t = switchDevices[\"vlan\"]\n\t\t\t\t\telse: vlan = \"\"\n\n\t\t\t\t\tif \"age\" in switchDevices:\t\tage\t = switchDevices[\"age\"]\n\t\t\t\t\telse: age = \"\"\n\n\t\t\t\t\tif \"ip\" in switchDevices:\n\t\t\t\t\t\t\t\t\t\t\t\t\tip\t = switchDevices[\"ip\"]\n\t\t\t\t\t\t\t\t\t\t\t\t\ttry:\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ip\n\t\t\t\t\t\t\t\t\t\t\t\t\texcept: continue\n\t\t\t\t\telse:\t\t\t\t\t\t\tip = \"\"\n\n\t\t\t\t\tif \"uptime\" in switchDevices:\tnewUp = \"{}\".format(switchDevices[\"uptime\"])\n\t\t\t\t\telse: newUp = \"\"\n\n\t\t\t\t\tnameSW = \"empty\"\n\t\t\t\t\tif \"hostname\" in switchDevices: nameSW = \"{}\".format(switchDevices[\"hostname\"]).strip()\n\t\t\t\t\tif nameSW == \"?\": nameSW = \"empty\"\n\t\t\t\t\tif len(nameSW) == 0: nameSW = \"empty\"\n\n\t\t\t\t\tipx = self.fixIP(ip)\n\t\t\t\t\tnew = True\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1 # is here\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10, \"{} {} wrong {}\".format(MAC, self.MAC2INDIGO[xType][MAC][\"devId\"], devType))\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states:\t\t\tcontinue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC:\t\tcontinue\n\t\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC, init=False)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif not new:\n\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1\n\t\t\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-00 {:15s} {}; {}; IP:{}; AGE:{}; newUp:{}; hostN:{}\".format(useIP, MAC, dev.name, ip, age, newUp, nameSW))\n\n\t\t\t\t\t\tif not ( isUpLink or isDownLink ): # this is not for up or downlink \n\t\t\t\t\t\t\tpoe = \"\"\n\t\t\t\t\t\t\tif MACSW in self.MAC2INDIGO[\"SW\"]: # do we know the switch\n\t\t\t\t\t\t\t\tif portN in self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"]: # is the port in the switch\n\t\t\t\t\t\t\t\t\tif \"nClients\" in self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN] and self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN][\"nClients\"] == 1: \n\t\t\t\t\t\t\t\t\t\tif \"poe\" in self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN] and self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN][\"poe\"] != \"\": # if empty dont add \"-\"\n\t\t\t\t\t\t\t\t\t\t\tpoe = \"-\"+self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN][\"poe\"]\n\t\t\t\t\t\t\t\t\t\tif len(dev.states[\"AP\"]) > 5: # fix if direct connect and poe is one can not have wifi for this MAC, must be ethernet, set wifi to \"-\"\n\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", \"-\")\n\n\t\t\t\t\t\t\tnewPort = swN+\":\"+portN+poe\n\t\t\t\t\t\t\t#self.indiLOG.log(10,\"portInfo MACSW: \"+MACSW +\" hostnameSW:\"+hostnameSW+\" \"+useIP +\" \"+ MAC+\" portN:\"+portN+\" MACSW-poe:\"+ self.MAC2INDIGO[\"SW\"][MACSW][\"ports\"][portN][\"poe\"]+\"; nameSW:{}\".format(nameSW)+\" poe:\"+poe+\" newPort:\"+newPort)\n\n\t\t\t\t\t\t\tif dev.states[\"SW_Port\"] != newPort:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"SW_Port\", newPort)\n\n\n\t\t\t\t\t\tprops=dev.pluginProps\n\n\t\t\t\t\t\tnewd = False\n\t\t\t\t\t\tdevidd = \"{}\".format(dev.id)\n\t\t\t\t\t\tif ip != \"\":\n\t\t\t\t\t\t\tif dev.states[\"ipNumber\"] != ip:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ip\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN] = age\n\t\t\t\t\t\tif dev.states[\"name\"] != nameSW and nameSW !=\"empty\":\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"name\", nameSW)\n\n\t\t\t\t\t\tif \"vlan\" in dev.states and dev.states[\"vlan\"] != vlan:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"vlan\", vlan)\n\n\n\t\t\t\t\t\tnewStatus = \"up\"\n\t\t\t\t\t\toldStatus = dev.states[\"status\"]\n\t\t\t\t\t\toldUp\t = self.MAC2INDIGO[xType][MAC][\"upTime\" + suffixN]\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\" + suffixN] = \"{}\".format(newUp)\n\t\t\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"] in [\"SWITCH\",\"OptDhcpSwitch\"]:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-01 {:15s} {} {}; oldStatus:{}; IP:{}; AGE:{}; newUp:{}; oldUp:{} hostN:{}\".format(useIP, MAC, dev.name, oldStatus, ip, age, newUp, oldUp, nameSW))\n\t\t\t\t\t\t\tif oldUp ==\t newUp and oldStatus ==\"up\":\n\t\t\t\t\t\t\t\tif \"useupTimeforStatusSWITCH\" in props and props[\"useupTimeforStatusSWITCH\"] :\n\t\t\t\t\t\t\t\t\tif \"usePingDOWN\" in props and props[\"usePingDOWN\"]\tand self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doSWITCHdictClients\") == 0:\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-1 {} reset timer for status up notuptime const\tbut answers ping\".format(MAC))\n\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-2 {} SW DICT network_table , Uptime not changed, continue expiration timer\".format(MAC))\n\t\t\t\t\t\t\t\telse: # will only expired if not in list anymore\n\t\t\t\t\t\t\t\t\tif \"usePingDOWN\" in props and props[\"usePingDOWN\"]\t and status !=\"up\" and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doSWITCHdictClients\") != 0:\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-3 {} SW DICT network_table , but does not answer ping, continue expiration timer\".format(MAC))\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-4 {} reset timer for status up answers ping in DHCP list\".format(MAC))\n\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\n\t\t\t\t\t\t\tif oldUp != newUp:\n\t\t\t\t\t\t\t\tif \"usePingUP\" in props and props[\"usePingUP\"] and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doSWITCHdictClients\") != 0:\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-5 {} SW DICT network_table , but does not answer ping, continue expiration timer\".format(MAC))\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-6 {} SW DICT network_tablerestart exp timer \".format(MAC))\n\n\t\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\t\toldIPX = dev.description.split(\"-\")\n\t\t\t\t\t\t\tif ipx !=\"\" and (oldIPX[0] != ipx or ( (dev.description != ipx + \"-\" + nameSW or len(dev.description) < 5) and nameSW !=\"empty\" and (dev.description).find(\"=WiFi\") ==-1 )) :\n\t\t\t\t\t\t\t\tif oldIPX[0] != ipx and oldIPX[0] !=\"\":\n\t\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_IPNumber_Change\",\"{}/{}/{}/{}\".format(dev.name, dev.states[\"MAC\"], oldIPX[0], ipx) )\n\t\t\t\t\t\t\t\tdev.description = ipx + \"-\" + nameSW\n\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-7 updating description for {} to....{}\".format(dev.name, dev.description) )\n\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\t\t\t\t\t\t#break\n\n\t\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName+ \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=ipx + \"-\" + nameSW,\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"SWITCH\",\"useupTimeforStatusSWITCH\":\"\",isType:True})\n\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"SW_Port\", \"\")\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN] = age\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN] = newUp\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, ip, \"\", \"\", \" status up SWITCH DICT new Device\", \"STATUS-SW\")\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}/{}/{}\".format(dev.name, MAC, ipx) )\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\n\t\t\tself.doInList(suffixN)\n\t\t\tself.executeUpdateStatesList()\n\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doSWITCHdict\")\n\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef doGWHost_table(self, gwDict, ipNumber):\n\n\t\tself.setBlockAccess(\"doGWHost_table\")\n\n\n\t\ttry:\n\t\t\tdevType = \"UniFi\"\n\t\t\tisType\t= \"isUniFi\"\n\t\t\tdevName = \"UniFi\"\n\t\t\tsuffixN = \"DHCP\"\n\t\t\txType\t= \"UN\"\n\n\t\t\t###########\t do DHCP devices\n\n\t\t\tif \"network_table\" not in gwDict:\n\t\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"DC-DHCP-E0 network_table not in dict {}\".format(gwDict[0:100]) )\n\t\t\t\treturn\n\n\n\t\t\thost_table = \"\"\n\t\t\tfor item in gwDict[\"network_table\"]:\n\t\t\t\tif \"host_table\" not in item: continue\n\t\t\t\thost_table = item[\"host_table\"]\n\t\t\t\tbreak\n\t\t\tif host_table == \"\":\n\t\t\t\tif \"host_table\" not in gwDict[\"network_table\"]:\n\t\t\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10,\"DC-DHCP-E1 no DHCP in gwateway ?.. skipping info \".format(gwDict[\"network_table\"][0:100]) )\n\t\t\t\t\treturn # DHCP not enabled on gateway, no info from GW available\n\n\t\t\tif \"connect_request_ip\" in gwDict:\n\t\t\t\tipNumber = gwDict[\"connect_request_ip\"]\n\t\t\telse:\n\t\t\t\tipNumber = \" \"\n\n\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 0\n\n\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DHCP-0 host_table len:{} {}\".format( len(host_table), host_table[0:100]) )\n\t\t\tif len(host_table) > 0:\n\t\t\t\tfor item in host_table:\n\n\n\n\t\t\t\t\tif \"ip\" in item: ip = item[\"ip\"]\n\t\t\t\t\telse:\t\t\t ip = \"\"\n\t\t\t\t\tMAC\t\t\t\t\t = item[\"mac\"]\n\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"GW-Dict\"): continue\n\t\t\t\t\tage\t\t\t\t\t = item[\"age\"]\n\t\t\t\t\tuptime\t\t\t\t = item[\"uptime\"]\n\t\t\t\t\tnew\t\t\t\t\t = True\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-DHCP-E1 {} {} wrong {}\".format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC, init=False)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif not new:\n\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DHCP-1 {:15s} {}; {}; ip:{}; age:{}; uptime:{}\".format(ipNumber, MAC, dev.name,ip, age, uptime))\n\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = True\n\n\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tif MAC != dev.states[\"MAC\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\t\t\t\t\tif ip != \"\":\n\t\t\t\t\t\t\t\tif ip != dev.states[\"ipNumber\"]:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ip\n\n\t\t\t\t\t\t\tnewStatus = \"up\"\n\t\t\t\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"] in [\"DHCP\",\"OptDhcpSwitch\",\"WiFi,DHCP\"]:\n\n\t\t\t\t\t\t\t\tif \"useAgeforStatusDHCP\" in props and props[\"useAgeforStatusDHCP\"] != \"-1\" and float(age) > float( props[\"useAgeforStatusDHCP\"]):\n\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif \"usePingDOWN\" in props and props[\"usePingDOWN\"] and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doGWHost_table\") == 0: # did answer\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\" {} restart exptimer DICT network_table AGE>max:{}, but answers ping, exp-time left:{:5.1f}\".format(MAC, props[\"useAgeforStatusDHCP\"], self.getexpT(props) -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"]) ))\n\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"up\"\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\" {} set timer for status down GW DICT network_table AGE>max:{}\".format(MAC, props[\"useAgeforStatusDHCP\"]))\n\t\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"startDown\"\n\n\t\t\t\t\t\t\t\telse: # good data, should be up\n\t\t\t\t\t\t\t\t\tif \"usePingUP\" in props and props[\"usePingUP\"] and dev.states[\"status\"] == \"up\" and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doGWHost_table\") == 1:\t# did not answer\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time() - self.getexpT(props) # down immediately\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"down\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1= dev.name.ljust(30) + \"set timer for status down ping does not answer\", iType=\"DC-DHCP-4 \",reason=\"DICT \"+suffixN+\" up\")\n\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"down\"\n\t\t\t\t\t\t\t\t\telif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1= dev.name.ljust(30) + \" status up GW DICT network_table\", iType=\"DC-DHCP-4 \",reason=\"DICT \"+suffixN+\" up\")\n\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"up\"\n\n\t\t\t\t\t\t\t\tif newStatus == \"up\":\n\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN]\t\t= age\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN]\t= uptime\n\n\t\t\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\t\t\toldIPX = dev.description.split(\"-\")\n\t\t\t\t\t\t\t\tipx = self.fixIP(ip)\n\t\t\t\t\t\t\t\tif ipx != \"\" and oldIPX[0] != ipx and oldIPX[0] != \"\":\n\t\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_IPNumber_Change\", \"{}/{}/{}/{}\".format(dev.name, dev.states[\"MAC\"], oldIPX[0], ipx) )\n\t\t\t\t\t\t\t\t\toldIPX[0] = ipx\n\t\t\t\t\t\t\t\t\tdev.description = \"-\".join(oldIPX)\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"updating description for {} to....{}\".format(dev.name, dev.description) )\n\t\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\n\t\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName + \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=self.fixIP(ip),\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={ \"useWhatForStatus\":\"DHCP\",\"useAgeforStatusDHCP\": \"-1\",\"useWhatForStatusWiFi\":\"\", isType:True})\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN]\t\t\t= age\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN]\t\t= uptime\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN]\t\t= True\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, ip, \"\", \"\", \" status up GW DICT new device\",\"DC-DHCP-3 \")\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}/{}\".format(dev.name, MAC) )\n\n\n\n\t\t\t## now check if device is not in dict, if not ==> initiate status --> down\n\t\t\tself.doInList(suffixN)\n\t\t\tself.executeUpdateStatesList()\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.unsetBlockAccess(\"doGWHost_table\")\n\t\t\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef doGWDvi_stats(self, gwDict, ipNumber):\n\n\t\tself.setBlockAccess(\"doGWDvi_stats\")\n\n\n\t\ttry:\n\t\t\tdevType = \"UniFi\"\n\t\t\tisType\t= \"isUniFi\"\n\t\t\tdevName = \"UniFi\"\n\t\t\tsuffixN = \"DHCP\"\n\t\t\txType\t= \"UN\"\n\n\t\t\t###########\t do DHCP devices\n\n\t\t\t### UDM does not have DHCP info use DPI info, cretae an artificial age # by adding rx tx packets\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1: key = \"dpi_stats\"\n\t\t\telse:\t\t\t\t\t\t\t\t\t\t key = \"dpi-stats\"\n\t\t\tif key not in gwDict or gwDict[key] == []: \n\t\t\t\tif False and self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-DPI dpi-stats not in dict or empty \" )\n\t\t\t\treturn \n\t\t\tdpi_table =[]\n\t\t\txx = {}\n\t\t\tfor dd in gwDict:\n\t\t\t\tif len(dd) < 1: continue\n\t\t\t\tif \"ip\" not in dd: \t\t\n\t\t\t\t\t#if self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-DPI \\\"ip\\\" not in gWDict\" )\n\t\t\t\t\tcontinue\n\t\t\t\tif type(dd) != type({}): \n\t\t\t\t\t#if self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-DPI dict in gwDict :\".format(gwDict) )\n\t\t\t\t\tcontinue\n\t\t\t\txx = {\"age\": 99999999999,\n\t\t\t\t\t \"authorized\": False,\n\t\t\t\t\t \"ip\": dd[\"ip\"],\n\t\t\t\t\t \"mac\": dd[\"mac\"],\n\t\t\t\t\t \"tx_bytes\": 0,\n\t\t\t\t\t \"tx_packets\": 0,\n\t\t\t\t\t \"uptime\": 0}\n\t\t\t\tfor yy in gwDict[key][\"stats\"]:\n\t\t\t\t\txx[\"rx_packets\"] += yy[\"rx_packets\"]\n\t\t\t\t\txx[\"tx_packets\"] += yy[\"tx_packets\"]\n\t\t\t\tif xx[\"rx_packets\"] + xx[\"tx_packets\"] > 0:\n\t\t\t\t\txx[\"age\"] \t = 0\n\t\t\t\t\txx[\"uptime\"] = int(time.time()*1000 - float(gwDict[\"initialized\"]))\n\t\t\t\t\tdpi_table.append(xx)\n\n\t\t\tfor item in dpi_table:\n\t\t\t\t\tif \"ip\" in item: ip = item[\"ip\"]\n\t\t\t\t\telse:\t\t\t ip = \"\"\n\t\t\t\t\tMAC\t\t\t\t\t = item[\"mac\"]\n\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"GW-Dict\"): continue\n\t\t\t\t\tage\t\t\t\t\t = item[\"age\"]\n\t\t\t\t\tuptime\t\t\t\t = item[\"uptime\"]\n\t\t\t\t\tnew\t\t\t\t\t = True\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-DPI-E1 {} {} wrong devType:{}\".format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC, init=False)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tif not new:\n\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DPI-1 {} {} {} ip:{} age:{} uptime:{}\".format(ipNumber, MAC, dev.name, ip, age, uptime))\n\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = True\n\n\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tif MAC != dev.states[\"MAC\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\t\t\t\t\tif ip != \"\":\n\t\t\t\t\t\t\t\tif ip != dev.states[\"ipNumber\"]:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ip\n\n\t\t\t\t\t\t\tnewStatus = \"up\"\n\t\t\t\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"] in [\"DHCP\",\"OptDhcpSwitch\",\"WiFi,DHCP\"]:\n\n\t\t\t\t\t\t\t\tif \"useAgeforStatusDHCP\" in props and props[\"useAgeforStatusDHCP\"] != \"-1\" and float(age) > float( props[\"useAgeforStatusDHCP\"]):\n\t\t\t\t\t\t\t\t\t\tif dev.states[\"status\"] == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif \"usePingDOWN\" in props and props[\"usePingDOWN\"] and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom = \"doGWHost_table\") == 0: # did answer\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DPI-2 {} ==> restart exptimer DICT network_table AGE>max, but answers ping {}, exp-time left:{:5.1f}\".format(MAC,props[\"useAgeforStatusDHCP\"], self.getexpT(props) -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"up\"\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DPI-3 {} set timer for status down GW DICT network_table AGE>max:\" .format(MAC,props[\"useAgeforStatusDHCP\"]))\n\t\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"startDown\"\n\n\t\t\t\t\t\t\t\telse: # good data, should be up\n\t\t\t\t\t\t\t\t\tif \"usePingUP\" in props and props[\"usePingUP\"] and dev.states[\"status\"] == \"up\" and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doGWHost_table\") == 1:\t# did not answer\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time() - self.getexpT(props) # down immediately\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"down\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1= dev.name.ljust(30) + \"set timer for status down ping does not answer\", iType=\"DC-DHCP-4 \",reason=\"DICT \"+suffixN+\" up\")\n\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"down\"\n\t\t\t\t\t\t\t\t\telif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1= dev.name.ljust(30) + \" status up \tGW DICT network_table\", iType=\"DC-DHCP-4 \",reason=\"DICT \"+suffixN+\" up\")\n\t\t\t\t\t\t\t\t\t\t\tnewStatus = \"up\"\n\n\t\t\t\t\t\t\t\tif newStatus == \"up\":\n\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN]\t\t= age\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN]\t= uptime\n\n\t\t\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\t\t\toldIPX = dev.description.split(\"-\")\n\t\t\t\t\t\t\t\tipx = self.fixIP(ip)\n\t\t\t\t\t\t\t\tif ipx!=\"\" and oldIPX[0] != ipx and oldIPX[0] !=\"\":\n\t\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_IPNumber_Change\",\"{}/{}/{}/{}\".format(dev.name, dev.states[\"MAC\"], oldIPX[0], ipx) )\n\t\t\t\t\t\t\t\t\toldIPX[0] = ipx\n\t\t\t\t\t\t\t\t\tdev.description = \"-\".join(oldIPX)\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-DPI-4 updating description for {} to: {}\".format(dev.name, dev.description) )\n\t\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\n\t\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName + \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=self.fixIP(ip),\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={ \"useWhatForStatus\":\"DHCP\",\"useAgeforStatusDHCP\": \"-1\",\"useWhatForStatusWiFi\":\"\", isType:True})\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"+suffixN]\t\t\t= age\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN]\t\t= uptime\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN]\t\t= True\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, ip, \"\", \"\", \" status u GW DICT new device\",\"DC-DPI-3 \")\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\",\"{}/{}\".format(dev.name, MAC) )\n\n\n\n\t\t\t## now check if device is not in dict, if not ==> initiate status --> down\n\t\t\tself.doInList(suffixN)\n\t\t\tself.executeUpdateStatesList()\n\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.unsetBlockAccess(\"doGWDvi_stats\")\n\t\t\n\t\treturn\n\n\n\n\n\t####-----------------\t ---------\n\tdef doWiFiCLIENTSdict(self,adDict, GHz, ipNDevice, apN, ipNumber):\n\t\ttry:\n\t\n\n\t\t\tif len(adDict) == 0:\n\t\t\t\treturn\n\n\t\t\tself.setBlockAccess(\"doWiFiCLIENTSdict\")\n\n\t\t\tif self.decideMyLog(\"Dict\") or self.debugDevs[\"AP\"][int(apN)]: self.indiLOG.log(10,\"DC-WF-00 {:13s}#{} ... vap_table..[sta_table]: {}\".format(ipNumber,apN, adDict) )\n\n\t\t\ttry:\n\t\t\t\tdevType = \"UniFi\"\n\t\t\t\tisType\t= \"isUniFi\"\n\t\t\t\tdevName = \"UniFi\"\n\t\t\t\tsuffixN = \"WiFi\"\n\t\t\t\txType\t= \"UN\"\n\t\t\t\tclientHostNames = \"\"\n\t\t\t\tfor MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\tif \"AP\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\tself.indiLOG.log(30,\"DC-WF-ER {} # type:{} is not properly defined, please check config and fix, then restart plugin\".format(MAC, xType) )\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"AP\"] != ipNumber: continue\n\t\t\t\t\tif self.MAC2INDIGO[xType][MAC][\"GHz\"] != GHz:\t\tcontinue\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 0\n\n\n\t\t\t\tfor ii in range(len(adDict)):\n\n\t\t\t\t\tnew\t\t\t\t= True\n\t\t\t\t\tif \"mac\" not in adDict[ii] : continue\n\t\t\t\t\tMAC\t\t\t\t= adDict[ii][\"mac\"]\n\t\t\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"WF-Dict\"): continue\n\t\t\t\t\tif \"ip\" not in adDict[ii]: continue\n\t\t\t\t\tip\t\t\t\t= adDict[ii][\"ip\"]\n\t\t\t\t\ttxRate\t\t\t= \"{}\".format(adDict[ii][\"tx_rate\"])\n\t\t\t\t\trxRate\t\t\t= \"{}\".format(adDict[ii][\"rx_rate\"])\n\t\t\t\t\trxtx\t\t\t= rxRate+\"/\"+txRate+\" [Kb]\"\n\t\t\t\t\tsignal\t\t\t= \"{}\".format(adDict[ii][\"signal\"])\n\t\t\t\t\tnoise\t\t\t= \"{}\".format(adDict[ii][\"noise\"])\n\t\t\t\t\tidletime\t\t= \"{}\".format(adDict[ii][\"idletime\"])\n\t\t\t\t\ttry:state\t\t= format(int(adDict[ii][\"state\"]), '08b')\t## not in controller\n\t\t\t\t\texcept: state\t= \"\"\n\t\t\t\t\tnewUpTime\t\t= \"{}\".format(adDict[ii][\"uptime\"])\n\t\t\t\t\ttry:\n\t\t\t\t\t\tnameCl\t\t= adDict[ii][\"hostname\"].strip()\n\t\t\t\t\texcept:\n\t\t\t\t\t\tnameCl\t\t= \"\"\n\t\t\t\t\tif nameCl !=\"\": clientHostNames += nameCl+\"; \"\n\t\t\t\t\tpowerMgmt = \"{}\".format(adDict[ii][\"state_pwrmgt\"])\n\t\t\t\t\tipx = self.fixIP(ip)\n\t\t\t\t\t#if\t MAC == \"54:9f:13:3f:95:25\":\n\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1/0\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"AP\"]\t\t \t\t = ipNumber\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"GHz\"]\t\t \t\t = GHz\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"{}; {} has wrong devType:{}\".format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC, init=False)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] \t\t\t = time.time()\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"GHz\"] \t\t\t = GHz\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"AP\"] \t\t\t\t = ipNumber\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\" + suffixN] = 1\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\telse:\n\t\t\t\t\t\tpass\n\n\n\t\t\t\t\tif not new:\n\t\t\t\t\t\t\tprops=dev.pluginProps\n\t\t\t\t\t\t\tdevidd = \"{}\".format(dev.id)\n\n\t\t\t\t\t\t\toldAssociated =\t dev.states[\"AP\"].split(\"#\")[0]\n\t\t\t\t\t\t\tnewAssociated =\t ipNumber\n\t\t\t\t\t\t\ttry:\t oldIdle =\tint(self.MAC2INDIGO[xType][MAC][\"idleTime\" + suffixN])\n\t\t\t\t\t\t\texcept:\t oldIdle = 0\n\n\t\t\t\t\t\t\t# this is for the case when device switches betwen APs and the old one is still sending.. ignore that one\n\t\t\t\t\t\t\tif dev.states[\"AP\"].split(\"-\")[0] != ipNumber:\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif oldIdle < 600 and int(idletime) > oldIdle: \n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC) or self.decideMyLog(\"Logic\") or self.debugDevs[\"AP\"](int[apN]):\n\t\t\t\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"DC-WF-old {:15s} oldAP:{}; {}; idletime old:{}/new:{} reject entry, still managed by old AP, not switched yet.. expired?\".format(ipNumber, dev.states[\"AP\"], MAC, oldIdle, idletime ))\n\t\t\t\t\t\t\t\t\t\tcontinue # oldIdle < 600 is to catch expired devices\n\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\tpass\n\n\t\t\t\t\t\t\tif dev.states[\"AP\"] != ipNumber+\"-#{}\".format(apN):\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", ipNumber+\"-#{}\".format(apN))\n\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1\n\n\t\t\t\t\t\t\tif ip != \"\":\n\t\t\t\t\t\t\t\tif dev.states[\"ipNumber\"] != ip:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ip\n\n\t\t\t\t\t\t\tif dev.states[\"name\"] != nameCl and nameCl !=\"\":\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"name\", nameCl)\n\n\t\t\t\t\t\t\tif dev.states[\"GHz\"] != GHz:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"GHz\", GHz)\n\n\t\t\t\t\t\t\tif dev.states[\"powerMgmt\"+suffixN] != powerMgmt:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"powerMgmt\"+suffixN, powerMgmt)\n\n\t\t\t\t\t\t\tif dev.states[\"rx_tx_Rate\"+suffixN] != rxtx:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"rx_tx_Rate\"+suffixN, rxtx)\n\n\t\t\t\t\t\t\tif dev.states[\"noise\"+suffixN] != noise:\n\t\t\t\t\t\t\t\tuD = True\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif abs(int(dev.states[\"noise\"+suffixN])- int(noise)) < 3:\n\t\t\t\t\t\t\t\t\t\tuD=\t False\n\t\t\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\t\t\tif uD: self.addToStatesUpdateList(dev.id,\"noise\"+suffixN, noise)\n\n\t\t\t\t\t\t\tif dev.states[\"signal\"+suffixN] != signal:\n\t\t\t\t\t\t\t\tuD = True\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif abs(int(dev.states[\"signal\"+suffixN])- int(signal)) < 3:\n\t\t\t\t\t\t\t\t\t\tuD=\t False\n\t\t\t\t\t\t\t\texcept: pass\n\t\t\t\t\t\t\t\tif uD: self.addToStatesUpdateList(dev.id,\"signal\"+suffixN, signal)\n\n\t\t\t\t\t\t\ttry:\toldUpTime = \"{}\".format(int(self.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN]))\n\t\t\t\t\t\t\texcept: oldUpTime = \"0\"\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN] = newUpTime\n\n\t\t\t\t\t\t\tif dev.states[\"state\" + suffixN] != state:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"state\" + suffixN, state)\n\n\t\t\t\t\t\t\tif dev.states[\"AP\"].split(\"-\")[0] != ipNumber:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", ipNumber+\"-#{}\".format(apN) )\n\n\t\t\t\t\t\t\tif idletime != \"\":\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"idleTime\" + suffixN] = idletime\n\n\t\t\t\t\t\t\toldStatus = dev.states[\"status\"]\n\n\t\t\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\t\t\toldIPX = dev.description.split(\"-\")\n\t\t\t\t\t\t\t\tif oldIPX[0] != ipx or (dev.description != ipx + \"-\" + nameCl+\"=WiFi\" or len(dev.description) < 5):\n\t\t\t\t\t\t\t\t\tif oldIPX[0] != ipx and oldIPX[0] !=\"\":\n\t\t\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_IPNumber_Change\",\"{}/{}/{}/{}\".format(dev.name, dev.states[\"MAC\"], oldIPX[0], ipx) )\n\t\t\t\t\t\t\t\t\tif len(oldIPX) < 2:\n\t\t\t\t\t\t\t\t\t\toldIPX.append(nameCl.strip(\"-\"))\n\t\t\t\t\t\t\t\t\telif len(oldIPX) == 2 and oldIPX[1] == \"\":\n\t\t\t\t\t\t\t\t\t\tif nameCl != \"\":\n\t\t\t\t\t\t\t\t\t\t\toldIPX[1] = nameCl.strip(\"-\")\n\t\t\t\t\t\t\t\t\toldIPX[0] = ipx\n\t\t\t\t\t\t\t\t\tnewDescr = \"-\".join(oldIPX)\n\t\t\t\t\t\t\t\t\tif (dev.description).find(\"=WiFi\")==-1:\n\t\t\t\t\t\t\t\t\t\tdev.description = newDescr+\"=WiFi\"\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tdev.description = newDescr\n\t\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\t\t\t\t\t\t\texpTime = self.getexpT(props)\n\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC) or self.decideMyLog(\"Logic\") or self.debugDevs[\"AP\"][int(apN)]:\n\t\t\t\t\t\t\t\tself.indiLOG.log(10,\"DC-WF-01 {:15s} {}; {}; ip:{}; GHz:{}; txRate:{}; rxR:{}; new-oldUPtime:{}-{}; idletime:{}; signal:{}; hostN:{}; powerMgmt:{}; old/new assoc {}/{}; curr status:{}\".format(ipNumber, MAC, dev.name, ip, GHz, txRate, rxRate, newUpTime, oldUpTime, idletime, signal, nameCl, powerMgmt, oldAssociated.split(\"-\")[0], newAssociated, dev.states[\"status\"]))\n\n\n\t\t\t\t\t\t\t# check what is used to determine up / down, make WiFi a priority\n\t\t\t\t\t\t\tif ( \"useWhatForStatus\" not in\tprops ) or ( \"useWhatForStatus\" in props and props[\"useWhatForStatus\"].find(\"WiFi\") == -1 ):\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif time.time() - time.mktime(datetime.datetime.strptime(dev.states[\"created\"], \"%Y-%m-%d %H:%M:%S\").timetuple()) <\t 60:\n\t\t\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\t\t\t\tprops[\"useWhatForStatus\"]\t\t= \"WiFi,DHCP\"\n\t\t\t\t\t\t\t\t\t\tprops[\"useWhatForStatusWiFi\"]\t= \"Optimized\"\n\t\t\t\t\t\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"created\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\t\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\t\t\tprops[\"useWhatForStatus\"]\t\t= \"WiFi,DHCP\"\n\t\t\t\t\t\t\t\t\tprops[\"useWhatForStatusWiFi\"]\t= \"Optimized\"\n\t\t\t\t\t\t\t\t\tdev.replacePluginPropsOnServer(props)\n\t\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\n\t\t\t\t\t\t\tif \"useWhatForStatus\" in props and props[\"useWhatForStatus\"].find(\"WiFi\") > -1:\n\n\t\t\t\t\t\t\t\tif \"useWhatForStatusWiFi\" not in props or (\"useWhatForStatusWiFi\" in props and\tprops[\"useWhatForStatusWiFi\"] !=\"FastDown\"):\n\n\t\t\t\t\t\t\t\t\ttry:\t newUpTime = int(newUpTime)\n\t\t\t\t\t\t\t\t\texcept:\t newUpTime = int(oldUpTime)\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\tidleTimeMaxSecs\t = float(props[\"idleTimeMaxSecs\"])\n\t\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\t\tidleTimeMaxSecs = 5\n\n\t\t\t\t\t\t\t\t\tif \"useWhatForStatusWiFi\" in props and ( props[\"useWhatForStatusWiFi\"] == \"Optimized\"):\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC) or self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o1 {:15s} {}; .. using optimized for status; idle uptimes {} < {} or uptime (new){} != {}(Old)\" .format(ipNumber, MAC, idletime, idleTimeMaxSecs, newUpTime, oldUpTime))\n\n\t\t\t\t\t\t\t\t\t\tif oldStatus == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif ( float(newUpTime) != float(oldUpTime)\t) or ( float(idletime) < idleTimeMaxSecs ):\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o2R {:15s} {}; ==> restart exptimer use idleTime, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif ( oldAssociated.split(\"-\")[0] != newAssociated ): # ignore new AP\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o3R {:15s} {}; ==> restart exptimer, associated to new AP:{} from:{}, exp-time left:{:5.1f}\".format(ipNumber, MAC, oldAssociated, newAssociated, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])) )\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\telse: # same old\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o4DN {:15s} {}; set timer to expire in 10 secs use idleTime/uptime\".format(ipNumber, MAC))\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()- expTime + 10\n\n\t\t\t\t\t\t\t\t\t\telse: # old = down\n\t\t\t\t\t\t\t\t\t\t\tif ( float(newUpTime) != float(oldUpTime) ) or ( float(idletime) <= idleTimeMaxSecs ):\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o5 {:15s} {}; status Down->up; ==> restart exptimer, use idleTime/uptime, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"]) ))\n\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=oldStatus,ts=time.time(),reason=\"DICT \"+suffixN+\" \"+ipNumber+\" up Optimized\")\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif ( oldAssociated.split(\"-\")[0] != newAssociated ): # ignore new AP\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-o6R {:15s} {}; ==> restart exptimer, status up new AP; use idleTime/uptime, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\n\t\t\t\t\t\t\t\t\telif \"useWhatForStatusWiFi\" in props and (props[\"useWhatForStatusWiFi\"] ==\"IdleTime\" ):\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC) or self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-i0- {:15s} {};. IdleTime.. checking IdleTime {} < {} old/new associated {}/{}\".format(ipNumber, MAC,idletime, idleTimeMaxSecs, oldAssociated.split(\"-\")[0], newAssociated)) \n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\tif float(idletime)\t> idleTimeMaxSecs and oldStatus == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif ( oldAssociated.split(\"-\")[0] == newAssociated ):\n\t\t\t\t\t\t\t\t\t\t\t\tif \"usePingDOWN\" in props and props[\"usePingDOWN\"] and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doWiFiCLIENTSdict\") ==0:\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\"): self.indiLOG.log(10,\"DC-WF-i1R {:15s} {}; reset exptimer, ping was test up, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])) )\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-i2DN {:15s} {}; status down in 10 secs\".format(ipNumber, MAC))\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()- expTime + 10\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-i3R {:15s} {}; status up new AP use IdleTime, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\t\t\t\t\t\t\t\t\t\telif float(idletime) <= idleTimeMaxSecs:\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-i4R {:15s} {}; ==> restart exptimer, use IdleTime, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\tif oldStatus != \"up\":\n\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=oldStatus,ts=time.time(),reason=\"DICT \"+ipNumber+\" \"+suffixN+\" up IdleTime\")\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-i5R {:15s} {}; status up, use IdleTime\".format(ipNumber, MAC))\n\n\n\t\t\t\t\t\t\t\t\telif \"useWhatForStatusWiFi\" in props and (props[\"useWhatForStatusWiFi\"] == \"UpTime\" ):\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC) or self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-U1 .. using UpTime for status\" )\n\t\t\t\t\t\t\t\t\t\tif newUpTime == oldUpTime and oldStatus == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif \"usePingUP\" in props and props[\"usePingUP\"] and self.sendWakewOnLanAndPing(MAC,dev.states[\"ipNumber\"], props=props, calledFrom =\"doWiFiCLIENTSdict\") == 0:\n\t\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC):self.indiLOG.log(10,\"DC-WF-u2 {:15s} {}; ==> restart exptimer, ping test ok, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])) )\n\t\t\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-u3DN {:15s} {}; let timer expire, Uptime is not changed\".format(ipNumber, MAC) )\n\n\t\t\t\t\t\t\t\t\t\telif newUpTime != oldUpTime and oldStatus != \"up\":\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=oldStatus, ts=time.time(), level=1, text1=dev.name.ljust(30) + \" \"+MAC+\" status up WiFi DICT Uptime\",iType=\"DC-WF-U2\",reason=\"DICT \"+ipNumber+\" \"+suffixN+\" up time\")\n\n\t\t\t\t\t\t\t\t\t\telif oldStatus == \"up\":\n\t\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-WF-u4 {:15s} {}; ==> restart exptimer, normal extension, exp-time left:{:5.1f}\".format(ipNumber, MAC, expTime -(time.time()-self.MAC2INDIGO[xType][MAC][\"lastUp\"])))\n\t\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\n\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\t\t\t\tif oldStatus != \"up\":\n\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\", oldStatus=oldStatus,level=1, text1=dev.name.ljust(30) + \" \"+MAC+\" status up WiFi DICT general\", iType=\"DC-WF-UE \",reason=\"DICT \"+suffixN+\" \"+ipNumber+\" up else\")\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\t#break\n\n\t\t\t\t\tif new and not self.ignoreNewClients:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname=\t\t\tdevName + \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=ipx + \"-\" + nameCl+\"=Wifi\",\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"WiFi,DHCP\", \"useWhatForStatusWiFi\":\"Expiration\",isType:True})\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tdevName += \"_\"+( \"{}\".format(time.time() - int(time.time())) ).split(\".\")[1] # create random name\n\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"trying again to create device with different name \"+devName)\n\t\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\t\tname\t\t\t=devName + \"_\" + MAC,\n\t\t\t\t\t\t\t\t\tdescription\t\t=ipx + \"-\" + nameCl+\"=Wifi\",\n\t\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"WiFi,DHCP\", \"useWhatForStatusWiFi\":\"Expiration\",isType:True})\n\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\t\tcontinue\n\n\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, ip, ipNumber, \"\", \" status up new Device\", \"DC-AP-WF-f \")\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"AP\", ipNumber+\"-#{}\".format(apN))\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"powerMgmt\"+suffixN, powerMgmt)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"name\", nameCl)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"rx_tx_Rate\" + suffixN, rxtx)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"signal\" + suffixN, signal)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"noise\" + suffixN, noise)\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"idleTime\" + suffixN] = idletime\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"inList\"+suffixN] = 1\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"state\"+suffixN, state)\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"+suffixN] = newUpTime\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}/{}\".format(dev.name, MAC, ipx) )\n\n\t\t\t\t\tself.executeUpdateStatesList()\n\n\t\t\t\tself.doInList(suffixN,wifiIPAP=ipNumber)\n\t\t\t\tself.executeUpdateStatesList()\n\n\t\t\texcept\tException as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\tself.unsetBlockAccess(\"doWiFiCLIENTSdict\")\n\t\t\t\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn \tclientHostNames\n\n\n\n\n\t####-----------------\t ---------\n\t## AP devices themselves DICT\n\t####-----------------\t ---------\n\tdef doAPdictsSELF(self,apDict, apNumb, ipNDevice, MAC, hostname, ipNumber, clientHostNames):\n\n\t\tself.setBlockAccess(\"doAPdictsSELF\")\n\n\t\tif \"model_display\" in apDict: model = (apDict[\"model_display\"])\n\t\telse:\n\t\t\tself.indiLOG.log(30,\"model_display not in dict doAPdicts\")\n\t\t\tmodel = \"\"\n\n\n\t\tdevType =\"Device-AP\"\n\t\tisType\t= \"isAP\"\n\t\tdevName = \"AP\"\n\t\txType\t= \"AP\"\n\t\ttry:\n\n\n\t\t\tfor GHz in [\"2\",\"5\"]:\n\t\t\t\tnClients = 0\n\t\t\t\tessid\t = \"\"\n\t\t\t\tradio\t = \"\"\n\t\t\t\ttx_power = \"\"\n\t\t\t\tfor jj in range(len(apDict[\"vap_table\"])):\n\t\t\t\t\tshortC\t= apDict[\"vap_table\"][jj]\n\t\t\t\t\tif \"usage\" in shortC: #skip if not wireless\n\t\t\t\t\t\tif shortC[\"usage\"] == \"downlink\": continue\n\t\t\t\t\t\tif shortC[\"usage\"] == \"uplink\":\t continue\n\t\t\t\t\tchannel = shortC[\"channel\"]\n\t\t\t\t\tif not( GHz == \"2\" and channel < 14 or GHz == \"5\" and channel > 13): continue \n\t\t\t\t\tnClients += shortC[\"num_sta\"]\n\t\t\t\t\tessid\t += \"{}\".format(shortC[\"essid\"]) + \"; \"\n\t\t\t\t\tradio\t = \"{}\".format(shortC[\"radio\"])\n\t\t\t\t\ttx_power = \"{}\".format(shortC[\"tx_power\"])\n\t\t\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"doAPdictsSELF {} - GHz:{}, sta:{}, essid:{}, radio:{}, tx:{}\".format(MAC, GHz, nClients, essid, radio, tx_power) )\n\n\t\t\t\t\tnew = True\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\"): self.indiLOG.log(10, \"{} {} wrong {}\".format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif not new:\n\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-AP--- {} hostname:{} MAC:{}; GHz:{}; essid:{}; channel:{}; nClients:{}; tx_power:{}; radio:{}\".format(ipNumber, hostname, MAC, GHz, essid, channel, nClients, tx_power, radio))\n\t\t\t\t\t\t\tif \"uptime\" in apDict and apDict[\"uptime\"] !=\"\":\n\t\t\t\t\t\t\t\tif \"upSince\" in dev.states:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"upSince\", time.strftime(\"%Y-%d-%m %H:%M:%S\", time.localtime(time.time() - apDict[\"uptime\"])) )\n\t\t\t\t\t\t\tif tx_power != dev.states[\"tx_power_\" + GHz]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"tx_power_\" + GHz, tx_power)\n\t\t\t\t\t\t\tif \"{}\".format(channel) != dev.states[\"channel_\" + GHz]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"channel_\" + GHz, \"{}\".format(channel) )\n\t\t\t\t\t\t\tif essid.strip(\"; \") != dev.states[\"essid_\" + GHz]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"essid_\" + GHz, essid.strip(\"; \"))\n\t\t\t\t\t\t\tif \"{}\".format(nClients) != dev.states[\"nClients_\" + GHz]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"nClients_\" + GHz, \"{}\".format(nClients) )\n\t\t\t\t\t\t\tif radio != dev.states[\"radio_\" + GHz]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"radio_\" + GHz, radio)\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ipNumber\n\t\t\t\t\t\t\tif ipNumber != dev.states[\"ipNumber\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ipNumber)\n\t\t\t\t\t\t\tif hostname != dev.states[\"hostname\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"hostname\", hostname)\n\t\t\t\t\t\t\tif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\", oldStatus=dev.states[\"status\"],ts=time.time(), level=1, text1=dev.name.ljust(30) + \" status up AP DICT\", reason=\"AP DICT\", iType=\"STATUS-AP\")\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\tif dev.states[\"model\"] != model and model != \"\":\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\t\t\tif dev.states[\"apNo\"] != apNumb:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"apNo\", apNumb)\n\n\t\t\t\t\t\t\tself.setStatusUpForSelfUnifiDev(MAC)\n\n\n\t\t\t\t\tif new:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName + \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=self.fixIP(ipNumber) + \"-\" + hostname,\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameIDCreated,\n\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"\",isType:True})\n\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, \"AP\", ipNumber,\"\", \"\", \" status up AP DICT new AP\", \"STATUS-AP\")\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"essid_\" + GHz, essid)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"channel_\" + GHz, channel)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"hostname\", hostname)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"nClients_\" + GHz, \"{}\".format(nClients) )\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"radio_\" + GHz, radio)\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"tx_power_\" + GHz, tx_power)\n\t\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\t\tself.buttonConfirmGetAPDevInfoFromControllerCALLBACK()\n\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}/{}\".format(dev.name, MAC, ipNumber) )\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\t\tself.indiLOG.log(40,\"failed to create dev: {}_ \".format(devName, MAC))\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"clientList_\"+GHz+\"GHz\", clientHostNames[GHz].strip(\"; \"))\n\n\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doAPdictsSELF\")\n\t\t\n\t\treturn\n\n\n\n\n\t####-----------------\t ---------\n\tdef doGatewaydictSELF(self, gwDict, ipNumber):\n\n\t\tself.setBlockAccess(\"doGatewaydictSELF\")\n\n\t\ttry:\n\n\t\t\tdevType = \"gateway\"\n\t\t\tdevName = \"gateway\"\n\t\t\tisType\t = \"isGateway\"\n\t\t\txType\t = \"GW\"\n\t\t\tsuffixN\t = \"DHCP\"\n\t\t\t##########\tdo gateway params ###\n\n\n\t\t\t# get lan info ------\n\t\t\tipNDevice\t= \"\"\n\t\t\tMAClan\t\t= \"\"\n\t\t\tlan\t\t\t= {}\n\t\t\tmodel\t\t= \"\"\n\t\t\tcpuPercent\t= \"\"\n\t\t\tmemPercent\t= \"\"\n\t\t\ttemperature = \"\"\n\t\t\ttemperature_Board_CPU \t= \"\"\n\t\t\ttemperature_Board_PHY \t= \"\"\n\t\t\ttemperature_CPU \t\t= \"\"\n\t\t\ttemperature_PHY \t\t= \"\"\n\n\t\t\tpublicIP\t= \"\"\n\t\t\twan\t\t\t= {}\n\t\t\tMAC\t\t\t= \"\"\n\t\t\tgateways\t= \"\"\n\t\t\twanUP\t\t= False\n\t\t\twanPingTime = \"\"\n\t\t\twanSpeedTest= \"\"\n\t\t\twanLatency\t= \"\"\n\t\t\twanDownload = \"\"\n\t\t\twanUpload\t= \"\"\n\t\t\tnameservers = \"-\"\n\t\t\twanRunDate\t= \"\"\n\t\t\twanUpTime\t= \"\"\n\t\t\tgateways\t= \"-\"\n\n\t\t\tpublicIP2\t= \"\"\n\t\t\twan2\t\t= {}\n\t\t\tMACwan2\t\t= \"\"\n\t\t\tgateways2\t= \"-\"\n\t\t\twan2UP\t\t= False\n\t\t\twan2PingTime = \"\"\n\t\t\twan2SpeedTest= \"\"\n\t\t\twan2Latency\t= \"\"\n\t\t\twan2Download = \"\"\n\t\t\twan2Upload\t= \"\"\n\t\t\tnameservers2 = \"-\"\n\t\t\twan2RunDate\t= \"\"\n\t\t\twan2UpTime\t= \"\"\n\t\t\tgateways2\t= \"-\"\n\n\t\t\twanSetup\t= \"wan1 only\"\n\n\n\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"doGw unifiControllerType:{}; if.. find UDM >-1:{}\".format(self.unifiControllerType, self.unifiControllerType.find(\"UDM\") > -1) )\n\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1: \n\n\t\t\t\tif \"if_table\" not in gwDict: \n\t\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"doGw UDM gateway \\\"if_table\\\" not in gwDict\")\n\t\t\t\t\treturn\n\n\t\t\t\tif \"ip\" in gwDict:\t \n\t\t\t\t\tpublicIP\t = gwDict[\"ip\"].split(\"/\")[0]\n\t\t\t\telse:\n\t\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"doGw UDM gateway no public IP number found: \\\"ip\\\" not in gwDict\")\n\t\t\t\t\treturn \n\n\t\t\t\tnameList = {}\n\t\t\t\tfor table in gwDict[\"if_table\"]:\n\t\t\t\t\tif \"name\" in table: \n\t\t\t\t\t\tnameList[table[\"name\"]] = \"\"\n\t\t\t\t\t\tif \"mac\" in table:\n\t\t\t\t\t\t\tnameList[table[\"name\"]] = table[\"mac\"]\n\n\t\t\t\tfor ethName in nameList:\n\t\t\t\t\tfor table in gwDict[\"port_table\"]:\n\t\t\t\t\t\tif ethName in table:\n\t\t\t\t\t\t\tif \"mac\" in table: \n\t\t\t\t\t\t\t\tnameList[ethName] = mac\n\n\n\t\t\t\t### wan default\n\t\t\t\t# udm-pro\n\t\t\t\t# wan = eth8\n\t\t\t\t# wan2 = eth9\n\t\t\t\t# udm has no second wan on the dedicated Unifi lte modem allows inetrnet backup \n\t\t\t\t# not supported yet , only use wan not wan2\n\t\t\t\twan = {}\n\t\t\t\tfor table in gwDict[\"if_table\"]:\n\t\t\t\t\tif self.unifiControllerType == \"UDM\":\n\t\t\t\t\t\tif table[\"ip\"] == publicIP:\n\t\t\t\t\t\t\twan = table\n\t\t\t\t\t\t\tif \"speedtest-status\" in table:\n\t\t\t\t\t\t\t\twan[\"latency\"] \t\t= table[\"speedtest-status\"][\"latency\"]\n\t\t\t\t\t\t\t\twan[\"xput_down\"] \t\t= table[\"speedtest-status\"][\"xput_download\"]\n\t\t\t\t\t\t\t\twan[\"xput_up\"] \t\t= table[\"speedtest-status\"][\"xput_upload\"]\n\t\t\t\t\t\t\t\twan[\"speedtest_ping\"] \t= table[\"speedtest-status\"][\"status_ping\"]\n\t\t\t\t\t\t\tif \"name\" in table:\n\t\t\t\t\t\t\t\tif table[\"name\"] in nameList:\n\t\t\t\t\t\t\t\t\twan[\"mac\"] = nameList[table[\"name\"]]\n\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\telse:\n\t\t\t\t\t\tif table[\"name\"] == \"eth8\":\n\t\t\t\t\t\t\twan = table\n\t\t\t\t\t\t\tif \"speedtest-status\" in table:\n\t\t\t\t\t\t\t\twan[\"latency\"] \t\t= table[\"speedtest-status\"][\"latency\"]\n\t\t\t\t\t\t\t\twan[\"xput_down\"] \t\t= table[\"speedtest-status\"][\"xput_download\"]\n\t\t\t\t\t\t\t\twan[\"xput_up\"] \t\t= table[\"speedtest-status\"][\"xput_upload\"]\n\t\t\t\t\t\t\t\twan[\"speedtest_ping\"] \t= table[\"speedtest-status\"][\"status_ping\"]\n\t\t\t\t\t\t\tif \"name\" in table:\n\t\t\t\t\t\t\t\tif table[\"name\"] in nameList:\n\t\t\t\t\t\t\t\t\twan[\"mac\"] = nameList[table[\"name\"]]\n\t\t\t\t\t\t\tbreak\n\n\n\t\t\t\twan2 = {}\n\t\t\t\tif self.unifiControllerType != \"UDM\": # for UDM pro only\n\t\t\t\t\tfor table in gwDict[\"if_table\"]:\n\t\t\t\t\t\tif table[\"name\"] == \"eth9\":\n\t\t\t\t\t\t\twan2 = table\n\t\t\t\t\t\t\tif \"speedtest-status\" in table:\n\t\t\t\t\t\t\t\twan2[\"latency\"] \t\t\t= table[\"speedtest-status\"][\"latency\"]\n\t\t\t\t\t\t\t\twan2[\"xput_down\"] \t\t\t= table[\"speedtest-status\"][\"xput_download\"]\n\t\t\t\t\t\t\t\twan2[\"xput_up\"] \t\t\t= table[\"speedtest-status\"][\"xput_upload\"]\n\t\t\t\t\t\t\t\twan2[\"speedtest_ping\"] \t= table[\"speedtest-status\"][\"status_ping\"]\n\t\t\t\t\t\t\tif \"name\" in table:\n\t\t\t\t\t\t\t\tif table[\"name\"] in nameList:\n\t\t\t\t\t\t\t\t\twan2[\"mac\"] = nameList[table[\"name\"]]\n\t\t\t\t\t\t\tbreak\n\n\n\t\t\t\tlan = {}\n\t\t\t\tfor table in gwDict[\"if_table\"]:\n\t\t\t\t\tif \"ip\" not in table: continue\n\t\t\t\t\tif table[\"ip\"] == ipNumber:\n\t\t\t\t\t\tlan = table\n\t\t\t\t\t\tif \"name\" in table:\n\t\t\t\t\t\t\tif table[\"name\"] in nameList:\n\t\t\t\t\t\t\t\twan[\"mac\"] = nameList[table[\"name\"]]\n\t\t\t\t\t\tbreak\n\n\t\t\t\tif lan == {} or wan == {}: \n\t\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"doGw UDM gateway nameList:{}; ip:{}; wan:{} / lan:{}; not found in \\\"if_table\\\"\".format(ipNumber, nameList, lan, wan) )\n\t\t\t\t\treturn \n\n\t\t\t\tipNDevice = ipNumber\n\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"doGw UDM gateway ip:{}; nameList:{}\\nwan:{}\\nlan:{}\".format(ipNumber, lan, wan, nameList) )\n\n\n\t\t\telse: # non UDM type \n\t\t\t\tif \"if_table\"\t\t\t not in gwDict: \n\t\t\t\t\treturn\n\t\t\t\tif\t \"config_port_table\"\t in gwDict: table = \"config_port_table\"\n\t\t\t\telif \"config_network_ports\" in gwDict: table = \"config_network_ports\"\n\t\t\t\telse:\t\t\t\t\t\t\t\t\t \n\t\t\t\t\treturn\n\n\t\t\t\tif \"connect_request_ip\" in gwDict:\n\t\t\t\t\tipNDevice = self.fixIP(gwDict[\"connect_request_ip\"])\n\t\t\t\tif ipNDevice == \"\": \n\t\t\t\t\treturn\n\n\t\t\t\tif table == \"config_network_ports\":\n\t\t\t\t\t\tif \"LAN\" in gwDict[table]:\n\t\t\t\t\t\t\tifnameLAN = gwDict[table][\"LAN\"]\n\t\t\t\t\t\t\tfor xx in range(len(gwDict[\"if_table\"])):\n\t\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameLAN:\n\t\t\t\t\t\t\t\t\tlan = gwDict[\"if_table\"][xx]\n\t\t\t\t\t\tif \"WAN\" in gwDict[table]:\n\t\t\t\t\t\t\tifnameWAN = gwDict[table][\"WAN\"]\n\t\t\t\t\t\t\tfor xx in range(len(gwDict[\"if_table\"])):\n\t\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameWAN:\n\t\t\t\t\t\t\t\t\twan = gwDict[\"if_table\"][xx]\n\t\t\t\t\t\tif \"WAN2\" in gwDict[table]:\n\t\t\t\t\t\t\tifnameWAN2 = gwDict[table][\"WAN2\"]\n\t\t\t\t\t\t\tfor xx in range(len(gwDict[\"if_table\"])):\n\t\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameWAN2:\n\t\t\t\t\t\t\t\t\twan2 = gwDict[\"if_table\"][xx]\n\n\t\t\t\telif table == \"config_port_table\":\n\t\t\t\t\tfor xx in range(len(gwDict[table])):\n\t\t\t\t\t\tif \"name\" in gwDict[table][xx] and gwDict[table][xx][\"name\"].lower() == \"lan\":\n\t\t\t\t\t\t\tifnameLAN = gwDict[table][xx][\"ifname\"]\n\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameLAN:\n\t\t\t\t\t\t\t\tlan = gwDict[\"if_table\"][xx]\n\t\t\t\t\t\tif \"name\" in gwDict[table][xx] and gwDict[table][xx][\"name\"].lower() ==\"wan\":\n\t\t\t\t\t\t\tifnameWAN = gwDict[table][xx][\"ifname\"]\n\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameWAN:\n\t\t\t\t\t\t\t\twan = gwDict[\"if_table\"][xx]\n\t\t\t\t\t\tif \"name\" in gwDict[table][xx] and gwDict[table][xx][\"name\"].lower() ==\"wan2\":\n\t\t\t\t\t\t\tifnameWAN2 = gwDict[table][xx][\"ifname\"]\n\t\t\t\t\t\t\tif \"name\" in gwDict[\"if_table\"][xx] and gwDict[\"if_table\"][xx][\"name\"] == ifnameWAN2:\n\t\t\t\t\t\t\t\twan2 = gwDict[\"if_table\"][xx]\n\n\n\n\t\t\tif \"model_display\" \tin gwDict:\t\t\t\t\t\tmodel\t\t= gwDict[\"model_display\"]\n\t\t\telse:\n\t\t\t\tself.indiLOG.log(10,\"model_display not in dict doGatewaydict\")\n\n\t\t\tif \"uptime\" in wan2:\t\t\t\t\t\t\t\twan2UpTime = self.convertTimedeltaToDaysHoursMin(wan2[\"uptime\"])\n\t\t\tif \"uptime\" in wan:\t\t\t\t\t\t\t\t\twanUpTime = self.convertTimedeltaToDaysHoursMin(wan[\"uptime\"])\n\n\t\t\tif \"gateways\" \t\tin wan:\t\t\t\t\t\t\tgateways\t\t= \"-\".join(wan[\"gateways\"])\n\t\t\tif \"gateways\" \t\tin wan2:\t\t\t\t\t\tgateways2\t\t= \"-\".join(wan2[\"gateways\"])\n\t\t\tif \"nameservers\" \tin wan:\t\t\t\t\t\t\tnameservers\t\t= \"-\".join(wan[\"nameservers\"])\n\t\t\tif \"nameservers\" \tin wan2:\t\t\t\t\t\tnameservers2\t= \"-\".join(wan2[\"nameservers\"])\n\t\t\tif \"mac\" \t\t\tin wan:\t\t\t\t\t\t\tMAC\t\t\t\t= wan[\"mac\"]\n\t\t\tif \"mac\" \t\t\tin wan2:\t\t\t\t\t\tMACwan2\t\t\t= wan2[\"mac\"]\n\n\n\t\t\tif \"up\" in wan:\t\t\t\t\t\t\t\t\twanUP = wan[\"up\"]\n\t\t\tif \"up\" in wan2:\t\t\t\t\t\t\t\t\twan2UP = wan2[\"up\"]\n\n\t\t\tif not wanUP and wan2UP: \t\t\t\t\t\t\twanSetup = \"failover\"\n\t\t\telif not wanUP and not wan2UP: \t\t\t\t\t\twanSetup = \"wan down\"\n\t\t\telif wanUP and wan2UP: \t\t\t\t\t\t\twanSetup = \"load balancing\"\n\t\t\telse: \t\t\t\t\t\t\t\t\t\t\t\twanSetup = \"wan1 only\"\n\n\t\t\tif \"ip\" in wan and wan[\"ip\"] != \"\" and wanUP: \tpublicIP = wan[\"ip\"].split(\"/\")[0]\n\t\t\telif \"ip\" in wan2 and wan2[\"ip\"] != \"\" and wan2UP:\tpublicIP2 = wan2[\"ip\"].split(\"/\")[0]\n\n\n\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"gw dict parameters wan:{}, wan2:{}, macwan:{}, macwan2:{}, publicIP:{}, publicIP2:{}\".format(wan,wan2,MAC,MACwan2,publicIP,publicIP2))\n\n\t\t\tif \"mac\" in lan:\t\t\t\tMAClan\t\t\t= lan[\"mac\"]\n\t\t\tif \"system-stats\" in gwDict:\n\t\t\t\tsysStats = gwDict[\"system-stats\"]\n\t\t\t\tif \"cpu\" in sysStats:\t cpuPercent = sysStats[\"cpu\"]\n\t\t\t\tif \"mem\" in sysStats:\t memPercent = sysStats[\"mem\"]\n\t\t\t\tfor xx in [\"temps\"]:\n\t\t\t\t\tif xx in sysStats:\n\t\t\t\t\t\tif len(sysStats[xx]) > 0:\n\t\t\t\t\t\t\tif type(sysStats[xx]) == type({}):\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tfor key in sysStats[xx]:\n\t\t\t\t\t\t\t\t\t\tif key == \"Board (CPU)\": temperature_Board_CPU \t= GT.getNumber(sysStats[xx][key])\n\t\t\t\t\t\t\t\t\t\telif key == \"Board (PHY)\":\ttemperature_Board_PHY \t= GT.getNumber(sysStats[xx][key])\n\t\t\t\t\t\t\t\t\t\telif key == \"CPU\": \t\ttemperature_CPU \t\t= GT.getNumber(sysStats[xx][key])\n\t\t\t\t\t\t\t\t\t\telif key == \"PHY\": \t\ttemperature_PHY \t\t= GT.getNumber(sysStats[xx][key])\n\t\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"doGatewaydictSELF sysStats[temp]err : {}, key:{}, value:{}, error:{}\".format(sysStats[xx], key, sysStats[xx][key], e))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\ttemperature\t = GT.getNumber(sysStats[xx])\n\t\t\tfor xx in [\"temperatures\"]:\n\t\t\t\t\tif xx in gwDict:\n\t\t\t\t\t\tif len(gwDict[xx]) > 0:\n\t\t\t\t\t\t\tif type(gwDict[xx]) == type([]):\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tfor yy in gwDict[xx]:\n\t\t\t\t\t\t\t\t\t\tif \"name\" in yy: \n\t\t\t\t\t\t\t\t\t\t\tif yy[\"name\"] == \"Local\": \ttemperature\t\t \t= GT.getNumber(yy[\"value\"])\n\t\t\t\t\t\t\t\t\t\t\tif yy[\"name\"] == \"PHY\": \ttemperature_Board_PHY \t= GT.getNumber(yy[\"value\"])\n\t\t\t\t\t\t\t\t\t\t\tif yy[\"name\"] == \"CPU\": \ttemperature_Board_CPU \t= GT.getNumber(yy[\"value\"])\n\t\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\t\tself.indiLOG.log(30,\"doGatewaydictSELF temperatures[temp]err : {}\".format(gwDict[xx]))\n\n\t\t\tif \"speedtest_lastrun\" in wan and wan[\"speedtest_lastrun\"] !=0:\n\t\t\t\t\t\t\t\t\t\t\twanSpeedTest\t= datetime.datetime.fromtimestamp(float(wan[\"speedtest_lastrun\"])).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\tif \"speedtest_lastrun\" in wan2 and wan2[\"speedtest_lastrun\"] !=0:\n\t\t\t\t\t\t\t\t\t\t\twan2SpeedTest\t= datetime.datetime.fromtimestamp(float(wan2[\"speedtest_lastrun\"])).strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\tif \"speedtest_ping\" in wan:\twanPingTime\t\t= \"%4.1f\" % wan[\"speedtest_ping\"] + \"[ms]\"\n\t\t\tif \"latency\" in wan:\t\t\twanLatency\t\t= \"%4.1f\" % wan[\"latency\"] + \"[ms]\"\n\t\t\tif \"xput_down\" in wan:\t\t\twanDownload\t\t= \"%4.1f\" % wan[\"xput_down\"] + \"[Mb/S]\"\n\t\t\tif \"xput_up\" in wan:\t\t\twanUpload\t\t= \"%4.1f\" % wan[\"xput_up\"] + \"[Mb/S]\"\n\n\t\t\tif \"speedtest_ping\" in wan2:\twan2PingTime\t= \"%4.1f\" % wan2[\"speedtest_ping\"] + \"[ms]\"\n\t\t\tif \"latency\" in wan2:\t\t\twan2Latency\t\t= \"%4.1f\" % wan2[\"latency\"] + \"[ms]\"\n\t\t\tif \"xput_down\" in wan2:\t\twan2Download\t= \"%4.1f\" % wan2[\"xput_down\"] + \"[Mb/S]\"\n\t\t\tif \"xput_up\" in wan2:\t\t\twan2Upload\t\t= \"%4.1f\" % wan2[\"xput_up\"] + \"[Mb/S]\"\n\n\n\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"UDM gateway MAC:{} -MAClan{}\".format(MAC,MAClan))\n\n\t\t\tisNew = True\n\n\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\ttry:\n\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\tisNew = False\n\t\t\t\texcept:\n\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"{} {} wrong {}\" .format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\tif \"MAC\" not in dev.states:\t\t\tcontinue\n\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC:\t\tcontinue\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\t\t\t\tisNew = False\n\t\t\t\t\t\tbreak\n\n\n\t\t\tif isNew:\n\t\t\t\ttry:\n\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\tprotocol\t\t= indigo.kProtocol.Plugin,\n\t\t\t\t\t\taddress \t\t= MAC,\n\t\t\t\t\t\tname \t\t\t= devName+\"_\" + MAC,\n\t\t\t\t\t\tdescription \t= self.fixIP(ipNDevice),\n\t\t\t\t\t\tpluginId \t\t= self.pluginId,\n\t\t\t\t\t\tdeviceTypeId \t= devType,\n\t\t\t\t\t\tfolder \t\t\t= self.folderNameIDCreated,\n\t\t\t\t\t\tprops \t\t\t= {\"useWhatForStatus\":\"\",isType:True, \"failoverSettings\":\"copypublicIP\", \"useWhichMAC\":\"MAClan\",\"enableBroadCastEvents\":\"0\"})\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, ipNDevice, \"\", \"\", \" status up GW DICT new gateway if_table\", \"STATUS-GW\")\n\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\tself.setUpDownStateValue(dev)\n\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}/{}\".format(dev.name, MAC, ipNDevice) )\n\t\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC): self.indiLOG.log(10,\"DC-GW-1--- {} ip:{} {} new device\".format(MAC, ipNDevice, dev.name) )\n\t\t\t\t\tisNew = False # fill the rest in next section\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\tif not isNew:\n\t\t\t\tif \"uptime\" in gwDict and gwDict[\"uptime\"] != \"\" and \"upSince\" in dev.states:\t\t\t\tself.addToStatesUpdateList(dev.id,\"upSince\",time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()-gwDict[\"uptime\"])) )\n\t\t\t\tprops = dev.pluginProps\n\t\t\t\tif wanSetup == \"failover\": \n\t\t\t\t\tif \"failoverSettings\" in props and props[\"failoverSettings\"] == \"copypublicIP\":\n\t\t\t\t\t\tpublicIP = publicIP2 \n\t\t\t\t\t\tgateways = gateways2 \n\t\t\t\t\t\tnameservers = nameservers2 \n\n\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ipNDevice\n\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] \t = time.time()\n\n\t\t\t\tif dev.states[\"wanSetup\"] \t\t\t\t!= wanSetup: \t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanSetup\", wanSetup)\n\t\t\t\tif dev.states[\"MAClan\"] \t\t\t\t!= MAClan:\t\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAClan\", MAClan)\n\t\t\t\tif dev.states[\"ipNumber\"] \t\t\t\t!= ipNDevice: \t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ipNDevice)\n\t\t\t\tif dev.states[\"model\"] \t\t\t\t!= model and model != \"\":\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\tif dev.states[\"memPercent\"] \t\t\t!= cpuPercent and memPercent != \"\":\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"memPercent\", memPercent)\n\t\t\t\tif dev.states[\"cpuPercent\"] \t\t\t!= cpuPercent and cpuPercent != \"\":\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"cpuPercent\", cpuPercent)\n\t\t\t\tif dev.states[\"temperature\"] \t\t\t!= temperature and temperature != \"\": \t\t\t\t\tself.addToStatesUpdateList(dev.id,\"temperature\", temperature)\n\t\t\t\tif dev.states[\"temperature_Board_CPU\"]\t!= temperature_Board_CPU and temperature_Board_CPU != \"\": self.addToStatesUpdateList(dev.id,\"temperature_Board_CPU\", temperature_Board_CPU)\n\t\t\t\tif dev.states[\"temperature_Board_PHY\"]\t!= temperature_Board_PHY and temperature_Board_PHY != \"\": self.addToStatesUpdateList(dev.id,\"temperature_Board_PHY\", temperature_Board_PHY)\n\t\t\t\tif dev.states[\"temperature_CPU\"]\t\t!= temperature_CPU \t\t and temperature_CPU != \"\":\t\tself.addToStatesUpdateList(dev.id,\"temperature_CPU\", temperature_CPU)\n\t\t\t\tif dev.states[\"temperature_PHY\"]\t\t!= temperature_PHY \t\t and temperature_PHY != \"\":\t\tself.addToStatesUpdateList(dev.id,\"temperature_PHY\", temperature_PHY)\n\n\t\t\t\tif dev.states[\"wan\"] \t\t\t\t\t!= wanUP:\t\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan\", \"up\" if wanUP else \"down\")\t\n\t\t\t\tif dev.states[\"MAC\"] \t\t\t\t\t!= MAC:\t\t\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\t\tif dev.states[\"nameservers\"]\t\t\t!= nameservers:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"nameservers\", nameservers)\n\t\t\t\tif dev.states[\"gateways\"] \t\t\t\t!= gateways:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"gateways\", gateways)\n\t\t\t\tif dev.states[\"publicIP\"] \t\t\t\t!= publicIP:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"publicIP\", publicIP)\n\t\t\t\tif dev.states[\"wanPingTime\"] \t\t\t!= wanPingTime: \t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanPingTime\", wanPingTime)\n\t\t\t\tif dev.states[\"wanLatency\"] \t\t\t!= wanLatency: \t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanLatency\", wanLatency)\n\t\t\t\tif dev.states[\"wanUpload\"] \t\t\t!= wanUpload:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanUpload\", wanUpload)\n\t\t\t\tif dev.states[\"wanSpeedTest\"] \t\t\t!= wanSpeedTest:\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanSpeedTest\", wanSpeedTest)\n\t\t\t\tif dev.states[\"wanDownload\"] \t\t\t!= wanDownload:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanDownload\", wanDownload)\n\t\t\t\tif dev.states[\"wanUpTime\"] \t\t\t!= wanUpTime: \t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wanUpTime\", wanUpTime)\n\n\t\t\t\tif dev.states[\"wan2\"] \t\t\t\t\t!= wan2UP:\t\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2\", \"up\" if wan2UP else \"down\")\n\t\t\t\tif dev.states[\"MACwan2\"] \t\t\t\t!= MACwan2:\t\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MACwan2\", MACwan2)\n\t\t\t\tif dev.states[\"wan2Nameservers\"]\t\t!= nameservers2:\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2Nameservers\", nameservers2)\n\t\t\t\tif dev.states[\"wan2Gateways\"] \t\t\t!= gateways2:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2Gateways\", gateways2)\n\t\t\t\tif dev.states[\"wan2PublicIP\"] \t\t\t!= publicIP2:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2PublicIP\", publicIP2)\n\t\t\t\tif dev.states[\"wan2PingTime\"] \t\t\t!= wan2PingTime: \t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2PingTime\", wan2PingTime)\n\t\t\t\tif dev.states[\"wan2Latency\"] \t\t\t!= wan2Latency:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2Latency\", wan2Latency)\n\t\t\t\tif dev.states[\"wan2Upload\"] \t\t\t!= wan2Upload:\t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2Upload\", wan2Upload)\n\t\t\t\tif dev.states[\"wan2SpeedTest\"] \t\t!= wan2SpeedTest:\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2SpeedTest\", wan2SpeedTest)\n\t\t\t\tif dev.states[\"wan2Download\"] \t\t\t!= wan2Download:\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2Download\", wan2Download)\n\t\t\t\tif dev.states[\"wan2UpTime\"] \t\t\t!= wan2UpTime: \t\t\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"wan2UpTime\", wan2UpTime)\n\n\t\t\t\tif dev.states[\"status\"] \t\t\t\t!= \"up\":\t\t\t\t\t\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1=dev.name.ljust(30) + \" status up GW DICT if_table\", reason=\"gateway DICT\", iType=\"STATUS-GW\")\n\n\n\t\t\t\tif self.decideMyLog(\"Dict\", MAC=MAC) or self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-GW-1-- {} ip:{} {} new GW data\".format(MAC,ipNDevice, dev.name))\n\n\t\t\t\tself.setStatusUpForSelfUnifiDev(MAC)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doGatewaydictSELF\")\n\t\t\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef convertTimedeltaToDaysHoursMin(self,uptime):\n\t\ttry:\n\t\t\tret = \"\"\n\t\t\tuptime = float(uptime)\n\t\t\txx = \"{}\".format(datetime.timedelta(seconds=uptime)).replace(\" days\",\"\").replace(\" day\",\"\").split(\",\")\n\t\t\tif len(xx) ==2:\n\t\t\t\tret = xx[0]+\"d \"\n\t\t\t\tyy = xx[1].split(\":\")\n\t\t\t\tif len(yy) >1:\n\t\t\t\t\tret += yy[0]+\"h \" +yy[1]+\"m\"\n\t\t\tif len(xx) ==1:\n\t\t\t\tyy = xx[0].split(\":\")\n\t\t\t\tif len(yy) >1:\n\t\t\t\t\tret += yy[0]+\"h \" +yy[1]+\"m\"\n\t\t\treturn ret\n\t\texcept: pass\n\n\n\t####-----------------\t ---------\n\tdef doNeighborsdict(self,apDict,apNumb, ipNumber):\n\t\tself.setBlockAccess(\"doNeighborsdict\")\n\n\t\ttry:\n\t\t\tdevType =\"neighbor\"\n\t\t\tdevName =\"neighbor\"\n\t\t\tisType = \"isNeighbor\"\n\t\t\txType = \"NB\"\n\t\t\tfor kk in range(len(apDict)):\n\n\t\t\t\tshortR = apDict[kk][\"scan_table\"]\n\t\t\t\tfor shortC in shortR:\n\t\t\t\t\tMAC = \"{}\".format(shortC[\"bssid\"])\n\t\t\t\t\tchannel = \"{}\".format(shortC[\"channel\"])\n\t\t\t\t\tessid = \"{}\".format(shortC[\"essid\"])\n\t\t\t\t\tage = \"{}\".format(shortC[\"age\"])\n\t\t\t\t\tadhoc = \"{}\".format(shortC[\"is_adhoc\"])\n\t\t\t\t\ttry:\n\t\t\t\t\t\trssi = \"{}\".format(shortC[\"rssi\"])\n\t\t\t\t\texcept:\n\t\t\t\t\t\trssi = \"\"\n\t\t\t\t\tif \"model_display\" in shortC: model = (shortC[\"model_display\"])\n\t\t\t\t\telse:\n\t\t\t\t\t\tmodel = \"\"\n\n\t\t\t\t\tnew = True\n\t\t\t\t\tif int(channel) >= 36:\n\t\t\t\t\t\tGHz = \"5\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tGHz = \"2\"\n\t\t\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\t\t\tif dev.deviceTypeId != devType: 1 / 0\n\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,MAC + \" {}\".format(self.MAC2INDIGO[xType][MAC]) + \" wrong \" + devType)\n\t\t\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\t\t\tself.setupStructures(xType, dev, MAC, init=False)\n\t\t\t\t\t\t\t\tnew = False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif not new:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ipNumber\n\t\t\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-NB-0- \"+ipNumber+ \" MAC: \" + MAC + \" GHz:\" + GHz + \" essid:\" + essid + \" channel:\" + channel )\n\t\t\t\t\t\t\tif MAC != dev.states[\"MAC\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\t\t\t\t\tif essid != dev.states[\"essid\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"essid\", essid)\n\t\t\t\t\t\t\tif channel != dev.states[\"channel\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"channel\", channel)\n\t\t\t\t\t\t\tif channel != dev.states[\"adhoc\"]:\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"adhoc\", adhoc)\n\n\t\t\t\t\t\t\tsignalOLD = [\" \" for iii in range(_GlobalConst_numberOfAP)]\n\t\t\t\t\t\t\tsignalNEW = copy.copy(signalOLD)\n\t\t\t\t\t\t\tif rssi != \"\":\n\t\t\t\t\t\t\t\tsignalOLD = dev.states[\"Signal_at_APs\"].split(\"[\")[0].split(\"/\")\n\t\t\t\t\t\t\t\tif len(signalOLD) == _GlobalConst_numberOfAP:\n\t\t\t\t\t\t\t\t\tsignalNEW = copy.copy(signalOLD)\n\t\t\t\t\t\t\t\t\tsignalNEW[apNumb] = \"{}\".format(int(-90 + float(rssi) / 99. * 40.))\n\t\t\t\t\t\t\tif signalNEW != signalOLD or dev.states[\"Signal_at_APs\"] == \"\":\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"Signal_at_APs\", \"/\".join(signalNEW) + \"[dBm]\")\n\n\t\t\t\t\t\t\tif model != dev.states[\"model\"] and model != \"\":\n\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"] = age\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1=dev.name.ljust(30) + \" status up neighbor DICT \", reason=\"neighbor DICT\", iType=\"DC-NB-1 \")\n\t\t\t\t\t\t\tif self.updateDescriptions\tand dev.description != \"Channel= \" + channel.rjust(2).replace(\" \", \"0\") + \" - SID= \" + essid:\n\t\t\t\t\t\t\t\tdev.description = \"Channel= \" + channel.rjust(2).replace(\" \", \"0\") + \" - SID= \" + essid\n\t\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\n\t\t\t\t\tif new and not self.ignoreNewNeighbors:\n\t\t\t\t\t\tself.indiLOG.log(10,\"new: neighbor \" +MAC)\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\t\t\tprotocol\t\t=indigo.kProtocol.Plugin,\n\t\t\t\t\t\t\t\taddress\t\t\t=MAC,\n\t\t\t\t\t\t\t\tname\t\t\t=devName + \"_\" + MAC,\n\t\t\t\t\t\t\t\tdescription\t\t=\"Channel= \" + channel.rjust(2).replace(\" \", \"0\") + \" - SID= \" + essid,\n\t\t\t\t\t\t\t\tpluginId\t\t=self.pluginId,\n\t\t\t\t\t\t\t\tdeviceTypeId\t=devType,\n\t\t\t\t\t\t\t\tfolder\t\t\t=self.folderNameNeighbors,\n\t\t\t\t\t\t\t\tprops\t\t\t={\"useWhatForStatus\":\"\",isType:True})\n\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"channel\", channel)\n\t\t\t\t\t\tsignalNEW = [\" \" for iii in range(_GlobalConst_numberOfAP)]\n\t\t\t\t\t\tif rssi != \"\":\n\t\t\t\t\t\t\tsignalNEW[apNumb] = \"{}\".format(int(-90 + float(rssi) / 99. * 40.))\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"Signal_at_APs\", \"/\".join(signalNEW) + \"[dBm]\")\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"essid\", essid)\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"age\"] = age\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"adhoc\", adhoc)\n\t\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, \"\", \"\", \"\", \" status up neighbor DICT new neighbor\", \"DC-NB-2 \")\n\t\t\t\t\t\tself.executeUpdateStatesList()\n\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}\".format(dev.name) )\n\t\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doNeighborsdict\")\n\t\t\n\t\treturn\n\n\n\n\n\t####-----------------\t ---------\n\tdef doMimiTypeSwitchesWithControllerData(self, apDict, apNumbSW, found):\n\t\ttry:\n\t\t\t\tif not self.isMiniSwitch[apNumbSW]: return \n\t\t\t\t\n\t\t\t\tif(\t\"mac\"\t\t\tin apDict and \n\t\t\t\t \t\"port_table\"\tin apDict and\n\t\t\t\t \t\"name\"\t \t\tin apDict and\n\t\t\t\t \t\"ip\"\t\t\tin apDict and \n\t\t\t\t\t\"model\"\t\t\tin apDict):\n\n\t\t\t\t\tif apDict[\"model\"].find(\"MINI\") == -1: return\n\n\t\t\t\t\tMACSW = apDict[\"mac\"]\n\t\t\t\t\thostname = apDict[\"name\"].strip()\n\t\t\t\t\tipNDevice = apDict[\"ip\"]\n\t\t\t\t\tipNumber = apDict[\"ip\"]\n\t\t\t\t\tapDict[\"model_display\"] = hostname\n\t\t\t\t\t#self.indiLOG.log(20,\"doMimiTypeSwitchesWithControllerData #{}, host:{}, ip:{}, found:{}\".format(apNumbSW, hostname, ipNDevice, found))\n\n\t\t\t\t\t################# update SWs themselves\n\t\t\t\t\tself.doSWdictSELF(apDict, apNumbSW, ipNDevice, MACSW, hostname, ipNumber)\n\n\t\t\t\t\t################# now update the clients on switch, no usefull information\n\t\t\t\t\t#self.doSWITCHdictClients(apDict, apNumbSW, ipNDevice, MACSW, hostname, ipNumber)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n\n\t####-----------------\t ---------\n\t#### this does the unifswitch device itself\n\t####-----------------\t ---------\n\tdef doSWdictSELF(self, theDict, apNumb, ipNDevice, MAC, hostname, ipNumber):\n\n\t\tself.setBlockAccess(\"doSWdictSELF\")\n\n\t\tif \"model_display\" in theDict:\tmodel = (theDict[\"model_display\"])\n\t\telse:\n\t\t\tself.indiLOG.log(30,\"model_display not in dict doSWdictSELF\")\n\t\t\tmodel = \"\"\n\n\n\t\tdevName = \"SW\"\n\t\txType\t= \"SW\"\n\t\tisType\t= \"isSwitch\"\n\n\t\ttry:\n\t\t\tif \"uptime\" not in theDict: return \n\n\t\t\tfanLevel\t= \"\"\n\t\t\tif \"fan_level\" in theDict:\n\t\t\t\tfanLevel = \"{}\".format(theDict[\"fan_level\"])\n\n\t\t\ttemperature = \"\"\n\t\t\tif \"general_temperature\" in theDict:\n\t\t\t\tif \"{}\".format(theDict[\"general_temperature\"]) !=\"0\":\n\t\t\t\t\ttemperature = GT.getNumber(theDict[\"general_temperature\"])\n\t\t\tif \"overheating\" in theDict:\toverHeating\t= theDict[\"overheating\"]# not in UDM\n\t\t\telse:\t\t\t\t\t\t\toverHeating = False\n\t\t\tuptime\t\t\t= \"{}\".format(theDict.get(\"uptime\",0))\n\t\t\tportTable\t\t= theDict[\"port_table\"]\n\t\t\tnports\t\t\t= len(portTable)\n\t\t\tnClients\t\t= 0\n\n\t\t\tif nports not in _numberOfPortsInSwitch:\n\t\t\t\tfor nn in _numberOfPortsInSwitch:\n\t\t\t\t\tif nports < nn:\n\t\t\t\t\t\tnports = nn\n\t\t\t\t\tif MAC not in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\tself.indiLOG.log(30,\"switch device model {} not support: please contact author. This has {} ports; supported are {} ports only - remember there are extra ports for fiber cables , using next highest..\".format(model, nports, _numberOfPortsInSwitch))\n\n\t\t\tif nports > _numberOfPortsInSwitch[-1]: return\n\n\n\t\t\tdevType = \"Device-SW-{}\".format(nports)\n\t\t\tnew = True\n\n\t\t\tif MAC in self.MAC2INDIGO[xType]:\n\t\t\t\ttry:\n\t\t\t\t\tdev = indigo.devices[self.MAC2INDIGO[xType][MAC][\"devId\"]]\n\t\t\t\t\tif dev.deviceTypeId != devType: raise error\n\t\t\t\t\tnew = False\n\t\t\t\texcept:\n\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"{} {} wrong {}\".format(MAC, self.MAC2INDIGO[xType][MAC], devType) )\n\t\t\t\t\tfor dev in indigo.devices.iter(\"props.\"+isType):\n\t\t\t\t\t\tif \"MAC\" not in dev.states: continue\n\t\t\t\t\t\tif dev.states[\"MAC\"] != MAC: continue\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"devId\"] = dev.id\n\t\t\t\t\t\tnew = False\n\t\t\t\t\t\tbreak\n\n\t\t\tUDMswitch = False\n\t\t\tuseIP = ipNumber\n\t\t\tif self.unifiControllerType.find(\"UDM\") > -1 and apNumb == self.numberForUDM[\"SW\"]:\n\t\t\t\tif self.decideMyLog(\"UDM\"): self.indiLOG.log(10,\"DC-SW-UDM using UDM mode for {}; IP process:{}; #Dict{}\".format(MAC, ipNumber, ipNDevice ) )\n\n\n\n\t\t\tif not new:\n\t\t\t\t\tif self.decideMyLog(\"DictDetails\", MAC=MAC): self.indiLOG.log(10,\"DC-SW-S0 {}/{}; SW hostname:{}; MAC:{}\".format(ipNumber, ipNDevice, hostname, MAC) )\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ipNumber\"] = ipNumber\n\n\t\t\t\t\tself.deviceUp[\"SW\"][ipNumber]\t= time.time()\n\n\n\t\t\t\t\tif \"uptime\" in theDict and theDict[\"uptime\"] !=\"\":\n\t\t\t\t\t\tif \"upSince\" in dev.states:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"upSince\", time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()-theDict[\"uptime\"])) )\n\n\t\t\t\t\tports = {}\n\t\t\t\t\tif dev.states[\"switchNo\"] != apNumb:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"switchNo\", apNumb)\n\n\t\t\t\t\tif \"ports\" not in self.MAC2INDIGO[xType][MAC]:\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ports\"]={}\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"nPorts\"] = len(portTable)\n\n\t\t\t\t\tfor port in portTable:\n\n\t\t\t\t\t\tif \"port_idx\" not in port: continue\n\t\t\t\t\t\tID = port[\"port_idx\"]\n\t\t\t\t\t\tidS = \"{:02d}\".format(ID) # state name\n\n\t\t\t\t\t\tif \"{}\".format(ID) not in self.MAC2INDIGO[xType][MAC][\"ports\"]:\n\t\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"ports\"][\"{}\".format(ID)] = {\"rxLast\": 0, \"txLast\": 0, \"timeLast\": 0,\"poe\":\"\",\"fullDuplex\":\"\",\"link\":\"\",\"nClients\":0}\n\t\t\t\t\t\tportsMAC = self.MAC2INDIGO[xType][MAC][\"ports\"][\"{}\".format(ID)]\n\t\t\t\t\t\tif portsMAC[\"timeLast\"] != 0. and \"tx_bytes\" in port:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tdt = max(5, time.time() - portsMAC[\"timeLast\"]) * 1000\n\t\t\t\t\t\t\t\trxRate = \"{:.1f}\".format( (port[\"tx_bytes\"] - portsMAC[\"txLast\"]) / dt + 0.5)\n\t\t\t\t\t\t\t\ttxRate = \"{:.1f}\".format( (port[\"rx_bytes\"] - portsMAC[\"rxLast\"]) / dt + 0.5)\n\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\terrors = \"{}\".format(port[\"tx_dropped\"] + port[\"tx_errors\"] + port[\"rx_errors\"] + port[\"rx_dropped\"])\n\t\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\t\terrors = \"?\"\n\t\t\t\t\t\t\tif port[\"full_duplex\"]:\n\t\t\t\t\t\t\t\tfullDuplex = \"FD\"\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tfullDuplex = \"HD\"\n\t\t\t\t\t\t\tportsMAC[\"fullDuplex\"] = fullDuplex+\"-\" + (\"{}\".format(port[\"speed\"]))\n\n\t\t\t\t\t\t\tnDevices = 0\n\t\t\t\t\t\t\tif \"mac_table\" in port:\n\t\t\t\t\t\t\t\tnDevices = len(port[\"mac_table\"])\n\t\t\t\t\t\t\tportsMAC[\"nClients\"] = nDevices\n\t\t\t\t\t\t\tppp = \"#C: {:02d}\" .format(nDevices) # of clients\n\n\n\t\t\t\t\t\t\tSWP = \"\"\n\t\t\t\t\t\t\tif \"is_uplink\" in port and port[\"is_uplink\"]:\n\t\t\t\t\t\t\t\tSWP = \"UL\"\n\t\t\t\t\t\t\t\tppp += \";\"+SWP\n\n\n\t\t\t\t\t\t\t### check if another unifi switch or gw is attached to THIS port , add SW:# or GW:0to the port string\n\t\t\t\t\t\t\tif SWP == \"\" and \"lldp_table\" in port and len(port[\"lldp_table\"]) >0:\n\t\t\t\t\t\t\t\tlldp_table = port[\"lldp_table\"][0]\n\t\t\t\t\t\t\t\tif \"lldp_chassis_id\" in lldp_table and \"lldp_port_id\" in lldp_table and \"lldp_system_name\" in lldp_table:\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\tLinkName = \t\t\tlldp_table[\"lldp_system_name\"].lower()\n\t\t\t\t\t\t\t\t\t\tmacUPdowndevice = \tlldp_table[\"lldp_chassis_id\"].lower()\n\t\t\t\t\t\t\t\t\t\tportID = \t\t\tlldp_table[\"lldp_port_id\"].lower()\n\n\t\t\t\t\t\t\t\t\t\tif\tSWP == \"\" and macUPdowndevice in self.MAC2INDIGO[\"GW\"]:\n\t\t\t\t\t\t\t\t\t\t\tppp += \";GW\"\n\t\t\t\t\t\t\t\t\t\t\tSWP = \"GW\"\n\n\t\t\t\t\t\t\t\t\t\tif\tSWP == \"\" and macUPdowndevice in self.MAC2INDIGO[\"AP\"]:\n\t\t\t\t\t\t\t\t\t\t\tppp += \";AP\"\n\t\t\t\t\t\t\t\t\t\t\tSWP = \"AP\"\n\n\t\t\t\t\t\t\t\t\t\tif\tSWP == \"\" and \"gatew\" in LinkName or \"udm\" in LinkName and LinkName.find(\"switch\") ==-1:\n\t\t\t\t\t\t\t\t\t\t\tppp += \";GW\"\n\t\t\t\t\t\t\t\t\t\t\tSWP = \"GW\"\n\n\t\t\t\t\t\t\t\t\t\tif SWP == \"\" and macUPdowndevice in self.MAC2INDIGO[xType]:\n\t\t\t\t\t\t\t\t\t\t\ttry:\tportNatSW = \",P:\"+portID.split(\"/\")\n\t\t\t\t\t\t\t\t\t\t\texcept: portNatSW = \"\"\n\t\t\t\t\t\t\t\t\t\t\tSWP = \"DL\"\n\t\t\t\t\t\t\t\t\t\t\tdevIdOfSwitch = self.MAC2INDIGO[\"SW\"][macUPdowndevice][\"devId\"]\n\t\t\t\t\t\t\t\t\t\t\tppp+= \";\"+SWP+\":{}\".format(indigo.devices[devIdOfSwitch].states[\"switchNo\"])+portNatSW\n\n\t\t\t\t\t\t\t\t\t\tif SWP == \"\" and \"switch\" in LinkName:\n\t\t\t\t\t\t\t\t\t\t\tppp += \";DL\"\n\t\t\t\t\t\t\t\t\t\t\tSWP = \"DL\"\n\n\t\t\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\t\t\t\t\t\tportsMAC[\"link\"] = SWP\n\n\t\t\t\t\t\t\tif self.count_APDL_inPortCount == \"0\":\n\t\t\t\t\t\t\t\tdontCountIf = [\"UL\",\"GW\",\"AP\",\"DL\"]\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tdontCountIf = [\"UL\",\"GW\"]\n\n\t\t\t\t\t\t\tif SWP not in dontCountIf: \n\t\t\t\t\t\t\t\tnClients += nDevices\n\t\t\t\t\t\t\tif SWP == \"\":\n\t\t\t\t\t\t\t\tppp += \"; \"\n\n\t\t\t\t\t\t\tpoe = \"\"\n\t\t\t\t\t\t\tif \"poe_enable\" in port:\n\t\t\t\t\t\t\t\tif port[\"poe_enable\"]:\n\t\t\t\t\t\t\t\t\tif (\"poe_good\" in port and port[\"poe_good\"])\t:\n\t\t\t\t\t\t\t\t\t\tpoe=\"poe1\"\n\t\t\t\t\t\t\t\t\telif (\"poe_mode\" in port and port[\"poe_mode\"] == \"passthrough\") :\n\t\t\t\t\t\t\t\t\t\tpoe=\"poeP\"\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tpoe=\"poe0\"\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tpoe=\"poeX\"\n\t\t\t\t\t\t\tportsMAC[\"poe\"] = poe\n\n\t\t\t\t\t\t\tif poe != \"\":\n\t\t\t\t\t\t\t\tppp += \";\"+poe\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tppp += \"; \"\n\n\t\t\t\t\t\t\tif \"port_\" + idS in dev.states:\n\t\t\t\t\t\t\t\tif nDevices > 0:\n\t\t\t\t\t\t\t\t\tppp += \";\" + fullDuplex + \"-\" + (\"{}\".format(port[\"speed\"]))\n\t\t\t\t\t\t\t\t\tppp += \"; err:\" + errors\n\t\t\t\t\t\t\t\t\tppp += \"; rx-tx[kb/s]:\" + rxRate + \"-\" + txRate\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tppp += \"; ; ;\"\n\n\t\t\t\t\t\t\t\tif ppp != dev.states[\"port_\" + idS]:\n\t\t\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"port_\" + idS, ppp)\n\n\n\n\n\t\t\t\t\t\tportsMAC[\"txLast\"]\t = port[\"tx_bytes\"]\n\t\t\t\t\t\tportsMAC[\"rxLast\"]\t = port[\"rx_bytes\"]\n\t\t\t\t\t\tportsMAC[\"timeLast\"] = time.time()\n\n\t\t\t\t\tif model != dev.states[\"model\"] and model !=\"\":\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\tif uptime != self.MAC2INDIGO[xType][MAC][\"upTime\"]:\n\t\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"] =uptime\n\t\t\t\t\tif temperature !=\"\" and \"temperature\" in dev.states and temperature != dev.states[\"temperature\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"temperature\", temperature)\n\t\t\t\t\tif \"overHeating\" in dev.states and overHeating != dev.states[\"overHeating\"]:\n\t\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"overHeating\", overHeating)\n\t\t\t\t\tif useIP != dev.states[\"ipNumber\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", useIP)\n\t\t\t\t\tif hostname != dev.states[\"hostname\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"hostname\", hostname)\n\t\t\t\t\tif dev.states[\"status\"] != \"up\":\n\t\t\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1=dev.name.ljust(30) + \" status up SW DICT\", reason=\"switch DICT\", iType=\"STATUS-SW\")\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"lastUp\"] = time.time()\n\t\t\t\t\tif \"fanLevel\" in dev.states and fanLevel != \"\" and fanLevel != dev.states[\"fanLevel\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"fanLevel\", fanLevel)\n\n\t\t\t\t\tif \"nClients\" in dev.states and nClients != \"\" and nClients != dev.states[\"nClients\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"nClients\", nClients)\n\n\n\t\t\t\t\tif self.updateDescriptions:\n\t\t\t\t\t\tipx = self.fixIP(useIP)\n\t\t\t\t\t\toldIPX = dev.description.split(\"-\")\n\t\t\t\t\t\tif oldIPX[0] != ipx or ( (dev.description != ipx + \"-\" + hostname) or len(dev.description) < 5):\n\t\t\t\t\t\t\tif oldIPX[0] != ipx and oldIPX[0] !=\"\":\n\t\t\t\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_IPNumber_Change\", \"{}/{}/{}/{}\".format(dev.name, dev.states[\"MAC\"], oldIPX[0], ipx) )\n\t\t\t\t\t\t\tif len(oldIPX) < 2:\n\t\t\t\t\t\t\t\toldIPX.append(hostname.strip(\"-\"))\n\t\t\t\t\t\t\telif len(oldIPX) == 2 and oldIPX[1] == \"\":\n\t\t\t\t\t\t\t\tif hostname != \"\":\n\t\t\t\t\t\t\t\t\toldIPX[1] = hostname.strip(\"-\")\n\t\t\t\t\t\t\toldIPX[0] = ipx\n\t\t\t\t\t\t\tnewDescr = \"-\".join(oldIPX)\n\t\t\t\t\t\t\tdev.description = newDescr\n\t\t\t\t\t\t\tdev.replaceOnServer()\n\n\n\t\t\t\t\tself.setStatusUpForSelfUnifiDev(MAC)\n\t\t\t\t\t#break\n\n\t\t\tif new:\n\t\t\t\tnewName = devName+\"_\" + MAC\n\t\t\t\tself.indiLOG.log(30,\"creating new unifi switch device:{}; MAC:{}; IP#in dict:{}; ip# dev:{}; Model:{}; devType:{}; nports:{}\".format(newName, MAC, ipNDevice, ipNumber, model, devType, nports) )\n\t\t\t\ttry:\n\t\t\t\t\tdev = indigo.device.create(\n\t\t\t\t\t\tprotocol \t\t= indigo.kProtocol.Plugin,\n\t\t\t\t\t\taddress \t\t= MAC,\n\t\t\t\t\t\tname \t\t\t= newName,\n\t\t\t\t\t\tdescription \t= self.fixIP(useIP) + \"-\" + hostname,\n\t\t\t\t\t\tpluginId \t\t= self.pluginId,\n\t\t\t\t\t\tdeviceTypeId \t= devType,\n\t\t\t\t\t\tfolder \t\t\t= self.folderNameIDCreated,\n\t\t\t\t\t\tprops \t\t\t= {\"useWhatForStatus\":\"\",isType:True})\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\t\tself.MAC2INDIGO[xType][MAC][\"upTime\"] = uptime\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"model\", model)\n\t\t\t\t\tif temperature != \"\" and \"temperature\" in dev.states and temperature != dev.states[\"temperature\"]:\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"temperature\", temperature)\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"overHeating\", overHeating)\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"hostname\", hostname)\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"switchNo\", apNumb)\n\t\t\t\t\tself.setupBasicDeviceStates(dev, MAC, xType, useIP, \"\", \"\", \" status up SW DICT new SWITCH\", \"STATUS-SW\")\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_New_Device\", \"{}/{}/{}\".format(dev.name, MAC, useIP) )\n\t\t\t\t\tdev = indigo.devices[dev.id]\n\t\t\t\t\tself.setupStructures(xType, dev, MAC)\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\tself.indiLOG.log(40,\" for mac#{}; hostname: {}\".format(MAC, hostname))\n\t\t\t\t\tself.indiLOG.log(40,\"MAC2INDIGO: {}\".format(self.MAC2INDIGO[xType]))\n\n\t\t\tself.executeUpdateStatesList()\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\tself.unsetBlockAccess(\"doSWdictSELF\")\n\t\t\n\n\t\treturn\n\n\t####----------------- if FINGSCAN is enabled send update signal\t ---------\n\tdef setStatusUpForSelfUnifiDev(self, MAC):\n\t\ttry:\n\n\t\t\tif MAC in self.MAC2INDIGO[\"UN\"]:\n\t\t\t\tself.MAC2INDIGO[\"UN\"][MAC][\"lastUp\"] = time.time()+20\n\t\t\t\tdevidUN = self.MAC2INDIGO[\"UN\"][MAC][\"devId\"]\n\t\t\t\ttry:\n\t\t\t\t\tdevUN = indigo.devices[devidUN]\n\t\t\t\t\tif devUN.states[\"status\"] !=\"up\":\n\t\t\t\t\t\tself.addToStatesUpdateList(devidUN,\"status\", \"up\")\n\t\t\t\t\t\tself.addToStatesUpdateList(devidUN,\"lastStatusChangeReason\", \"switch message\")\n\t\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC) : self.indiLOG.log(10,\"updateself setStatusUpForSelfUnifiDev: updating status to up MAC:\" + MAC+\" \"+devUN.name+\" was: \"+ devUN.states[\"status\"] )\n\t\t\t\t\tif \"{}\".format(devUN.displayStateImageSel) !=\"SensorOn\":\n\t\t\t\t\t\tdevUN.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\t\t\texcept:pass\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\t####----------------- if FINGSCAN is enabled send update signal\t ---------\n\tdef sendUpdatetoFingscanNOW(self, force=False):\n\t\ttry:\n\t\t\tx = \"\"\n\t\t\tif not self.enableFINGSCAN:\n\t\t\t\tself.sendUpdateToFingscanList ={}\n\t\t\t\treturn x\n\t\t\tif self.sendUpdateToFingscanList =={} and not force:\n\t\t\t\treturn x\n\t\t\tif self.countLoop < 10:\n\t\t\t\tself.sendUpdateToFingscanList ={}\n\t\t\t\treturn x ## only after stable ops for 10 loops ~ 20 secs\n\n\t\t\tplug = indigo.server.getPlugin(\"com.karlwachs.fingscan\")\n\t\t\tif not plug.isEnabled():\n\t\t\t\tself.sendUpdateToFingscanList ={}\n\t\t\t\treturn x\n\n\t\t\tif not force:\n\t\t\t\tlocalF = copy.copy(self.sendUpdateToFingscanList)\n\t\t\t\tfor devid in localF:\n\t\t\t\t\tif devid !=\"\":\n\t\t\t\t\t\t\tdev= indigo.devices[int(devid)]\n\t\t\t\t\t\t\tif dev.deviceTypeId != \"neighbor\" or ( dev.deviceTypeId == \"neighbor\" and not self.ignoreNeighborForFing) :\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"Fing\"): self.indiLOG.log(10,\"FINGSC--- \"+\"updating fingscan with \" + dev.name + \" = \" + dev.states[\"status\"])\n\t\t\t\t\t\t\t\t\tplug.executeAction(\"unifiUpdate\", props={\"deviceId\": [devid]})\n\t\t\t\t\t\t\t\t\tself.fingscanTryAgain = False\n\t\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t\t\t\tself.fingscanTryAgain = True\n\n\t\t\telse:\n\t\t\t\tdevIds\t = []\n\t\t\t\tdevNames = []\n\t\t\t\tdevValues = {}\n\t\t\t\tstringToPrint = \"\\n\"\n\t\t\t\tfor dev in indigo.devices.iter(self.pluginId):\n\t\t\t\t\tif dev.deviceTypeId == \"client\": continue\n\t\t\t\t\tdevIds.append(\"{}\".format(dev.id))\n\t\t\t\t\tstringToPrint += dev.name + \"= \" + dev.states[\"status\"] + \"\\n\"\n\n\t\t\t\tif devIds != []:\n\t\t\t\t\tfor i in range(3):\n\t\t\t\t\t\tif self.decideMyLog(\"Fing\"): self.indiLOG.log(10,\"FINGSC--- \"+\"updating fingscan try# {}\".format(i + 1) + \"; with \" + stringToPrint )\n\t\t\t\t\t\tplug.executeAction(\"unifiUpdate\", props={\"deviceId\": devIds})\n\t\t\t\t\t\tself.fingscanTryAgain = False\n\t\t\t\t\t\tbreak\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\tself.sendUpdateToFingscanList ={}\n\t\treturn x\n\n\t####----------------- if FINGSCAN is enabled send update signal\t ---------\n\tdef sendBroadCastNOW(self):\n\t\ttry:\n\t\t\tx = \"\"\n\t\t\tif\tself.enableBroadCastEvents ==\"0\":\n\t\t\t\tself.sendBroadCastEventsList = []\n\t\t\t\treturn x\n\t\t\tif self.sendBroadCastEventsList == []:\n\t\t\t\treturn x\n\t\t\tif self.countLoop < 10:\n\t\t\t\tself.sendBroadCastEventsList = []\n\t\t\t\treturn x ## only after stable ops for 10 loops ~ 20 secs\n\n\t\t\tmsg = copy.copy(self.sendBroadCastEventsList)\n\t\t\tself.sendBroadCastEventsList = []\n\t\t\tif len(msg) >0:\n\t\t\t\tmsg ={\"pluginId\":self.pluginId,\"data\":msg}\n\t\t\t\ttry:\n\t\t\t\t\tif self.decideMyLog(\"BC\"): self.indiLOG.log(10,\"BroadCast- updating BC with {}\".format(msg) )\n\t\t\t\t\tindigo.server.broadcastToSubscribers(\"deviceStatusChanged\", json.dumps(msg))\n\t\t\t\texcept\tException as e:\n\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn x\n\n\t####-----------------\t ---------\n\tdef setupBasicDeviceStates(self, dev, MAC, devType, ip, ipNDevice, GHz, text1, type):\n\t\ttry:\n\t\t\tself.addToStatesUpdateList(dev.id,\"created\", datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n\t\t\tself.addToStatesUpdateList(dev.id,\"MAC\", MAC)\n\t\t\tself.MAC2INDIGO[devType][MAC][\"lastUp\"] = time.time()\n\t\t\tif ip !=\"\":\n\t\t\t\tself.addToStatesUpdateList(dev.id,\"ipNumber\", ip)\n\n\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), level=1, text1=dev.name.ljust(30) + text1, iType=type,reason=\"initialsetup\")\n\t\t\tvendor = self.getVendortName(MAC)\n\t\t\tif vendor != \"\":\n\t\t\t\t\tself.addToStatesUpdateList(dev.id,\"vendor\", vendor)\n\t\t\t\t\tself.moveToUnifiSystem(dev, vendor)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef testIgnoreMAC(self, MAC, fromSystem=\"\") :\n\t\tignore = False\n\t\tif MAC in self.MACignorelist:\n\t\t\tif self.decideMyLog(\"IgnoreMAC\"): self.indiLOG.log(10,\"{:10}: ignore list.. ignore MAC:{}\".format(fromSystem, MAC))\n\t\t\treturn True\n\n\t\tif len(self.MACSpecialIgnorelist) == 0:\n\t\t\treturn False\n\n\t\tMACSplit = (MAC.lower()).split(\":\")\n\t\tfor MACsp in self.MACSpecialIgnorelist:\n\t\t\tMACSPSplit = (MACsp.lower()).split(\":\")\n\t\t\tignore = True\n\t\t\tfor nn in range(6):\n\t\t\t\tif MACSPSplit[nn] !=\"xx\" and MACSPSplit[nn] != MACSplit[nn]:\n\t\t\t\t\tignore = False\n\t\t\t\t\tbreak\n\t\t\tif ignore:\n\t\t\t\tif self.decideMyLog(\"IgnoreMAC\"): self.indiLOG.log(10,\"{:10}: ignore list.. ignore MAC:{}; is member of ignore list:{}\" .format(fromSystem, MAC, MACsp))\n\t\t\t\treturn True\n\t\treturn False\n\n\t####-----------------\t ---------\n\tdef moveToUnifiSystem(self,dev,vendor):\n\t\ttry:\n\t\t\tif vendor.upper().find(\"UBIQUIT\") >-1:\n\t\t\t\tindigo.device.moveToFolder(dev.id, value=self.folderNameIDSystemID)\n\t\t\t\tself.indiLOG.log(10,\"moving \"+dev.name+\"; to folderID: {}\".format(self.folderNameIDSystemID))\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef getVendortName(self,MAC):\n\t\tif self.enableMACtoVENDORlookup !=\"0\" and not self.waitForMAC2vendor:\n\t\t\tself.waitForMAC2vendor = self.M2V.makeFinalTable()\n\n\t\treturn\tself.M2V.getVendorOfMAC(MAC)\n\n\n\t####-----------------\t ---------\n\tdef setImageAndStatus(self, dev, newStatus, oldStatus=\"123abc123abcxxx\", ts=\"\", level=1, text1=\"\", iType=\"\", force=False, fing=True,reason=\"\"):\n\t\ttry:\n\t\t\tif \"{}\".format(dev.id) not in self.xTypeMac: \n\t\t\t\tself.indiLOG.log(10,\"STAT-Chng {} not properly setup, missing in xTypeMac\".format(dev.name.ljust(20)) )\n\t\t\t\treturn \n\n\t\t\tMAC\t =\t self.xTypeMac[\"{}\".format(dev.id)][\"MAC\"]\n\t\t\tif self.testIgnoreMAC(MAC, fromSystem=\"set-image\"): return \n\n\t\t\tif self.decideMyLog(\"\", MAC=MAC): self.indiLOG.log(10,\"STAT-Chang {} data in: newSt:{}; oldStIn:{}; oldDevSt:{}\".format(MAC, newStatus, oldStatus, dev.states[\"status\"]))\n\t\t\tif oldStatus == \"123abc123abc\":\n\t\t\t\toldStatus = dev.states[\"status\"]\n\n\t\t\ttry:\txType = self.xTypeMac[\"{}\".format(dev.id)][\"xType\"]\n\t\t\texcept: \n\t\t\t\tself.indiLOG.log(10,\"STAT-Chang error for devId:{} xType bad:{}\".format(dev.id, self.xTypeMac[\"{}\".format(dev.id)]))\n\t\t\t\treturn\n\n\t\t\tif oldStatus != newStatus or force:\n\n\t\t\t\tif oldStatus != newStatus:\n\t\t\t\t\tif fing and oldStatus != \"123abc123abcxxx\":\n\t\t\t\t\t\tself.sendUpdateToFingscanList[\"{}\".format(dev.id)] = \"{}\".format(dev.id)\n\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"status\", newStatus)\n\n\t\t\t\t\tif \"lastStatusChangeReason\" in dev.states and reason != \"\":\n\t\t\t\t\t\tself.addToStatesUpdateList(dev.id, \"lastStatusChangeReason\", reason)\n\t\t\t\t\tif self.decideMyLog(\"Logic\", MAC=MAC): self.indiLOG.log(10,\"STAT-Chang {} st changed {}->{}; {}\".format(dev.states[\"MAC\"], dev.states[\"status\"], newStatus, text1))\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn\n\n\t####-----------------\t ---------\n\t#### wake on lan and pings\tSTART\n\t####-----------------\t ---------\n\tdef sendWakewOnLanAndPing(self, MAC,IPNumber, nBC=2, waitForPing=500, countPings=1, waitBeforePing=0.5, waitAfterPing=0.5, nPings =1, calledFrom=\"\", props=\"\"):\n\t\ttry:\n\t\t\tdoWOL = True\n\t\t\tif props != \"\" and \"useWOL\" in props and props[\"useWOL\"] ==\"0\": doWOL = False\n\t\t\tif doWOL:\n\t\t\t\tself.sendWakewOnLan(MAC, calledFrom=calledFrom)\n\t\t\t\tif nBC ==2:\n\t\t\t\t\tself.sleep(0.05)\n\t\t\t\t\tself.sendWakewOnLan(MAC, calledFrom=calledFrom)\n\t\t\t\tself.sleep(waitBeforePing)\n\t\t\treturn self.checkPing(IPNumber, waitForPing=waitForPing, countPings=countPings, nPings=nPings, waitAfterPing=waitAfterPing, calledFrom=calledFrom)\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef checkPing(self, IPnumber , waitForPing=100, countPings=1,nPings=1, waitAfterPing=0.5, calledFrom=\"\",verbose=False):\n\t\ttry:\n\t\t\tWait = \"\"\n\t\t\tif waitForPing != \"\":\n\t\t\t\tWait = \"-W {}\".format(waitForPing)\n\t\t\tCount = \"-c 1\"\n\n\t\t\tif countPings != \"\":\n\t\t\t\tCount = \"-c {}\".format(countPings)\n\n\t\t\tif nPings == 1 :\n\t\t\t\twaitAfterPing = 0.\n\n\t\t\tretCode =1\n\t\t\tfor nn in range(nPings):\n\t\t\t\tretCode = subprocess.call('/sbin/ping -o '+Wait+' '+Count+' -q '+IPnumber+' >/dev/null',shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE) # \"call\" will wait until its done and deliver retcode 0 or >0\n\t\t\t\tif self.decideMyLog(\"Ping\"): self.indiLOG.log(10,calledFrom+\" \"+\"ping resp:{} :{}\".format(IPnumber,retCode))\n\t\t\t\tif retCode ==0: return 0\n\t\t\t\tif nn != nPings-1: self.sleep(waitAfterPing)\n\t\t\tif retCode !=0 and verbose: self.indiLOG.log(10,\"ping to:{}, dev not responding\".format(IPnumber))\n\t\t\treturn retCode\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef sendWakewOnLan(self, MAC, calledFrom=\"\"):\n\t\tif self.broadcastIP !=\"9.9.9.255\":\n\t\t\tbc = self.broadcastIP\n\t\t\tmacaddress = MAC.upper().replace(\":\",'')\n\t\t\tif sys.version_info[0] > 2: # > python 2\n\t\t\t\ttemp = b''\n\t\t\t\tfor i in range(0, len(macaddress), 2):\n\t\t\t\t\ttemp += struct.pack('B', int(macaddress[i: i + 2], 16))\n\t\t\t\tdata = b'FFFFFFFFFFFF' + temp * 16\n\n\t\t\telse:\n\t\t\t\tdata = ''.join(['FFFFFFFFFFFF', macaddress * 16])\n\t\t\t\tdata = data.encode(\"hex\")\n\t\t\tsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\t\t\tsock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)\n\t\t\ttry:\n\t\t\t\tsock.sendto(data, (bc, 9))\n\t\t\texcept Exception as e:\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: \n\t\t\t\t\tself.indiLOG.log(30,\"\", exc_info=True)\n\t\t\t\t\tself.indiLOG.log(30,\"sendWakewOnLan, type:{}, data:{} broadcastIP type:{}, {}\".format(type(data), data, type(self.broadcastIP), self.broadcastIP))\n\t\t\tif self.decideMyLog(\"Ping\"): self.indiLOG.log(10,\"{} sendWakewOnLan for {}; called from {}; bc ip: {}\".format(calledFrom, MAC, calledFrom, self.broadcastIP))\n\t\treturn\n\n\t####-----------------\t ---------\n\t#### wake on lan and pings\tEND\n\t####-----------------\t ---------\n\n\n\n####-------------------------------------------------------------------------####\n\tdef getHostFileCheck(self):\n\t\tif self.pluginPrefs.get(\"hostFileCheck\",\"\") == \"ignore\":\n\t\t\treturn \" yes \"\n\t\treturn \" no \"\n\n\n\t####-----------------\t ---------\n\tdef manageLogfile(self, apDict, apNumb, unifiDeviceType):\n\t\ttry:\n\t\t\tif self.decideMyLog(\"DictFile\"):\n\t\t\t\tself.writeJson( apDict, fName=\"{}dict-{}#{}.json\".format(self.indigoPreferencesPluginDir, unifiDeviceType, apNumb), sort=False, doFormat=True )\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef exeDisplayStatus(self, dev, status, force=True):\n\t\tif status.lower() in [\"up\",\"on\",\"connected\"] :\n\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOn)\n\t\telif status.lower() in [\"down\",\"off\",\"adopting\",\"offline\"]:\n\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorOff)\n\t\telif status.lower() in [\"expired\",\"rec\",\"event\",\"motion\",\"ring\",\"person\",\"vehicle\"]:\n\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorTripped)\n\t\telif status.lower() in [\"susp\"] :\n\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.PowerOff)\n\t\telif status == \"\" :\n\t\t\tdev.updateStateImageOnServer(indigo.kStateImageSel.SensorTripped)\n\t\tif force or status == \"\":\n\t\t\tdev.updateStateOnServer(\"displayStatus\",self.padDisplay(status)+datetime.datetime.now().strftime(\"%m-%d %H:%M:%S\"))\n\t\t\tdev.updateStateOnServer(\"status\",status)\n\t\t\tdev.updateStateOnServer(\"onOffState\",value= dev.states[\"status\"].lower() in [\"up\",\"rec\",\"on\",\"connected\"], uiValue= dev.states[\"displayStatus\"])\n\t\treturn\n\n\n\t####-----------------\t ---------\n\tdef addToStatesUpdateList(self,devid, key, value):\n\t\ttry:\n\t\t\tdevId = \"{}\".format(devid)\n\t\t\t#if self.decideMyLog(\"Special\") and (key == \"status\" or key == \"displayStatus\"): self.indiLOG.log(10,\"addToStatesUpdateList (1) devId {} key:{}; value:{}\".format(devid, key, value ) )\n\t\t\t### no down during startup .. 100 secs\n\t\t\tif key == \"status\" and value.lower() not in [\"up\", \"connected\", \"event\", \"rec\", \"motion\", \"vehicle\", \"person\"]:\n\t\t\t\tif time.time() - self.pluginStartTime < 0:\n\t\t\t\t\t#self.indiLOG.log(10,\"in addToStatesUpdateList reject update at startup for devId:{} key:{}; value:{}\".format(devid, key, value ) )\n\t\t\t\t\treturn\n\n\t\t\tlocalCopy = copy.deepcopy(self.devStateChangeList)\n\t\t\tif devId not in localCopy:\n\t\t\t\tlocalCopy[devId] = {}\n\n\t\t\tif key in localCopy[devId]:\n\t\t\t\tif value != localCopy[devId][key]:\n\t\t\t\t\tlocalCopy[devId][key] = {}\n\n\t\t\tlocalCopy[devId][key] = value\n\t\t\tself.devStateChangeList = copy.deepcopy(localCopy)\n\t\t\t#if self.decideMyLog(\"Special\") and (key == \"status\" or key == \"displayStatus\"): self.indiLOG.log(10,\"addToStatesUpdateList (2) devId {} key:{}; value:{}\".format(devid, key, value ) )\n\n\n\t\texcept\tException as e:\n\t\t\tif len(\"{}\".format(e))\t> 5 :\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn\n\n\n\n\n\t####-----------------\t ---------\n\tdef executeUpdateStatesList(self):\n\t\tdevId = \"\"\n\t\tkey = \"\"\n\t\tlocal = \"\"\n\t\ttry:\n\t\t\tif len(self.devStateChangeList) == 0: return\n\t\t\tlocal = copy.deepcopy(self.devStateChangeList)\n\t\t\tself.devStateChangeList ={}\n\t\t\tchangedOnly = {}\n\t\t\ttrigList=[]\n\t\t\tfor devId in local:\n\t\t\t\ttry: int(devId)\n\t\t\t\texcept: continue\n\t\t\t\tif len( local[devId]) > 0:\n\t\t\t\t\tdev =indigo.devices[int(devId)]\n\t\t\t\t\tfor key in local[devId]:\n\t\t\t\t\t\tvalue = local[devId][key]\n\t\t\t\t\t\t#if self.decideMyLog(\"Special\"): self.indiLOG.log(10,\"executeUpdateStatesList (1) dev {} key:{}; value:{}\".format(dev.name, key, value ) )\n\t\t\t\t\t\tif \"{}\".format(value) != \"{}\".format(dev.states[key]):\n\t\t\t\t\t\t\tif devId not in changedOnly: changedOnly[devId]=[]\n\t\t\t\t\t\t\tchangedOnly[devId].append({\"key\":key,\"value\":value})\n\t\t\t\t\t\t\tif key == \"status\":\n\t\t\t\t\t\t\t\t#if \"MAC\" in dev.states and self.decideMyLog(\"\", MAC=dev.states[\"MAC\"]): self.indiLOG.log(10,\"executeUpdateStatesList(2) dev {} key:{}; value:{}\".format(dev.name, key, value ) )\n\t\t\t\t\t\t\t\tts = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\t\t\t\t\tchangedOnly[devId].append({\"key\":\"lastStatusChange\", \"value\":ts})\n\t\t\t\t\t\t\t\tif \"previousStatusChange\" in dev.states:\n\t\t\t\t\t\t\t\t\tchangedOnly[devId].append({\"key\":\"previousStatusChange\", \"value\":dev.states[\"lastStatusChange\"]})\n\t\t\t\t\t\t\t\tchangedOnly[devId].append({\"key\":\"displayStatus\",\t \"value\":self.padDisplay(value)+ts } )\n\t\t\t\t\t\t\t\tchangedOnly[devId].append({\"key\":\"onOffState\",\t \"value\":value in [\"up\",\"rec\",\"ON\"], \"uiValue\":self.padDisplay(value)+ts } )\n\t\t\t\t\t\t\t\tself.exeDisplayStatus(dev, value, force=False)\n\n\t\t\t\t\t\t\t\tself.statusChanged = max(1,self.statusChanged)\n\t\t\t\t\t\t\t\ttrigList.append(dev.name)\n\t\t\t\t\t\t\t\tval = \"{}\".format(value).lower()\n\t\t\t\t\t\t\t\tif self.enableBroadCastEvents !=\"0\" and val in [\"up\",\"down\",\"expired\",\"rec\",\"ON\", \"event\"]:\n\t\t\t\t\t\t\t\t\tprops = dev.pluginProps\n\t\t\t\t\t\t\t\t\tif\tself.enableBroadCastEvents == \"all\" or\t(\"enableBroadCastEvents\" in props and props[\"enableBroadCastEvents\"] == \"1\" ):\n\t\t\t\t\t\t\t\t\t\tmsg = {\"action\":\"event\", \"id\":\"{}\".format(dev.id), \"name\":dev.name, \"state\":\"status\", \"valueForON\":\"up\", \"newValue\":val}\n\t\t\t\t\t\t\t\t\t\tif self.decideMyLog(\"BC\"):\tself.indiLOG.log(10,\"BroadCast {:30} {}\".format(dev.name, msg))\n\t\t\t\t\t\t\t\t\t\tself.sendBroadCastEventsList.append(msg)\n\n\n\n\t\t\t\t\tif devId in changedOnly and changedOnly[devId] !=[]:\n\t\t\t\t\t\tif self.decideMyLog(\"UpdateStates\"):\tself.indiLOG.log(10,\"update device:{:30} states:{}\".format(dev.name, changedOnly[devId]))\n\n\t\t\t\t\t\tself.dataStats[\"updates\"][\"devs\"]\t +=1\n\t\t\t\t\t\tself.dataStats[\"updates\"][\"states\"] +=len(changedOnly)\n\t\t\t\t\t\tif self.indigoVersion >6:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tdev.updateStatesOnServer(changedOnly[devId])\n\t\t\t\t\t\t\texcept\tException as e:\n\t\t\t\t\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfor uu in changedOnly[devId]:\n\t\t\t\t\t\t\t\tdev.updateStateOnServer(uu[\"key\"],uu[\"value\"])\n\n\t\t\tif len(trigList) >0:\n\t\t\t\tfor devName\t in trigList:\n\t\t\t\t\tindigo.variable.updateValue(\"Unifi_With_Status_Change\",devName)\n\t\t\t\t#self.triggerEvent(\"someStatusHasChanged\")\n\t\texcept\tException as e:\n\t\t\tif len(\"{}\".format(e))\t> 5 :\n\t\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\ttry:\n\t\t\t\t\tself.indiLOG.log(40,\"{} {} {}; devStateChangeList:\\n{}\".format(dev.name, devId , key, local) )\n\t\t\t\texcept:pass\n\t\tif len(self.sendBroadCastEventsList) >0: self.sendBroadCastNOW()\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef padDisplay(self,status):\n\t\tif\t status == \"up\":\t\t return status.ljust(11)\n\t\telif status == \"expired\":\t return status.ljust(8)\n\t\telif status == \"down\":\t\t return status.ljust(9)\n\t\telif status == \"susp\":\t\t return status.ljust(9)\n\t\telif status == \"changed\":\t return status.ljust(8)\n\t\telif status == \"double\":\t return status.ljust(8)\n\t\telif status == \"ignored\":\t return status.ljust(8)\n\t\telif status == \"off\":\t\t return status.ljust(11)\n\t\telif status == \"REC\":\t\t return status.ljust(9)\n\t\telif status == \"ON\":\t\t return status.ljust(10)\n\t\telse:\t\t\t\t\t\t return status.ljust(10)\n\t\treturn\n\n\t####-----------------\t ---------\n\tdef escapeExpect(self, inString):\n\t\treturn inString.replace(\"#\",\"\\\\#\")\n\n\n\t########################################\n\t# General Action callback\n\t######################\n\tdef actionControlUniversal(self, action, dev):\n\t\t###### BEEP ######\n\t\tif action.deviceAction == indigo.kUniversalAction.Beep:\n\t\t\t# Beep the hardware module (dev) here:\n\t\t\t# ** IMPLEMENT ME **\n\t\t\tindigo.server.log(\"sent \\\"{}\\\" beep request not implemented\".format(dev.name) )\n\n\t\t###### STATUS REQUEST ######\n\t\telif action.deviceAction == indigo.kUniversalAction.RequestStatus:\n\t\t\t# Query hardware module (dev) for its current status here:\n\t\t\t# ** IMPLEMENT ME **\n\t\t\tindigo.server.log(\"sent \\\"{}\\\" status request not implemented\".format(dev.name) )\n\t\treturn\n\n\t####-----------------\n\t########################################\n\t# Sensor Action callback\n\t######################\n\tdef actionControlSensor(self, action, dev):\n\t\t###### TURN ON ######\n\t\tif action.sensorAction == indigo.kSensorAction.TurnOn:\n\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), iType=\"actionControlSensor\",reason=\"TurnOn\")\n\n\t\t###### TURN OFF ######\n\t\telif action.sensorAction == indigo.kSensorAction.TurnOff:\n\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), iType=\"actionControlSensor\",reason=\"TurnOff\")\n\n\t\t###### TOGGLE ######\n\t\telif action.sensorAction == indigo.kSensorAction.Toggle:\n\t\t\tif dev.onState:\n\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), iType=\"actionControlSensor\",reason=\"toggle\")\n\t\t\telse:\n\t\t\t\tself.setImageAndStatus(dev, \"up\",oldStatus=dev.states[\"status\"], ts=time.time(), iType=\"actionControlSensor\",reason=\"toggle\")\n\n\t\tself.executeUpdateStatesList()\n\t\treturn\n\n\n\t####---------------- wait for other tasks to finish (ie main and fill messages ) wait max 9 secs ---------\n\t#### unblock \n\tdef unsetBlockAccess(self, waitingPgm):\n\t\ttry:\n\t\t\tqLen = self.blockWaitQueue.qsize()\n\t\t\tif qLen == 0: return \n\t\t\tif qLen == 1:\n\t\t\t\tself.blockWaitQueue = PriorityQueue()\n\t\t\t\treturn \n\n\t\t\t#if qlengthNow == 1: \n\t\t\t#\tself.blockWaitQueue.get()\n\t\t\t#\treturn\n\t\t\ttempQueue = PriorityQueue()\n\t\t\tblockingPGM = \"\"\n\t\t\tfor nn in range(qLen):\n\t\t\t\ttry: \n\t\t\t\t\tblockingPGM = self.blockWaitQueue.queue[qLen-nn-1]\n\t\t\t\texcept: \n\t\t\t\t\ttempQueue = PriorityQueue()\n\t\t\t\t\tbreak \n\t\t\t\tif blockingPGM == waitingPgm: continue\n\t\t\t\ttempQueue.put(blockingPGM)\n\n\t\t\tself.blockWaitQueue = tempQueue\n\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn \n\n\t#### block\n\tdef setBlockAccess(self, waitingPgm):\n\t\ttry:\t\n\t\t\twaitTime = time.time()\n\t\t\tblockingPgm = \"None\"\n\t\t\tqLenMax = 0\n\t\t\tfor ii in range(90):\n\t\t\t\tqlengthNow = self.blockWaitQueue.qsize()\n\t\t\t\tqLenMax = max(qLenMax, qlengthNow)\n\t\t\t\tif qlengthNow == 0:\tbreak\n\t\t\t\tif blockingPgm == \"None\":\n\t\t\t\t\ttry:\tblockingPgm = self.blockWaitQueue.queue[0]\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tpass\n\t\t\t\t\t\t#if \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\t\t\t\t\t#self.indiLOG.log(40, \"setBlockAccess err waiting for: {}, pgmWaiting:{} queue is:{}\".format(blockingPgm, waitingPgm, self.blockWaitQueue.queue))\n\t\t\t\t#self.indiLOG.log(10, \"setBlockAccess waiting for: {}, pgmWaiting:{} qlen:{}; queue is:{}\".format(blockingPgm, waitingPgm, self.blockWaitQueue.qsize(), self.blockWaitQueue.queue))\n\t\t\t\tself.sleep(0.1)\n\n\t\t\tif not self.blockWaitQueue.empty(): blockingPgm = self.blockWaitQueue.get()\n\t\t\tself.blockWaitQueue.put(waitingPgm)\n\n\t\t\t## init dicts if not present \n\t\t\tif \"yesterday\" not in self.waitTimes:\n\t\t\t\tself.waitTimes = {\"today\":{}, \"yesterday\":{} }\n\n\t\t\tif \"startDate\" not in self.waitTimes[\"today\"]:\n\t\t\t\tself.waitTimes[\"today\"][\"startDate\"] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\t\t\t\tself.waitTimes[\"today\"][\"startTime\"] = time.time()\n\t\t\t\tself.waitTimes[\"today\"][\"lastPrint\"] = time.time()\n\t\t\t\tself.waitTimes[\"today\"][\"WaitingPgm\"] = {}\n\t\t\t\tself.waitTimes[\"today\"][\"BlockingPgm\"] = {}\n\t\t\t\tself.waitTimes[\"today\"][\"QlenGT1\"] = 0\n\t\t\t\tself.waitTimes[\"today\"][\"QlenMax\"] = 0\n\n\t\t\twaitTime = time.time() - waitTime\n\t\t\tif qlengthNow >0: \n\t\t\t\tself.waitTimes[\"today\"][\"QlenMax\"] = max(self.waitTimes[\"today\"][\"QlenMax\"], qlengthNow)\n\t\t\t\tif qlengthNow > 1: \n\t\t\t\t\tself.waitTimes[\"today\"][\"QlenGT1\"] += 1\n\n\t\t\tfor tagCat, waitOrBlock in [[\"---TOTAL----\", \"WaitingPgm\"], [waitingPgm, \"WaitingPgm\"], [blockingPgm, \"BlockingPgm\"], [\"---TOTAL----\", \"BlockingPgm\"] ]:\n\t\t\t\tif tagCat not in self.waitTimes[\"today\"][waitOrBlock]: \n\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat] = {\"n\":0, \"tot\":0., \"max\":0., \".1\":0, \".5\":0, \"1\":0, \"3\":0, \"6\":0, \"12\":0, \"20\":0}\n\t\t\t\tif waitOrBlock == \"BlockingPgm\" and blockingPgm == \"\": continue\n\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"n\"] += 1\n\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"tot\"] += waitTime\n\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"max\"] = max(waitTime, self.waitTimes[\"today\"][waitOrBlock][tagCat][\"max\"])\n\n\t\t\t\tif waitTime > 0.1:\n\t\t\t\t\tif waitTime <= 0.5: \n\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\".1\"] += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tif waitTime <= 1: \n\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\".5\"] += 1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tif waitTime <= 3: \n\t\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"1\"] += 1\n\t\t\t\t\t\t\telse: \n\t\t\t\t\t\t\t\tif waitTime <= 6: \n\t\t\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"3\"] += 1\n\t\t\t\t\t\t\t\telse: \n\t\t\t\t\t\t\t\t\tif waitTime <= 12: \n\t\t\t\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"6\"] += 1\n\t\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\t\tif waitTime <= 20: \n\t\t\t\t\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"12\"] += 1\n\t\t\t\t\t\t\t\t\t\telse: \n\t\t\t\t\t\t\t\t\t\t\tself.waitTimes[\"today\"][waitOrBlock][tagCat][\"20\"] += 1\n\n\t\t\tself.waitTimes[\"today\"][\"endDate\"] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\t\treturn \n\n\t\n####-------------------------------------------------------------------------####\n\tdef readPopen(self, cmd):\n\t\ttry:\n\t\t\tret, err = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\n\t\t\treturn ret.decode('utf-8'), err.decode('utf-8')\n\n\t\texcept Exception as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\n\n####-------------------------------------------------------------------------####\n\tdef openEncoding(self, ff, readOrWrite, showError=True):\n\n\t\ttry:\n\t\t\tif readOrWrite.find(\"b\") >-1:\n\t\t\t\treturn open( ff, readOrWrite)\n\n\t\t\tif sys.version_info[0] > 2:\n\t\t\t\treturn open( ff, readOrWrite, encoding=\"utf-8\")\n\n\t\t\telse:\n\t\t\t\treturn codecs.open( ff, readOrWrite, \"utf-8\")\n\n\t\texcept\tException as e:\n\t\t\tif showError: self.indiLOG.log(40,\"{}\".format(ff))\n\t\t\tif showError: self.indiLOG.log(40,\"\", exc_info=True)\n\n########################################\n########################################\n####----------------- logging ---------\n########################################\n########################################\n\n\n\t####-----------------\t ---------\n\tdef decideMyLog(self, msgLevel, MAC=\"\"):\n\t\ttry:\n\t\t\tif MAC != \"\" and MAC in self.MACloglist:\t\t\t\treturn True\n\t\t\tif msgLevel\t == \"all\" or \"all\" in self.debugLevel:\t\treturn True\n\t\t\tif msgLevel\t == \"\" and \"all\" not in self.debugLevel:\treturn False\n\t\t\tif msgLevel in self.debugLevel:\t\t\t\t\t\t\treturn True\n\n\t\texcept\tException as e:\n\t\t\tif \"{}\".format(e).find(\"None\") == -1: self.indiLOG.log(40,\"\", exc_info=True)\n\t\treturn False\n####----------------- valiable formatter for differnt log levels ---------\n# call with: \n# formatter = LevelFormatter(fmt='', level_fmts={logging.INFO: ''})\n# handler.setFormatter(formatter)\nclass LevelFormatter(logging.Formatter):\n\tdef __init__(self, fmt=None, datefmt=None, level_fmts={}, level_date={}):\n\t\tself._level_formatters = {}\n\t\tself._level_date_format = {}\n\t\tfor level, format in level_fmts.items():\n\t\t\t# Could optionally support level names too\n\t\t\tself._level_formatters[level] = logging.Formatter(fmt=format, datefmt=level_date[level])\n\t\t# self._fmt will be the default format\n\t\tsuper(LevelFormatter, self).__init__(fmt=fmt, datefmt=datefmt)\n\t\treturn\n\n\tdef format(self, record):\n\t\tif record.levelno in self._level_formatters:\n\t\t\treturn self._level_formatters[record.levelno].format(record)\n\n\t\treturn super(LevelFormatter, self).format(record)\n\n","sub_path":"uniFiAP.indigoPlugin/Contents/Server Plugin/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":563938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"197105194","text":"class Solution:\n def canCompleteCircuit(self, gas: List[int], cost: List[int]) -> int:\n total = 0 # 判断整个数组是否有解\n sum = 0 # 判断当前指针的有效性\n j = -1\n for i in range(len(gas)):\n sum += gas[i] - cost[i]\n total += gas[i] - cost[i]\n if sum < 0:\n j = i\n sum = 0\n return j + 1 if total >= 0 else -1","sub_path":"Huang_LeetCode/134-Gas Station.py","file_name":"134-Gas Station.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"604377107","text":"from django.shortcuts import render\nfrom product.models import Product, Category\nfrom .forms import SaveAddress\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth import get_user_model\nfrom .models import Address\nfrom .models import Orders\n\n# Create your views here.\ndef checkout(request,slug):\n pro = Product.objects.filter(slug=slug)\n pro1 = Address.objects.filter(user=request.user.username)\n if request.method =='POST':\n Name = request.POST['Name']\n new_price = request.POST['new_price']\n First_Name = request.POST['First_Name']\n Last_Name = request.POST['Last_Name']\n Address_Line_1 = request.POST['Address_Line_1']\n Address_Line_2 = request.POST['Address_Line_2']\n City = request.POST['City']\n Phone = request.POST['Phone']\n\n od = Orders(product_name=Name,price=new_price,user=request.user.username,First_Name=First_Name,Last_Name=Last_Name,Address_Line_1=Address_Line_1,Address_Line_2=Address_Line_2,City=City,Phone=Phone)\n od.save()\n return render(request, \"thanks.html\")\n\n lis = {'pro':pro,'pro1':pro1}\n return render(request, \"checkout.html\",lis)\n\ndef Addaddress(request):\n if request.method =='POST':\n fm = SaveAddress(request.POST)\n if fm.is_valid():\n fn = fm.cleaned_data['First_Name']\n ln = fm.cleaned_data['Last_Name']\n a1 = fm.cleaned_data['Address_Line_1']\n a2 = fm.cleaned_data['Address_Line_2']\n ci = fm.cleaned_data['City']\n ph = fm.cleaned_data['Phone']\n reg = Address(user=request.user.username,First_Name=fn,Last_Name=ln,Address_Line_1=a1,Address_Line_2=a2,City=ci,Phone=ph)\n fm.save()\n return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\n else:\n fm = SaveAddress()\n lis = {'form':fm}\n return render(request, \"address.html\",lis)","sub_path":"order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"470773296","text":"from django.urls import path\nfrom .views import QuizesList, QuizDetail, QuestionsList, CreateChoice, ResultsList, QuizesActiveList\n\nurlpatterns = [\n path(\"quiz/\", QuizesList.as_view(), name=\"quiz_list\"),\n path(\"quiz//\", QuizDetail.as_view(), name=\"quiz_detail\"),\n path(\"quiz//questions/\", QuestionsList.as_view(), name=\"questions_list\"),\n path(\"quiz//questions//choice/\", CreateChoice.as_view(), name=\"choice_create\"),\n path(\"results//\", ResultsList.as_view(), name=\"results\"),\n path(\"activequizes/\", QuizesActiveList.as_view(), name=\"activequixes\"),\n]\n\n","sub_path":"drfquestions/apiquestions/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"114608329","text":"#Juego Snake completo usando el módulo turtle.\r\nimport turtle\r\nimport time\r\nimport random\r\n\r\n# Declarations\r\nwindow = turtle.Screen()\r\nplayer = turtle.Turtle()\r\nfood = turtle.Turtle()\r\nscore = turtle.Turtle()\r\nsnakebody = []\r\nmilisecond = 0.1\r\nactual_score = 0\r\nhigh_score = 0\r\n\r\n# Initialize\r\ndef startwindow():\r\n window.title(\"Game_001\")\r\n window.bgcolor(\"#D5B0A8\")\r\n window.setup(600, 600)\r\n window.tracer(0)\r\n\r\n\r\ndef startplayer():\r\n player.speed(0)\r\n player.shape(\"square\")\r\n player.penup() # No deja rastro al moverse\r\n player.goto(0, 0)\r\n player.direction = \"stop\"\r\n\r\n\r\ndef spawnfood():\r\n food.speed(0)\r\n food.shape(\"circle\")\r\n food.penup()\r\n food.color(\"green\")\r\n food.goto(100, 0)\r\n\r\n\r\ndef startscore():\r\n score.speed(0)\r\n score.penup()\r\n score.hideturtle()\r\n score.goto(0, 260)\r\n score.write(\"Score: 0 High Score: 0\", align = \"center\", font = (\"Arial\",22, \"normal\"))\r\n\r\ndef addbody():\r\n body = turtle.Turtle()\r\n body.speed(0)\r\n body.shape(\"square\")\r\n body.color(\"darkgrey\")\r\n body.penup() # No deja rastro al moverse\r\n return body\r\n\r\n\r\n# Controls\r\ndef playerup():\r\n player.direction = \"up\"\r\n\r\n\r\ndef playerdown():\r\n player.direction = \"down\"\r\n\r\n\r\ndef playerright():\r\n player.direction = \"right\"\r\n\r\n\r\ndef playerleft():\r\n player.direction = \"left\"\r\n\r\n\r\ndef keyboard():\r\n window.listen()\r\n window.onkeypress(playerup, \"Up\")\r\n window.onkeypress(playerdown, \"Down\")\r\n window.onkeypress(playerleft, \"Left\")\r\n window.onkeypress(playerright, \"Right\")\r\n\r\n\r\n# Collision\r\ndef foodcollision():\r\n if player.distance(food) < 20:\r\n # newfood\r\n randx = random.randint(-280, 280)\r\n randy = random.randint(-280, 280)\r\n food.goto(randx, randy)\r\n # addbody\r\n snakebody.append(addbody())\r\n #up score\r\n global actual_score\r\n global high_score\r\n actual_score += 1\r\n if actual_score > high_score:\r\n high_score = actual_score\r\n score.clear()\r\n score.write(\"Score: {} High Score: {}\".format(actual_score,high_score), align = \"center\", font = (\"Arial\", 22, \"normal\"))\r\n\r\ndef wallcollision():\r\n if player.xcor() > 290 or player.xcor() < -290 or player.ycor() > 290 or player.ycor() < -290:\r\n #Send player back to 0,0\r\n time.sleep(1)\r\n player.goto(0, 0)\r\n player.direction = \"stop\"\r\n #Yeet previous body\r\n for body in snakebody:\r\n body.goto(1000, 1000)\r\n snakebody.clear()\r\n score.clear()\r\n global actual_score\r\n global high_score\r\n actual_score = 0\r\n score.write(\"Score: {} High Score: {}\".format(actual_score,high_score), align = \"center\", font = (\"Arial\", 22, \"normal\"))\r\n\r\n# Movement()\r\ndef bodymovement():\r\n bodylen = len(snakebody)\r\n for index in range(bodylen - 1, 0, -1):\r\n x = snakebody[index - 1].xcor()\r\n y = snakebody[index - 1].ycor()\r\n snakebody[index].goto(x, y)\r\n\r\n if bodylen > 0:\r\n x = player.xcor()\r\n y = player.ycor()\r\n snakebody[0].goto(x, y)\r\n\r\n\r\n\r\n\r\ndef movement():\r\n if player.direction == \"up\":\r\n y = player.ycor()\r\n player.sety(y + 20)\r\n\r\n if player.direction == \"down\":\r\n y = player.ycor()\r\n player.sety(y - 20)\r\n\r\n if player.direction == \"right\":\r\n x = player.xcor()\r\n player.setx(x + 20)\r\n\r\n if player.direction == \"left\":\r\n x = player.xcor()\r\n player.setx(x - 20)\r\n\r\n\r\n# Window loop\r\ndef updatewindow():\r\n while True:\r\n window.update()\r\n foodcollision()\r\n bodymovement()\r\n keyboard()\r\n movement()\r\n wallcollision()\r\n\r\n for body in snakebody:\r\n if body.distance(player) < 20:\r\n time.sleep(1)\r\n player.goto(0, 0)\r\n player.direction = \"stop\"\r\n\r\n # Yeet the body\r\n for body in snakebody:\r\n body.goto(1000, 1000)\r\n snakebody.clear()\r\n actual_score = 0\r\n\r\n time.sleep(milisecond)\r\n\r\n\r\n# Main program\r\n\r\n#Initialize variables\r\nstartwindow()\r\nstartscore()\r\nstartplayer()\r\nspawnfood()\r\n\r\n#Infinite Loop\r\nupdatewindow()\r\n","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":4247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"368698378","text":"#importation des modules\r\nfrom tkinter import *\r\nimport random\r\n\r\n#déclaration des variables et du tableau de verbes\r\nv = 0\r\nscore = 0\r\nvaleurs = []\r\ntableau_vi= [[\"be\",\"was/were\",\"been\",\"etre\"],\r\n [\"become\",\"became\",\"become\",\"devenir\"],\r\n [\"begin\",\"began\",\"begun\",\"commencer\"],\r\n [\"break\",\"broke\",\"broken\",\"casser\"],\r\n [\"bring\",\"brought\",\"brought\",\"apporter\"],\r\n [\"build\",\"built\",\"built\",\"construire\"],\r\n [\"burn\",\"burnt\",\"burnt\",\"bruler\"],\r\n [\"buy\",\"bought\",\"bought\",\"acheter\"],\r\n [\"catch\",\"caught\",\"caught\",\"attraper\"],\r\n [\"choose\",\"chose\",\"chosen\",\"choisir\"],\r\n [\"come\",\"came\",\"come\",\"venir\"],\r\n [\"cost\",\"cost\",\"cost\",\"couter\"],\r\n [\"cut\",\"cut\",\"cut\",\"couper\"],\r\n [\"do\",\"did\",\"done\",\"faire\"],\r\n [\"draw\",\"drew\",\"drawn\",\"dessiner\"],\r\n [\"dream\",\"dreamt\",\"dreamt\",\"rever\"],\r\n [\"drink\",\"drank\",\"drunk\",\"boire\"],\r\n [\"drive\",\"drove\",\"driven\",\"conduire\"],\r\n [\"eat\",\"ate\",\"eaten\",\"manger\"],\r\n [\"fall\",\"fell\",\"fallen\",\"tomber\"],\r\n [\"feel\",\"felt\",\"felt\",\"ressentir\"],\r\n [\"fight\",\"fought\",\"fougth\",\"se battre\"],\r\n [\"find\",\"found\",\"found\",\"trouver\"],\r\n [\"fly\",\"flew\",\"flown\",\"voler\"],\r\n [\"forget\",\"forgot\",\"forgotten\",\"oublier\"],\r\n [\"forgive\",\"forgave\",\"forgiven\",\"pardonner\"],\r\n [\"get\",\"got\",\"got\",\"obtenir\"],\r\n [\"give\",\"gave\",\"given\",\"donner\"],\r\n [\"go\",\"went\",\"gone\",\"aller\"],\r\n [\"have\",\"had\",\"had\",\"avoir\"],\r\n [\"hear\",\"heard\",\"heard\",\"entendre\"],\r\n [\"hit\",\"hit\",\"hit\",\"frapper\"],\r\n [\"hold\",\"held\",\"helt\",\"tenir\"],\r\n [\"hurt\",\"hurt\",\"hurt\",\"blesser\"],\r\n [\"keep\",\"kept\",\"kept\",\"garder\"],\r\n [\"know\",\"knew\",\"known\",\"savoir\"],\r\n [\"lead\",\"led\",\"led\",\"mener\"],\r\n [\"learn\",\"learnt\",\"learnt\",\"apprendre\"],\r\n [\"leave\",\"left\",\"left\",\"partir\"],\r\n [\"lose\",\"lost\",\"lost\",\"perdre\"],\r\n [\"make\",\"made\",\"made\",\"fabriquer\"],\r\n [\"mean\",\"meant\",\"meant\",\"signifier\"],\r\n [\"meet\",\"met\",\"met\",\"rencontrer\"],\r\n [\"pay\",\"paid\",\"paid\",\"payer\"],\r\n [\"put\",\"put\",\"put\",\"mettre\"],\r\n [\"read\",\"read\",\"read\",\"lire\"],\r\n [\"ride\",\"rode\",\"ridden\",\"faire\"],\r\n [\"ring\",\"rang\",\"rung\",\"sonner\"],\r\n [\"run\",\"ran\",\"run\",\"courir\"],\r\n [\"say\",\"said\",\"said\",\"dire\"],\r\n [\"see\",\"saw\",\"seen\",\"voir\"],\r\n [\"sell\",\"sold\",\"sold\",\"vendre\"],\r\n [\"send\",\"sent\",\"sent\",\"envoyer\"],\r\n [\"shoot\",\"shot\",\"shot\",\"tirer\"],\r\n [\"show\",\"showed\",\"shown\",\"montrer\"],\r\n [\"shut\",\"shut\",\"shut\",\"fermer\"],\r\n [\"sing\",\"sang\",\"sung\",\"chanter\"],\r\n [\"sit\",\"sat\",\"sat\",\"asseoir\"],\r\n [\"sleep\",\"slept\",\"slept\",\"dormir\"],\r\n [\"smell\",\"smelt\",\"smelt\",\"sentir\"],\r\n [\"speak\",\"spoke\",\"spoken\",\"spoken\"],\r\n [\"spell\",\"spelt\",\"spelt\",\"epeler\"],\r\n [\"spend\",\"spent\",\"spent\",\"depenser\"],\r\n [\"stand\",\"stood\",\"stood\",\"etre debout\"],\r\n [\"steal\",\"stole\",\"stolen\",\"derober\"],\r\n [\"swim\",\"swam\",\"swum\",\"nager\"],\r\n [\"take\",\"took\",\"taken\",\"prendre\"],\r\n [\"teach\",\"taught\",\"taught\",\"enseigner\"],\r\n [\"tell\",\"told\",\"told\",\"raconter\"],\r\n [\"think\",\"thought\",\"thought\",\"penser\"],\r\n [\"throw\",\"threw\",\"thrown\",\"lancer\"],\r\n [\"understand\",\"understood\",\"understood\",\"comprendre\"],\r\n [\"wake\",\"woke\",\"woken\",\"se reveiller\"],\r\n [\"wear\",\"wore\",\"worn\",\"porter\"],\r\n [\"win\",\"won\",\"won\",\"gagner\"],\r\n [\"write\",\"wrote\",\"written\",\"ecrire\"]]\r\n\r\n#cette définition permet de choisir aléatoirement x et y\r\ndef initxy():\r\n global x, y, valeurs\r\n x = random.randint(0, 75) #tirer x entre 0 et 75\r\n y = random.randint(0,3) #tirer y entre 0 et 3\r\n while x in valeurs != True : #tant que la ligne tirée a déja été tiré\r\n x = random.randint(1, 75) #retirer x\r\n valeurs.append(x) #ajouter x aux valeurs\r\n\r\n#cette définition permet de générer une nouvelle ligne\r\ndef generate():\r\n hide = \"******\"\r\n global x\r\n global y\r\n\r\n initxy() #appel de la def aléatoire de x et y\r\n\r\n if y ==0: #si premiere colonne tirée\r\n baser = Label(frame, text=hide, font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #cacher la premiere colonne\r\n baser.place(x=-230, y=-50, width=110, height=25) #donne la position du label\r\n preteritr = Label(frame, text=tableau_vi[x][1], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #montrer la deuxieme colonne\r\n preteritr.place(x=-110, y=-50, width=110, height=25) #donne la position du label\r\n participer = Label(frame, text=tableau_vi[x][2], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #montrer la troisieme colonne\r\n participer.place(x=10, y=-50, width=110, height=25) #donne la position du label\r\n traductionr = Label(frame, text=tableau_vi[x][3], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #montrer la dernier colonne\r\n traductionr.place(x=130, y=-50, width=110, height=25) #donne la position du label\r\n\r\n if y ==1: #si deuxieme colonne tirée\r\n baser = Label(frame, text=tableau_vi[x][0], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n baser.place(x=-230, y=-50, width=110, height=25)\r\n preteritr = Label(frame, text=hide, font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #cacher la deuxieme colonne\r\n preteritr.place(x=-110, y=-50, width=110, height=25)\r\n participer = Label(frame, text=tableau_vi[x][2], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n participer.place(x=10, y=-50, width=110, height=25)\r\n traductionr = Label(frame, text=tableau_vi[x][3], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n traductionr.place(x=130, y=-50, width=110, height=25)\r\n\r\n if y ==2: #si troisieme colonne tiree\r\n baser = Label(frame, text=tableau_vi[x][0], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n baser.place(x=-230, y=-50, width=110, height=25)\r\n preteritr = Label(frame, text=tableau_vi[x][1], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n preteritr.place(x=-110, y=-50, width=110, height=25)\r\n participer = Label(frame, text=hide, font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #cacher la troisieme colonne\r\n participer.place(x=10, y=-50, width=110, height=25)\r\n traductionr = Label(frame, text=tableau_vi[x][3], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n traductionr.place(x=130, y=-50, width=110, height=25)\r\n\r\n if y ==3: #si quatrieme colonne tiree\r\n baser = Label(frame, text=tableau_vi[x][0], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n baser.place(x=-230, y=-50, width=110, height=25)\r\n preteritr = Label(frame, text=tableau_vi[x][1], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n preteritr.place(x=-110, y=-50, width=110, height=25)\r\n participer = Label(frame, text=tableau_vi[x][2], font=(\"Courrier\", 10), bg='#FFFFFF', fg='black')\r\n participer.place(x=10, y=-50, width=110, height=25)\r\n traductionr = Label(frame, text=hide, font=(\"Courrier\", 10), bg='#FFFFFF', fg='black') #cacher la derniere colonne\r\n traductionr.place(x=130, y=-50, width=110, height=25)\r\n\r\n\r\n\r\n#cette définition détermine les bonnes ou mauvaises réponses et ajoute le score et les couleurs pour chaque réponses\r\ndef validate():\r\n global tirage1, tirage2, tirage3,tirage4, tirage5, tirage6, tirage7, tirage8, tirage9, tirage10, score, v, x, y, reponse1, reponse2, reponse3, reponse4, reponse5, reponse6, reponse7, reponse8, reponse9, reponse10, color1, color2, color3, color4, color5, color6, color7, color8, color9, color10\r\n\r\n if v < 10: #si il y a eu moins de 10 tirages\r\n if reponse.get() == tableau_vi[x][y]: #si il s'agit de la bonne réponse\r\n score = score + 1 #ajouter 1 au score\r\n if v == 0: #si il s'agit du premier tirage\r\n color1 = \"green\" #mettre la couleur 1 à vert\r\n if v == 1: #si il s'agit du deuxieme tirage\r\n color2 = \"green\" #mettre la couleur 2 à vert\r\n if v == 2: #si il s'agit du troisieme tirage\r\n color3 = \"green\"\r\n if v == 3:\r\n color4 = \"green\"\r\n if v == 4:\r\n color5 = \"green\"\r\n if v == 5:\r\n color6 = \"green\"\r\n if v == 6:\r\n color7 = \"green\"\r\n if v == 7:\r\n color8 = \"green\"\r\n if v == 8:\r\n color9 = \"green\"\r\n if v == 9:\r\n color10 = \"green\"\r\n\r\n#les labels ci dessous pemettent d'afficher le score au fur et à mesure\r\n scorec = Label(frame, text=\"Score :\", font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scorec.place(x=80, y=90, width=90, height=25)\r\n scorec = Label(frame, text=score, font=(\"Courrier\", 10), bg='#FF7433', fg='white') #affichage du score\r\n scorec.place(x=170, y=90, width=25, height=25)\r\n scorec = Label(frame, text=\"/\", font=(\"Courrier\", 10), bg='#FF7433', fg='white') #sur\r\n scorec.place(x=195, y=90, width=10, height=25)\r\n scorec = Label(frame, text=v+1, font=(\"Courrier\", 10), bg='#FF7433', fg='white') #le nombre de tirage effectué\r\n scorec.place(x=205, y=90, width=25, height=25)\r\n\r\n#la partie ci dessous permet d'enregistrer dans une variable le contenu de chaque ligne tirée ainsi que la réponse du joueur\r\n if y == 0:\r\n if v == 0: #si il s'agit du premier tirage\r\n tirage1 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3] #enregistre le contenu de la ligne\r\n reponse1 = reponse.get() #enregistre la reponse du joueur\r\n if v == 1:\r\n tirage2 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse2 = reponse.get()\r\n if v == 2:\r\n tirage3 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse3 = reponse.get()\r\n if v == 3:\r\n tirage4 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse4 = reponse.get()\r\n if v == 4:\r\n tirage5 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse5 = reponse.get()\r\n if v == 5:\r\n tirage6 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse6 = reponse.get()\r\n if v == 6:\r\n tirage7 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse7 = reponse.get()\r\n if v == 7:\r\n tirage8 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse8 = reponse.get()\r\n if v == 8:\r\n tirage9 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse9 = reponse.get()\r\n if v == 9:\r\n tirage10 = tableau_vi[x][0].upper() + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse10 = reponse.get()\r\n\r\n if y == 1:\r\n if v == 0: #si il s'agit du premier tirage\r\n tirage1 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3] #enregistre le contenu de la ligne\r\n reponse1 = reponse.get() #enregistre la reponse du joueur\r\n if v == 1:\r\n tirage2 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse2 = reponse.get()\r\n if v == 2:\r\n tirage3 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse3 = reponse.get()\r\n if v == 3:\r\n tirage4 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse4 = reponse.get()\r\n if v == 4:\r\n tirage5 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse5 = reponse.get()\r\n if v == 5:\r\n tirage6 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse6 = reponse.get()\r\n if v == 6:\r\n tirage7 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse7 = reponse.get()\r\n if v == 7:\r\n tirage8 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse8 = reponse.get()\r\n if v == 8:\r\n tirage9 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse9 = reponse.get()\r\n if v == 9:\r\n tirage10 = tableau_vi[x][0] + \" \" + tableau_vi[x][1].upper() + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3]\r\n reponse10 = reponse.get()\r\n\r\n if y == 2:\r\n if v == 0: #si il s'agit du premier tirage\r\n tirage1 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3] #enregistre le contenu de la ligne\r\n reponse1 = reponse.get() #enregistre la reponse du joueur\r\n if v == 1:\r\n tirage2 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse2 = reponse.get()\r\n if v == 2:\r\n tirage3 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse3 = reponse.get()\r\n if v == 3:\r\n tirage4 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse4 = reponse.get()\r\n if v == 4:\r\n tirage5 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse5 = reponse.get()\r\n if v == 5:\r\n tirage6 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse6 = reponse.get()\r\n if v == 6:\r\n tirage7 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse7 = reponse.get()\r\n if v == 7:\r\n tirage8 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse8 = reponse.get()\r\n if v == 8:\r\n tirage9 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse9 = reponse.get()\r\n if v == 9:\r\n tirage10 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2].upper() + \" \" + tableau_vi[x][3]\r\n reponse10 = reponse.get()\r\n\r\n if y == 3:\r\n if v == 0: #si il s'agit du premier tirage\r\n tirage1 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper() #enregistre le contenu de la ligne\r\n reponse1 = reponse.get() #enregistre la reponse du joueur\r\n if v == 1:\r\n tirage2 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse2 = reponse.get()\r\n if v == 2:\r\n tirage3 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse3 = reponse.get()\r\n if v == 3:\r\n tirage4 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse4 = reponse.get()\r\n if v == 4:\r\n tirage5 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse5 = reponse.get()\r\n if v == 5:\r\n tirage6 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse6 = reponse.get()\r\n if v == 6:\r\n tirage7 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse7 = reponse.get()\r\n if v == 7:\r\n tirage8 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse8 = reponse.get()\r\n if v == 8:\r\n tirage9 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse9 = reponse.get()\r\n if v == 9:\r\n tirage10 = tableau_vi[x][0] + \" \" + tableau_vi[x][1] + \" \" + tableau_vi[x][2] + \" \" + tableau_vi[x][3].upper()\r\n reponse10 = reponse.get()\r\n\r\n reponse.delete(first=0, last=100) #vider la zone d'entrée\r\n\r\n v = v + 1 #ajouter 1 au nombre de tirage\r\n\r\n generate() #générer la nouvelle ligne (appel de la définition generate)\r\n\r\n if v == 10: #si le nombre de tirage est egal à 10\r\n resultat() #afficher les resultats (appel de la definition resultat)\r\n\r\n#cette définition permet d'afficher les résultats\r\ndef resultat():\r\n global tirage1, tirage2, tirage3,tirage4, tirage5, tirage6, tirage7, tirage8, tirage9, tirage10, reponse1, reponse2, reponse3, reponse4, reponse5, reponse6, reponse7, reponse8, reponse9, reponse10, color1, color2, color3, color4, color5, color6, color7, color8, color9, color10\r\n\r\n labelr = Label(frame, text=\"\", font=(\"Courrier\", 10), bg='#DEF6FF', fg='black') #label vide qui se supperpose au dessus de tous les labels, boutons et entréé précédement définis\r\n labelr.place(x=-300, y=-200, width=600, height=600) #placement du label\r\n\r\n#la partie ci dessous permet d'afficher le score final\r\n scorea = Label(frame, text=\"Votre score :\", font=(\"Arial\", 10), bg='purple', fg='white')\r\n scorea.place(x=50, y=-110, width=100, height=25)\r\n scoreb = Label(frame, text=score, font=(\"Arial\", 10), bg='purple', fg='white') #score\r\n scoreb.place(x=145, y=-110, width=25, height=25)\r\n scorey = Label(frame, text=\"/ 10\", font=(\"Arial\", 10), bg='purple', fg='white') #sur 10\r\n scorey.place(x=165, y=-110, width=40, height=25)\r\n\r\n#entete du tableau\r\n repc = Label(frame, text=\"Vos réponses\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n repc.place(x=-200, y=-78, width=140, height=23)\r\n scorec = Label(frame, text=\"Bonnes réponses\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n scorec.place(x=-55, y=-78, width=300, height=23)\r\n\r\n#premiere ligne\r\n numc = Label(frame, text=\"1\", font=(\"Courrier\", 10), bg='grey', fg='white') #numero de la ligne\r\n numc.place(x=-230, y=-50, width=25, height=23)\r\n repc = Label(frame, text=reponse1, font=(\"Courrier\", 10), bg=color1, fg='white') #reponse du joueur\r\n repc.place(x=-200, y=-50, width=140, height=23)\r\n scorec = Label(frame, text=tirage1, font=(\"Courrier\", 10), bg='#FF7433', fg='white') #bonne réponse de la ligne tirée\r\n scorec.place(x=-55, y=-50, width=300, height=23)\r\n\r\n#deuxieme ligne\r\n numd = Label(frame, text=\"2\", font=(\"Courrier\", 10), bg='grey', fg='white') #numero de la ligne\r\n numd.place(x=-230, y=-25, width=25, height=23)\r\n repd = Label(frame, text=reponse2, font=(\"Courrier\", 10), bg=color2, fg='white') #reponse du joueur\r\n repd.place(x=-200, y=-25, width=140, height=23)\r\n scored = Label(frame, text=tirage2, font=(\"Courrier\", 10), bg='#FF7433', fg='white') #bonne réponse de la ligne tirée\r\n scored.place(x=-55, y=-25, width=300, height=23)\r\n\r\n#troisieme ligne\r\n nume = Label(frame, text=\"3\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n nume.place(x=-230, y=0, width=25, height=23)\r\n repe = Label(frame, text=reponse3, font=(\"Courrier\", 10), bg=color3, fg='white')\r\n repe.place(x=-200, y=0, width=140, height=23)\r\n scoree = Label(frame, text=tirage3, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scoree.place(x=-55, y=0, width=300, height=23)\r\n\r\n numf = Label(frame, text=\"4\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numf.place(x=-230, y=25, width=25, height=23)\r\n repf = Label(frame, text=reponse4, font=(\"Courrier\", 10), bg=color4, fg='white')\r\n repf.place(x=-200, y=25, width=140, height=23)\r\n scoref = Label(frame, text=tirage4, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scoref.place(x=-55, y=25, width=300, height=23)\r\n\r\n numg = Label(frame, text=\"5\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numg.place(x=-230, y=50, width=25, height=23)\r\n repg = Label(frame, text=reponse5, font=(\"Courrier\", 10), bg=color5, fg='white')\r\n repg.place(x=-200, y=50, width=140, height=23)\r\n scoreg = Label(frame, text=tirage5, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scoreg.place(x=-55, y=50, width=300, height=23)\r\n\r\n numh = Label(frame, text=\"6\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numh.place(x=-230, y=75, width=25, height=23)\r\n reph = Label(frame, text=reponse6, font=(\"Courrier\", 10), bg=color6, fg='white')\r\n reph.place(x=-200, y=75, width=140, height=23)\r\n scoreh = Label(frame, text=tirage6, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scoreh.place(x=-55, y=75, width=300, height=23)\r\n\r\n numi = Label(frame, text=\"7\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numi.place(x=-230, y=100, width=25, height=23)\r\n repi = Label(frame, text=reponse7, font=(\"Courrier\", 10), bg=color7, fg='white')\r\n repi.place(x=-200, y=100, width=140, height=23)\r\n scorei = Label(frame, text=tirage7, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scorei.place(x=-55, y=100, width=300, height=23)\r\n\r\n numj = Label(frame, text=\"8\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numj.place(x=-230, y=125, width=25, height=23)\r\n repj = Label(frame, text=reponse8, font=(\"Courrier\", 10), bg=color8, fg='white')\r\n repj.place(x=-200, y=125, width=140, height=23)\r\n scorej = Label(frame, text=tirage8, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scorej.place(x=-55, y=125, width=300, height=23)\r\n\r\n numk = Label(frame, text=\"9\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numk.place(x=-230, y=150, width=25, height=23)\r\n repck = Label(frame, text=reponse9, font=(\"Courrier\", 10), bg=color9, fg='white')\r\n repck.place(x=-200, y=150, width=140, height=23)\r\n scorek = Label(frame, text=tirage9, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scorek.place(x=-55, y=150, width=300, height=23)\r\n\r\n numl = Label(frame, text=\"10\", font=(\"Courrier\", 10), bg='grey', fg='white')\r\n numl.place(x=-230, y=175, width=25, height=23)\r\n repcl = Label(frame, text=reponse10, font=(\"Courrier\", 10), bg=color10, fg='white')\r\n repcl.place(x=-200, y=175, width=140, height=23)\r\n scorel = Label(frame, text=tirage10, font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\n scorel.place(x=-55, y=175, width=300, height=23)\r\n\r\n#paramettre de la fenetre\r\nwindow = Tk() #fenetre tkinter\r\nwindow.title(\"Irregular Verbs\") #titre de la page\r\nwindow.geometry(\"1600x900\") #taille de base de la page\r\nwindow.minsize(500, 325) #taille minimale de page\r\n#window.iconbitmap(\"flag.ico\") #logo de la page\r\nwindow.config(bg='#DEF6FF') #couleur de fond de la page\r\n\r\ncolor1 = color2 = color3 = color4 = color5 = color6 = color7 = color8 = color9 = color10 = \"red\"\r\n\r\n#boite\r\nframe= Frame(window, bg='#DEF6FF', bd=1, pady=125, padx=250) #boite contenant tous les éléments\r\n\r\n#entete (label avec \"Base verbale, preterit ...)\r\nbase = Label(frame, text=\"Base Verbale\", font=(\"Courrier\", 10), bg='#FF7433', fg='white') #label base verbale\r\nbase.place(x=-230, y=-90, width=110, height=25) #placement du label\r\npreterit = Label(frame, text=\"Preterit\", font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\npreterit.place(x=-110, y=-90, width=110, height=25)\r\nparticipe = Label(frame, text=\"Participe Passé\", font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\nparticipe.place(x=10, y=-90, width=110, height=25)\r\ntraduction = Label(frame, text=\"Traduction\", font=(\"Courrier\", 10), bg='#FF7433', fg='white')\r\ntraduction.place(x=130, y=-90, width=110, height=25)\r\n\r\n#reponse\r\nreponset = Label(frame, text=\"La partie manquante est \", font=(\"Courrier\", 10), bg='#DEF6FF', fg='black')\r\nreponset.place(x=-140, y=0, width=160, height=25)\r\nreponse = Entry(frame, bg='#DEF6FF', font=(\"Courrier\", 10), fg='blue', relief='groove', borderwidth=0) #entrée de texte ou le joueur rentre sa réponse\r\nreponse.place(x=10, y=0, width=110, height=25) #placement de l'entrée\r\nbas = Label(frame, text=\"¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\", font=(\"Courrier\", 10), bg='#DEF6FF', fg='black') #label __\r\nbas.place(x=10, y=20, width=110, height=25) #placement du label\r\npoint = Label(frame, text=\".\", font=(\"Courrier\", 15), bg='#DEF6FF', fg='black') #label .\r\npoint.place(x=120, y=0, width=5, height=25) #placement du label\r\n\r\n#boutons\r\nbuttonv = Button(frame, text=\"VALIDER\", font=(\"Courrier\", 10), bg='#DEF6FF', fg='black', relief='groove', command = validate) #bouton valider qui appel la définition validate\r\nbuttonv.place(x=10, y=50, width=110, height=25) #placement du bouton\r\nlabelp = Label(frame, text=\" \", font=(\"Courrier\", 10), bg='#DEF6FF', fg='black')\r\nlabelp.pack(pady=25, fill=X) #label de hauteur 25 et de largeur ajustable\r\n\r\ngenerate() #appel de la fonction generate\r\n\r\nframe.pack(expand=YES) #affichage de la boite\r\nwindow.mainloop() #met la fenetre en boucle principale\r\n","sub_path":"Verbes irréguliers.py","file_name":"Verbes irréguliers.py","file_ext":"py","file_size_in_byte":26963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"569176661","text":"# -*- coding: utf-8 -*-\n\n'''\nCreated on Jun 23, 2014\n\n@author: David K. Laundav\n'''\n\n#===============================================================================\n# IMPORTS\n#===============================================================================\n\n\"\"\" Self-defined libs \"\"\"\nimport common.utility as util\n\n\"\"\" Python libs \"\"\"\nimport sys\nimport re\nimport scipy.io\nimport numpy as np\nfrom collections import OrderedDict\n\n#===============================================================================\n# VARIABLES\n#===============================================================================\n\n_regex_trial_file = re.compile(r'^\\S+.mat$')\n\n#===============================================================================\n# METHODS\n#===============================================================================\n\ndef get_trial_dict(data_folder, annotation_list):\n #===========================================================================\n # Retrieve an ordered dictionary of trials based on the name\n #===========================================================================\n count = 0\n trial_dict = {}\n \"\"\" Parse the matlab trial list \"\"\"\n matlab_trial_list = _parse_matlab_data(data_folder)\n for trial_name in matlab_trial_list.keys():\n \"\"\" Using the trial name as key \"\"\"\n trial_dict[trial_name] = annotation_list[count]\n \"\"\" Insert the movie category and the ratings of valence and arousal \"\"\"\n trial_dict[trial_name]['arousal'] = matlab_trial_list[trial_name]['arousal']\n trial_dict[trial_name]['valence'] = matlab_trial_list[trial_name]['valence']\n trial_dict[trial_name]['category'] = _get_movie_category(trial_name)\n \"\"\" Increment the count used by the annotation list \"\"\"\n count = count + 1\n \"\"\" Returns the dictionary sorted by the trial name \"\"\"\n return OrderedDict(sorted(trial_dict.items()))\n\ndef _parse_matlab_data(data_folder):\n #===========================================================================\n # Parse and return the content of the matlab files as a dictionary:\n # 1) Key = trial name\n # 2) Values = valence & arousal\n #===========================================================================\n trial_dict = OrderedDict()\n directory = data_folder + '/' + 'TI' + '/'\n \"\"\" Find the trial file and load it with scipy \"\"\"\n trial_file = util.find_file_with_regex(_regex_trial_file, directory)\n matlab_file = scipy.io.loadmat(trial_file)\n \"\"\" The sequence of trials are found in the trialList variable \"\"\"\n \"\"\" The valence assessment is found in the valence_resp variable \"\"\"\n \"\"\" The arousal assessment is found in the arousal_resp variable \"\"\"\n trial_list = np.asarray(matlab_file['trialList'][0])\n valence_list = np.asarray(matlab_file['valence_resp'][0])\n arousal_list = np.asarray(matlab_file['arousal_resp'][0])\n for trial_number in range(len(trial_list)):\n \"\"\" The trial name is found in the first position of the trial variable \"\"\"\n \"\"\" The valence and arousal variables are found in the first position of a two-dimensional matrix \"\"\"\n trial = str(trial_list[trial_number][0].replace('.mp4', ''))\n valence = float(valence_list[trial_number][0][0])\n arousal = float(arousal_list[trial_number][0][0])\n \"\"\" Insert the valence and arousal categories for the specific trial \"\"\"\n trial_dict[trial] = {'valence': valence, 'arousal': arousal}\n return trial_dict\n\ndef _get_movie_category(trial_name):\n #===========================================================================\n # Returns the category for the specific movie\n #===========================================================================\n is_first = True\n category_file = '../data/categories.txt'\n with open(category_file) as f:\n for line in f:\n if is_first:\n is_first = False\n continue\n \"\"\" Split the line, formatted with a tabulator, into a list \"\"\"\n line = line.split('\\t')\n if line[0] == trial_name:\n return line[1].replace('\\n', '')","sub_path":"common/parsers/matlab_parser.py","file_name":"matlab_parser.py","file_ext":"py","file_size_in_byte":4150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"346315906","text":"import re\n\nimport networkx as nx\n\nfrom pyflakes import api as pyflakes_api\nfrom pyflakes import reporter as pyflakes_reporter\n\nfrom kale.static_analysis.ast import get_all_names\n\n\nclass StreamList:\n\n def __init__(self):\n self.out = list()\n\n def write(self, text):\n self.out.append(text)\n\n def reset(self):\n self.out = list()\n return self\n\n def __call__(self):\n return self.out\n\n\ndef pyflakes_report(code):\n \"\"\"Inspect code using PyFlakes to detect any 'missing name' report.\n\n Args:\n code: A multiline string representing Python code\n\n Returns: a list of names that have been reported missing by Flakes\n \"\"\"\n flakes_stdout = StreamList()\n flakes_stderr = StreamList()\n rep = pyflakes_reporter.Reporter(\n flakes_stdout.reset(),\n flakes_stderr.reset())\n pyflakes_api.check(code, filename=\"kale\", reporter=rep)\n\n # Match names\n p = r\"'(.+?)'\"\n\n out = rep._stdout()\n # Using a `set` to avoid repeating the same var names in case they are\n # reported missing multiple times by flakes\n undef_vars = set()\n # iterate over all the flakes report output, keeping only lines\n # with 'undefined name' reports\n for line in filter(lambda a: a != '\\n' and 'undefined name' in a, out):\n var_search = re.search(p, line)\n undef_vars.add(var_search.group(1))\n return undef_vars\n\n\ndef detect_in_dependencies(nb_graph: nx.DiGraph, ignore_symbols: set = None):\n \"\"\"Detect missing names from the code blocks in the graph.\n\n Args:\n nb_graph: nx DiGraph with pipeline code blocks\n ignore_symbols: names to be ignored from the report\n \"\"\"\n block_names = nb_graph.nodes()\n for block in block_names:\n source_code = nb_graph.nodes(data=True)[block]['source']\n ins = pyflakes_report(code=source_code)\n\n if ignore_symbols:\n ins.difference_update(set(ignore_symbols))\n nx.set_node_attributes(nb_graph, {block: {'ins': sorted(ins)}})\n\n\ndef detect_out_dependencies(nb_graph: nx.DiGraph, ignore_symbols: set = None):\n \"\"\"Detect the 'out' dependencies of each code block.\n\n These deps represent the variables that each code block must marshal to\n child steps of the pipeline. Out deps are detected by cycling though all\n the ancestors of each block. By knowing the 'ins' deps (e.g. missing names)\n of the current block, we can get the blocks were those names were declared.\n If an ancestor matches the `ins` entry then it will have a matching `outs`.\n\n Args:\n nb_graph: nx DiGraph with pipeline code blocks\n ignore_symbols: names to be ignored\n \"\"\"\n for block_name in reversed(list(nx.topological_sort(nb_graph))):\n ins = nb_graph.nodes(data=True)[block_name]['ins']\n # for _a in nb_graph.predecessors(block_name):\n for _a in nx.ancestors(nb_graph, block_name):\n father_data = nb_graph.nodes(data=True)[_a]\n # Intersect the missing names of this father's child with all\n # the father's names. The intersection is the list of variables\n # that the father need to serialize\n outs = set(ins).intersection(father_data['all_names'])\n # include previous `outs` in case this father has multiple\n # children steps\n outs.update(father_data['outs'])\n # remove symbols to ignore\n if ignore_symbols:\n ins = list(set(ins) - set(ignore_symbols))\n # add to father the new `outs` variables\n nx.set_node_attributes(nb_graph, {_a: {'outs': sorted(outs)}})\n\n\ndef dependencies_detection(nb_graph: nx.DiGraph, ignore_symbols: set = None):\n \"\"\"Analyze the code blocks in the graph and detect the missing names.\n\n in each code block, annotating the nodes with `in` and `out` dependencies\n based in the topology of the graph.\n\n Args:\n nb_graph: nx DiGraph with pipeline code blocks\n ignore_symbols: names to be ignored\n\n Returns: annotated graph\n \"\"\"\n # First get all the names of each code block\n for block in nb_graph:\n block_data = nb_graph.nodes(data=True)[block]\n all_names = get_all_names(block_data['source'])\n nx.set_node_attributes(nb_graph, {block: {'all_names': all_names}})\n\n # annotate the graph inplace with all the variables dependencies between\n # graph nodes\n detect_in_dependencies(nb_graph, ignore_symbols)\n detect_out_dependencies(nb_graph, ignore_symbols)\n\n return nb_graph\n","sub_path":"kale/static_analysis/dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":4522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"} +{"seq_id":"94405057","text":"import scr.FormatFunctions as Format\nimport scr.StatisticalClasses as Stat\nimport Parameters as P\n\n\ndef print_outcomes(sim_output, strategy_name):\n\n rewards_mean_CI_text=Format.format_estimate_interval(\n estimate=sim_output.get_ave_reward(),\n interval=sim_output.get_CI_reward(alpha=P.ALPHA),\n deci=1\n )\n\n print(strategy_name)\n print(\"Estimate of mean game rewards and {:.{prec}%} confidence interval:\".format(1 - P.ALPHA, prec=1),\n rewards_mean_CI_text)\n\n\ndef print_comparative_outcomes(sim_output_unfair_game,sim_output_fair_game):\n increase=Stat.DifferenceStatIndp(\n name='Increase in game rewards',\n x=sim_output_unfair_game(),\n y_ref=sim_output_fair_game()\n )\n\n estimate_CI=Format.format_estimate_interval(\n estimate=increase.get_mean(),\n interval=increase.get_t_CI(alpha=P.ALPHA),deci=1)\n\n print(\"Average increase in game rewards and {:.{prec}%} confidence interval:\".format(1 - P.ALPHA, prec=1),\n estimate_CI)\n\n\n","sub_path":"SupportSteadyState.py","file_name":"SupportSteadyState.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"58"}